A weighted, multi-method approach for accurate basin-wide streamflow estimation in an ungauged watershed

Shope, Christopher L.; Bartsch, Svenja; Kim, Ki-Yong; Kim, Bomchul; Tenhunen, John; Peiffer, Stefan; Park, Ji‐Hyung; Ok, Yong Sik; Fleckenstein, Jan H.; Koellner, Thomas

doi:10.1016/j.jhydrol.2013.04.035

Cited by 17 publications

(21 citation statements)

References 14 publications

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“…They can also influence the peak flow and the time of concentration to peak discharge of the simulated hydrograph (Khakbaz et al, 2012;Wilson et al, 1979). Our weather analysis revealed heterogeneous meteorological conditions throughout the Haean catchment that are dependent on elevation and aspect and largely focused in subregions (Choi et al, 2010;Shope et al, 2013). These meteorological variations have a direct influence on the relative humidity and therefore, the spatial variability of plant growth parameters between subbasins was significant (Fig.…”

Section: Meteorological Drivers and The Effects Of Interpolationmentioning

confidence: 85%

“…The average catchment discharge at the outlet is 4.32 m 3 s −1 (1.20-379 m 3 s −1 ) while the average discharge at the S1 headwater monitoring location is 0.03 m 3 s −1 (1.4 × 10 −4 -10.0 m 3 s −1 ). The catchment hydrology is further described in Shope et al (2013). The catchment is 56 % forested and 44 % agricultural LULC.…”

Section: Catchment Characteristicsmentioning

confidence: 99%

“…1) through multiple methods as described by Shope et al (2013). Observed streamflow at interior locations within the catchment (S1, S4, S5, and S6) and the catchment outlet (S7) were utilized for daily and monthly model calibration to better parameterize spatial variability in hydrologic partitioning.…”

Section: Discharge and Evapotranspiration Estimatesmentioning

confidence: 99%

“…Because of the robust and highfrequency spatially variable micrometeorologic data available through the TERRECO project, we simulated daily PET using the Penman-Monteith method (Penman, 1948). As described in and Shope et al (2013), the weather conditions throughout the catchment are heterogeneous and therefore, the physically based Penman-Monteith estimates were preferred over the alternative methods. Soil evaporation and crop transpiration were estimated using the FAO Penman-Monteith equation as described in Allen et al (1988).…”

Section: Discharge and Evapotranspiration Estimatesmentioning

confidence: 99%

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Using the SWAT model to improve process descriptions and define hydrologic partitioning in South Korea

Shope

Maharjan

Tenhunen

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. Watershed-scale modeling can be a valuable tool to aid in quantification of water quality and yield; however, several challenges remain. In many watersheds, it is difficult to adequately quantify hydrologic partitioning. Data scarcity is prevalent, accuracy of spatially distributed meteorology is difficult to quantify, forest encroachment and land use issues are common, and surface water and groundwater abstractions substantially modify watershed-based processes. Our objective is to assess the capability of the Soil and Water Assessment Tool (SWAT) model to capture eventbased and long-term monsoonal rainfall-runoff processes in complex mountainous terrain. To accomplish this, we developed a unique quality-control, gap-filling algorithm for interpolation of high-frequency meteorological data. We used a novel multi-location, multi-optimization calibration technique to improve estimations of catchment-wide hydrologic partitioning. The interdisciplinary model was calibrated to a unique combination of statistical, hydrologic, and plant growth metrics. Our results indicate scale-dependent sensitivity of hydrologic partitioning and substantial influence of engineered features. The addition of hydrologic and plant growth objective functions identified the importance of culverts in catchment-wide flow distribution. While this study shows the challenges of applying the SWAT model to complex terrain and extreme environments; by incorporating anthropogenic features into modeling scenarios, we can enhance our understanding of the hydroecological impact.

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Section: Meteorological Drivers and The Effects Of Interpolationmentioning

confidence: 85%

Section: Catchment Characteristicsmentioning

confidence: 99%

Section: Discharge and Evapotranspiration Estimatesmentioning

confidence: 99%

Section: Discharge and Evapotranspiration Estimatesmentioning

confidence: 99%

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Using the SWAT model to improve process descriptions and define hydrologic partitioning in South Korea

Shope

Maharjan

Tenhunen

et al. 2014

Hydrol. Earth Syst. Sci.

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“…The combination of high elevation gradients and the bowl-shaped geologic structure drastically alters the local meteorological conditions. The Haean catchment is located in a region with a monsoonal climate and an average temperature of 8.65 ± 0.35 ∘ C that ranges between −26.9 ∘ C in January and 33.4 ∘ C in August [28]. The climate of Republic of Korea is humid continental to subtropical and is heavily influenced by the East Asian summer monsoon.…”

Section: Study Location and Physiographymentioning

confidence: 99%

Modeling Spatiotemporal Precipitation: Effects of Density, Interpolation, and Land Use Distribution

Shope

Maharjan

2015

Advances in Meteorology

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Characterization of precipitation is critical in quantifying distributed catchment-wide discharge. The gauge network is a key driver in hydrologic modeling to characterize discharge. The accuracy of precipitation is dependent on the location of stations, the density of the network, and the interpolation scheme. Our study examines 16 weather stations in a 64 km 2 catchment. We develop a weighted, distributed approach for gap-filling the observed meteorological dataset. We analyze five interpolation methods (Thiessen, IDW, nearest neighbor, spline, and ordinary Kriging) at five gauge densities. We utilize precipitation in a SWAT model to estimate discharge in lumped parameter simulations and in a distributed approach at the multiple densities (1, 16, 50, 142, and 300 stations). Gauge density has a substantial impact on distributed discharge and the optimal gauge density is between 50 and 142 stations. Our results also indicate that the IDW interpolation scheme was optimum, although the Kriging and Thiessen polygon methods produced similar results. To further examine variability in discharge, we characterized the land use and soil distribution throughout each of the subbasins. The optimal rain gauge position and distribution of the gauges drastically influence catchmentwide runoff. We found that it is best to locate the gauges near less permeable locations.

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Evaluating the impact of watershed management on the environmental sustainability of the Taftan watershed in arid areas of southeast Iran

Jahantigh

2023

Arab J Geosci

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A weighted, multi-method approach for accurate basin-wide streamflow estimation in an ungauged watershed

Cited by 17 publications

References 14 publications

Using the SWAT model to improve process descriptions and define hydrologic partitioning in South Korea

Using the SWAT model to improve process descriptions and define hydrologic partitioning in South Korea

Modeling Spatiotemporal Precipitation: Effects of Density, Interpolation, and Land Use Distribution

Evaluating the impact of watershed management on the environmental sustainability of the Taftan watershed in arid areas of southeast Iran

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