DL_10.1: Modelled and evaluated whatif? scenarios of predicted climate change

Transcription

1 Project no: GOCE Project acronym: BRAHMATWINN Instrument: Specific Targeted Research Project Thematic Priority: Global Change and Ecosystems Project title: Twinning European and South Asian River Basins to enhance capacity and implement adaptive management approaches Deliverable Report DL_10.1: Modelled and evaluated whatif? scenarios of predicted climate change Due date of deliverable October 2009 Actual submission date December 2009 Start date of project: Duration: 36 Month Organisation name of lead contractor for this deliverable: LMU Project homepage: Dissemination Level: PU

2 Content Introduction... 5 Methodology... 5 Achievements... 5 Directory of figures Figure 1: Developing of annual air temperature in the UDRB for the past and the four scenarios from 1971 to Figure 2: Developing of the annual air temperature in the UBRB for the past and the four scenarios from 1971 to Figure 3: Developing of the annual precipitation sum in the UDRB for the past and the four scenarios from 1971 to Figure 4: Developing of the annual percentage of snow precipitation in the UDRB for the past and the four scenarios from 1971 to Figure 5: Developing of the annual precipitation sum in the UBRB for the past and the four scenarios from 1971 to Figure 6: Developing of the annual percentage of snow precipitation in the UBRB for the past and the four scenarios from 1971 to Figure 7: Developing of the annual evapotranspiration sum in the UDRB for the past and the four scenarios from 1971 to Figure 8: Developing of the annual evapotranspiration in the UBRB for the past and the four scenarios from 1971 to Figure 9: Developing of the mean annual discharge in the UDRB for the past and the four scenarios from 1971 to Figure 10: Developing of the mean annual discharge in the UBRB for the past and the four scenarios from 1971 to Figure 11: The mean annual precipitation sum [mm] in the UDRB and the UBRB for the past 30years period from 1971 to 2000 and for the future 30years periods from and 2051 to 2080 according to the A1B and B1 scenario Figure 12: Difference of the mean annual precipitation sum [mm] in the UDRB and the UBRB of the future 30years periods from and 2051 to 2080 according to the A1B and B1 scenario to the past 30years period from 1971 to

3 Figure 13: The mean annual evapotranspiration sum [mm] in the UDRB and the UBRB for the past 30years period from 1971 to 2000 and for the future 30years periods from and 2051 to 2080 according to the A1B and B1 scenario Figure 14: Difference of the mean annual evapotranspiration sum [mm] in the UDRB and the UBRB of the future 30years periods from and 2051 to 2080 according to the A1B and B1 scenario to the past 30years period from 1971 to Figure 15: The mean annual runoff [mm] in the UDRB and the UBRB for the past 30years period from 1971 to 2000 and for the future 30years periods from and 2051 to 2080 according to the A1B and B1 scenario Figure 16: Difference of the mean annual runoff [mm] in the UDRB and the UBRB of the future 30years periods from and 2051 to 2080 according to the A1B and B1 scenario to the past 30years period from 1971 to Figure 17: Developing of the mean monthly discharge in the UBRB for the past from 1971 to 2000 in comparison to the 30year periods from 2011 to 2040 and 2051 to Figure 18: Developing of the mean monthly discharge in the UDRB for the past from 1971 to 2000 in comparison to the 30year periods from 2011 to 2040 and 2051 to Figure 19: Developing of the percentage of ice melt on the mean monthly discharge in the Ötztaler Ache at the gauge Huben and in the UDRB at the gauge Achleiten for the decades from 1991 to 2000, and according to Glowa-Danube-Projekt ( ) Figure 20: Developing of the percentage of ice melt on the mean monthly discharge in the Lhasa River catchment for the 30-years-periods from 1971 to 2000, from 2011 to 2040 and from 2051 to 2080 according to the A1B scenario

4 Directory of tables Table 1: Results delivered for the implementation in RBIS between 2011 and 2080 with a daily temporal and a 1km² spatial resolution Table 2: Mean changes of annual precipitation, percentage of snowfall, evapotranspiration and runoff for all scenarios in the UDRB and the UBRB in comparing the future 30years periods from 2011 to 2040 and from 2051 to 2080 to the past from 1971 to

5 Introduction To determine the impact of global climate change on the water availability in both, the UDRB and the UBRB, DANUBIA was applied for the four IPCC SRES scenarios A1B, B1, A2 and the commitment run. As meteorological drivers the downscaled CLM output data of work package 2 were used. In order to calculate the impact of climate change and to provide the basis for the deviation of IWRMS options, the hydrological model results were then analysed. Methodology The regional climate model data of the CLM, driven by the global climate model ECHAM 5, were provided by work package 2 for the four SRES emissions scenarios A2, A1B, B1 and the Commitment (see WP 2). They were used as meteorological drivers for the hydrological model DANUBIA (see WP 7) to calculate the future time series of the water balance parameters for the UDRB and the UBRB for the period from 2011 to The results of the DANUBIA model runs provide distributed raster fields in a spatial resolution of 1km² and a temporal resolution of one day. The following table lists the results for both catchments which were transferred to Jena to implement it into RBIS, so that all project partners as well as stakeholders can derive relevant information. Table 1: Results delivered for the implementation in RBIS between 2011 and 2080 with a daily temporal and a 1km² spatial resolution. Meteorological parameters Unit Hydrological parameters Unit Minimum air temperature K Maximum air temperature K Mean air temperature K Precipitation sum mm Relative humidity % Direct radiation W/m² Diffuse radiation W/m² Evapotranspiration Snow water equivalent Surface runoff Percolation only UDRB: River runoff only UBRB: interflow mm mm mm mm m³/s mm To compare the model results to the data for the past period from 1971 to 2000 and determine the impact of future climate change, the results were edited according to the analysis for the past period in work package 7 (see WT 7.4). Hence, at first, the annual averages for air temperature, precipitation, evapotranspiration, rain- and snowfall as well as runoff were calculated to derive the development for the future until the model year Then the average of the periods from 2011 to 2040 and from 2051 to 2080 of the mentioned output parameters was calculated for both catchments to determine their spatial distribution. In the next step, the monthly runoff was studied to find out the seasonal water availability in the future. The influence of snow and ice on the runoff was analysed as well. In the last step, the future data were compared to the historical values and changes were assessed. Finally the regime indicators, fixed in work package 6 were edited and provided to the other project partners. Achievements The results of the future model runs and their analysis are presented in the following section. The annual averages of the air temperature (Figure 1 and Figure 2) from 1971 to 2000 and then from 2011 to 2080 show in both the UDRB and the UBRB the increase according to the four different IPCC SRES emission scenarios. For further details see WP 2. In the development of the mean annual precipitation sum (Figure 3 and Figure 5) there is no significant trend in the UDRB except of the B1 scenario where the precipitation sum decreases. On the contrary in the UBRB in all four scenario runs 5

6 a decrease of the precipitation sum is simulated. The influence of snow clearly decreases because the percentage of snowfall of the annual precipitation decreases in all four scenarios in both catchments (Figure 4 and Figure 6). Due to the increasing temperatures and longer vegetation periods the evapotranspiration sum (Figure 7 and Figure 8) increases for all scenarios in the UBRB. In the UDRB the increasing trend is clear for the scenarios A1B, B1 and the commitment run, but not for the A2 scenario. The changes in the parameters, described before, cause a reduced mean annual runoff (Figure 9 and Figure 10) in both catchments. Hence, the water availability in the catchments is decreasing in average according to the future simulations, using the CLM Echam 5 data as meteorological drivers. Then the averages from 2011 to 2040 and from 2051 to 2080 of the mentioned output parameters were calculated and compared to the past period from 1971 to Table 2 summarizes the mean changes of precipitation, percentage of snowfall, evapotranspiration and runoff for all scenarios in both catchments in the comparison of the future 30years periods from 2011 to 2040 and from 2051 to 2080 to the past from 1971 to The results described before are confirmed. Furthermore the average from 2011 to 2040 and from 2051 to 2080 of the mentioned output parameters was analysed in its spatial distribution. Thereby the consortium agreed on focussing on the B1 and the A1B scenario results. Figure 11, Figure 13 and Figure 15 show the distributed values of precipitation, evapotranspiration and runoff for the three 30 years periods, whereas on Figure 12, Figure 14 and Figure 16 the difference of the future values to the past can be seen. In the UDRB in both scenarios in the eastern parts of the catchment, especially in the south an increase of the mean annual precipitation can be determined whereas in the eastern parts precipitation decreases according to the chosen scenarios. The UBRB is also divided into regions with increasing precipitation in the Tibetan parts, whereas in the southern Indian parts, precipitation will decrease. The impact of climate change on the annual evapotranspiration is an increase in the low and high mountain ranges due to higher temperatures and therefore longer growing seasons in the UDRB. In the lower valleys, especially in the northern parts of the watershed, there is a small decrease modelled which might be caused by water stress of the plants. In the UBRB the evapotranspiration is determined by the water availability due to precipitation. With increasing precipitation in the Tibetan parts, the evapotranspiration also increases a little whereas in Assam there is an increasing as well as a decreasing trend simulated. The changes in water availability due to climate change will be split in the UDRB. In the western parts, especially in the south a decrease can be seen whereas in the eastern parts an increase in runoff is simulated. Reasons are the different precipitation and evapotranspiration patterns. Due to the scenarios there will be more water available in the northern parts of the UBRB, whereas in Assam due to the decreasing precipitation the runoff will decrease, too. In the next step, the monthly runoff was studied to find out seasonal changes in the water availability in the future. Therefore the mean 30years periods were compared. According to the scenarios the runoff will decrease in all four scenarios in the first 30 years of the modelled future period in the UBRB, especially during the summer months. The decreasing trend is going on in the second period from 2051 to Regarding the peak of the runoff, no clear trend can be seen. Figure 17 shows the mean monthly runoff in the UBRB for the three periods and the four different scenarios. In the UDRB especially during the summer month the runoff will be lower than in the past period. This trend goes on in the last period of the scenario time. As this is the growing season it is especially of importance 6

7 for the agriculture but also for energy production and shipping in the Danube, not only in the Upper basin, but also in the attached regions of Austria and Hungary. Besides the decrease of snow precipitation in both catchments, the influence of glacier melt on the runoff was analysed as well. As the modelling of the glacier dynamics cannot be reproduced in using CLM data, the station driven model runs as outcome of the Glowa-Danube project (Glowa-Danube- Project 2009) were used to show the decreasing influence of ice melt on the runoff because of the melting of the glaciers due to climate change. As analysed in work package 7, the influence of ice melt is only relevant in the alpine headwatersheds, the melt out has little effect on the runoff in the UDRB and cannot compensate future low flow during summer. Although in the first future decades the amount of glacier melt water increases and accordingly goes down because the glacier ice is melted away (Figure 19). In the Lhasa catchment, for which the glacier study was carried out in the UBRB, the melting periods are longer because of the increasing temperatures. So the influence of ice melt during the first 30-years-period from increases compared to the past period from 1971 to Afterwards the influence goes down heavily because the water reservoir of the glacier ice will almost have been melted away (Figure 20). As the influence is little compared to the influence of the monsoon precipitation this will only be of importance during spring before the monsoon precipitation starts. Summing up, there will be less water available in the UBRB as well as in the UDRB according to the model results driven by the CLM Echam 5 ouput data. These results agree with other future projections like the KLIWA ( or the Glowa Danube Results (Glowa-Danube-Projekt ( )) in the UDRB. Reasons for the decreasing runoff can be seen in less precipitation and higher evapotranspiration, although there are regional differences where even more runoff will occur, e.g. in Tibet in the UBRB or in the south eastern parts of the UDRB. Snowfall will decrease because of the increasing temperatures and the glaciers will melt away. However their melt water only has a small influence on the water availability, so the melt water will not be able to compensate less water in the rivers due to an increasing amount of melt water, because the influence is little compared to precipitation. In the UDRB it is only of importance in the alpine headwatersheds where the glaciation is higher than 20 percent. In analysing the results of the impact of future climate change, one should always take into account that the trends are not a prediction, but are the simulations under the assumption that climate develops according to the chosen scenarios. Different scenarios might produce different results. Nevertheless the results now can be used to assess the impact of climate change on water availability and for the derivation of the IWRMS options. Therefore the results were edited by appropriate means, transferred to Jena for the stakeholders and provided to all project partners. References Glowa-Danube-Projekt (Edt.) ( ): Global Change Atlas. Einzugsgebiet Obere Donau, München. 7

8 Table 2: Mean changes of annual precipitation, percentage of snowfall, evapotranspiration and runoff for all scenarios in the UDRB and the UBRB in comparing the future 30years periods from 2011 to 2040 and from 2051 to 2080 to the past from 1971 to SRES Scenario UDRB UBRB precipitation [mm] change [%] precipitation [mm] change [%] A A1B B Com snow precipitation [%] change [%] snow precipitation [%] change [%] A A1B B Com evapotranspiration [mm] change [%] evapotranspiration [mm] change [%] A A1B B Com runoff [m³/s] change [%] runoff [m³/s] change [%] A A1B B Com

9 Mean annual air temperature in the UDRB Past ERA B1 A2 A1B COM Air temperature [ C] Figure 1: Developing of annual air temperature in the UDRB for the past and the four scenarios from 1971 to Mean annual air temperature in the UBRB Past ERA B1 A2 A1B COM Air temperature [ C] Figure 2: Developing of the annual air temperature in the UBRB for the past and the four scenarios from 1971 to

10 Mean annual precipitation sum in the UDRB Past (ERA) A1B A2 B1 Com Precipitation [mm] Figure 3: Developing of the annual precipitation sum in the UDRB for the past and the four scenarios from 1971 to Percentage of snow precipitation in the UDRB Past ERA B1 A2 A1B COM snow precipitation [%] Figure 4: Developing of the annual percentage of snow precipitation in the UDRB for the past and the four scenarios from 1971 to

11 Mean annual precipitation sum in the UBRB Past ERA B1 A2 A1B COM Annual precipitation [mm] Figure 5: Developing of the annual precipitation sum in the UBRB for the past and the four scenarios from 1971 to Percentage of snow precipitation in the UBRB Past ERA B1 A2 A1B COM snow precipitation [%] Figure 6: Developing of the annual percentage of snow precipitation in the UBRB for the past and the four scenarios from 1971 to

