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BY 4.0 license Open Access Published by De Gruyter Open Access March 29, 2022

Curve number estimation using rainfall and runoff data from five catchments in Sudan

Salma Ibrahim, Babikir Brasi, Qingchun Yu and Magdi Siddig
From the journal Open Geosciences

Abstract

The United States Natural Resources Conservation Services Curve Number (NRCS-CN) method uses the CN and rainfall to calculate runoff. However, there are still some uncertainties in the method, such as choosing the most appropriate CN value. Therefore, this study attempts to evaluate the effectiveness of using the NRCS-CN method to estimate the runoff of five catchments in Sudan. For each catchment, CN values were obtained from the number of observed rainfall-runoff events using the NRCS table, arithmetic mean, median, and geometric mean methods. For each method, Nash–Sutcliffe efficiency (NSE) was obtained to evaluate the fit between the observed and runoff, and negative NSE values were found for all methods. Negative values of NSE indicate that the observed runoff and estimated runoff are not well fitted, and the NRCS-CN method is not suitable for runoff calculation in the study areas.

1 Introduction

Runoff estimation from rainfall is of great significance in flood control research. In addition, direct runoff estimates are important for power generation, agriculture, and irrigation in a lot of countries [1]. There are various ways to estimate runoff. The Soil Conservation Service Curve Number (SCS-CN) method is the most commonly used method for estimating runoff, and it was renamed as the Natural Resources Conservation Services Act (NRCS) in 1994 [2]. CN tables were developed by the United States Department of Agriculture. CN usually ranges between 30 and 10 [3]. Low runoff is represented by minimum values of CN, while CN values approaching 100 represent high runoff. The United States Department of Agriculture developed this method; it relies on land use and land cover types of the American agricultural catchments. The characteristics of land use and land cover in Sudan are likely to be different from those in the United States. Land use, soil type, antecedent water conditions, treatment, and surface conditions are the main factors affecting runoff generation. The disadvantages of the NRCS-CN method include a single parameter containing a variety of factors (such as land use, soil type, antecedent water condition, and surface conditions) rendering the method very delicate, the influence of spatial scale, intensity, and temporal distribution of rainfall and the effect of adjacent moisture condition are not considered [2]. In general, the Sudan curve numbers are obtained from the NRCS-CN tables, and the curve numbers in these tables may not be applicable to Sudan, as the NRCS-CN tables depend on the land features of the United States. In this article, the authors attempt to evaluate the use of the NRCS-CN method to estimate runoff in five catchments in Sudan. Using NRCS-CN tables, CN values were estimated in a Geographic Information System (GIS) [4]. Shuttle Radar Topography Mission 30m resolution (SRTM30) is the Digital Elevation Model (DEM) used in this research [5]. Rainfall-runoff events were used to calculate the CN using three statistical methods (arithmetic mean, median, and geometric mean).

2 Methods

2.1 Study area

The study areas are distributed in different parts of Sudan. The first study area is the Abufargha catchment in the eastern region of Sudan in Al Gadarif State (Figure 1a). Abufargha catchment has a drainage area of 92.4 km2 and its outlet coordinates are 14°02′ N and 35°22′ E. The second study area is the Eldilling catchment in the southern region of Sudan in South Kordofan State (Figure 1b). The Eldilling catchment has a drainage area of 484.84 km2 and its outlet coordinates are 13°11′ N and 29°59′ E. The third study area is the Erigi catchment in the western region of Sudan in North Darfor State (Figure 1c). The Erigi catchment has a drainage area of 73.3 km2 and its outlet coordinates are 14°00′ N and 24°31′ E. The fourth study area is the Tiflo catchment in the western region of Sudan in North Darfor State (Figure 1d). The Tiflo catchment has a drainage area of 329.87 km2 and its outlet coordinates are 14°24′ N and 23°32′ E. The last one is the Abasya catchment in the southern region of Sudan in South Kordofan State (Figure 1e).” The Abasya catchment has a drainage area of 289.23 km2 and its outlet coordinates are 12°10′ N and 31°19′ E. The DEM is a fundamental dataset for the simulation of the stream network; it is useful for the delineation of the watershed and the illustration of the topographic variations in the area. In this study, the DEM with 30 m horizontal spatial resolution was used, mosaicked, and processed.

