Modelling surface runoff using the soil conservation service-curve number method in a drought prone agro-ecological zone in Rwanda

Uwizeyimana, Dieudonne; Mureithi, Stephen Mwangi; Mvuyekure, Simon Martin; Karuku, George N.; Kironchi, G.

doi:10.1016/j.iswcr.2018.12.001

Cited by 33 publications

(17 citation statements)

References 16 publications

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“…During inconsistencies, the authors found that there is a significant decrease in the performance of the SCS method by USDA (1986) in more forested areas, which did not bring good correlations between precipitation and CN. Uwizeyimana et al (2019) performed the classification of land use and slope of a watershed in Rwanda, where they found an increase in the runoff in dry soil environments, being more expressive in high slopes.…”

Section: Resultsmentioning

confidence: 99%

Sensitivity analysis of rainfall–runoff parameters models to estimate flows

et al. 2021

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Determinate the runoff of a watershed is a challenge due to the complexity of representing all “inlets” and “outlets” involved in a rainfall–runoff model. Therefore, methodologies applied for this purpose should have a good representation of the variables that most influence in this process. One of the models used to calculate the design flow is the (USDA in Urban Hydrology for Small. Technical release, no 55 (TR-55). Soil Conservation Service. Washigton, DC, http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Urban+Hydrology+for+Small+watersheds#1, 1986), which considers the analysis changes in soil coverage, time of concentration (tc), and recurrence period (T). In this way, this study sought to evaluate the hydrological behavior of a watershed with an increase in soil waterproofing. These modifications were correlated with the variation of runoff coefficients (CN), modifications of the periods of recurrence indicated by the literature, and different equations of the time of concentration. Its application was carried out in the Ribeirão do Suru watershed, Santana de Parnaíba, SP, Brazil. The CN {75; 80; 85; 90} increased 3.14, 5.61, 10.90 and 15.85%, respectively. In the most critical situation, runoff was 15.85% higher in estimated CN. The variation of precipitation as a function of T (2, 5, 10, 25, 50, 100 and 500) and application of 11 time of concentration methods designed 132 hydrographs and flow values that were statistically treated in T of Student and in the Analysis of Variance (ANOVA). Except for Bransby Willians associated Cinematic Method, Dooge with Johnstone and CTH with Tsuchyia, the pairs showed degrees of correlation below 59%. The greatest correlation was observed in Jonhstone with Dooge (90%), followed by the Kinematic Method with the Soil Conservation Service Method (83%) and with Dodge (74%). As a result, it was possible to demonstrate the behavior of the SCS parameters to minimize subjectivities and revealing how each parameter impacts the flow of the watershed. Finally, the sensitivity attributed to T was the highest among the three analyzed.

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Section: Resultsmentioning

confidence: 99%

Sensitivity analysis of rainfall–runoff parameters models to estimate flows

et al. 2021

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“…Different parameters of the soil moisture budget were computed using the Thornthwaite Water Balance software as applied elsewhere [29] utilizing the PET (calculated from daily maximum and minimum temperature) and rainfall obtained from the gauging stations and the Tanzania Meteorological Agency (TMA). The water balance referred to in this method is basically the balance between the incoming water from precipitation and the outflow of water by evapotranspiration [30,31]. Calculations to determine S B and APWL were performed for each day using daily precipitation (P) and potential evapotranspiration (PET) data.…”

Section: The Modified Soil Moisture Balance Methods Coupled With Curve Number For Recharge Assessmentmentioning

confidence: 99%

Natural Groundwater Recharge Response to Climate Variability and Land Cover Change Perturbations in Basins with Contrasting Climate and Geology in Tanzania

