Review of water, crop production and system modelling approaches for food security studies in the Eastern Gangetic Plains

Transcription

1 SUSTAINABLE AGRICULTURE FLAGSHIP Review of water, crop production and system modelling approaches for food security studies in the Eastern Gangetic Plains Mac Kirby, Mobin-ud Din Ahmad, Perry Poulton, Zili Zhu, Geoff Lee and Mohammed Mainuddin July 2013

2 Citation Kirby M, Ahmad MD, Poulton P, Zhu Z, Lee G and Mainuddin M (2013) Review of water, crop production and system modelling approaches for food security studies in the Eastern Gangetic Plains. CSIRO, Australia. Copyright and disclaimer 2013 CSIRO To the extent permitted by law, all rights are reserved and no part of this publication covered by copyright may be reproduced or copied in any form or by any means except with the written permission of CSIRO. Important disclaimer CSIRO advises that the information contained in this publication comprises general statements based on scientific research. The reader is advised and needs to be aware that such information may be incomplete or unable to be used in any specific situation. No reliance or actions must therefore be made on that information without seeking prior expert professional, scientific and technical advice. To the extent permitted by law, CSIRO (including its employees and consultants) excludes all liability to any person for any consequences, including but not limited to all losses, damages, costs, expenses and any other compensation, arising directly or indirectly from using this publication (in part or in whole) and any information or material contained in it.

3 Table of Contents Executive Summary Introduction The challenge what this report is about Approach how this study and this report is organised Water resources in the Eastern Gangetic Plains Climate Surface water Groundwater Water use irrigation Regional water balances (NW Bangladesh as example) Climate change impacts (general overview) Potential trends in water use Concluding summary: gaps in data / knowledge / modelling capabilities Crop production in the lower Gangetic plains General overview: crops and crop seasons Rice Wheat Other crops Climate change impacts (general overview) Trends and potential future crop production Concluding summary: gaps in data and knowledge Systems modelling for food security studies General overview: the potential role of systems modelling Crop systems modelling and catchment / regional water resources overview of approaches Scenarios and systems modelling Conclusion: suggested approach for FSIFS project Uncertainty, decisions and systems modelling General overview: the role of uncertainty analysis in systems modelling Uncertainty modelling overview of approaches Example application: the effect of uncertain rainfall on the cropped area of rice Conclusion: suggested approach for FSIFS project Conclusions

4 7 References Figures Figure 2.1 Spatial pattern of annual precipitation ( ) across east Ganges plain Figure 2.2 Spatial pattern of mean annual temperature ( ) across east Ganges plain Figure 2.3 Mean monthly rainfall ( ) at 5 selected cities in the east Ganges plain. Data source: Hijmans et al Figure 2.4 Mean monthly temperature ( ) at 5 selected cities in the east Ganges plain. Data source: Hijmans et al Figure 2.5 Time-series ( ) monthly rainfall at Rajshahi, Bangladesh. Data source: Bangladesh Meteorological Department Figure 2.6 Observed and calculated monthly flows (in million cubic metres, mcm) at Farakka on the Ganges, just upstream of the Bangladesh India border. Source: Eastham et al. (2010) Figure 2.7 Floods in the lower Ganges region in 2004 (red and pink shades) and maximum extent in earlier years (blue shades). Source: G.R.Brakenridge, "Global Active Archive of Large Flood Events", Dartmouth Flood Observatory, University of Colorado, (accessed March 2013) Figure 2.8 District level 2009 pre-monsoon (March-April) groundwater depth in the east Ganges plain Figure 2.9 District level 2009 post-monsoon (November) groundwater depth in the east Ganges plain Figure 2.10 Temporal changes in post-monsoon groundwater depth north-west region of Bangladesh Figure 2.11 Temporal behaviour of groundwater depth in Bihar, Jharkhand and west Bengal, India. The lines are the average depth of all boreholes in Bihar and West Bengal, and the average of boreholes in districts in Jharkhand shown in Figure 2.8 and Figure 2.9. Source: Central Ground Water Board water information system at 19 Figure 2.12 Northwest Bangladesh schematic water balance for 1996 (a slightly drier than average year); left, May October; right, November April. The width of the arrows is proportional to the volume of flow, with the wet season rain being 60 km 3 for scale. The numbers (+7.8 and -8 km3) in the groundwater box are the seasonal net gain (wet season) or loss (dry season). The groundwater lateral flows (depicted as?? in the figure) are unknown Figure 3.1 Area of wet season (Kharif) rice and dry season rice in Punjab, Bihar and West Bengal. Source Directorate of Economics and Statistics (2013) Figure 3.2 Area of wet season (Aman), dry season (boro) and late dry / early wet season (Aus) rice in northwest Bangladesh (top) and the whole of Bangladesh (bottom). Source: Ministry of Agriculture, GoB (2013) and BBS (2013). (Note that the vertical axes uses the same scale as Figure 3.1.) Figure 3.3 Yield of wet season (Kharif) rice and dry season rice in Punjab, Bihar and West Bengal. Source Directorate of Economics and Statistics (2013) Figure 3.4 Yield of wet season (Aman), dry season (boro) and late dry / early wet season (Aus) rice in northwest Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013) Figure 3.5 Production of wet season (Kharif) rice and dry season rice in Punjab, Bihar and West Bengal. Source Directorate of Economics and Statistics (2013)

5 Figure 3.6 Production of wet season (Aman), dry season (boro) and late dry / early wet season (Aus) rice in northwest Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013). (Note that the vertical axes uses the same scale as Figure 3.5.) Figure 3.7 Area of wheat in Punjab, Bihar and West Bengal. Source Directorate of Economics and Statistics (2013) Figure 3.8 Area of wheat in northwest Bangladesh and the whole of Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013). (Note that the vertical axis uses the same scale as Figure 3.7.) Figure 3.9 Yield of wheat in Punjab, Bihar and West Bengal. Source Directorate of Economics and Statistics (2013) Figure 3.10 Yield of wheat in northwest Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013) Figure 3.11 Production of wheat in Punjab, Bihar and West Bengal. Source Directorate of Economics and Statistics (2013) Figure 3.12 Production of wheat in northwest Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013). (Note that the vertical axis uses the same scale as Figure 3.11.) Figure 3.13 Area of pulses in Bihar and West Bengal. Source Directorate of Economics and Statistics (2013) Figure 3.14 Area of pulses in northwest Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013). (Note that the vertical axis uses the same scale as Figure 3.13 Area of pulses in Bihar and West Bengal. Source Directorate of Economics and Statistics (2013).Figure 3.7.) Figure 3.15 Production of pulses in Bihar and West Bengal. Source Directorate of Economics and Statistics (2013) Figure 3.16 Production of pulses in northwest Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013). (Note that the vertical axis uses the same scale as Figure 3.15Figure 3.13 Area of pulses in Bihar and West Bengal. Source Directorate of Economics and Statistics (2013).Figure 3.7.) Figure 3.17 Area of oilseeds in Bihar and West Bengal. Source Directorate of Economics and Statistics (2013) Figure 3.18 Area of oilseeds in northwest Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013). (Note that the vertical axis uses the same scale as Figure 3.17.) Figure 3.19 Production of oilseeds in Bihar and West Bengal. Source Directorate of Economics and Statistics (2013) Figure 3.20 Area of oilseeds in northwest Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013). (Note that the vertical axis uses the same scale as Figure 3.19.) Figure 4.1 Overall system modeling approach, schematic. The blue arrows depict flows of water information between components, and the green arrows depict flows of crop information between components Figure 4.2 Northwest Bangladesh regional dynamic water balance assessment: from the top, modelled irrigation requirement; modelled groundwater height above or below an arbitrary datum; 12 month moving average rain (green) and modelled groundwater level (red); 5 year moving average rain (green) and modelled groundwater level (red) Figure 4.3 Demonstration application of northwest Bangladesh regional dynamic water balance assessment to assess potential climate change impact: (top) 12 month moving average modelled groundwater level ; 5 year moving average modelled groundwater level. The red lines are the same as those in the bottom two panels of Figure Figure 4.4 Patterns of actual evapotranspiration for groundwater dominant irrigation areas in the northwestern part of Bangladesh

6 Figure 4.5 Volume of net groundwater use for boro rice production in the north-western part of Bangladesh. (Grey and black colour lines represents Thana and District boundaries respectively.) Figure 4.6 Spatial variation of boro yield in 2009 in northwest Bangladesh. (Grey and black colour lines represents Thana and District boundaries respectively.) Figure 4.7 Spatial variation of boro water productivity in 2009 in northwest Bangladesh. (Grey and black colour lines represents Thana and District boundaries respectively) Figure 4.8 Mean daily solar radiation (calculated) for Rajshahi for years Solar radiation (NASA-POWER) at top of Atmosphere (ToA) and calculated clear sky radiation (Clear sky) are included for comparison. Mean daily rainfall highlights the monsoon period Figure 4.9 Predicted (lines) and observed (points) district level rice yields for local, HYV and Hybrid varieties for 2008 to Figure 4.10 (a) Simulated daily ET (ET -HYV), radiation derived ET (ET Radiation) and potential ET (Eo) during a boro (HYV) crop between 1 Jan 2009 and 30 April Symbols (ET RS derived) represent ET estimates based on remote sensing for selected dates. (b) Total ET (accumulated) between 1 Jan 2009 and 30 April 2009 for local, HYV and Hybrid varieties. Symbols represent maximum and minimum total ET estimates derived from remote sensing Figure 4.11 Probability of exceedance graphs for simulated (a) yield and (b) ET for local, HYV, Hybrid and an optimised potential based on current irrigation and fertiliser management options for Rajshahi district, Figure 4.12 Probability of exceedence graphs for simulated (a) applied irrigation and (b) drainage for local, HYV, Hybrid and an optimised potential based on current irrigation and fertiliser management options for Rajshahi district, Figure 4.13 Probability of exceedence graphs for simulated (a) yield and (b) ET for a HYV boro crop grown using current practice (baseline), optimised water and nitrogen management (optimal) using past Dhaka climate data for 1962 to Additional simulations for two future climate scenarios based on downscaled GCM data from the ECHAM5 and GFDLCM2 models are include for comparison Figure 4.14 Probability of exceedence graphs for simulated (a) applied irrigation and (b) drainage for a HYV boro crop grown using current practice (baseline), optimised water and nitrogen management (optimal) and two future climate scenario (ECHAM5 & GFDLCM2) Figure 5.1 The futures price of rice in U.S. dollars per kilogram of produced rice Figure 5.2 A constant mean irrigation water level over time, with its time-varying standard deviation Figure 5.3 Alternative behaviour of the mean water level where the level decreases smoothly in time Figure 5.4 Groundwater table: mean water table level and the standard-deviation in time Figure 5.5 Decreasing groundwater table: mean water table level and the standard-deviation in time Figure 5.6 Sample stochastic trajectories for the price of rice as a function of time Figure 5.7 Fifty sample stochastic trajectories for the irrigation water level as a function of time Figure 5.8 Real option values for a constant (blue curve) and decreasing (red curve) available water level.. 63 Figure 5.9 Fifty simulated stochastic paths of the rice price Figure 5.10 The rice price averaged over 1000 stochastic trajectories Figure 5.11 Fifty simulated stochastic paths of a scenario of rainfall where the rainfall on average drops from 2000mm to 1450mm Figure 5.12: The average annual rainfall for two different scenarios obtained by averaging over 1000 trajectories

7 Figure 5.13: The water table level above an arbitrary datum resulting from maintaining the cropped area (blue curve) and allowing the area to vary (red curve) Figure 5.14: The cropped area resulting from maintaining a constant area (blue curve) and allowing the area to vary (red curve) Figure 5.15: The simulated water table level for five different values of the water table limit Figure 5.16: The simulated cropped area for five different values of the limit on the water table level Figure 5.17: The water table level resulting from three different rainfall scenarios. The scenarios correspond to those displayed in Figure Figure 5.18: The cropped area resulting from three different rainfall scenarios. The scenarios correspond to those displayed in Figure

8 Tables Table 4.1 Rajshahi district (NW Bangladesh) statistical data for boro production in

9 Acknowledgements This study was funded by AusAid and CSIRO, through the AusAID CSIRO Research for Development Alliance and through CSIRO Water for a Healthy Country. In conducting this study, we have benefitted from the help and advice of many people. We particularly appreciate the support given by Dr Christian Roth. 7

10 Executive Summary Our aim in this study is to describe the current state of water resources and cropping systems in the Eastern Gangetic Plains, the likely future challenges resulting from changes such as population increase and climate change, and to identify knowledge gaps in the assessment. We approached the task partly by reviewing the literature and data, and partly by some preliminary modelling. The Eastern Gangetic Plains has a humid sub-tropical climate, with a distinct wet summer season and a cooler dry season. Rivers likewise have a distinct high flow period and frequent flooding in the wet season and lower flows in the dry season. The region is mostly underlain by a large aquifer system which receives considerable recharge from rain and the rivers during the wet season and contributes to dry season river flows. Groundwater is extensively used for irrigation, particularly in northwest Bangladesh where dry season irrigation using groundwater provides for the high yielding and highly productive dry season rice crop. In parts of northwest Bangladesh, groundwater use is probably unsustainable. Elsewhere, it appears that groundwater could be used more, and there are calls for greater use of groundwater to boost food production in West Bengal. However, there are few studies of how much groundwater can be sustainably used, in part because of a lack of models. Aside from issues of groundwater quantity, groundwater must be used with care because of natural contamination with arsenic and other water quality issues. The population of the region is likely to increase to one and a half times the present population by 2050, and food demand is likely to increase by between one and a half and two times. The increased demand for food will in turn lead to increased demand for water, to perhaps as much as twice the current demand by Satisfying this increased demand will be a challenge, and will likely involve more use of groundwater. Climate change projections suggest increased rainfall in the wet season and also an increase in extreme rainfall which, if realised, would lead to greater flooding. However, rainfall projections are uncertain and the impact on water resources, particularly on groundwater, is uncertain and the changes not necessarily large. Crop production in the Eastern Gangetic Plains is dominated by rice in the wet season; in the dry season wheat dominates in Bihar while boro rice dominates in West Bengal and northwest Bangladesh, the latter relying on extensive groundwater irrigation. In Bihar and West Bengal, increased crop production to meet the greater demand in the future may come from both increased use of irrigation and from improved varieties, management and other factors. In northwest Bangladesh, on the other hand, irrigation is already intensively used, and increased production will depend more on the technical factors of improved varieties, management and so on. Systems models are useful in understanding constraints and opportunities in crop production for food security. Furthermore, considerable understanding is derived from linking models from the plot or farm scale to the regional scale. At the farm scale we use the APSIM cropping systems model. Remote sensing modeling provides assessments of the spatial variability of crop performance and water use. At the regional scale, we use a dynamic regional water balance. Assessments at these three scales together show how much yield may be improved (and how this varies through years of varying climate), where yield may be improved, and how this adds up to an increase in production at a regional scale given constraints on how much water may be sustainably used. The systems modelling approach applied to northwest Bangladesh shows that there is potential for improving yields and optimising water use through improved irrigation and fertiliser application, both in the current climate and under a projected future climate. Yields are lower in some parts of the area than others, and these are probably the most fruitful areas to target with research and extension. Regional groundwater level trends are probably influenced by long term rainfall variability, and this should be taken into greater account when assessing sustainable groundwater use. The median climate change expectation of greater rainfall in the region will, if realised, lead to higher groundwater levels than at present, possibly 8

