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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]

The projection of runoff in a large‐scale basin under climate change and human activities is crucial for the future management of water supplies. The impacts of climate change on runoff projections are associated with large uncertainties. In this study, using the optimization and selection of climate models, as well as an uncertainty analysis via the global Bayesian model averaging (BMA) approach, an ensemble projection framework was established to significantly improve the reliability of runoff projections in the future. The global BMA algorithm proposed in this study is an improvement on the BMA algorithm for large basins and is designed to reflect the differences among various models across the basin. In this algorithm, comprehensive BMA weights are obtained by considering the number (N) of stations in a basin. To verify the feasibility and improvement of this method, the runoff in 2050 and 2070 was projected with data from the Yellow River Basin, China. The runoff and its 90% confidence intervals at the six main stations in the Yellow River Basin were obtained. The increased evapotranspiration will exceed the increase in runoff generated by increase in precipitation in the future. The runoff in the upper and middle reaches of the Yellow River in 2050 and 2070 are projected to be 9% and 7% lower, respectively, than those in the reference period. The ensemble projection method proposed in this paper can be used as a widely applicable process in hydrometeorological ensemble projection and provides a basis for water resource management planning. Key Points: An ensemble projection framework was established to significantly improve the reliability of runoff in the futureThe global BMA algorithm is proposed to obtain comprehensive BMA weights by considering number of stations in a basinThe runoff of the Yellow River Basin in 2050 and 2070 will decrease by 9% and 7%, respectively [ABSTRACT FROM AUTHOR]

To better understand the changes in the atmospheric evaporative demand (AED) in the context of global warming, the anomaly contribution analysis is presented to estimate mid‐long term contributions of meteorological factors to the AED. The Pearson correlation coefficient (RP) between the total contribution (ψ) of meteorological factors and the relative variation (ϕ) of the AED, and the Sen's trend slope (βS) of (ϕ, ψ) scatters are used to evaluate the applicability of the method. The smaller the values of |1 − RP| and |1 − βS|, the more applicable the method is. To validate the method, the reference crop evapotranspiration is employed as a proxy for the AED. The multi‐year contribution analysis is used as a comparison approach, which can only investigate the dominant meteorological factors of the AED in long term. Moreover, the Huaihe River basin of China is taken as a case study. Results show that (a) the values of |1 − RP| and |1 − βS| in mid‐long term are less than 0.1 in most cases when applying the anomaly contribution analysis, and the mid‐long term contribution processes of meteorological factors to the AED are clearly demonstrated; and (b) the wind speed and sunshine hours are the two most dominant factors (the total absolute contribution exceeds 60%) in long term, but they are not always the dominant factors in mid‐long term (e.g., wind speed in summer, and sunshine hours in winter). Therefore, the anomaly contribution analysis is a reasonable and effective method, which can help to gain insights into the changes in the AED. Key Points: Anomaly contribution analysis can identify the dominant meteorological factorsAnomaly contribution analysis is better than multi‐year contribution analysisDominant meteorological factors may not be consistent across time scales [ABSTRACT FROM AUTHOR]

