HESSD Hydrology and Earth System Sciences Discussions HESSD Hydrol. Earth Syst. Sci. Discuss. 1812-2116 Göttingen, Germany 10.5194/hess-2017-441 Modeling water balance using the Budyko framework over variable timescales under diverse climates Wu Chuanhao 1 Yeh Pat J.-F. https://orcid.org/0000-0001-7629-3362 2 Xu Kai 1 Hu Bill X. 1 Huang Guoru 3 4 Wang Peng 1 5 Institute of Groundwater and Earth Sciences, Jinan University, Guangzhou 510632, China Department of Civil and Environmental Engineering, National University of Singapore, Singapore School of Civil Engineering and Transportation, South China University of Technology, Guangzhou 510640, China State Key Laboratory of Subtropical Building Science, South China University of Technology, Guangzhou 510640, China Chongqing Key Laboratory of Karst Environment, Chongqing 400715, China 21617301 Cqk 201702 41530316 2016YFC0402805 25 08 2017 2017 1 33 Copyright: © 2017 Chuanhao Wu et al. 2017 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://hess.copernicus.org/preprints/hess-2017-441/ The full text article is available as a PDF file from https://hess.copernicus.org/preprints/hess-2017-441/hess-2017-441.pdf

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 (<i>WSC</i>, <i>dS/dt</i>) 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 <i>WSC</i> 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 <i>WSC</i> is incorporated into BM by replacing precipitation (<i>P</i>) with effective precipitation (i.e., <i>P</i> minus <i>WSC</i>), significant improvements are found in arid climates, but to a lesser extent in humid climates. The ratio of the standard deviation of <i>WSC</i> to that of evapotranspiration (<i>E</i>), 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 <i>WSC</i>. The modeling errors of BM are positively correlated with the temporal variability of <i>WSC</i> and hence larger in humid climates, and also found to be proportional to the ratio of potential evapotranspiration (<i>PET</i>) to <i>E</i>. More sophisticated models than the BM which explicitly incorporate the effect of <i>WSC</i> are required to improve water balance modeling in humid climates particularly at all the annual, seasonal, and monthly timescales.