Prediction of flow characteristics using multiple regression and neural networks: A case study in Zimbabwe

Mazvimavi, Dominic; Meijerink, A.M.J.; Savenije, Hubert; Stein, Alfred

doi:10.1016/j.pce.2005.08.003

Cited by 63 publications

(66 citation statements)

References 19 publications

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“…In order words, they are not applicable to ungauged areas where records of streamflow do not exist. Previous studies have used regression analysis extensively to estimate baseflow at ungauged sites in various regions of the world [22][23][24][25][26][27][28]. For example, Santhi et al [23] utilized regression analysis to relate relief, percentage of sand and effective rainfall to baseflow index (BFI) and baseflow volume for the conterminous United States.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Santhi et al [23] utilized regression analysis to relate relief, percentage of sand and effective rainfall to baseflow index (BFI) and baseflow volume for the conterminous United States. Mazvimavi et al [24] also used multiple regressions to predict BFI from mean annual precipitation, watershed slope, and proportion of a basin with grasslands in Zimbabwe. Longobardi and Villani [25] relied on regression analysis to develop regional equations for BFI prediction for Italy.…”

Section: Introductionmentioning

confidence: 99%

“…The most common watershed characteristics that influence baseflow and streamflow variations reported in the scientific literature include topography, relief, climate, rainfall, evapotranspiration, slope, basin drainage area, geologic and hydrogeologic variables, soils infiltration rate, baseflow factor, and land cover [23,24,[27][28][29][30][31][32]. Based on previous studies, regression models have the advantage of being implemented relatively easily to estimate baseflow with reasonable accuracy [21,25,27,31,33].…”

Section: Introductionmentioning

confidence: 99%

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Regression Modeling of Baseflow and Baseflow Index for Michigan USA

Zhang

Ahiablame

Engel

et al. 2013

Water

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Abstract:Baseflow plays an important role in maintaining streamflow. Seventeen gauged watersheds and their characteristics were used to develop regression models for annual baseflow and baseflow index (BFI) estimation in Michigan. Baseflow was estimated from daily streamflow records using the two-parameter recursive digital filter method for baseflow separation of the Web-based Hydrograph Analysis Tool (WHAT) program. Three equations (two for annual baseflow and one for BFI estimation) were developed and validated. Results indicated that observed average annual baseflow ranged from 162 to 345 mm, and BFI varied from 0.45 to 0.80 during 1967-2011. The average BFI value during the study period was 0.71, suggesting that about 70% of long-term streamflow in the studied watersheds were likely supported by baseflow. The regression models estimated baseflow and BFI with relative errors (RE) varying from −29% to 48% and from −14% to 19%, respectively. In absence of reliable information to determine groundwater discharge in streams and rivers, these equations can be used to estimate BFI and annual baseflow in Michigan.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Regression Modeling of Baseflow and Baseflow Index for Michigan USA

Zhang

Ahiablame

Engel

et al. 2013

Water

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“…PET " K pˆEpan (5) where, PET is the potential evapotranspiration in mm¨day´1, Epan represents the pan evaporation in mm¨day´1, and K p is the pan coefficient, which is the adjustment factor that depends on mean relative humidity, wind speed, and ground cover.…”

Section: Verification Of Model Outputsmentioning

confidence: 99%

“…Anthropogenic activities add more uncertainties to this task by inducing changes to land and climate at different scales [1,2]. This situation is more pronounced in developing countries, where in many river basins no runoff data are available [3][4][5][6][7] and the existing ones are of questionable quality or, at best, short or incomplete.…”

Section: Introductionmentioning

confidence: 99%

Multi-Site Validation of the SWAT Model on the Bani Catchment: Model Performance and Predictive Uncertainty

Begou

Jomaa

Benabdallah

et al. 2016

Water

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Abstract:The objective of this study was to assess the performance and predictive uncertainty of the Soil and Water Assessment Tool (SWAT) model on the Bani River Basin, at catchment and subcatchment levels. The SWAT model was calibrated using the Generalized Likelihood Uncertainty Estimation (GLUE) approach. Potential Evapotranspiration (PET) and biomass were considered in the verification of model outputs accuracy. Global Sensitivity Analysis (GSA) was used for identifying important model parameters. Results indicated a good performance of the global model at daily as well as monthly time steps with adequate predictive uncertainty. PET was found to be overestimated but biomass was better predicted in agricultural land and forest. Surface runoff represents the dominant process on streamflow generation in that region. Individual calibration at subcatchment scale yielded better performance than when the global parameter sets were applied. These results are very useful and provide a support to further studies on regionalization to make prediction in ungauged basins.

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Global patterns in base flow index and recession based on streamflow observations from 3394 catchments

et al. 2013

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[1] Numerous previous studies have constructed models to estimate base flow characteristics from climatic and physiographic characteristics of catchments and applied these to ungauged regions. However, these studies generally used streamflow observations from a relatively small number of catchments (<200) located in small, homogeneous study areas, which may have led to less reliable models with limited applicability elsewhere. Here, we use streamflow observations from a highly heterogeneous set of 3394 catchments (<10,000 km 2 ) worldwide to construct reliable, widely applicable models based on 18 climatic and physiographic characteristics to estimate two important base flow characteristics: (1) the base flow index (BFI), defined as the ratio of long-term mean base flow to total streamflow; and (2) the base flow recession constant (k), defined as the rate of base flow decay. Regression analysis results revealed that BFI and k were related to several climatic and physiographic characteristics, notably mean annual potential evaporation, mean snow water equivalent depth, and abundance of surface water bodies. Ensembles of artificial neural networks (ANNs; obtained by subsampling the original set of catchments) were trained to estimate the base flow characteristics from climatic and physiographic data. The catchment-scale estimation of the base flow characteristics demonstrated encouraging performance with R 2 values of 0.82 for BFI and 0.72 for k. The connection weights of the trained ANNs indicated that climatic characteristics were more important for estimating k than BFI. Global maps of estimated BFI and k were obtained using global climatic and physiographic data as input to the derived models. The resulting global maps are available for free download at http://www.hydrology-amsterdam.nl.

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Prediction of flow characteristics using multiple regression and neural networks: A case study in Zimbabwe

Cited by 63 publications

References 19 publications

Regression Modeling of Baseflow and Baseflow Index for Michigan USA

Regression Modeling of Baseflow and Baseflow Index for Michigan USA

Multi-Site Validation of the SWAT Model on the Bani Catchment: Model Performance and Predictive Uncertainty

Global patterns in base flow index and recession based on streamflow observations from 3394 catchments

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