Figure 1 
                  (a) Location of Abufargha catchment, (b) location of Eldilling catchment, (c) location of Erigi catchment, (d) location of Tiflo catchment, and (e) location of Abasya catchment.
Figure 1 
                  (a) Location of Abufargha catchment, (b) location of Eldilling catchment, (c) location of Erigi catchment, (d) location of Tiflo catchment, and (e) location of Abasya catchment.

Figure 1

(a) Location of Abufargha catchment, (b) location of Eldilling catchment, (c) location of Erigi catchment, (d) location of Tiflo catchment, and (e) location of Abasya catchment.

3 Data sets

Daily rainfall-runoff events from five catchments were obtained from Sudan Meteorological Authority and Ministry of Water Resources, Irrigation, and Electricity. Each of the catchment’s daily events were used to estimate the CN. Measuring stations at each catchment outlet were used to measure runoff. Daily rainfall and runoff were measured in millimeter (mm) units. Thiessen Polygon method was used to represent the average rainfall over study areas. In this study, events having runoff coefficients greater than 1% were used. The number of events for Abufargha, Eldilling, Erigi, Tiflo, and Abasya catchments is 24, 28, 36, 17, and 18, respectively. The period of daily events for the Abufargha catchment is from 1981 to 2008, from 1974 to 1986 for Eldilling catchment, from 1966 to 1975 for Erigi catchment, from 1965 to 1973 for Tiflo catchment, and from 1968 to 1973 for the Abasya catchment.

4 Curve number estimation

4.1 CN estimation using NRCS-CN tables

To estimate CN using NRCS-CN tables, Arcmap 10.5 was used. The common estimation method of NRCS-CN by using Arcmap 10.5 is as follows:

  1. Selection and drawing of the outlines of the catchments.

  2. Determination of the catchment areas.

  3. Mapping of the soil types and land use for catchment areas.

  4. Changing of the soil types to hydrologic soil groups.

  5. Overlaying of the land use and hydrologic soil group maps to identify each unique land use, soil group polygon, and to compute the area of each polygon.

  6. Allocation of a CN to each singular polygon based on the NRCS-CN tables [6].

The total CN of the catchment is calculated by area weighting of the polygons of the land use-soil group in the catchment.

5 NRCS-CN estimation from daily rainfall and runoff data

The formula for estimating runoff depth using NRCS-CN is as follows:

(1) Q = ( P 0.2 S ) 2 P + 0.8 S , P > 0.2 S ,

(2) Q = 0 , P < 0.2 S ,

where P is the depth of rainfall (mm), Q is the depth of runoff (mm), and S is the potential maximum storage [7]. The general equation of NRCS-CN is given by:

(3) S = P λ + ( 1 λ ) Q ( 1 λ ) 2 Q 2 + 4 λ P Q 2 λ 2 ,

where P is the depth of rainfall (mm), Q is the depth of runoff (mm), λ is the initial abstraction (Ia) coefficient, and S is potential maximum storage [2]. By knowing the rainfall and runoff data in the catchments, CN is estimated by applying equations (3) and (4). Considering λ to be equal to 0.2, equation (3) will be [8]:

(4) S = 5 [ P + 2 Q ( 4 Q 2 + 5 P Q ) 1 2 ] .

CN is estimated from S:

(5) CN = 100 254 S + 254 .

Arithmetic mean [9], the median, and geometric mean [10] were used to obtain CN values from rainfall-runoff data.

