High‐Resolution Land Surface Modeling of Hydrological Changes Over the Sanjiangyuan Region in the Eastern Tibetan Plateau: 1. Model Development and Evaluation

Abstract

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.

High‐Resolution Land Surface Modeling of Hydrological Changes Over the Sanjiangyuan Region in the Eastern Tibetan Plateau: 2. Impact of Climate and Land Cover Change

“…Located in the eastern TP, the Sanjiangyuan area is the headwaters region of the Yellow River, the Yangtze River, and the Lancang‐Mekong River, three major rivers in East Asia. As shown in the first part (Yuan, Ji, et al, ) of this series, streamflow decreased significantly during 1980s–1990s, and recovered slightly in the recent decade. The Terrestrial Water Storage (TWS) also experienced significant decadal variations over the Sanjiangyuan region (Yuan, Ji, et al, ).…”

“…As shown in the first part (Yuan, Ji, et al, ) of this series, streamflow decreased significantly during 1980s–1990s, and recovered slightly in the recent decade. The Terrestrial Water Storage (TWS) also experienced significant decadal variations over the Sanjiangyuan region (Yuan, Ji, et al, ). Changes in streamflow and TWS not only impact the regional hydrological cycle but also put pressure on the water security over downstream regions.…”

“…Figure shows annual mean surface air temperature (T2 m) and precipitation changes over the Sanjiangyuan region during 1979–2005. The observations come from modified CMFD data set (Yuan, Ji, et al, ) and results for CMIP5 experiments are after bias correction. A rapid warming rate (0.54 °C/decade; p < 0.01) occurred during this period, which is among the warming rates of TP of 0.50–0.67 °C/decade (Kuang & Jiao, ).…”

“…As the second part of the series, this study detected and attributed the hydrological changes over the Sanjiangyuan region with consideration of anthropogenic climate change, natural climate change, and land cover change, based on the simulations from the CSSPv2 land surface model developed by Yuan, Ji, et al (). During 1979–2005, it is found that spatial averaged temperature increased rapidly which increased ground temperature and shortened soil frozen days significantly ( p < 0.01).…”

“…Considering the systematic bias of CMIP5 models in simulating surface variables (e.g., precipitation and temperature) especially at regional scale (Christensen et al, ; Mueller & Seneviratne, ), a bias correction method which depends on cumulative distribution function (CDF) matching was used to adjust the CDFs from CMIP5 ALL simulations to the observed CDFs (Wood et al, ; Yuan, Wang et al, ). The observed CDFs were estimated from the China Meteorological Forcing Dataset (CMFD; He & Yang, ) modified by Yuan, Ji, et al (), with precipitation and temperature observations from 2474 China Meteorological Administration stations incorporated. As there are no observations for NAT and CTL scenario experiments, we used the same CDFs of ALL simulations to bias‐correct the NAT and CTL simulations following Yuan, Jiao, et al (), assuming that the bias was an inherent characteristic of the model under different climate forcings.…”