A new approach combining groundwater storage change (GWSC) derived from Gravity Recovery and Climate Experiment (GRACE) satellite data and baseflow was proposed to estimate groundwater recharge at large spatial scales but a short (monthly) time scale. This method was applied in two typical karst basins of southwest China, the Wujiang River basin (WRB, ∼87,900 km2, ∼70% karstification) and Xijiang River Basin (XRB, ∼360,000 km2, ∼44% karstification). The 2006–2012 monthly baseflow was first separated from in situ streamflow through multivariate regression analysis. Groundwater recharge was estimated using separated baseflow and GWSC estimated from GRACE data and observation‐based groundwater‐level data. The comparison between GRACE‐ and observation‐based recharge in the larger XRB shows better consistency, while that in the smaller WRB shows larger discrepancies. Considering the associated uncertainty (60–93 mm/yr), the 2006–2012 mean recharge in two basins, ranging 199–225 mm/yr (17%–19% of precipitation) is comparable to bulletin‐reported estimates (∼145 mm/yr). Both the degree of karstification and aquifer water table depth influence recharge and discharge processes in karst areas of southwest China. The more karstic WRB exhibits more rapid infiltration (due to more developed permeable epikarst zone) and higher discharge capability (due to more developed underground drainage systems), particularly during dry periods (2009, 2011, and winter). Baseflow exhibits a shorter time lag to recharge and GWSC in the XRB than WRB owing to more quick flow, lower storage with more rapid infiltration, and shorter mean groundwater residence time. Observation‐based monthly recharge reflects different rainfall‐infiltration‐runoff processes under different rainfall intensities, particularly in the WRB. Plain Language Summary: Groundwater recharge is a critical water budget component. This study proposed a new method to estimate groundwater recharge at large spatial scales but on a short time scale. This method combines the groundwater storage changes estimated from gravity satellite data and baseflow (i.e., the main groundwater discharge component) separated from in situ streamflow through multiple regression analysis. We applied this method in two karst basins in southwest China, the Wujiang and Xijiang River Basins. We validated groundwater recharge estimates from gravity satellite data against those only from ground observations. At a larger scale in the Xijiang River Basin, the satellite‐ and ground‐based estimates of groundwater recharge showed better agreement. In the Wujiang River Basin, relatively more rainfall infiltrated into the subsurface to form recharge and more groundwater drained out during dry periods because this basin has a larger proportion of karst areas where the underlying rocks were eroded with large voids and spaces to transfer water. In the Xijiang River Basin, the groundwater storage requires less time to drain out to form baseflow because the flow velocity in this basin is faster. Compared to the gravity satellite data, the ground‐based estimates better reveal the runoff generation mechanism in karst areas. Key Points: A new approach combining Gravity Recovery and Climate Experiment (GRACE) and baseflow is proposed for estimating groundwater recharge at large spatial scales but a short time scaleGRACE‐ and observation‐based recharge estimates are overall consistent, but they indicate different recharge processesGRACE reveals different behaviors of recharge and baseflow during dry periods between two basins with different degrees of karstification [ABSTRACT FROM AUTHOR]

Quantification of the dependence characteristics in hydrological time series is essential for understanding hydrological variability and for managing water resources. However, how determining a suitable model for describing the dependent components is still a challenge. In this article, we proposed a correlation coefficient‐based information criterion (CCIC) to determine the optimal model order of regression‐based models for quantifying the dependence characteristics in hydrological time series. CCIC was developed by combining the index of correlation coefficient and the information entropy index. The former was used to reflect the fitting error of the model and the latter was used as a penalty term to reflect the model uncertainty. They have similar roles as the two terms of the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) used commonly in hydrology. Results of Monte‐Carlo experiments verified higher and more stable accuracy of determining the true model order by CCIC than by AIC and BIC. Moreover, results indicated that the mean value of a time series had a big impact on the accuracy of CCIC. If the mean value of a time series was far from its initial value, the estimation of CCIC would have a big bias, causing low accuracy in the determination of suitable model order. The application of CCIC to annual precipitation from 520 stations in China further confirmed its advantages over AIC and BIC and indicated the significant short‐term dependence characteristics of annual precipitation in the Yangtze River basin. The proposed CCIC approach has potential for wider use in hydrometeorology. Key Points: Correlation coefficient‐based information criterion (CCIC) was proposed to determine the optimal order of regression models for quantifying dependence characteristics in hydrologic time seriesMC experiments verified higher and more stable accuracy of determining the true model order by CCIC than by Akaike Information Criterion and Bayesian Information Criterion used commonlyMean value of a time series had a big impact on the accuracy of CCIC for determining optimal order for regression models [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]

