Soil Conservation Service Curve Number Research Articles

Assessment of the Impacts of Land Use/Cover Change and Rainfall Change on Surface Runoff in China

Assessment of the impacts of land use/cover change (LUCC) and rainfall change on surface runoff depth can help provide an understanding of the temporal trend of variation of surface runoff and assist in urban construction planning. This study evaluated the impacts of LUCC and rainfall change on surface runoff depth by adopting the well-known Soil Conservation Service-Curve Number (SCS-CN) method and the widely used Long-Term Hydrologic Impact Assessment (L-THIA) model. National hydrologic soil group map of China was generated based on a conversion from soil texture classification system. The CN values were adjusted based on the land use/cover types and soil properties in China. The L-THIA model was configured by using the adjusted CN values and then applied nationally in China. Results show that nationwide rainfall changes and LUCC from 2005 to 2010 had little impact on the distribution of surface runoff, and the high values of runoff depth were mainly located in the middle and lower reaches of the Yangtze River. Nationally, the average annual runoff depths in 2005, 2010 and 2015 were 78 mm, 83 mm and 90 mm, respectively. For the 2015 land use data, rainfall change caused the variation of surface runoff depth ranging from −203 mm to 476 mm in different regions. LUCC from 2005 to 2015 did not cause obvious change of surface runoff depth, but expansion of developed land led to runoff depth increases ranging from 0 mm to 570 mm and 0 mm to 742 mm from 2005 to 2010 and 2010 to 2015, respectively. Potential solutions to urban land use change and surface runoff control were also analyzed.

ANN-SCS-based hybrid model in conjunction with GCM to evaluate the impact of climate change on the flow scenario of the River Subansiri

Abstract The River Subansiri, one of the largest tributaries of the Brahmaputra, makes a significant contribution towards the discharge at its confluence with the Brahmaputra. This study aims to investigate an appropriate model to predict the future flow scenario of the river Subansiri. Two models have been developed. The first model is an artificial neural network (ANN)-based rainfall-runoff model where rainfall has been considered as the input. The future rainfall of the basin is calculated using a multiple non-linear regression-based statistical downscaling technique. The proposed second model is a hybrid model developed using ANN and the Soil Conservation Service (SCS) curve number (CN) method. In this model, both rainfall and land use/land cover have been incorporated as the inputs. The ANN models were run using time series analysis and the method selected is the non-linear autoregressive model with exogenous inputs. Using Sen's slope values, the future trend of rainfall and runoff over the basin have been analyzed. The results showed that the hybrid model outperformed the simple ANN model. The ANN-SCS-based hybrid model has been run for different land use/land cover scenarios to study the future flow scenario of the River Subansiri.

Computation of Runoff by SCS-CN Method from micro watersheds of Urmodi basin in Maharashtra state using RS and GIS

Flood is a natural or manmade phenomenon and timely and accurate forecasting of flood is very important. However forecasting of flood is a difficult task due to influence of rainfall-runoff process which depends on various factors. Estimation of surface runoff in a watershed is based on the rate of precipitation and discharge at the outlet. In this study, runoff from micro watersheds of Urmodi basin in Maharashtra state was computed by Soil Conservation Service-Curve Number method using remote sensing and Geographic Information System (GIS) techniques. Various thematic maps such as soil map, land use/land cover, stream order, slope etc. were prepared using remote sensing and GIS. Daily rainfall data was used for determining runoff. Antecedent moisture conditions were determined from daily rainfall data and for different CNs with the help of combined land use land cover and hydrologic soil group map in GIS environment. Results showed that the highest runoff for Bharatgaon and Nagthane micro watersheds was 46.20 mm and 54 mm respectively. Total runoff depth for the year 2014 was computed as 215.05 mm for Bharatgaon micro watershed and 277.68 mm for Nagthane micro watershed. Different soil and water conservation measures and water harvesting structures were recommended to control soil erosion and to harness the surface runoff.

