Modeling water balance using the Budyko framework over variable timescales under diverse climates

Abstract. Understanding the effects of climate and catchment characteristics on overall water balance at different temporal scales remains a challenging task due to the large spatial heterogeneity and temporal variability. Based on a long-term (1960–2008) land surface hydrologic dataset over China, this study presented a systematic examination of the applicability of the Budyko model (BM) under various climatic conditions at long-term mean annual, annual, seasonal and monthly temporal scales. The roles of water storage change ( WSC , dS/dt ) in water balance modeling and the dominant climate control factors on modeling errors of BM are investigated. The results indicate that BM performs well at mean annual scale and the performance in arid climates is better than humid climates. At other smaller timescales, BM is generally accurate in arid climates, but fails to capture dominant controls on water balance in humid climates due to the effects of WSC not included in BM. The accuracy of BM can be ranked from high to low as: dry seasonal, annual, monthly, and wet seasonal timescales. When WSC is incorporated into BM by replacing precipitation ( P ) with effective precipitation (i.e., P minus WSC ), significant improvements are found in arid climates, but to a lesser extent in humid climates. The ratio of the standard deviation of WSC to that of evapotranspiration ( E ), which increases from arid to humid climates, is found to be the key indicator of the BM simulation errors due to the omission of the effect of WSC . The modeling errors of BM are positively correlated with the temporal variability of WSC and hence larger in humid climates, and also found to be proportional to the ratio of potential evapotranspiration ( PET ) to E . More sophisticated models than the BM which explicitly incorporate the effect of WSC are required to improve water balance modeling in humid climates particularly at all the annual, seasonal, and monthly timescales.