Predicting Ground‐Water Response to Precipitation

Sangrey, Dwight A.; Harrop‐Williams, Kingsley; Klaiber, Jeffrey A.

doi:10.1061/(asce)0733-9410(1984)110:7(957)

Cited by 70 publications

(28 citation statements)

References 6 publications

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“…Both slope (HRU_SLP) and soil available water content (SOL_AWC) were particularly sensitive for the base flow simulation because they affect lateral flow within the kinematic storage model in SWAT (Sloan and Moore, 1984). The aquifer percolation coefficient (RCHRG_DP) and the base flow alpha factor (ALPHA_BF) strongly influenced base flow calculations (Sangrey et al, 1984), as did the channel's Manning N value (CH_N2), which is used to estimate channel flow (Chow, 2008). For SS loads, 12 and four parameters, respectively, were identified as sensitive in relation to the simulations of base flow and quick flow (parameters above the dashed line in Fig.…”

Section: Separated Parameter Sensitivitymentioning

confidence: 99%

Effects of hydrologic conditions on SWAT model performance and parameter sensitivity for a small, mixed land use catchment in New Zealand

Abell

Hamilton

2015

Hydrol. Earth Syst. Sci.

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Abstract. The Soil Water Assessment Tool (SWAT) was configured for the Puarenga Stream catchment (77 km 2 ), Rotorua, New Zealand. The catchment land use is mostly plantation forest, some of which is spray-irrigated with treated wastewater. A Sequential Uncertainty Fitting (SUFI-2) procedure was used to auto-calibrate unknown parameter values in the SWAT model. Model validation was performed using two data sets: (1) monthly instantaneous measurements of suspended sediment (SS), total phosphorus (TP) and total nitrogen (TN) concentrations; and (2) high-frequency (1-2 h) data measured during rainfall events. Monthly instantaneous TP and TN concentrations were generally not reproduced well (24 % bias for TP, 27 % bias for TN, and R 2 < 0.1, NSE < 0 for both TP and TN), in contrast to SS concentrations (< 1 % bias; R 2 and NSE both > 0.75) during model validation. Comparison of simulated daily mean SS, TP and TN concentrations with daily mean dischargeweighted high-frequency measurements during storm events indicated that model predictions during the high rainfall period considerably underestimated concentrations of SS (44 % bias) and TP (70 % bias), while TN concentrations were comparable (< 1 % bias; R 2 and NSE both ∼ 0.5). This comparison highlighted the potential for model error associated with quick flow fluxes in flashy lower-order streams to be underestimated compared with low-frequency (e.g. monthly) measurements derived predominantly from base flow measurements. To address this, we recommend that high-frequency, event-based monitoring data are used to support calibration and validation. Simulated discharge, SS, TP and TN loads were partitioned into two components (base flow and quick flow) based on hydrograph separation. A manual procedure (one-at-a-time sensitivity analysis) was used to quantify parameter sensitivity for the two hydrologically separated regimes. Several SWAT parameters were found to have different sensitivities between base flow and quick flow. Parameters relating to main channel processes were more sensitive for the base flow estimates, while those relating to overland processes were more sensitive for the quick flow estimates. This study has important implications for identifying uncertainties in parameter sensitivity and performance of hydrological models applied to catchments with large fluctuations in stream flow and in cases where models are used to examine scenarios that involve substantial changes to the existing flow regime.

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Section: Separated Parameter Sensitivitymentioning

confidence: 99%

Effects of hydrologic conditions on SWAT model performance and parameter sensitivity for a small, mixed land use catchment in New Zealand

Abell

Hamilton

2015

Hydrol. Earth Syst. Sci.

