Simulating hydrologic impacts of climate change in the Dongjiang Basin, South China: Comparison of six monthly models Yongqin David Chen Department of Geography and Resource Management Centre of Strategic Environmental Assessment for China The Chinese University of Hong Kong, China Email: ydavidchen@cuhk.edu.hk ICCC Pre-conference Seminars, 6 Oct 2009
Background and Motivation • Global climate has been changing as increasingly evidenced by temperature increase driven by anthropogenic warming. Availability and variability of water resources will be affected by global warming. • Research on the effects of climate change on water resources includes the use of – climate models, – downscaling techniques, and – hydrological models. • Uncertainties exist in every step of the investigation. • Uncertainties in GCMs and downscaling techniques have been widely discussed in the literature. • However, the uncertainties resulting from the use of different hydrologic models have not been widely studied and reported in the literature.
Hydrologic Model Types and Use for Different Purposes • Monthly water balance models – assessment of water availability under different climatic conditions to support water resources management • Simple statistical models – estimation of changes in the average annual runoff for different climate change scenarios • Conceptual rainfall-runoff models – detailed simulation of surface, subsurface, and groundwater flow components to support a wide range of hydrologic analyses hydrologic analyses • Physically-based distributed-parameter models – simulation of spatial patterns of hydrologic processes in response to rainfall input for understanding the amount, pathway, and timing of flow
Study basin and data 50.0 350.0 Runoff 45.0 Rainfall 300.0 Pan Evaporation 40.0 Air Temperature 250.0 Depth of water (mm) 35.0 o C ） Air temperature 30.0 （ 200.0 25.0 150.0 20.0 15.0 100.0 10.0 50.0 5.0 0.0 0.0 Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Month
Objective and Procedure • Objective: – to quantify how large the difference one would expect when using different hydrologic models to simulate the impacts of climate change as compared to their capabilities in simulating historical water balance components • Procedure: – Evaluate the performance of the tested models in reproducing historical water balance components – Compare the difference of various models in simulating the hydrologic consequence of changed climate – Discuss the model structural effects on the difference in the simulation
Selection of Six Monthly Hydrologic Models • Thornthwaite water balance model (TM, Alley, W.M., 1984) • Vrije Universitet Brussel model (VUB, Vandewiele, G.L., Xu, C.Y. and Ni- Lar-Win, 1992) • The Xinanjiang model (XAJ, Chen, X., Chen Y.D. and Xu, C-Y, 2007) • The model by Guo Shenglian (GM, Guo, S.L., 1992) • The WatBal model (WM, Kaczmarek, Z., 1993) • The model by Schaake (SM, Schaake, J.C., 1990)
References for the Six Models • Alley, W.M., 1984. On the treatment of evapotranspiration, soil moisture accounting and aquifer recharge in monthly water balance models. Water Resources Research , 20(8):1137-1149. • Vandewiele, G.L., Xu, C.Y. and Ni-Lar-Win, 1992. Methodology and comparative study of monthly water balance models in Belgium, China and Burma. Journal of Hydrology , 134; 315-347. • Chen, X., Chen Y.D. and Xu, C-Y, 2007. A Distributed Monthly Hydrological Model for Integrating Spatial Variations of Basin Topography and Rainfall. Hydrological Processes. Integrating Spatial Variations of Basin Topography and Rainfall. Hydrological Processes. 21:242-252. • Guo, S.L., 1992. Impact of climatic change on hydrological regimes in the Dongjiang Basin. Proceedings of First National Post-doctoral Conference. National Defence Industry Press, Beijing, pp.2034-2037. • Kaczmarek, Z., 1993. Water balance model for climate impact analysis. ACTA Geophysica Polonica , 41(4):1-16. • Schaake, J.C., 1990. From climate to flow. In P. E. Waggoner (Editor.), Climate change and U.S. Water Resources. John Wiley & Sons, New York, pp. 177-206.