11 implying that more groundwater could be sustainably used in some areas. However, uncertainty in climate change projections means that median expectation may well not be realised: the rainfall could decrease. Decision making must deal with the uncertainty of future events such as droughts, floods, costs of production and prices for produce, combined with the further uncertainties of climate change. We have shown that real options valuation is an approach which combines decision making with uncertainty, and can be applied both at the farm and regional level. Two hypothetical case studies, both involving groundwater use in northwest Bangladesh, demonstrate the potential of the approach. In both cases, we used the regional water balance model to investigate decisions in the face of uncertain futures. In the first case study, we showed that there may be circumstances in which purchasing irrigation water is best (in terms of expected average value), whereas other circumstances favour investing in a tubewell. The second case study shows how the approach may link decisions (such as limits to groundwater extraction) to the trade-off between production and sustainable use. A decision to limit the fall in water levels leads to an expected or average reduction in the cropped area and hence financial returns to cropping. We suggest that the approach may be further developed into a useful tool for making decisions about cropping and water use in the region when the circumstances are uncertain. While much can be done with existing readily available water resources data and crop data in the region, nevertheless there are some large knowledge gaps. There will be much value in better data, particularly in readily available river flow data and long records of groundwater level data. Overall, the Eastern Gangetic Plains presents a picture of a large and growing population that will demand more food, and place the water resources under greater pressure. The levels of water use (particularly groundwater) differs in northwest Bangladesh from that in Bihar and West Bengal, and dry season rice yields are generally higher the northwest Bangladesh. Northwest Bangladesh may therefore have the greater challenge in increasing food production. Nevertheless, there appears to be scope across the region to increase yields and production. Climate change impacts are uncertain, with the median projections for greater monsoon rain; however, drier conditions cannot be ruled out. Modelling at scales from farm to region is a useful way of testing impacts and policy or management options to meet these challenges to increasing food production for the future. 9

12 1 Introduction 1.1 The challenge what this report is about The Ganges Basin is one of the global mega food baskets. Its agriculture feeds the roughly 400 million people who live in the basin, and the western parts (including parts of Haryana) export much grain to other parts of India. However, its ability to continue to as a cornerstone of food security in South Asia is under threat. Population growth and increasing competition between urban, industrial and agricultural water use is leading to physical and economical water scarcity in some parts of the basin, while climate change impacts are raising additional uncertainties over future supply and recharge regimes. The implications for future development are unclear. Within the Ganges Basin, the Eastern Gangetic Plains (broadly, Bihar and northern West Bengal in India, the Terai in Nepal and Northwest Bangladesh) are believed to have significant potential for intensification of agricultural production and to offer underutilised opportunities to improve livelihoods of smallholder farmers. The magnitude and distribution of untapped water resources underpinning this perceived potential are not fully understood. Also, northwest Bangladesh has been more successful in tapping into this potential than the biophysically similar neighbouring states in India, which raises the question about the nature of social and institutional constraints to rural development holding back smallholders in India. Against this background, the AusAID - CSIRO Alliance has funded a scoping inquiry into food security in the Eastern Gangetic Plains, as part of a wider project on Food Systems Innovation for Food Security. The aim is to analyse biophysical and socio-economic constraints to the wider adoption of climate-resilient farming systems and to identify potential entry points for R4D intervention in the Eastern Gangetic Plain. In addition, the inquiry will investigate the wider basin level setting of climate-resilient farming systems within the context of the Ganges Basin food-water-energy-climate change nexus. The current report deals with the biophysical aspects of the inquiry. In it, we describe the current state of water resources and cropping systems, and the likely future challenges resulting from changes such as population increase and climate change; we also identify gaps in knowledge in the assessment. Anticipating that systems modelling is likely to be an important part of research in food security in the region, we also describe modelling approaches to food production systems. Also anticipating that our knowledge of the current state and likely future of food security is quite uncertain, we describe approaches to modelling uncertainty and decision making. 1.2 Approach how this study and this report is organised The study is partly based on a review of literature and data, and partly on some preliminary modelling assessments. We review literature and data describing water resources (in Chapter 2) and crop production (Chapter 3) in the Eastern Gangetic Plains. In so doing, we contrast the Indian states of Bihar and West Bengal with the biophysically similar region of northwest Bangladesh. We examine the projections for population growth, economic development and climate change, and briefly assess the likely impacts on demand for water and food. We also identify gaps in knowledge of water resources and food production. We then describe the literature on crop systems modelling for food security studies (Chapter 4), and demonstrate some preliminary modelling. We conclude the chapter by suggesting the approach to adopt in future studies and outlining the data requirements. In Chapter 5, we discuss uncertainty and decision making, and again demonstrate some preliminary modelling. We conclude this chapter by recommending an approach and outlining the data requirements. 10

13 2 Water resources in the Eastern Gangetic Plains 2.1 Climate The Eastern Gangetic Plains has a humid sub tropical climate. As shown in Figure 2.1 and Figure 2.2, extracted from very high resolution interpolated climate surfaces (Hijmans et al. 2005), there is a gradual decrease in rainfall and increase in mean annual temperature from north-east to central parts of the east Ganges plain. The spatial variation in rainfall is greater than that in temperature. Rainfall varies from over 3500 mm/year in north east of West Bengal to under 900 mm/year in central parts of Bihar. In contrast mean annual temperature varies from 22 to 27 C (except the hilly tracts) from high rainfall to low rainfall areas respectively. The winter in east Ganges is dry and most of the rainfall occurs during summer months. Figure 2.1 Spatial pattern of annual precipitation ( ) across east Ganges plain. The monthly precipitation and mean temperature profile for 5 cities across the study area, Rajshahi, Kolkata, Khumarkhand, Pantna and Kalaiya, is presented in Figure 2.3 and Figure 2.4. (The location of these cities are marked in Figure 2.1 and Figure 2.2). Highest temperatures are generally observed in the period between May and June and most of the rainfall is concentrated in June to September. There is very high year to year variation in rainfall. For example, in Rajshahi, Bangladesh, the annual rainfall varied from 792 mm in 2010 to 2062 mm in 1997 (Figure 2.5); the average annual rainfall is 1422 mm ( ). As a result of this climatic pattern, the east Ganges basin is prone to water scarcity in winter dry months and abundant water availability often leading to floods during monsoon period. 11

14 Figure 2.2 Spatial pattern of mean annual temperature ( ) across east Ganges plain. Figure 2.3 Mean monthly rainfall ( ) at 5 selected cities in the east Ganges plain. Data source: Hijmans et al

15 Figure 2.4 Mean monthly temperature ( ) at 5 selected cities in the east Ganges plain. Data source: Hijmans et al Figure 2.5 Time-series ( ) monthly rainfall at Rajshahi, Bangladesh. Data source: Bangladesh Meteorological Department. 2.2 Surface water The surface water resources of the Eastern Gangetic Plains comprise inflows from the Ganges and several tributaries that enter the region, and surface water resources resulting from rainfall within the region. The main rivers entering the region are the Ganges itself and several tributaries, including the Kosi, Narayani and Karnali / Ghaghara which enter the region from the Himalayas to the north, while the Son enters the region from the south. Fed by the monsoon (section 2.1), the rivers all have high flow in the wet season and low flows in the dry season. The northern tributaries generally have greater discharges due to the higher rainfall over the Himalayas. Few measured river flow data are publicly available for the Ganges Basin, and most available data are from the 1960s and 1970s for the tributaries in Nepal, so the actual volumes of flow in most of the basin are not accurately known. Better data are available for the lower Ganges near the border with Bangladesh and in Bangladesh itself. Eastham et al. (2010) used flow data from gauges in Nepal on the Kosi, Narayani and Karnali, and at Farakka on the Ganges near the border with Bangladesh, together with modelled water withdrawals for irrigation, 13

16 to estimate flows in the Ganges and its other tributaries. The total mean annual flow entering the region is about 360 km 3 per year, of which about 100 km 3 is from the Ganges, with most of the rest from the northern (Himalayan) tributaries. About % of the annual flow is in the monsoon months of June to October, and roughly 100 km 3, slightly more than 25 % of the annual total, is in the peak flow month of August. Figure 2.6 shows the monthly flows at Farakka, and demonstrates the large difference between monsoon and dry season flows, as well as the variation in flows from year to year. The annual cycle of low dry season flows and high wet season flows are broadly similar upstream in the Ganges and in the tributaries, but with smaller monthly volumes. In the Himalayan tributaries, the low flows in the dry season generally are larger relative to the wet season flows. Flow, mcm Observed flow Calculated flow Figure 2.6 Observed and calculated monthly flows (in million cubic metres, mcm) at Farakka on the Ganges, just upstream of the Bangladesh India border. Source: Eastham et al. (2010). The large peak flows of the Ganges and its tributaries cause some flooding in most years, and extensive flooding in the worst years (Figure 2.7). The figure also shows that much flooding does not derive from the major rivers in the region. Broadly, the blue shades of floods before 2004 are floods by spilling of the Ganges and some tributaries (and, in the east, the Hooghly distributary), whereas the red and pink shades (with different dates referring to different dates in 2004) are floods caused by insufficiently rapid drainage of local runoff in Bihar (in the west part of the image) and Bangladesh (in the east part of the image). The floods and associated waterlogging lead to reduced crop production (Lohani et al., 2004). The flow of the Ganges into Bangladesh during the dry season (1 st January to 31 st May) is governed by the Treaty of 1996, which provides a formula for calculating the sharing of the water (Nishat and Pasha, 2001; Rahaman, 2006). Critiques of the treaty, including the two references mentioned, point out that the formula defines how the flow should be divided, but makes no provision for what the flow should be, and does not even guarantee a minimum flow. There appear to be few reviews of its operation in practice. The best we know of is in a blog by Khalequzzaman and Islam (2012), which analyses four years of recent flow data to show that, while Bangladesh has received what it should under the treaty 80 % of the time, it is receiving a (mostly fair) share of a flow that is about half what would have been expected from the period of , which is the reference period for assessing the flow. 14

17 Figure 2.7 Floods in the lower Ganges region in 2004 (red and pink shades) and maximum extent in earlier years (blue shades). Source: G.R.Brakenridge, "Global Active Archive of Large Flood Events", Dartmouth Flood Observatory, University of Colorado, (accessed March 2013). 2.3 Groundwater The Eastern Gangetic Plain is underlain by one of the largest alluvial unconfined aquifers in the world. The southern parts of Bihar, large parts of Jharkhand and the western part of West Bengal are hard rock formations that are not part of the aquifer system. The main source of groundwater recharge is rainfall and river flooding during wet months. In dry months, this groundwater resource contributes significantly to dry season flows in the Ganges river and to meeting irrigation and drinking water demand. In order to understand seasonal changes in groundwater depth in the Eastern Gangetic Plains, for this scoping study, the publicly available groundwater level data for India (Central Groundwater Board), and data for Bangladesh was used to develop groundwater maps (as shown in Figure 2.8 and Figure 2.9). Currently, on average at a district level, the groundwater table is within 14 meters from the surface towards the end of dry season (March-April) and is much shallower in November following recharge in the monsoon. The spatial variation of groundwater table is generally due to ground surface elevation. For an example, the deep groundwater in the southwestern part of the northwest region of Bangladesh is found under the elevated Barind tract area. In terms of seasonal patterns, groundwater fluctuations are more in the north-western region of Bangladesh than in the Indian states. This distinct difference, particularly in pre-monsoon water table conditions, we assess to be primarily due to drawdown by intensive groundwater pumping for dry season boro rice irrigation. The extent of groundwater use for irrigation between Bangladesh and India largely appears to be related to Government policies: in Bangladesh, to encourage groundwater use for dry season irrigation, government policies were relaxed in the 1970s and 1980s to 15

18 limit government involvement and encourage private development (Bhuiyan, 1984), eliminate duties on diesel pumps and allow the import of agricultural equipment without permits (Hossain, 2010). In contrast, in India, despite abundant groundwater availability, the government has provided the lowest level of electricity subsidy in the Eastern Gangetic Plains (Mukherji, 2007; Mukherji et al., 2009; Mukherji et al., 2012) which resulted a lower level of groundwater use than in the neighbouring region in Bangladesh. Figure 2.8 District level 2009 pre-monsoon (March-April) groundwater depth in the east Ganges plain. The information about the sustainability of groundwater use, the potential for further development and associated implications for drinking water supply and quality, is limited and requires detailed investigation. Our initial analysis in the north-west region of Bangladesh and India suggests, with current level of groundwater use, there is no immediate threat for sustainability of irrigated agriculture in much of the region. However, some areas within it (particularly the area known as the Barind Tract in northwest Bangladesh) are impacted by excessive and probably unsustainable use. Because of the concerns over excessive groundwater use in the Barind Tract, it has received the greatest attention in the literature, and we discuss it in some detail below. 16

19 Figure 2.9 District level 2009 post-monsoon (November) groundwater depth in the east Ganges plain. Shamsudduha et al. (2009), Jahan et al. (2010), Shahid et al. (2010), and Rahman and Mahbub (2012) all show that groundwater levels are falling in the Barind Tract. Rahmatullah Imon and Ahmed (2013) also show that groundwater levels are falling generally in the Barind area, but in some small parts they are steady or rising. Shamsudduha et al. (2009) conclude that the use of shallow aquifers for irrigation in the area is unsustainable. As shown in Figure 2.10 (where the bright red patch in the right hand image of the figure is the area of the Barind Tract), which is based on our analysis of Bangladesh groundwater data, there has indeed been a sharp decline of groundwater depth in Barind Tract in the southwestern part of north-west Bangladesh. The decline is more noticeable in the decade from , than in the preceding decade. The greater decline of the recent decade may partly result from rainfall. As shown in Figure 2.5, the decade from ended wetter than it started, whereas the decade ended drier than it started. All other things being equal, this would result in greater groundwater declines in the second decade than the first. Shahid et al. (2010), based on data up to 2002, conclude that both rainfall variation and excessive groundwater pumping is responsible for groundwater droughts (periods when shallow tube wells run dry), with excessive pumping more important after Rahman and Mahbub (2012), based on data up to 2010, also conclude that groundwater levels are falling independently of rainfall. 17

20 Figure 2.10 Temporal changes in post-monsoon groundwater depth north-west region of Bangladesh. Withdrawal of groundwater will always lead to some decline in the water table. But is it unsustainable? It may lead to undesirable impacts, such as shallow wells and tubewells running dry (Shahid et al., 2010), but that does not necessarily mean the practice is unsustainable in the sense of leading to a continuing and ultimately catastrophic or irreversible decline. Sustainability in groundwater use may better defined in terms of recharge. Thus, if the use is less than the recharge, the use is in principle sustainable. Adham et al. (2010) suggest that the recharge potential of a small area within the Barind Tract is quite low, and actual recharge (roughly equivalent to 100 mm of recharge per year) is insufficient to replenish the water withdrawn for irrigation. Rahman and Roehrig (2006) show that recharge is insufficient in some parts of the Barind area, but (prior to 2006) was just sufficient in others, and is generally in the range of mm per year. The latter estimates are more consistent with our own estimates of recharge based on regional water balance modelling (section 2.5 below). While the recharge may have been just sufficient prior to 2006 in some areas (Rahman and Roehrig, 2006), continued development of irrigation presumably means that more of the area has since fallen into the category of insufficient recharge, and the situation thus overall is becoming more unsustainable. Our analysis of the northwest Bangladesh water balance (section 2.5 below), suggests that a considerable quantity of water flows from the groundwater to the rivers as baseflow (see also Ahmad et al., 2008). Jahan et al. (2008) show that the groundwater under the Barind area (which is elevated above the surrounding landscape) forms a mound, from which water flows laterally in all directions, which is consistent with the assessment that there is baseflow to the rivers. Despite lower water groundwater levels in recent years, the groundwater remained as a mound, albeit smaller and lower. Water would still flow away, but at a lower rate. Rahman and Mahbub (2012) show similar behaviour in one small area of the Barind Tract. Thus, the impact of pumping and falling groundwater levels may be to have increased evapotranspiration (from irrigated crops) and reduced baseflow to rivers. This may be undesirable, but is not necessarily unsustainable. However, groundwater levels continue to fall, and the groundwater mound will eventually disappear if current trends continue; this is unsustainable. In the wider region of northwest Bangladesh, the changes in groundwater level are neither so pronounced as those in the Barind Tract, nor is the water falling everywhere (Figure 2.10). In the Indian part of the 18