Mussa

Mjemah

Machunda

2021

Earth

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The response of aquifers with contrasting climate and geology to climate and land cover change perturbations through natural groundwater recharge remains inadequately understood. In Tanzania and elsewhere in the world, studies have been conducted to assess the impact of climate change and variability, and land use/cover changes on stream flow using different models, but similar studies on groundwater dynamics are inadequate. This study, therefore, examined the influence of land use/cover and climate dynamics on natural groundwater recharge in basins with contrasting climate and geology in Tanzania, applying the modified soil moisture balance method, coupled with the curve number (CN). The method hinges on the balance between the incoming water from precipitation and the outflow of water by evapotranspiration. The different parameters in the soil moisture balance method were computed using the Thornthwaite Water Balance software. The potential evapotranspiration (PET) was calculated using the daily maximum and minimum temperatures, utilizing two-temperature-based PET methods, Penman–Monteith (PM) and Hargreaves–Samani (HS). The rainfall data were obtained from the gauging stations under the Tanzania Meteorological Agency and some additional data were acquired from climate observatories management by water basins. The results show that there has been a quasi-stable CN in the Singida semi-arid, fractured crystalline basement aquifer (74.2 in 1997, 73.64 in 2005, and 73.87 in 2018). In the Kimbiji, humid, Neogene sedimentary aquifer, the CN has been steadily increasing (66.69 in 1997, 69.08 in 2008, and 71.42 in 2016), indicating the rapid land cover changes in the Kimbiji aquifer as compared to the Singida aquifer. For the Kimbiji humid aquifer, the PET calculated using the Penman–Monteith (PM) method for the 1996/1997, 2007/2008, and 2015/2016 hydrological years were 1156.5, 1079.5, and 1143.9 mm/year, respectively, while for the Hargreaves–Samani (HS) method, the PET was found to be 1046.1, 1138.3, and 1204.4 mm/year for the 1996/1997, 2007/2008, and 2015/2016 hydrological years, respectively. For the Singida semi-arid aquifer, the PM PET method resulted in 2083.3, 2053.6, and 1875.4 mm/year for the 1996/1997, 2004/2005, and 2017/2018 hydrological years, respectively. The HS method produced relatively lower PET values for the semi-arid area (1839.4, 1814.7, and 1710.2 mm/year) for the 1996/1997, 2004/2005, and 2017/2018 hydrological years, respectively. It was equally revealed that the recharge and aridity indices correspond with the PET calculated using two temperature-dependent methods. The decline of certain land covers (forests) and increase in others (built-up areas) have contributed to the increase in surface runoff in each study area, possibly resulting in the decreasing trend of groundwater recharge. An overestimation of the PET using the HS method in the Kimbiji humid aquifer was observed, which was relatively smaller than the overestimation of the PET using the PM method in the Singida semi-arid aquifer. Despite the difference in climate and geology, the response of the two aquifers to rainfall is similar. The combined influence of climate and land cover changes on natural groundwater recharge was observed to be prominent in the Kimbiji aquifer, while only climate variability appreciably influences natural groundwater recharge in the Singida semi-arid aquifer. El Nino and the Southern Oscillation as part of the climate variability phenomenon dwarfed the time lags between rainfall and recharge in the two basins, regardless of their difference in climate and geology.

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“…The vector of means and the variance-covariance matrix are then used to estimate the probability by which one given pixel belongs to all classes. Finally, pixels were assigned to the class to which it belonged with the highest probability (Tso & Mather, 2009). A Bayesian approach was used and considered was the a priori probability, whether a pixel belonged to a certain class.…”

Section: Minimum Distancementioning

confidence: 99%

Determination of field capacity in the Chibunga and Guano rivers micro-basins

et al. 2021

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Background: The micro-basins of the Chibunga and Guano rivers are located within the sub-basin of the Chambo River, which starts at the thaw of the Chimborazo, crosses the cities of Guano and Riobamba, and ends in the Chambo River. These rivers are considered fluvial hydrological forces and geological limits of the aquifer, located in this sub-basin. For this reason, our investigation addressed the field capacity in the micro-basins of Chibunga and Guano rivers, to determine the maximum retention potential, i.e., the saturation of water in the soil. Methods: We investigated the change of precipitation to runoff through the correlations between the characteristics of the soil and its vegetation. We applied the Curve Number (CN) method introduced by the United States Soil Conservation Service (USSCS); this represents an empirical model, which relates the vegetation cover to the geological and topographic conditions of the soil. Along with the geographic information system, the model allows to represent the variation of runoffs for each micro-basin, according to the different land use categories, over the time frame from 2010 to 2014. Results: We found that the maximum retention potential is directly affected by CN values, representing the runoff potential. Highest values of 100 belong to the wetlands, urban area, snow, and water, as rain is converted directly into runoff, being impervious areas. The Guano river micro-basin possesses clay soil with CN of 78, the soil texture for eucalyptus forest is clay loam, and its CN value, 46, is the lowest of the data set. Knowledge of field capacity allows to properly evaluate the storage capacity of soil and water conservation. Conclusions: Results of this work will be useful in the quantification of the water balance, to determine the water supply and demand.

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Modelling surface runoff using the soil conservation service-curve number method in a drought prone agro-ecological zone in Rwanda

Cited by 33 publications

References 16 publications

Sensitivity analysis of rainfall–runoff parameters models to estimate flows

Sensitivity analysis of rainfall–runoff parameters models to estimate flows

Natural Groundwater Recharge Response to Climate Variability and Land Cover Change Perturbations in Basins with Contrasting Climate and Geology in Tanzania

Determination of field capacity in the Chibunga and Guano rivers micro-basins

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