The topographic wetness index (TWI) has been widely used for determining the potential of each digital elevation model (DEM) grid to develop a saturated condition, which allows for the investigation of topographic control on the hydrologic response of a watershed. Many studies have evaluated TWI, its components, and the impacts of DEM resolution on its computation. However, the majority of the studies are concerned with typical dendritic watersheds, and the effectiveness of TWI computations for depression-dominated areas has been rarely evaluated. The objectives of this study are (1) to develop a modified TWI computation procedure for depression-dominated areas, (2) to examine the differences between the new and existing TWI computation procedures using different DEMs, and (3) to assess the impact of DEM resolution on the new TWI procedure. In particular, a bathymetry survey was conducted for a study area in the Prairie Pothole Region (PPR), and the DEM representing the actual surface topography was created. The statistical analyses of TWI highlighted a two-hump pattern for the depression-dominated surface, whereas a one-hump pattern was observed for the dendritic surface. It was observed that depressional DEM grids accounted for higher values of TWI than other grids. It was demonstrated that a filled DEM led to misleading quantity and distribution of TWI for depression-dominated landscapes. The modified TWI computation procedure proposed in this study can also be applied to other depression-dominated areas.

Traditional delineation and modeling methods do not consider the spatial arrangement and dynamic threshold control of surface depressions. Instead, full structural hydrologic connectivity, uniform well‐connected drainage networks, and an invariant contributing area are often assumed. In reality, depressions play an important role in quantifying functional connected areas (ACs) and contributing area. This study is aimed to develop a new procedure to analyze functional hydrologic connectivity related to topography at a mesoscale, specifically in depression‐dominated areas by (a) characterizing surface topography, (b) quantifying and locating dynamic hydrologic connectivity, and (c) analyzing hydrologic connectivity and threshold‐controlled dynamics of contributing area using a set of dimensionless indicators and a new normalized connected area function. Thorough analyses for different topographic surfaces provided improved understanding of the intrinsic relationship and interaction between structural and functional hydrologic connectivity patterns. In addition, the new procedure was compared against a traditional delineation method, terrain analysis using digital elevation models (TauDEM), to determine structural and functional connectivity. It was found that spatial arrangement and scale of depressions had a direct effect on hydrologic connectivity. A stepwise trend, unique to depression‐dominated areas, highlighted the effect of threshold behaviors on contributing area and ACs. Conversely, dendritic surfaces showed an expedited surface connectivity due to the assumption of depressionless topography. Thus, precisely locating and quantifying ACs and contributing area via the new analysis procedure improve our understanding of the mechanisms of topography‐controlled overland flow and sediment transport dynamics, and hence, the findings are valuable in making informed decisions about water quantity and quality across varying topographic surfaces.

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Watershed hydrologic models often possess different structures and distinct methods and require dissimilar types of inputs. As spatially-distributed data are becoming widely available, macro-scale modeling plays an increasingly important role in water resources management. However, calibration of a macro-scale grid-based model can be a challenge. The objective of this study is to improve macro-scale hydrologic modeling by joint simulation and cross-calibration of different models. A joint modeling framework was developed, which linked a grid-based hydrologic model (GHM) and the subbasin-based Soil and Water Assessment Tool (SWAT) model. Particularly, a two-step cross-calibration procedure was proposed and implemented: (1) direct calibration of the subbasin-based SWAT model using observed streamflow data; and (2) indirect calibration of the grid-based GHM through the transfer of the well-calibrated SWAT simulations to the GHM. The joint GHM-SWAT modeling framework was applied to the Red River of the North Basin (RRB). The model performance was assessed using the Nash–Sutcliffe efficiency (NSE) and percent bias (PBIAS). The results highlighted the feasibility of the proposed cross-calibration strategy in taking advantage of both model structures to analyze the spatial/temporal trends of hydrologic variables. The modeling approaches developed in this study can be applied to other basins for macro-scale climatic-hydrologic modeling.

Non-point source (NPS) pollution from agricultural lands is the leading cause of various water quality problems across the United States. Particularly, surface depressions often alter the releasing patterns of NPS pollutants into the environment. However, most commonly-used hydrologic models may not be applicable to such depression-dominated regions. The objective of this study is to improve water quantity/quality modeling and its calibration for depression-dominated basins under wet and dry hydroclimatic conditions. Specifically, the Soil and Water Assessment Tool (SWAT) was applied for hydrologic and water quality modeling in the Red River of the North Basin (RRB). Surface depressions across the RRB were incorporated into the model by employing a surface delineation method and the impacts of depressions were evaluated for two modeling scenarios, MS1 (basic scenario) and MS2 (depression-oriented scenario). Moreover, a traditional calibration scheme (CS1) was compared to a wet-dry calibration scheme (CS2) that accounted for the effects of hydroclimatic variations on hydrologic and water quality modeling. Results indicated that the surface runoff simulation and the associated water quality modeling were improved when topographic characteristics of depressions were incorporated into the model (MS2). The Nash–Sutcliffe efficiency (NSE) coefficient indicated an average increase of 30.4% and 19.6% from CS1 to CS2 for the calibration and validation periods, respectively. Additionally, the CS2 provided acceptable simulations of water quality, with the NSE values of 0.50 and 0.74 for calibration and validation periods, respectively. These results highlight the enhanced capability of the proposed approach for simulating water quantity and quality for depression-dominated basins under the influence of varying hydroclimatic conditions.