Evaluation of loss models and effect of LU/LC changes on surface runoff in Subarnarekha river basin

ABSTRACT The main focus of study is to compute the surface runoff and to assess the impact of land use/land cover changes (LU/LCC) with the help of hydrological model (SWAT) in Subarnarekha River Basin (SRB) over a period of 1999–2014. For runoff simulation, two different loss models were employed like SCS-CN (Soil Conservation Services – Curve Number) model and Green-Ampt model (GA). Drainage map, elevation map, land use/land cover map, slope map, and soil map of the watershed are prepared using remote sensing and GIS approaches as input data to the model. Simulation has been done at six observing stations located at Adityapur, Jamshedpur, Ghatsila, Jamsholaghat, Fekoghat, and Rajghat. During this period, runoff is found to increase by 6.06% due to decrease in infiltration rate. The comparison between the observed and estimated data shows the Nash–Sutcliffe efficiency (NSE) values are 0.91 and 0.90, and coefficient of determination (R 2) values are 0.89 and 0.88 for SCS-CN and GA methods, respectively. Thus, SCS model is found to do well than GA model in Subarnarekha River Basin. This type of study would be beneficial for the watershed development at that particular watershed.

An Integrated Hydrological-CFD Model for Estimating Bacterial Levels in Stormwater Ponds

A hydrological model was integrated with a computational fluid dynamics (CFD) model to determine bacteria levels distributed throughout the Inverness stormwater pond in Calgary, Alberta. The Soil Conservation Service (SCS) curve number model was used as the basis of the hydrological model to generate flow rates from the watershed draining into the pond. These flow rates were then used as input for the CFD model simulations that solved the Reynolds-Averaged Navier-Stokes (RANS) equations with k-ɛ turbulence model. E. coli, the most commonly used fecal indicator bacteria for water quality research, was represented in the model by passive scalars with different decay rates for free bacteria and attached bacteria. Results show good agreement with measured data in each stage of the simulations. The middle of the west wing of the pond was found to be the best spot for extracting water for reuse because it had the lowest level of bacteria both during and after storm events. In addition, only one of the four sediment forebays was found efficient in trapping bacteria.

Soil Moisture Accounting (SMA) based sediment graph models for small watersheds

The sediment graph models are useful for computation of sediment yield as well as total sediment out flow from watershed. In this study, the analytical development of proposed sediment graph models is based on Soil Moisture Accounting (SMA) procedure coupled Soil Conservation Service-Curve Number (SCS-CN) method, Nash’s Instantaneous Unit Sediment Graph (IUSG)model and Power law. This coupling has led to the development of four sediment graph models (SGMs), i.e., SMA-SGM1, SMA-SGM2, SMA-SGM-3 and SMA-SGM4 depending on the four different hydrologic conditions as: (i) initial soil moisture (V0) = 0 and initial abstraction (Ia) = 0, (ii) initial soil moisture (V0)≠0andinitialabstractionIa=0, (iii) initial soil moisture (V0)=0and(Ia)≠0, and (iv) initial soil moisture (V0)≠0andinitialabstration(Ia)≠0, respectively. These models are applied on six natural watersheds with nineteen storm events having different land use/land cover, climatic condition (arid, semi-arid, humid and sub-tropical), rainfall and land slope conditions. The goodness-of-fit statistics is evaluated in terms of Nash Sutcliffe efficiency (NSE) and relative error (RE) between observed and simulated (calibrated and validated) sediment graphs. Further, the performance of these models is also compared with the sediment graph model of Bhunya et al. (2010) (BSGM) on all the six study watersheds. It is found that the proposed models perform very well in simulating sediment yield generation process for all the watersheds and show significant improvement over the BSGM model.

Flash flood schlep ability estimation in vertical distribution law of the precipitation area: a case of Xulong gully, Southwest China

This study focused on the influence of rainfall vertical distribution on the flood schlep ability of Xulong gully, located upstream of Jinsha River. The mountain precipitation vertical distribution formula (MPVDF) was employed to fit the rainfall data, the Soil Conservation Service curve number (SCS-CN) method to calculate the runoff and the isochrone method to calculate the flood peak flow. In addition, the maximum diameter of the gravel transported by flood is used to evaluate the flood schlep ability. The calculation results of the traditional method (TM) and inverse distance weighting (IDW) are also used for comparison. The results show that the maximum error of fitting for rainfall data is 11.58% and the maximum error of the calculated peak flow compared with the calculation of the morphological survey method (MSM) is 3.66%, which highlights MPVDF as more appropriate for the catchments in which the precipitation follows a significant vertical distribution law. The results of the maximum diameter of the gravel transported by flood of MPVDF, TM and IDW were 86 cm, 66 cm and 69 cm, respectively, which means the rainfall vertical distribution has great influence on the schlep ability of flood.

