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The Natural Resources Conservation Service Curve Number (NRCS-CN) method was developed in agricultural basins with slopes less than or equal to 5%. This research investigates the relationship between the basin slope and runoff depth in arid basins by the NRCS-CN method. The results show that there is a clear effect of the proposed curve number equation with slope (the slope adjusted CN, CNIIα) on the runoff depth in the studied basins, and this is consistent with the literature. However, large basin areas with a high percentage of alluvium area and vegetation, and high infiltration rates, counteract the slope of the basin, and consequently the basin slope will not have a significant effect on runoff. The correlation coefficient of various developed equations varies between 0.89 and 0.93 and the root mean square error of runoff depth varies between 11 mm for λ = 0.2 and 5.2 mm for λ = 0.01. [ABSTRACT FROM AUTHOR]

Of the numerous methods applied in rainfall-runoff models, the most common is the NRCS-CN method that is applied to calculate raised-water runoffs and compare them with the runoff values measured for 12 selected rainfall-runoff events. This study was conducted on three experimental forest catchments with an area ranging from 67.6 to 747 ha. Total rainfall values ranging from 22.2 to 84.1 mm were analysed. Relatively low effective rainfall values were obtained for the lowest average for catchment 1 (Pe = 0.23 mm) and the runoff coefficient (α = 0.40%) and for the highest average for catchment 3 (Pe = 1.35 mm) and an average runoff coefficient (α = 3.12%). The maximum potential retention Si value, corresponding to each pair of P-Pe events, was the effect of the catchment's moisture and absorptive capacity conditions. The lowest retention S value was calculated for catchment 3. The highest average retention value was calculated for catchment 1, in which the lightest soils were found. The best fit of the initial loss coefficient for the majority of rainfall-runoff events occurred for the λ coefficient values of 0.05 and 0.075. At higher λ, the effective rainfall Pe was not generated. LAG times calculated using 10 methods yielded diverse values. The fit of a specific formula was largely influenced by the size of the catchment, as well as the number and type of parameters considered during model calibration. The method based on catchment width demonstrated the best fit for all catchments, with R2 ranging from 0.77 to 0.78 and RMSE from 0.52 for catchment 2 to 1.11 for catchment 1. [ABSTRACT FROM AUTHOR]

Soil erosion is the major environmental risk that causes topsoil loss, which decreases fertility in agricultural land. Spatial estimation of soil erosion is essential for conserving land resources and for developing control plans. This study evaluated soil erosion in 17 sub-basins of the Wadi Hanifa catchment using the Gavrilović model. The soil erosion rate is categorized into three classes based on the annual volume of rainfall and runoff. About 21% of the drainage area of Wadi Hanifa is affected by erosion severity, and 33% of the area is affected by severe erosion. The correlation obtained between the annual soil erosion and annual volumes of rainfall and runoff is significant. It is concluded from the results that the drainage area of the north-western part suffers from a high soil erosion risk. The NRCS-CN and erosion predict that the Gavrilović model can be applied for estimating and analyzing the spatial distribution of soil erosion over more drainage watersheds in Saudi Arabia. Now, the GIS techniques are one of the best tools for developing the conservation and management planning processes of soil resources.

Of the numerous methods applied in rainfall-runoff models, the most common is the NRCS-CN method that is applied to calculate raised-water runoffs and compare them with the runoff values measured for 12 selected rainfall-runoff events. This study was conducted on three experimental forest catchments with an area ranging from 67.6 to 747 ha. Total rainfall values ranging from 22.2 to 84.1 mm were analysed. Relatively low effective rainfall values were obtained for the lowest average for catchment 1 (Pe = 0.23 mm) and the runoff coefficient (α = 0.40%) and for the highest average for catchment 3 (Pe = 1.35 mm) and an average runoff coefficient (α = 3.12%). The maximum potential retention Si value, corresponding to each pair of P-Pe events, was the effect of the catchment’s moisture and absorptive capacity conditions. The lowest retention S value was calculated for catchment 3. The highest average retention value was calculated for catchment 1, in which the lightest soils were found. The best fit of the initial loss coefficient for the majority of rainfall-runoff events occurred for the λ coefficient values of 0.05 and 0.075. At higher λ, the effective rainfall Pe was not generated. LAG times calculated using 10 methods yielded diverse values. The fit of a specific formula was largely influenced by the size of the catchment, as well as the number and type of parameters considered during model calibration. The method based on catchment width demonstrated the best fit for all catchments, with R2 ranging from 0.77 to 0.78 and RMSE from 0.52 for catchment 2 to 1.11 for catchment 1.

