HGS RESEARCH HIGHLIGHT – Evaluating Landscape Influences on Hydrologic Behavior with a Fully-Integrated Groundwater – Surface Water Model
AUTHORS: S.K. Frey, K. Miller, O. Khader, A. Taylor, D. Morrison, X. Xu, S.J. Berg, H.-T. Hwang, E.A. Sudicky & D.R. Lapen
New research has been published on Aquanty’s work modeling the Oak River Watershed in south-west Manitoba, Canada. The work here seeks to quantify the impact of land use change on the hydrologic system.
Characterizing how land management practices and depressional storage, together, influence the hydrologic behaviour of a watershed under flood and drought conditions is challenging because of the complex interaction between land use and the land surface, soils, and groundwater. In this study, a high-resolution model is used to establish a process-based understanding of how the landscape and management practices interact with the hydrologic system. The HydroGeoSphere model employed in this study was able to reproduce extreme hydrologic behavior over a five-year time interval, including flood and drought conditions. Results suggest that landscape changes associated with development have led to a 22 % to 25 % increase in flood peak flows in the subject watershed.
This paper also introduces a watershed-scale application of the numerical formulation to apply spatially variable rill (depressional) storage, and one-dimensional channel flow using HydroGeoSphere.
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Abstract:
Optimizing hydrologic resiliency in agricultural landscapes is critical for the sustainability of food production and ecosystem services, and optimization strategies must include a holistic understanding of the hydrologic cycle, including groundwater-surface water interactions. Here, a large-scale (2056 km2), high resolution (50–150 m), structurally complex, fully-integrated groundwater-surface water model was constructed for an agriculturally-dominated watershed in the Northern Great Plains.
The sensitivity of surface water flow and groundwater conditions were examined in the context of wetland/depression water storage capacity, soil hydraulic conductivity (Ks), field surface roughness (Manning’s n), as well as variability in precipitation and potential evapotranspiration (PET). The model evaluation interval extended from 2010 to 2015, which included both flood and drought conditions.
Results showed that stream flow rates in the watershed exhibit a larger response to 10 % changes in either precipitation or PET relative to the response associated with a 50 % change in depression storage, a 4x change in Ks, or a 50 % change in n, thus highlighting the region’s susceptibility to weather variability. Of the landscape parameters examined, increasing depression storage provided a greater reduction in peak flood flows than an increase in Ks or n; however, increases in all three landscape parameters contributed to flood peak mitigation as well as groundwater recharge.
When model configuration approximated predevelopment (primarily native prairie) landscape conditions, simulated flood peaks were reduced by 22 to 25 %. This study presents a fully-integrated groundwater-surface water modeling framework to determine the hydrologic influences and risk mitigation value of landscape management practices.