Curve Number Method Research Articles

Application of Soil Conservation Service Curve Number Method for Runoff Estimation in Sebou Watershed, Morocco

Application of Soil Conservation Service Curve Number Method for Runoff Estimation in Sebou Watershed, Morocco

The State of the Curve-Number Method

The State of the Curve-Number Method

An improved MUSLE model incorporating the estimated runoff and peak discharge predicted sediment yield at the watershed scale on the Chinese Loess Plateau

An accurate prediction of sediment yield at the watershed scale is critical not only for sustainable watershed management but also for improving knowledge regarding the relationship between sediment yield and its determinant factors, which often rely on the varying model complexity. In this study, a modified sediment yield formula based on the modified universal soil loss equation (MUSLE) model was developed by introducing a channel factor into the original formula. The reliability of the proposed method was tested using data from 1,341 storm events in 38 watersheds and was applied to 256 storm events in five application watersheds using the optimized parameters. Results indicated that the proposed method is very accurate, as demonstrated by the Nash–Sutcliffe efficiency (NSE) values of 88.18 %, 85.72 %, and 85.51 % during calibration, validation, and application, respectively. The performance of the proposed model was superior to that of the original MUSLE model. Subsequently, the proposed method was used to predict sediment yield from the last five typical watersheds. This prediction utilized the parameters derived from the initial 38 watersheds; the peak discharge predicted by the modified Chemicals, Runoff, and Erosion from Agricultural Management Systems (CREAMS); and the runoff estimated by the modified Soil Conservation Service Curve Number (SCS-CN) method. Elevated NSE (61.88–81.42 %) and low root mean square error values (2.35–11.40 t ha−1) were calculated for the five watersheds. From the results, the proposed sediment yield model, combined with the modified SCS-CN method and CREAMS, was found to accurately predict the event-based sediment yield, peak discharge, and runoff in the Loess Plateau region under varying hydrological and geomorphic conditions.

Mitigation of Urban Flooding using Blue-Green Infrastructure: A Case of Dehradun City, India

Increasing frequency and intensity of floods caused lives of 6.7 million people between 1920-2020. Rapid urbanization and climate change are the two leading causes of urban flooding. During the past several years, India has seen an increase in the number of urban flooding catastrophes. The city of Dehradun in Uttarakhand, India situated in a valley in the Himalayan foothills, suffers from the contradictory problems of urban flooding due to increase in the builtup area and water supply gaps. The aim of the study is to mitigate urban flooding through blue-green infrastructure development. The study uses Geographic Information System (GIS) and remote sensing to understand Land Use Land Cover (LULC) change in the period 1995-2020 and to estimate the surface run-off generation of this period using the weighted co-efficient and Soil Conservation Service Curve Number (SCS-CN) methods. The result reveals that with the blue-green infrastructure, rainwater harvesting potential can be increased to 82.03 MLD and groundwater recharge would increase to 2.66 MLD. With this, the rainwaer harvesting potential becomes equivalent to 37% of the water supply for the design period of 2031.

Capacity of River Valleys to Retain Nutrients from Surface Runoff in Urban and Rural Areas (Southern Poland)

Studies on water quality are necessary, as catchments of small watercourses are exposed to anthropogenic influences associated with agricultural activities, settlement, transport and other undertakings, leading to water pollution. There has been insufficient research performed on the valley’s ability to retain nutrients during floods, contributing to water accumulation. The main object of the study was to identify the retention capacity of river valleys under various aspects of human urbanization. To represent soil water retention, the Soil Conservation Service Curve Number (SCS-CN) method was used. Spatiotemporal autoregressive models were exploited to investigate the relationship between pollutants in precipitation and surface water in rivers. In contrast, multivariate analysis was used to identify and reveal patterns of land use for specific chemical compounds in the headwaters. The canonical-correlation analysis (CCA) showed that Mg+2 and Ca+2 cations in rainwater and surface waters play the main roles in the geochemical cycle in urban and rural areas. In the urban catchment area, the strongest relations were found for NO3−, K+ and Na+. The average NO3− concentration in urban headwater was 8.3 mg·dm−3, the highest in the study area. The relationship between NO3− concentration in headwater and rainwater was found for all study catchments using spatial autoregression (SAR). High concentrations of SO42− in surface water have been identified in urban areas. Severe water erosion raises the risk of nutrient leaching in soils prone to surface runoff. As a consequence of low soil permeability and urbanization, retention capacity is significantly reduced in areas with low soil permeability. Land development plans should take spatial retention capacity into consideration. To ensure that large reservoirs can retain water in the face of climate change, riparian buffer zones (protective zones in valleys for small water bodies as well as Nature-based Solution) are important.