The present study is aimed to perform flood susceptibility mapping in the rapidly developing Gurugram district of India. Major flood-triggering factors, namely, slope, elevation, land use land cover, precipitation, drainage density, and soil texture, were selected, and their relative weights were assessed using the Analytical Hierarchy Process method through an expert survey. The highest weightage was attributed to precipitation (34%), while the least (7%) was assigned to elevation and soil texture each. Flood hazard maps were developed using overlay analysis in the Geographic Information System (GIS) environment to provide a quantitative assessment of flood vulnerability covering the time periods of 1985–1994, 1995–2004, 2005–2014, 2015–2019, and 2020–2021. A surface runoff estimation was done using the GIS-aided Natural Resource Conservation Service-Curve Number method for the past thirty-seven years (1985–2021) to explore how land use alterations have impacted surface runoff. The results from flood hazard maps and surface runoff estimation together revealed that the northern part of the district is the most likely to experience flooding. This was in concurrence with the locations that were inundated in the recent past. To the best of our knowledge, no current studies have used these two approaches together to detect flood-prone areas. The developed methodology can be easily employed in regions with limited data for urban drainage planning and undertaking disaster management measures by prioritising areas depending on the likelihood of flood occurrence. The inclusion of more flood-triggering factors and encouraging micro-level studies can be done as part of future research. [ABSTRACT FROM AUTHOR]

The NRCS-CN (Natural Resources Conservation Service curve number) method, developed by the USDA (U.S. Department of Agriculture) is among the most widely used for the estimation of surface runoff from watersheds. Ever since its introduction in the 1950s, although it has been used to a great extent by engineers and hydrologists, the applicability of the original procedure used to determine its main parameter, the curve number (CN), to various regions with diverse environmental conditions, is still subject to many uncertainties and debates. This study presents a comparative analysis of different methods applied to determine curve numbers from local data in four watersheds located in the central part of Romania, within the mountain region surrounding the Brașov Depression. The CN values were not only computed using rainfall–runoff records from 1991 to 2020, but also determined from the standard NRCS tables documented in the National Engineering Handbook part 630 (NEH-630), for comparison purposes. Thus, a total of 187 rainfall–runoff data records from the study watersheds and five different methods were used to assess the accuracy of various procedures for determining the CN values, namely: tabulated CN (CN values selected from NRCS tables, TAB), asymptotic fitting (AF) of both natural and ordered data, median CN (MD), geometric mean CN (GM) and the arithmetic mean CN (AM) methods. The applicability of the aforementioned methods was investigated both for the original fixed initial abstraction ratio λ = 0.2 and its adjustment to λ = 0.05. Relatively similar results were found for the curve number-based runoff estimates related to the field data analysis methods, yet slightly better when the λ was reduced to 0.05. A high overall performance in estimating surface runoff was achieved by most CN-based methods, with the exception of the asymptotic fitting of natural data and the tabulated CN method, with the latter yielding the lowest results in the study area. [ABSTRACT FROM AUTHOR]

Identifying suitable zones for surface water storage and groundwater recharge is needed to enhance irrigation water availability. This study was undertaken to map rainwater harvesting (RWH) potential zones using geospatial tools and analytic hierarchy process (AHP). The suitable locations for RWH were also mapped using the Boolean logic concept. The surface runoff is a vital factor to demarcate the appropriate zones for RWH. The curve number approach was used to estimate the surface runoff potential. The runoff coefficient (RC) map was generated based on rainfall and surface runoff depth. Weights have been allocated to selected themes of RC, drainage density, and slope. The themes were integrated using geographic information system (GIS) and AHP to demarcate the suitable zones for RWH. The derived RWH potential map was categorized into zones like "very good," "good," "moderate," "poor," and "very poor" with an aerial extent of 14.3%, 24.7%, 28.3%, 20.2%, and 12.6%, respectively. The area suitable for farm ponds was found to be about 9% (408 km2), 13% (329 km2), and 4% (244 km2) in Mirzapur, Chandauli, and Sonbhadra districts, respectively. Furthermore, 22, 15, and 21 locations were found suitable for check dams in Mirzapur, Chandauli, and Sonbhadra districts, respectively. At a large scale, effective planning of water management strategies based on multicriteria decision analysis and GIS offers increased availability of surface and groundwater resources and may help for enhancing the agricultural land use options. The higher resolution maps may be further utilized to plan RWH strategies at village level. [ABSTRACT FROM AUTHOR]