Characteristics of impervious surface and its effect on direct runoff: a case study in a rapidly urbanized area

Abstract In recent years, many cities have experienced serious urban flood and non-point pollution issues due to hydrological process changes in rapidly urbanizing areas. Understanding the relationship between impervious surface and direct runoff is important for urban planning to protect the urban hydrological system. In this study, we used a mixed spectral decomposition method to interpret the long-term series of impervious surface of Shenyang, China. Direct runoff was evaluated by an improved SCS-CN (Soil Conservation Service curve number) model, and the relative influences of five underlying surface factors on the direct runoff of each period were analyzed by boosted regression trees. The overall impervious area was significantly increased in both the study area and built-up area from 1984 to 2015. The impervious ratio showed a decreasing trend in the built-up area and increasing trend in the whole study area. The runoff coefficient of the built-up area showed a significantly decreasing trend. The runoff ratio of the built-up area to the whole study area was increased dramatically, reaching 0.26 by 2015. NDVI (normalized distribution vegetation index), vegetation, and impervious surface were the most important urban surface conditions in the study area for direct runoff generation. The relative influence of impervious surface showed a rapidly increasing trend and then gradually decreased from 2000.

Application of HEC-HMS Model for Flow Simulation in the Lake Tana Basin: The Case of Gilgel Abay Catchment, Upper Blue Nile Basin, Ethiopia

Understanding the complex relationships between rainfall and runoff processes is necessary for the proper estimation of the quantity of runoff generated in a watershed. The surface runoff was simulated using the Hydrologic Modelling System (HEC-HMS) for the Gilgel Abay Catchment (1609 km2), Upper Blue Nile Basin, Ethiopia. The catchment was delineated and its properties were extracted from a 30 m × 30 m Digital Elevation Model (DEM) of the Lake Tana Basin. The meteorological model was developed within HEC-HMS from rainfall data and the control specifications defined the period and time step of the simulation run. To account for the loss, runoff estimation, and flow routing, Soil Conservation Service Curve Number (SCS-CN), Soil Conservation Service Unit Hydrograph (SCS-UH) and Muskingum methods were used respectively. The rainfall-runoff simulation was conducted using six extreme daily time series events. Initial results showed that there is a clear difference between the observed and simulated peak flows and the total volume. Thereafter, a model calibration with an optimization method and sensitivity analysis was carried out. The result of the sensitivity analysis showed that the curve number is the sensitive parameter. In addition, the model validation results showed a reasonable difference in peak flow (Relative Error in peak, REP = 1.49%) and total volume (Relative Error in volume, REV = 2.38%). The comparison of the observed and simulated hydrographs and the model performance (NSE = 0.884) and their correlation (R2 = 0.925) showed that the model is appropriate for hydrological simulations in the Gilgel Abay Catchment.

Curve Number Estimation of Ungauged Catchments considering Characteristics of Rainfall and Catchment

The Soil Conservation Service Curve Number (CN) method is widely used to calculate the flood runoff in ungauged catchments. However, the existing CN calculation method has a disadvantage in that it cannot calculate the CNs considering the spatiotemporal variability of rainfall in the ungauged catchments. In this study, the authors used a distributed rainfall-runoff model and a simple runoff generation method to generate the hourly runoffs of the ungauged catchments considering the spatiotemporal variability of rainfall, and estimate the CNs of the ungauged catchments using the generated runoff and the CN back calculation method. As a result of calculating the CNs of the ungauged catchments for past 20 rainfall events, the CNs had a large variability even in the same catchment. In addition, the mean CNs for the 20 rainfall events of independent ungauged catchments differed even if the ungauged catchments were close to each other. The method of estimating the CNs of the ungauged catchments presented in this study properly reflects the characteristics of actual rainfall and catchment, and thus can be usefully used to estimate valid CN.

Estimating flow rate in gauged and ungauged stations in Kuantan river basin using Clark method in Hec-HMS

Hydrologic simulation that engages incomputer models is the cutting edge to boost the understanding and provide further reliable outcomes in estimating flow rate. US Army Corps of Engineers has taken an initiative to develop a stable and dependent model, HEC-HMS that can be used for many hydrological simulations. The value of parameter needed for the simulation basically depends on the method chosen for loss, transform and base flow. In this study, the model parameters are changed and the model calibration had been performed separately for the three selected methods, the Soil Conservation Service Curve Number (SCS CN) loss method, the Constant Monthly base flow and the Clark Unit Hydrograph for transform method, to determine the most suitable simulation and to obtain the highest peak discharge for every sub-basin of the Kuantan River. Every flow simulated undergoes the validation and calibration process, with the Nash-Sutcliffe index used as a criterion to compare the outcomes between observed and simulated hydrographs. At the end of the study, it can be concluded that SCS Curve Number, Constant Monthly and Clark Unit Hydrograph is the best method for loss, base flow and transform method consecutively in estimating the flow rate in Kuantan River Basin.

