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Free, publicly-accessible full text available June 19, 2025
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Abstract Using the existing measures for training numerical (non-categorical) prediction models can cause misclassification of droughts. Thus, developing a drought category-based measure is critical. Moreover, the existing fixed drought category thresholds need to be improved. The objective of this research is to develop a category-based scoring support vector regression (CBS-SVR) model based on an improved drought categorization method to overcome misclassification in drought prediction. To derive variable threshold levels for drought categorization, K-means (KM) and Gaussian mixture (GM) clustering are compared with the traditional drought categorization. For drought prediction, CBS-SVR is performed by using the best categorization method. The new drought model was applied to the Red River of the North Basin (RRB) in the USA. In the model training and testing, precipitation, temperature, and actual evapotranspiration were selected as the predictors, and the target variables consisted of multivariate drought indices, as well as bivariate and univariate standardized drought indices. Results indicated that the drought categorization method, variable threshold levels, and the type of drought index were the major factors that influenced the accuracy of drought prediction. The CBS-SVR outperformed the support vector classification and traditional SVR by avoiding overfitting and miscategorization in drought prediction.
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Abstract Devils Lake is a terminal lake located in northeast North Dakota. Because of its glacial origin and accumulated salts from evaporation, the lake has a high concentration of sulfate compared to the surrounding water bodies. From 1993 to 2011, Devils Lake water levels rose by ~10 m, which flooded surrounding communities and increased the chance of an overspill to the Sheyenne River. To control the flooding, the State of North Dakota constructed two outlets to pump the lake water to the river. However, the pumped water has raised concerns about of water quality degradation and potential flooding risk of the Sheyenne River. To investigate these perceived impacts, a Soil and Water Assessment Tool (SWAT) model was developed for the Sheyenne River and it was linked to a coupled SWAT and CE‐QUAL‐W2 model that was developed for Devils Lake in a previous study. While the current outlet schedule has attempted to maintain the total river discharge within the confines of a two‐year flood (36 m3/s), our simulation from 2012 to 2018 revealed that the diversion increased the Sheyenne River sulfate concentration from an average of 125 to >750 mg/L. Furthermore, a conceptual optimization model was developed with a goal of better preserving the water quality of the Sheyenne River while effectively mitigating the flooding of Devils Lake. The optimal solution provides a “win–win” outlet management that maintains the efficiency of the outlets while reducing the Sheyenne River sulfate concentration to ≤600 mg/L.
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Abstract Prior to hydrologic modelling, topographic features of a surface are derived, and the surface is divided into sub‐basins. Surface delineation can be described as a procedure, which leads to the quantitative rendition of surface topography. Different approaches have been developed for surface delineation, but most of them may not be applicable to depression‐dominated surfaces. The main objective of this study is to introduce a new depression‐dominated delineation (D‐cubed) method and highlight its unique features by applying it to different topographic surfaces. The D‐cubed method accounts for the hierarchical relationships of depressions and channels by introducing the concept of channel‐based unit (CBU) and its connection with the concept of puddle‐based unit (PBU). This new delineation method implements a set of new algorithms to determine flow directions and accumulations for puddle‐related flats. The D‐cubed method creates a unique cascaded channel‐puddle drainage system based on the channel segmentation algorithm. To demonstrate the capabilities of the D‐cubed method, a small laboratory‐scale surface and 2 natural surfaces in North Dakota were delineated. The results indicated that the new method delineated different surfaces with and without the presence of depressional areas. Stepwise changes in depression storage and ponding area were observed for the 3 selected surfaces. These stepwise changes highlighted the dynamic filling, spilling, and merging processes of depressions, which need to be considered in hydrologic modelling for depression‐dominated areas. Comparisons between the D‐cubed method and other methods emphasized the potential consequences of use of artificial channels through the flats created by the depression‐filling process in the traditional approaches. In contrast, in the D‐cubed method, sub‐basins were further divided into a number of smaller CBUs and PBUs, creating a channel‐puddle drainage network. The testing of the D‐cubed method also demonstrated its applicability to a wide range of digital elevation model resolutions. Consideration of CBUs, PBUs, and their connection provides the opportunity to incorporate the D‐cubed method into different hydrologic models and improve their simulation of topography‐controlled runoff processes, especially for depression‐dominated areas.