12 550 Mean annual evapotranspiration sum in the UDRB Past (ERA) A2 Com Linear (B1) Linear (A1B) A1B B1 Linear (Past (ERA)) Linear (A2) Linear (Com) Evapotranspiration [mm] Figure 7: Developing of the annual evapotranspiration sum in the UDRB for the past and the four scenarios from 1971 to Annual evapotranspiration sum in the UBRB Past ERA B1 A2 A1B COM Annual evapotranspiration [mm] Figure 8: Developing of the annual evapotranspiration in the UBRB for the past and the four scenarios from 1971 to

13 3200 Mean annual discharge in the UDRB Past (ERA) A2 Com Linear (B1) Linear (A1B) A1B B1 Linear (Past (ERA)) Linear (A2) Linear (Com) 2700 Mean discharge [m³/s] Figure 9: Developing of the mean annual discharge in the UDRB for the past and the four scenarios from 1971 to Mean discharge in the UBRB Past ERA B1 A2 A1B COM Mean discharge [mm] Figure 10: Developing of the mean annual discharge in the UBRB for the past and the four scenarios from 1971 to

14 (A1B) (B1) (B1) (B1) (A1B) (A1B) (B1) (A1B) (B1) Figure 11: The mean annual precipitation sum [mm] in the UDRB and the UBRB for the past 30years period from 1971 to 2000 and for the future 30years periods from and 2051 to 2080 according to the A1B and B1 scenario. 14

15 Δ ( ) to ( ) (A1B) Δ ( ) to ( ) (B1) Δ ( ) to ( ) (A1B) Δ ( ) to ( ) (B1) Δ ( ) to ( ) (A1B) Δ ( ) to ( ) (B1) Δ ( ) to ( ) (A1B) Δ ( ) to ( ) (B1) Figure 12: Difference of the mean annual precipitation sum [mm] in the UDRB and the UBRB of the future 30years periods from and 2051 to 2080 according to the A1B and B1 scenario to the past 30years period from 1971 to

16 (A1B) (B1) (A1B) (B1) (A1B) (B1) (A1B) (B1) Figure 13: The mean annual evapotranspiration sum [mm] in the UDRB and the UBRB for the past 30years period from 1971 to 2000 and for the future 30years periods from and 2051 to 2080 according to the A1B and B1 scenario. 16

17 Δ ( ) to ( ) (A1B) Δ ( ) to ( ) (B1) Δ ( ) to ( ) (A1B) Δ ( ) to ( ) (B1) Δ ( ) to ( ) (A1B) Δ ( ) to ( ) (B1) Δ ( ) to ( ) (A1B) Δ ( ) to ( ) (B1) Figure 14: Difference of the mean annual evapotranspiration sum [mm] in the UDRB and the UBRB of the future 30years periods from and 2051 to 2080 according to the A1B and B1 scenario to the past 30years period from 1971 to

18 (A1B) (B1) (A1B) (B1) (A1B) (B1) (A1B) (B1) Figure 15: The mean annual runoff [mm] in the UDRB and the UBRB for the past 30years period from 1971 to 2000 and for the future 30years periods from and 2051 to 2080 according to the A1B and B1 scenario. 18

19 Δ ( ) to ( ) (A1B) Δ ( ) to ( ) (B1) Δ ( ) to ( ) (A1B) Δ ( ) to ( ) (B1) Δ ( ) to ( ) (A1B) Δ ( ) to ( ) (B1) Δ ( ) to ( ) (A1B) Δ ( ) to ( ) (B1) Figure 16: Difference of the mean annual runoff [mm] in the UDRB and the UBRB of the future 30years periods from and 2051 to 2080 according to the A1B and B1 scenario to the past 30years period from 1971 to

20 mean monthly discharge [m³/s] B B A A A1B A1B Com Com Jan Feb Mar Apr Mai Jun Jul Aug Sep Oct Nov Dec Figure 17: Developing of the mean monthly discharge in the UBRB for the past from 1971 to 2000 in comparison to the 30year periods from 2011 to 2040 and 2051 to B B A A A1B A1B com Com mean monthly discharge [m³/s] month Figure 18: Developing of the mean monthly discharge in the UDRB for the past from 1971 to 2000 in comparison to the 30year periods from 2011 to 2040 and 2051 to

21 Figure 19: Developing of the percentage of ice melt on the mean monthly discharge in the Ötztaler Ache at the gauge Huben and in the UDRB at the gauge Achleiten for the decades from 1991 to 2000, and according to Glowa-Danube-Projekt ( ). Monthly runoff and percentage of ice melt in the Lhasa River basin ice melt ice melt ice melt monthly runoff [m³/s] percentage of ice melt [%] Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 20: Developing of the percentage of ice melt on the mean monthly discharge in the Lhasa River catchment for the 30-years-periods from 1971 to 2000, from 2011 to 2040 and from 2051 to 2080 according to the A1B scenario. 0 21

22 Project no: GOCE Project acronym: BRAHMATWINN Instrument: Specific Targeted Research Project Thematic Priority: Global Change and Ecosystems Project title: Twinning European and South Asian River Basins to enhance capacity and implement adaptive management approaches Deliverable Report DL 10.2: Analysis of integrated indicators for what-if? scenarios Due date of deliverable September 2009 Actual submission date December 2009 Start date of project: Duration: 36 Month Organisation name of lead contractor for this deliverable: Project homepage: FSU Dissemination Level: PU

23 Content 1 Introduction Applied IPCC scenarios A1B and B Storyline for scenario A1B Storyline for scenario B Selected integrated indicators Analysis of integrated indicators for the storylines Environmental indicators Climate indicators Measured historical climate time series Modelled climate dynamics using CLM ERA data Downscaled base line and climate projections Hydrology indicators River discharge Surface runoff in the UBRB Melt water runoff Area of Glacier Cover (AGC) Wetlands Socio-economic indicators Vulnerability against floods Governance Climate change impacts on the water balance and vulnerabilities Challenges for sustainable IWRM References Introduction Indicators have been identified in WP6 to quantify what-if? scenarios developed in WP8 for the chosen IPCC scenarios A1B and B1. By means of selected integrated indicators what-if? scenarios were comparatively evaluated with respect to climate change impacts on the natural environment and its human dimension, e.g. the socio-economic development and sustainable IWRM. This evaluation was done in cooperation and by discussions with stakeholders from the twinning basins, Upper Brahmaputra River Basin (UBRB) and the Upper Danube River Basin (UDRB), during the stakeholder symposium in Kathmandu, November Applied IPCC scenarios A1B and B1 For the various analyses in the frame of the BRAHMATWINN project the IPCC scenarios (IPPC 2000) were used to project future climate conditions for the twinning river basins of the UDRB in Europe and the UBRB in South-East Asia. The A1B, A2, B1 and the commitment scenario runs of the ECHAM5 model were dynamically downscaled in WP2 with the CLM for the years and the CLM output was corrected for 2m temperature and precipitation biases. 2

24 For the socio-economic vulnerability studies done in WP4 and the hydrological modelling analysis done in WP7 it was decided to use the A1B and B1 scenarios, commonly considered as most representative and maybe realistic, for further processing. Both scenarios are described by the IPCC (2000) which were presented to the stakeholders as information base for further discussion. 2.1 Storyline for scenario A1B The A1B group belongs to the A1 storyline and scenario family, which describes a sort of realistic but less optimistic case scenario of the future world described of (a) very rapid economic growth, (b) global population that peaks in mid-century and declines thereafter, and (c) the rapid introduction of new and more efficient technologies. Major underlying assumptions are convergence among regions, capacity building and increased cultural and social interactions, with a substantial reduction in regional differences in per capita income. The A1 scenario family has been differentiated into three groups that describe alternative directions of technological change in the energy system. The A1B belongs to the first group of A1 scenarios, and assumes "balanced" progress across all resources and technologies from energy supply to end use, as well as "balanced" land-use changes. It should be noted, that different interpretations of what such a "balanced" resource-technology portfolio could be in the 21st century are applied. The assumed rapid technology dynamics that underlie the A1 scenario storyline requires a significant change of technologies and resource exploitation. Hence, the concept of "balanced" development addresses the general scenario's development path throughout the 21st century and as other scenarios the A1B group heavily relies on the slow turnover rates in the capital stock of the energy sector. 2.2 Storyline for scenario B1 The B1 storyline and scenario family describes a more optimistic convergent world with a global population that peaks in midcentury and declines thereafter, as in the A1 storyline, but with rapid changes in economic structures toward a service and information economy, with reductions in material intensity, and the introduction of clean and resource-efficient technologies. The emphasis is on global solutions to economic, social, and environmental sustainability, including improved equity, but without additional climate initiatives. 3 Selected integrated indicators Indicators identified in WP6 have been grouped in three domains of sustainability: social, economic and environmental indicators. From the numerous indicators identified in WP6 those have been selected by means of an expert assessment that quantify climate change impacts for the what-if? scenarios developed in WP8 for the A1B and B1 scenarios. They are named integrated indicators as they comprise meanings for both the natural environment and its socio-economic development and are considered relevant for vulnerability analysis and sustainable IWRM respectively. Required data for the indicator calculation have been collected and analysed in WP2 till WP5 and WP7 and respective information is available for the A1B, B1 and B2 scenarios from the decision information support tools (DIST) BrahmaRBIS and DanubeRBIS ( both implemented for the twinning basins. They originate from the modelling exercises in WP2, WP5 and WP7 as well as from the stakeholder workshops and expert assessments done in WP4, WP6 and WP8. 3

25 4 Analysis of integrated indicators for the storylines The selected integrated indicators that have been selected by the experts and were discussed with the stakeholders in the Kathmandu stakeholder workshop in 2009 are listed in Tab The historical dynamics ( ) is referred to as the base line against which the scenario projects have been evaluated. The results derived from Tab. 4.1 are differentiated between environment and socioeconomic indicators and discussed in the subsequent paragraphs for the UBRB in example. The conclusions, however, also apply for the UDRB in Europe as has been shown in the test basin of the Salzach River. Tab. 4.1: Integrated indicators to evaluate climate change impact on sustainable IWRM Integrated Indicator Scenarios and Projections Domain (subdomain) Indicator Base Line A1B B1 env (climate) air temperature (T) precipitation (P) evapotranspiration (ET) Different regional trends depend on seasonal dynamics T C P + in Bhutan P + in Tibet P in Assam T C P + in Bhutan P + in Tibet P in Assam env (hydrology) surface runoff (Sr) interflow (Int) groundwater flow (Gf) snow and glacier melt (SGM) Changes of flow volume and seasonal flow distribution 15% till 28% less mean annual discharge and changing runoff regimes 15% till 23% less mean annual discharge and changing runoff regimes env (glaciology) Δ Area of Glacier Cover (AGC) ΔAGC ( ) - 17% till -20% glaciers and permafrost will melt away less pace of glacier and permafrost melt env (hydrobiology) wetlands with ecosystem services (ESS) and ecosystem functions (ESF) regional diversity with functioning ESS and ESF strong pressure from GDP and population growth on ESS and ESF less pressure from GDP and population growth on ESS and ESF soc-econ (vulnerability) Gross domestic product (GDP) population pressure (PP) high vulnerability in flood prone areas with high PP high GDP development decreases vulnerability lower plus of GDP reduces decrease of vulnerability soc-econ (governance) governance and policy IWRM is not in place but first attempts are made on a transnational level less good with respect to sustainability but governance levels as good as in B1 best setup for management of the whole river basin and IWRM 4.1 Environmental indicators This type of indicators was derive from the analysis of the natural environment done in WP2 till WP5 and WP7 and have been described in detail in the respective deliverables form these work packages. Their calculation and qualitative evaluation was based on historical time series analysis, modelling 4

26 exercises, field campaigns and application of integrated geoinformatics methods, i.e. satellite image interpretation and GIS analysis Climate indicators The main climate parameters used for this analysis are measured and modelled air temperature, precipitation and evapotranspiration. For the indicator analysis three different types of data were analysed and are discussed below Measured historical climate time series An intensive climate data collection was done during the course of the BRAHMATWINN project and hundreds of stations have been input into the BrahmaRBIS and DanubeRBIS respectively. The quality assessment revealed that most of these stations unfortunately have significant data gaps and therefore cannot be used for a comparative assessment. After a thorough screening the remaining stations presented the regional climate trends shown in Fig. 4.1, which can be described as follows: (i) Throughout the UBRB, i.e. from the arid western till the monsoon driven eastern part of the basin there is a positive trend in air temperature. (ii) The precipitation trend is negative in the arid western and Tibetan part of the UBRB and turns into a positive trend in the eastern part of the UBRB, i.e. in Assam and the windward located slopes of the Himalaya mountain ridge. (iii) As shown in Fig. 4.2 for Fig. 4.1: Regional trends for air temperature (T) and precipitation (P) the station Dibrugarh in Assam the increase of precipitation is also significant on a monthly scale indicating a substantial change of precipitation pattern dynamics in principle. Fig. 4.2: Mean monthly and annual distribution of precipitation (mm) in Dibrugarh, Assam 5

27 Modelled climate dynamics using CLM ERA data The climate modelling results from the CLM ERA data that refer to the historical period 1970 till 2000 constitute what is called the base line climate situation to which the climate projections are referred to. The climate modelling projections obtained from WP2 by means of the Climate Model (CLM) Fig. 4.4: Changing temperature ( C/100 yr) for the scenarios A1B (left) and the B1 (right) confirm the continuation of warming of the UBRB but as listed in Tab. 4.1 with a much higher warming for the A1B scenario than for the B1 scenario. The precipitation trend for the historical time series is confirmed in the arid and semi-arid western Tibetan part of the basin but is not confirmed for the eastern part of the UBRB. As can be seen from Fig. 4.4 there is a significant increase of 5-day precipitation storms projected over the middle part of the UBRB which is of relevance for flood generation. This trend is complemented by a significant increase of dry periods projected for both scenarios. Fig. 4.4: Changing greatest 5-day precipitation (%) between June and September projected for the scenarios A1B (left) and the B1 (right) It should be noted that these projections refer to a 0,5 x 0.5 grid cell resolutions and therefore not necessarily are reflected by the trends observed in the historical time series measured within the UBRB, as local influences might become dominant and cover or compensate the regional trend Downscaled base line and climate projections The modelled time series obtained from the climate modelling (see DL2) for the meso-scale grid resolution have been downscaled to the 1 x 1 km grid cell. For details about the downscaling procedure the discussion supplied in DL7 and Marke (2008) is referred to. The results are shown as 6

