1. Evaluation of the Surface Water Balance of Southeast Asia from a Land Surface Model and ERA40 Reanalysis Mergia Y. Sonessa 1, Jeffrey E. Richey 2 and Dennis P. Lettenmaier 1 1 Department of Civil and Environmental Engineering, Box 352700, University of Washington, Seattle, WA 98195 2 School of Oceanography Campus Box 355351, University of Washington, Seattle, WA 98195 - 7940 Steve Burges retirement symposium, March 24 - 26, 2010 ABSTRACT Understanding the water and energy balances of a region (which are linked through evapotranspiration, a common term) is a key first step toward understanding regional hydroclimatic sensitivities to long-term climate change. The Southeast Asia region is one of the most populous on the globe, and therefore key practical issues, such as the region’s food and fiber production capabilities, are intricately linked to possible changes in its hydroclimatic regime. Furthermore, the region contributes much higher particulate organic carbon (POC) fluxes to the sea on a per unit area basis than most of the global land area. We compare two approaches to estimating the surface energy and moisture flux terms under recent historical conditions for a region consisting of seven major southeast Asia river basins plus the Kingdom of Bhutan. The first approach is application of the Variable Infiltration Capacity (VIC) land surface hydrology model forced by gridded precipitation and temperature, and other gridded radiant fluxes and meteorological variables. The second is analysis of the ERA-40 reanalysis, which result from application of a frozen version of the European Centre for Medium Range Weather Forecasting (ECMWF) weather forecast model with data assimilation as used in operational weather forecasting. Both approaches produce gridded (at one-half degree spatial resolution in the case of VIC, and about one degree spatial resolution for ERA-40) surface moisture and energy fluxes. In the case of VIC, both water and energy budgets close by construct, where ERA-40 budgets must include a non-closure (so-called analysis increment) term to account for the effects of data assimilation. We compare the model-simulated moisture and energy flux terms with independent observations from in situ sources (in the case of streamflow and precipitation) and satellite (downward solar and longwave radiation, and total water storage change). Observed streamflow from different gauging stations will be used to evaluate the modeled water balance. Computations are also made of estimates from both approaches of moisture recycled within SEA, and its seasonal variations. 4 CONCLUDING REMARKS  VIC simulated the streamflow reasonably well with high correlation coefficients and Nash-Sutcliffe efficiencies especially for the Mekong basin for which a number of gauging stations with observed flow records are available.  The P and RF by ERA40 and VIC show similar seasonal patterns. ERA40 RF is generally higher than VIC RF while P from both are quite comparable.  All water balance terms, VIC RF, ET and P show high variability across the basins than ERA RF, ET and P, respectively.  Although streamflow observations are yet to be obtained for complete assessment of the entire basin, from the results of the three basins, Mekong, Irrawaddy and Sittang, VIC simulation capability for the region can be considered very promising. 1 VIC and ERA40 Model Forcings and Parameters Forcing Min/max Temperature (T) data obtained from Climate Research Unit (CRU) Monthly precipitation (P)statistics was obtained from the University of Delaware (UDel) Missing data for observed data (CRU and UDel) was filled using quantile mapping with NCEP/NCAR data. Precipitation (P) – ET – Runoff (RF) 3 Streamflow Simulations – VIC – Seasonal flow and Monthly Timeseries Future Work References  Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, A Simple hydrologically Based Model of Land Surface Water and Energy Fluxes for GSMs, J. Geophys. Res., 99(D7), 14,415-14,428, 1994 Uppala, S. M., and Coauthors, 2005. The ERA-40 Re-analysis. Quart. J. Royal Meteor. Soc., 131, 2961-3012, doi:10.1256/qj.04.176. 5 6 Basins included Mekong Salween Song Ma Irrawaddy Sittang Chao Phraya 2 Red River Mekong at Luang Prabang Mekong at Chiang Sean Mekong at Mukdahan Large scale hydrologic model ( Liang et al. 1994 ) that solves: the full water and energy balances Run at 1/4 degree spatial resolution for this study Uses as an input: Climatic forcing (P, T, wind speed), downward solar and longwave radiation, humidity Soil and vegetation parameters Three soil layers Large scale hydrologic model ( Liang et al. 1994 ) that solves: the full water and energy balances Run at 1/4 degree spatial resolution for this study Uses as an input: Climatic forcing (P, T, wind speed), downward solar and longwave radiation, humidity Soil and vegetation parameters Three soil layers Mekong at Nong Khai Mekong at Pakse Irrawaddy SagaingIrrawaddy at Sagaing Sittang at Toungoo ERA40 - Reanalysis (Uppala et al. 2005) was used for comaprison with VIC simulation. The ERA40 data are available at 1.125 degree spatial resolution and were interpolated to the ¼ degree grid using an inverse distance interpolation. The ERA40 data cover 45 years from September 1957 to August 2002 of which 32 years (1970 – 2001) of precipitation, evapotranspiration and runoff were used. ERA40 - Reanalysis (Uppala et al. 