Streamflow simulation of the headwater catchment of the Yellow River basin (HCYRB) in China is important for water resources management of the Yellow River basin. A statistical‐dynamical model, combining regular vine copulas with an optimization method for structure estimation, is presented with an application for simulating the monthly streamflow with local climate drivers at HCYRB. Local climate drivers for streamflow in every month are analyzed using rank‐based correlation. Precipitation, evaporation, and temperature generally show strong associations with streamflow. Winter streamflows relate to total precipitation of the wet season and total evaporation of October and November, while unfrozen‐month streamflows are correlated with evaporation and precipitation of current month and previous 1 month in the wet season. Both canonical vine and D‐vine copulas are applied to develop different conditional quantile functions for streamflows in different months with their dynamical covariates. The covariates are selected from historical streamflows and climate drivers with appropriate lags using partial correlations. The optimal vine trees are selected using the sequential maximum spanning tree algorithm with the weight based on both dependence and goodness of fit. The model demonstrates higher skill than existing vine‐based models and the seasonal autoregressive integrated moving average model. The enhanced skill of the hybrid statistical‐dynamical model comes from an improved capability of capturing nonlinear correlation and tail dependence of streamflow and climate drivers with the optimization of vine structure selection. The model provides an effective advance to enhance water resources planning and management for HCYRB and the whole basin.

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The reservoir operation is a notable source of uncertainty in the natural streamflow and it should be represented in hydrological modelling to quantify the reservoir impact for more effective hydrological forecasting. While many researches focused on the effect of large reservoirs only, this study developed an online reservoir module where the small reservoirs were aggregated into one representative reservoir by employing a statistical approach. The module was then integrated into the coupled Noah Land Surface Model and Hydrologic Model System (Noah LSM-HMS) for a quantitative assessment of the impact of both large and small reservoirs on the streamflow in the upper Gan river basin, China. The Noah LSM-HMS was driven by the China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool (SWAT) model (CMADS) with a very good performance and a Nash-Sutcliffe coefficient of efficiency (NSE) of 0.89, which proved to be more effective than the reanalysis data from the National Centers for Environmental Prediction (NCEP) over China. The simulation results of the integrated model indicate that the proposed reservoir module can acceptably depict the temporal variation in the water storage of both large and small reservoirs. Simulation results indicate that streamflow is increased in dry seasons and decreased in wet seasons, and large and small reservoirs can have equally large effects on the streamflow. With the integration of the reservoir module, the performance of the original model is improved at a significant level of 5%.

A few relatively large reservoirs, hundreds of small reservoirs, and numerous farm dams were built in the upper Gan River Basin, China. The operation of such a reservoir network can serve as a significant source of variability in the local hydrological regime and should be included in research to better understand the interaction between multiple hydrological processes and watershed management. In this study, a reservoir network module that included reservoirs of multiple sizes was developed and fully integrated into the coupled land surface and distributed hydrologic model system (CLHMS), for a detailed description of the hydrological impact of a reservoir network. A generalized release scheme was employed to determine the outflow of both large and small reservoirs. The integrated model was then evaluated against observations and reanalysis data, which indicate that the model can reasonably reconstruct the reservoir operation, streamflow, and other hydrological variables. Results quantitatively demonstrate that a reservoir network can result in an increased streamflow in dry seasons, a decreased streamflow in wet seasons, a generally larger groundwater discharge, higher groundwater level, a slightly damper soil condition, and a larger amount of evapotranspiration at the basin level. With the integrated model, it is feasible to achieve more sustainable watershed planning and management.

The regional terrestrial water cycle is strongly altered by human activities. Among them, reservoir regulation is a way to spatially and temporally allocate water resources in a basin for multi-purposes. However, it is still not sufficiently understood how reservoir regulation modifies the regional terrestrial-and subsequently, the atmospheric water cycle. To address this question, the representation of reservoir regulation into the terrestrial component of fully coupled regional Earth system models is required. In

During the last century, reservoirs were intensively built around the world for a variety of purposes, such as flood control, irrigation and water supply (Mulligan et al., 2020). By far, the global reservoir capacity has exceeded 8,000 km 3 , equivalent to one-sixth of the river discharge to oceans (Boulange et al., 2021). The massive water stored in the reservoirs can bring about a long-term alteration to the terrestrial water storage (TWS), a key component of the global water budget (Lv et al., 2019(Lv et al., , 2021. Knowledge of the TWS variations under the reservoir impact can contribute to quantifying and predicting water-related hazards (

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