28 mean values for the base line period 1970 till 2000 in Fig. 4.5 and their spatial distribution can be described as follows: (i) There is a clear differentiation of temperature between the subtropical North-East Indian and the cold temperate Tibetan part of the basin. The deep valleys which are protected from the cold wind in winter show up as warmer corridors. (ii) The rainfall distribution is showing a similar distribution with a major water input during the monsoon season in North-Eastern India, windward located slopes of the Himalaya, and the NE parts of the basin. The Tibetan central and western parts show up as relative dry regions. It should be noted that in this region the downscaling algorithms applied failed to realistically reflect the lee effect of the Himalaya ridge. Fig. 4.5: Mean climate indicators for the base line 1971 till 2000 situation in the UBRB. (iii) The mean snow cover of the UBRB was estimated to about 12% and is restricted to the mountain ridges and high Tibetan plateau. (iv) Temperature and precipitation both control the distribution of potential Evapotranspiration (ET) which is low because of the cold climate in the western and central Tibetan part of the UBRB. It increases with temperature towards the North-Eastern temperate and subtropical parts of the UBRB. Fig. 4.6: Changing ET (mm) for the two time periods of the A1B scenario within the UBRB. 7

29 This figure will change with increasing temperatures in both A1B and B1 scenarios. However, due to the higher warming in the A1B scenario the changing ET becomes more obvious in the latter scenario and as shown in Fig. 4.6 reduce the availability of water for discharge and groundwater recharge all over the UBRB thereby aggravating the semi-arid and arid character of the western and middle part of the UBRB Hydrology indicators From the hydrological modelling done with the DANUBIA hydrological model numerous indicators describing and quantifying the hydrological dynamics in the twinning basin were derived. From these indicators surface runoff, interflow and groundwater flow have been selected. The latter will also be used as indicator for long term groundwater recharge, which is an important eco-system service (ESS) of the basin and the base of water supply especially in rural areas River discharge Discharge in the Brahmaputra River at Guwahati (Gauhati in Fig. 4.1) is the reference point for the water balance and indicator discussions. The DANUBIA hydrological model was applied for the historical time series (base line) and the two IPCC scenarios A1B and B1. Input data in all three cases were the modelled climate data time series supplied by WP2, which were downscaled on a 1 x 1 km grid cell resolution. Both scenarios A1B and B1 indicate a continuous reduction of annual river discharge in the Brahmaputra River but as shown in Fig. 4.7 the negative trend is much stronger for the A1B scenario then for the B1 scenario. The reason is related to the lower warming in the B1 scenario and the lower increase of ET in the basin. Fig. 4.7: Annual discharge modelled for the historical base line and the A1B and B1 scenarios. Fig. 4.7 also shows that in the historical base line time series there is no such negative trend developed that would fit into that one modelled for the projected A1B and B1 scenario periods. One reason for this discrepancy can be seen in the fact that the historical CLM ERA driven meteorological input time series used for the base line modelling is not reflecting properly the observed trends from the climate stations. Unfortunately the discharge time series available at Guwahati are not of sufficient quality to validate the modelled base line runoff. This will be subject of further analysis to be done in cooperation with stakeholders from North-East India, i.e. the states Assam, Arunachal Pradesh, Nagaland, Manipur, Tripura, Meghalaya, West Bengal and from Bhutan Surface runoff in the UBRB The change of runoff contribution from the UBRB for the two A1B and B1 scenarios with reference to the historical base line model results is shown in Fig. 4.8 and allows a more detailed differentiation of the information already derived from Fig (i) The two scenarios differ quite significant in the characteristic and magnitude of surface runoff change distributed within the UBRB. 8

30 (ii) They both show a decrease of surface runoff contribution in the first projection period for the monsoon driven rivers of the windward located Himalaya mountain ridge in North-East India and Bhutan. (iii) In opposite the Tibetan central and Western part of the UBRB have higher surface runoff contributions that can be explained from the surplus generated from snow and glacier melt. Fig. 4.8: Change of annual surface runoff (mm) for the A1B and B1 scenarios. (iv) In the second projection time period the dynamics is changing considerably as the Tibetan part of the UBRB is producing less surface runoff as the snow and glaciers have been melted away to a large extent. (v) Meanwhile in the A1B scenario the reduction of surface runoff contribution continues only in North-East India and Bhutan due to higher rainfall input will experience an surface runoff increase the B1 scenario indicates a reduced surface runoff dynamics also for this part of the UBRB. (vi) The distinct difference between the A1B and B1 scenario in the second projected period is not reflected by a corresponding annual increase of precipitation but can be related to the increased frequency of greatest 5-day precipitation shown in Fig Melt water runoff Melt water runoff contribution originates from the melting of permanent snow fields and exposed glaciers and generates a characteristic snow melt driven hydrological regime. This dynamic, however will not become recognizable at the outlet of the UBRB in Guwahati as the percentage of such frozen storages is too small. Consequently the runoff contribution from melting storages will be covered by the high runoff volumes generated from the monsoon dynamics. It therefore was analysed in the Lhasa River basin (A = km 2 ) in Tibet from which mean monthly discharge values were available starting from 1957 onwards for the gauging station Lhasa. The results are shown in Fig. 4.9 and indicate: 9

31 (i) The Lhasa River has a typical snow and glacier melt driven runoff regime with a distinct annual hydrograph during the summer months. (ii) The first two time periods covering about 30 years have a similar discharge dynamics and only differ in magnitude corresponding to the respective rainfall pattern. Fig. 4.9: Monthly discharge measured in the Lhasa River at the gauging station Lhasa. (iii) The second time period of 14 years is showing a significant different monthly discharge dynamic as the annual hydrograph is starting earlier, has a much broader peak and drops less steep if compared to the previous two periods. This clearly indicates a change of the onset of melt water runoff and a longer and stronger snow melt period due to the climate warming discussed in section The discharge dynamics derived from the measured runoff values are confirmed by the modelling results obtained from the hydrological modelling using the base line ERA data and the A1B projections. The model also provided the melt water runoff components and results are shown in Fig which support the previous interpretation as follows: (iv) The melt water contribution in the historical base line time period peaked to about 12% and was generated from glacier and snow melt. Fig. 4.10: Modelled monthly discharge in the Lhasa River at the gauging station Lhasa. new balance with the changing climate is established. (v) In the first projected time period the glacier melt water is increasing to almost 25% reducing the frozen water storages, i.e. glaciers, permanent snow fields and permafrost till a (vi) In the second projected time period most of the former frozen storage is already gone and a new balance is reached between glacier and snow storage in the winter and melting in the summer. The new glacier melt contribution is significantly lower than in the first projected period and even lower and differently distributed than in the base line period. (vii) The annual hydrograph of the first projected period doesn t differ if compared with the base line but that one of the second projected period differs significantly as the glacier and snow melt during the rising limb of the hydrograph is starting later and is contributing less to the discharge volume. In conclusion it is likely that such glaciered river basin will yield less river discharge during the beginning of the melting period and less water will be available for irrigation during the period May till August. 10

32 4.1.3 Area of Glacier Cover (AGC) The influence of melting water from frozen storages, i.e. permanent snow fields, glaciers and permafrost has been analysed with respect to the discharge dynamics in river basins that show significant area of glacier cover (AGC). AGC used as an indicator to validate these interpretations has been analysed in WP4 and is presented in the respective deliverable. The compressed results are presented in Fig and present the following findings: (i) Glaciers throughout the UBRB show a general tendency to retreat due to continuous glacier melting. (ii) The melting dynamics is linked to the temperature distribution within the basin and is highest in the high altitude parts of the Himalaya where the AGC is ranging between -7 and Fig. 4.11: Analysed glacier retreat between 1970 and % per decade. (iii) Within the region of the Lhasa River basin a retreat rate of -7% per decade was found that matches well with the discharge analysis presented in the previous section. (iv) Increasing glacier melt is resulting in the formation of glacier lakes with instable dam structures and increases the hazard of glacier lake outbreak floods (GLOFS). Complement to the glacier retreat is an increased melting of permafrost in the slopes that is reducing slope stability and supports landslides that in turn can dam rivers and cause hazards when the dam wall is breaking. Fig. 4.12: Distribution of different wetland types Wetlands The different types of wetlands and their hydrological and biological eco-system functions (ESF) have been described in detail in DL3. Their eco-system services (ESS) with respect to water resources of a river basin are firstly the storage of surface runoff during snow melt and in the rainfall season resulting in the buffering of hydrograph peaks, and secondly the continue release of water between hydrographs and especially during the dry season establishing base 11

33 flow and guaranteeing environmental flow requirements. Their spatial distribution is shown in Fig for the Lhasa River basin and Assam in India. The first region is representative for the Tibetan wetland classification and the latter one for the North-East India monsoon region and the floodplain of the Brahmaputra River. When comparing both maps the following findings can be presented: (i) There are five types of wetlands in the Tibetan part of the UBRB which are characteristic for the alpine character of the glaciered Tibetan river basins and four wetland types which are representative for the monsoon region of the Brahmaputra floodplain in North-East India. (ii) Both regions have alluvial and floodplain wetlands in common, of which the first ones are often washed away during floods and the latter, are inundated during floods. (iii) In the Lhasa River basin melt water is stored in lakes and swamps, meanwhile in Assam the Beels are major stores of monsoon rain and flood water inflowing from the braiding main stem of the river. Analysis was done in the study regions shown in Fig on the vulnerability of wetlands with respect to impacts from human activity and climate change and the results are listed in Tab. 4.2 indicate that the classified wetlands are in the same way vulnerable against human activities, i.e. overgrazing and drainage as against the change of water supply due to climate change. Tab. 4.2: Vulnerability of wetlands against impacts from human activities and climate change Study Region Lhasa River basin, Tibet Assam, North- East India Vulnerability Type of Wetland classified to Alluvial Floodplan Swamp Lake Alpine Meadow Beel human activity low low climate change high high low high medium human activity high climate change 4.2 Socio-economic indicators Two socio-economic indicators were selected to be included into the discussion because of their integrated nature. A detailed description of the development of the indicators for the quantification of vulnerability and risk with respect to the IPCC approach is given in DL4 and DL6. The Total Sustainably Index (TSI) approach, which initially was planned to be applied, could not be used because of lack of data. The use of the TSI requires substantial details of land management practice, historical context and future land management planning and in addition considers soil quality changes, GHG emissions as well as complex inter-relations with environmental management policy. This was deemed to be excessive in terms of the data requirement but also in terms of producing a template for assessment that might be taken up by decision making bodies within governance in the twinning basins. The IPCC approach (IPCC 2001, 2007) adopted instead is a more strategic and conceptual oriented and this provided a number of critical advantages: (i) Both in Assam and Bhutan there was sufficient information available to generate IPCC based outputs of relevance to decision making processes whereas this was certainly not the case for a TSI approach. (ii) The IPCC approach fits better with a specific study relating to climate change with a conceptual framework aimed at the issues raised by climate change. (iii) It is substantially more strategic at a state or district level and would allow for comparison over time without the requirement for large new data sets. 12

34 (iv) There is a far greater likelihood of uptake of the IPCC approach as it is a simpler and more transparent approach in a development context (v) The IPCC approach includes resilience and vulnerability as a component of its framework which address the requirements of the work task with reliability considered as an aspect of hazard within the overall risk model Vulnerability against floods This indicator was extensively studied in WP4 and is discussed in detail in DL4. The vulnerability against floods was established for Assam in North-West India by using census data and Landsat satellite image analysis and is shown in Fig The analysis of Fig reveals: Fig. 4.13: Total population and flood vulnerability quintiles in Assam derived from Indian census data and Landsat satellite image analysis 13 (i) High vulnerabilities are strongly related to population centres like in the west of Assam at the city of Guwahati. (ii) Higher vulnerabilities can be found along the main stem of the Brahmaputra River and its tributaries because of bank erosion and flooding of the Beels which are used for complement food supply and grazing. Future vulnerability scenarios following SRES projections (A1, B1) for the time steps 2000, 2020 and 2050 have been modelled for both case studies in the UDRB and UBRB. The analysis clearly shows that GDP and population growth impacts on household and community factors that predict socioeconomic vulnerability to climate hazards such as the proportion of the population working in agriculture, proportion of roads that are, proportion of households with a television, proportion of houses with burnt brick wall and proportion of households using firewood for cooking. The impact of GDP and population growth is highest in areas where levels of vulnerability are already high. The results clearly depicts that a slow growth in population with a concurrent rapid growth in GDP is important in reducing levels of vulnerability. With respect to the IPCC scenarios A1B and B1 for 2050, it can be noted that under scenario B1 population growth is and real GDP growth is and for A1 population growth is and real GDP growth is , nonetheless although scenario B1 has a lower GDP compared to scenario A1, it impact on reducing levels of vulnerability is stronger because of the low population growth rate Governance Each of the storylines for the SRES scenarios A1B and B1 makes certain assumptions about the balance of the socio-economic drivers in place in 2050 but fails to make comments regarding the governance regimes expected to support the scenarios. It is recognised, however, that the extent to which response options will be effective or not depends to a significant degree on the governance and policy positions in place (Ministerial Declaration 2000). It is therefore necessary to assess the

35 suitability of a response option on the SRES storylines together with the governance capacity to support its implementation. Consequently, the characteristics of the governance system that would be needed to support the storylines must be determined to assess their practicality of the proposed response options. The SRES storylines must therefore be deconstructed to identify the particular strands relevant to water, land and disaster management and the resulting projected governance frameworks and their potential for institutional and international cooperation; the relative balancing of economic, social and environmental concerns; the capacity for land use control; and the likelihood of effective enforcement. Table 4.3: Evaluation of suitability of Assam response strategies against projected governance characteristics of climate change scenarios A1, A2, B1 and B2 (IPPC 2000) Governance issue Response strategy A1 A2 B1 B2 Time Slice Awareness of population Increase awareness of the population on risks, conservation and on risks, conservation and WRM WRM Integration of research in decision-making Community involvement in decision making See comment 1 Early warning system See comment 2 Integration and coordination among different sectors of research and decision making Improve community involvement and foster participatory processes for decision-making Foster livelihood practices based on conservation, rehabilitation and sustainability Early warning system Disaster risk management Hazard zoning IWRM Design and implement IWRM plans Multi-purpose dam construction Long term vision and Flood and erosion control measures vs. Short term Land use planning engineering solutions Environmental impact assessment for new dams Design and implement relief and rehabilitation plans Relief and rehabilitation Soil conservation efforts Renaturation Accountability and transparency in Policy making and government actions implementation of laws Implement and enforce existing laws and design new and more effective laws Coordination among Resolve conflicts and strengthen institutions coordination among institutions Inter-state conflict, cross Inter-state coordination and conflict boundary issues resolution Totals The responses options to the SRES A1, A2, B1 and B2 (IPCC 2000) have been analysed from the perspective of their appropriateness to the projected governance frameworks. For example, the governance related issues and corresponding response strategies that were raised by stakeholders in the local actors workshop in Guwahati were evaluated against the projected governance regimes for