Streamflow simulations at daily time steps are vital to water resources management, especially in arid regions. Previously, data‐driven models have been used as an effective tool for daily streamflow simulation. However, the accuracy of conventional data‐driven approaches is affected by the temporal autocorrelation of daily streamflow, especially in irrigated watersheds where the persistence of saturated flows dominates irrigation seasons. This study presents a Stepwise Clustered Regression Tree Ensemble (SCRTE) to address the streamflow autocorrelation. With the provision of a state‐of‐the‐art data‐driven model Stepwise Cluster Analysis (SCA), the SCRTE enables both single‐ and multi‐output settings (i.e., model predictand can be either a scalar or a vector), which can thus address interactions among streamflow values over multiple consecutive days. The autocorrelation effect of daily streamflow is evaluated based on single‐ and multi‐output SCA ensembles, which can then be aggregated according to their performance for various streamflow quantile ranges. To facilitate the irrigation scheduling decision‐making under rigorous transboundary water regulations, the SCRTE is applied to three interconnected watersheds with mixed land use, located in a floodplain of the Yellow River basin in China. The results show that the SCRTE outperforms seven well‐known benchmark models across seven evaluation metrics. Our findings reveal that the SCRTE can reflect the varying effects of autocorrelation over different streamflow quantile ranges, thereby improving the streamflow simulation. The multi‐output SCA ensembles are more capable of addressing the medium flows, while the single‐output one can better simulate the low and high flows. Key Points: A regression tree ensemble is proposed to address the temporal autocorrelation of daily streamflowA Bayesian model average with a stratified sampling ensemble strategy is provided to improve the simulation accuracy for medium flowsAutocorrelation of daily streamflow is particularly important for establishing reliable irrigation‐discharge relationships [ABSTRACT FROM AUTHOR]

Prior to their application in a land surface data assimilation (DA) system, remotely sensed (RS) soil moisture (SM) products are typically rescaled to remove systematic differences with respect to comparable SM estimates obtained from a land surface model (LSM). This preprocessing of RS SM data—commonly referred to as bias correction—implicitly discards spatial information present in the RS SM retrievals. Here, based on dense SM network measurements obtained within the Huai River Basin of China, we demonstrate that L‐band Soil Moisture Active Passive (SMAP) L3 surface SM retrievals provide a better representation of spatial variability in time‐averaged SM fields than a LSM—suggesting that, as typically applied, bias correction is neglecting important spatial information present in RS SM products. To maximally use RS information, we propose an alternative bias correction approach for SM DA that provides the benefits of traditional rescaling while also improving modeled SM spatial patterns. Specifically, the regional mean of SMAP SM is first corrected using modeled SM of the Variable Infiltration Capacity (VIC) model. Then, two key VIC soil parameters (EXPT and BULKD) are calibrated to match the relative subregional spatial variability captured by the SMAP SM product. Results show that our model calibration method successfully improves VIC SM spatial patterns and pixel‐wise time series. Furthermore, these SM improvements translate into enhanced VIC streamflow estimates. Overall, results suggest that the proposed bias correction framework can improve current land surface DA systems by maximally utilizing spatial information contained in RS SM products. Key Points: Spatial information contained in the SMAP L3 SM product is used for VIC model calibrationThe model calibration improves VIC SM spatial patterns for the surface and second soil layersThe model calibration also enhances VIC streamflow estimates [ABSTRACT FROM AUTHOR]

