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In developing countries in Africa and Asia, meeting challenges of water scarcity and pollution has often been hampered by shortcomings in higher education, including insufficient research productivity and funding, lack of opportunity for university graduates, and a mismatch between university activities and societal needs. To address these issues, we developed novel programs integrating technical instruction and preparation for professional practice in hydrology for cohorts of graduate students from Morocco and Egypt (2012–2013) and from Türkiye and Indonesia (2013–2014). Students participated in an initial online course and a follow‐up workshop featuring geographic information systems (GIS), remote sensing, and hydrologic modeling with internet‐based data sets. Field activities in the USA (first cohort) and in the students' home countries (second cohort) included stream gauging, measurement of water levels in wells, water sampling, and measurement of hydrochemical parameters. A subsequent online course focused on research ethics, preparing proposals and publications, and presenting findings to technical audiences and the public, culminating in presentations at conferences in the USA. Participants mentored other students at their home institutions and at K‐12 schools in Türkiye and Indonesia. Participant feedback during and after the programs tended to be strongly positive, and participants have continued to engage with project leaders and mentor students in their home countries and the USA. Our modular, hybrid approach offers a template for students in hydrology and related fields to develop relevant skills and engage internationally. [ABSTRACT FROM AUTHOR]

Summary A priori parameterization approaches that improve our ability to provide reliable hydrologic predictions in ungauged and poorly gauged basins, as well as in basins undergoing change are currently receiving considerable attention. However, such methods are typically based on local-scale process understanding and simplifying assumptions and an increasing body of evidence suggests that hydrologic models that utilize parameters estimated via such approaches may not always perform well. This paper proposes a Maximum Likelihood multi-criteria penalty function strategy for evaluating a priori parameter estimation approaches. We demonstrate the method by examining the extent to which a priori parameter estimates specified for the Hydrology Laboratory’s Research Distributed Hydrologic Model (via a set of pedotransfer functions) are consistent with the optimal model parameters required to simulate the dynamic input–output response of the Blue River basin. Our results indicated that whereas simulations using the a priori parameter estimates give consistently positive flow bias, unconstrained optimization to the response data results in parameter values that are very different from the a priori parameter set. Moreover, although unconstrained optimization performed best (as measured by the calibration criteria), poor hydrograph simulation performance was evident when evaluated in terms of multiple performance statistics not used in the calibration. On the other hand, the multi-criteria compromise solutions provided improved input–output performance in terms of measures not used in calibration, with generally more consistent behavior across calibration and evaluation years, while maintaining physically realistic a priori values for most of the model parameter estimates; adjustments were found to be necessary for only a few key model parameters. [ABSTRACT FROM AUTHOR]

Summary: This paper explores the use of a semi-automated multiple-criteria calibration approach for estimating the parameters of the spatially distributed HL-DHM model to the Blue River basin, Oklahoma. The study was performed in the context of Phase 2 of the DMIP project organized by the Hydrology Lab of the NWS. To deal with the problem of ill conditioning, we employ a regularization approach that constrains the search space using information contained in a priori estimates of the spatially distributed parameter fields developed from soils and other geo-spatial datasets. Unlike the commonly used spatial-multiplier method, our more general approach allows the parameters to depart non-uniformly (to some degree) from the a priori spatial pattern. The approach reduces the number of unknowns to be estimated using historical input–output data from 860 to 35. Two commonly used summary statistics of the model residuals, MSE and MSEL, are used to optimize fitting of the model to both the peaks and the recession periods of the time series data. A signature measure approach is used to select parameter sets that are close to Pareto-optimal in terms of MSE and MSEL, but which provide more consistent representation of the hydrologic behavior of the watershed as summarized by measures derived from the flow duration curve. While the results support the methods used in this analysis and show considerable improvement over the a priori parameter estimates, we find that the basin has some peculiar behaviors (including time non-stationarity) that the HL-DHM model as implemented is not set up to reproduce. [ABSTRACT FROM AUTHOR]

Floods are among the most catastrophic natural disasters around the globe impacting human lives and infrastructure. Implementation of a flood prediction system can potentially help mitigate flood-induced hazards. Such a system typically requires implementation and calibration of a hydrologic model using in situ observations (i.e., rain and stream gauges). Recently, satellite remote sensing data have emerged as a viable alternative or supplement to in situ observations due to their availability over vast ungauged regions. The focus of this study is to integrate the best available satellite products within a distributed hydrologic model to characterize the spatial extent of flooding and associated hazards over sparsely gauged or ungauged basins. We present a methodology based entirely on satellite remote sensing data to set up and calibrate a hydrologic model, simulate the spatial extent of flooding, and evaluate the probability of detecting inundated areas. A raster-based distributed hydrologic model, Coupled Routing and Excess STorage (CREST), was implemented for the Nzoia basin, a subbasin of Lake Victoria in Africa. Moderate Resolution Imaging Spectroradiometer Terra-based and Advanced Spaceborne Thermal Emission and Reflection Radiometer-based flood inundation maps were produced over the region and used to benchmark the distributed hydrologic model simulations of inundation areas. The analysis showed the value of integrating satellite data such as precipitation, land cover type, topography, and other products along with space-based flood inundation extents as inputs to the distributed hydrologic model. We conclude that the quantification of flooding spatial extent through optical sensors can help to calibrate and evaluate hydrologic models and, hence, potentially improve hydrologic prediction and flood management strategies in ungauged catchments. [ABSTRACT FROM AUTHOR]