36 the scenario storylines and ranked according to their suitability for each scenario. A ranking of 4 indicates the most appropriate and 1 showing the storyline with which the response is least suitable. The individual and final scores for each storyline are listed in Tab. 4.3 and indicate those strategies most in accord with what the storylines might reflect thereby generating the following comments: Comment 1: Both A1 and A2 scenarios depend on a high quality foundation of good governance and this would demand effective participation of communities at all levels, contrary to what may at first sight be the case. In the A2 and B2 scenarios the storylines that ostensibly rely on community involvement most heavily, participation at the local levels would be necessary, but would not function well at the national level. Strategies that encourage and enable participation at the community level only would best be suited to the A2 and B2 scenarios. Comment 2: Note that although hazard zoning forms part of Indian union water policy currently, it is difficult to see how it could work in Assam, given that the floodplain coincides with the bulk of the productive farmland in the state. The impact of climate change depends on the scenario one considers, and the broad conclusions from the evaluation of the responses against the governance context suggest: (i) While the A2 and B2 scenarios were the least compatible scenarios (scoring 36 and 41 respectively), B1 ranked as the best (62, and the most 4-rated responses), with A1 closely following (52). (ii) The relatively heavy weighting of economic interests in the A1 scenario is most consistent with effective disaster risk management and infrastructure investment. (iii) Given the correlation between high income levels and good governance (Kaufmann et al, 2005), the A1 scenario would also suggest good accountability and transparency in government actions, along with effective enforcement, characteristics that would be shared with the B1 scenario. 5 Climate change impacts on water balance and vulnerabilities The integrated climate indicators if analysed with respect to the implementation of SRES response options as means of sustainable IWRM in the UBRB reveal significant climate change impacts on the water balance in both twinning basins which generate significant challenges for adaptive IWRM to account for changing environments in the UDRB and the UBRB: (i) Rainfall in the arid western regions of the UBRB will become less in the forthcoming decades. In the eastern part of the UBRB and especially in the monsoon regions, like in Assam the historical increasing precipitation trend likely will turn into negative. The significant increase of 5-day rainfall indicates the possibility of higher flood risk and on the other side the significant increase of projected dry periods will likely enhance the threat of drought. (ii) Temperature will increase throughout the UBRB, which will experience a considerable warming according to the A1B scenario of up to 6 C and a moderate increase of about 2 C in case of the B1 scenario. (iii) Evaporation is going to increase in both of the IPCC scenarios following the trend of temperature increase and will contribute to the reduction of permanent snow fields and glaciers till a new balance is established. As a result of this process less water will be available for IWRM and in the Tibetan part of the UBRB and consequently aridity will increase. Water resources might even become insufficient to support sustainable irrigation agriculture in the medium and long term. 15

37 (iv) Discharge of the Brahmaputra River is showing a decreasing trend in both scenarios although this tendency is much stronger in the A1B scenario if compared to the B1 scenario. Both indicate a changing dynamics of surface runoff contribution within the UBRB but show different magnitudes and spatial distribution. In the first projected period surface runoff in the Tibetan central and western part of the UBRB is likely to increase from melting from permanent snow fields and glaciers. In North-India and Bhutan the decrease of surface runoff is high due to increasing ET values. In the second projected period the Tibetan part of the UBRB also contributes less surface runoff as most of the frozen water storages have disappeared. In the A1B scenario the surface runoff contribution is rising in the Bhutan and western Assam region but continues to decrease in the rest of North-East India. In the B1 scenario the decreasing surface runoff contributions is relevant for the whole area of North-East India and Bhutan. (v) Melt water runoff from permanent snow field, glaciers and permafrost are already changing the river runoff regimes in glaciered basins such as the Lhasa River and this runoff component is going to increase till about From there onwards a new balance between the precipitation input and the energy available for melting frozen water storages will be established. Consequently melt water volumes will be reduced to levels below the reference base line (vi) Wetlands in the floodplain are inundated during floods and release water with the flood peak recession. In the Tibetan part lakes and swamps store surplus water from rainfall or snow melt and release it during low flow periods. The same ESS is provided by the Beels in North-East India. Wetlands in the UBRB in general are vulnerable to decreasing rainfall and to population pressure which causes their degradation by grazing or when drained even completely turns them into agricultural lands. Reducing wetlands will have impacts to IWRM as the natural flood buffering of the basin is reduced and the low flow situations will become more extreme as base flow contribution from the wetlands is getting smaller. (vii) Socio-economic vulnerability against flooding is strongly related to the water balance and the related hydrological runoff regime in the UDRB and the UBRB and especially in the Brahmaputra floodplains in North-East India this relationship will be even more evident. The analysis done clearly shows that GDP and population growth impact is highest in areas where levels of vulnerability are already high. The results clearly depicts that a slow growth in population with a concurrent rapid growth in GDP is important in reducing levels of vulnerability. With respect to the IPCC scenarios for 2050 B1 because of its lower GDP has a stronger impact on reducing levels of vulnerability because of the low population growth rate if compared to scenario A1. 6 Challenges for sustainable IWRM A summary of the water balance indicators is given in Tab From these environment indicators the following challenges for the implementation of a sustainable IWRM UBRB can be identified: (1) Higher temperature will lead to an increase of potential evapotranspiration of up to 23 % and less water will be available from the river basin for future water management. (2) This process will be aggravated by a decreasing trend of precipitation and less snow cover in winter. 16

38 (3) The discharge in the Brahmaputra River will be reduced considerable but the extreme events will increase in terms of floods and droughts requiring consequent IWRM adaptations to ensure the quality of water supply from surface and subsurface resources for agriculture, human consumption and the environment. (4) IWRM in glaciered basins from the second half of the 21 st century onwards will have to account for significantly less discharge volumes available during the months May till August when irrigation demand is high. (5) The risk of glacier lake outbreak floods will further increase complemented by an increased risk of landslides from reduced slope stability due to permafrost melting. (6) Wetlands will suffer from less surface runoff inflow and increasing population pressure degrading their ESF and impacting their buffering discharge ESS. Tab. 5.1: Summary of environment indicators analysis in the UBRB for the IPCC scenarios A1B and B1 Indicator (Dim) Temperature ( C) Precipitation (mm) Snow precipitation (%) Evapotranspiration (mm) Brahamputra River discharge (m 3 /s) IPCC Indicator value Change to base line SRES base line (%) (%) A1B 9,0 11,0 1,2 15,4 3,2 41,0 7,8 B1 8,8 10,0 1,0 12,8 2,2 28,2 A1B , , B , ,5 A1B , ,7 12 B , ,0 A1B , ,4 321 B , ,1 A1B , , B , ,6 According to the environment indicators water resources managers must not only account for intensified extreme events like floods and droughts but in general will have less surface runoff water available for regional and long distance distribution, i.e. to irrigation agriculture. In addition water quantity and quality standards for human consumption and environmental flow requirements will be harder to meet especially if the population pressure continues. Attention must also be given to the increasing runoff contribution from Bhutan and western Assam indicated for the second period of the A1B scenario. This could mean a higher frequency of floods and higher erosion rates from river systems having their headwaters in this region. With respect to the socio-economic indicators water managers must account for high vulnerabilities in flood prone areas and along the river banks with severe bank erosion. The impact of GDP and population growth is highest in areas where levels of vulnerability are already high. Slow growth in population with a concurrent rapid growth in GDP is important in reducing levels of vulnerability. Although scenario B1 has a lower GDP compared to scenario A1, it impact on reducing levels of vulnerability is stronger because of the low population growth rate. In terms of the specific responses preferred by the stakeholders, A1 would seem best suited to Disaster Risk Management but less strong on environmental protection. Given the increased focus of the B1 scenario on environmental and social issues, better integration of Land Use Planning with impacts on water and disaster risk management suggest that flood risk zoning for hazard prevention might be more successful in B1 than in A1. The latter will prefer traditional engineering solutions and 17

39 it appears from the existing Governance and Policy frameworks in place in Assam currently, that the Assamese government is strongest in areas that are perhaps more aligned with the A1 scenario. 7 References IPCC (2000). Special Report on Emissions Scenarios : A special report of Working Group III of the Intergovernmental Panel on Climate Change, 27 p. - In: Nebojsa Nakicenovic & Rob Swart (Eds.). Cambridge University Press, UK. IPCC (2001). Climate Change 2001: Impacts, Adaptation and Vulnerability. Contribution to the Working Group I to the third assessment report of the Intergovernmental Panel on Climate Change (IPCC), Cambridge University Press, Cambridge. IPCC (2007). Climate Change 2007, Impacts, Adaption and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the IPCC. http,// Kaufmann, D., Kraay, A., and Mastruzzi, M., Governance Matters IV (2005): Governance Indicators for , World Bank Policy Research Working Paper 3630, June 2005, 3. Available at Marke, T. (2008): Development and Application of a Model Interface to couple Regional Climate Models with Land Surface Models for Climate Change Risk Assessment in the Upper Danube Watershed, Dissertation der Fakultät für Geowissenschaften, Digitale Hochschulschriften der LMU München, 188, München. Ministerial Declaration of the Hague on Water Security in the 21 st Century, The Hague, The Netherlands, 22 nd Mar Available at 18

40 Project no: GOCE Project acronym: BRAHMATWINN Instrument: Specific Targeted Research Project Thematic Priority: Global Change and Ecosystems Project title: Twinning European and South Asian River Basins to enhance capacity and implement adaptive management approaches Deliverable Report 10.3: Adaptive IWRM options for mitigation Due date of deliverable November 2009 Actual submission date December 2009 Start date of project: Duration: 36 Month Organisation name of lead contractor for this deliverable: Project homepage: FEEM

41 Table of Content Introduction... 3 Method... 4 The framework... 4 DSS Design... 5 Results... 6 References... 8 Annex... 9 List of figures & tables Figure 1 The NetSyMoD flowchart... 4 Figure 2 Brainstorming session during the symposium, ICIMOD, Kathmandu 9 November Figure 3 Specific actions fort the implementation of responses, screenshot of FreeMind List of acronyms CSM DPSIR DSS GCM HRU IIT IWRM IWRMS MCA mdss NE NetSyMoD UBRB UDRB VulnUs WP WRRU creative system modelling Driving force Pressure State Impact Response Decision Support System Global Circulation Model Hydrological Response Units integrated indicators table Integrated Water Resources Management Integrated Water Resources Management Strategies Multi Criteria Analysis Mulino Decision Support System natural environment Network analysis, Creative System Modelling, Decision Support Upper Brahmaputra River Basin Upper Danube River Basin vulnerability units Workpackage Water Resources Response Units List of partners involved Partner 1 Partner 2 Partner 4 Partner 5 Partner 6 Partner 7 Partner 9 Partner 12 Partner 13 Partner 19 Partner 20 FSU LMU ZGIS UniVie GeoDa UniDun FEEM ICIMOD UniBu JWG IITR 2

42 Definition of responses to cope with what-if? scenarios Introduction This deliverable is strongly based on outcomes of previous workpackages (WP), especially WP_6 and WP_8, which are in turn based on previous WPs (WP_2, WP_3, WP_4). We can thus say that with the phase described here we have achieved the in depth definition of Integrated Water Resource Management Strategies (IWRMS) options envisaged as a conclusion of deliverable 8.3. In Dl_8.3, in fact, we had ended with the outcome of the two workshops carried out with local actors 1 in Salzburg for the UDRB and in Kathmandu for the UBRB, in October and November of 2008 respectively. The local actors that participated in both workshops evaluated responses 2 based on the Planning category as the most promising to cope with flood risk, which should increase because of the impacts of climate change. Local actors that participated in two ad hoc workshops (see Dl_8.3 and Dl_8.4) were first presented with scenarios of climate change based on the results of the downscaling of the Global Circulation Model (GCM) done by the research partners of the University of Frankfurt (JWG) in WP_2; and then requested to evaluate the effectiveness of four categories of responses to cope with flood risk in the climate change scenarios presented. Climate Change scenarios presented in the workshops provided climate simulations using three IPCC- SRES scenarios (A1B, A2 and B1) and the COMMIT scenario (i.e. the consequence of committing world economies to limit GHG concentrations at 2000 levels), five data sets (GPCC, UDEL, CRU, EAD, F&S) and four models (ERA40, CLM-ERA40, ECHAM5, ECHAM5-Γ). The four categories of responses presented are: (1) Engineering solutions and land management, (2) Knowledge improvement and capacity building, (3) Governance and institutional strength, (4) Planning. According to the Multi Criteria Analysis (MCA) carried out with the help of the Mulino Decision Support System (mdss) software, Planning was chosen as the preferred response. From that analysis of the effectiveness of responses comes the need to better define what IWRM option should be implemented. The categories of responses analysed are, in fact, too broad to be implemented, thus the need for further specification arises. This deliverable is thus the conclusion of a participative process carried out during the Brahmatwinn project, aimed at identifying pressing issues in the UDRB and in the UBRB, and possible responses to cope with them. The three Delphi Rounds carried out among Brahmatwinn project partners 1 We have preferred to use the term local actor (LA), to identify all the people involved in the case study activities instead of the more commonly used term stakeholder, to emphasise the fact that they were people who did not belong to the project consortium (typically local experts or policy makers), involved in project activities by partners responsible for the management of case studies to provide advice and steer project activities, without the ambition to assess their representativeness with robust procedures, such as Social Network Analysis. 2 In this context the word response is defined according to the DPSIR framework (EEA, 1999) as strategies to be put in place to cope with environmental issues identified. 3