The NRCS-CN method, integrated with GIS and remote sensing, can be used for estimating curve numbers (CN) and surface runoff in geohydrological systems. The study area is divided into 63 sub-basins, and the land use land cover (LULC)-hydrologic soil group (HSG) complex is identified for each sub-basin. The CN values for three antecedent soil moisture (AMC) conditions are calculated and corrected for surface slope variations. The surface runoff depth is determined using the rainfall data for 16 years (2005–2020). The average runoff depth and mean annual precipitation ranges from 444.50 to 1960.55 mm and 936.99 to 3520.55 mm, respectively. For all sub-basins, strong correlations between runoff depth and rainfall (R2 ≥ 0.8) as well as between simulated runoff and measured runoff (R2 ≥ 0.8) are observed. The Nash–Sutcliffe model efficiency coefficient (NSE) values suggest that the model's efficiency is good to satisfactory.

Rispana River flows through the heart of Dehradun, the capital city of Uttarakhand State, India. Uttarakhand had separated from Uttar Pradesh State in the year 2000; since then, Dehradun City has witnessed numerous changes. Both urban sprawl and densification were noticed, with around a 32% increase in population. The city had faced recurrent high runoff and urban flood situations in these last 2 decades. Therefore, the study was conducted to detect the change in land use/land cover (LULC), especially urbanization, through remote sensing data; and later to determine the impacts of such changes on the Rispana watershed hydrology. The LULC maps for the year 2003 and the 2017 were generated through supervised classification technique using the Landsat Series satellite datasets. The LULC change analysis depicted that mainly the urban settlement class increased with significant area among other classes from the year 2003–2017. It was noticed that majorly agriculture and fallow land (8.18 km2, which is 13.52% of total watershed area) converted to urban, increasing the impervious area. Almost all the municipal wards, falling in the Rispana watershed, showed urbanization during the said period, with an increase of as high as 71%. The change in LULC or effect of urbanization on the hydrological response of the watershed was assessed using the most widely used Natural Resources Conservation Services Curve Number method. It was noticed that the area under moderated runoff potential (approx. 10.23 km2) steeply increased during the lean season, whereas, high runoff potential zones (5 km2) increased significantly under wet season. Therefore, it was concluded that an increase in impervious surface resulted in high runoff generation. Further, such LULC change along with climate might lead to high runoff within the watershed, which the present storm drainage network could not withstand. The situation generally led to urban floods and affected urban dwellers regularly. Therefore, it is critical to assess the hydrological impacts of LULC change for land use planning and water resource management. Furthermore, under the smart city project, the local government has various plans to improve present infrastructure; therefore, it becomes necessary to incorporate such observations in the policies. [ABSTRACT FROM AUTHOR]

The NRCS-CN (Natural Resources Conservation Service curve number) method, developed by the USDA (U.S. Department of Agriculture) is among the most widely used for the estimation of surface runoff from watersheds. Ever since its introduction in the 1950s, although it has been used to a great extent by engineers and hydrologists, the applicability of the original procedure used to determine its main parameter, the curve number (CN), to various regions with diverse environmental conditions, is still subject to many uncertainties and debates. This study presents a comparative analysis of different methods applied to determine curve numbers from local data in four watersheds located in the central part of Romania, within the mountain region surrounding the Brașov Depression. The CN values were not only computed using rainfall–runoff records from 1991 to 2020, but also determined from the standard NRCS tables documented in the National Engineering Handbook part 630 (NEH-630), for comparison purposes. Thus, a total of 187 rainfall–runoff data records from the study watersheds and five different methods were used to assess the accuracy of various procedures for determining the CN values, namely: tabulated CN (CN values selected from NRCS tables, TAB), asymptotic fitting (AF) of both natural and ordered data, median CN (MD), geometric mean CN (GM) and the arithmetic mean CN (AM) methods. The applicability of the aforementioned methods was investigated both for the original fixed initial abstraction ratio λ = 0.2 and its adjustment to λ = 0.05. Relatively similar results were found for the curve number-based runoff estimates related to the field data analysis methods, yet slightly better when the λ was reduced to 0.05. A high overall performance in estimating surface runoff was achieved by most CN-based methods, with the exception of the asymptotic fitting of natural data and the tabulated CN method, with the latter yielding the lowest results in the study area.