Because remote sensing (RS) data are spatially and temporally explicit and available across the globe, they have the potential to be used for predicting runoff in ungauged catchments and poorly gauged regions, a challenging area of research in hydrology. There is potential to use remotely sensed data for calibrating hydrological models in regions with limited streamflow gauges. This study conducts a comprehensive investigation on how to incorporate gridded remotely sensed evapotranspiration (AET) and water storage data for constraining hydrological model calibration in order to predict daily and monthly runoff in 30 catchments in the Yalong River basin in China. To this end, seven RS data calibration schemes are explored and compared to direct calibration against observed runoff and traditional regionalization using spatial proximity to predict runoff in ungauged catchments. The results show that using bias‐corrected remotely sensed AET (bias‐corrected PML‐AET data) for constraining model calibration performs much better than using the raw remotely sensed AET data (nonbias‐corrected AET obtained from PML model estimate). Using the bias‐corrected PML‐AET data in a gridded way is much better than using lumped data and outperforms the traditional regionalization approach especially in headwater and large catchments. Combining the bias‐corrected PML‐AET and GRACE water storage data performs similarly to using the bias‐corrected PML‐AET data only. This study demonstrates that there is great potential in using bias‐corrected RS‐AET data to calibrating hydrological models (without the need for gauged streamflow data) to estimate daily and monthly runoff time series in ungauged catchments and sparsely gauged regions. Key Points: Using bias‐corrected remote sensing data to calibrate hydrological model shows great potential especially in ungauged catchmentsCompared to raw PML‐AET, bias‐corrected PML‐AET improves runoff prediction noticeably and adding GRACE shows limited benefitGridded application performs better than lumped catchment modeling application for maximizing the benefit from the spatial PML‐AET data [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]

A comprehensive and reliable assessment of the water resources in China's transboundary river basins is vital for water resources management and peaceful development. In this study, we built machine learning (random forest, gradient boosting, and stacking) and traditional linear models to identify the relation between the runoff coefficient and its influencing factors, including topography, climate, land cover, and soil. The cross‐validation results show that the machine learning models greatly outperform the traditional linear model in predicting runoff coefficient. High‐resolution (0.1°) runoff coefficient and runoff maps for the China's transboundary river basins riparian countries were produced and compared with other estimates at the country level. The best water resources estimates achieved from the machine learning model are consistent with the Food and Agriculture Organization of the United Nations AQUASTAT database (root‐mean‐square error  = 76.97 km3/year, normalized root‐mean‐square error = 12%) at the country level. This outperformed two currently available runoff products: the UNH/GRDC Global Composite Runoff Fields and the Global Streamflow Characteristics Dataset. The study also demonstrated that accurate precipitation data can improve runoff and water resources estimation accuracy and that climate and topographic factors have a controlling role in prediction, whereas the influences of land cover and soils are weak. Finally, China's transboundary water resources were calculated and thoroughly assessed at basin and country levels. Key Points: High‐resolution (0.1 degrees) runoff coefficient and runoff maps of all the CTRB riparian countries were producedMachine learning models were more accurate than a traditional linear model and two popular runoff data productsThe water resources of China's transboundary river basins were comprehensively assessed at basin and country level for the first time [ABSTRACT FROM AUTHOR]

Systematic uncertainty analysis (UA) has rarely been conducted for integrated modeling of surface water-groundwater (SW-GW) systems, which is subject to significant uncertainty, especially at a large basin scale. The main objective of this study was to explore an innovative framework in which a systematic UA can be effectively and efficiently performed for integrated SW-GW models of large river basins and to illuminate how process understanding, model calibration, data collection, and management can benefit from such a systematic UA. The framework is based on the computationally efficient Probabilistic Collocation Method (PCM) linked with a complex simulation model. The applicability and advantages of the framework were evaluated and validated through an integrated SW-GW model for the Zhangye Basin in the middle Heihe River Basin, northwest China. The framework for systematic UA allows for a holistic assessment of the modeling uncertainty, yielding valuable insights into the hydrological processes, model structure, data deficit, and potential effectiveness of management. The study shows that, under the complex SW-GW interactions, the modeling uncertainty has great spatial and temporal variabilities and is highly output-dependent. Overall, this study confirms that a systematic UA should play a critical role in integrated SW-GW modeling of large river basins, and the PCM-based approach is a promising option to fulfill this role. [ABSTRACT FROM AUTHOR]

