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Reliable urban flood modeling is highly demanded in emergency response, risk management, and urban planning related to urban flooding. In this paper, the Storm Water Management Model (SWMM) is adapted to simulate urban rainfall‐runoff and pipe drainage processes within the Dominant river tracing‐Routing Integrated with VIC Environment (DRIVE) model which accounts for natural river basin runoff generation and routing processes. The integrated DRIVE‐SWMM model (referred to as DRIVE‐Urban) allows to explicitly delineate the mass‐energy interactions between urban drainage system (e.g., pipes and dikes) and river networks. This presents a further step model development for accurate urban flooding prediction which is lacking in existing urban flood models and traditional hydrological models. The validity of the DRIVE‐Urban model is evaluated for three case studies in Haikou City, China, with camera observations of street inundation during typhoon landfalls and heavy rainfall events. The results show that the DRIVE‐Urban model successfully captures 62%, 69%, and 77% of the total observed inundated road‐sections for the three cases respectively. The third case study with severe flooding situation shows that the DRIVE‐Urban performance is further improved when given reliable river and tidal level information, indicating the importance of integrating river‐basin with urban hydrological and hydraulic modeling. Plain Language Summary: This study mainly focuses on the development of an urban flood model (Dominant river tracing‐Routing Integrated with VIC Environment, DRIVE‐Urban), which couples the river‐basin and urban hydrological modeling, with a great potential for large‐scale real‐time urban flood monitoring and forecasting through the current Global Flood Monitoring System. Two inundation algorithms are developed within the DRIVE‐Urban model. One is for the river inundation caused urban flooding, and the other one is for the urban inundation caused by manhole overflow or excessive runoff drainage over the city land surface. Multi‐source observations including the closed‐circuit television video, citizen photos and municipal reports are collected for model validation. The results show that the DRIVE‐Urban model simulates well the different type of urban inundations at the 5‐m high spatial resolution with a promising hit success (i.e., POD >60%) according to three case studies. In particular, the results for the typhoon Kalmaegi case show that the model performance can be improved significantly by coupling the upstream river flow simulation with the urban runoff‐routing process, which validates our strategy of including the upstream river‐basin hydrological process in the urban flood modeling. Key Points: The Dominant river tracing‐Routing Integrated with VIC Environment (DRIVE)‐Urban flood model is developed by coupling river basin and urban hydrological and hydraulic processTwo 2D inundation algorithms are implemented within the model to simulate the manhole inundating and river overflow flooding respectivelyThe validity of the DRIVE‐Urban model is well established through three case studies with observations during typhoon and rainfall events [ABSTRACT FROM AUTHOR]

Afforestation to control soil erosion has been implemented throughout China over the past few decades. The long‐term hydrological effects, such as total water yield and baseflow, of this large‐scale anthropogenic activity remain unclear. Using six decades of hydrologic observations and remote sensing data, we explore the hydrological responses to forest expansion in four basins with contrasting climates across China. No significant change in runoff was found for the period 1970–2012 for the cold and dry Hailar River Basin in northeastern China. However, both forest expansion and reduced precipitation contributed to the runoff reduction after afforestation since the late 1990s. Similarly, afforestation and drying climate since the mid‐1990s induced a significant decrease in runoff for the Weihe River Basin in semi‐arid northwestern China. In contrast, the two wet basins in the humid southern China, Ganjiang River Basin and Dongjiang River Basin, showed insignificant changes in total runoff during their study periods. However, the baseflow in the winter dry seasons in these two watersheds significantly increased since the 1950s. Our results highlight the long‐term variable effects of forest expansion and local climatic variability on basin hydrology in different climatic regions. This study suggests that landuse change in the humid study watersheds did not cause dramatic change in river flow and that region‐specific afforestation policy should be considered to deal with forestation‐water quantity trade‐off. Conclusions from this study can help improve decision‐making for ecological restoration policies and water resource management in China and other countries where intensive afforestation efforts are taking place. Key Points: Both precipitation and afforestation explained reduction in runoff for dry basinsForest recovery did not result in significant changes in total runoff for two wet basinsDry season baseflow in two wet basins had an upward trend [ABSTRACT FROM AUTHOR]

