Understanding the role of reservoirs in the terrestrial water cycle is critical to support the sustainable management of water resources especially for China where reservoirs have been extensively built nationwide. However, this has been a scientific challenge due to the limited availability of continuous, long‐term reservoir operation records at large scales, and a process‐based modeling tool to accurately depict reservoirs as part of the terrestrial water cycle is still lacking. Here, we develop a continental‐scale land surface‐hydrologic model over the mainland China by explicitly representing 3,547 reservoirs in the model with a calibration‐free conceptual operation scheme for ungauged reservoirs and a hydrodynamically based two‐way coupled scheme. The model is spatially calibrated and then extensively validated against streamflow observations, reservoir storage observations and GRACE‐based terrestrial water storage anomalies. A 30‐year simulation is then performed to quantify the seasonal dynamics of China’s reservoir water storage (RWS) and its role in China's terrestrial water storage (TWS) over recent decades. We estimate that, over a seasonal cycle, China's RWS variation is 15%, 16%, and 25% of TWS variation during 1981–1990, 1991–2000, and 2001–2010, respectively, and one‐fifth of China’s reservoir capacity are effectively used annually. In most regions, reservoirs play a growing role in modulating the water cycle over time. Despite that, an estimated 80 million people have faced increasing water resources challenges in the past decades due to the significantly weakened reservoir regulation of the water cycle. Our approaches and findings could help the government better address the water security challenges under environmental changes.

Although there is mounting concern about how high vapor pressure deficit (VPD) and low soil moisture (SM) affect ecosystem productivity, their relative importance is still under debate. Here, we comprehensively quantified the relative impacts of these two factors on ecosystem gross primary production (GPP) using observations from a global network of eddy‐covariance towers and two approaches (sensitivity analysis and linear regression model). Both approaches agree that a higher percentage of sites experience GPP reduction from high VPD than from low SM over the growing season. However, the constraint of high VPD and low SM on GPP reduction is tightly linked with climates and plant functional types. Humid and mesic ecosystems including forests and grasslands are dominated by VPD, while the semi‐arid and arid ecosystems including shrublands and savannas are dominated by SM. The varying dominant role of these two factors on GPP is closely related to plant stomatal behavior, as predicted by a stomatal conductance model. Additionally, we highlight the non‐linear impact of SM on GPP during droughts and the possible underestimation of the SM effects for deep‐rooted plants when only using surface‐layer SM. Our results shed light on a better understanding of the impacts of VPD and SM on vegetation productivity, with important implications for modeling the response and feedback of ecosystem dynamics to current and future climates.

The Yarlung Zangbo River (YZR) is the largest river system in the Tibetan Plateau, and its basin is one of the centers of human economic activity in Tibet. Large uncertainties exist in several previous climate change studies in this basin because of limited climate observations. In this paper, we used a meteorological drought index (Standardized Precipitation Evapotranspiration Index, SPEI) and a newly-released gridded climate forcing dataset based on high-quality climate station data to re-evaluate climate change in the YZR Basin during the period of 1961–2014. Results showed that precipitation experienced a statistically insignificant increasing trend at a rate of 6.32 mm/10 years, and its annual mean was 512.40 mm. The basin was sensitive to climate change in terms of the air temperature that significantly increased at the rate of 0.32 °C/10 years. This warming rate was obviously larger than that in many other regions. Analysis of SPEI showed that the basin had no obvious statistical trends in the number of dry/wet episodes, but the severity of dry episode aggravated in terms of duration and magnitude. This study provides a reliable analysis of climate change in the YZR Basin, and suggests this large Tibetan river basin is sensitive to climate change.

The Tropical Rainfall Measuring Mission (TRMM) Multi-Satellite Precipitation Analysis (TMPA) products have been widely used, but their error and uncertainty characteristics over diverse climate regimes still need to be quantified. In this study, we focused on a systematic evaluation of TMPA’s error characteristics over mainland China, with an improved error-component analysis procedure. We performed the analysis for both the TMPA real-time and research product suite at a daily scale and 0.25° × 0.25° resolution. Our results show that, in general, the error components in TMPA exhibit rather strong regional and seasonal differences. For humid regions, hit bias and missed precipitation are the two leading error sources in summer, whereas missed precipitation dominates the total errors in winter. For semi-humid and semi-arid regions, the error components of two real-time TMPA products show an evident topographic dependency. Furthermore, the missed and false precipitation components have the similar seasonal variation but they counter each other, which result in a smaller total error than the individual components. For arid regions, false precipitation is the main problem in retrievals, especially during winter. On the other hand, we examined the two gauge-correction schemes, i.e., climatological calibration algorithm (CCA) for real-time TMPA and gauge-based adjustment (GA) for post-real-time TMPA. Overall, our results indicate that the upward adjustments of CCA alleviate the TMPA’s systematic underestimation over humid region but, meanwhile, unfavorably increased the original positive biases over the Tibetan plateau and Tianshan Mountains. In contrast, the GA technique could substantially improve the error components for local areas. Additionally, our improved error-component analysis found that both CCA and GA actually also affect the hit bias at lower rain rates (particularly for non-humid regions), as well as at higher ones. Finally, this study recommends that future efforts should focus on improving hit bias of humid regions, false error of arid regions, and missed snow events in winter.