Assessing the benefit of improved precipitation input in SWAT model simulations

Transcription

1 Assessing the benefit of improved precipitation input in SWAT model simulations Ilyas Masih 1,2, Shreedhar Maskey 2, Stefan Uhlenbrook 2,3, Vladimir Smakhtin 1 1 International Water Management Institute (IWMI), P. O. Box. 275, Colombo, Sri Lanka, (i.masih@cgiar.org). 2 UNESCO-IHE Institute for Water Education, P.O. Box 315, 261 DA Delft, The Netherlands. 3 Delft University of Technology, Department of Water Resources, P.O. Box 548, 26 GA Delft, The Netherlands. Paper presentation at the 5th International Watershed Modelling (SWAT) Conference, August 5-7, 29, University of Colorado, Boulder, Colorado, USA.

2 Introduction Immense progress in hydrological model development and use Addressing involved uncertainties (e.g., due to data, model) is still a key issue in order to improve hydrological predictions and management of water resources (e.g., Beven 21) SWAT has significantly improved over last 3 years (e.g. Gassman et al., 27), e.g., Physical basis and structure Integration of various processes Efficient data processing (e.g. GIS interfaces) and viewing of output Sensitivity/uncertainty/auto-calibration Research has also been devoted to evaluate the impact of different quality data sets (e.g. Resolution of DEM, soil and land use data) But there are few studies on the issue of climatic data input (e.g., Chaplot et al., 25; Jayakrishnan et al., 25; Tobin and Bennett 29) This critical aspect needs further attention

3 Introduction (Cont.) Because Climatic input (e.g., precipitation) is the major driver of hydrology, sediment and nutrient processes, and The current way of climatic data input in SWAT is rather simple One station nearest to the centroid of a catchment Gauge nearest to the centroid may not be the best representative This can undermine the full use of available data Quality of the climatic data input will have serious implications for the model formulation (e.g., parameterization) and quality of (spatial and temporal) the results-all these aspects require further research

4 Main focus of this study Main research question: how improved precipitation input influences the SWAT s hydrological simulations across a large River basin? The specific objectives: to compare the SWAT performance (observed versus simulated streamflow hydrograph) achieved by using the areal precipitation input with that using the rain gauge data as per SWAT s current settings of the climatic data input to examine spatial and temporal performance of the model simulations under both precipitation scenarios

5 Materials and Methods

6 Study area: the Karkheh basin, Iran Some basic facts and figures Drainage area of 5, 764 Km 2 More than 8 % is mountainous Divided into five subbasins Mediterranean climate: Cool and wet winter; Dry and hot summers Precipitation 45 mm/year, range: 15 mm to 75 mm

7 Climate and hydrology Precipitation (mm/month) Kermanshah Sanandaj Hamedan Khorramabad Arak Ahw az Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Temperature ( o C/month) Kermanshah Sanandaj Hamedan Khorramabad Arak Ahw az Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Mean monthly river flow (m 3 /sec) 5 45 Pole Chehre 4 Ghore Baghestan Holilan 35 Pole Dokhtar 3 Jelogir 25 Paye Pole 2 Hamedieh Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Month Source: Masih et al., Submitted for publication

8 The Karkheh basin: Key issues and knowledge gaps Very important for food and hydropower production, and environmental point of view (Hoor-Al-Azim SWAMP) The pressure on available water resources is increasing The improved knowledge and understanding of the basin hydrology and impact of these interventions are lacking The ultimate goal of this SWAT modeling exercise is to contribute in improving the understanding of basin hydrology and devising options for better water management under various agricultural development scenarios, including the climatic variability and change Water allocations in 21 (4949 MCM) Others, 14 Environment, 5 Environment, 5 Domestic, 262 Industry, 23 Irrigation, 4149 Water allocations in 225 (893 MCM) Others, 512 Domestic, 362 Industry, 113 Irrigation, 7416 Source: JAMAB 26

9 SWAT calibration and performance evaluation Daily climatic data of (Precipitation: 41 stations; Temperature: 11 stations) Case I: station precipitation input Case II: areal precipitation input Performance evaluation: NSE, R 2 and annual volume balance Spatially at 15 streamflow gauges across the Karkheh River System Temporally at daily, monthly and annual time scales, over period of (Calibration: Oct 1887-Sep1994; Validation: Oct 1994-Sep 21)

