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Green infrastructures have been widely used to manage urban stormwater, especially in water-stressed regions. They also pose new challenges to urban and watershed water resources management. This paper focuses on the green infrastructure-induced dynamics of water sharing in a watershed from three spatial scales. A multiagent socio-hydrologic model framework is developed to provide an optimization-simulation method for city-, inter-city-and watershed-scale integrated green infrastructures and water resource management (IGWM) that comprehensively considers the watershed circumstances-, the urban water managers-and the watershed manager-urban water managers interactions. We apply the framework to conduct three simulating experiments in the Upper Mississippi River Basin, the US. Four patterns in city-scale IGWM are classified and two dynamics of cost and equity in inter-city- and watershed-scale IGWM are characterized through various sensitivity, scenario, and comparative analyses. The modeling results could advance our understanding of the role of green infrastructures in urban and watershed water resources management and assist water managers in making associated decisions. [ABSTRACT FROM AUTHOR]

Low impact development (LID) practices are rarely implemented on slopes due to concerns about their poor hydrological performance and the potential impact on slope stability. Implementing LIDs on slopes, involving alterations to surface topography and subsurface hydrology (specifically, the formation of a groundwater mound), can pose challenges in maintaining slope stability. However, sloping areas often require LIDs for sustainable development. It is imperative to supplement the conventional design standard to address these challenges. Before proposing specific solutions, the impact of LIDs on slope stability should be understood first. This includes quantifying the extent of changes in slope stability before and after LID implementation, as well as identifying the key factors that influence stability. To address these, we developed a numerical model in HYDRUS‐2D to simulate the hydrological and geotechnical processes of slopes implemented with a two‐stepped bioretention cell (BC) system. The numerical model was calibrated and validated using monitoring data from a stepped BC in Cincinnati, Ohio, USA. Simulation scenarios encompassed three generic slope angles (15°, 20°, and 25°) with and without BCs, two initial groundwater table positions and two inflow volumes. The hydrological process was characterized by the evolution of the groundwater mound, and the geotechnical process was quantified using the factor of safety (FoS). Our findings indicated slope cutting and filling was likely to enhance stability with an increase in the FoS of 0.1. However, the formation of groundwater mounds, resulting from exfiltration, led to an approximate 0.1–0.2 reduction in the FoS, depending on the inflow volume. The subsequent groundwater mound evolution had no further impact on stability. Ultimately, the FoS was slightly reduced by around 0–0.1 due to the combined effect of cutting and filling operations and groundwater mounds. It was generally safe to implement LIDs on 15° slopes. For steeper slopes, special design considerations are necessary, such as reducing the drainage area to strike a balance between hydrological performance and slope safety. [ABSTRACT FROM AUTHOR]

Hydrological models are simplified representations of catchments. Finding an appropriate model instance (i.e., a combination of structure and parameter values) for a given catchment is a fundamental problem in hydrological modeling. This study develops a latent factor‐based machine learning method to learn to predict the pairwise association between the behavioral characteristics under climate forcing of a catchment and a hydrological model instance. The underlying assumption is that the behaviors of catchments and model instances can be sufficiently well characterized by sets of latent factors that allow accurate prediction of the associations between observed and modeled behaviors. In this study, the proposed method was applied to learn the latent factor vectors (which are numerical values) of 2,649 catchments worldwide and over 1,000,000 model instances from their pairwise associations (i.e., similarities between observed and simulated catchment behaviors). The model instances were created using 36 conceptual model structures or modeling software, and the their parameter values were randomly sampled. The results show that the learned latent factor vectors are able to accurately characterize the associations between catchments and hydrological model instances. The latent factor vector of a "new" catchment (not seen by the training procedures) can be estimated by running a small number of randomly sampled model instances, from which well‐fitting model instances created using different conceptual models can be recommended. The recommended instances can be of comparable quality to those derived using a calibration algorithm, and the method is useful for catchments outside the regions from which the latent factor vectors of the model instances were derived. Plain Language Summary: Are hydrological models with random parameter values useful? Yes, they are. For a given weather event, by comparing the actual response of a catchment with that of random models, we can gain valuable information about the similarities between the catchment and the specific model representations. Is it possible to create a profile for each catchment and model representation, so that the similarity between a catchment and a model representation can be predicted from the profiles alone (without running the model)? This study shows that by learning from the similarities between catchment‐model representation pairs, we can assign each catchment and each model representation a numerical vector, and from which highly accurate predictions of catchment‐model representation similarity can be derived. This result indicates that the hydrological functions of the catchments and model representations can be adequately characterized by a few latent factors. A new and effective model calibration method is proposed, where the best fitting model representations of a new catchment (not in the database) can be easily retrieved from the database containing models with different conceptual model structures by estimating its latent factor values through running a number of random models. Key Points: Behavioral characteristics of catchments and model instances created using different conceptual models are profiled by fixed‐length vectorsThe fit between a catchment and a model instance can be accurately predicted by the dot product of their associated vectorsAn efficient method for simultaneously searching for well‐fitting model instances created using multiple conceptual models is proposed [ABSTRACT FROM AUTHOR]

Most current modelling practices for sediment trapping in vegetative filter strips (VFS) assumed a constant sediment trapping efficiency of VFS, while, in practice, it was found to decrease greatly with the increasing trapped sediment amount in VFS (decreasing sediment trapping capacity of VFS). To account for this impact, a widely used VFS sediment transport module was modified to consider the changing sediment trapping capacity of VFS, which was expressed by a power‐law relationship with the inflow discharge, sediment concentration, slope gradient, and antecedent deposition amount. An integrated model that coupled a slope infiltration‐runoff module and the modified sediment transport module was then developed to simulate hydrology and suspended sediment transport in VFS on sloping surface. The model is calibrated and validated by 18 VFS experiments with different inflow, sediment, and slope conditions from Luo et al. (Science of the Total Environment, 725, 138361, 2020). Good agreements between observed and simulated runoff and sediment transport processes were found for nearly all cases. Due to the impact of changing sediment trapping capacity of VFS, the trapped sediment amount and the sediment transport rate of outflow varies greatly in space and time. With the increase of inflow discharge, sediment concentration, and slope gradient, the overall sediment trapping efficiency of VFS tends to decrease greatly and then remains almost unchanged. The comparison between proposed model and original physically based model shows that neglecting the impacts of changing sediment trapping capacity of VFS would lead to a constant and overly large sediment trapping efficiency of VFS, especially on steep slopes. Moreover, comparing with the empirical expression, the model with well‐founded theory can provide higher fidelity and more detailed results. Thus, the modelling framework is expected to provide a more effective approach for VFS assessment and maintenance in local or watershed scale. [ABSTRACT FROM AUTHOR]