The depths of rainfall and runoff are substituted into equation (4) to obtain the values of the computed and measured S. When S is substituted into equation (5), the computed and measured CN can be obtained. Equation (6) was used to obtain the CN using the geometric mean method.

(6) CN = 100 1 + 10 log S ¯ 254 .

5.1 Statistical analyses

The coefficient of determination (R 2), the NSE, and percentage mean bias (PBIAS) were utilized for the statistical analyses. R 2, NSE, and PBIAS were computed using equations (7)–(9) [11].

(7) R 2 = i = 1 n ( Q obs Q ¯ obs ) ( Q est Q ¯ est ) i = 1 n ( Q obs Q ¯ obs ) 2 × i = 1 n ( Q est Q ¯ est ) 2 0.5 2 ,

(8) NSE = i = 1 n ( Q est Q obs ) 2 i = 1 n ( Q obs Q ¯ obs ) 2 ,

(9) PBIAS = i = 1 n ( Q obs Q est ) × X 100 i = 1 n Q obs ,

where Q obs is the value of the observed runoff, Q ¯ obs is the average of the observed runoff, Q est is the value of the estimated runoff, and Q ¯ est is the average value of the estimated runoff.

If R 2 is greater than 0.5, it is considered “acceptable.” NSE values range from −∞ to 1, and can be considered as “very good” if 0.75 < NSE ≤1; “good,” if 0.65 < NSE ≤75; “satisfactory,” if 0.50 <NSE ≤0.65; and “unsatisfactory,” if NSE ≤0.50. The PBIAS positive and negative values indicate model underestimation bias. The PBIAS can be considered “very good” if PBIAS < ±10%; “good,” if 10% ≤ PBIAS < ±15%; “satisfactory,” if 15% ≤ PBIAS < ±25%; and “unsatisfactory,” if PBIAS ≥ ±25% [11].

6 Results and discussion

In order to generate the CN based on NRCS-CN tables, soil maps, land use maps, and hydrological soil maps were prepared. The results are as described below:

6.1 Hydrological soil map

For each catchment, the generated hydrologic soil group was used as the input of the CN in ArcMap 10.5. It is a feature of the soil mapping unit and each soil mapping unit is allocated to a particular hydrological group: A, B, C, and D [10]. The watershed has different soil groups according to which the soil code has been assigned. Clay loam was taken as hydrologic group D, sandy clay loam as soil group C, loam as soil group B, and sandy loam as hydrologic group A. The hydrologic groups A, B, C, and D formed in Arcmap 10.5 are shown in Table 1 and Figure 2(a)–(e). Due to different infiltration rates, each soil group has different runoff. The soil in group A has the lowest runoff potential, while the soil in group D has the highest runoff potential [12].

Table 1

Soil groups of catchments

Catchment Hydro group Area (%)
Abufargha C 100
Eldilling A 22.4
C 0.76
D 76.9
Erigi A 97.7
B 2.3
Tiflo B 100
Abasya C 4.6
D 95.6
Figure 2 
                  (a) Soil group of Abufargha catchment, (b) soil group of Eldilling catchment, (c) soil group of Erigi catchment, (d) soil group of Tiflo catchment, and (e) soil group of Abasya catchment.

Figure 2

(a) Soil group of Abufargha catchment, (b) soil group of Eldilling catchment, (c) soil group of Erigi catchment, (d) soil group of Tiflo catchment, and (e) soil group of Abasya catchment.

6.2 Land cover maps

In the present study, the supervised classification was carried out in ArcMap 10.5. Two land use land cover grades such as residential and agricultural exist [4]. The land use land cover maps are shown in Table 2 and Figure 3(a)–(e).

