Variability of Modeled Runoff Over China and Its Links to Climate Change
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Climatic Change (2017) 144:433–445 DOI 10.1007/s10584-015-1593-x Variability of modeled runoff over China and its links to climate change Mingxing Li1 & Zhuguo Ma1 & Meixia Lv1 Received: 27 September 2015 /Accepted: 23 December 2015 /Published online: 18 January 2016 # The Author(s) 2016. This article is published with open access at Springerlink.com Abstract Runoff is a key component of the water cycle over land, with direct impact on regional ecosystems and water resources. This study investigates historical runoff variability and change over China in 1951–2008 using the Community Land Model and in situ observations of atmospheric forcing fields. Model simulations are first evaluated against in situ observations of streamflow for four major rivers, as well as soil moisture and water table depths, before further analysis is conducted. Then, quantile regression is used to analyze runoff variability and its relation to precipitation and temperature. The spatial pattern of monthly climatological runoff over China is characterized by maxima in the humid south and a gradual decrease toward the arid northwest. Runoff increases in the humid south, slightly decreases in the transition zone, and shows nonsignificant trends in the arid northwest. The footprint of decadal variability can be seen from 1951 to 2008. The annual precipitation advances the spatiotemporal variability of runoff despite locally distinct runoff–precipitation responses. The runoff-temperature relationship shows complex spatiotemporal characteristics that depend on the feedback from precipitation. 1 Introduction Runoff variability and change have attracted attention because of their impacts on water resources management and use and the consequent redistribution of economic and environ- mental benefits (e.g., Oki and Kanae 2006). Such concerns stem from observations that show significant changes in the Earth’s hydrological cycle in the context of climate change and the This article is part of a Special Issue on "Regional Earth System Modeling" edited by Zong-Liang Yang and Congbin Fu. Electronic supplementary material The online version of this article (doi:10.1007/s10584-015-1593-x) contains supplementary material, which is available to authorized users. * Mingxing Li [email protected] 1 RCE-TEA, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China 2 Huayanli 40#, Chaoyang District, Beijing 100029, People’sRepublicChina 434 Climatic Change (2017) 144:433–445 increased risk for extreme runoff and flooding (Wentz et al. 2007). In the case of China, one of the most water-scarce countries in the world, water shortage has long been a serious problem. To address this problem, the government has invested huge resources in water and ecohydraulic infrastructure. However, such efforts are subject to uncertainty because of the considerable changes in the runoff regime. The Fifth Assessment Report of the Inter-Governmental Panel on Climate Change (IPCC) argues that the global surface air temperature has increased by 0.72–0.85 °C since the beginning of industrialization, with a particularly steep increase in recent decades (IPCC 2013). Presumably, because of global warming, the annual maximum daily precipitation has increased 8.5 % over the last 110 years, resulting in runoff variability (Asadieh and Krakauer 2015). In China, a large body of work has been amassed for various watersheds, addressing the runoff variability during recent decades. For instance, increasing annual mean runoff was observed in the East River and the Yangtze River basins, South China (Zhang et al. 2015;Zhangetal.2006). In Northwest China, runoff also increased during the past 50 years, with a step-like change in precipitation at approximately 1986 (Chen et al. 2006). In contrast, in the Yellow River basin, runoff has been decreasing in recent decades (Zhang et al. 2009). The combination of decreased precipitation and increased human activities is considered to decrease runoff, as in the Northeast from 1965 to 2005 (Zhang et al. 2012). However, it is yet unclear whether these changes have any coherent countrywide spatial patterns and what drives these changes. Regarding the link of runoff variability to climate change, water balance modeling suggests that the water-year precipitation accounts for almost all of the runoff varia- tions. Furthermore, the temperature effect on runoff is small even during the period of significantly increased temperature (McCabe and Wolock 2011). Observations have shown that seasonal variations in runoff and summer monsoon precipitation, as well as pronounced warming and drying (runoff), are strongly correlated on decadal time- scales (Xue et al. 2005). For example, in the Yangtze