High-resolution land surface modeling of hydrological changes over the Sanjiangyuan region in the eastern Tibetan plateau: 1. Model Development and Evaluation

High-resolution modeling became popular in recent years due to the availability of multisource observations, advances in understanding fine-scale processes, and improvements in computing facilities. However, modeling of hydrological changes over mountainous regions is still a great challenge due to the sensitivity of highland water cycle to global warming, tightly coupled hydrothermal processes, and limited observations. Here we show a successful high-resolution (3 km) land surface modeling over the Sanjiangyuan region located in the eastern Tibetan plateau, which is the headwater of three major Asian rivers. By developing a new version of a Conjunctive Surface-Subsurface Process model named as CSSPv2, we increased Nash-Sutcliffe efficiency by 62–130% for streamflow simulations due to the introduction of a storage-based runoff generation scheme, reduced errors by up to 31% for soil moisture modeling after considering the effect of soil organic matter on porosity and hydraulic conductivity. Compared with ERA-Interim and Global Land Data Assimilation System version 1.0 reanalysis products, CSSPv2 reduced errors by up to 30%, 69%, 92%, and 40% for soil moisture, soil temperature, evapotranspiration, and terrestrial water storage change, respectively, as evaluated against in situ and satellite observations. Moreover, CSSPv2 well captured the elevation-dependent ground temperature warming trends and the decreased frozen dates during 1979–2014, and significant increasing trends (p < 0.05) in evapotranspiration and terrestrial water storage during 1982–2011 and 2003–2014 respectively, while ERA-Interim and Global Land Data Assimilation System version 1.0 showed no trends or even negative trends. This study implies the necessity of developing high-resolution land surface models in realistically representing hydrological changes over highland areas that are sentinels to climate change.