NRCS Curve Number Method: Comparison of Methods for Estimating the Curve Number from Rainfall-Runoff Data

A data set comprising rainfall-runoff data gathered at 31 Agricultural Research Service experimental watersheds was used to explore curve number calibration. This exploration focused on the calibrated value and goodness-of-fit as a function of several items: calibration approach, precipitation event threshold, data ordering approach, and initial abstraction value. Calibration methods explored were least-squares, the National Engineering Handbook (NEH) median, and an asymptotic approach. Results were quantified for events exceeding two precipitation thresholds: 0 and 25.4 mm. Natural and frequency-matched data ordering methods were analyzed. Initial abstraction ratios of 0.05 and 0.20 were examined. Findings showed that the least-squares calibration approach applied directly to rainfall-runoff data performed best. Initial abstraction ratios clearly influenced the magnitude of the calibrated curve number. However, neither ratio outperformed the other in terms of goodness-of-fit of predicted runoff to observed runoff. Precipitation threshold experiments produced mixed results, with neither threshold level producing a clearly superior model fit. Frequency-matching was not considered to be a valid analysis approach, but was contrasted with naturally ordered results, indicating a bias toward producing calibrated curve numbers that were 5–10 points larger.

Erratum for “Essential Considerations in Applying the Curve-Number Method” by David A. Chin

Erratum for “Essential Considerations in Applying the Curve-Number Method” by David A. Chin

Integrated Surface Water and Groundwater Modeling in Arid Environment, Al-Lusub Watershed, Saudi Arabia

Coupling surface and groundwater for a better understanding of the hydrologic response in arid basins is essential for managing water resources. The study aims to integrate surface water (SW) and groundwater (GW) modeling in an arid environment and to identify groundwater sources at Hadat Ash-Sham farm station in the Al-Lusub watershed, located in the western part of Saudi Arabia. The curve number (CN) method for SW modeling is primarily used to estimate GW potential recharge based on a developed correlation equation between event-based rainfall and potential recharge, which is processed in the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) software. Monthly potential recharge is utilized to compute GW modeling in MODFLOW. Van Mullem’s equation is used to calculate hydraulic conductivity (K) from CN. The calibration of steady-state GW modeling reveals that the K (from Van Mullem’s equation) is within the range of measured K in the field. The transient groundwater modeling phase concludes that the groundwater system in the Al-Lusub watershed can be interpreted based on two different scenarios. The first is that an extra recharge from a nearby watershed flows through the geological structure into a downstream area. The second scenario involves potential recharge from surface water flowing on the Al-Lusub watershed’s mainstream bed. Validation reveals a strong relationship between predicted and observed water tables. The root mean square error (RMSE) for Scenarios 1 and 2 are 0.6 m and 0.7 m, respectively. Further investigation is needed to determine the region’s most common scenario.