43 (described in Dl_6.1), along with the Creative System Modelling workshops in which local actors expressed their opinions (described in Dl_4.1 and Dl_6.1), resulted in the creation of the Integrated Indicator Table (IIT). The IIT enables measurement of issues by means of an indicator. Moreover the IIT lists possible responses, in relation to the governance framework, to cope with scenarios of climate change, i.e. the defined what-if? scenarios. The creation of the IIT is the result of the interaction of Brahmatwinn scientists with local actors. Continuous feedback opportunities have been created during the project years, so that the two communities could share knowledge among and within them. Method The framework The approach adopted for the analysis of alternative IWRMS options is based on the NetSyMoD methodological framework (Giupponi et al., 2008; for the management of participatory modelling and decision processes (Figure 1). This methodology relies on the DPSIR framework (Driving forces, Pressures, State, Impacts, and Responses; EEA, 1999). opinions & interests ACTOR ANALYSIS communication & adaptation PROBLEM ANALYSIS CREATIVE SYSTEM MODELLING DSS DESIGN scenarios & models Thematic structure information management ACTIONS & MONITORING decisions ANALYSIS OF OPTIONS preferences Figure 1 The NetSyMoD flowchart The NetSyMoD methodology is organised in six main phases. The first three (Actors Analysis, Problem Analysis, Creative System Modelling) provided the Brahmatwinn Project with (1) an in depth analysis of general problems related to water resources management in the two upper river basins, with the participation of the communities of interested parties in the case study areas, and (2) mental model representations of the problems, i.e. qualitative descriptions of the causal links between the various components of the local socio-ecosystems by means of cognitive maps clustered in order to be consistent with the DPSIR framework, used as an upper aggregated level communication interface. The subsequent phases, DSS Design and Analysis of Options contributed in WP_8 to the design and evaluation of a set of alternative categories of responses (Engineering solutions and land management, Knowledge improvement and capacity building, Governance and institutional strength, Planning) obtained with group elicitation techniques and with the application of the DSS tool. The result of this iteration of the DSS Design and Analysis of Options was that the 4

44 Planning category, evaluated as the most effective to cope with flood risk under the impact of the scenarios of climate change, needed to be analysed and defined in a more specific way. This second DSS Design phase, object of this deliverable, consists of a brainstorming, aimed at a more in depth specification and definition of what is meant for responses based on Planning. The Analysis of Options that followed will be described in WP_10.4. DSS Design Building upon the information acquired in the participatory activities carried out in the first two years of the project one new workshop was hosted by ICIMOD, and held in Kathmandu, Nepal (November 2009), with the aim of providing the project consortium with a more detailed definition of responses to cope with flood risks under the pressure of climate change, related to the Planning category. Due to the high participation of end users from the UBRB in general (see Annex), and specifically from the Assam case study area, the symposium focused on the Assam State of India. However, all end users opinion were collected, thus the outcomes can be generally thought as Brahmatwinn outcomes. The workshop activities have been carried out during the symposium in which end users of the final outcomes have been invited. This symposium is one of the dissemination activities organized by the Brahmatwinn project partners to facilitate understanding of the project outcomes. During the symposium, in fact, possibilities of interaction between researchers and end-users were encouraged. The workshop, therefore, relied on the presentation made by the workpackage leader, during which an overview of the Brahmatwinn activities was given. The following presentations were given: 1. Downscaling of General Circulation Model predictions in the Himalayan region; Andreas Dobler (JWG Univ. of Frankfurt, Germany) 2. Assessment of the natural environment; Petra Füreder (Z_GIS, Univ. of Salzburg, Austria) 3. Modeling socio-economic vulnerability to floods: Comparison of methods developed for European and Asian case studies; Craig Hutton (GeoData Institute, Univ. of Southampton, United Kingdom) and Stefan Kienberger (Z_GIS, Univ. of Salzburg, Austria) 4. Analysis of present IWRM practices in the Brahmaputra basin; Anita Bartosch (FSU-Jena, Germany) 5. Identification and selection of indicators of environmental change; Valentina Giannini (FEEM, Venice, Italy) 6. Using the hydrological model DANUBIA for water availability scenarios in the upper Brahmaputra basin; Monika Prasch (LMU, Munich, Germany) 7. Stakeholder presentation to Present IWRM in Upper Danube river basin ; Hans Wiesenegger (Government Salzburg) 8. Presentation of likely what-if scenarios in the UBRB; Prof Wolfgang Flügel (FSU-Jena, Germany) 5

45 Figure 2 Brainstorming session during the symposium, ICIMOD, Kathmandu 9 November 2009 Having introduced the project and its main outcomes, such as the climate change scenarios, a brainstorming session was conducted to elicit and consolidate the sets of possible responses within the Planning category. This section created the basis for the correct implementation of the ensuing steps, and led to the identification of specific actions, within the proposed broad Planning category of responses. The responses analysed were: DISASTER RISK MANAGEMENT FLOOD RISK ZONING FOR HAZARD PREVENTION LAND-USE PLANNING RELIEF AND REHABILITATION PLANS Results The end users present in the workshop took part in a very informative discussion, each contributing to it by sharing knowledge and understanding. The brainstorming was facilitated by Craig Hutton and Valentina Giannini, who was also registering the contributions by means of a freeware, FreeMind, which was projected on the big screen for everybody to see (see Figure 3 and Annex). The goal of the workshop was to elicit what we have defined as actions, i.e. specific IWRMS that could be implemented under each response. The brainstorming time, roughly two hours, was divided into four sections. During each the participants were asked to define and identify what kind of actions are existing or needed with reference to the four responses presented. It must be said that all actions were collected, and no statements were made as to preferences in this phase. 6

46 The use of a software for the registration is useful in many respects: (1) it enables the in time visualization of all that is being said, ensuring the right action was registered, (2) it creates a visual aid, i.e. a reminder for participants of what has been said, (3) it structures actions in the given framework, (4) it enables conversion of outcomes in a.html file, which can be then elaborated. Figure 3 Specific actions fort the implementation of responses, screenshot of FreeMind. The further elaboration of the outcomes is necessary in order to systematize in a coherent way what was expressed. Repetition of similar actions, in fact, may occur. Also some actions that during the brainstorming were attribute to one of the proposed response, but were thought to fit better in another response, were moved. However, some of the actions are not easily attributed to one or the other response. Actions were also divided between: (1) existing and (2) needing improvement or demanded (see Annex). Most of the outcomes regard the Assam case study. However, some information was elicited from participants of the other case studies. The only European present, Hans Wiesenegger from Salzburg, gave a presentation during the symposium on water resources management in the municipality of Salzburg. Hans also intervened in the brainstorming sharing his experience. His contribution was much appreciated by the Brahmatwinn research partners and by other participants. Participants from Bhutan briefly, but effectively, described their country projects on water resources management, and environmental issues in general. They stressed the fact that in Bhutan they are just in the starting phase, therefore, many institutions are created or regulations defined, but little has been implemented, as of now. 7

47 The situation in Tibet is very different. The government seems to be well aware of the environmental problems, and very defined plans are being implemented. Unfortunately, a very generic contribution was given by participants in the brainstorming, it would have been very interesting to learn more about implementation mechanisms, for instance. Last but not least, the Assam State of India. Most of the words spoken in the brainstorming came from participants of this region. These participants were generally very well informed and thus their contributions were possibly of inspiration for the others. It emerges that a framework is in place, even if more needs to be done for the implementation and further specification of it. However, the brief comments outlined here are based on the mere interpretation of the brainstorming outcomes. A more in depth analysis of the responses with respect to the governance framework was carried out by the University of Dundee in other deliverables (Dl_4.2, Dl_4.3, Dl_8.4). Our hope is that this brainstorming in particular, as well as the symposium in general, are seen as a good opportunity to share information and learn from each other, integrating knowledge from the different disciplines involved. References EEA (1999). Environmental Indicators: typology and overview. European Environment Agency (EEA) (ed.), Technical report n 25, Copenhagen Giupponi C., et al. (2008) NetSyMoD: an integrated approach for water resources management. Integrated water management: practical experiences and case studies, Springer,

48 Annex Bhutan 1. Karma Dupchu Sr. Hydrology Officer Department of Energy (Hydromet Service Division) Ministry of Economic Affairs, Bhutan Tel No , Fax No , Cell No G. Karma Chhopel Head National Environment Commission, Water Resorces P.O.Box 466, Thimphu, Bhutan Tel No , Fax No , Cell No: Chimi Wangmo Associate Lecturer College of Science and Technology 9

49 Phuentsholing, Bhutan Tel No , Fax No , Cell No Tashi Lhamo Assistant Engineer Druk Green Power Corporation Limited Project Department, Bhutan 5. Ugyen Rinzin Executive Engineer Public Health Engineering Division Department of Public Health, Ministry of Health, Bhutan Tel No , Cell No rinzin.ugyen@yahoo.com 6. Tenzin Executive Engineer, Engineer Cell Department of Agriculture Ministry of Agriculture, Bhutan Cell No tuzutinzin@yahoo.com 7. Ugyen Tenzin Lecturer Team Leader (P-13) Brahamatwinn Project Royal University of Bhutan Cell No utinzin_2000@yahoo.com China 8. Jianhu Hu Water Resources and Hydrology Bureau, Ministry of Water Resorces and Power of China Lane 2, Baiguang Road, Beijing Tel No , Fax No , Cell No jwhu@mwr.gov.cn 9. Jingshi Liu Research Professor Institutes of Tibetan Plateau Research Chinese Academy of Science Shuangqing Rd 18, Haidian, Beijing, P.R. China Tel No , Cell No jsliu@itpcas.ac.cn 10

50 10. Dongqi Zhang Scientist Chinese Academy of Meteorological Science No. 46, Zhangguancun South Street, Beijing, China Tel No Bian Duo Institute of Tibetan Plateau Atmospheric & Environmental Science Tibet Meteorological Bureau No. 2, North Linkuo Road, Lhasa Tel No , Cell No Chu Duo Institute of Tibetan Plateau Atmospheric and Environmental Sciences Tibet Meteorological Bureau No.2 North Linkuo Road, Lhasa Tel/Fax: , Cell No Luo Xinghong Institute of Tibetan Plateau Atmospheric and Environmental Sciences Tibet Meteorological Bureau No.2 North Linkuo Road, Lhasa Tel/Fax: , Cell No Yan XianMa Institute of Tibetan Plateau Atmospheric and Environmental Sciences Tibet Meteorological Bureau No.2 North Linkuo Road, Lhasa Tel/Fax: , Cell No Sou Lang Duo Li Institute of Tibetan Plateau Atmospheric and Environmental Sciences Tibet Meteorological Bureau No.2 North Linkuo Road, Lhasa Tel/Fax: , Cell No

51 16. Zhigang Yang Tibet Climate Centre Tibet Meteorological Bureau No.2 North Linkuo Road, Lhasa Tel/Fax: , Cell No Zhuo Ga Institute of Tibetan Plateau Atmospheric and Environmental Sciences Tibet Meteorological Bureau No.2 North Linkuo Road, Lhasa Tel/Fax: , Cell No India 18. Yuping Lei Centre for Agriculture Resource Research, IGDB CAS 286 Huaizhong Rd. Shijiazhuang , China Tel No / Cell No Fax No , leiyp@sjziam.ac.cn 19. Amiya Sharma Executive Director Rastriya Gramin Vikas Nidhi RGVN Oppo. 8 th Bye line, Rajgarh Road, Guwahati Tel No , Cell No amiya_sharma@hotmail.com 20. Rupak K. Mazumdar Government Administrator Government of Assam, Director of Food and Civil Suppliers Bhawgagarh Sethi Trust Building, Guwahati , Assam Tel No , Cell No rupak.mazumder25@gmail.com 21. Dulal Chandra Goswami Retd. Professor, Environmental Science Guwahati University Quarter No. 2, Guwahati , Assam Cell No dulalg@yahoo.com 12

52 22. Trilochan Baruah Superintending Engineer Brahmapurtra Board (MOWR), Govt. of India Basistha Guwati 29, Assam Tel No , Cell No Nawajyoti Sharma Advisor, (Imig. Flood Control) North Eastern Council IIT Roorki Nangrim Hills, Shillong Meghalaya India Tel No , Cell No Pradip Sharma Selection Grade Lecturer Cotton College Department of Geography Guwahati , Assam, India Cell No / Abhijit Dutta Secretary to the Govt. of Assam, Public Health Engineering Department, IIT-Roorki B Block, 2 nd Floor, Assam, Guwahati, India Tel No (o)/ / duttaabhijit@gmail.com 26. Roopak Goswami Principal Corespondent The Telegraph 3 rd Floor, Jupitar Place, GS Road, Bhangagach, Guwahati Tel No , Cell No roopakgoswami@gmail.com 27. Anup Mitra Adviser ADB Project, Government of India Block B, Dispur, Guwahati , Assam Tel No , Cell No anup.fcontrol@indiatimes.com 28. Tapan Dutta Retd. Professor 13

53 Govt. of India Advisor to the Chief Minister, ARIASP Building Khanapara, Guwahati Tel No , Cell No Padma Sharma Goswami Selection Grade Lecturer Cotton College, Guwahati Cell No Nayan Sharma Dept. of Water Resources Development & Management Indian Institute of Technology Roorkee Roorkee , Uttarakhand, India Tel No (Tele Fax) / Home Fax No , , / Cell: nayanfwt@gmail.com, nayanfwt@iitr.ernet.in Nepal 31. Dilip kumar Gautam Sr. Divisional Hydrologist Department of Hydrology and Meteorology P. O. Box 406, Babarmahal, Kathmandu, Nepal Tel No Fax No Cell No dilip_gautam@hotmail.com 32. Tirtha Raj Adhikari Lecturer Central Department of hydrology and Meteorology Tribhuvan University, Kritipur, Kathmandu Tel No Cell No tirtha43@yahoo.com 33. Kumud Raj Kafle Assistant Professor Department of Environmental Sciences and Engineering Kathmandu University, Dhulikhel, Kavre District, Nepal Tel No , Cell No krkafle@ku.edu.np krkafle@yahoo.com 34. Sharad Upadhyaya Engineer Institute of Engineering, Tribhuwan University 14

54 Thapathali Campus, Kathmandu Nepal Cell No Gautam Rajkarnikar Sr. Divisional Engineer and Chief Koshi River Basin Management Cell Water and Energy Commission Secretariat Singhdurbar, Kathmandu, Nepal Tel ( Cell), gautamraj@hotmail.com 36. Bijay Kumar Pokhrel Hydrologist Engineer Department of Hydrology and Meteorology Babarmahal, Kathmandu Nepal Tel No Fax No Cell No bijaypokharel@hotmail.com 37. Bed Kumar Dhakal Department of National Parks and Wildlife Conservation G.P.O Box 860 Babarmahal, Kathmandu Tel No / / Fax No info@dnpwc.gov.np/ bedkumar@gmail.com 38. Shreekamal Duibedi Department of Water Induced Disaster Prevention P. Box No , Pulchowk, Lalitpur Tel No / / Fax No dwidp@ntc.net.np/ shreekamal@gmail.com 39. Neera Shrestha Pradhan (Regret) WWF Nepal P.O.Box No. 7660, Baluwatar Kathmandu Tel No Fax No info@wwfnepal.org 40. Dhurba R. Pant (Regret) Head International Water Management Institute (IWMI)-Nepal Department of Irrigation Building #