2005) was used for comaprison with VIC simulation. The ERA40 data are available at 1.125 degree spatial resolution and were interpolated to the ¼ degree grid using an inverse distance interpolation. The ERA40 data cover 45 years from September 1957 to August 2002 of which 32 years (1970 – 2001) of precipitation, evapotranspiration and runoff were used. Land Surface Parameters Soil data prepared using FAO Soil Program. Soil depth was initially approximated and corrected using iterative calibration of streamflow by the UA-SCEM method Veg data prepared from MODIS 2000. R 2 = 0.91 NS = 0.80 R 2 = 0.94 NS = 0.85 R 2 = 0.96 NS = 0.92 R 2 = 0.75 NS = 0.55 R 2 = 0.92 NS = 0.81 R 2 = 0.92 NS = 0.83 R 2 = 0.93 NS = 0.83 Streamlows simulated by VIC for the different basins are well correlated with their corresponding observed flow especially in the Mekong Basin, although some seasonal biases are apparent The relatively high values of Nash-Sutcliffe Efficiency (NS), which describes the prediction skill of the modeled flows as compared to observations, are also indicative of a good simulation For the other two basins, Sittang and Irrawaddy, for which observed stream flow is obtained (though for only ten years, 1978-1988), the simulations were not as good as for the Mekong basin. Investigation as to the reasons is ongoing. For the Irrawaddy basin, while the low flows are simulated well, the VIC simulated flows during peak flow months July – September are substantially higher than the observed flow. In the case of Sittang basin, the timing of the peak flows is shifted forward by about a month. Streamlows simulated by VIC for the different basins are well correlated with their corresponding observed flow especially in the Mekong Basin, although some seasonal biases are apparent The relatively high values of Nash-Sutcliffe Efficiency (NS), which describes the prediction skill of the modeled flows as compared to observations, are also indicative of a good simulation For the other two basins, Sittang and Irrawaddy, for which observed stream flow is obtained (though for only ten years, 1978-1988), the simulations were not as good as for the Mekong basin. Investigation as to the reasons is ongoing. For the Irrawaddy basin, while the low flows are simulated well, the VIC simulated flows during peak flow months July – September are substantially higher than the observed flow. In the case of Sittang basin, the timing of the peak flows is shifted forward by about a month. The annual mean P, ET and RF over the seven basins are shown above. The P range is higher for the VIC simulation (380– 4400 mm) than for ERA-40 which ranges from 560 – 3400 mm per annum. However, both of them show similar patterns in P distribution over the region with highest P for Irrawaddy and lowest P in the north of Salween and Mekong basins. Overall, the VIC P field shows more variability over the area than the ERA40 P. ERA ET varies less than that of VIC ET, which ranges from 0 – 2900 mm per annum. VIC ET is highest in the Sittang basin while ERA40 ET is highest in the southern part of the Mekong basin. ERA40 RF ranges from 2 – 2200 mm annually and VIC RF ranges from 2 to 2800 mm. The annual mean P, ET and RF over the seven basins are shown above. The P range is higher for the VIC simulation (380– 4400 mm) than for ERA-40 which ranges from 560 – 3400 mm per annum. However, both of them show similar patterns in P distribution over the region with highest P for Irrawaddy and lowest P in the north of Salween and Mekong basins. Overall, the VIC P field shows more variability over the area than the ERA40 P. ERA ET varies less than that of VIC ET, which ranges from 0 – 2900 mm per annum. VIC ET is highest in the Sittang basin while ERA40 ET is highest in the southern part of the Mekong basin. ERA40 RF ranges from 2 – 2200 mm annually and VIC RF ranges from 2 to 2800 mm.  Computing Moisture recycling  Runoff Ratio  Energy Balance terms ( downward solar radiation and long-wave radiation)  Total water storage change.  All the basins have a clearly defined P cycle that peaks during the months of June to September except for Chao-phraya and Songma which have two peaks in May and September. For these two basins and Salween the VIC P is always lower than ERA40 P; while for the remaining basins they match each other quite well. Both ERA40 and VIC P show similar patterns.  RF shows similar pattern as P. There is a big discrepancy between ERA40 RF and VIC RF. Especially between months of May and November, ERA40 RF is much higher than VIC RF for all basins.  ERA40 ET varies very small throughout the year while VIC ET has the same pattern as VIC P having peaks during the months of June to September.  = 317.1 mm SD = 272.3 mm  = 1687.5 mm SD = 776.5 mm  = 847.6 mm SD = 298.8 mm  = 1944.3mm SD = 654.9 mm  = 853.3mm SD = 511.5 mm  = 946 mm SD = 232.9 mm Chao-Phraya - PPTNIrrawaddy- PPTNMekong - PPTN Red River- PPTN Chao-Phraya - RF Irrawaddy- RFMekong - RF Red River- RF Key  = Mean SD = Standard Deviation PPTN = Precipitation RF = Runoff ET = Evapotranspiration NS = Nash-Sutcliff Efficiency RF, mm/month PPTN, mm/month RF, mm/month PPTN, mm/month ET, mm/month RF, mm/month PPTN, mm/month Salween- RFSittang - RF Songma - RF Salween- ET Sittang - ET Songma - ET Salween- PPTN Sittang - PPTN Songma - PPTN Chao-Phraya - ETIrrawaddy- ET Mekong - ETRed River- ET ET, mm/month