An improved method that incorporates the estimated runoff for peak discharge prediction on the Chinese Loess Plateau

An accurate prediction of peak discharge in watersheds is critical not only for water resource management, but also for understanding the complex relationships of hydrological processes. In this study, a modified peak discharge formula based on the Chemicals, Runoff, and Erosion from Agricultural Management Systems (CREAMS) model was developed by introducing rainfall intensity and soil moisture factors. The reliability of the proposed method was tested with data from 1464 storm events in 41 watersheds and was applied to 256 storm events in five remaining typical watersheds using the optimized parameters. The results indicate that the proposed method is highly accurate in terms of model efficiency, as determined by Nash–Sutcliffe efficiencies (NSEs) of 88.60%, 74.04%, and 90.12% during the calibration, validation, and application cases, respectively. Furthermore, it performed better than the original and modified CREAMS methods. Subsequently, using the parameters derived from the initial 41 watersheds and the runoff estimated using the modified Soil Conservation Service curve number (SCS–CN) method, the proposed method was used to predict the peak discharge from the last five typical watersheds. Large NSE (63.88–80.83%) and low root mean square error (RMSE) values (0.31–35.93 m3s-1) were obtained for the five watersheds. Overall, the proposed peak discharge model, combined with the modified SCS-CN method, may accurately predict event-based peak discharge and runoff for general applications under various hydrological and geomorphic conditions in the Loess Plateau region.

Long-Term (1990–2013) Changes and Spatial Variations of Cropland Runoff across China

Quantitative information on regional cropland runoff is important for sustainable agricultural water quantity and quality management. This study combined the Soil Conservation Service Curve Number (SCS-CN) method and geostatistical approaches to quantify long-term (1990–2013) changes and regional spatial variations of cropland runoff in China. Estimated CN values from 17 cropland study sites across China showed reasonable agreement with default values from the National Engineering Handbook (R2 = 0.76, n = 17). Among four commonly used geostatistical interpolation methods, the inverse distance weighting (IDW) method achieved the highest accuracy (R2 = 0.67, n = 209) for prediction of cropland runoff. Using default CN values and the IDW method, estimated national annual cropland runoff volume and runoff depth in 1990–2013 were 253 ± 25 km3 yr−1 and 182 ± 15 mm yr−1, respectively. Estimated cropland runoff depth gradually increased from the drier northwest inland region to the wetter southeast coastal region (range: 2–1375 mm yr−1). Regionally, eastern, central and southern China accounted for 39% of the cultivated area and 53% of the irrigated land area and contributed to 68% of the national cropland runoff volume. In contrast, northwestern, northern, southwestern and northeastern China accounted for 61% of the cultivated area and 47% of the irrigated land area and contributed to 32% of the runoff volume. Rainfall was the main source (72%) of cropland runoff for the entire nation, while irrigation became the main source of cropland runoff in drier regions (northwestern and southwestern China). Over the 24-year study period, estimated cropland runoff depth showed no significant trends, whereas cropland runoff volume and irrigation-contributed percentages decreased by 7% and 35%, respectively, owing to implementation of water-saving irrigation technologies. To reduce excessive runoff and increase water utilization efficiencies, regionally specific water management strategies should be further promoted. As the first long-term national estimate of cropland runoff in China, this study provides a simple framework for estimating regional cropland runoff depth and volume, providing critical information for guiding developments of management practices to mitigate agricultural nonpoint source pollution, soil erosion and water scarcity.

Analysis of Flood Discharge due to Impact of Tropical Cyclone

Tropical Cyclone Seroja, which occurred between April 2 to 6, 2021, is one of the strongest storms ever in East Nusa Tenggara. The track results of the cyclone showed that Seroja, formed at coordinates 10.5° S and 123° E, moved towards west longitude to Sumba Island and continued towards Australia. Moreover, the Global Precipitation Measurement (GPM) output was used to analyze the rainfall conditions at the center of the Seroja cyclone through the Kambaniru watershed in East Sumba, and the results showed that the precipitation continued to increase during Seroja's development to reach 225 mm. Therefore, this study aimed to analyze the effect of the rainfall during the storm on the maximum runoff experienced in the Kambaniru watershed through the application of quantitative analysis on the rainfall data from GPM data. The process involved analyzing the flood discharge using the HSS-SCS Curve Number method and GPM data, which were initially used to evaluate the rainfall during the TC Seroja due to limited field data. The results showed that the CN value in the Kambaniru watershed was in the AMC III condition with a curve number of 88.90 and the maximum flood during the Seroja storm was recorded to be 2,987 m3/s which is higher than the flood discharge for the 500 year return period. It was also discovered that the narrowing of the river channel on the Kambaniru Bridge section contributed to the collapse of the bridge. Doi: 10.28991/CEJ-2022-08-09-01 Full Text: PDF