Spatio-temporal variability in the land use/land cover (LULC) complex occurs due to rapid expansion in activities such as urbanization, socio-economic activities, and environmental changes. Hence, detecting the effects of spatio-temporal variability on rainfall-runoff transformation is crucial. In this study, the conventional Natural Resources Conservation Service Curve Number (NRCS-CN) method was modified using slope factor as the conventional NRCS-CN method does not consider slope. The maximum change in spatial extent was observed in the case of bare soil followed by built-up land, agricultural land, dense forest, open forest, and water body over the time interval of 13 years (1999 to 2011). The area under low vegetative coverage (LVC) was observed to be increased from 697.68 to 999.30 km2 over 13 years. The areas under medium vegetative coverage (MVC) and high vegetative coverage (HVC) were observed to be decreased from 1081.93 to 914.54 km2 and 137.56 to 3.33 km2, respectively over 13 years. These changes in the spatial extent of LVC, MVC, and HVC were found to be responsible to increase the curve number (CN) of the study area. Over the time interval of 13 years, the slope-corrected weighted curve number (CNwα) values under dry (AMC-I), normal (AMC-II), and wet (AMC-III) conditions were observed to be increased from 66.55 to 69.54, 80.65 to 82.34, and 90.23 to 91.05, respectively. It was observed that the average runoff coefficient has been increased from 0.41 to 0.43 over 13-year interval which is responsible to increase the runoff. The coefficient of determination (R2) between estimated and observed runoff was found to be about 0.77 which indicated the good predictive performance of the modified NRCS-CN method. This study is helpful for detecting the temporal land use/land cover change and its effects on runoff generation. This study provides importance of construction of rainwater harvesting structures for the purpose of groundwater recharge. [ABSTRACT FROM AUTHOR]

The NRCS-CN method, integrated with GIS and remote sensing, can be used for estimating curve numbers (CN) and surface runoff in geohydrological systems. The study area is divided into 63 sub-basins, and the land use land cover (LULC)-hydrologic soil group (HSG) complex is identified for each sub-basin. The CN values for three antecedent soil moisture (AMC) conditions are calculated and corrected for surface slope variations. The surface runoff depth is determined using the rainfall data for 16 years (2005-2020). The average runoff depth and mean annual precipitation ranges from 444.50 to 1960.55 mm and 936.99 to 3520.55 mm, respectively. For all sub-basins, strong correlations between runoff depth and rainfall (R² ≥ 0.8) as well as between simulated runoff and measured runoff (R² ≥ 0.8) are observed. The Nash-Sutcliffe model efficiency coefficient (NSE) values suggest that the model's efficiency is good to satisfactory. [ABSTRACT FROM AUTHOR]

This study investigated the suitable Initial abstraction ratio (λ) for estimating the depth of runoff in northern Thailand. The Initial abstraction ratio (λ) and curve number (CN) are two catchment parameters in the Natural Resources Conservation Service-Curve Number method (NRCS-CN). In addition, the λ is considered a regional parameter, therefore, a rainfall-runoff model for estimating streamflow in first-order and ungauged headwater of the tropically medium-sized watersheds in northern Thailand should be created to gain the more accurate runoff for this particular region. NRCS-CN was employed to determine the λ in five watersheds, evaluating the performance of the modified λ values and determining the correlation for potential maximum retention (S0.2) from the original Curve Number (CN) equation with the revised Sλ value. A set of instruments comprised a rain gauge and a water stage recorder installed at each watershed. The initial abstraction (Ia), λ and CN of 80 storm events in 2018 and the Sλ-S0.20 correlation equation were calculated. The runoff model assessment from 72 selected events in 2019 was validated. The results revealed a median λ of 0.045. The runoff model assessment used the data from the measuring station compared to those calculated from the created rainfall-runoff model provided more accurate runoff and better responded to Nash-Sutcliff coefficient (NSE), coefficient of determination (R²) and percent of bias (PBIAS) (0.92, 0.92 and 1.91 respectively). Regarding the Watershed-Specific S correlation equation based on the S correlation equation between S0.045 and S0.20 values, R² was 0.97. It is recommended that the created model with the λ of 0.045 should be used for future simulations in the 100 to 250 km2 tropically medium-sized watershed. [ABSTRACT FROM AUTHOR]