21 region, publicly available records are not long enough to show whether the post monsoon minimum and pre monsoon maximum groundwater depths are changing (Figure 2.11). Figure 2.11 Temporal behaviour of groundwater depth in Bihar, Jharkhand and west Bengal, India. The lines are the average depth of all boreholes in Bihar and West Bengal, and the average of boreholes in districts in Jharkhand shown in Figure 2.8 and Figure 2.9. Source: Central Ground Water Board water information system at Rahman and Roehrig (2006) analysed the complete water balance, groundwater balance, and recharge for the Barind region, and their work may be used to estimate the sustainable water use for the area. However, we are not aware of a similar study in northwest Bangladesh or the wider Eastern Gangetic Plains more generally. Thus, other than Rahman and Roehrig (2006), we know of no estimate of sustainable groundwater use, which is presumably less than current use in the Barind area (though Rahman and Roehrig s analysis shows this to be true only of part of the area), but may be more than current use elsewhere in the Eastern Gangetic Plains. Given the importance of groundwater for irrigation and hence food security in the region, we conclude that this is an area which requires further detailed work, including detailed analysis of groundwater flow and surface-groundwater interactions. Groundwater quality Groundwater quality is also a major concern in the Eastern Gangetic Plains. The most important quality concern is natural arsenic contamination in much of the lower Ganges basin (Chakraborti et al., 2004). In Bangladesh, the greatest arsenic contamination is in the south and south-east of the country and the least contamination in the north-west and in the uplifted areas of north-central Bangladesh (BGS and DPHE, 2001, Acharyya et al., 2000). However, there are occasional hotspots in low arsenic-contaminated areas in northern Bangladesh, which makes it difficult to predict the arsenic contamination. Extensive arsenic contamination is also found in southern West Bengal (Acharyya et al., 2000), and hotspots elsewhere in West Bengal, Jharkhand (Bhattacharjee et al., 2005) and Bihar (Saha et al., 2010). Kumar and Shah (2006) suggest that other groundwater quality problems are widespread in India, including Bihar and West Bengal: the issues include fluoride, salinity, and rural, urban and industrial pollution. 2.4 Water use irrigation Irrigation is used extensively in the Eastern Gangetic Plains, though more in northwest Bangladesh than in the Indian states, as we will show. The main months of irrigation are in the dry winter months from November to May. Surface water availability is limited in the dry season, so much of the water for irrigation is pumped from groundwater. Rice is the most important crop in the region. In northwest Bangladesh, dry season boro rice is all irrigated. The publicly available crop statistics for India (source indicate the area of rice in 19

22 the wet and dry season, and the area of rice that is irrigated, but do not indicate whether the irrigation is applied to the dry season rice or the wet season rice. However, the area of rice reported as irrigated exceeds the area of dry season rice, so at least some of the irrigation must be applied to the wet season crop, presumably as supplementary irrigation. (Supplementary irrigation is irrigation applied to a primarily rainfed crop, to ease it through dry periods, or to boost yields even when dry periods are not the limiting factor.) In Bihar, there is little dry season rice, so the irrigation must be mostly applied to the wet season rice. The area irrigated (average from to ) is about 50 % of the total rice area in each state. Wheat is the second most important crop in the region (though its importance relative to rice declines from Bihar to West Bengal and Bangladesh), and is mostly irrigated, though in Bangladesh often with too few irrigations to reach maximum yield (Hasan et al., 2011). The source of irrigation water (for all crops) is about 97 % groundwater in northwest Bangladesh (BADC, yearly report) and in Bihar it was about 80 % groundwater in (source The greater use of irrigation in Bangladesh, and the greater use of groundwater as a source, is probably related to the lesser restrictions on sinking a well, and greater access to cheap pumps and to subsidised diesel to power them. In Bihar and West Bengal, policies are restrictive (though they are easing in West Bengal), diesel is not subsidised and, in contrast to other parts of India (such as the Punjab and Haryana), there is limited access to subsidised electricity to power pumps (Mukherji, 2007; Mukherji et al., 2009; Mukherji et al., 2012). The actual volumes of water used in irrigation are not measured (by metering pumps, for example). We rely on our own estimates, mainly for northwest Bangladesh, made using remote sensing based surface energy balance (more details are under section 4.3 on scenario and system modelling) and water use modelling (see Box 1 for method). Box 1: Irrigation water use modelling The irrigation water requirement is calculated from the sum of the district water requirements, which are based on the areas and crop coefficients of five crops: Aus (which receives only supplementary irrigation early in its growth), boro, wheat, potatoes and other (which occupies only modest areas). For each crop in each district in each month, the actual ET in mm, ET a, is given by: ET = a K o ET o where K o is a crop coefficient, and ET o is the reference ET in mm. The total requirement in bcm for disctrict i in month j, IR ij, is given by: IR IR ij ij = = nc k= 1 0 ( ET R ) aki i A ik R < ET i i R ET where k is a crop, nc is the total number of crops, ET oki is the actual ET of crop k in district i, A ik is the area in km 2 of crop k in disctrict i, R i is the rain in district i, and the factor converts from km 2 and mm to bcm. The irrigation requirements for each month are summed to give monthly total requirements. The calculations use the meteorological data from the Bangladesh Meteorological Department, the areas of crops from the Ministry of Agriculture ( aki aki In northwest Bangladesh, irrigated crops occupy up to about 23,000 km 2 (the actual amount varying from month to month as different crops are planted and harvested) in a total area of about 34,500 km 2. About 16,000 km 2 of the irrigated are is planted to boro rice. We estimate that about 10 km 3 of irrigation water is applied in an average year, but some of this is effectively returned to the groundwater as drainage. The net irrigation water use (consumed as evapotranspiration) we estimate to be about 6.3 km 3. 20

23 In Bihar and West Bengal, irrigated crops occupy about 45,000 and 41,000 km 2 respectively (again with the actual amount varying from month to month as different crops are planted and harvested) in total areas of about 94,000 km 2 (Bihar) and 89,000 km 2 (West Bengal) (source Irrigated dry season rice occupies about 19,000 km 2 in Bihar and 27,000 km 2 in West Bengal (source For Bihar and West Bengal, unlike for northwest Bangladesh, we lack detailed information on crop planting and harvesting dates and other factors, and so cannot make a detailed calculation of water use. However, assuming that the water use is roughly the same per unit area of irrigated land as it is in northwest Bangladesh (and allowing for the lesser proportion of irrigated dry season rice, the heaviest water user, in the overall crop mix), the net irrigation water use would be about 8 to 12 km 3 of water in each state. If it were spread evenly over the area of each state, this is equivalent to about half or a little more of the amount of water withdrawn per unit area in northwest Bangladesh. 2.5 Regional water balances (NW Bangladesh as example) A regional water balance is a useful, high-level account of the water resources of a region. It provides a statement of how much water is available, in both surface and groundwater, and also how much is used in irrigation or other uses. It also provides a tool for examining broad scale impacts of change on the water balance. Example question to be addressed include: what are the regional water balance implications if the crop water demand changes because of climate change?, and what is the regional consequence if the area of irrigated crops or irrigation efficiency is increased?. We have calculated a monthly water balance for northwest Bangladesh from 1985 to The water balance for the 1996 dry and wet seasons is shown schematically in Figure The figure shows that the water balance in the wet season is dominated by rain, evapotranspiration, runoff and river flow. In the dry season, evaporation, irrigation, recharge and baseflow are the more important components of the balance. Baseflow to the rivers and pumping for irrigation remove nearly 8 km 3 (net, after returns from irrigation are counted) from the groundwater in the dry season, which is more or less regained from recharge in the wet season. The regional water balance treats the region as one large water balance. It does not allow separate accounting of the water balance of areas within the region. We showed in section 2.3 above that water use in the Barind Tract is excessive and very likely unsustainable, but that detail is lost in the regional water balance. A water balance could be constructed for the Barind area alone, but that is not our purpose here. 21

24 Irr Rain ET 60 km 3 River inflow Irr Rain ET River inflow Pumping Runoff Baseflow Runoff Rivers Recharge Rivers Recharge km 3-8 km 3???? River River outflow outflow Pumping Baseflow Figure 2.12 Northwest Bangladesh schematic water balance for 1996 (a slightly drier than average year); left, May October; right, November April. The width of the arrows is proportional to the volume of flow, with the wet season rain being 60 km 3 for scale. The numbers (+7.8 and -8 km3) in the groundwater box are the seasonal net gain (wet season) or loss (dry season). The groundwater lateral flows (depicted as?? in the figure) are unknown. 2.6 Climate change impacts (general overview) Climate change may alter the temperature and rainfall within the region, the flow of rivers into the Eastern Gangetic Plains, and also the crop water demand. Temperature is projected to increase in India and surrounding areas (Kumar et al., 2006) and in the Ganges basin in particular (Moors et al., 2011, Mulligan et al., 2011), while the trend for precipitation in the Ganges basin is less certain (Moors et al., 2011, Mulligan et al., 2011). There is uncertainty in the magnitude of the increases, which vary with the climate change scenario and with climate change model. Extreme temperatures and precipitation are expected to increase (Kumar et al, 2006). However, natural variability is expected to dominate the climate change signal, at least up to 2050 (Moors et al., 2011). Projected changes to the flow of rivers resulting from climate change arise from several effects. Firstly, the storage of water in glaciers is affected by temperature changes and precipitation changes. As recently reviewed by Bolch et al. (2012), the state and projected future of Himalayan glaciers is poorly understood. Glaciers have lost mass since the mid 19 th century, and loss rates appear to have increased in recent decades. However, the glacier contribution to flow is modest in the Ganges (Immerzeel et al., 2010), so overall changes in flow are likely to be modest. Since summer melting and peak monsoon precipitation coincide in the Ganges catchments, rivers will maintain their peak summer discharge with reduced glacier melt partly replaced by direct runoff (Immerzeel et al, 2010). There may be an increase in spring flows in the next few decades with increased melting. The second potential impact on river flow results from precipitation and temperature changes in the basin overall. While there is considerable uncertainty in projections, the expectation from several studies is for greater overall discharge and greater peak discharge (hence flooding) in the Ganges (Mirza et al., 2003; Nohara et al., 2006, Mulligan et al., 2011) and Brahmaputra (which borders the region considered in this report) (Gain et al., 2011). While there appears to be no detailed study of low flows, Sharma and Ambili 22

25 (2009) expect diminished low flows in the dry season in the Indus and Ganges, which would pose difficulties for irrigation supply and for river transport and the environment in Bangladesh. On the other hand, the detailed study of the Brahmaputra by Gain et al.(2011) suggests that extreme low flows will occur less frequently. Overall, according to Moors et al. (2011), average water availability in the Ganges basin is not expected to change much until 2050, and any average change will be less than the natural variability. Yu et al. (2010) also conclude that, whereas temperatures are projected to increase beyond the range of historical variation,... in the climate scenarios of the 2030s, 2050s and 2080s, precipitation does not separate itself from the historical variability for any month or season. On the other hand, Mulligan et al. (2011) suggest that the mean change in rainfall will be several times larger than the standard deviation in historic annual rainfall totals. The difference between the two interpretations perhaps emphasises the uncertainty in projections in the region. The projected increases to rainfall and temperature will also affect vegetation water use and recharge to groundwater within the Eastern Gangetic Plains. We will discuss the potential impacts on cropping and food production in section 3.5. The ways in which climate change may impact groundwater are complex and poorly understood (Kumar, 2012). Recharge will be affected by changes to rainfall, flood extent and duration (and hence river inflows from upstream), and the consumption of surface water by vegetation. Kumar (2012) and Shah (2009) suggest that, while there is much uncertainty, recharge may decline, partly because rainfall is expected in fewer, more intense storms which will lead to greater runoff and shorter periods for recharge. 2.7 Potential trends in water use Water use in the Eastern Gangetic Plains is likely to change in coming years. Climate change may alter the availability of water and the times when it is available but, as described above, the impact is uncertain and the changes not necessarily large. The demand for water will increase considerably with population growth and economic growth, which will increase the demand for irrigation water to produce food, drinking water, and water for industrial uses. Hosterman et al. (2012) suggest that the population of the Ganges basin will grow from about 500 million in 2001 to 720 million in 2025, an overall increase of about 44 %; Sharma et al (2008) give slightly lower figures for the Indian part of the basin. Estimates by BIDS (made as part of the AusAID CSIRO Alliance funded Bangladesh Integrated Water Resources Assessment project) suggest that the population of northwest Bangladesh will grow from about 35 million in 2011 to just about 50 million in 2050, an increase of just slightly less than that given by Hosterman et al. (2012) for the whole Ganges basin. The current (2011 census) population of Bihar is m, and that of West Bengal is 91.3 m but we do not have separate population growth figures for the Indian states. In addition to population growth, India and Bangladesh both have enjoyed strong economic growth for the last two decades or more (about 7 % for India and 5.5 % for Bangladesh, average : APO, 2012), and the growing economy is accompanied by growing individual prosperity. In the Eastern Gangetic Plains, the Bihar economy in recent years has grown more rapidly than that of India overall, whereas that of West Bengal has grown at rates similar or slightly less than India overall (MOSPI, 2012). The increased population will result in greater demand for food, and hence water for irrigated crops. The greater demand for irrigation water will also come from population growth outside the study area: in Bangladesh, for example, the NW region supplies food to Dhaka and population growth there will increase the demand for food and hence irrigation water in the NW region. The increased population will also increase the demand for drinking water and water for industrial uses. We do not know of a study that details the increased demand for water in the region. All other things being equal, a population of about one and a half times the present population by 2050 would demand one and a half times the amount of water. However, rising prosperity is likely to be accompanied by a greater water demand per capita, through changed food preferences, growing household water use, and growing industrial water use. Thus, the overall demand for water is likely to increase more than one and a half times, and perhaps double to

26 Water policy may also impact water use in the region. In West Bengal, the barriers to using groundwater have led to limited use of groundwater (Mukherji, 2007; Mukherji et al., 2009; Mukherji et al., 2012). Mukherji et al. (2012) suggest that with recent easing of the barriers, the area irrigated by groundwater could nearly double in just a few years. On the other hand, concerns about contamination (particularly arsenic contamination) and over-use of groundwater in Bangladesh are leading to suggestions that surface water be used more. The National Water Management Plan has suggestions for barrages on the Ganges and Brahmaputra (Jamuna) rivers to allow continued expansion of irrigation, amongst other things (WARPO, 2001). Mondal et al. (2010) show that the Brahmaputra (Jamuna) barrage will not be able to supply water reliably alone, and that groundwater must also be used. 2.8 Concluding summary: gaps in data / knowledge / modelling capabilities Much is known in a general way about the water resources of the Eastern Gangetic Plains, and the available data allow the piecing together of a reasonable general description. We have concentrated on the volumes of water available for use, but also have noted the critical issue of water quality, particularly concerns over natural arsenic contamination of groundwater. We have shown roughly how much water is available from surface water resources, how much is used by irrigation, and how much is depleted from and recharged into the groundwater of the region. In northwest Bangladesh, more irrigation water is used per unit area, and the impact on groundwater correspondingly greater than in Bihar and West Bengal. In the Barind Tract area in northwest Bangladesh, groundwater use for irrigation is probably unsustainable. Climate change will impact water resources, but the details are uncertain and may not be especially great. Increased demand for water, perhaps as much as twice the current demand by 2050 due to population and economic growth, will likely lead to greater water use in the future. At the same time, changing water policy will affect use. In northwest Bangladesh, there are concerns about overuse of groundwater and plans for greater use of surface water, while in Bihar and West Bengal there are calls for greater use of groundwater. Some additional irrigation may be possible in northwest Bangladesh through the development of surface water resources, but the low river flows in the dry season are already of concern for keeping channels open, keeping saline intrusion at bay and satisfying ecosystem demands. However, detailed data, models and analyses are lacking, and the figures we have presented in the analysis are rough. There are many gaps. In India, data on river flow are not publicly available, and our best estimate of water availability in the rivers comes from some rather simple and general modelling of river water balances. We know of few hydrology models of the Ganges Basin. Groundwater level data are available for recent years only in India, but for longer in northwest Bangladesh. We know of no detailed model of regional shallow groundwater or groundwater surface water interactions in India or northwest Bangladesh (though there is one model, with no reported use after its initial development, of the Barind Tract part of northwest Bangladesh). This is despite the importance of groundwater surface water interactions in the region in determining the availability of water for irrigation and other uses. It is simply not known what are the sustainable levels of use but, outside the Barind Tract, the sustainable level appears to be more than current use. Climate change projections for precipitation in the region are uncertain, and any changes may lie within the range of historic variation. On the one hand, this is cause for concern and raises calls for more study because of the potentially large impacts on water resources. On the other, it sets a requirement for adaptive management to cope with whatever change there may be, and also suggests that planning for variability and extreme events will also cope with the impacts of climate change. 24