10 Salient features of the streamflow gauges Sub-basin River Name Station Name ID Long Lat Elevation, masl Gamasiab Qarasou Kashkan Saymareh Khorram Rod Toyserkan Gamasiab Qarsou Abe Marg Qarasou Har Rod Doab Aleshtar Khorramabad Chalhool Kashkan Bad Avar Saymareh Karkheh Karkheh Aran Firozabad Pole Chehre Doabe Merek Khersabad Ghore Baghestan Kaka Raza Sarab Seidali Cham Injeer Afarineh Pole Dokhtar Noorabad Holilan Jelogir Paye Pole D.area, km

11 Preparation of areal precipitation input 1) Rain gauge data 2) Gauge location 3) DEM /Elevation 4) Sub-basin ID Interpolation using IDW including elevation weighting Cross validation Areal average for sub-catchment Virtual rain gauge data In put for each sub-catchment

12 SWAT calibration for Case I Sub-catchment delineation Land cover and soil properties HRU definition Elevation bands Setting up other routines and parameters, e.g. Snow Groundwater Channel routing Land cover management etc Land cover map of the Upper Karkheh basin (Ahmad et al., 29) We followed a physically based approach for model set up and calibration. Suitable values/ranges of the parameters were defined using various sources of information that included measured data, global data sources, SWAT soil and land cover data base, literature, discussion with the local experts and field visits.

13 SWAT calibration: Final values/ranges of the selected parameters Parameter Suggeste d ranges Final value or range Remarks Snow routine Snow fall temperature, SFTMP ( o C) Snow melt temperature, SMTMP ( o C) Maximum melt rate of snow during a year (occur in summer solstice), (mm/ o C -day) Minimum melt rate of snow during a year (occur in winter solstice), (mm/ o C -day) Snow pack temperature lag factor (TIMP) Soil routine Soil available water capacity (SOL_AWC), mm/mm Soil saturated hydraulic conductivity (SOL_K), mm/hr Maximum Soil Depth (SOL_ZMX), mm Soil evaporation compensation factor (ESCO) Surface runoff routine SCS CN Manning s n value for overland flow Surface runoff lag coefficient (SURLAG) Groundwater routine Base flow recession constant (ALPHA_BF) Groundwater delay from soil to groundwater, (GW_DELAY), day to Snow parameters were modified to get snow results in accordance with the study of Saghafian and Davatalab (27) on snow cover dynamics in the Karkheh basin. Ranges were defined based on various sources of information. The used values differed based on soil types and land use, e.g., coarse soils had lowest SOL_AWC and highest SOL_K. Used values were in close agreement with literature (e.g., SCS Engineering Division, Engman 1983). Varied based on base flow features of the streams (e.g. Masih et al., 29)

14 Results and Discussions

15 Comparison of the input precipitation: Case II VS Case I: Spatial view Sub-catchment precipitation (Case II) Precipitation difference (Case II VS Case 1) High spatial variability, mainly influenced by topography (left Fig.) The precipitation difference in Case II compared to Case I ranged from - 4 to 4 % (right Fig.)

16 Comparison Case II VS Case I: Temporal view Divergent variation by subcatchment, illustrated by four selected cases Precipitation dynamics in Case II could be different in many respects. P Case I (mm/day) P Case I (mm/day) Daily Monthly Annual Sub-catchment ID: P Case II (mm/day) Sub-catchment ID: P Case II (mm/day) P Case I (mm/month) P Case I (mm/month) Sub-catchment ID: P Case II (mm/month) Sub-catchment ID: P Case II (mm/month) P Case I (mm/year) P Case I (mm/year) Sub-catchment ID: P Case II (mm/year) Sub-catchment ID: P Case II (mm/year) For instance, daily values can be higher/lower. They also show clear pattern in extreme values: 1) lower P events can be totally missed out be a single rain gauge; 2) extremes in Case II are comparatively small in most cases, though could be other way around for some subcatchments and P events. P Case I (mm/day) P Case I (mm/day) Sub-catchment ID: P Case II (mm/day) Sub-catchment ID: P Case II (mm/day) P Case I (mm/month) P Case I (mm/month) Sub-catchment ID: P Case II (mm/month) Sub-catchment ID: P Case II (mm/month) P Case I (mm/year) P Case I (mm/year) Sub-catchment ID: P Case II (mm/year) Sub-catchment ID: P Case II (mm/year)

17 Comparison: R 2 under Case I & Case II Calibration Validation 1. Case I Case II 1. Case I Case II.8.8 Daily R Daily R Gauge ID Gauge ID 1. Case I Case II 1. Case I Case II Monthly R Monthly R Gauge ID Gauge ID

18 Daily NSE Monthly NSE Comparison: NSE under Case I & Case II Calibration Case I Case II Case I Gauge ID Case II Gauge ID Daily NSE Monthly NSE Validation Case I Case II Case I Gauge ID Case II Gauge ID