Despite a multitude of models predicting sediment transport dynamics in open‐channel flow, self‐organized vertical density stratification that dampens flow turbulence due to the interaction between fluid and sediment has not been robustly validated with field observations from natural rivers. Turbulence‐suppressing density stratification can develop in channels with low channel‐bed slope and high sediment concentration. As the Yellow River, China, maintains one of the highest sediment loads in the world for a low sloping system, this location is ideal for documenting particle and fluid interactions that give rise to density stratification. Herein, we present analyses from a study conducted over a range of discharge conditions (e.g., low flow, rising limb, and flood peak) from a lower reach of the Yellow River, whereby water samples were collected at targeted depths to measure sediment concentration and, simultaneously, velocity measurements were collected throughout the flow depth. Importantly, sediment concentration varied by an order of magnitude between base and flood flows. By comparing measured concentration and velocity profiles to predictive models, we show that the magnitude of density stratification increases with sediment concentration. Furthermore, a steady‐state calculation of sediment suspension is used to determine that sediment diffusivity increases with grain size. Finally, we calculate concentration and velocity profiles, showing that steady‐state sediment suspensions are reliably predicted over a range of stratification conditions larger than had been previously documented in natural river flows. We determine that the magnitude of density stratification can be predicted by a function considering an entrainment parameter, sediment concentration, and bed slope. Key Points: Detailed concentration profiles reveal grain‐size specific density stratificationSediment diffusivity effects are overwhelmed by density stratification at high concentrationsFine washload sediment (<25 μm) is vertically stratified in the Yellow River [ABSTRACT FROM AUTHOR]

We present a multi‐objective, multi‐stage stochastic programming with recourse model for reservoir management and operation, where we use utility theory to select the best compromise solution from the Pareto front. A multi‐stage streamflow scenario tree is generated first by the neural gas method. Then the Pareto front at each stage is produced by a modified constrained NSGA‐II. A single best compromise solution on the Pareto front must be selected for the immediate stage and the model moves forward one stage and is re‐optimized over a moving planning horizon of fixed duration. A key contribution of this study is the selection of the best compromise solution, which is achieved by a proposed linear spline utility function allied with regression. Our proposed utility function has the following advantages: (a) It satisfies the law of diminishing marginal rate of substitution, (b) it does not rely on the pre‐specified weight or goal, and (c) it selects the best compromise solution that is likely to fall in the "knee regions" of the Pareto front. We apply the proposed optimization model to the Three Gorges Reservoir in China. The two conflicting objectives are (a) maximizing the total expected energy output in the planning horizon, and (b) maximizing the average expected ecological benefits in the planning horizon. The results show that the proposed model produces the optimal water release policy successfully under different hydrological scenarios, considering both the inflow uncertainty and the tradeoff between the two conflicting objectives. Key Points: Presents a multi‐objective, multi‐stage stochastic programming with recourse model for reservoir operationsDevelops a linear spline utility function for selecting the most preferred solution on the Pareto frontEvaluates the performance of the proposed optimization framework with a real‐world case study [ABSTRACT FROM AUTHOR]

There is a drastic geographic imbalance in available global streamflow gauge and catchment property data, with additional large variations in data characteristics. As a result, models calibrated in one region cannot normally be migrated to another without significant modifications. Currently in these regions, non‐transferable machine learning models are habitually trained over small local data sets. Here we show that transfer learning (TL), in the senses of weight initialization and weight freezing, allows long short‐term memory (LSTM) streamflow models that were pretrained over the conterminous United States (CONUS, the source data set) to be transferred to catchments on other continents (the target regions), without the need for extensive catchment attributes available at the target location. We demonstrate this possibility for regions where data are dense (664 basins in Great Britain), moderately dense (49 basins in central Chile), and scarce with only remotely sensed attributes available (5 basins in China). In both China and Chile, the TL models showed significantly elevated performance compared to locally trained models using all basins. The benefits of TL increased with the amount of available data in the source data set, and seemed to be more pronounced with greater physiographic diversity. The benefits from TL were greater than from pretraining LSTM using the outputs from an uncalibrated hydrologic model. These results suggest hydrologic data around the world have commonalities which could be leveraged by deep learning, and synergies can be had with a simple modification of the current workflows, greatly expanding the reach of existing big data. Finally, this work diversified existing global streamflow benchmarks. Plain Language Summary: We introduced a method to utilize available big data to better start and warm up a machine learning streamflow model that is later fine‐tuned for prediction in basins on other continents (Asia, South America and Europe). This procedure noticeably improved streamflow volume prediction for different scenarios with varying amounts of data in the target basins (in terms of time period, length of collected data, and number of basins having data). This allows thousands of basins across the world with only a few years' worth of streamflow observations to benefit from improved modeling and accuracy resulting from the use of deep learning. Key Points: Basins around the world can be better modeled by applying transfer learning (TL) to a deep network trained in the US, and tuning it locallyThe benefits of TL increased with the amount and diversity of the source data, and were larger than from pretraining with a hydrologic modelThis work greatly expands the reach of deep learning, adds to the value of existing big data, and calls for synergy of global data sets [ABSTRACT FROM AUTHOR]