27 3 Crop production in the Eastern Gangetic Plains 3.1 General overview: crops and crop seasons The monsoon climate in the Eastern Gangetic Plain results in two main crop seasons: the summer, monsoon or wet season, also known as Kharif after an Arabic word for autumn (harvest time for the summer crop); and the winter or dry season crop, also known as Rabi after an Arabic word for spring (harvest time for the winter crop) 1. However, since temperatures are suitable for cropping all year round, and water (with irrigation in the dry season) is available, crops are sown and harvested at all times of year. The main staple crops are rice and to a lesser extent wheat. In Bihar, in the western and drier part of the Eastern Gangetic Plains, wheat in the dry season is almost as important as rice, whereas in West Bengal and northwest Bangladesh it is much less important than rice. Many other crops are grown but, while they may be important parts of the diet, their area and production is much less than that of rice. In this section, we describe the area, production and yield of rice, wheat and some other crops, based on state and district statistics from India and Bangladesh. The Indian state of Bihar was split into Bihar and Jharkhand in 2000, and the area and production statistics for Bihar were similarly split. To maintain comparability, we combine Bihar and Jharkhand statistics from 2000, though for most statistics the values (of area and production) for Bihar are considerably greater than those for Jharkhand so the correction is small. Much of the intensively cropped area in Jharkhand is in the north of the state, within the Ganges plain, and so might in any event be considered as part of the Eastern Gangetic Plains. We also show statistics for the Punjab which generally has higher yields than Bihar and West Bengal, and helps illuminate production and productivity trends in the Eastern Gangetic Plains. 3.2 Rice Area sown to rice Rice cropping in the west of the Ganges basin is dominantly a wet season rainfed crop, with dry season irrigated rice becoming increasingly important to the east. As shown in Figure 3.1 and Figure 3.2 (top), there is nearly no dry season rice in the Punjab, little in Bihar, and more in West Bengal. In northwest Bangladesh the dry season boro crop now occupies about the same area as the wet season Aman crop. The crop statistics for northwest Bangladesh are for a to ; the longer period of national statistics show shows the great increase in the area of dry season boro rice in the last 25 years or so (Figure 3.2, bottom). The figures also show that the area of wet season (Aman) rice in the whole of Bangladesh is approximately equal to the area of wet season (Kharif) rice in Bihar or West Bengal, and about three times the area in northwest Bangladesh. Bihar and West Bengal occupy roughly 90,000 km 2 each, whereas northwest Bangladesh occupies about 35,000 km 2. Rice thus occupies a greater proportion of northwest Bangladesh than it does in Bihar or West Bengal. 1 A confusion arises in the naming of the rice crops in India and Bangladesh. In the official statistics for India (Directorate of Economics and Statistics, 2012), Kharif is also described as autumn and winter and Rabi is also described as summer, presumably because harvest of the Kharif (summer) crop may be as late as winter, and harvest of the Rabi (winter) crop may be as late as summer. In Bangladesh, three rice crops are distinguished, Aman (wet season or Kharif), boro, (dry season or Rabi), and Aus (late dry season / early wet season). 25

28 60000 Rice area, km Bihar & Jharkhand Kharif Bihar & Jharkhand dry season West Bengal Kharif West Bengal dry season Punjab Kharif Figure 3.1 Area of wet season (Kharif) rice and dry season rice in Punjab, Bihar and West Bengal. Source Directorate of Economics and Statistics (2013) Rice area, km Aus Aman Boro Rice area, km Aman Aus Boro

29 Figure 3.2 Area of wet season (Aman), dry season (boro) and late dry / early wet season (Aus) rice in northwest Bangladesh (top) and the whole of Bangladesh (bottom). Source: Ministry of Agriculture, GoB (2013) and BBS (2013). (Note that the vertical axes uses the same scale as Figure 3.1.) Rice irrigation In northwest Bangladesh, the dry season boro rice crop is almost all irrigated, whereas little irrigation is applied to the wet season Aman crop. In Bihar and West Bengal, about half the rice crop is irrigated, but the official statistics do not indicate whether the irrigation is for the dry season or wet season (Kharif) crop. Inasmuch as the area of irrigation is greater than the area of dry season crop, at least some of the irrigation must be for the wet season crop. In the Punjab, the rice crop (all wet season / Kharif) is almost all irrigated; the irrigation in the wet season will be to supplement the rainfall, rather than the main supply of water. Irrigation of wet season rice in Bihar and West Bengal may also be practiced to supplement the rainfall. Rice yield The Punjab (wet season rice) and northwest Bangladesh (dry season boro rice) achieve the highest yields, at around 4 tonnes per hectare, followed by dry season rice in West Bengal (Figure 3.3 and Figure 3.4). Wet season rice in West Bengal (Kharif) and northwest Bangladesh (Aman) yield around 2 tonnes per hectare. The least yields are achieved in the Aus (late dry / early wet season) rice in northwest Bangladesh and in Bihar, with dry season rice barely achieving 2 tonnes per hectare, and wet season (Kharif) rice yielding little over 1 tonne per hectare. The highest yields are from the irrigated Punjab and northwest Bangladesh boro crops, followed by the West Bengal dry season crop which we assume is also irrigated. The lowest yields are from the crops which are prone to flooding in the wet season. Rice yield, tonnes/ha Bihar Kharif Bihar dry season West Bengal Kharif West Bengal dry season Punjab Kharif

30 Figure 3.3 Yield of wet season (Kharif) rice and dry season rice in Punjab, Bihar and West Bengal. Source Directorate of Economics and Statistics (2013). yield, tones/ha Aus Aman Boro Figure 3.4 Yield of wet season (Aman), dry season (boro) and late dry / early wet season (Aus) rice in northwest Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013). The difference shown here between the higher yielding Punjab region to the lower yielding region of the Eastern Gangetic Plain was also found by Sharma et al. (2010), who showed that the lower yield per unit of land area was matched by lower water productivity. Similar differences were found by Priya and Shibasaki (2001). Rice production The high yield of rice in the Punjab leads to that state having the largest production of any of the rice crops considered here. The production of the high yielding irrigated boro (dry season) crop of northwest Bangladesh, at about 6 m tonnes, is similar to that of the larger area of wet season (Kharif) rice in Bihar and Jharkhand (Figure 3.5 and Figure 3.6). The combined Bihar and Jharkhand Kharif rice production jumped markedly in 2012 (the year after the last year graphed in Figure 3.5). Overall the Eastern Gangetic Plains region produces about 30 m tonnes of rice, of which about 10 m tonnes is from northwest Bangladesh (which produces about one third of the overall Bangladesh rice crop). Rice production, m tonnes Bihar & Jharkhand Kharif Bihar & Jharkhand dry season West Bengal Kharif West Bengal dry season Punjab Kharif

31 Figure 3.5 Production of wet season (Kharif) rice and dry season rice in Punjab, Bihar and West Bengal. Source Directorate of Economics and Statistics (2013). production, m tones Aus Aman Boro Figure 3.6 Production of wet season (Aman), dry season (boro) and late dry / early wet season (Aus) rice in northwest Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013). (Note that the vertical axes uses the same scale as Figure 3.5.) 3.3 Wheat Area sown to wheat Wheat is grown on almost as much area as rice in the Punjab, but there is less in Bihar, and little in West Bengal and northwest Bangladesh (Figure 3.7 and Figure 3.8). This is presumably due to the winters being warmer in the eastern parts of the region. The northwest of Bangladesh with some slightly elevated and drier ground experiences the coolest conditions in Bangladesh and produces much of the country s wheat crop. The combined area of wheat and dry season rice in Bihar is approximately half the area of wet season (Kharif) rice; in West Bengal the ratio is about 30 %; and in northwest Bangladesh, dry season (boro) rice plus wheat occupies slightly more area than that of wet season (Aman) rice. This presumably results from the greater use of irrigation in northwest Bangladesh Wheat area, km Bihar & Jharkhand West Bengal Punjab

32 Figure 3.7 Area of wheat in Punjab, Bihar and West Bengal. Source Directorate of Economics and Statistics (2013) Total area NW area Wheat area, km Figure 3.8 Area of wheat in northwest Bangladesh and the whole of Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013). (Note that the vertical axis uses the same scale as Figure 3.7.) Wheat irrigation The wheat crops throughout the region (including the Punjab), being dry season (winter) crops, are almost all irrigated. Wheat yield The Punjab has by far the highest wheat yields in the Ganges basin, at around 4 tonnes per hectare, while in the Eastern Gangetic Plains, wheat yields are about half that (Figure 3.9 and Figure 3.10). Wheat yield, tonnes/ha Bihar West Bengal Punjab 30

33 Figure 3.9 Yield of wheat in Punjab, Bihar and West Bengal. Source Directorate of Economics and Statistics (2013) yield, tones/ha Figure 3.10 Yield of wheat in northwest Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013). The trend from the higher yielding Punjab to the lower yielding region of the Eastern Gangetic Plain was also found by Sharma et al. (2010), who showed that the lower yield per unit of land area was matched by lower water productivity. Similar trends were found by Priya and Shibasaki (2001). Wheat production The high yield of rice in the Punjab leads to that state having the largest production of any of the wheat crops considered here. The production of Bihar and Jharkhand (Figure 3.5 and Figure 3.6). Overall the Eastern Gangetic Plains region produces about 5.5 m tonnes of wheat, of which about 80 % is from Bihar. About 0.5 m tonnes is from northwest Bangladesh, which produces more than one half of the overall Bangladesh wheat crop. Wheat production, m tonnes Bihar & Jharkhand West Bengal Punjab

34 Figure 3.11 Production of wheat in Punjab, Bihar and West Bengal. Source Directorate of Economics and Statistics (2013). production, m tones Figure 3.12 Production of wheat in northwest Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013). (Note that the vertical axis uses the same scale as Figure 3.11.) 3.4 Other crops Many other crops are grown for food and fibre in the Eastern Gangetic Plains, including grain crops other than rice and wheat (barley, millet, etc.), pulses (chickpea, pigeon pea, black gram, etc.), oilseeds (linseed, mustard, sunflower, etc.), potatoes, sugar cane, cotton and jute. In addition, there are many tree crops producing fruit and nuts: for example, the Rajshahi district in northwest Bangladesh is the country s main area of mango production. Here we focus just on the overall areas producing crops other than rice and wheat, and on the area and production of total pulses and oilseeds. Pulses and oilseeds are important in the local diet, partly for their high protein content in a diet low in animal protein. Comparisons amongst Bihar, West Bengal and northwest Bangladesh are meaningless for most other crops due to the great variety of crops (which may differ in different parts of the region) and due to the lack of detail in the statistics on matters such the cropping season and whether irrigated or not. Areas of other crops Rice and wheat dominate the area of cropping in the Eastern Gangetic Plains, particularly in West Bengal and northwest Bangladesh. In Bihar and Jharkhand, the wet season (Kharif) rice crop occupies about 63 % of the total cropped area, with about 27,000 km 2 occupied by other crops. In West Bengal, wet season (Kharif) rice occupies about 80 % of the total cropped area, with about 11,000 km 2 occupied by other crops. In northwest Bangladesh, the area of wet season (Aman) rice is 80 % or more of the total cropped area; dry season (boro) rice plus wheat is also 80 % or more of the total cropped area; about 3,700 km 2 is sown to other crops. In terms of area, the most important crop in northwest Bangladesh after rice and wheat is potato. (Note: these figures exclude fallow land and cultivable land that is not currently cultivated. They are based on data from Directorate of Economics and Statistics 2013, Ministry of Agriculture GoB, 2013, and BBS, 2013.) Thus, the area devoted to other crops is least in northwest Bangladesh and most in Bihar. Furthermore, the most important other crop in northwest Bangladesh is potato, another staple; less area is devoted to nonstaples, which are important for proteins, micronutrients and so on. Area and production of pulses 32

35 The area of pulses declines from about 10,000 km 2 in Bihar to about 500 km 2 in northwest Bangladesh ( Figure 3.13 and Figure 3.14), and the production declines from 0.8 m tonnes to about 0.04 m tonnes (Figure 3.15 and Figure 3.16). In terms of area sown and production, pulses are clearly of much lesser importance relative to staples (rice, wheat and others) in northwest Bangladesh than in the Indian states. In all three regions, the area declined over the period of the statistics we had available. Production, however, remained at about the same level throughout the period, with increasing yields making up for reduced areas Total pulses area, km Bihar & Jharkhand West Bengal Figure 3.13 Area of pulses in Bihar and West Bengal. Source Directorate of Economics and Statistics (2013) Pulses area, km

36 Figure 3.14 Area of pulses in northwest Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013). (Note that the vertical axis uses the same scale as Figure 3.13 Area of pulses in Bihar and West Bengal. Source Directorate of Economics and Statistics (2013).Figure 3.7.) Figure 3.15 Production of pulses in Bihar and West Bengal. Source Directorate of Economics and Statistics (2013). 1.0 Pulses production, m tones Figure 3.16 Production of pulses in northwest Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013). (Note that the vertical axis uses the same scale as Figure 3.15Figure 3.13 Area of pulses in Bihar and West Bengal. Source Directorate of Economics and Statistics (2013).Figure 3.7.) Area and production of oilseeds As with the pulses, the area of oilseeds declines from Bihar to northwest Bangladesh (from about 6,000 km 2 to about 800 km 2, Figure 3.17 and Figure 3.18), and the production declines from 0.6 m tonnes to about 0.09 m tonnes (Figure 3.19 and Figure 3.20). The areas and production are similar to those of the pulses (above), so like the pulses, oilseeds (in terms of area and production) are of lesser importance relative to staples (rice, wheat and others) in northwest Bangladesh than in the Indian states. In all three regions, the area remained about the same or increased slightly over the period of the statistics we had available. The combined increasing area and increasing yields led to rising production in the Indian states. In northwest Bangladesh, yields varied more from year to year, and the static area with varying yields led to varying production which peaked in , after which it fell. 34

37 Total oilseeds area, km Bihar & Jharkhand West Bengal Figure 3.17 Area of oilseeds in Bihar and West Bengal. Source Directorate of Economics and Statistics (2013) Oilseeds area, km Figure 3.18 Area of oilseeds in northwest Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013). (Note that the vertical axis uses the same scale as Figure 3.17.) 0.8 Total oilseeds production, m tonnes Bihar & Jharkhand West Bengal

38 Figure 3.19 Production of oilseeds in Bihar and West Bengal. Source Directorate of Economics and Statistics (2013). 0.8 Oilseds production, m tones Figure 3.20 Area of oilseeds in northwest Bangladesh. Source: Ministry of Agriculture, GoB (2013) and BBS (2013). (Note that the vertical axis uses the same scale as Figure 3.19.) 3.5 Climate change impacts (general overview) As discussed in section 2.6, climate change may alter the temperature and rainfall within the region, the flow of rivers into the Eastern Gangetic Plains, and also the crop water demand. Temperature is projected to increase in the Ganges basin, while the trend for precipitation is less certain (Moors et al., 2011). There is uncertainty in the magnitude of the increases, which vary with the climate change scenario and with climate change model. Extreme temperatures and precipitation are expected to increase (Kumar et al, 2006). However, natural variability is expected to dominate the climate change signal, at least up to 2050 (Moors et al., 2011). Climate change may thus alter factors such as temperature and rainfall that influence crop growth and yield. It may also affect the availability of water which will affect how much crop can be irrigated and hence affect overall production, and it may affect the extent of flooding which will also affect how much crop can be grown. As described above, climate change may alter the availability of water and the times when it is available but the impact is uncertain and the changes not necessarily large. In terms of the direct impacts, the literature carries projections of anything from large decreases in yields and production to small increases. Aggarwal (2008) reviewed studies of both recent trends and projections in India of yields of rice, wheat, and other crops, and concluded that large production losses in some crops due to climate trends are already evident, and future losses due to climate change could be large up to 40 % for wheat, for example. Some of the projected losses could be mitigated by various adaptations. Similarly, Boomiraj et al. (2010) expect large reductions (50 % and more in eastern India) in yields of rainfed and irrigated mustard (as an example of oilseed crops). In contrast, Roth and Laing (2013) suggest that climate change may result in slightly higher yields of Aman rice and cowpeas in southwest Bangladesh. In addition, boro rice, which currently suffers a yield penalty if sown earlier, could be sown earlier without the yield penalty, giving more flexibility in overall cropping. Yu et al. (2010) fall between the two foregoing projections, and expect wet season (Aman) rice production to decline about 1.5 % and dry season (boro) to decline from 3 to 5 % by 2050; however, wheat production may increase by about 3 %. In making these projections, Yu et al. (2010) assumed that access to water for irrigation will not be constrained but, as noted in section 2.7, several factors may affect water availability. 36