The current study investigates the variability of vertical structure of precipitation in the Huaihe River Basin (HRB) of China. The precipitation characteristics have been revealed by the long-term observations of vertical profile of reflectivity (VPR) from the first spaceborne precipitation radar (PR) onboard the National Aeronautics and Space Administration (NASA)'s Tropical Rainfall Measuring Mission (TRMM) satellite. This study has statistically analyzed the latest TRMM 2A-23 and 2A-25 products (version 7, released in 2012) with ∼15 years time span (from 11 December 1997 to 19 August 2012). First, the spatial and seasonal variations of storm height and freezing level have been investigated. The results show a climatological relation connecting the storm height with the rainfall rate in HRB. Second, mean VPRs have been studied for the stratiform and convective precipitation. The VPR variability has been analyzed for different seasons and rain intensities. Third, the characteristics of rain intensification and weakening in the vertical direction have been examined by the statistical analysis of VPR slope below the melting layer. The results show that the rainfall tends to be reduced (or intensified) with the height changing downward in the light (or moderate and heavy) precipitating clouds, no matter stratiform or convection. Finally, the S-band climatological VPRs have been characterized by converting the VPR from Ku-band to S-band. Considering the wide application of national radar network for weather surveillance in China, the developed S-band climatological VPRs can be potentially applied in a VPR correction scheme to improve the ground radar-based quantitative precipitation estimation (QPE) in this river basin. [ABSTRACT FROM AUTHOR]

Sustainable water resource developments require reservoir operations that provide environmental flows (e-flows) to support the downstream riverine ecosystem by minimizing the degree of hydrologic alteration under given water supply constraints. The effective management of e-flows below dams was explored by combining three e-flow management strategies for 'normal,' wet, and dry year situations, delivering a range of e-flow regimes for different reservoir storage levels and different natural flow (reservoir inflow) conditions. We combined these into a single combined e-flow strategy and coupled this with reservoir operating rule curves to form a reservoir operating approach that optimized e-flow provision under given water supply constraints. We also explored constraints imposed by the dam's valve release capacity. To investigate the capability of the approach to minimize the degree of hydrologic alteration downstream from a reservoir, we presented a case study of the Tanghe Reservoir in China's Tang river basin. The approach triggered a preferred e-flow strategy when the reservoir water level was high to better maintain the ecological functions within downstream riverine ecosystems, a basic e-flow strategy when the water level was low to reduce the water supply risk, and an acceptable strategy under intermediate conditions. The approach effectively balanced human and ecosystem needs and demonstrated the minimum levels of flow regime alteration achievable within the regulated river. [ABSTRACT FROM AUTHOR]

Model parameter calibration is a fundamentally important stage that must be completed before applying a model to address practical problems. In this study, we describe an automatic calibration framework that combines sensitivity analysis (SA) and an adaptive surrogate modeling‐based optimization (ASMO) algorithm. We use this framework to calibrate catchment‐specific sensitive parameters for streamflow simulation in the variable infiltration capacity (VIC) model with a 0.25° spatial resolution over 10 major river basins of China from 1960 to 1979. We found that three parameters—the infiltration parameter (B) and two of the soil depth parameters (D1, D2)—are highly sensitive in most basins, while other parameter sensitivities are strongly related to the dynamic environment of the basin. Compared with directly calibrating the seven parameters recommended for the default calibration procedure, our framework not only reduced the computing time by two thirds through opting out of insensitive parameters (type I error) but also improved the Nash‐Sutcliffe model efficiency coefficient (NSE) for optimized results when it identified a missing sensitive parameter (type II error) in the case study river basins. Results show that the SA‐based ASMO framework is an effective and efficient model‐optimization technique for matching simulated streamflow with observations across China. The NSE for monthly streamflow ranged from 0.75 to 0.97 and from 0.71 to 0.97 during the validation and calibration periods, respectively. The calibrated parameters can be applied directly in streamflow simulations across China, and the proposed calibration framework holds important implications for relevant simulation studies in other regions. Key Points: We proposed a calibration framework that combines sensitivity analysis (SA) and an adaptive surrogate modeling‐based optimization (ASMO)Calibrating unnecessary parameters (Type I error) and missing sensitive parameters (Type II error) were detected in default VIC calibrationThis SA‐based ASMO framework is an effective and efficient model‐optimization technique for streamflow simulations across China [ABSTRACT FROM AUTHOR]