Research results on the effects of land cover change on water resources vary greatly and the topic remains controversial. Here we use published data worldwide to examine the validity of Fuh's equation, which relates annual water yield (R) to a wetness index (precipitation/ potential evapotranspiration; P/PET) and watershed characteristics (m). We identify two critical values at P/PET ¼ 1 and m ¼ 2. m plays a more important role than P/PET when mo2, and a lesser role when m42. When P/PETo1, the relative water yield (R/P) is more responsive to changes in m than it is when P/PET41, suggesting that any land cover changes in non-humid regions (P/PETo1) or in watersheds of low water retention capacity (mo2) can lead to greater hydrological responses. m significantly correlates with forest coverage, watershed slope and watershed area. This global pattern has far-reaching significance in studying and managing hydrological responses to land cover and climate changes.

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Extreme climate events such as droughts and heat waves exert strong impacts on ecosystems and human well-being. Estimations of the risks of climate extremes typically focus on one variable in isolation. In this study, we present a method to examine the likelihood of concurrent extreme temperature and precipitation modes at the interannual scale, including compound cool/dry and cool/wet events during the cold season as well as compound hot/dry and hot/wet events during the warm season. A comparison of changes in the likelihood of such joint climate extremes was then conducted between the first and second halves of the full observed records.Our findings indicate a decrease in the occurrence probability for most concurrent modes over much of China, despite positive shifts found over southwestern and northeastern parts of China for the compound hot/dry events in the warm season. We further examined changes in likelihood related to these four compound climate extremes between the historical observed period and the future period (2021-2080) based on climate model simulations with the RCP8.5 scenario. Our results show widespread increases in the occurrence probability for wintertime cool/dry and summertime hot/dry and hot/wet events over most parts of China but with different magnitudes, while much of China may experience declining likelihood of the wintertime cool/wet extremes in the future. OPEN ACCESS RECEIVED

The effective prediction and estimation of hydrometeorological variables are important for water resources planning and management. In this study, we propose a multivariate conditional model for streamflow prediction and the refinement of spatial precipitation estimates. This model consists of high dimensional vine copulas, conditional bivariate copula simulations, and a quantile‐copula function. The vine copula is employed because of its flexibility in modeling the high dimensional joint distribution of multivariate data by building a hierarchy of conditional bivariate copulas. We investigate two cases to evaluate the performance and applicability of the proposed approach. In the first case, we generate one month ahead streamflow forecasts that incorporate multiple predictors including antecedent precipitation and streamflow records in a basin located in South China. The prediction accuracy of the vine‐based model is compared with that of traditional data‐driven models such as the support vector regression (SVR) and the adaptive neuro‐fuzzy inference system (ANFIS). The results indicate that the proposed model produces more skillful forecasts than SVR and ANFIS. Moreover, this probabilistic model yields additional information concerning the predictive uncertainty. The second case involves refining spatial precipitation estimates derived from the tropical rainfall measuring mission precipitationproduct for the Yangtze River basin by incorporating remotely sensed soil moisture data and the observed precipitation from meteorological gauges over the basin. The validation results indicate that the proposed model successfully refines the spatial precipitation estimates. Although this model is tested for specific cases, it can be extended to other hydrometeorological variables for predictions and spatial estimations.

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Soil organic carbon (SOC) plays critical roles in stabilizing atmospheric CO2 concentration, but the mechanistic controls on the amount and distribution of SOC on global scales are not well understood. In turn, this has hampered the ability to model global C budgets and to find measures to mitigate climate change. Here, based on the data from a large field survey campaign with 2600 plots across China's forest ecosystems and a global collection of published data from forested land, we find that a low litter carbon-to-nitrogen ratio (C/N) and high wetness index (P/PET, precipitation-to-potential-evapotranspiration ratio) are the two factors that promote SOC accumulation, with only minor contributions of litter quantity and soil texture. The field survey data demonstrated that high plant diversity decreased litter C/N and thus indirectly promoted SOC accumulation by increasing the litter quality. We conclude that any changes in plant-community composition, plant-species richness and environmental factors that can reduce the litter C/N ratio, or climatic changes that increase wetness index, may promote SOC accumulation. The study provides a guideline for modeling the carbon cycle of various ecosystem scales and formulates the principle for land-based actions for mitigating the rising atmospheric CO2 concentration.