Estimation of Rainwater Harvesting Potential in an Educational Institute of Faisalabad, Pakistan

Pakistan is on verge of facing severe water scarcity issues by 2025. An exponential increase in population increases the water demand as most of the population in the country depends on groundwater. Over extraction of groundwater is causing depletion in the groundwater table at an alarming rate. Water conservation is becoming the need of time. Rainwater Harvesting is one the best methods that can be used to conserve water. This paper aims to determine the total volume of water that can be conserved from Rainfall on the Rooftop at the University of Faisalabad, Amin Campus located in an industrial city Faisalabad, Pakistan. For this purpose, water usage on the campus is estimated. The area of the campus rooftop is measured by conducting a topographic survey. Rainfall data from the year 1981-2021 is collected and average monthly rainfall is calculated. The soil Conservation Service Curve Number method is adopted to calculate the run-off. Results indicated that about 1300 cubic meters of water can be conserved per year which will cover about 20-25 percent of the total water demand of the campus if a Rainwater Harvesting System is installed at the campus.Keyword: Rainwater Harvesting, Water Conservation, Water Scarcity

Minimum Rainfall for Applying the Curve-Number Method

Minimum Rainfall for Applying the Curve-Number Method

A Comparative Evaluation of Using Rain Gauge and NEXRAD Radar-Estimated Rainfall Data for Simulating Streamflow

Ascertaining the spatiotemporal accuracy of precipitation is a challenge for hydrologists and planners for flood protection measures. The objective of this study was to compare streamflow simulations using rain gauge and radar data from a watershed in Southern Ontario, Canada, using the Hydrologic Engineering Center’s event-based distributed Hydrologic Modeling System (HEC-HMS). The model was run using the curve number (CN) and the Green and Ampt infiltration methods. The results show that the streamflow simulated with rain gauge data compared better with the observed streamflow than the streamflow simulated using radar data. However, when the Mean Field Bias (MFB) corrections were applied, the quality of the streamflow results obtained from radar rainfall data improved. The results showed no significant difference between the simulated streamflow using the SCS and the Green and Ampt infiltration approach. However, the SCS method is reasonably more appropriate for modeling the runoff at the sub-basin-scale than the Green and Ampt infiltration approach. With the SCS method, the simulated and observed runoff amount obtained using rain gauge rainfall showed an R2 value of 0.88 and 0.78 for MFB-corrected radar and 0.75 for radar only. For the Green and Ampt modeling option, the R2 value for the simulated and observed runoff amounts were 0.87 with rain gauge, 0.66 with radar only, and 0.68 with MFB-corrected radar rainfall inputs. The NSE values for rain gauge input ranged from 0.65 to 0.35. Overall, three values were less than 0.5 for streamflow for both the methods. For seven radar rainfall events, the NSE was greater than 0.5, with a range of very good to satisfactory. The analysis of RSR showed a very good comparison of stream flow using the SCS curve number method and Green and Ampt method using different rainfall inputs. Only one value, the 2 November 2003 event, was above 0.7 for rain gauge-based streamflow. The other RSR values were in the range of “very good”. Overall, the study showed better results for the simulated runoff with the MFB-corrected radar rainfall when compared with the simulations obtained using radar rainfall only. Therefore, MFB-corrected radar could be explored as a substitute rainfall source.

