Integrating depression storages and their spatial distribution in watershed-scale hydrologic modeling

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The SCS curve number (SCS-CN) method has gained widespread popularity for simulating rainfall excess in various rainfall events due to its simplicity and practicality. However, it possesses inherent structural issues that limit its performance in accurately simulating rainfall excess and infiltration over time. The objective of this study was to develop a modified CN method with temporally varying rainfall intensity (MCN-TVR) by combining a soil moisture accounting (SMA) based SCS-CN method with the SMA method in the Hydrologic Engineering Center’s Hydrologic Modeling System (HEC-HMS). In the MCN-TVR, the SMA-based SCS-CN method is utilized to simulate the cumulative rainfall excess and infiltration, while the SMA method in the HEC-HMS serves as an infiltration control function. A key advantage of the MCN-TVR is that it eliminates the need for additional input parameters by inherently linking the parameters in the two SMA-based methods. Sixteen hypothetical 24 h SCS Type II rainfall events with different soil types and five real rainfall events for the Rush River Watershed in North Dakota were used to assess the performances of the MCN-TVR method and the SMA-based SCS-CN method. In the hypothetical simulations, the rainfall excess simulated by the SMA-based SCS-CN and MCN-TVR models was compared to that simulated by a Green–Ampt model. Discrepancies were observed between the rainfall excess simulated by the SMA-based SCS-CN and Green–Ampt models, especially for coarse soils under relatively light rainfall. However, the MCN-TVR model, incorporating an infiltration control function, demonstrated its improved performance closer to the Green–Ampt model. For all the hypothetical events, the Nash–Sutcliffe efficiency (NSE) coefficient of the rainfall excess simulated by the MCN-TVR method compared to the Green–Ampt model was greater than 0.99, while the root mean standard deviation ratio (RSR) was less than 0.03. In the real applications, the SMA-based SCS-CN model failed to provide acceptable simulation of the direct runoff for rainfall events with durations of less than the time of concentration. In contrast, the MCN-TVR model successfully simulated the direct runoff for all the events with NSE values ranging from 0.65 to 0.91 and RSR values from 0.31 to 0.56.

Section: Performance Of the Proposed Methodsmentioning

confidence: 63%

A Modified SCS Curve Number Method for Temporally Varying Rainfall Excess Simulation

Wang

Chu

2023

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“…The algorithm of HUD-DC was primarily based on the depression-oriented delineation methods [37][38][39]. The related delineation approaches and algorithms have been widely tested over a decade in a series of depression-oriented hydrologic modeling studies (e.g., [33,[37][38][39][40][41][42][43]). Based on both the original DEM and the fully filled DEM, HUD-DC identifies depressions, channels, and their contributing areas as two types of hydrologic units (i.e., puddle-based units and channel-based units), as well as the connectivity between the hydrologic units.…”

Section: Overall Modeling Frameworkmentioning

confidence: 99%

Incorporating Wetland Delineation and Impacts in Watershed-Scale Hydrologic Modeling

Khanaum

Boutin

et al. 2023

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In semi-distributed hydrologic models, it is difficult to account for the impacts of wetlands on hydrologic processes, as they are based on lumped, subbasin-scale wetland concepts. It is a challenge to incorporate the influences of individual small wetlands into watershed-scale models by using lumped parameterization. The objective of this study was to improve watershed-scale hydrologic modeling by taking into account real wetland features during the wetland parameterization. To achieve this objective, a joint modeling framework was proposed to couple a surface delineation algorithm with a semi-distributed hydrologic model and then applied to the Upper Turtle River watershed in North Dakota, USA. The delineation algorithm identified the topographic properties of wetlands, which were further utilized for wetland parameterization. A nonlinear area–storage relationship was determined and used in the estimation of the wetland-related parameters. The results demonstrated that the new joint modeling approach effectively avoided misestimating the wetland-related parameters by accounting for real topographic characteristics (e.g., storage, ponding area, and contributing area) of identified wetlands and their influences, and provided improved modeling of the hydrologic processes in such a wetland-dominated watershed.

“…Depression removal comes with the risk of eliminating naturally occurring depressions and overly smoothing the topography. For these reasons, the effects of topographic depressions on river networks connectivity and in watershed-scale modeling are analyzed increasingly in the literature [35][36][37][38][39][40][41].…”

Section: Introductionmentioning

confidence: 99%

Effects of DEM Depression Filling on River Drainage Patterns and Surface Runoff Generated by 2D Rain-on-Grid Scenarios

Costabile

Costanzo

Gandolfi

et al. 2022

Topographic depressions in Digital Elevation Models (DEMs) have been traditionally seen as a feature to be removed as no outward flow direction is available to route and accumulate flows. Therefore, to simplify hydrologic analysis for practical purposes, the common approach treated all depressions in DEMs as artefacts and completely removed them in DEMs’ data preprocessing prior to modelling. However, the effects of depression filling on both the geomorphic structure of the river network and surface runoff is still not clear. The use of two-dimensional (2D) hydrodynamic modeling to track inundation patterns has the potential to provide novel point of views on this issue. Specifically, there is no need to remove topographic depression from DEM, as performed in the use of traditional methods for the automatic extraction of river networks, so that their effects can be directly taken into account in simulated drainage patterns and in the associated hydrologic response. The novelty introduced in this work is the evaluation of the effects of DEM depression filling on both the structure of the net-points characterizing the simulated networks and the hydrologic response of the watersheds to simplified rainfall scenarios. The results highlight how important these effects might be in practical applications, providing new insights in the field of watershed-scale modeling.