Rainfall-runoff estimation of Bojiang lake watershed using SCS-CN model coupled with GIS for watershed management

A proper understanding of watershed spatio-temporal hydrological characteristics is critical to the management of a watershed and its natural resources such as water and vegetation. Rainfall runoff estimation plays an important role as an integral part of watershed management. Runoff volume and distribution data provides valuable information for water management strategies such as selection of artificial water abstraction sites, water storage facilities, and soil erosion control strategies. In the present study Bojiang lake watershed was used to indicate the application of Soil Conservation Service Curve Number method (SCS-CN) coupled with Geographic Information System (GIS) and Remote Sensing (RS) techniques. The watershed falls within Erdos Larus Relictus National Nature Reserve (ELRNNR) which was listed under the wetlands of international importance in 2002. Rainfall runoff is influenced by a variety of factors within a watershed such as soil and land use/cover types, soil moisture content, rainfall, drainage density, and shape and size of the watershed. The SCS Curve number is the most popular and widely applied method for runoff estimation. GIS and Remote Sensing play an important role in estimating surface runoff by SCS-CN method. ArcGIS 10.2 software was used to overlay different thematic layers and develop an attribute table and calculate a weighted curve number. The weighted curve number was applied to the SCS-CN equations to estimate daily, monthly, and yearly runoff. Correlation coefficient (r) was used to test for the relationship between rainfall and runoff, and verify the computation of the method. The results show an average runoff of 17.78 mm which is about 7.18% of the annual average rainfall for the years 2001-2016. The derived output maps can assist in identifying suitable areas for water recharge/abstraction. The study demonstrates that SCS-CN in conjunction with GIS and RS can be used to calculate runoff for ungagged watersheds and assist in watershed management strategies.

Phenology-adjusted dynamic curve number for improved hydrologic modeling

The Soil Conservation Service Curve Number (SCS-CN, or CN) is a widely used method to estimate runoff from rainfall events. It has been adapted to many parts of the world with different land uses, land cover types, and climatic conditions and successfully applied to situations ranging from simple runoff calculations and land use change assessment to comprehensive hydrologic/water quality simulations. However, the CN method lacks the ability to incorporate seasonal variations in vegetated surface conditions, and unnoticed landuse/landcover (LULC) change that shape infiltration and storm runoff. Plant phenology is a main determinant of changes in hydrologic processes and water balances across seasons through its influence on surface roughness and evapotranspiration. This study used regression analysis to develop a dynamic CN (CNNDVI) based on seasonal variations in the remotely-sensed Normalized Difference Vegetation Index (NDVI) to monitor intra-annual plant phenological development. A time series of 16-day MODIS NDVI (MOD13Q1 Collection 5) images were used to monitor vegetation development and provide NDVI data necessary for CNNDVI model calibration and validation. Twelve years of rainfall and runoff data (2001–2012) from four small watersheds located in the Konza Prairie Biological Station, Kansas were used to develop, calibrate, and validate the method. Results showed CNNDVI performed significantly better in predicting runoff with calibrated CNNDVI runoff increasing by approximately 0.74 for every unit increase in observed runoff compared to 0.46 for SCS-CN runoff and was more highly correlated to observed runoff (r = 0.78 vs. r = 0.38). In addition, CNNDVI runoff had better NSE (0.53) and PBIAS (4.22) compared to the SCS-CN runoff (−0.87 and −94.86 respectively). In the validated model, CNNDVI runoff increased by approximately 0.96 for every unit of observed runoff, while SCS-CN runoff increased by 0.49. Validated runoff was also better correlated to observed runoff than SCS-CN runoff (r = 0.52 vs. r = 0.33). These findings suggest that the CNNDVI can yield improved estimates of surface runoff from precipitation events, leading to more informed water and land management decisions.