Since 1999, the Loess Plateau, China, has undergone one of the world's largest revegetation programs (Grain for Green Project, GfGP). Revegetation has profound impacts on hydrological cycle and water balance, especially in arid and semi‐arid areas. As an essential component of water balance, the long‐term change in water storage (ΔS) is generally treated as zero. However, it remains unclear how to define the time scale of "long‐term," especially over regions undergoing dramatic vegetation restoration. In this study, we quantify ΔS over the Loess Plateau from 1982 to 2009 from a water balance perspective, with a particular focus on the impact of vegetation on ΔS at different time scales. Results show that a 44.78% increase in vegetation coverage could lead to a 37.72% decrease in ΔS. Moreover, the better the vegetation growth, the lower the ratio of ΔS to precipitation (ΔS/P) in the Loess Plateau. ΔS/P achieved a local minimum at the 4‐ to 5‐year scale and reached a plateau after 10 years. We further use the Budyko framework to verify the above conclusions. We find that errors of simulated streamflow over 11/16 of catchments become stable after 4–5 years, and all 16 catchments become stable after 10 years. Ignoring ΔS at a 10‐year scale has the least influence on water budget closure. These findings highlight the importance of considering time scales when ignoring ΔS in the water balance analysis, especially when the length of measurement is limited. Key Points: We use simulated evapotranspiration (ET) from the PT‐JPL model, observed streamflow and precipitation to quantify ΔS from 1982 to 2009 on 16 catchments over the Loess Plateau, ChinaWe find that a 44.78% increase in vegetation coverage could reduce ΔS by 37.72%Neglecting ΔS on a time scale of about 10 years would have the least impact on the streamflow error simulated by the Budyko framework in the Loess Plateau [ABSTRACT FROM AUTHOR]

How human activities have altered hydrological droughts (streamflow deficits) in China during the past five decades (1961–2016) is investigated using the latest version (v2.0) of PCR‐GLOBWB model at high spatial resolution (~10 km). Although both human activities and climate variability have significant effects on river flows over China, there are large regional north‐south contrasts. Over northern China, human activities generally intensify hydrological droughts. We find that human activities exacerbated drought deficit by about 70–200% from 2004 to 2015. In contrast, droughts over southern China are generally alleviated by human activities. For instance, irrigation and water management (such as reservoir operation and water abstraction) increase drought StDef (standardized drought deficit volume) by about 80% in the Yellow River (north) but reduce it by about 20% in the Yangtze River (south). Human activities slightly reduce drought deficit in the Yangtze River due to the combination of large reservoir storage and low ratio of agriculture consumption to abstracted irrigation water. In contrast, hydrological drought is aggravated in the semiarid Yellow River basin because of high water consumption from agricultural sectors. This study suggests that human activities have contrasting influences on hydrological drought characteristics in the northern (intensification) and southern (mitigation) parts of China. Therefore, it is critical to consider the variable roles of human activities on hydrological drought in China when developing mitigation and adaptation strategies. Plain Language Summary: China faces unprecedented challenges for water resources management under a changing climate, which is expected to lead to more frequent and severe droughts in the future. Of particular importance is streamflow drought, which jeopardizes regional water supply and local ecosystem services. On one hand, human activities through reservoir operation can effectively alleviate drought by releasing water during the low flow period. But on the other hand, water abstraction to meet sectoral water demand (such as irrigation) could exacerbate the streamflow deficit. To what extent such human activities differ across regions is not clear. In this study, we use a physically based hydrological and water resources model to investigate how human activities have altered streamflow droughts in China during the past five decades (1961–2016). We find that human activities generally alleviate streamflow droughts in the southern region (e.g., Yangtze River) but intensify them in the northern part of China (e.g., Yellow River). Our research highlights the contrasting geographical differences of human influences on hydrological drought across China, which can be useful for making more effective drought adaptation strategies. Key Points: We used the PCR‐GLOBWB model at high spatial resolution to investigate the effects of human activities on hydrological drought over ChinaInfluences of human activities on hydrological drought characteristics have a strong and contrasting north‐south gradientReservoir operation, water abstraction, and irrigation increase drought deficit in the Yellow River but reduce it in the Yangtze River [ABSTRACT FROM AUTHOR]

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. Key Points: The associations of streamflow in every month and different climate indices in Headwater Catchment of the Yellow River Basin are analyzedThe sequential maximum spanning tree optimization method is proposed for vine‐based monthly streamflow simulation with multiple predictorsThe model is verified skillful for its capability of capturing nonlinearity and tail dependence with the optimization of vine structures [ABSTRACT FROM AUTHOR]