55 Jawalakhel, Lalitpur Tel No / Fax No Luna Bharati (Regret) International Water Management Institute (IWMI)-Nepal Department of Irrigation Building # 413 Jawalakhel, Lalitpur Tel No / Fax No Kiran Shankar Yogacharya Chairperson SOHAM Nepal Kupandole Height - 10 Tel No (o) / Cell No , kiran1126@yahoo.com 43. Jagat Kumar Bhusal Vice Chairperson SOHAM Nepal Kupandole Height - 10 Tel No (o) ihpnepal@gmail.com / bhusaljagat@yahoo.com 44. Mr.Dhiraj Pradhananga, General Secretary, SOHAM-Nepal (President, The Small Earth - Nepal, 626 Bhakti Thapa Sadak, Naya Baneshwor) Tel: / Mob: dhirajpradhananga@yahoo.com/ dhirajmet@hotmail.com 45. Narendra Man Shakya Civil Engineering Department Institute of Engineering Pulchowk, Lalitpur Tel No / Fax No Cell No nmsioe@yahoo.com; nms@ioe.edu.np 46. Dr. Madan Lal Shrestha (Regret) Academician Nepal Academy of Science and Technology P.O.Box 19444, Kathmandu, NEPAL Tel: /Cell No madanls@hotmail.com / malashre@gmail.com 16

56 47. Adarsha Prasad Pokharel Bhanimandal, Kathmandu Cell No Ram Chandra khanal (Regret) IUCN Nepal P.O.Box 3923 Kupondole, Lalitpur, Nepal Tel: (977-1) Fax (977-1) Pakistan 49. Farrah Zulfiqar Lecturer Department of Earth Sciences Quaid-i-Azam University, Islamabad Tel No / Fax No Cell No , BRAHMATWINN 50. Zulfiqar Ahmad Professor & Chairman Department of Earth Sciences Quaid-i-Azam University, Islamabad Tel No / Fax No Kimberly Casey Scientist University of Oslo, Department of Geosciences P.B. 1047, Blindern 0316 Oslo, Norway Tel No / 52. Andreas Dobler PhD Student, Mesoskalige Meteorologie und Klima Institut fuer Atmosphaere und Umwelt / Geozentrum Riedberg Goethe- Universitaet Altenhoeferallee 1 D Frankfurt am Main Tel: +49-(0) dobler@iau.uni-frankfurt.de 17

57 53. Bodo Ahrens Professor Goethe-University Frankfurt Alien Hoeferallee 1, Frankfurt, Germany Craig Hutton Researcher GeoData Institute University of Southampton Southampton, SO17 1BJ, UK Tel. No. +44 (0) , Fax No. +44 (0) Monika Prasch Researcher Ludwig Maximilians University, Luisen str. 37, Munich 80333, Germany Tel No Valentina Giannini Researcher Fondazione Eni Enrico Mattei castello 5252, I venezia Tel. No , Fax No Petra Füreder Researcher University of Salzburg, Schiller Str. 30, Salzburg Tel No / Fax No Stefan Kienberger Centre for Geoinformatics Salzburg University Schillerstrasse 30, 5020 Salzburg, Austria Tel. No / Fax No Ivo Cerny Researcher VODNÍ ZDROJE, a.s., Komunardů 309/ Praha 7, CZECH REPUBLIC Tel No , Cell No

58 60. Zuzana Boukalová Hydrodeologist Head of the International Department, Hydrogeologist VODNÍ ZDROJE, a.s., Komunardů 309/ Praha 7, CZECH REPUBLIC Tel. No / zboukalova@gmail.com 61. Anita Bartosch Researcher FRIEDRICH SCHILLER UNIVERSITY OF JENA Department of Geoinformatics Loebdergraben 32, D Jena Cell No ANITA.BARTOSCH@UNI-JENA.DE 62. Jörg Pechstädt FRIEDRICH SCHILLER UNIVERSITY OF JENA Guiclgasn 6, Jena Tel No joerg.pechstaedt@uni-jena.dc 63. Carsten Busch Scientist Codematix GmbH Felsbach Str. 5/7, Jena, Germany Tel No , Cell No carsten.busch@codematix.de 64. Boehm Cristoph MD GDS Talstrasse 84, Germany Tel No , Cell No c8boch@googl .com 65. Hans Wiesenegger Head of Department, Regional Government of Salzburg M. Pacher Str. 36, 5020 Salzburg Austria Tel No Hans.WIESENEGGER@salzburg.fo.et 66. Norbert Exler Researcher University of Vieana Tel No NORBERT.EXLER@UNIVIE.AC.AT 19

59 67. Georg Janauer Professor University of Vienna Althanstr. 14, A Vienna, Austria Tel No , Fax No georg.janauer@univie.ac.at 68. Wolfgang-Albert Flügel Department of Geoinformatics, Hydrology and Modelling Friedrich-Schiller University (FSU-Jena) Löbdergraben 32 D Jena, GERMANY Tel No. +49 (0) / Fax No. +49 (0) C5WAFL@uni-jena.de 69. Andrew Allen Lecturer University of Dundee Perth Road, Peters Building, DUNDEE Tel No , Cell. No A.A.ALLAN@dundee.ac.uk 70. Znamenackova Jitka Diplomat Embassy of the Czech Republic in India 50 Niti Marg, Chanakyapuri, New Delhi Cell No JITKA_ZNAMENACKOVA@MZV.CZ 71. Kzeoter Jan Diplomat Embassy of the Czech Republic in India 50 Niti Marg, Chanakyapuri, New Delhi jan.kzeoter@gmail.com ICIMOD 72. Hua Ouyang Program Manager IWHM 73. Mats Eriksson Water Specialist 74. Arun B. Shrestha Climate Specialist 75. Rajesh Thapa 20

60 Land & Water Analyst 76. Sagar R. Bajracharya Satellite Hydrology Officer 77. Binod Gurung 78. Sarita Joshi Sr. Program Assistant 79. Rekha Rasaily Program Assistant 21

61 Specific actions fort the implementation of DISASTER RISK MANAGEMENT responses, screenshot of FreeMind.

62 Specific actions fort the implementation of FLOOD RISK ZONING FOR HAZARD PREVENTION responses, screenshot of FreeMind. 23

63 Specific actions fort the implementation of LAND-USE PLANNING responses, screenshot of FreeMind. 24

64 Specific actions fort the implementation of RELIEF AND REHABILITATION PLANS responses, screenshot of FreeMind. 25

65 1. DISASTER RISK MANAGEMENT EXISTING A 1. FLOOD GUIDELINES preparedness mitigation (engineering and non structural interventions) response planning A 2. UN DISASTER RISK MANAGEMENT PROGRAMME 3 tier system task forces created for each district to create awareness response is effective training provided: first aid, for masons to build stronger buildings, programmes for engineers and architects protection of life, property and environment A, T 3. EARLY WARNING SYSTEM CWC collects data and to the state governments, which pass it to district authorities on Brahmaputra and tributaries especially in remote areas of Tibet A 4. INFORMATION FROM ASSAM POLICE RADIO people send information to police A, B every police station has radio 5. COMMUNICATION NETWORK mobile phone network is needed (A) reach communities in remote areas, which are cut off as soon as the flood happens (A) deliver information to communities (B) B 6. DEPARTMENT OF DISASTER MANAGEMENT recent establishment started to prepare guidelines IMPROVEMENTS / DEMANDS: A, 7. CAPACITY BUILDING B A, 8. FUND RAISING B A 9. EARLY WARNING SYSTEM flood water warning system early warning system on tributaries for cloud bursts and flash floods modernize monitoring system for tributaries A, T 10. COMMUNICATION NETWORK mobile phone network improvement (A) temporary system is available, only for government level (T) 26

66 A A A 11. ANALYSIS OF REAL TIME INFORMATION 12. DISASTER PREPAREDNESS 13. PROVISION OF CELL PHONES AND OTHER GOODS appoint volunteers for provision 2. FLOOD RISK ZONING FOR HAZARD PREVENTION EXISTING A B B T A A 14. TEMPORARY RESETTLEMENT OF PEOPLE 15. DEVELOPMENT OF FRAMEWORK 16. GLOFS ARE A PRIORITY 17. PERMANENT RESETTLEMENT OF PEOPLE IMPROVEMENTS / DEMANDS 18. TAKE INTO ACCOUNT BANK EROSION 19. SPECIFY RULES FOR LOCATION OF EMBANKMENTS define flood prone areas calculate period of return(hq) superimpose weather prediction data to make decisions map areas most prone to flooding connect wetlands to river A 20. ENABLE THE USE OF WETLANDS AS RETENTION AREAS A 21. CONTROL RELEASE OF WATER FROM DAMS A 22. DIVERSE LANDSCAPE SHOULD BE TAKEN INTO ranging from 4000 m, to 100 m foothills ACCOUNT A 23. NEED TO INCLUDE RIVERINE POPULATIONS difficult to reach them some communities are not adapted and are hit hard A 24. DEAL WITH EMBANKMENTS embankments are becoming shallower people are worried A B B B 25. BUILD RAISED PLATFORM REFUGES IN FLOOD PRONE AREAS 26. MITIGATION RELATED TO GLOFS 27. PROTECT CULTURAL AND RELIGIOUS SITES AND MONUMENTS 28. PROTECT HYDROPOWER PLANTS raised platform are needed so people and cattle can find refuge 27

67 3. LAND-USE PLANNING EXISTING A A A 29. SOCIAL MAP 30. RESOURCES MAP 31. WETLAND DEVELOPMENT AUTHORITY, STATE OF ASSAM T 32. URBAN PLANNING mainly people live in cities T 33. PLANS short and long time T 34. FARM PROTECTION PROGRAMME farmland cannot be transformed degradation affects grass land only IMPROVEMENTS / DEMANDS A 35. ENFORCEMENT OF LAW regulations to protect wetlands exist but are not implemented A 36. UNDERSTANDING OF WETLANDS A 37. REGULATORY MEASURES TO PROTECT wetlands are reduced because of increase productivity WETLANDS A 38. STOP ENCROACHMENT prevent construction in flood plain assess impact of infrastructure deal with shifting cultivation A 39. STOP SILTATION siltation disconnects water from river A 40. BRAHMAPUTRA RIVER BASIN AUTHORITY provide inter state Indian basin management 28

68 4. RELIEF AND REHABILITATION PLANS EXISTING A 41. PROVISION OF FOOD TO FLOOD AFFTECTED COMMUNITIES army helicopter are deployed to bring rice National disaster response force also distributes food Food corporation of India makes food available communities have adaptation strategies: they reserve stocks of food that can sustain them for 3-4 days A 42. ASSAM RELIEF MANUAL instructions for natural disasters (1976) information for different types of people (young, grownups ) includes rehabilitation elements CRF: 27 items, 25% Assam money, 75% State of India funding scheme for flood affected people B 43. CHAIN OF COMMAND under the Ministry of Agriculture at the block level the local government is answerable to it arrives at the local level B 44. FIRST RESPONSE at very local level army personnel can intervene, if necessary IMPROVEMENTS / DEMANDS A 45. ASSAM RELIEF MANUAL establish a way to calculate compensation develop more effective measures develop funding scheme for people affected by erosion A 46. PAY ATTENTION TO PEOPLE AFFECTED BY FLASH FLOODS people affected by flooding have somewhat adapted people affected by flash floods need to develop adaptation strategies A 47. COORDINATION IS NEEDED A 48. MAP EXISTING ASSETS documentation needed to allocate compensation equitably In the first column the letter identifies the origin of the end user: A: Assam B: Bhutan T: Tibet 29

69 Project no: GOCE Project acronym: BRAHMATWINN Instrument: Specific Targeted Research Project Thematic Priority: Global Change and Ecosystems Project title: Twinning European and South Asian River Basins to enhance capacity and implement adaptive management approaches Deliverable Report Dl_10.4: IWRM strategy ranking based on stakeholder acceptance, gender issues, and legal implementation Due date of deliverable November 2009 Actual submission date December 2009 Start date of project: Duration: 36 Month Organisation name of lead contractor for this deliverable: Project homepage: FEEM

70 Table of Content 1. Introduction Method... 4 The framework... 4 DSS Design and Analysis of Options Results Conclusions Annexes List of end users that participated in the symposium Analysis Matrix List of figures & tables Figure 1 The NetSyMoD flowchart... 5 Figure 2 Wolfgang Flügel, the coordinator, explaining to Bhutanese end users capabilities of the River Basin Information System Figure 3 Weights attributed to criteria by local actors during the workshop held in Kathmandu in Figure 4 Screenshot of mdss with indicators attributed to DPSIR framework... 9 Figure 5 Screenshot of mdss with ranking obtained with the Electre III method (average matrix)... 9 Figure 6 Screenshot of mdss: compromise solution based on descending order with Condorcet rule Figure 7 Screenshot of mdss : compromise solution based on descending order with Borda rule Figure 8 Screenshot of mdss : compromise solution based on descending order with Extended Borda rule Figure 9 End users and Brahmatwinn research partners on the first day of the symposium Figure 10 (top left) Screenshot of mdss: compromise solution based on ascending order with Condorcet rule Figure 11 (top right) Screenshot of mdss : compromise solution based on ascending order with Borda rule Figure 12 (bottom left) Screenshot of mdss : compromise solution based on ascending order with Extended Borda rule Figure 13 Screenshot of mdss: compromise solution based on intersection order with Condorcet rule Figure 14 Screenshot of mdss : compromise solution based on intersection order with Borda rule.. 29 Figure 15 Screenshot of mdss : compromise solution based on intersection order with Extended Borda rule Table 1 Selected criteria and relative indicators

71 List of acronyms CSM DPSIR DSS HRU IIT IWRM NE NetSyMoD UBRB UDRB VulnUs WRRU creative system modelling Driving force Pressure State Impact Response Decision Support System Hydrological Response Units integrated indicators table Integrated Water Resources Management natural environment Network analysis, Creative System Modelling, Decision Support Upper Brahmaputra River Basin Upper Danube River Basin vulnerability units Water Resources Response Units List of partners involved Partner 1 Partner 2 Partner 4 Partner 6 Partner 7 Partner 9 Partner 12 Partner 13 Partner 20 FSU LMU ZGIS GeoDa UniDun FEEM ICIMOD UniBu IITR 3