19 Comparison: Volume balance under Case I & Case II Calibration Validation Volume balance Case I Case II Gauge ID Volume balance Case I Case II Gauge ID

20 Comparison: highlighting scale dependency Change in Daily NSE and R 2 (Case II VS Case I) Change in NSE and R R square Calibration R square Validation NSE Calibration NSE Validation Drainge area (km 2 ) Less changes for the gauges draining large areas More changes in case of smaller catchments

21 Gauges performing good in both cases Jelogir station at the Karkheh River Mean Daily Discharge (m 3 /sec) Observed Simulated Case I Simulated Case II 1/1/1987 7/1/1987 1/1/1988 7/1/1988 1/1/1989 7/1/1989 1/1/199 7/1/199 1/1/1991 7/1/1991 1/1/1992 7/1/1992 1/1/1993 7/1/1993 1/1/1994 7/1/1994 1/1/1995 7/1/1995 1/1/1996 7/1/1996 1/1/1997 7/1/1997 1/1/1998 7/1/1998 1/1/1999 7/1/1999 1/1/2 7/1/2 1/1/21 7/1/21 Date The stations on the Karkheh River and its major tributaries perform well in both cases Indicating averaging out effect of the precipitation input Also, no remarkable differences were seen in spatial and temporal term, as results are in acceptable ranges for all these cases

22 Gauges performing good in cases II Sarab Seidali station at the Doab Aleshtar River Mean Daily Discharge (m 3 /sec) Observed Simulated Case I Simulated Case II 1/1/1987 7/1/1987 1/1/1988 7/1/1988 1/1/1989 7/1/1989 1/1/199 7/1/199 1/1/1991 7/1/1991 1/1/1992 7/1/1992 1/1/1993 7/1/1993 1/1/1994 7/1/1994 1/1/1995 7/1/1995 1/1/1996 7/1/1996 1/1/1997 7/1/1997 1/1/1998 7/1/1998 1/1/1999 7/1/1999 1/1/2 7/1/2 1/1/21 7/1/21 Most of the smaller tributaries performed well in Case II Better performance in Case II for sub-catchments in the Kashkan River region, where rain gauge density is comparatively low Temporally, performance improved for all the studied scales in general Changes in precipitation regime contributed in a number of ways, e.g., Better peak value estimates (e.g., in case of Sarab Seidali) Better volume estimations and more representative event scale dynamics Date

23 Gauges performing good in cases I Firozabad station at the Toyserkan River Mean Daily Discharge (m 3 /sec) Observed Simulated Case I Simulated Case II 1/1/1987 7/1/1987 1/1/1988 7/1/1988 1/1/1989 7/1/1989 1/1/199 7/1/199 1/1/1991 7/1/1991 1/1/1992 7/1/1992 1/1/1993 7/1/1993 1/1/1994 7/1/1994 1/1/1995 7/1/1995 1/1/1996 7/1/1996 1/1/1997 7/1/1997 1/1/1998 7/1/1998 1/1/1999 7/1/1999 1/1/2 7/1/2 1/1/21 7/1/21 Date They generally perform poor in both cases, as indicated by lower values of R 2 and NSE and high volume error This stresses the need for accumulating more data on different aspects such as climatic data, laps rates of precipitation and temperature, snow fall and snow melt dynamics, soil properties, abstractions for different uses and groundwater and surface water interactions including the influence of the karst formations in these areas.

24 Conclusions and recommendations It can be concluded that the use of areal precipitation input (Case II) helped in improving the SWAT model formulation and consequently improved the reliability of the simulated streamflows in the study area, both in term of spatial and temporal aspects The improved precipitation input is likely to result in better simulations of other processes modeled in the SWAT such as sediment and pollution, but deserve further research.

25 Conclusions and recommendations (Cont.,) Development of an additional (optional) component for the interpolation of climatic data within the existing SWAT model framework is recommended Considering the profound historic development of SWAT over the last 3 years, attention towards this addition would be in line with the overall (ongoing) development of the other components of the model Although the study focuses on improvement of precipitation input in the SWAT model, the procedures and results are instructive for rainfall-runoff modeling in general

26 Acknowledgments: Thanks to: Ministry of Energy of Iran for streamflow data Ministry of Jihad-e-Agriculture of Iran, particularly AREO, Iran, for support in data collection and field visits IWMI, CPWF, TUD and their donors for funding IWMI-CPWF, IDIS team for the data management support

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28 Hydrology in SWAT: an overview