1. Assessment of Hydrology of Bhutan What would be the impacts of changes in agriculture (including irrigation) and forestry practices on local and regional water balances? E.g., will forest harvests lead to an increase in stream flow and potential erosion? How would changes in land use practices, with varying climate, affect water supply and water quality? If some indication of climate over a growing season was provided, could crop selection (and fire management) be improved? Can floods or droughts be predicated, or at least anticipated, one or two months into the future, as an early-warning system? These models compute not only stream discharge, but such intermediate products as soil moisture and evapotranspiration. If a model is maintained in an operational mode, the current conditions of soil moisture (the antecedent for floods or drought) can be monitored. If the hydrology modeled is then driven by forecasts from regional climate models (see below), then near-future potential conditions can be tracked, and warnings given. How would developments of infrastructure affect downstream flow, water quality, and hydro power and fisheries resources? How would changes in stream flow affect fisheries (including through changes in water levels of nursery areas)?

2.  Data Acquisition  Hydrologic Model (VIC)  Bias Correction and Data Analysis  Results  Summary  Data Acquisition  Hydrologic Model (VIC)  Bias Correction and Data Analysis  Results  Summary Outline

3. Forcing Data Preparation Min/max Temperature (T) data obtained from Climate Research Unit (CRU) Monthly precipitation (P)statistics was obtained from the University of Delaware (UDel) Orographic correction for P was made Missing data for observed data (CRU and UDel) was filled using quantile mapping with NCEP/NCAR data. Forcing Data Preparation Min/max Temperature (T) data obtained from Climate Research Unit (CRU) Monthly precipitation (P)statistics was obtained from the University of Delaware (UDel) Orographic correction for P was made Missing data for observed data (CRU and UDel) was filled using quantile mapping with NCEP/NCAR data. 1. Data Acquisition

4. The daily variability of NCEP/NCAR is used to create daily P and T data using monthly CRU (for T) and Udel (for P) as a control. So, for a given month the daily precipitation varies like the NCEP/NCAR data while the amount is controlled by (add up to) that month's U-Delaware precipitation. The data prepared using the above mentioned steps are of 0.5 degree resolution. Those data were interpolated to 1/24 grid cells using the SYMAP algorithm (Shepard, 1984) to obtain daily time series of precipitation and maximum and minimum temperature for each grid cell. Temperature data were interpolated using a lapse rate of -6.5 °C per km to adjust temperature from the 0.5 degree grid cell to each elevation of the 1/24 grid cell. The daily variability of NCEP/NCAR is used to create daily P and T data using monthly CRU (for T) and Udel (for P) as a control. So, for a given month the daily precipitation varies like the NCEP/NCAR data while the amount is controlled by (add up to) that month's U-Delaware precipitation. The data prepared using the above mentioned steps are of 0.5 degree resolution. Those data were interpolated to 1/24 grid cells using the SYMAP algorithm (Shepard, 1984) to obtain daily time series of precipitation and maximum and minimum temperature for each grid cell. Temperature data were interpolated using a lapse rate of -6.5 °C per km to adjust temperature from the 0.5 degree grid cell to each elevation of the 1/24 grid cell.

5. Soil Data Preparation Soil Physical Properties were obtained from the FAO Soil Program Bulk Density Sand/Clay content From sand and clay content, each 1/12 degree pixel grid cell is assigned to one of the twelve FAO soil textural classes Soil hydrologic parameters estimated from the USDA soil texture class, following Schaake (2000). These includes: Porosity Saturated Hydraulic Conductivity Field Capacity Wilting Point Soil Data Preparation Soil Physical Properties were obtained from the FAO Soil Program Bulk Density Sand/Clay content From sand and clay content, each 1/12 degree pixel grid cell is assigned to one of the twelve FAO soil textural classes Soil hydrologic parameters estimated from the USDA soil texture class, following Schaake (2000). These includes: Porosity Saturated Hydraulic Conductivity Field Capacity Wilting Point

6. Soil depths are taken as 10, 20 and 120 cm as initial guess for the layers one to three respectively. It is to be changed after calibration of simulated to observed flows. Other soil parameters are either computed from those already obtained ; for instance, particle density computed from Bulk density and porosity or recommended values from previous studies are used ; for instance soil thermal damping depth for which 4 m is a recommended value. Vegetation Data Preparation Vegetable parameter for VIC Model is extracted from the MODIS 2000(???) Satellite map Soil depths are taken as 10, 20 and 120 cm as initial guess for the layers one to three respectively. It is to be changed after calibration of simulated to observed flows. Other soil parameters are either computed from those already obtained ; for instance, particle density computed from Bulk density and porosity or recommended values from previous studies are used ; for instance soil thermal damping depth for which 4 m is a recommended value. Vegetation Data Preparation Vegetable parameter for VIC Model is extracted from the MODIS 2000(???) Satellite map

7. Large scale hydrologic model ( Liang et al. 1994 ) that solves: Full water and energy balance Run at 1/24 degree resolution for this study Uses as an input: Climatic forcing (P, T, wind speed) Soil and vegetation parameters Three soil layers Large scale hydrologic model ( Liang et al. 1994 ) that solves: Full water and energy balance Run at 1/24 degree resolution for this study Uses as an input: Climatic forcing (P, T, wind speed) Soil and vegetation parameters Three soil layers 2. VIC ( Variable Infiltration Capacity) Model