39 3.6 Trends and potential future crop production Crop production in the Eastern Gangetic Plains is likely to change in coming years. As noted in section 2.7, the population is likely to increase to about one and a half times the present population by Rising prosperity is likely to be accompanied by a greater food demand per capita. Rising prosperity is also accompanied by changed food preferences, particularly for more animal protein: this trend is evident in India, but is less strong than in other developing countries (Pingali, 2006). The overall demand for crops (for direct consumption and for feeding animals) is thus likely to increase more than one and a half times, and perhaps double to Crop production in the region could in principle meet the growing demand by increasing the areas of crops, increasing irrigation, and / or increasing yields (which might come about through increased irrigation, but could also be achieved even amongst irrigated crops). The area of crops can be greatly expanded by double-cropping where there is currently one crop, and triple-cropping where there are currently two. The area of dry season crops in Bihar and West Bengal is much less than the area of wet season crops (section 3.2 and 3.3 for rice and wheat, the dominant crops), suggesting that there is scope to increase the area that is double-cropped. An increase would presumably require irrigation. As discussed in section 2.4 (Water use irrigation), the dry season irrigated area and estimated water use in Bihar and West Bengal is, in proportion to the total area of the states, about half the area and water use in northwest Bangladesh. As noted in section 2.7 (Potential trends in water use), Mukherji et al. (2012) suggest that with recent easing of the barriers to irrigation in West Bengal, the area irrigated by groundwater could nearly double in just a few years. Such an increase in area using groundwater would lead to a considerable increase in crop production. Sulser et al. (2010) project that the area of irrigated cropping in the Ganges basin will increase by about 40 % from 2000 to 2050, though this would be partly offset by a reduction in the area of rainfed cropping. Any expansion of irrigation using groundwater should take account of contamination, particularly arsenic contamination. The Aus rice crop in northwest Bangladesh is ideally sown and harvested between the dry season and wet season crops (boro and Aman in the case of rice). However, the crop has low yields, and the area and production of Aus have been declining, and the crop is now a minor one. Development of shorter season crops and earlier dry season crops could see the window for the Aus crop increase, and production to rise again: this is the aim of a research project at the Bangladesh Rice Research Institute (Project 02 listed at The yield of rice and wheat in the region is generally low, except for the boro (dry season) rice crop in northwest Bangladesh and, to a lesser extent, the dry season rice crop in West Bengal (section 3.2 and 3.3). The low yield of other rice and wheat crops may be partly due to limited use of irrigation (dry season crops), or to the flooding and water logging during the wet season which necessitates the use of traditional low yielding varieties that can withstand a period of deep flooding. The wheat (dry season) crop in the region has a yield of about half that of wheat elsewhere in the Ganges basin, and presumably there is much scope to improve yields. Sharma et al. (2010) also conclude that production could be lifted considerably at current levels of water use, by improving the water productivity of current crops. Overall, then, there is considerable scope to increase crop production in Bihar and West Bengal, with current practices and knowledge, simply by growing more dry season crops, using more irrigation (from groundwater) and increasing yields. There is some scope also in northwest Bangladesh but the land is already more intensively used (particularly for dry season crops) and irrigation also used more, so the scope is more limited than in Bihar and West Bengal. Beyond these simple measures, improvements to yields have been occurring steadily for several decades, and much research is devoted and planned to further increase yields; given the lower yields of some crops in the region, it is reasonable to anticipate continued improvements in yields. 37

40 3.7 Concluding summary: gaps in data and knowledge There is much literature and many official statistics on crop yields and production in the eastern Gangetic Plains, and we may readily discern the major trends and problems. Crop production, particularly of dry season rice, has increased in northwest Bangladesh in recent years. In Bihar and West Bengal, crop production has increased, but the trend has been less than in northwest Bangladesh, and there has been more year to year variation. We have shown that the dry season cropping and irrigation is more developed in northwest Bangladesh, than in Bihar and West Bengal. As shown in Chapter 2, the present level groundwater use in the Barind Tract area of northwest Bangladesh is probably unsustainable, but groundwater use elsewhere may be less than can be sustained. Thus, increased use of water, particularly in the dry season, could lead to large increases in production in Bihar and West Bengal, with further gains from improved varieties, management and other factors. However, in northwest Bangladesh there is less scope for increasing production through increased water use particularly in the Barind area. Thus, increases in production will have to rely more on the agronomic factors of improved varieties, management and so on. Increased supplementary irrigation of Aman rice is likely to be one factor leading to improved yields. The Indian and Bangladesh parts of the Eastern Gangetic Plains have about the same expected proportional increase in population, and a demand to increase production by between one and a half and two times to There appears to be reasonable scope for increased production to meet the rising demand in the next few decades and a likelihood that it will do so. However, there is much detail yet to be worked out to achieve this. The main gaps in data and knowledge are to do with increasing yield and production in the future. This is in part a matter of conducting research into improved crop varieties, management and so on. It is also in part a matter of understanding the institutional constraints and incentives to produce crops: as shown by Mukherji (2012), institutional constraints have held back irrigation in eastern India. It is also partly a matter of understanding the limits to sustainable production. As we noted in Chapter 2, there are concerns about unsustainable groundwater use in parts of northwest Bangladesh. While it appears reasonable to expect that much additional groundwater could be used for irrigation in Bihar and West Bengal, how much could be sustainably used appears not to be known. 38

41 4 Systems modelling for food security studies 4.1 General overview: the potential role of systems modelling Systems models and system modelling mean different things to different people. In the context of food security, for some it is a model of a farming system (e.g. the APSIM model; Keating et al., 2003). For others, it is a hydrology model of river basin (such as the system of hydrology models for the Murray-Darling Basin; CSIRO, 2008). The basin model may have some modelling of crop water use and production built into it, but it is likely to be much simpler than that of the farming system model. For yet others, it is an integrated model of the hydrology and economics of a river basin (Kirby et al., 2012a). Again, the wider focus is likely to be associated with simpler descriptions of the farming system, the hydrology and the economics than individual models of the component parts or processes. Meeting future regional food security requires ongoing scientific advances in understanding the physiological basis of crop yield potential and the relationship between agricultural soils, crop productivity, and plant ecology in relation to environmental factors that determine crop yields (Cassman, 2009). The science driving on-going field experimentation has and will continue to underpin our knowledge and understanding of major farming systems and production constraints affecting the livelihoods of millions of small holder farmers, worldwide. However, the rate of socioeconomic and environmental change regionally requires timely adaption and adoption strategies in meeting future food security and necessitates better understanding of the complex relationship between crops, soils, water availability and climate. Suitable analytical tools that integrate our knowledge of cropping systems, water allocation, climate variability and economic constraints need to be utilised. Farming system models are now considered an assessable tool in evaluation of cropping systems performance worldwide by providing accurate predictions of crop production in relation to climate, genotype, soil and management factors, whilst addressing long-term resource management issues in farming systems (Keating et al., 2003). System models have been employed in evaluation of critical crop, soil nutrition, soil water and management factors limiting production by providing insight into adaption strategies aimed at closing the gap between on-farm yields and crop potential. Models help direct research priorities and evaluate the impact from seasonal climate variability and future climate change at the farming system or regional level and provide key inputs to economic models considering food security at temporal and spatial scales (Ittersum et al., 2012). Crop growth models do not replace traditional experimental research but support the comparison and extrapolation of field data to wider environments and potential management options and are one approach for understanding the influence of climatic variables on crop productivity (Pathak et al., 2003). An integrated system model decreases the time and risk associated with exploring climate induced adaption options available to farmers, reduces uncertainty on the part of their advisors and contributes to learning and farmer researcher dialogue (Hansen, 2005). Scenario analysis using historic weather records isolate the effect of experimental management and weather conditions experienced during a field experiment (Verburg and Bond, 2003), captures spatial and temporal variation in production not possible with empirical methods (Ittersum et al., 2012) and contributes to quantifying environmental impact and economic productivity associated with regional crop, water and nitrogen management. The exploration of scenarios, whether the impact of an externally imposed constraint or of a management decision or policy, is also performed by systems models at larger scales. Thus, Kirby et al. (2012b) used an integrated hydrology economics model of the Murray-Darling Basin to explore the interactions of climate change and proposed policy to limit the diversions of irrigation water from the rivers in the basin. 39

42 4.2 Crop systems modelling and catchment / regional water resources overview of approaches We briefly discuss models that link water use and crop production from the region to the plot. Our aim is to show that models at the large scale help estimate regional water availability and the impact of large scale policy and management. This is complemented by a more spatially explicit form of model based on remote sensing, which helps identify well-performing areas from those where crop water use may be inefficient, or yields lower than adjacent areas. The remote sensing modeling also links the regional water balances to crop systems models, which help assess yields, yield constraints and opportunities for improvement at the plot level. The value of this linking of analysis from crop to regional scales is illustrated by the example of increasing crop production by reducing inefficient leaks from an irrigation system that is, by using more of the water conveyed to an area. The leaked water previously percolated to the groundwater where it could be reused by farmers through pumping: this second group will no longer have access to the water. Thus, the greater crop in one area is balanced by lesser crop production in another, and there is no net gain (Ahmad et al., 2007a; 2007b). Linking crop to regional scale analyses helps distinguish the false gains in the example from solutions which result in real gains in the system overall. Use of river basin / regional water balance models River basin models are used for many purposes, and there are many approaches to modelling (Loucks and van Beek, 2005). Here, we restrict the discussion to models and approaches that focus on water use, particularly water use for food production. Generally, such models are at larger scales and longer timesteps (many just use annual averages, and thus have no timestep as such) than hydrological models which focus more on river flows (for flood forecasting, for example, which requires fairly short timesteps in a model). Such models often include land use and crop water use simulations. The water accounting approach of Molden and Sakthivadivel (1999), based on annual average water availability and use, is widely used to assess water availability in large river basins with particular application to use for irrigation and food production. For example, Karimi et al. (2012) used the approach to show that crop water use in the Indus basin is inefficient, and sustainable water use would be enhanced by reducing soil evaporation. Eastham et al. (2010) extended the approach to include a simple monthly rainfall-runoff and river flow model. They determined water balances and water use in the Ganges basin at a monthly timestep over a period of 50 years, and showed that increasing irrigation efficiency locally within the basin would not necessarily change water availability overall, since the water made available through increased efficiency can be consumed downstream. Vaghefi et al., (2013) applied the Soil and Water Assessment Tool (SWAT) to all the Karkheh River basin in Iran, and showed that climate change may increase water availability and wheat yields overall, though some parts of the basin may experience declines. Kirby et al. (2012b), extended the approach of Eastham et al. (2010) to link a hydrological and water use model of the Murray-Darling Basin with a model of irrigation economics. They showed that projected climate change impacts may nullify the expected benefits of policy to recover water for the environment in the basin; in contrast, the gross revenues to irrigation would be but moderately impacted. The examples above are not an exhaustive account of river basin or regional water balance models. They demonstrate a range of sophistication of hydrology modelling and integration with crop and economics modelling. They have been used to model scenarios from externally imposed change (climate change) to policy and management options. Underlying all of the models is a water balance a statement of inflows, internal flows and storage, and outflows often in the form of beneficial uses of water. Use of remote sensing For data scarce regions, such as Eastern Gangetic Plains, remote sensing datasets and modelling presents unique opportunities to understand cropping patterns, water use and agricultural performance from field to river basin scales. Such applications range from detailed energy balance modelling to classical classification techniques. For example, Ahmad et al. (2005; 2009) demonstrated the use of remote sensing based energy balance modelling to estimate net groundwater use for irrigation and water use performance 40

43 in Rechna Doab, Pakistan. Since this approach is based on the solving of energy and radiation balance, it provides a powerful means to compute water consumption under actual field conditions over large irrigation system which is not possible using conventional approaches. Similar to this Cai and Sharma (2009), used remote sensing (MODIS NDVI), weather and field survey data in estimating yield, consumptive use and water productivity of wheat and rice crops in the IGB. They used a simple surface energy balance model (ET0 X crop coefficients) to calculate potential ET. Results from the analysis produced yield maps, ET maps, water productivity maps covering Pakistan, India, Nepal and Bangladesh. Similarly, Panigrahy et al., 2010 used SPOT VGT NDVI data to identify major crops of rice, wheat, sugarcane, potato, and cotton in the IGP. They identified double cropping as the major cropping pattern with rice-wheat the dominant rotation with 40% of the net sown area. Results presented a spatial representation of the major cropping systems and percentage of cover for the total IGP estimated from NDVI data highlighting that 65 % of the agricultural area is sown to rice during the kharif and wheat sown to 67 % during the rabi (notable exception is West Bengal). Remote sensing and GIS was applied by Chowdary et al. ( 2008), in mapping of surface and sub-surface, perennial and seasonal waterlogged areas in 132 irrigation command areas in Bihar. Remote sensing observation and modelling results can be used to apply point/field scale crop simulation models in distribution fashion and/or upscale modelling results on water use and productivity at large irrigation systems (Droogers and Bastiaanssesn, 2002; De Wit and van Diepen, 2008). Use of crop modelling Process based simulation models have a demonstrated history of capturing climate, crop and nutrient interactions of cereal systems and have been widely employed by researchers in evaluation of experimental results from agricultural systems throughout south Asia (List of crop modelling studies for the South Asia region compiled by Joost Wolf, Wageningen University; joost.wolf@wur.nl). Use of simulation models in participatory research with farmers has been well documented (Carberry et al, 2002) in some countries but struggles for real-world relevance with small-holder farmers in developing countries. The Indo-Gangetic Plains has been the focus of numerous studies targeting both policy and technology approaches aimed at reducing the vulnerability of the region s food systems to climate change. Suitable analytical tools such as simulation models of cropping systems, water allocation and economic parameters need to be brought together within purpose-built decision support systems (Aggarwal et al., 2004). A number of modelling approaches have been employed in the region from semi empirical methods using census data and semiphysically based remote sensing analysis through to crop and water balance models (CROPWAT, WTGROW, InfoCrop) and integrated modelling platforms such as DSSAT, SWAP and APSIM. Inputs of weather, soil and cultivar-specific parameters (genetic coefficients) and crop management are key in using crop growth simulation models. All models require calibration and validation of the processes that they are trying to simulate, for example: crop phenology, crop growth, yields and soil water and N dynamics. Once validated then potential scenarios can be evaluated with some level of confidence. A number of examples of model use and contribution to the research are reported here but a clear lack of a participatory focus in employing modelling tools in helping evaluate options for intervention in meeting food security needs of smallholder farmers is evident. Simulated datasets of biomass production and grain yield from models such as the Wheat Growth Simulator WTGROWS (Aggarwal et al., 1994) and InfoCrop (Aggarwal et al., 2006) have been used in deriving water production functions based on seasonal evapo-transpiration. Aggarwal et al. (2001), applying a simulation approach in analysing trade-offs between food production, irrigation water used, NO 3 leaching, biocide residue, employment, and income. Pathak et al. (2003) concluded that although earlier studies had considered an integrated assessment of the effects of climate change on regional food chains, studies using actual climate data in simulating crop yield trends with a South Asia regional focus were negligible. They went on to simulate potential yields of rice (CERES-RICE, Singh et al., 1998) and wheat (CERES-WHEAT, Ritchie et al., 1998). Aggarwal et al. (2004) identified that climate change and competition for land and resources (non agricultural sector) presented a major threat to long-term food security in the region. The authors concluded the importance of maintaining the natural resource base in the face of socioeconomic development and that new information and tools are needed to help bridge science and policy; analytical tools such as simulation models of cropping systems, water allocation and economic 41