Quantification of Recharge and Runoff from Rainfall Using New GIS Tool: Example of the Gaza Strip Aquifer

The Gaza Strip forms a transition zone between the semi-humid coastal zone in the north, the semi-arid zone in the east, and the Sinai desert in the south. Groundwater is the only water source for 1.94 million inhabitants, where the only fresh replenishment water for the aquifer comes from rainfall. This study focuses on testing a newly developed GIS tool to estimate the spatial and temporal distribution of runoff and recharge from rainfall. The estimation of surface runoff was made using the Soil Conservation Services Curve Number Method, while groundwater recharge was estimated using Thornthwaite and Mather’s Soil Moisture Balance approach. The new tool was applied to the Gaza aquifer for the year 1935 and for the period from 1973 to 2016. A comparison was made between the results obtained with the developed GIS tool and the frequently used Thiessen polygon method for rainfall distribution. Runoff and recharge were estimated for the year 1935 (prior to development) to compare with the current developed conditions. It was found that the built-up and sand dune areas stand in an inverse relationship, where the former is replacing the latter (built-up area expanded from 30.1 km2 in 1982 to 92.1 km2 in 2010). Recharge takes place in the sand dune area, whereas runoff increases in the built-up area. Due to development, runoff almost tripled from 9 million m3 in 1982 to 22.9 million m3 in 2010, while groundwater recharge was reduced from 27.3 million m3 in 1982 to 23 million m3 in 2010, even though the rainfall increased between 1982 and 2010 by 11%. Comparison between the newly developed GIS tool and the Thiessen polygon-based estimation shows that the former leads to higher values of runoff and recharge for dry years.

Assessment of the Soil Conservation Service–Curve Number method performance in a tropical Oxisol watershed

The Soil Conservation Service–Curve Number (SCS-CN) method is a rainfall-runoff model intended to estimate direct surface runoff (DSR) from rainfall. This method is based on an empirical approach of the relationship between rainfall (P) and ground conditions (soils, management, and antecedent moisture content). SCS-CN method can be applied using published tables containing a CN value for each combination of soil hydrology, land use, and management, and the total five-day antecedent rainfall (P₅). However, its accuracy increases as observed rainfall-runoff events are used for calibration. The standard SCS-CN method assigns 0.2 to the initial abstraction coefficient (λ), which refers to the ratio between initial rainfall abstraction (I_a) and the potential maximum retention (S). In this study, λ was evaluated for a tropical watershed with predominance of Oxisols using 15 methodologies for CN characterization, based on CN values published by <i>National Engineering Handbook</i>, Section 4 (NEH-4) and also derived from 166 monitored rainfall-runoff events. Such methodologies include the use of λ equal to 0.2—standard approach, 0.02—average value obtained from the 166 events, and 0.05—a value that has been suggested in some studies. The results demonstrated that a fixed area-weighted CN representing the entire watershed gave poor accuracy even when antecedent runoff condition (ARC) was considered. In contrast, the methodology based on calibration to the observed hydrological events provided good results. Improvements in DSR estimates were found for most of the methodologies applied when λ was set equal to 0.05 and 0.02 instead of 0.2, except for the spatially distributed CN model (Heterogeneity Model), in which the use of λ = 0.2 implied improvement of DSR estimates.

Evolution of runoff and groundwater recharge in the Gaza Strip over the last four decades

Gaza Strip is experiencing a severe water crisis caused mainly by overexploitation of the groundwater source. Rainfall is the only source of freshwater replenishment for the Gaza coastal aquifer. Groundwater level has dropped to more than 10 m below mean sea level as a result of aquifer exploitation and imbalance between recharge and abstraction in the past decades, which exposes the groundwater to seawater intrusion. As the only fresh water source recharging the aquifer is from rainfall, it is essential to estimate the annual recharge volume from rainfall. This paper is focusing on recharge estimation based on historical daily rainfall data for the past 41 years (1973–2014). An estimation of surface runoff was made using soil conservation services curve number method. An estimation of groundwater recharge has been calculated using two different methods: Thornthwaite and Mather soil moisture balance approach and chloride mass balance. Four land use maps and data have been used for the entire calculation period, three of them based on actual surveys carried out in 1994, 2004 and 2010, while the fourth map was developed based on the population expansion trend to cover the period before 1993. It was found that the proportion of built-up area has expanded from 8.25% in 1982 to 25.23% in 2010, while the sand dune area has shrunk from 31.46% in 1982 to 8.64% in 2010. Runoff has doubled from 6.9 million m3 in 1982 to 13.7 million m3 in 2010, while groundwater recharge was reduced from 24.4 million m3 in 1982 to 18.1 million m3 in 2010.

