As a vital land surface parameter, soil moisture influences climate through its impact on water and energy cycles. However, the effect of soil moisture on precipitation has been strongly debated. In this study, a new causal detection method, convergent cross mapping (CCM), was applied to explore the causality between soil moisture and precipitation over low- and mid- latitude regions in the Northern Hemisphere. CCM method generally identified a strong effect of soil moisture on precipitation. Specifically, the optimal effect of soil moisture on precipitation occurred with a lag of one month and clearly decreased after four months, suggesting that soil moisture has potentials to improve the accuracy of precipitation forecast at a sub-seasonal scale. In addition, as climate (i.e., aridity index) changed from dry to wet, the effect of soil moisture on precipitation first increased and then decreased with peaks in semi-arid and semi-humid areas. These findings statistically support the hypothesis that soil moisture impacts precipitation and also provide a reference for the design of climate prediction systems.

Soil moisture plays a crucial role in the hydrological cycle and climate system. The reliable estimation of soil moisture in space and time is important to monitor and even predict hydrological and meteorological disasters. Here we studied the spatiotemporal variations of soil moisture and explored the effects of precipitation and temperature on soil moisture in different land cover types within the Tarim River Basin from 2001 to 2015, based on high-spatial-resolution soil moisture data downscaled from the European Space Agency’s (ESA) Climate Change Initiative (CCI) soil moisture data. The results show that the spatial average soil moisture increased slightly from 2001 to 2015, and the soil moisture variation in summer contributed most to regional soil moisture change. For the land cover, the highest soil moisture occurred in the forest and the lowest value was found in bare land, and soil moisture showed significant increasing trends in grassland and bare land during 2001~2015. Both partial correlation analysis and multiple linear regression analysis demonstrate that in the study area precipitation had positive effects on soil moisture, while temperature had negative effects, and precipitation made greater contributions to soil moisture variations than temperature. The results of this study can be used for decision making for water management and allocation.

Soil moisture plays a critical role in land-atmosphere interactions. Quantifying the controls on soil moisture is highly valuable for effective management of water resources and climatic adaptation. In this study, we quantified the effects of precipitation, temperature, and vegetation on monthly soil moisture variability in an arid area, China. A non-linear Granger causality framework was applied to examine the causal effects based on multi-decadal reanalysis data records. Results indicate that precipitation had effects on soil moisture in about 91% of the study area and explained up to 40% of soil moisture variability during 1982–2015. Temperature and vegetation explained up to 8.2% and 3.3% of soil moisture variability, respectively. Climatic extremes were responsible for up to 10% of soil moisture variability, and the importance of climatic extremes was low compared to that of the general climate dynamics. The time-lagged analysis shows that the effects of precipitation and temperature on soil moisture were immediate and dissipated shortly. In addition, the effects of precipitation on soil moisture decreased with the increase of precipitation, soil moisture, and elevation. This study provides deep insight for uncovering the drivers of soil moisture variability in arid regions.

Quantifying the uncertainty sources in assessment of climate change impacts on hydrological processes is helpful for local water management decision-making. This paper investigated the impact of the general circulation model (GCM) structural uncertainty on hydrological processes in the Kaidu River Basin. Outputs of 21 GCMs from the Coupled Model Intercomparison Project Phase 5 (CMIP5) under two representative concentration pathway (RCP) scenarios (i.e., RCP4.5 and RCP8.5), representing future climate change under uncertainty, were first bias-corrected using four precipitation and three temperature methods and then used to force a well-calibrated hydrological model (the Soil and Water Assessment Tool, SWAT) in the study area. Results show that the precipitation will increase by 3.1%–18% and 7.0%–22.5%, the temperature will increase by 2.0 °C–3.3 °C and 4.2 °C–5.5 °C and the streamflow will change by −26% to 3.4% and −38% to −7% under RCP4.5 and RCP8.5, respectively. Timing of snowmelt will shift forward by approximately 1–2 months for both scenarios. Compared to RCPs and bias correction methods, GCM structural uncertainty contributes most to streamflow uncertainty based on the standard deviation method (55.3%) while it is dominant based on the analysis of variance approach (94.1%).