44 parameters need to be brought together. The study raised key questions around the type and sources of information required to help shape future research. A study by Kalra et al. (2007), discussed growth and yield of wheat with respect to varying agronomic and resource management practices. The study evaluated yield variation in relation to agronomic inputs (fertilizer, irrigation and variety) and management and stress factors (biotic and abiotic) using historic data and simulation models WTGROWS (Aggarwal et al., 1994) and InfoCrop (Aggarwal et al., 2006) for regional locations in Ranchi, Patna and Samastipur in Bihar. Results show wheat yields have plateaued in the western IGP (Punjab) requiring improved genetic material however, there is scope for yield improvements in the eastern region with increases in water and nutrients. Optimum sowing date for wheat in Bihar was estimated at around day 325. A yield gap analysis for the Eastern IGP suggest potential increases from an increase in input of water and nutrients. Modelled results for water and nitrogen production functions were used to aid irrigation and fertilizer application. Simulation using WTGROWS, estimated a 30 to 40 percent reduction in wheat yield for the major wheat growing regions of India with a delay in sowing of one month after the local optimum sowing date. Matching sowing date with seasonal conditions has potential benefits as an adaptation strategy in managing future on-farm yields for climate change. Kumar et al. (2008), compared CERES-wheat and CropSyst models as evaluation tools for semi-arid irrigated ecosystems in the Indo-Genetic Plains. Experimental data based on a silty clay loam near Delhi. CropSyst generally performed better in prediction of grain yield for a range of nitrogen treatments. In comparison with traditional crop models, the Agricultural Production Systems Simulator (APSIM) was designed as a farming systems simulator (Keating et al., 2003) for dryland agriculture, able to simulate multiple crops in rotation or intercropped and has a proven track record in modelling the performance of diverse cropping systems (Gaydon et al., 2012). Incorporation of the ORYZA2000 rice model into the APSIM framework with additional processes for simulating long-term flooded or saturated soil conditions encountered in rice-based systems as described by Gaydon et al. (2012a, 2012b) provides a useful tool in evaluation of rice wheat dominated farming system of the Indo Gangetic Plains. 4.3 Scenarios and systems modelling We have adopted the top down modelling approach, depicted schematically in Figure 4.1. Initially, we developed a regional water balance to quantify the overall water resources availability and sustainable limits for potential use. Then, using the remote sensing based energy balance modelling and secondary datasets, we have mapped spatial variations in land and water productivity to indentify high and low productivity areas. This was followed by detailed APSIM modelling to understand the impediments for crop growth and impact of climate change on crop water productivity. We have demonstrated the use of this area for north-west region of Bangladesh, for which some data are more readily available than is the case in Bihar and West Bengal. Finally, we applied an approach to be described in Chapter 5 to uncertainty and decision making. 42

45 Basin surface water supply constraints Regional water balance Climate inputs Scenarios Input water requirements Farm / plot / crop system Outputs - crop production, drainage Regional crop water use, productivity Groundwater supply constraints Uncertainty, economics and decision making: What are the trade-offs of production, price, water, electricity? Population growth Climate change Policy change (eg electricity supply) Figure 4.1 Overall system modeling approach, schematic. The blue arrows depict flows of water information between components, and the green arrows depict flows of crop information between components. Regional Water Balance Scenario Modelling We have calculated a regional water balance for northwest Bangladesh, using a simple top-down method described in Box 2. Box 2: Regional water balance model The regional water balance comprises three linked water balances: the land surface, the rivers and the groundwater. The links amongst the balances are the flows of water from one to the next such as the drainage from the land surface to the groundwater, and the baseflow from the groundwater to the river. The groundwater balance is only a partial balance in which flows to and from the land surface and the rivers are considered, and lateral groundwater flows are ignored. The sowing dates and crop coefficients were refined with remote sensing observations to capture actual field conditions. Surface water balance: The surface water balance is calculated separately for irrigated and non-irrigated land. The areas of irrigated and non-irrigated land change throughout the year. The irrigated land water balance is described in Box 1. The water balance of non-irrigated land is calculated through a simple catchment rainfall-runoff model. We do not claim the model to be the best possible for Bangladesh, but it was convenient to use. We have used it several times in other places including in the Ganges (Eastham et al., 2010). However, in this study, we have also used the remote sensing datasets to refine crop growth window (especially for dry season rice) and surface energy balance modelling results to gain further confidence on actual evapotranspiration from irrigated and non-irrigated areas. In the rainfall-runoff model, we derive partitioning of rainfall using the reasoning of Budyko (1974), which applies to average annual runoff, with the addition of a storage that varies from month to month. The monthly extension is based on Zhang et al. (2008). A conceptually identical rainfall runoff model, but with equations formulated differently, was shown by Wang et al. (2011) to perform well for Australian catchments, including many within the Murray-Darling Basin. We firstly partition rainfall, P, at the land 43

46 surface into runoff, R o, and infiltration, I, where conservation of mass must be observed. The infiltration component, I, is an addition to a generalized surface store, which could include temporary free water such as puddles, as well as actual infiltration into the soil. Evapotranspiration from the generalized surface store will be dealt with separately after calculating the infiltration. Thus: P I R = 0 (1) o Rainfall is the supply limit, whereas the unfilled portion of a generalized surface storage, ΔS smax, is the capacity limit governing the partition and includes soil storage and small surface stores. We use a Budykolike equation to smooth the transition from the supply limit to the capacity limit: S I = s max 1 a1 ( P Ss max ) + ( P S ) a1 s max 1 a1 (2) where a 1 is a parameter and S smax is the maximum capacity of the generalized surface store. The generalized surface store includes soil water and temporary surface water such as puddles and ponds. Figure B2.1 shows that with larger values of the parameter a1, this equation makes a sharper transition from the supply limit to the capacity limit. Thus, given precipitation and the parameter a1, equation (2) gives the runoff and equation (3) gives the infiltration into the generalized surface store I / S max (P+ Ir )/ S max Figure B2.1. Behaviour of the runoff infiltration partition equation with different values of the parameter. a 1 The evapotranspiration depends on the potential evapotranspiration, ET pot (the capacity limit), and the surface storage, S s (the supply limit). Although we do not differentiate between soil and other surface stores, the implication is that evaporation occurs from small ponds, puddles, and the soil surface, in contrast to transpiration, which comes from deeper soil storage. A similar equation to equation (3) above, with a second adjustable parameter, a 2, is used to smooth the transition from the supply limit to the capacity limit: 44

47 ET ET pot = 1 t t a2 ( Ss ETpot ) t t + ( S ET ) s a2 pot 1 a2 (3) This equation also behaves as shown in Figure 3 with the obvious changes to the variables. Infiltration increases the water stored in the generalized surface store, while it is decreased by evapotranspiration and a drainage-to-groundwater component, D: S t s = S t t s + I ET D (4) where t is time and Δ t is the time step (one month). We model the drainage-to-groundwater component as a fraction of the generalized surface store: D = c 1 (5) t t S s where c 1 is a fraction of the surface store (0 < c1 < 1). The relative proportions of ET, runoff and drainage are determined by S smax, a 1, a 2, and c 1. Groundwater balance: the groundwater balance comprises drainage inflow, calculated according to equation (5) above, minus baseflow to the rivers, and minus abstractions for irrigation calculated according to the irrigation water requirement in Box 1 and constrained by net groundwater use estimated by remote sensing analysis and net changes in groundwater level (a similar approach adopted by Ahmad et al The baseflow to rivers is calculated as a constant times the level of the groundwater above or below an arbitrary datum. River balance: the river balance and river flows are taken from separate calculations of the river flow done by the Institute for Water Modelling as part of an allied project, the Bangladesh Integrated Water Resources Assessment. Overall balance: The model is adjusted by changing S smax, a 1, a 2, and c 1 together with the parameter controlling drainage beneath irrigation and another parameter controlling baseflow, such that balance is achieved, and plausible values obtained for overall ET, drainage, runoff and baseflow. To run the balances, we use climate data from 1985 to 2010 from the Bangladesh Meteorological Department, the areas of crops and non-cropped area from the Ministry of Agriculture ( and crop coefficients from the FAO. Some components of the water balance output for 25 years of historical climate records are shown in Figure 4.2. The top panel shows that the irrigation requirement varies from year to year, driven by rainfall and potential evapotranspiration in the dry season. The groundwater (second panel) varies in response particularly to wet season rainfall and consequent recharge, as well as to extractions for irrigation. The decline in groundwater following the dry year of 1994 is particularly evident. The bottom two panels of the figure show that the groundwater appears to respond to rain both over a short period and to longer term trends. The association between rainfall and groundwater level is significant in assessing the extent to which groundwater declines may be attributed to excessive pumping. As far as we know, previous assessments of groundwater level decline have not paid much attention to the rainfall (for example, little is said about rainfall in Shamsudduha, 2009, as discussed in section 2.3). We have not attempted a regional water balance for Bihar and West Bengal as part of this project. However, with the probable increase in irrigation groundwater use, a regional water balance will be very useful to examine the impacts on the groundwater and rivers, and to address questions such as the sustainable level of groundwater use. The regional water balance described above and in Box 2 above can be used to model the consequences in northwest Bangladesh of climate change and other scenarios. As a demonstration, we model a

48 median climate change scenario in which the average annual rainfall is projected to increase from 1982 mm to 2090 mm, and potential evapotranspiration is projected to increase from 1312 to 1358 mm. In Chapter 2 we noted that climate change projections of rainfall for the region are uncertain, with projected mean changes remaining within the historical variation. Nevertheless, the mean changes are for greater annual rainfall (Yu et al., 2010, Moors et al., 2011, Mulligan et al., 2011), mainly because of increased rainfall in the wet season; our assumed changes are consistent with these projections. We assume that the area and mix of crops remains the same as in the analysis above. We use the historic rainfall and potential evapotranspiration as before. The demonstration scenario results in Figure 4.3 show that the greater rain results in somewhat higher groundwater tables. The wetter climate also leads to slightly reduced irrigation extraction, down from 11.9 to 11.6 km 3 (gross extraction, including irrigation water that returns to groundwater as drainage). It also leads to somewhat greater baseflow, up from 6.0 to 7.1 km 3. We emphasise that this is a demonstration application of a simple overview model: the groundwater surface water interactions in northwest Bangladesh have not been studied in detail and no detailed model exists. We recommend much more detailed study, which may confirm or refute the simple demonstration analysis done here. Irrigation water use, bcm GW height, m irrigation GW height above datum GW height, m GW 12 month moving average rain 12 month total Rain, mm GW height, m GW 5 year moving average rain 5 year moving average Rain, mm 46

49 Figure 4.2 Northwest Bangladesh regional dynamic water balance assessment: from the top, modelled irrigation requirement; modelled groundwater height above or below an arbitrary datum; 12 month moving average rain (green) and modelled groundwater level (red); 5 year moving average rain (green) and modelled groundwater level (red). GW height, m middle climate change historical climate GW height, m middle climate change historical climate Figure 4.3 Demonstration application of northwest Bangladesh regional dynamic water balance assessment to assess potential climate change impact: (top) 12 month moving average modelled groundwater level ; 5 year moving average modelled groundwater level. The red lines are the same as those in the bottom two panels of Figure 4.2. Spatial patterns of agricultural water consumption and productivity To understand spatial patterns of agricultural water consumption during dry season, we have used satellite images and production statistics for north-west Bangladesh. We have applied a well-tested and widely used the surface energy balance (SEBAL) model to compute the variation in evapotranspiration under actual field conditions for the area covered by Landsat image in the north-west Bangladesh (Bastianssen et al and 2002, Ahmad et al. 2009). SEBAL is an image processing model which computes a complete radiation and energy balance along with the resistances for momentum, heat and water vapour transport for each pixel. The key input data for SEBAL consists of spectral radiance in the visible, near-infrared and thermal infrared part of the spectrum and routine meteorological data. For this study we used 6 Landsat 5 images (path 138/row 043) from January to April 2009 to cover the boro irrigation season. The number and temporal spacing images was chosen based on availability of cloud free images and critical crop growth stages. Actual evaportranspiration (ETa) results on image acquisition dates are shown in Figure 4.4. The relatively low spatial variation of ETa in January image is related to the low evaporative demand in the winter season (and very little rice area). But as the evaporative demand increase from January to April, high spatial variations are discerned within the same and between different land uses. For example, the greenish blue to dark blue areas in March to April represent boro rice and water bodies. In order to estimate seasonal water consumption and net groundwater use (for dry/boro season), daily evapotranspiration values were temporally integrated using available climatic information (using approach Bastiaanssen et al. 2002, Teixeira et al and Ahmad et al. 2005). As the key water sources contributing to actual evaportranspiration in dry season are rainfall, irrigation (almost entirely from groundwater as explained under section 2.4), capillary rise and (to a lesser extent) residual soil moisture, net groundwater use 47

50 (Ahmad et al. 2005) is computed using spatial estimates of actual evapotranspiration and rainfall data. Such estimates of net groundwater use are regarded more accurate than those estimated using conventional techniques. However, it is pertinent to note that net groundwater use is not equal to pumping but it reflects net depletion of groundwater. The detailed field monitoring and modelling studies in the rice systems of the Indo-Gangetic basin has confirmed that irrigation applications for rice are much more than actual consumption as a large fraction (up to 50%) of applied irrigation water returns back to the aquifer as deep percolation (Ahmad et al. 2002; Jalota and Arora, 2002; Humphreys et al. 2010). Therefore actual groundwater pupmage for rice crop could be almost double than of net groundwater use. In order to account water consumption and water use performance, using the boro land use classification map and thana (sub-district level administrative) boundaries, seasonal volumes of net groundwater use were computed (Figure 4.5). The net groundwater use in the modelled area is about 4.4 km 3 (55 % of which comes from about 46 % of the gross area under boro rice); the total regional water use for the whole northwest region in the same period was estimated at about 7.5 km 3 using the regional water balance model. These estimates provide the first comprehensive and independent assessment on the extent of net groundwater use for boro rice production. Such information is lacking in south Asia particularly for Bangladesh where tubewells used for groundwater pumping are generally private, and there is little information about the extent of groundwater use for dry season irrigation. 48

51 Figure 4.4 Patterns of actual evapotranspiration for groundwater dominant irrigation areas in the north-western part of Bangladesh. Figure 4.5 Volume of net groundwater use for boro rice production in the north-western part of Bangladesh. (Grey and black colour lines represents Thana and District boundaries respectively.) 49

52 Figure 4.6 Spatial variation of boro yield in 2009 in northwest Bangladesh. (Grey and black colour lines represents Thana and District boundaries respectively.) 50