The Natural Resources Conservation Service runoff curve number (NRCS-CN) method is widely used to simulate direct runoff, but the impact of rainfall intensity has not been considered. In this study, a rainfall intensity modification factor (γ) was incorporated into the Ia-S relationship of the NRCS-CN method, and the modified method (NRCS–CN–γ) was compared with the NRCS-CN method with λ = 0.2 and λ = 0.05 in three watersheds of the Walnut Gulch Experimental Watershed (WGEW). The results showed that for 2016–2018 period, the simulation performance of the NRCS–CN–γ method was close to the NRCS-CN (λ = 0.05) method and better than the NRCS-CN (λ = 0.2) method. When the new data (2009 data with high variance) was added, the significant improvement was observed by NRCS–CN–γ method with all the evaluation parameters being the best in the three watersheds, indicating a more adapted capability of the modified method with highly uneven rainfall intensities. The covariance between rainfall intensity and the simulated runoff were 19.01, 15.14, and 16.35 for the three methods, respectively. When the optimal CN changed, the relative errors representing CN sensitivity were 6.25, 6.49 and 17.39 for the methods, respectively. It is suggested that the NRCS–CN–γ method outperformed the other two methods and could contribute to a more accurate estimation of direct runoff where rainfall intensity greatly varied, especially in monsoon region or under the context of climate change.

Wide field application of curve number (CN) methodology in design flood estimation and forecasting studies is a well-accepted reality. The parameter CN is often regarded as a constant, but it is in fact a random variable, for it relies on a number of factors governing the process of rainfall-runoff. This paper suggests an alternative procedure to estimate design runoff (Q) using design storm (P) and design curve number (CN) values. To this end, design CN values for different durations and return periods have been derived from 25 years of daily annual maximum P-Q data of 10 Indian catchments. When compared, the computed design Q from design CN values was either very close to or slightly greater than the observed Q. The performance is also evaluated using coefficient of determination (R2), percentage of bias (PBIAS), and normalized Nash-Sutcliffe efficiency (NNSE). The magnitude of design CN is found to decrease for longer durations and smaller return periods, and vice versa, and such a behavior invoked the development of an empirical relation for estimation of design CN from given return period and duration for gauged as well as ungauged watersheds. The statistical analysis revealed the general extreme value (GEV) to fit best the P-CN series, whereas multiple probability distributions, viz., GEV, Gumbel max, log Pearson 3, and Gen Pareto fitted best the Q series. The 50-year, 100-year, and 200-year runoff estimated from the P-CN series exceeded only marginally those derived from the conventional Q series approach. [ABSTRACT FROM AUTHOR]

Stormwater, which cannot penetrate the soil surface due to the impermeable layer created by urbanization, inevitably causes floods. Therefore, the change in urban areas and calculation of flood size are of great importance in urban flood management. In this study, five basins are examined which have been experiencing floods caused by increasing urbanization in Bodrum, one of Turkey's most important tourism centres. Firstly, the effect of urbanization changes on the flood size was calculated, according to satellite images from 1984, 2010, 2011 and 2019, using the Natural Resources Conservation Service curve number method. Then, the multi-criteria decision-making model was used to create flood-risk maps of basins. As a result of the study, it was determined that the increase in urbanization in the period from 1984 to 2019 caused the flood flows to increase as well as the size of the areas at risk to increase. [ABSTRACT FROM AUTHOR]

CN method is a commonly used technique for estimation of direct runoff for flood mitigation studies. In Saudi Arabian (SA) arid basins, there is no analysis of NRCS-CN obtained from measured rainfall-runoff events. The research objectives are to find out the actual range of CN values in arid basins, the statistical distribution of CN, the confidence intervals of CNs, and the relation between the CN and initial abstraction factor, λ. Five basins with 19 sub-basins located in the southwest of Saudi Arabia were considered, and 161 rainfall-runoff events were analyzed during the period 1984–1987. The least squares method was used to obtain the optimum range of values for λ and CN based on three estimation methods. The rainfall and runoff exhibited log-normal distribution. The analysis showed that CN varied between 45 and 85 at λ = 0.2. The low CN values account for the transmission losses which is a typical phenomenon in arid regions. The initial abstraction ratio λ = 0.01 is found to be more representative to arid basins rather than λ = 0.2. The Beta distribution is the best to fit CN at both λ = 0.2 and 0.01. The confidence intervals are estimated and tabulated at different significant levels for flood risk assessment studies in arid basins. [ABSTRACT FROM AUTHOR]