8. 3. Bias Correction Data Analysis The simulated and observed flows were compared using : Correlation coefficient (R) and Root Mean Squared Error (RMSE) To avoid systematic bias, the monthly flow time series is bias corrected using quantile mapping bias correction method The quantile mapping method uses the empirical probability distributions for observed and simulated flows to remove biases. The quantile mapping method adjusts an simulated flow based on the cumulative distribution (CDF) observed flow; so that the simulated and observed flow have the same non-exceedence probability. This method of bias correction was used by (Wood, 2007; Hashino, et. al. 2007; among others) the bias-corrected simulated flow will be replaced by an observed flow with similar CDF value. The simulated and observed flows were compared using : Correlation coefficient (R) and Root Mean Squared Error (RMSE) To avoid systematic bias, the monthly flow time series is bias corrected using quantile mapping bias correction method The quantile mapping method uses the empirical probability distributions for observed and simulated flows to remove biases. The quantile mapping method adjusts an simulated flow based on the cumulative distribution (CDF) observed flow; so that the simulated and observed flow have the same non-exceedence probability. This method of bias correction was used by (Wood, 2007; Hashino, et. al. 2007; among others) the bias-corrected simulated flow will be replaced by an observed flow with similar CDF value.

9. Only one sub-basin out of the 17 sub-basin in Bhutan is treated here First, annual average monthly precipitation, ET and Runoff are presented Then, monthly flow time series for 9 gauging station in the sub-basin are depicted. Under each graph, R, RMSE for monthly time series and seasonal flow and mean observed flow are depicted. Since the calibration of the model is yet to be completed and the remaining sub-basin’s gauging station’s flow is yet to be compared with the simulated flow, only summary of the results will be given. No detailed discussion! Only one sub-basin out of the 17 sub-basin in Bhutan is treated here First, annual average monthly precipitation, ET and Runoff are presented Then, monthly flow time series for 9 gauging station in the sub-basin are depicted. Under each graph, R, RMSE for monthly time series and seasonal flow and mean observed flow are depicted. Since the calibration of the model is yet to be completed and the remaining sub-basin’s gauging station’s flow is yet to be compared with the simulated flow, only summary of the results will be given. No detailed discussion! 4. Results

11. ST112 Dorakha Bridge on Amo Chhu ST122 Haa on Damchhuzam ST126 Chimakoti Dam on Wang Chhu ST125 Tamchu on Wang Chhu ST124 Lungtenphug on Thimphu Chhu ST122 Paro on Paro Chhu ST113 Doyagang on Amo Chhu ST6 Tashiding on Daga Chhu

12. Haa on Damchhuzam Paro on Paro Chhu Tashiding on Daga Chhu

13. Mean Observed Flow 13.015 m/s 3 STAT2 – Seasonal Flow R RMSE Seasonal0.9730.9833.2622.195 Monthly0.8910.8935.5665.562

14. STAT1 – Seasonal FlowST126 – Seasonal Flow Mean Observed Flow 5.614 m/s 3 Mean Observed Flow 97.822 m/s 3 R RMSE Obs/RawObs/BCObs/RawObs/BC Seasonal0.9640.9822.1550.896 Monthly0.8650.8363.2582.955 R RMSE Obs/RawObs/BCObs/RawObs/BC Seasonal0.9900.99854.2885.517 Monthly0.9410.93468.38038.178

15. ST125 – Seasonal Flow ST124 – Seasonal Flow Mean Observed Flow 22.890 m/s 3 Mean Observed Flow 52.261 m/s 3 R RMSE Obs/RawObs/BCObs/RawObs/BC Seasonal0.9760.99512.7744.534 Monthly0.8810.88224.92824.442 R RMSE Obs/RawObs/BCObs/RawObs/BC Seasonal0.9790.9974.7711.762 Monthly0.9170.9129.2049.212

16. ST122 – Seasonal Flow ST113 – Seasonal Flow Mean Observed Flow 6.118 m/s 3 Mean Observed Flow 195.1120 m/s 3 R RMSE Obs/RawObs/BCObs/RawObs/BC Seasonal0.9910.9962.2200.377 Monthly0.9420.9432.5721.518 R RMSE Obs/RawObs/BCObs/RawObs/BC Seasonal0.9550.90148.22264.443 Monthly0.9080.84967.81583.639

17. ST112 – Seasonal FlowSTAT6 – Seasonal Flow R RMSE Obs/RawObs/BCObs/RawObs/BC Seasonal 0.9720.96935.63423.139 Monthly 0.8700.84956.96354.914 Mean Observed Flow 140.170 m/s 3 R RMSE Obs/RawObs/BCObs/RawObs/BC Seasonal 0.9890.9909.0462.626 Monthly 0.8910.89212.0608.638 Mean Observed Flow 23.967 m/s 3

18. FebruaryJanuaryMarchApril

19. JuneMayJunlyAugust

20. OctoberSeptemberNovemberDecember

21. 5. Summary Even though the calibration of the model is yet to be done and the results for the all stations is not done, the following can be said about the simulation based on the results available. 1.VIC simulation looks very promising with high R values and low RMSE. Thus, the model shows a greater potential for land surface modeling of the kingdom if Bhutan. 2.For the sub basin considered here, the discharge pattern follows the pattern of precipitation and thus indicating that it could be rainfall dominated than snow dominated. 3.In fact, from the Snow water equivalent data (not shown here), it is only small fraction of the basin that is covered with snow in part/through out the year