Reliability in Estimating Urban Groundwater Recharge Through the Vadose Zone

Friedel, Michael J.

doi:10.1007/1-4020-5175-1_13

Cited by 5 publications

(13 citation statements)

References 20 publications

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“…In this study, the uncertainty associated with model parameter values and extreme rainfall amounts is computed using a calibration-constrained Monte Carlo approach (Friedel 2006b). As the name implies, the calibration-constrained Monte Carlo approach is an inverse-based approach (as opposed to the conventional forward-based Monte Carlo approach) analogous to the generalized likelihood uncertainty estimation approach described by Beven and Binley (1992).…”

Section: Predictive Uncertaintymentioning

confidence: 99%

Regularized joint inverse estimation of extreme rainfall amounts in ungauged coastal basins of El Salvador

Friedel

2007

Nat Hazards

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A regularized joint inverse procedure is presented and used to estimate the magnitude of extreme rainfall events in ungauged coastal river basins of El Salvador: Paz, Jiboa, Grande de San Miguel, and Goascoran. Since streamflow measurements reflect temporal and spatial rainfall information, peak-flow discharge is hypothesized to represent a similarity measure suitable for regionalization. To test this hypothesis, peak-flow discharge values determined from streamflow recurrence information (10-year, 25-year, and 100-year) collected outside the study basins are used to develop regional (country-wide) regression equations. Peak-flow discharge derived from these equations together with preferred spatial parameter relations as soft prior information are used to constrain the simultaneous calibration of 20 tributary basin models. The nonlinear range of uncertainty in estimated parameter values (1 curve number and 3 recurrent rainfall amounts for each model) is determined using an inverse calibration-constrained Monte Carlo approach. Cumulative probability distributions for rainfall amounts indicate differences among basins for a given return period and an increase in magnitude and range among basins with increasing return interval. Comparison of the estimated median rainfall amounts for all return periods were reasonable but larger (3.2-26%) than rainfall estimates computed using the frequency-duration (traditional) approach and individual rain gauge data. The observed 25-year recurrence rainfall amount at La Hachadura in the Paz River basin during Hurricane Mitch (1998) is similar in value to, but outside and slightly less than, the estimated rainfall confidence limits. The similarity in joint inverse and traditionally computed rainfall events, however, suggests that the rainfall observation may likely be due to under-catch and not model bias.

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Section: Predictive Uncertaintymentioning

confidence: 99%

Regularized joint inverse estimation of extreme rainfall amounts in ungauged coastal basins of El Salvador

Friedel

2007

Nat Hazards

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“…This method facilitates the crossover of information between dependent variables (the crossover effect), and its success is commonly evaluated through observed reductions in transport parameter uncertainty (Sonnenberg and others, 1996;Hendricks-Franssen, H.J. and others, 2003;Friedel, 2002Friedel, , 2005Friedel, , 2006aFriedel, , 2006b.…”

Section: Information Contentmentioning

confidence: 99%

“…If the information quality is comparatively poor (that is, limited in one or more of the following: number, type, space, and time), the calibration process may become numerically intractable. In general, the limited information content of field measurements used to constrain the model calibration process requires some form of regularization (Tikhonov, 1977;McLaughlin and Townley, 1996;Friedel, 2002Friedel, , 2005Friedel, , 2006aFriedel, , 2006b.…”

Section: Information Contentmentioning

confidence: 99%

Hydrologic modeling strategy for the Islamic Republic of Mauritania, Africa

Friedel¹

2008

Open-File Report

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The government of Mauritania is interested in how to maintain hydrologic balance to ensure a long-term stable water supply for minerals-related, domestic, and other purposes. Because of the many complicating and competing natural and anthropogenic factors, hydrologists will perform quantitative analysis with specific objectives and relevant computer models in mind. Whereas various computer models are available for studying water-resource priorities, the success of these models to provide reliable predictions largely depends on adequacy of the model-calibration process. Predictive analysis helps us evaluate the accuracy and uncertainty associated with simulated dependent variables of our calibrated model. In this report, the hydrologic modeling process is reviewed and a strategy summarized for future Mauritanian hydrologic modeling studies.

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“…The accuracy of spatial modeling methods of soil properties has been analyzed in several studies (Saito et al, 2005;Seyedmohammadi et al, 2016;Tripathi et al, 2015;Varouchakis & Hristopulos, 2013). Geostatistical methods have been used in several studies, to estimate electrical conductivity (Bhunia et al, 2018;Friedel, 2006;Khosravi et al, 2016;Seyedmohammadi et al, 2016;Tripathi et al, 2015;Zehtabian et al, 2012). However, it is appropriate to adopt an effective technique to predict the spatial distribution of certain soil characteristics (Emadi & Baghernejad, 2014;Seyedmohammadi et al, 2016;Tripathi et al, 2015;Zarco-Perello & Simões, 2017).…”

Section: Introductionmentioning

confidence: 99%

Modeling the Spatial Variability of the Electrical Conductivity of the Soil Using Differents Spatial Interpolation Methods: Case of the Dawling National Park in Mauritania

Abidine¹,

Aboudi²,

Kebd³

et al. 2018

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One of the most important indicators for land degradation is the progressive salinization of soils. This work, conducted at the Dawling National Park (southern Mauritania), assess the effects of salinity on soil quality. Analyzes of spatial variation in salinity were performed using interpolation and spatial analysis (GIS) methods. Thus, maps of electrical conductivity have been developed using several methods of interpolation and spatial analysis: Inverse Weighting (IDW), Local Polynomial Interpolation (LPI), Radial Base Function (RBF) and Ordinary Kriging (OK). The obtained results showed that the best estimator is IDW method, which provides a good ability to predict electrical conductivity, with a mean squared error (RMSE) of 0.34 mS/cm and a correlation coefficient (R) of 0.99.

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Reliability in Estimating Urban Groundwater Recharge Through the Vadose Zone

Cited by 5 publications

References 20 publications

Regularized joint inverse estimation of extreme rainfall amounts in ungauged coastal basins of El Salvador

Regularized joint inverse estimation of extreme rainfall amounts in ungauged coastal basins of El Salvador

Hydrologic modeling strategy for the Islamic Republic of Mauritania, Africa

Modeling the Spatial Variability of the Electrical Conductivity of the Soil Using Differents Spatial Interpolation Methods: Case of the Dawling National Park in Mauritania

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