River basin, the correlation coefficient between runoff and precipitation is up to 0.89 (Chen et al. 2014). However, land use changes (i.e., forestation), glacial melting, and human activities make the relationship much more complex, especially in recent decades. For instance, Bi et al. (2009) reported that forestation had reduced the annual streamflow in a watershed in the Loess Plateau by 49.63 % from 1954 to 2008. Therefore, at a countrywide scale, the pattern of interaction between runoff variability and climate change remains an open question. At present, global climate projections suggest that the global water cycle will be strengthened due to the increased moisture-holding capacity of the atmosphere and the surface energy supply (Wu et al. 2013;Wuetal.2010). Land runoff plays a very important role in the global hydrological cycle. Regionally, runoff is a vital component of the water cycle of a watershed, and it is critical to ecosystem services and water resource management, particularly in the arid and semiarid areas of China. Thus, this study examines countrywide patterns of runoff variability, along with extremes over critical regions, and the links to climatic covariates by quantile regression. In section 2, we discuss the model development and data. In section 3, we discuss the evaluation of the modeling, the spatial patterns of runoff variability, the variability over critical regions, and the relations of runoff to precipitation and temperature. The conclusions are summarized in section 4. Climatic Change (2017) 144:433–445 435 2 Model and data 2.1 Experimental design 2.1.1 Model description The Community Land Model (CLM) is the land model for the Community Earth System Model. CLM represents several aspects of the land surface, including surface heterogeneity, and it consists of components or submodels related to the hydrologic cycle, land biogeophysics, ecosystem dynamics, biogeochemistry, and human dimensions. From version 3.5 onward, CLM contains water-related parameterization schemes [for instance, a simple TOPMODEL-based runoff scheme (SIMTOP) and a simple ground- water scheme (SIMGM)] along with improvements in soil water availability and resis- tance terms to reduce the overestimated soil evaporation (Niu et al. 2005;Olesonetal. 2008;YangandNiu2003). CLM version 3.5 (CLM 3.5) offers significant improvements in estimating the subcomponents of the land water cycle (Oleson et al. 2008). Neverthe- less, CLM 3.5 produces higher soil moisture and lower variability than observations in the rooting zone. To reduce these biases, Li and Ma (2015) introduced a factor to describe soil porosity, increase the recharging water from the soil column to the aquifer, and reduce the flux in the opposite direction, as achieved by Lawrence et al. (2011)andNiuetal.(2011). Newer CLM versions (4.0 and 4.5) offer improved solutions related to soil moisture and biogeochemical processes. However, the soil moisture variability remains low compared with observations (Lawrence et al. 2011). Based on our assessment of the applicability of CLM 3.5 across China and the observation-based datasets (Li and Ma 2010;Lietal.2011), we opted for CLM 3.5 to regenerate the long-term runoff variability. For a detailed description of CLM 3.5, the readers are referred to Oleson et al. (2004) and the references therein. 2.1.2 Atmospheric forcing and land data We constructed an observation-based atmospheric forcing dataset involving four state variables (air temperature, pressure, wind speed, and specific humidity) and two flux variables (precipitation and radiation). Historical measurements of precipitation, air temperature, pressure, and wind speed were taken from the China Meteorological Administration (CMA), and the specific humidity was calculated from dry- and wet- bulb temperature observations. We adopted the radiation data of Sheffield et al. (2006) by bilinear interpolation to compensate for the scarcity of radiative observations. Using Kriging for spatial and curve fitting for temporal interpolations, the resulting atmospheric forcing dataset has 0.5° × 0.5° horizontal resolution, a 3-h temporal interval, and a 58- year time span (1951–2008). As for the details of establishing the forcing field and the evaluation of the modeling of the land water cycle, the readers are referred to Li and Ma (2010). The land data used in this study consist of topography, soil attributes, plant functional types, and physiological parameters of vegetation. We retained the default data in CLM 3.5, which were mostly taken from the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface mapping (Lawrence and Chase 2007). 436 Climatic Change (2017) 144:433–445 2.1.3 Simulation configuration The simulation of runoff was conducted in the off-line mode over China and was driven by the aforementioned