53 Figure 4.7 Spatial variation of boro water productivity in 2009 in northwest Bangladesh. (Grey and black colour lines represents Thana and District boundaries respectively). Based on the actual evapotranspiration estimates for boro (dry season irrigated rice), and thana production records (with yields shown spatially in Figure 4.6), we have estimated the water productivity (in kilograms of grain per m 3 of actual evapotranspiration) by thana ( Figure 4.7). The results shows considerable variation in both land and water productivity within and between administrative units. Although the overall boro yield and water productivity values are higher in north-west region of Bangladesh than the neighbouring Indian states in the Eastern Gangetic Plain, considerable scope exists for further productivity improvements within Bangladesh by reducing the gaps between high and low productivity areas. The detailed crop/apsim modelling, as described in next section, was applied to identify key impediments and management opportunities to improve productivity. APSIM crop growth modelling Aligning point scale models such as APSIM with estimates of ET at a regional scale enables evaluation of water productivity at a regional scale as a consequence of change at the farm level. This simple example describes one approach for model calibration using Thana level statistical and published rice yield data. Simulated yield and ET estimates using long-term historic records and downscaled GCM derived future climate scenarios can provide suitable data in assessment of water productivity (WP). This example will focus on the area planted to boro rice in the Rajshahi region of NW Bangladesh (Table 1). Estimates of daily boro ET on six days between 1 Jan 2009 and 30 Apr 2009 and estimates of total ET (maximum and minimum) for the entire period were derived from the remote sensing and spatial analysis (last section). Table 4.1 Rajshahi district (NW Bangladesh) statistical data for boro production in District Rajshahi Thana Boro area (ha) Boro production (million t) Boro yield (t ha -1 ) Rajshahi city corporation area Rajshahi Paba 5,450 18, Rajshahi Durgapur 6,000 23, Rajshahi Godagari 19,200 65, Rajshahi Tanore 17,500 65, Rajshahi Mohanpur 7,180 27, Rajshahi Bagmara 20,000 77, Rajshahi Putia 3,550 15, Rajshahi Chargat 800 3, Rajshahi Bagha 1,350 5, Maximum 4.41 Minimum 3.36 Mean 3.83 Long-term daily climate data obtained from the Bangladesh Meteorological Department (BMD) for Rajshahi from 1964 to 2010 was converted to a suitable format for APSIM. Total solar radiation was generated from both sunshine hours using Angstrom (1924) and from maximum and minimum temperature using a Campbell - Donatelli method (Donatelli et al., 2003) and compared with remote sensing data from 1983 to 2012 (NASA-POWER). The temperature derived radiation values are used in this example. Generated mean daily radiation is compared with long-term monthly mean values for Rajshahi along with radiation 51

54 intercepted at the top of the atmosphere (ToA) and clear sky radiation, calculated on a daily basis (Figure 4.8). Simulation models, such as APSIM, based on radiation use efficiency (RUE) require some effort on the part of the user in assessing the quality of key climatic inputs of solar radiation, temperature and rainfall. Access to quality long-term solar radiation has been a limiting factor as has poorly generated values derived from sunshine hour data. A generic soil based on a previously characterised soil from the BARI campus, Gazipur modified with locally measured soil properties was derived for this example. Hydraulic conductivity estimates for soils in this region were used to limit drainage loss from these ponded systems under boro rice. Sensitivity to changing hydraulic conductivity on required irrigation and potential ET need further quantification. Figure 4.8 Mean daily solar radiation (calculated) for Rajshahi for years Solar radiation (NASA-POWER) at top of Atmosphere (ToA) and calculated clear sky radiation (Clear sky) are included for comparison. Mean daily rainfall highlights the monsoon period. The majority of the boro rice planted is of a HYV variety (87%) with hybrid and local varieties (11% and 0.1 %) the balance of the ha planted in the Rajshahi district in Three varieties were initially selected for use in APSIM (local-generic, HYV-BR28 and Hybrid-BDan7). All varieties received adequate levels of irrigation (15-16 irrigations of 50 mm) and fertiliser (60, 80 and 130 kg N ha -1 ). Simulated yields were compared with district rice yields (if available) for 2007 to Simulated yields compared favourably with district averages for each of the local, HYV and hybrid varieties ( Figure 4.9). Simulated daily ET measurements were compared with ET estimates derived from remote sensing (Figure 4.10a). Calculated daily values for atmospheric evaporative demand (Eo) and radiation derived ET are included in Figure 4.10a for comparison. In this example crop ET was limited to atmospheric demand Eo during crop growth. Although only one soil type (generic) and one climate location was considered for the entire Rajshahi region the simulation was able to closely capture the 464 mm total ET (based on remote sensing) between 1 January and 30 April Characterisation of spatial soil mapping in specifying the key soil types and their area planted to boro and quantifying actual irrigation applied and potential system losses (drainage) will improve the accuracy and therefore the value of this approach in WP assessment. That we 52

55 have obtained a reasonable match between the crop model and the remote sensing model improves our confidence in our ability to simulate crop performance, and also provides the link from field scale processes (and how alleviation of constraints to production at the field scale may be tested via modelling) to regional water management and sustainability. Figure 4.9 Predicted (lines) and observed (points) district level rice yields for local, HYV and Hybrid varieties for 2008 to Figure 4.10 (a) Simulated daily ET (ET -HYV), radiation derived ET (ET Radiation) and potential ET (Eo) during a boro (HYV) crop between 1 Jan 2009 and 30 April Symbols (ET RS derived) represent ET estimates based on remote sensing for selected dates. (b) Total ET (accumulated) between 1 Jan 2009 and 30 April 2009 for local, HYV and Hybrid varieties. Symbols represent maximum and minimum total ET estimates derived from remote sensing. This configuration for each boro type was then run for a 45 year period ( ) with the addition of a hypothetical (potential) management treatment where water and nitrogen are applied in response to the onset of water or nitrogen generated stress in a hybrid rice crop (Hybrid-BDan7). Simulated yield and ET for 53

56 all four treatments are compared as probability of exceedence graphs (Figure 4.11). Applied irrigation and drainage is compared in Figure Considering the uncertainty associated with specifying APSIM at one point and then extrapolating simulated outputs to represent a broader regional scale the simulation results indicate scope for improved water and nitrogen management in these rice systems. Once calibrated and validated against local observations cropping system models become useful tools for scenario analysis. In this example current practice is compared with improved management (applied water and nitrogen) and two future climate scenarios using downscaled GCM data (ECHAM5 & GFDLCM2) for 2021 to 2040 (Figure 4.13 and Figure 4.14). Future scenarios (in this example) only compare climatic differences influencing crop growth and reflect the underlying assumptions made in specifying APSIM for this case study. This example does demonstrate one method by which models can be employed in exploring changes to existing irrigation and fertiliser application practices and the scope for improving overall water productivity. Transferability of simulation results to actual on-farm management will require rigorous testing. Combining modelling with strategic field experimentation will be critical in filling knowledge gaps in our understanding of constraints to local farming systems, help reduce the risks associated long-term field trials and contributes to future predictive capability of the models in addressing key issues around food security. Figure 4.11 Probability of exceedance graphs for simulated (a) yield and (b) ET for local, HYV, Hybrid and an optimised potential based on current irrigation and fertiliser management options for Rajshahi district,

57 Figure 4.12 Probability of exceedence graphs for simulated (a) applied irrigation and (b) drainage for local, HYV, Hybrid and an optimised potential based on current irrigation and fertiliser management options for Rajshahi district, Figure 4.13 Probability of exceedence graphs for simulated (a) yield and (b) ET for a HYV boro crop grown using current practice (baseline), optimised water and nitrogen management (optimal) using past Dhaka climate data for 1962 to Additional simulations for two future climate scenarios based on downscaled GCM data from the ECHAM5 and GFDLCM2 models are include for comparison. Figure 4.14 Probability of exceedence graphs for simulated (a) applied irrigation and (b) drainage for a HYV boro crop grown using current practice (baseline), optimised water and nitrogen management (optimal) and two future climate scenario (ECHAM5 & GFDLCM2). 4.4 Conclusion: suggested approach for FSIFS project We have attempted to show that systems models are useful in understanding constraints and opportunities in crop production for food security. Furthermore, considerable understanding is derived from linking models from the plot or farm scale to the regional scale. At the plot or farm scale, crop systems models are useful in assessing yields, yield constraints and opportunities for improvement; they are particularly useful in assessing the impact of improvements over a long run of years with variation in rain, temperature and so on. This is complemented by spatially explicit modeling based on remote sensing, to identify well 55

58 performing areas from those where crop water use may be inefficient, or yields are lower than adjacent areas. The remote sensing models also give estimates of regional evapotranspiration. At the regional scale, water balance models help estimate regional water availability and the impact of large scale policy and management. Assessments at these three scales together show how much yield may be improved (and how this varies through years of varying climate), where yield may be improved, and how this adds up to an increase in production at a regional scale given constraints on how much water may be sustainably used. Another advantage of the systems approach we have put forward here is that the many gaps in data and knowledge may be dealt with by looking at the whole system, and by drawing on different sorts of data and modelling. In northwest Bangladesh this has given us a reasonable picture, if somewhat rough in terms of detailed numbers. However, it does show the value of looking at the whole water balance, which constrains many numbers to a modest range of values, and of cross-checking values with different methods and models, placing the understanding in the framework of the overall system. With a detailed look at northwest Bangladesh, we have demonstrated potential for improving yields and optimising water use through improved irrigation and fertiliser application, both in the current climate and under a projected future climate. We have further shown that the yields (whether per unit of land or per unit of water) are lower in some parts of the area than others, and these are probably the most fruitful areas to target with research and extension. The existence of the low performing areas reinforces the conclusion in Chapter 3 that there appears to be scope for increasing production. Finally, we showed that the regional groundwater level trends are probably influenced by long term rainfall variability, and this should be taken into greater account when assessing sustainable groundwater use. The median climate change expectation of greater rainfall in the region will, if realised, lead to higher groundwater levels than at present, possibly implying that more groundwater could be sustainably used in some areas. However, uncertainty in climate change projections means that median expectation may well not be realised: the rainfall could decrease. We suggest that a more detailed systems study of northwest Bangladesh would reveal the constraints, opportunities and sustainable levels of water use in the area as a whole. It would provide a better and more quantitative assessment than our mostly qualitative conclusion to Chapter 3 that the region may be able to increase production to meet future demand. Bihar and West Bengal lacks even a first exploratory study of the type we have made in this chapter. An exploratory study would be useful as an initial assessment of food production constraints and opportunities. It would also reveal uncertainties in the data (such as uncertainties in climate change impacts), and data availability and gaps. 56

59 5 Uncertainty, decisions and systems modelling It is impossible to predict future random events (drought, flood) and market prices (rice and wheat prices, and future prices of electricity and diesel ). However, when making decisions now, we need to be aware that the outcomes of the decisions today will be impacted on by these uncertain future events, either adversely or favourably. It is prudent to understand and hopefully quantify the impacts due to future uncertainty, particularly any major adverse impact. For example, in the food-security arena, we can support farmers to invest in a particular crop type, however, this crop type might need a fixed amount of water to have the desired yields. Future rainfall patterns can change significantly due to climate change scenarios, and it is also possible that a large proportion of farmers decide to plant the same crop type at the same time. In such scenarios, irrigation water will be in short supply and ground water levels can drop substantially. Consequently, the program to support planting the particular crop can potentially have substantial unforeseen negative results for the farmers. The uncertainty about future rainfall and the depletion of ground water due to overplanting of such a water intensive crop type should be analysed and quantified. Policies need to be explicitly set up in advance to prevent and contain potential adverse outcomes from such uncertainties. 5.1 General overview: the role of uncertainty analysis in systems modelling In systems modelling, particularly for water systems, multiple risk factors are present. Main risk factors are future rainfall, underground water table, and river inflows and outflows, other risk factors can include the population growth, crop prices on the market, energy cost such as electricity price, or even local GDP figures. These uncertain risk factors can directly affect the outcome of water balance system. These risk factors are impossible to predict and their aggregate impacts on a water system can be difficult to estimate because the impacts from these risk factors are not independent. For example, if the rainfall decreased in the future due to climate change, groundwater would likely be consumed far more than normal, and the watertable level would be lower, the cost of using groundwater would be higher, but the cost increase is not necessarily proportional to the decrease in rainfall. Farmers relying on groundwater for growing a particular crop type would thus be affected by this cost increase, however the crop price might increase at the same time, and thus the farmers income would not necessarily be affected. 5.2 Uncertainty modelling overview of approaches The valuation of development programs is similar to investment decisions for infrastructure under uncertainty, because infrastructure investments normally rely on annual cash returns stretching 20 or 30 years into the future. In standard investment decision making for infrastructure, net-present-value (NPV) is commonly accepted as the valuation approach. In the NPV approach, future cash flows are assumed to be known, and the cash flows of future years are discounted back to today, and the accumulated discounted value is the NPV value of the investment project. To account for uncertainty, future cash flows can be altered according to different assumptions, the sensitivity of the NPV value to these assumptions is used to indicate the level of uncertainty in the valuation. The NPV approach does not naturally include uncertainty due to random future events. Rather, future uncertain events are considered as a separate NPV valuation, and the different NPV valuations are used to indicate uncertainty. 57

60 Monte-Carlo simulation techniques can be used to simulate random events and market prices. These simulated events and prices are directly used in NPV valuations, thus the NPV method can adopt uncertainty in the valuation, in general, the NPV value is calculated as the future expectation of random payoffs. The hybrid approach of using simulated random events and prices for NPV calculation is an intuitive way to account for uncertainty. However, such a hybrid approach lacks the underlying mathematical consistency and vigour. There are other methods, such as statistical decision theory (Pratt et al., 2001) which relies on decision trees and uses heavily Bayesian framework to reach optimal solutions. In the past twenty years, increasingly, a Real-Option approach (Dixit and Pindyck, 1993) has been used to consider future uncertainty. The Real-Option approach is based on financial options-pricing theory. In the Real-Option approach, future cash flows are assumed to be unknown and uncertain, cash flows are determined in the future directly by future random events or market price signals. For example, when calculating future cash income from a particular crop, the future crop price is uncertain. Additionally, the cost of operating a water pump for irrigation is determined by the future electricity price. The operating cost is also affected by the depth of the watertable in any particular future year. The uncertainty in future electricity price and watertable level will also contribute to the uncertainty in future cash incomes. Real option approach is also more intuitive, and it often relies on the financial risk management of hedging to reduce or remove uncertainty in decision making. In Real-Option valuation approach, for the above example, the future crop price is assumed to follow a random-walk stochastic process, and the process is calibrated to historical price data or to forecasting scenarios. The future electricity prices can be similarly represented by another stochastic process. The watertable level is assumed to be deterministic in the following first case study, and known for the future. For the second case study of this Chapter, the watertable is dynamically determined through a water balance model. For more realistic modelling, the watertable can also be assumed to follow a stochastic process. For this example, we can then simulate the future prices of the crop and electricity for the future through a Monte-Carlo simulation technique. At each future year, the simulated crop price and electricity price are used to calculate the cash income for a given watertable level. Decisions can be made to determine if the crop should be planted when the cash return is not sufficient. These various simulated cash returns are then discounted back to today and averaged to produce the value of the investment in a particular crop for the future, say 10 years. We use the following case study to outline a stylised implementation of Real- Option valuation approach. Case study 1 We apply the machinery of real options valuation in decision making under uncertainty to the design of development programs in northern India. In rural northern India, different development programs can be deployed to increase farmers productivity in rice farming. One of the main focuses for increasing productivity is the access to water supply. There exist two different ways of acquiring water for rice farming: buying water from irrigation network, installing wells and buying pumps to access underground water supply. The decision on investing in which water resources is affected by the large degree of uncertainty in future rice price, irrigating water price, and in the case of using underground water, the uncertainty about future watertable level in the ground (particularly if underground water is depleted due to excessive consumption by the local community). Given these uncertainties, it is not straightforward to decide on which approach would provide the more efficient or higher-value development programs. Specifically, the investment decision on water supply for rice farming is to choose: 1) installing wells and pumping underground water onsite owned by the farmer (and the associated capital works and buying pumps), or 2), buying water directly from another irrigation source on the market. The objective is to select a more profitable way of using water resources to produce rice, and once the selection of water resources is made, the program will be in place for the subsequent 10 years. 58