Table 2

Land use of catchments

Catchment Land use Area (%) Land use reclassification
Abufargha Croplands 86.9 Agricultural
Cities 13.10 Medium residential
Eldilling Open deciduous Shrubland 4.70 Agricultural
Closed grassland 0.32 Agricultural
Cropland 1.67 Agricultural
Crop with opened woody vegetation 93.31 Agricultural
Erigi Open grassland with sparse shrubs 15.90 Agricultural
Cropland 84.10 Agricultural
Tiflo Open grassland 25.40 Agricultural
Spare grassland 74.64 Agricultural
Abasya Open deciduous Shrubland 0.22 Agricultural
Cropland 0.31 Agricultural
Crop with opened woody vegetation 99.47 Agricultural
Figure 3 
                  (a) Land use types of Abufargha catchment, (b) land use types of Eldilling catchment, (c) land use types of Erigi catchment, (d) land use types of Tiflo catchment, and (e) land use types of Abasya catchment.

Figure 3

(a) Land use types of Abufargha catchment, (b) land use types of Eldilling catchment, (c) land use types of Erigi catchment, (d) land use types of Tiflo catchment, and (e) land use types of Abasya catchment.

6.3 Generating CN maps

The land use map, soil map, and the DEM as inputs are first imported into ArcMap 10.5. The HEC-GeoHMS extension tool of Arcmap 10.5 was used to generate the CN grid [12]. The appropriate code is given to the soil type. Soil code provides the hydrologic soil group in the area. Then, the land use and hydrologic soil group are combined to form a new merged soil and land use map. The CN look up table (Table 3) was created and the appropriate CN value was assigned for each soil land map [13]. The CN values for each catchment using the NRCS-CN tables are shown in Table 3 and Figure 4(a)–(e).

Table 3

CN lookup table

Land use A B C D
Medium residential 57 72 81 86
Agricultural 66 77 78 87
Figure 4 
                  CN grid based on NRCS-CN tables, soil maps, and land use maps of (a) Abufargha catchment, (b) Eldilling catchment, (c) Erigi catchment, (d) Tiflo catchment, and (e) Abasya catchment.

Figure 4

CN grid based on NRCS-CN tables, soil maps, and land use maps of (a) Abufargha catchment, (b) Eldilling catchment, (c) Erigi catchment, (d) Tiflo catchment, and (e) Abasya catchment.

6.4 CN estimation using rainfall and runoff data

For each catchment, rainfall-runoff events were analyzed and the CN values, R 2, NSE, and PBIAS were estimated for five catchments as shown in Table 4. CN values for the Abufargha, Eldilling, Erigi, Tiflo, and Abasya catchments range from 82.94 (NRCS-CN table) to 90.96 (geometric mean), from 82.43 (NRCS-CN table) to 87.19 (median), from 67.13 (NRCS-CN table) to 93.11 (geometric mean), from 77 (NRCS-CN table) to 94.63 (median), and 86.597 (NRCS-CN table) to 98.71 (geometric mean), respectively. R 2 for Abufargha, Eldilling, Erigi, Tiflo, and Abasya catchments are 0.575–0.621, 0.509–0.5533, 0.239–0.625, 0.061–0.746, and 0.5368–0.632, respectively. NSE values for Abufargha, Eldilling, Erigi, Tiflo, and Abasya catchments are −2.941 to −20.441, −21.655 to −38.1557, −2.365 to −6.874, −161.937 to −1846.635, and −522.935 to −2763.9, respectively. The PBIAS for Abufargha, Eldilling, Erigi, Tiflo, and Abasya catchments are 217.565–400.011, 459.040–8599.886, 281.614–1614.411, 1194.876–1903.735, and 1149.784–2660.558, respectively.