A modified NRCS-CN method for eliminating abrupt runoff changes induced by the categorical antecedent moisture conditions

The popular Natural Resources Conservation Service Curve Number (NRCS-CN) (earlier known as Soil Conservation Service Curve Number (SCS-CN) method of rainfall-runoff modeling has often faced the criticism of exhibiting quantum jumps in runoff computations because of the sudden jumps appearing in CN-values derived from NEH-4 tables for three antecedent moisture conditions (AMC), viz., AMC-I, AMC-II, and AMC-III valid for dry, normal, and wet conditions, respectively. The variability of antecedent soil moisture within an AMC category is responsible for the abrupt jump and other deficiencies in the CN method for runoff estimation. This paper suggests a novel procedure to account for the antecedent moisture (M), preventing quantum jumps and eliminating deficiencies in determination of CN and, in turn, estimation of direct runoff. Its validity was verified utilizing the observed rainfall (P)-runoff (Q) events from 36 US watersheds, four sub-catchments of the Godavari basin, and small agricultural plots at Roorkee, India. The performance of the proposed model (M5) for runoff prediction was compared with the existing NRCS-CN (M1), Mishra and Singh (2002) (M2), Singh et al. (2015) (M3), and Verma et al. (2021) (M4) model using various performance indices. Using the CNs derived from observed events, model M5 was seen to have performed better than M1-M4 in terms of Nash Sutcliffe Efficiency (NSE), Root Mean Square Error (RMSE), and Percent Bias (PBIAS) for the data of US watersheds, and CN-P correlation improved as the coefficient of determination (R2) enhanced. Similarly, using the RS & GIS-based CNs on natural watersheds of the Godavari basin and considering AMC-I, the performance of M5 was again better than M1-M4 in terms of RMSE, Mean Bias Error (mBIAS), Mean Absolute Error (MAE), and Normalized-Nash Sutcliffe Efficiency (NNSE). Interestingly, there existed a significant (p < 0.05) relationship between the in-situ water content (w) measured for the experimental plots of Roorkee and the model input variable antecedent moisture (M), offering a physical touch to the conceptual model.

Updates to the Annual P Loss Estimator (APLE) model.

The Annual P Loss Estimator (APLE) is a spreadsheet-based model developed for predicting annual field-scale P loss in surface runoff and changes in soil test P. This empirically based model was designed for use by those without significant modeling experience. However, a significant limitation with the model is that it does not calculate runoff. Moreover, APLE is deterministic and thus predicts a single value for a given set of inputs, thereby ignoring any uncertainties associated with model inputs. Here, we describe modifications to APLE that allow users to estimate runoff using the Curve Number method. Using Monte Carlo simulations, the updated version of APLE also provides users the ability to account for model input uncertainties in estimating model prediction errors. We provide examples of using the revised version of APLE (ver. 3.0) for calculating P loss from two fields in Mississippi over a 4-yr period and calculating the change in Mehlich-3 P concentrations over a 9-yr period at three locations in Maryland following cessation of P application. Both examples demonstrate that incorporating estimates of uncertainties in both measured data and model predictions provides modelers with a more realistic understanding of the model's performance.

A New Scenario-Based Approach for Water Quality and Environmental Impact Assessment Due to Mining Activities

Water quality assessment and its monitoring are necessary for areas of mining activities. In Malaysia, the mining industry is the backbone of the manufacturing and construction sectors. This study used spatio-temporal water quality modeling along a reach with mining activities during high and low discharges at Sungai (river) Lebir and Sungai Aring, situated in Gua Musang, Kelantan, Peninsular Malaysia. The objective was to assess the spatio-temporal environmental impact of mining activities during the wet and dry seasons. Data were collected at different locations along the reach. Point and non-point sources were near the mining site. Overland flow calculation at the mining site was found with the widely used SCS (Soil Conservation Service) curve number method. Several scenarios were analyzed, such as baseline, worst-case, and with-mitigation. The study revealed that baseline values of all parameters were either in a natural condition or slightly polluted, except for aluminum. All parameters were estimated at a high concentration from the mining site to downstream during the worst case of the wet season. Whereas, during the worst case of the dry season, no significant differences were observed compared to baseline values. In the with-mitigation scenario, parameter concentrations were improved and similar to baseline values. Overall, the scenario selection was helpful in the environmental impact assessment. Furthermore, this study will be significant in pre- and post-mining assessment and environmental clearance.