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“…The water balance in an unconfined aquifer is calculated according to Equation (9): aq f HRU,i " aq f HRU,i´1`ωuncon f ,HRU´QBF,HRU (9) where aqf HRU,i is the amount of water stored in an unconfined aquifer on a given day (mm), aqf HRU,i-1 is the amount of water stored in the unconfined aquifer on the previous day (mm), ω unconf,HRU is the amount of recharge entering the unconfined aquifer on that day (mm) and Q BF,HRU is the amount of baseflow into the main channel (mm). The amount of water recharged into both aquifers (confined and unconfined) is estimated using the exponential decay weighting function (Equation (10)) proposed by Venetis [48] and used by Sangrey et al [49] in their precipitation-groundwater response model and by Neitsch et al [38] in the SWAT model:…”

Section: Development Of Baseflow Modulementioning

confidence: 99%

Development of a Watershed-Scale Long-Term Hydrologic Impact Assessment Model with the Asymptotic Curve Number Regression Equation

Ryu

Jang

Kim

et al. 2016

Water

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Abstract:In this study, 52 asymptotic Curve Number (CN) regression equations were developed for combinations of representative land covers and hydrologic soil groups. In addition, to overcome the limitations of the original Long-term Hydrologic Impact Assessment (L-THIA) model when it is applied to larger watersheds, a watershed-scale L-THIA Asymptotic CN (ACN) regression equation model (watershed-scale L-THIA ACN model) was developed by integrating the asymptotic CN regressions and various modules for direct runoff/baseflow/channel routing. The watershed-scale L-THIA ACN model was applied to four watersheds in South Korea to evaluate the accuracy of its streamflow prediction. The coefficient of determination (R 2 ) and Nash-Sutcliffe Efficiency (NSE) values for observed versus simulated streamflows over intervals of eight days were greater than 0.6 for all four of the watersheds. The watershed-scale L-THIA ACN model, including the asymptotic CN regression equation method, can simulate long-term streamflow sufficiently well with the ten parameters that have been added for the characterization of streamflow.

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“…In many cases of slow slope movements involving colluvial covers, a close correlation between the piezometric levels time evolution and the displacement rates inside the landslide bodies can be observed (Manfredini et al, 1981;Nakamura, 1984;Sangrey et al, 1984;Morgenstern, 1985;Bertini et al, 1986;Cascini et al, 1986;Iverson et al, 1987;Cartier et al, 1988;Sirangelo et al, 1992;Davis et al, 1993;Pouget et al, 1994;Desai et al, 1995;Urciuoli, 1998;Alonso et al, 1999;Russo et al, 1999;Vulliet, 1999;Pellegrino et al, 2000Pellegrino et al, , 2002Piscitelli et al, 2001;Crosta et al, 2003). So it should be taken into account in the numerical analysis: piezometric level variations during time can be obtained introducing the piezometric level time dependence, h w (t), in the Mohr-Coulomb-Terzaghi criterion used to represent τ lim (t), the soil yielding resistance:…”

Section: Figmentioning

confidence: 97%

Mathematical modelling of the landslide occurred at Gagliano Castelferrato (Italy)

Maugeri

Motta

Raciti

2006

Nat. Hazards Earth Syst. Sci.

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Abstract. Shallow slopes in clayey colluvial covers are often involved in progressive downhill motion with discontinuous rate of movements, depending on fluctuations in pore-water pressure.In geotechnical engineering research on natural slopes, the main efforts have been concentrated on stability analysis, always with a rigid perfectly plastic body assumption. In case of slow slope movements, however, the notion of stability losses its sense, so the main question is not to evaluate a stability factor, but to model a velocity field and to define the kinematic and dynamic features of the movement (mobility analysis).Many authors, in their researches, deal with slow slope movements and for the complexity of the problem and the great number of parameters involved they agree about applying numerical techniques (FEM, FDM) and advanced material modelling (elastoviscoplasticity) and suggest to calibrate the involved parameters values with the help of "back analyses" of existing case histories.In this paper a mathematical model predicting the landslide body viscous deformations, is presented. The model has been implemented in a computer FDM code, and has been tested on some well known case histories. Here it is applied to the case of a landslide occurred at Gagliano Castelferrato (Sicily -Italy), where a great number of field measurements was available.

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Predicting Ground‐Water Response to Precipitation

Cited by 70 publications

References 6 publications

Effects of hydrologic conditions on SWAT model performance and parameter sensitivity for a small, mixed land use catchment in New Zealand

Effects of hydrologic conditions on SWAT model performance and parameter sensitivity for a small, mixed land use catchment in New Zealand

Development of a Watershed-Scale Long-Term Hydrologic Impact Assessment Model with the Asymptotic Curve Number Regression Equation

Mathematical modelling of the landslide occurred at Gagliano Castelferrato (Italy)

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