Thornthwaite water balance model (TM) P PET E a Soil Moisture Storage (S) ∆ Q ∆ Q Water Surplus R (Q) Simulated Streamflow No. of soil No. of storages Types of storage (deficit) Runoff components zones (deficit) Soil moisture storage, Runoff 1 2 Water surplus
Vrije Universitet Brussel model (VUB) P PET E a R f Available Water Storage R s (W) (W) Simulated Streamflow Types of storage (deficit) Runoff components No. of soil No. of storages zones (deficit) Soil moisture storage Fast runoff, 1 1 Slow runoff
The Xinanjiang model (XAJ) PET P E a EU Upper Layer Soil Moisture Storage (WU) EL Low Layer Soil Moisture Storage (WL) ED Deep Layer Soil Moisture Storage (WD) R R f Free Water Storage No. of soil No. of soil No. of No. of Types of storage Types of storage Runoff Runoff (S) zones storages (deficit) components Δ GS (deficit) R s Groundwater Storage Upper layer tension Fast runoff, (GS) storage, Slow runoff Lower layer tension storage, Simulated 3 5 Deep layer tension Streamflow storage, Free water storage, Groundwater storage
The WatBal model (WM) T P P eff PET R d E a R s Relative Soil Moisture R ss Storage (Z) R b R b Simulated Streamflow No. of soil No. of storages Types of storage (deficit) Runoff components zones (deficit) Relative soil moisture Direct runoff, storage Surface flow, 1 1 Sub-surface flow, Baseflow
PET P E a The model by Guo R s Shenglian (GM) Soil Moisture Storage (S) R i WS Groundwater storage R g (GS) (GS) Simulated Streamflow No. of soil No. of storages Types of storage (deficit) Runoff components zones (deficit) Soil moisture storage, Surface runoff, Groundwater storage Interflow, 1 2 Groundwater flow
The model by Schaake (SM) T P P eff PET R d E a R s Relative Soil Moisture Relative Soil Moisture R ss Storage (Z) R b No. of No. of storages Types of storage Runoff components Simulated soil zones (deficit) (deficit) Streamflow Relative soil moisture Direct runoff, storage Surface flow, 1 1 Sub-surface flow, Baseflow
Monthly Water Balance Models • The common and basic water balance equation at the monthly time scale: • These models differ in how E and Q are conceptually considered and mathematically represented. • Actual E is estimated from potential E using soil moisture extraction function or coefficient of evapotranspiration. • Large differences exist in the treatment of soil moisture accounting. Except VUB, all models adopt a threshold value of soil moisture storage capacity. • River flow routing is not considered and all the flow components run off directly at the basin outlet on a monthly basis.
Model calibration and validation results for Dongjiang Basin (1960-1988) • All six models produced good results for the calibration and validation periods. • The XAJ model performed the best as indicated by the highest E value and the lowest RMSE value, followed by GM, VUB, TM, SM, and WM.
Comparison of model ability in reproducing the mean monthly runoff (1960-1988) All six models simulate quite well the mean monthly runoff except the WM model for June and July, and the SM model for October and November.
Comparison of model ability in reproducing the mean monthly actual ET (1960-1988) There is a good agreement in the mean monthly evapotranspiration simulated by the six models, except that the WM and SM models yield smaller values for winter and spring months.
Comparison of model ability in reproducing the mean monthly soil moisture (1960-1988) Large differences exist because the models conceptualize and estimate soil moisture dynamics quite differently. In particular, the soil depth is usually not explicitly defined in all monthly models. Similar results have been reported in the literature.
Comparison of model results in predicting changes of ( DT=1,2,4˚C; DP=0, ± ± ± 10, ± ± ± ± ± 20% ) mean annual runoff in response to changed climate • The differences between models increase as P change increases in both directions. • Decrease of P results in larger differences than increase of P by the same amount. same amount. • TM, XAJ and GM models behave similarly and produce larger changes in runoff for a given climate change scenario, while the SM model produces the smallest changes. • Runoff changes are more sensitive to precipitation than to temperature.
Comparison of model results in predicting changes of ( DT=1,2,4˚C; DP=0, ± ± 10, ± ± 20% ) ± ± ± ± mean annual evapotranspiration in response to changed climate • Even at the annual level there are large differences in ET responses simulated by the six models driven by the perturbed climate scenarios. • Models can be divided into two groups: TM, XAJ and GM respond almost TM, XAJ and GM respond almost identically, and the other models show different responses. • The effect of P changes on ET is smaller than on runoff. • Contrary to runoff, ET increases as temperature increases.
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