Watershed Delineation and Estimation of Groundwater Recharge for Ras Gharib Region, Egypt

The present work tried to estimate the runoff discharge and groundwater recharge volumes for the catchments of Ras Gharib area using the Soil Conservation Service curve number (SCS-curve number) and the water balance methods. The two methods were selected among other methods used by hydrologists due to simplicity and popularity for application in arid and semi-arid areas like Egypt. The watershed delineation and streamlines for Ras Gharib region have been accomplished using ArcMap 10 GIS and the 1-arc second DEM which demonstrated three basins in the study area. The rainfall data points nearby the study area, extracted from the TRMM data, have been used as input for the Log-Pearson III distribution in order to calculate the design storm for different return periods (100, 50, 25, and 10 years). The results of applying the SCS model estimated the runoff depths as 19.86, 8.00, 2.32, and 0.06 mm for the different return periods, respectively. The total surface runoff volumes reached the study area are 34.78, 14.02, 4.07, and 0.11 Mm3, respectively for the selected return periods, whereas the total groundwater recharge volumes for the selected storm return periods are 58.16, 31.34, 18.14, 3.18 Mm3, respectively.

Forecasting the Urban Expansion Effects on the Design Storm Hydrograph and Sediment Yield Using Artificial Neural Networks

Urban expansion substantially alters the impervious areas in a catchment, which in turn affects surface runoff and sediment yield in the downstream areas. In this study, the Land Transformation Model (LTM) was used to forecast the urban land expansion in a catchment, whilst future land use maps were employed according to the Soil Conservation Service Curve Number method (SCS-CN) and the Modified Universal Soil Loss Equation (MUSLE) model, so as to examine the urbanization effects on runoff and sediment yield production respectively. Compared to pristine conditions, urban land is anticipated to increase from 6% in 1979 to 31% by 2027. The latter expansion pointed to an increase of peak discharge by 2.2–2.6 times and of flood volume by 1.6–2.1 times, with the sediment yield ranging between 0.47 to 1.05 t/ha for the upcoming 2027 period. Furthermore, the urban sprawl effects on all the latter variables were more profound during short duration storm events. Forecasting urban expansion through integrated artificial neural networks (ANN) and geographic information system (GIS) techniques, in order to calculate the associated design storm hydrograph and sediment yield, is of great importance, in order to properly plan and design hydraulic works that can sustain future urban development.

Integrated Stormwater and Groundwater Management in Urban Areas, a Case Study

Urbanization and its further consequences and limitations, especially in arid and semi-arid regions, make societies use practices to return predevelopment conditions. In this study, stormwater runoff is aimed to comprehensively manage as a sustainable resource using the best management practices (BMPs). Potential groundwater recharge from stormwater runoff is subsequently estimated in Isfahan- the third-largest city of Iran, as the study area. To estimate the runoff volume, different watershed’s characteristics were overlaid using the Soil Conservation Service Curve Number (SCS-CN) method in the ArcGIS environment and runoff volume was calculated based on CN classes over the urban area (410 km2) in dry, normal and wet conditions. Then, according to flooding levels in the study area, three of the best management practices (BMPs) were chosen and considered as a source control method to infiltrate the calculated runoff to the zones as their potential recharges. Based on Isfahan Groundwater Model (IGM), the potential recharge volumes determined by BMPs were found to be 2.4, 11.1 and 37.5 million cubic meters per year (MCM/year) for dry, normal and wet years, respectively. These results could be a motivator for decision makers and may also change the attitudes of the managers when the optimal ways for stormwater control are addressed. Thus, it is more reasonable to primarily utilize optimal solutions to control stormwater in especially when water resources are scarce and invaluable.

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

Runoff farming is reported to improve land productivity and crop yields in hot and dry climates. This study was conducted to assess the available rainwater that can be harvested in a conserved catchment in a drought prone agro-ecological zone. The study was carried out in the Cyili sub-catchment, southern province of Rwanda, which has an irregular rainfall pattern and unexploited runoff water. Soil Conservation Service-Curve Number method (SCS-CN), CROPWAT model version 8 and Hazen model with an average daily rainfall data recorded from 1971 to 2016 were applied to estimate the runoff and water requirements in the study area. Findings of the study revealed that more than half rainfall water received in the catchment was lost through runoff (229.8 mm) and effective rainfall was lower (246.9 mm) than the actual crop water requirement for maize (330 mm). The expected seasonal surface runoff volume to be harvested by the farmers was 3008 m3 ha−1 per season and 1.29 × 106 m3 per season for the entire whole sub-catchment (430 ha). Based on Hazen model, the return period of low rainfall (dry spell) event would be expected every 2 years with a 98% probability of occurrence. Cyili sub-catchment has higher potential runoff volume to stabilize the deficit of water demand in the period of short rainy season. This suggests that rainwater harvesting through supplementary irrigation is an option to improve the crop yield in the dry period as well as in the annual dry season.