72 1. Introduction This deliverable is strongly based on outcomes of previous deliverables, especially relative to WP_6 and WP_8, which are in turn based on previous WPs (WP_2, WP_3, WP_4). We can thus say that with this phase we have achieved the in depth analysis of effectiveness of IWRMS options envisaged as a conclusion of deliverable 8.3. In Dl_8.3, in fact, we had ended with the outcome of the two workshops carried out with local actors 1 in Salzburg for the UDRB and in Kathmandu for the UBRB, respectively in October and November of The local actors that participated in both workshops evaluated responses based on the planning category as most promising to cope with flood risk, which should increase because of the impacts of climate change. From that analysis of the effectiveness comes the need to better define what IWRM option should be implemented. The categories of responses analysed are, in fact, too broad to be implemented, thus the need for further specification arises. This deliverable is thus the conclusion of a participative process carried out during the Brahmatwinn project, aimed at identifying pressing issues in the UDRB and in the UBRB, and possible responses to them. This process is the result of the interaction of Brahmatwinn scientists with local actors. Continuous feedback opportunities have been created during the project years, so that the two communities could share knowledge among and within them. 2. Method The framework The approach adopted for the analysis of alternative adaptation responses is based on the NetSyMoD methodological framework (Giupponi et al., 2008; for the management of participatory modelling and decision processes (Figure 1). This methodology relies on the DPSIR framework (Driving forces, Pressures, State, Impacts, and Responses; EEA, 1999). The DPSIR framework, although widely used, is the object of some criticism, because it is not believed to be a neutral framework, but rather one which is best suited for biodiversity management leading to Preservationist discourse options (Svarstad et al., 2008). Svarstad et al. (2008) conclude that the DPSIR framework should be expanded to incorporate social and economic concerns. In the research presented in this deliverable, we have shown a possible way to achieve this. The NetSyMoD methodology is organised in six main phases. The first three (Actors Analysis, Problem Analysis, Creative System Modelling) provided the Brahmatwinn Project with (1) an in depth analysis of general problems related to water resources management in the two upper river basins, with the participation of the communities of interested parties in the case study areas, and (2) mental model representations of the problems, i.e. qualitative descriptions of the causal links 1 We have preferred to use the term local actor (LA), to identify all the people involved in the case study activities instead of the more commonly used term stakeholder, to emphasise the fact that they were people who did not belong to the project consortium (typically local experts or policy makers), involved in project activities by partners responsible for the management of case studies to provide advice and steer project activities, without the ambition to assess their representativeness with robust procedures, such as Social Network Analysis. 4

73 between the various components of the local socio-ecosystems by means of cognitive maps clustered in order to be consistent with the DPSIR framework, used as an upper aggregated level communication interface. The subsequent phases, DSS Design and Analysis of Options contributed in WP_8 to the design and evaluation of a set of alternative categories of responses (Engineering solutions and land management, Knowledge improvement and capacity building, Governance and institutional strength, Planning) obtained with group elicitation techniques and with the application of the DSS tool. The result of this iteration of the DSS Design and Analysis of Options was that the Planning category needed to be analysed and defined in a more specific way. opinions & interests ACTOR ANALYSIS communication & adaptation PROBLEM ANALYSIS CREATIVE SYSTEM MODELLING DSS DESIGN scenarios & models Thematic structure information management ACTIONS & MONITORING decisions ANALYSIS OF OPTIONS preferences Figure 1 The NetSyMoD flowchart This second DSS Design phase consists -again- of system specification and development of software tools capable of managing the data required for informed and robust decisions. The Analysis of Options is also again performed with the mdss software (Mulino DSS) through Multi Criteria Decision Analysis (MCDA), which provides a framework for decision analysis, and with a set of techniques aiming at the elicitation and aggregation of decision preferences (Figueira et al., 2005). In this case, MCDA demonstrates how to assist a decision maker, or a group of decision makers, in identifying the best alternative from a range of alternatives in an environment of conflicting and competing criteria and interests (Belton and Stewart, 2002). DSS Design and Analysis of Options Building upon the information acquired in the participatory activities carried out in the first two years of the project one new workshop was hosted by ICIMOD, and held in Kathmandu, Nepal (November 2009), with the aim of providing the project consortium with an assessment of the expected effectiveness of the four Planning responses to cope with flood risks under the pressure of climate change. Due to the high participation of end users from the UBRB in general (see Annex), and specifically from the Assam case study, the symposium focused on the Assam State of India. However, all end users opinion were collected, thus the outcomes can be generally thought as Brahmatwinn outcomes! The workshop activities have been carried out during the symposium in which end users of the final outcomes have been invited. This symposium is one of the dissemination activities organized by the 5

74 Brahmatwinn project partners to facilitate understanding of the project outcomes. During the symposium, in fact, possibilities of interaction between researchers and end-users were encouraged. Figure 2 Wolfgang Flügel, the coordinator, explaining to Bhutanese end users capabilities of the River Basin Information System. The workshop, therefore, relied on the presentation made by the workpackage leader, during which an overview of the Brahmatwinn activities was given. Having introduced the problem and the climate change scenarios, a brainstorming session was conducted to elicit and consolidate the sets of possible responses within the Planning category (see deliverable_10.3 for a description of this phase). This section created the basis for the correct implementation of the ensuing steps, and led to the identification of sub-categories and specific actions, within the proposed Planning category of responses. The responses analysed were: DISASTER RISK MANAGEMENT FLOOD RISK ZONING FOR HAZARD PREVENTION LAND USE PLANNING RELIEF AND REHABILITATION PLANS Having consolidated the identification of responses, the participants were presented the criteria selected for the evaluation of responses, and the indicators chosen to describe each criterion. The criteria presented had been selected during the workshop held in Kathmandu in 2008, from the Subdomains listed in the Integrated Indicator Table (IIT) created by the whole Brahmatwinn consortium in WP_6, and described in Dl_6.1. The indicators have been chosen among those listed in the IIT, because judged the most fit to describe that specific criterion in the caste study. 6

75 Table 1 Selected criteria and relative indicators THEME CRITERION INDICATOR SOC 1 POVERTY 1. per capita income SOC 2 SOC 3 POPULATION DYNAMCS INFRASTRUCTURE PRESSURES 2. population growth; urbanization 3. measure of flood damage to property, to man, to cattle ENV 1 BASIN MORPHOLOGY 4. stream bank erosion ENV 2 FOREST MANAGEMENT 5. decline of per-capita availability of forest land ENV 3 VULNERABILTY 6. potential erosion prone stream bank line ECON 1 ENERGY PRODUCTION 7. construction of dams; use of dams for hydropower, water supply or both ECON 2 AGRICULTURE PRODUCTION 8. growth of area and number of tea estates 9. gross irrigated area ECON 3 EMPLOYMENT 10. share of secondary sector of GSDP; contribution of tertiary sector to NSDP 11. growth of industries Indicators/criteria and responses were used to define the entries of the Analysis Matrix (AM) (9 rows and 5 columns for criteria and response categories respectively) and were utilised for the subsequent evaluation exercise, by means of the MCDA methods provided by the mdss software. Participants were asked to fill in the matrix by evaluating the potential effectiveness of each response (columns) in coping with the issues expressed by the criteria (rows) by means of a Likert scale (from 1 to 5 ranging from very high effectiveness to very low effectiveness ). Forms were distributed to all the participants with a specific question aimed at understanding the effect each response would have on each indicator (see Annex). Moreover, in accordance with the Guidance Notes for the lead authors of IPCC 4th Assessment Report on Addressing Uncertainties (IPCC, 2005), a scale was added to the matrix to analyse the degree of confidence and uncertainty related to local actors opinion. Here, the concept of uncertainty was related to the unpredictability of the effectiveness of the responses, which can be due to various reasons: e.g. the unpredictable projections of human behaviour, the unpredictable evolution of political systems, the chaotic components of the eco-system, etc. Thus, a second question, What is your degree of confidence in giving your answer, considering its predictability? was added to the form sheets and a second Likert scale was added in the AM. 7

76 The compilation of the AM concluded the NetSyMoD workshop. All the data collected were coded with a spreadsheet software and then passed to the mdss tool, for Multi-Criteria Analysis (MCA) and Group Decision Making (GDM). The mdss software allowed for the comparison of the alternative options using MCA techniques, by operating parallel evaluation processes, representing the preferences of each participant. The alternative options (i.e. the four categories of responses), were assessed on the basis of their contributions to solve the expected impact due to flooding under a climate change scenario, and expressed through the criteria values. The weights used for the MCDA were those elicited in the Kathmandu workshop held in 2008 (see Figure 3). UBRB Weights weights (0-1) Poverty Population dynamics Infrastructure pressures Vulnerability Basin morphology Forest management Agricultural production Energy production Employment Figure 3 Weights attributed to criteria by local actors during the workshop held in Kathmandu in In practice, the qualitative evaluations contained in the Analysis Matrix were transformed into scores that expressed the performances of the responses by applying a normalisation procedure, which converted them into a continuous scale from zero to one, subsequently processed by means of MCA decision rules. For the purposes of the workshop the Electre III decision rule was utilised to rank the alternative responses. Electre III adopts a pairwise comparison of the alternatives, so it is computationally rather demanding, but very simple to be applied by practitioners. It imposes socalled outranking relation on a set of alternatives. An alternative a outranks an alternative b if a is at least as good as b and there is no strong argument against. Results of individual outranking procedures were subsequently combined in a Group Decision Making procedure by means of the Borda rule. The Borda rule is one of the most simple outranking procedures and it is provided by the mdss software, in which a total Borda mark is calculated by summing up all the (reversed) rankings obtained by the LAs (i.e. the best option is given, in this case, a value of 3, while the worst the fourth, is given a value of 0). The best (consensus) option is obviously the one with highest total Borda mark. 3. Results The analysis of the matrices compiled by the workshops participants was carried out with the mdss software. First of all a file was created where the indicators (rows of the matrix) were loaded and 8

77 attributed to the DPSIR framework. In the same file IWRM responses (columns of the matrix) analyzed were loaded (see Figure. 4). Figure 4 Screenshot of mdss with indicators attributed to DPSIR framework Then each matrix compiled was input into the mdss, and analysed with ELECTRE III. Also a matrix with the averages was input into mdss. To enable the ranking, the vector of weights, created in the previous Kathmandu workshop (2008) was loaded. Three outranking procedures are available: (1) descending, (2) ascending, and (3) intersection (see Figure 5, top left, top right and bottom, respectively). If we consider the matrix containing the average of the scores attributed by each participant, we see that Land use planning wins, regardless of the method applied! Figure 5 Screenshot of mdss with ranking obtained with the Electre III method (average matrix) 9

78 Another option given in mdss is the comparison of each participants votes. This is done in the Group decision Compromise function. After elaborating each participants matrix, output option files are created, which are later loaded in the Group decision Compromise section. Three rules can be used: (1) Condorcet, (2) Borda, and (3) Extended Borda. Each rule can be applied to the descending, ascending or intersection outranking procedures, generating nine rankings (see Figures 6 8 and Annex). This analysis confirms Land use planning as the preferred responses, thus assessing the robustness of this choice. Figure 6 Screenshot of mdss: compromise solution based on descending order with Condorcet rule Figure 7 Screenshot of mdss : compromise solution based on descending order with Borda rule 10

79 Figure 8 Screenshot of mdss : compromise solution based on descending order with Extended Borda rule 4. Conclusions Since participatory processes, where power is equally shared and expression of all opinions is facilitated, are increasingly being included in good governance principles (De La Vega-Leinert et al., 2008; Reed, 2008; Griffin, 2007), by choosing this methodology, we were able to include many local actors that have a stake. It was possible to compare several opinions. Moreover, since the goal of the process was not consensus building, we were able to consider and compare all opinions, avoiding the loss of minority views, e.g. those of less empowered stakeholders (Griffin, 2007). In our case, on the contrary, end users have been invited because they represent all issues at stake, and all opinions they expressed have the same importance. 11

80 5. Annexes List of end users that participated in the symposium Figure 9 End users and Brahmatwinn research partners on the first day of the symposium Bhutan 1. Karma Dupchu Sr. Hydrology Officer Department of Energy (Hydromet Service Division) Ministry of Economic Affairs, Bhutan Tel No , Fax No , Cell No kdupchu@druknet.bt 2. G. Karma Chhopel Head National Environment Commission, Water Resorces P.O.Box 466, Thimphu, Bhutan Tel No , Fax No , Cell No: gkchhopel@hotmail.com 3. Chimi Wangmo Associate Lecturer College of Science and Technology 12

81 Phuentsholing, Bhutan Tel No , Fax No , Cell No Tashi Lhamo Assistant Engineer Druk Green Power Corporation Limited Project Department, Bhutan 5. Ugyen Rinzin Executive Engineer Public Health Engineering Division Department of Public Health, Ministry of Health, Bhutan Tel No , Cell No rinzin.ugyen@yahoo.com 6. Tenzin Executive Engineer, Engineer Cell Department of Agriculture Ministry of Agriculture, Bhutan Cell No tuzutinzin@yahoo.com 7. Ugyen Tenzin Lecturer Team Leader (P-13) Brahamatwinn Project Royal University of Bhutan Cell No utinzin_2000@yahoo.com China 8. Jianhu Hu Water Resources and Hydrology Bureau, Ministry of Water Resorces and Power of China Lane 2, Baiguang Road, Beijing Tel No , Fax No , Cell No jwhu@mwr.gov.cn 9. Jingshi Liu Research Professor Institutes of Tibetan Plateau Research Chinese Academy of Science Shuangqing Rd 18, Haidian, Beijing, P.R. China Tel No , Cell No jsliu@itpcas.ac.cn 13

82 10. Dongqi Zhang Scientist Chinese Academy of Meteorological Science No. 46, Zhangguancun South Street, Beijing, China Tel No Bian Duo Institute of Tibetan Plateau Atmospheric & Environmental Science Tibet Meteorological Bureau No. 2, North Linkuo Road, Lhasa Tel No , Cell No Chu Duo Institute of Tibetan Plateau Atmospheric and Environmental Sciences Tibet Meteorological Bureau No.2 North Linkuo Road, Lhasa Tel/Fax: , Cell No Luo Xinghong Institute of Tibetan Plateau Atmospheric and Environmental Sciences Tibet Meteorological Bureau No.2 North Linkuo Road, Lhasa Tel/Fax: , Cell No Yan XianMa Institute of Tibetan Plateau Atmospheric and Environmental Sciences Tibet Meteorological Bureau No.2 North Linkuo Road, Lhasa Tel/Fax: , Cell No Sou Lang Duo Li Institute of Tibetan Plateau Atmospheric and Environmental Sciences Tibet Meteorological Bureau No.2 North Linkuo Road, Lhasa Tel/Fax: , Cell No