Table 4

Estimated curve numbers and statistical analyses

Catchment Method CN R 2 NSE PBIAS (%)
Abufargha NRCS table 82.94 0.575 −20.441 217.565
Arithmetic mean 87.88 0.619 −3.441 303.380
Median 87.54 0.621 −2.941 294.978
Geometric mean 90.96 0.598 −10.977 400.011
Eldilling NRCS table 82.43 0.509 −21.655 459.040
Arithmetic mean 83.12 0.5197 −24.543 85.999
Median 87.19 0.5533 −38.1557 583.051
Geometric mean 87.18 0.5533 −38.1152 582.755
Erigi NRCS table 67.13 0.239 −2.958 281.614
Arithmetic mean 90.74 0.625 −2.989 295.482
Median 91.19 0.624 −2.365 281.3296
Geometric mean 93.11 0.619 −6.874 1,614.411
Tiflo NRCS table 77 0.061 −161.937 1,194.876
Arithmetic mean 90.50 0.689 −883.373 1,358.468
Median 94.63 0.746 −1,846.635 1,903.735
Geometric mean 93.90 0.736 −1,604.722 1,767.090
Abasya NRCS table 86.597 0.5368 −522.935 1,149.784
Arithmetic mean 90.06 0.565 −801.583 1,284.274
Median 95.43 0.603 −1,698.02 1,823.094
Geometric mean 98.71 0.632 −2,763.9 2,660.558

It is noted that NSE values of the five catchments are all negative. These negative values indicate the difference between the observed and estimated runoff (poor fit). Previous studies have shown that SCS-CN has poor performance in areas with high infiltration rates, such as areas with predominant sand soils [14]. The literature review has also revealed that λ the initial abstraction (Ia) coefficient may not be equal to 0.2 [7], which would affect the fit between the observed and estimated runoff. All the values of PBIAS were positive, indicating that runoff was underestimated [15,16,17,18]. Except for the NRCS table method for the Erigi and Tiflo catchments, all methods have R 2 value greater than 0.5. This result shows the significance of prospective studies on the use of the CN method in the Sudanese catchments, such as the one developed in this study [19,20,21,22,23,24,25]. The author recommends measuring the initial abstraction (Ia) to understand the actual value of initial abstraction coefficient (λ) to know the actual value of λ. The initial abstraction coefficient (λ) will affect the fit between the observed and estimated runoff, because some previous studies have shown that λ may not be equal to 0.2 [26,27,28,29,30,31].

7 Conclusion

There are still some uncertainties in the (NRCS-CN) method, such as choosing the most appropriate CN value. This study aimed to evaluate using the NRCS-CN method to estimate the runoff of five catchments in Sudan. The first study area is the Abufargha catchment in the eastern region of Sudan. The second study area is the Eldilling catchment in the southern region of Sudan, the third study area is the Erigi catchment in the western region of Sudan, the fourth study area is the Tiflo catchment in the western region of Sudan, and the last one is the Abasya catchment in the southern region of Sudan. For each catchment, CN values were obtained from the number of observed rainfall-runoff events using the NRCS table, arithmetic mean, median, and geometric mean methods. The R 2, the NSE, and PBIAS were utilized for the statistical analysis. R 2, NSE, and PBIAS were computed for five catchments. NSE values of the five catchments are all negative. These negative values indicate the difference between the observed and estimated runoff and the NRCS-CN method is not suitable for runoff calculation in the study areas.

Acknowledgements

This study was supported by School of Water Resources and Environment, China university of Geosciences (Beijing). The authors are very grateful to Suzan Mustafa (Ministry of Water Resources, Irrigation, and Electricity-Khartoum, Sudan) for her support and guidance during the study.

  1. Author contributions: Salma Ibrahim and Babikir Barsi conceived the presented idea. Salma Ibrahim and Magdi Siddig collected the data. Salma Ibrahim developed the theory and performed the computations. Salma Ibrahim, Babikir Barsi, and Qingchun Yu verified the analytical methods. Salma Ibrahim wrote the manuscript with support from Babikir Barsi, Qingchun Yu, and Magdi Siddig. All authors have agreed to the final manuscript.

  2. Conflict of interest: Authors state no conflict of interest.

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Received: 2021-03-21
Revised: 2022-01-13
Accepted: 2022-02-21
Published Online: 2022-03-29

© 2022 Salma Ibrahim et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.