Determination of accurate baseline representation for three Central Iowa watersheds within a HAWQS-based SWAT analyses

Improving food systems to address food insecurity and minimize environmental impacts is still a challenge in the 21st century. Ecohydrological models are a key tool for accurate system representation and impact measurement. We used a multi-phase testing approach to represent baseline hydrologic conditions across three agricultural basins that drain parts of north central and central Iowa, U.S.: the Des Moines River Basin (DMRB), the South Skunk River Basin (SSRB), and the North Skunk River Basin (NSRB). The Soil and Water Assessment Tool (SWAT) ecohydrological model was applied using a framework consisting of the Hydrologic and Water Quality System (HAWQS) online platform, 40 streamflow gauges, the alternative runoff curve number method, additional tile drainage and fertilizer application. In addition, ten SWAT baselines were created to analyze both the HAWQS parameters (baseline 1) and nine alternative baseline configurations (considering the framework). Most of the models achieved acceptable statistical replication of measured (close to the outlet) streamflows, with Nash-Sutcliffe (NS) values ranging up to 0.80 for baseline 9 in the DMRB and SSRB, and 0.78 for baseline 7 in the NSRB. However, water balance and other hydrologic indicators revealed that careful selection of management data and other inputs are essential for obtaining the most accurate representation of baseline conditions for the simulated stream systems. Using cumulative distribution curves as a criterion, baselines 7 to 10 showed the best fit for the SSRB and NSRB, but none of the baselines accurately represented 20% of low flows for the DMRB. Analysis of snowmelt and growing season periods showed that baselines 3 and 4 resulted in poor simulations across all three basins using four common statistical measures (NS, KGE, Pbias, and R2), and that baseline 9 was characterized by the most satisfactory statistical results, followed by baselines 5, 7 and 1.

Rainfall-runoff models compared for tile-drained agricultural fields in the Western Lake Erie Basin, Ohio

Simple models like the curve number method are commonly used to predict runoff volumes from agricultural fields, playing a key role in nutrient transport modeling and watershed management; however, the curve number method has not been evaluated for use in tile-drained fields and it may therefore produce erroneous runoff predictions if applied in these settings. In this study, we evaluate the curve number method at 12 tile-drained research sites in the Western Lake Erie Basin of Ohio. Rainfall and runoff observations at each of these sites were used to calculate curve numbers using six published variations of the curve number method. These were compared to published curve numbers, selected from NRCS tables to correspond to land use in the study sites. In addition to the curve number methods, the complacent-violent method was also used to develop runoff model parameters for the research sites. Methods were compared to one another using Nash-Sutcliffe efficiency, bias, and R-squared. The curve number methods often performed poorly, and sometimes altogether failed to produce a real solution. Of the rainfall-runoff models evaluated, the complacent-violent method produced the most accurate results and should be used in place of the curve number method to make runoff predictions from tile-drained fields.

GIS- and ICPR-Based Approach to Sustainable Urban Drainage Practices: Case Study of a Development Site in Florida

Stormwater control is an urgent concern in cities where the increased impervious surface has disrupted natural hydrology, particularly causing a reduction in groundwater recharge which is the source of potable water supply for many communities. Water managers are increasingly turning towards infiltration-based stormwater management options (ISMOs) to help minimize flooding and mitigate the impact of urbanization on the local hydrologic systems. This paper offers a unique hydrologic and hydraulic (H&amp;H) modeling approach using the Geographic Information System (GIS) and Interconnected Pond and Channel Routing (ICPR) software to help quantify the associated flood stage and groundwater recharge benefits of using ISMOs. The proposed approach incorporated ICPR percolation links and utilization of the curve number and Green-Ampt infiltration methods into the case study design, as well as an analysis of the effectiveness of including low-impact development practices. This analysis shows a 13–36% reduction in stormwater volume leaving the proposed site when percolation links were utilized to account for percolation from the proposed ISMOs. These reduction provides an indirect estimate of groundwater recharge benefits. The conversion from impervious parking to a pervious one and inclusion of rainwater harvesting from the roof area resulted in a further reduction in peak stages ranging from 1.20 to 7.62 cm.