83 16. Zhigang Yang Tibet Climate Centre Tibet Meteorological Bureau No.2 North Linkuo Road, Lhasa Tel/Fax: , Cell No Zhuo Ga Institute of Tibetan Plateau Atmospheric and Environmental Sciences Tibet Meteorological Bureau No.2 North Linkuo Road, Lhasa Tel/Fax: , Cell No India 18. Yuping Lie Centre for Agriculture Resource Research, IGDB CAS 286 Huaizhong Rd. Shijiazhuang , China Tel No / Cell No Fax No , leiyp@sjziam.ac.cn 19. Amiya Sharma Executive Director Rastriya Gramin Vikas Nidhi RGVN Oppo. 8 th Bye line, Rajgarh Road, Guwahati Tel No , Cell No amiya_sharma@hotmail.com 20. Rupak K. Mazumdar Government Administrator Government of Assam, Director of Food and Civil Suppliers Bhawgagarh Sethi Trust Building, Guwahati , Assam Tel No , Cell No rupak.mazumder25@gmail.com 21. Dulal Chandra Goswami Retd. Professor, Environmental Science Guwahati University Quarter No. 2, Guwahati , Assam Cell No dulalg@yahoo.com 15

84 22. Trilochan Baruah Superintending Engineer Brahmapurtra Board (MOWR), Govt. of India Basistha Guwati 29, Assam Tel No , Cell No Nawajyoti Sharma Advisor, (Imig. Flood Control) North Eastern Council IIT Roorki Nangrim Hills, Shillong Meghalaya India Tel No , Cell No Pradip Sharma Selection Grade Lecturer Cotton College Department of Geography Guwahati , Assam, India Cell No / Abhijit Dutta Secretary to the Govt. of Assam, Public Health Engineering Department, IIT-Roorki B Block, 2 nd Floor, Assam, Guwahati, India Tel No (o)/ / duttaabhijit@gmail.com 26. Roopak Goswami Principal Corespndent The Telegraph 3 rd Floor, Jupitar Place, GS Road, Bhangagach, Guwahati Tel No , Cell No roopakgoswami@gmail.com 27. Anup Mitra Adviser ADB Project, Government of India Block B, Dispur, Guwahati , Assam Tel No , Cell No anup.fcontrol@indiatimes.com 28. Tapan Dutta Retd. Professor 16

85 Govt. of India Advisor to the Chief Minister, ARIASP Building Khanapara, Guwahati Tel No , Cell No Padma Sharma Goswami Selection Grade Lecturer Cotton College, Guwahati Cell No Nayan Sharma Dept. of Water Resources Development & Management Indian Institute of Technology Roorkee Roorkee , Uttarakhand, India Tel No (Tele Fax) / Home Fax No , , / Cell: nayanfwt@gmail.com, nayanfwt@iitr.ernet.in Nepal 31. Dilip kumar Gautam Sr. Divisional Hydrologist Department of Hydrology and Meteorology P. O. Box 406, Babarmahal, Kathmandu, Nepal Tel No Fax No Cell No dilip_gautam@hotmail.com 32. Tirtha Raj Adhikari Lecturer Central Department of hydrology and Meteorology Tribhuvan University, Kritipur, Kathmandu Tel No Cell No tirtha43@yahoo.com 33. Kumud Raj Kafle Assistant Professor Department of Environmental Sciences and Engineering Kathmandu University, Dhulikhel, Kavre District, Nepal Tel No , Cell No krkafle@ku.edu.np krkafle@yahoo.com 34. Sharad Upadhyaya Engineer Institute of Engineering, Tribhuwan University 17

86 Thapathali Campus, Kathmandu Nepal Cell No Gautam Rajkarnikar Sr. Divisional Engineer and Chief Koshi River Basin Management Cell Water and Energy Commission Secretariat Singhdurbar, Kathmandu, Nepal Tel ( Cell), gautamraj@hotmail.com 36. Bijay Kumar Pokhrel Hydrologist Engineer Department of Hydrology and Meteorology Babarmahal, Kathmandu Nepal Tel No Fax No Cell No bijaypokharel@hotmail.com 37. Bed Kumar Dhakal Department of National Parks and Wildlife Conservation G.P.O Box 860 Babarmahal, Kathmandu Tel No / / Fax No info@dnpwc.gov.np/ bedkumar@gmail.com 38. Shreekamal Duibedi Department of Water Induced Disaster Prevention P. Box No , Pulchowk, Lalitpur Tel No / / Fax No dwidp@ntc.net.np/ shreekamal@gmail.com 39. Neera Shrestha Pradhan (Regret) WWF Nepal P.O.Box No. 7660, Baluwatar Kathmandu Tel No Fax No info@wwfnepal.org 40. Dhurba R. Pant (Regret) Head International Water Management Institute (IWMI)-Nepal Department of Irrigation Building #

87 Jawalakhel, Lalitpur Tel No / Fax No Luna Bharati (Regret) International Water Management Institute (IWMI)-Nepal Department of Irrigation Building # 413 Jawalakhel, Lalitpur Tel No / Fax No Kiran Shankar Yogacharya Chairperson SOHAM Nepal Kupandole Height - 10 Tel No (o) / Cell No , kiran1126@yahoo.com 43. Jagat Kumar Bhusal Vice Chairperson SOHAM Nepal Kupandole Height - 10 Tel No (o) ihpnepal@gmail.com / bhusaljagat@yahoo.com 44. Mr.Dhiraj Pradhananga, General Secretary, SOHAM-Nepal (President, The Small Earth - Nepal, 626 Bhakti Thapa Sadak, Naya Baneshwor) Tel: / Mob: dhirajpradhananga@yahoo.com/ dhirajmet@hotmail.com 45. Narendra Man Shakya Civil Engineering Department Institute of Engineering Pulchowk, Lalitpur Tel No / Fax No Cell No nmsioe@yahoo.com; nms@ioe.edu.np 46. Dr. Madan Lal Shrestha (Regret) Academician Nepal Academy of Science and Technology P.O.Box 19444, Kathmandu, NEPAL Tel: /Cell No madanls@hotmail.com / malashre@gmail.com 19

88 47. Adarsha Prasad Pokharel Bhanimandal, Kathmandu Cell No Ram Chandra khanal (Regret) IUCN Nepal P.O.Box 3923 Kupondole, Lalitpur, Nepal Tel: (977-1) Fax (977-1) Pakistan 49. Farrah Zulfiqar Lecturer Department of Earth Sciences Quaid-i-Azam University, Islamabad Tel No / Fax No Cell No , BRAHMATWINN 50. Zulfiqar Ahmad Professor & Chairman Department of Earth Sciences Quaid-i-Azam University, Islamabad Tel No / Fax No Kimberly Casey Scientist University of Oslo, Department of Geosciences P.B. 1047, Blindern 0316 Oslo, Norway Tel No / 52. Andreas Dobler PhD Student, Mesoskalige Meteorologie und Klima Institut fuer Atmosphaere und Umwelt / Geozentrum Riedberg Goethe- Universitaet Altenhoeferallee 1 D Frankfurt am Main Tel: +49-(0) dobler@iau.uni-frankfurt.de 20

89 53. Bodo Ahrens Professor Goethe-University Frankfurt Alien Hoeferallee 1, Frankfurt, Germany Craig Hutton Researcher GeoData Institute University of Southampton Southampton, SO17 1BJ, UK Tel. No. +44 (0) , Fax No. +44 (0) Monika Prasch Researcher Ludwig Maximilian University, Luisen str. 37, Munich 80333, Germany Tel No Valentina Giannini Researcher Fondazione Eni Enrico Mattei castello 5252, I venezia Tel. No , Fax No Petra Füreder Researcher University of Salzburg, Schiller Str. 30, Salzburg Tel No / Fax No Stefan Kienberger Centre for Geoinformatics Salzburg University Schillerstrasse 30, 5020 Salzburg, Austria Tel. No / Fax No Ivo Cerny Researcher VODNÍ ZDROJE, a.s., Komunardů 309/ Praha 7, CZECH REPUBLIC Tel No , Cell No

90 60. Zuzana Boukalová Hydrodeologist Head of the International Department, Hydrogeologist VODNÍ ZDROJE, a.s., Komunardů 309/ Praha 7, CZECH REPUBLIC Tel. No / zboukalova@gmail.com 61. Anita Bartosch Researcher FRIEDRICH SCHILLER UNIVERSITY OF JENA Department of Geoinformatics Loebdergraben 32, D Jena Cell No ANITA.BARTOSCH@UNI-JENA.DE 62. Jörg Pechstädt FRIEDRICH SCHILLER UNIVERSITY OF JENA Guiclgasn 6, Jena Tel No joerg.pechstaedt@uni-jena.de 63. Carsten Busch Scientist Codematix GmbH Felsbach Str. 5/7, Jena, Germany Tel No , Cell No carsten.busch@codematix.de 64. Boehm Cristoph MD GDS Talstrasse 84, Germany Tel No , Cell No c8boch@googl .com 65. Hans Wiesenegger Head of Department, Regional Government of Salzburg M. Pacher Str. 36, 5020 Salzburg Austria Tel No Hans.WIESENEGGER@salzburg.fo.at 66. Norbert Exler Researcher University of Vieana Tel No NORBERT.EXLER@UNIVIE.AC.AT 22

91 67. Georg Janauer Professor University of Vienna Althanstr. 14, A Vienna, Austria Tel No , Fax No georg.janauer@univie.ac.at 68. Wolfgang-Albert Flügel Department of Geoinformatics, Hydrology and Modelling Friedrich-Schiller University (FSU-Jena) Löbdergraben 32 D Jena, GERMANY Tel No. +49 (0) / Fax No. +49 (0) C5WAFL@uni-jena.de 69. Andrew Allen Lecturer University of Dundee Perth Road, Peters Building, DUNDEE Tel No , Cell. No A.A.ALLAN@dundee.ac.uk 70. Znamenackova Jitka Diplomat Embassy of the Czech Republic in India 50 Niti Marg, Chanakyapuri, New Delhi Cell No JITKA_ZNAMENACKOVA@MZV.CZ 71. Kzeoter Jan Diplomat Embassy of the Czech Republic in India 50 Niti Marg, Chanakyapuri, New Delhi jan.kzeoter@gmail.com ICIMOD 72. Hua Ouyang Program Manager IWHM 73. Mats Eriksson Water Specialist 74. Arun B. Shrestha Climate Specialist 75. Rajesh Thapa 23

92 Land & Water Analyst 76. Sagar R. Bajracharya Satellite Hydrology Officer 77. Binod Gurung 78. Sarita Joshi Sr. Program Assistant 79. Rekha Rasaily Program Assistant 24

93 Analysis Matrix According to the climate change storyline presented, and referring to the issue of FLOOD RISK, we will evaluate the POTENTIAL EFFECTIVNESS of each RESPONSE to cope with the impacts of climate change referring to the selected CRITERIA.. Please fill the matrix in the next page, cell by cell, by answering to the two following questions: 1) What is the potential effectiveness of the RESPONSE (columns) to cope with each CRITERIA (rows)? 1 Very High effectiveness At least 9 out of 10 chance of being effective 2 High effectiveness About 8 out of 10 chance of being effective 3 Medium effectiveness About 5 out of 10 chance of being effective 4 Low effectiveness About 2 out of 10 chance of being effective 5 Very low effectiveness Less than 1 out of 10 chance of being effective 2) What is your degree of confidence in giving your answer, considering its predictability? The predictability of the responses effectiveness can be limited because of various reasons: e.g. the unpredictable projections of human behaviour; the unpredictable evolution of political systems; the chaotic components of the eco-system, etc. Thus, bearing in mind this, please also answer to this question: A Very High confidence At least 9 out of 10 chance of being correct B High confidence About 8 out of 10 chance of being correct C Medium confidence About 5 out of 10 chance of being correct D Low confidence About 2 out of 10 chance of being correct E Very low confidence Less than 1 out of 10 chance of being correct NOTE: If you have comments, please report them in the last page

94 effectiveness confidence 1 A very high 2 B high 3 C medium 4 D low 5 E very low RESPONSE OPTIONS BUSINES AS USUAL DISASTER RISK MANAGEMENT FLOOD RISK ZONING FOR HAZARD PREVENTION LAND USE PLANNING RELIEF AND REHABILITATION PLANS confidence in giving answer on each criterion 1. What is the potential effectiveness of each response to increase per capita income? A B C D E What is the potential effectiveness of each response to cope with population growth and urbanization? A B C D E What is the potential effectiveness of each response to limit damage to property, man and cattle? A B C D E What is the potential effectiveness of each response to decrease/control stream bank erosion or other degradation phenomena related to the morphology of the river basin? What is the potential effectiveness of each response to contribute to flood control through investments on forest management? What is the potential effectiveness of each response to decrease vulnerability to flood risk by reducing potential erosion of stream bank? What is the potential effectiveness of each response to increase construction of dams for energy production and flood control? A B C D E A B C D E A B C D E A B C D E 26

95 What is the potential effectiveness of each response to maintain commercial production of goods, such as tea, in the light of increase flood risk? What is the potential effectiveness of each response to increase gross irrigated area under the threat of increase flood risk? What is the potential effectiveness of each response to influence shares of economic sectors on the total GDP, and thus improve employment rates? What is the potential effectiveness of each response to cope with industrial growth, and thus improve employment rates? BUSINES AS USUAL DISASTER RISK MANAGEMENT FLOOD RISK ZONING FOR HAZARD PREVENTION LAND USE PLANNING RELIEF AND REHABILITATION PLANS A B C D E A B C D E A B C D E A B C D E A B C D E A B C D E A B C D E A B C D E A B C D E confidence in giving answer on each response effectiveness confidence 1 A very high 2 B high 3 C medium 4 D low 5 E very low RESPONSE OPTIONS 27

96 Figure 10 (top left) Screenshot of mdss: compromise solution based on ascending order with Condorcet rule Figure 11 (top right) Screenshot of mdss : compromise solution based on ascending order with Borda rule Figure 12 (bottom left) Screenshot of mdss : compromise solution based on ascending order with Extended Borda rule 28

97 Figure 13 Screenshot of mdss: compromise solution based on intersection order with Condorcet rule Figure 14 Screenshot of mdss : compromise solution based on intersection order with Borda rule Figure 15 Screenshot of mdss : compromise solution based on intersection order with Extended Borda rule 29