61 Because the impact from the investment decisions will last 10 years, the future rice price of the next 10 years will have a major impact on the profitability of the rice farming, the irrigation water available for sale, and the underground watertable level of the next ten years will also have a substantial impact on the profitability because when the level of underground watertable is low, the cost of pumping the required water will increase significantly, both in terms of the extra energy and time required. When the irrigation water level is low, the cost of buying water becomes high, through either market price increase and/or difficulty in acquiring the water. The future rice price, the underground watertable level and the irrigation water level are therefore treated as uncertain stochastic processes. It is possible to include the electricity price as an extra stochastic variable that can impact on the profitability of the decision. In this example, due to the lack of data on electricity prices from the region considered, we only consider rice price, irrigation water level and groundwater table level as the uncertain stochastic variables. Modelling of rice price, water table and irrigation water Our assumed future rice price, displayed in Figure 5.1, is obtained by taking the yearly historical price from the last 30 years from Kumar et al. (2011). The volatility of the rice price is then calculated as the standard deviation of the monthly prices over the preceding year. We note that the data over the last (12) years has been adjusted to account for the large change observed in the historical rice price. We selected a random 12 month sequence from an earlier time to smooth the futures price Value ($/kg) /01/ /12/ /11/2032 9/11/2043 Price Std Dev Figure 5.1 The futures price of rice in U.S. dollars per kilogram of produced rice. The irrigation water level in the future is also a random stochastic variable, and we assume it follows a mean-reverting process. Here, the availability of irrigation water is represented by a water level index and treated as a stochastic variable. The irrigation water level index is quantified by a mean value a fractional water level and the volatility about this mean. In this study, we model two separate irrigation water level behaviours: a constant water level shown in Figure 5.2, to represent the scenario of average water supply around the 80% capacity level. a smoothly decreasing level displayed in Figure 5.3, to represent the scenario of diminishing irrigation water supply, due to potential future drought, increasing competition from other water usage, and/or climate-change effect. 59

62 Irrigation Water Level Std Dev Fractional Water Level /01/ /12/ /11/2032 9/11/2043 Figure 5.2 A constant mean irrigation water level over time, with its time-varying standard deviation. Irrigation Water Level Std Dev Fractional Water Level /01/ /12/ /11/2032 9/11/2043 Figure 5.3 Alternative behaviour of the mean water level where the level decreases smoothly in time. We also assume the underground watertable follows a mean-reverting stochastic process, with the same two scenarios as for the irrigation water level; a constant average level at 50% shown in Figure 5.4 and, corresponding to the diminishing levels of irrigation water due to water shortage, a decreasing underground watertable level in time is shown in Figure 5.5. Watertable Level Std Dev Fractional Water Level /01/ /12/ /11/2032 9/11/

63 Figure 5.4 Groundwater table: mean water table level and the standard-deviation in time. Watertable Level Std Dev Fractional Water Level /01/ /12/ /11/2032 9/11/2043 Figure 5.5 Decreasing groundwater table: mean water table level and the standard-deviation in time. The stochastic process for rice price is assumed to be geometric Brownian motion, whereas the irrigation water level and underground watertable are assumed to be mean-reversion processes. The data shown in Figure 5.1 to Figure 5.5 are used to calibrate the stochastic processes for rice price, irrigation water level and underground watertable. Figure 5.6 displays 50 independent stochastic trajectories for the rice future price after calibration (the rice price data of Figure 5.1 is used to calibrate the rice price stochastic process). Figure 5.6 Sample stochastic trajectories for the price of rice as a function of time. After the stochastic process for irrigation water level is calibrated by the data shown in Figure 5.3, we can use the calibrated stochastic model to forecast a sample of 50 independent stochastic trajectories for the irrigation water level, as shown in Figure 5.7. In the real option valuation, we introduce boundary conditions such that the water levels remain between 0 and 1; for example, if the trajectory follows a path such that the water level goes beyond 1, the value is restricted to remain at 1 unless the stochastic path returns to a value less than 1. 61

64 Fractional Water Level /01/2011 1/01/2023 1/01/2035 1/01/2047 Figure 5.7 Fifty sample stochastic trajectories for the irrigation water level as a function of time. Payoffs from irrigation vs groundwater We use data from the case-study presented in the work of Kumar et al. (2011) which focused upon the effects of introducing metered power rates on groundwater levels and the sustainability of farms. Kumar et al. (2011) calculated the average cost of each water source by taking into account the cost of infrastructure (well, pumps etc), the cost of electricity, maintenance, the average usage as well as the amount of water pumped by the well. The average cost was found to be C w =0.77 Rs per cubic metre of water for well owners and C b =0.7 Rs per cubic metre to directly buy irrigation water. Converting to U.S. dollars, these values become C w = $/m 3 and C b = $/m 3. We set the initial cost of the well to be five times higher for the first year to account for installation costs and use the value C w to represent ongoing costs of maintaining the wells. We augment the cost of buying irrigation water by linking it to the water level. We create a disincentive to keep purchasing water as the water level drops by multiplying the buying cost C b or C w by the inverse of the water level. In this approach, the cost of the water will be higher or lower depending on if the current water level is lower or higher than the long-term average water level. The average water level is set to be a level of 0.8 or 80% of total available water for the irrigation source and 0.5 or 50% for the watertable. To find the value of the crop produced under either source of water, we use the physical water productivity data from Index Mundi ( expressed in units of kilograms of crop produced per cubic metre of irrigated water. Values for paddy rice in south Bihar are P o =2.5 kg/m 3 for well owners and P b =2.69 kg/m 3 for water buyers. These values are multiplied by the rice futures price to obtain the value of crop produced per cubic metre of irrigation water used. In this example, the decision for adoption of buying irrigation water or using underground water lasts for 10 years. We can calculate the Real Option value (ROV) or spread-profit from relying on buying irrigation water and not investing in using wells as water resources. This spread-profit is expressed as the extra profit from purchasing irrigation water while foregoing the opportunity for installing wells and pumps to produce rice. The produced rice is then sold on the open market: n 10 ROV = { e ri [(V R P b A b C b ) V R P w A w j=0 i=0 C V w } (1) w V b Here, V R is the future price of rice, A W =50% is the average underground watertable level. A b =80% is the average irrigation water level. V b is the current irrigation water level, whereas V w is the current underground watertable level. r is the domestic interest-rate. 62

65 In equation (1), the three quantities V R, V w V b are stochastic random variables simulated via their corresponding stochastic models. A total of n Monte-Carlo simulations are performed, these n simulated values are averaged to produce the expected Real-Option value of (1). In this study, the Monte Carlo engine generates 1000 different realisations of the future rice price and the future watertable and irrigation water level. For each decision date between year 2011 and 2040, the payoff of (1) is calculated for its subsequent 10 year investment period, and the real option value is computed as the average of the 1000 simulations. Varying Average Water Levels Constant Average Water Levels Value ($/m 3 ) /01/ /12/ /11/2032 9/11/ Figure 5.8 Real option values for a constant (blue curve) and decreasing (red curve) available water level. In Figure 5.8, the horizontal axis is the decision date, whereas the vertical axis is the spread-profit of equation (1) as the Real-Option value. At each future date, the Real Option value (spread profit) indicates the extra benefit from investing in directly buying irrigation water over pumping underground water for the subsequent 10 years. A negative Real-Option value indicates that the installation of wells to tap underground water is a better strategy. The more valuable the real option, the more money is earned from buying irrigation water than from installing and using a pump over the length of the investment period (10 years). A decrease in value of the option to 0 or negative indicates that installing a pump yields a better profit. In Figure 5.8, for the case of constant water levels, the value is positive for the whole period of This indicates that relying on buying irrigation water is a better choice. When the water levels change as shown in Figure 5.3 and Figure 5.4, the Real Option value decreases in value as the water levels decrease, i.e. the cost of buying water increases and the option value decreases. In year 2023, the value changes from positive to negative, indicating installation of wells and pumps should be more profitable than buying irrigation water. 5.3 Example application: the effect of uncertain rainfall on the cropped area of rice The availability of ground water to irrigate crops is a key component in food security, particularly in developing regions such as the Indo-Gangetic Basin (Sikka et al, 2009.). Policy settings implemented by governmental authorities may impact on the livelihoods of farming communities in the region, particularly under the uncertainty of the climate conditions into the future. For example, imposing a limiting minimum value on the ground watertable level may result in a reduction in crop production if rainfall decreases in the future there may be insufficient ground water available to irrigate a given area of crop, and so the cropped area may have to be reduced. Alternatively, different technologies may have to be deployed to improve the efficiency of irrigation, improving the yield of crops for a fixed volume of irrigated water. In particular, the choice of appropriate technology to apply may be dependent on the future rainfall, crop prices, the watertable level and electricity price amongst others. The future values of these quantities are uncertain and so the decision to invest in a particular technology should take into account these future uncertainties. This decision-making 63

66 process is therefore similar to making investment decisions in infrastructure under uncertainty, where real options valuation can be used to quantify these decisions. The behaviour of the watertable level over time may be useful in making investment decisions under the uncertainty of future rainfall. Specifically, the knowledge of the change in groundwater level caused by cropping a fixed area of crop at a given annual rainfall may allow an adjustment of the cropped area to maintain the water table above a critical limiting value. We use the water balance model presented in Section 4.3 to determine the water table level for a given rainfall, cropped area and evapotranspiration (ET) value. Utilising this water balance model, we developed a model that can dynamically compute the average cropped area that keeps the water table level above a critical value under the uncertainty of future rainfall. To develop this model we made three key assumptions. As a simplification, we assumed each region was identical, with an area equal to the average regional area. Furthermore, we calculated the average monthly ET and rainfall values (averaged over the 26 years of data provided). This provided an average profile of ET and rainfall for a single year. We then restricted the model to examine the change in water table level over a period of one year, rather than using monthly time steps. For the ET values, we could set the ET values to be equal to the 26 year average, or we could set the ET values to follow certain scenarios. We allow the annual rainfall amount to be stochastic. The annual rainfall amount was distributed monthly according to the average monthly distribution. We then calculated values of the change in water table level ( W) for different annual rainfall (R) amounts and cropped area (A) for the boro rice crop, thus generating numerically the function W = f(r, A). The change in water table level was calculated after replicating the annual rainfall over 26 years, allowing the water balance to reach a steady state independent of the initial water level. We can then use numerical interpolation to find W for any given rainfall value and cropped area. Different scenarios of future rainfall can then be implemented in our stochastic model, where we can model the rainfall as either a geometric Brownian motion or mean-reverting process. We then generate many different trajectories of the annual rainfall into the future. Starting at an initial cropped area A 0, and water table level W 0, we calculate W for each path as a function of time. If we find that the water table level W will drop below a critical value W C for a given annual rainfall R i, we can adjust the cropped area at a fixed rainfall value to find the critical change in water table level ΔW C such that W= W C. That is, we solve the equation f(r i, A) = ΔW C, for the area A. We restrict the region of area to be non-negative for obvious reasons. We can thus obtain the average cropped area of rice as a function of time by averaging over the different stochastic trajectories, in addition to the average water table level. We can then develop the cash-flow V generated from growing the crop by multiplying the area A by the yield of crop Y in kg/km 2 and the crop price P in $/kg, i.e. V = AYP. Case study 2 To illustrate the operation of this model, we examine the behaviour of the cropped area of boro rice under the uncertainty of future rainfall. We defined an initial cropped area of 1200 km 2 (representing approximately 55% of the available land), an initial water table height of 2m, and a critical lower limit on the water table level of 1m (in terms of the arbitrary datum of the model, see Box 2), here the initial absolute water table height can be set at any arbitrary value, the change in watertable level is the important indicator. Two stochastic risk factors are used the rice price and the annual rainfall. We calibrate the increasing rice price in US$/kg to a geometric Brownian motion process. 50 sample paths are shown below in Figure 5.9, while Figure 5.10 displays the average rice price over 1000 stochastic trajectories. 64

67 Rice price, $/kg Figure 5.9 Fifty simulated stochastic paths of the rice price Rice Price ($/kg) Time (years) Figure 5.10 The rice price averaged over 1000 stochastic trajectories The second risk factor, the annual rainfall, is assumed to follow two different scenarios, and is calibrated to a mean-reverting stochastic process. The annual rainfall in the first scenario is assumed to fall from approximately 2000mm to approximately 1450mm over a 26 year period. A second scenario is chosen such that the annual rainfall remains approximately constant. The standard deviation of the rainfall was assumed to be increasing with time T in years as R(T)=100 T mm. Fifty sample paths representing the first scenario is displayed in Figure 5.11, while Figure 5.12 displays the average annual rainfall computed over 1000 stochastic trajectories for both scenarios. 65

68 Rainfall, mm Figure 5.11 Fifty simulated stochastic paths of a scenario of rainfall where the rainfall on average drops from 2000mm to 1450mm. Figure 5.12: The average annual rainfall for two different scenarios obtained by averaging over 1000 trajectories. For scenario 1 where the average rainfall is assumed to drop, and the ET is assumed to correspond to dry seasons, we calculated the policy effect of setting a lower limit on the water table level on the cropped area. We performed simulations both with and without the limit on water table level. Figure 5.13 displays the resulting water table levels from with and without setting limits to the water table level, while Figure 5.14 exhibits the corresponding changing cropped area. Without the policy limit on watertable level, the water table level remains constant for approximately 10 years, and then decreases monotonically, reaching a level around 0m above the arbitrary datum at the end of the simulation. The cropped area remains constant at the initial value of A=1200 km 2. Introducing the limit on the water table level, the reduction in water table level is slower because the cropped area would decrease if the water table level is less than the policy limit, and on average does not reach the lower limit of 1m. The cropped area reduces to a value around 1000 km 2 after 26 years in order to maintain a water table level more than 1m above the arbitrary datum. 66

69 Figure 5.13: The water table level above an arbitrary datum resulting from maintaining the cropped area (blue curve) and allowing the area to vary (red curve) Cropped Area (km2) Fixed Area Variable Area Time (years) Figure 5.14: The cropped area resulting from maintaining a constant area (blue curve) and allowing the area to vary (red curve). The reduction in cropped area results in a corresponding decrease in cash-flows generated from selling the crop; without the policy limit on water table level, the average total cash-flow was 2.87 billion USD, while introducing the limit on water table level reduces the average total cash-flow to 2.73 billion USD. We then investigated the effect of setting different water table limits on the cropped area in more detail, varying the lower limit from 0m to 2m above the arbitrary datum, the initial starting height. Figure 5.15 displays the average water table levels resulting from simulations with 5 different watertable limit values (including setting no limit). For higher watertable limits, the water table level asymptotes to the limit within the simulation period. As the limit is set at lower values, the calculated average watertable levels remain higher than the preset limits, which indicates the model works as expected. 67

70 Figure 5.15: The simulated water table level for five different values of the water table limit. The corresponding cropped area is plotted in Figure 5.16 for the five policy limits for water table levels. A strong trend is evident the cropped area remains constant for the first 10 years, and then reduces in response to the reduction in rainfall and to the values set as the limit on watertable levels. Furthermore, the introduction of a stricter (higher) limit on the water table level results in a more severe decrease in cropped area at an earlier time. This causes a decrease in the average cash-flow generated from growing rice under the policy setting of a stricter water table limit. In the case of keeping the water table level to be constant at its initial value of 2m, the average cash-flow reduces to 2.66 billion USD, a reduction of approximately 200 million USD from the cash-flow value obtained from not imposing any limit on watertable level. Figure 5.16: The simulated cropped area for five different values of the limit on the water table level. We then examined the behaviour of the water table level and cropped area for the two different rainfall scenarios presented in Figure We fixed the water table limit to be 1m. The water table level for each scenario is displayed in Figure 5.17, while Figure 5.18 displays the average cropped area. The water table level and cropped area remain constant for scenario 2 (average rainfall constant), while the cropped area 68

71 reduces to below 1000 km 2 for the first scenario where the average rainfall reduces to approximately 1450mm. Therefore, we can observe that the predicted future scenario of rainfall has a significant effect on the cropped area (and therefore cash-flow) required to maintain a policy limit on water table level. Figure 5.17: The water table level resulting from three different rainfall scenarios. The scenarios correspond to those displayed in Figure Cropped Area (km2) Scenario 1 Scenario Time (years) Figure 5.18: The cropped area resulting from three different rainfall scenarios. The scenarios correspond to those displayed in Figure Conclusion: suggested approach for FSIFS project There are many ways of dealing with uncertainty, from Monte-Carlo simulation to statistical decision theory. We have demonstrated another approach, Real Option valuation, in which decisions are assessed in 69