This paper presents a methodology of calculating the water transfer capacity of a dyke pumping station in flood wave conditions in order to improve its functioning, especially with regards to the safety of the areas being drained. The exemplary analysis was carried out for a pumping station situated on a small right-bank tributary of the Odra River in the southwest part of Poland, which, due to the inadequate capacity of its pumps, extensively flooded the surrounding areas in May and June 2010. Hydrological analyses were conducted in order to determine the rate of the designed and control flows using a spatial regression equation, and as a comparison, the rainfall-runoff method was also used. The corresponding flood-wave hydrographs were also determined, which included total precipitation using the German Association For Water Resources and Land Improvement (DVWK) method, effective precipitation using the Natural Resource Conservation Service curve number (NRCS-CN) method, as well as hypothetical waves using the instantaneous unit hydrograph (IUH) method. Flood-wave routing was carried out and alternative solutions for both the output of the required pumps and the retarding reservoir capacity were highlighted on this basis. The paper presents the possibility of a correct pump capacity selection, and in turn, the size of the pumping station retarding reservoir that results from this selection. This will enable pumping station exploitation costs or maintenance costs of the retarding reservoir to be considerably reduced.

The Natural Resources Conservation Service (NRCS) Curve Number (CN) method is generally applied to estimate direct runoff from watersheds. However, its utility in estimating direct runoff in South Korean watersheds is limited due to steep slopes, shallow soil layers, less infiltration losses and sudden peak flow generation. The CN is the primary parameter used in the method and varies with watershed characteristics and measured rainfall runoff data. In this study, we evaluated the performance of NRCS-CN models employing weighted average CNs and watershed slope-adjusted CNs. Furthermore, various combinations of alternatives were investigated to determine the proper watershed average CN. Direct runoff was estimated using different CN approaches and these estimates were compared to the observed values. The results obtained in this study indicated that the slope-adjusted CNs were higher than the NRCS tabulated CNs. Similarly, the Weighted Runoff (WQ) method provided higher runoff estimates than the weighted CN (WCN) method. In conclusion, the runoff estimated using the combination of the WQ method and slope-adjusted CNs showed better agreement with the observed direct runoff compared to other methods. [ABSTRACT FROM AUTHOR]

Highlights: [•] A probabilistic constraint based binary CA model is developed to simulate the futuristic scenarios of urban growth. [•] Spatial data exploration techniques were used to derive the transition rules for CA model. [•] NRCS CN method is found to be suitable to assess the runoff characteristics of ungauged catchments. [•] CA model delivers the temporal inputs to the hydrologic model and is consistent with the latter. [•] Integration of these techniques is vital to assess the relative change in the runoff hydrographs on temporal scale. [ABSTRACT FROM AUTHOR]

In temperate regions, there are relatively enough measurements of rainfall and runoff and therefore estimation of curve number (CN) is made by inverse modelling. There is a lack of studies for CN in arid regions from rainfall-runoff measurements, therefore, a focus is made on analyzing CN in mountainous arid catchments in Saudi Arabia (SA) from measured events. The study is carried out on five basins with 19 sub-basins in the southwestern part of SA. 161 rainfall storms are analyzed. The asymptotic fitting method (AFM) is used to estimate the asymptotic CN (CN ∞). The analysis showed that the dominant curve number-precipitation (CN–P) relationship is the standard behavior and CN ∞ shows high variability. There is an overall trend of increasing CN ∞ with the increasing of the initial abstraction ratio (λ). The correlation coefficient, r, and root mean square error (RMSE, CN units) between the measured and fitted CN are 0.77 (14.8), 0.95 (3.3), and 0.96 (2.4) respectively for λ = 0.01, 0.2 and 0.3 respectively for the case of natural sorting of data pairs. The same trend is noticed for the case of ordered sorting of data pairs with relatively higher r, and lower RMSE (0.797 (9.7), 0.979 (1.9) and 0.985 (1.3), for λ 0.01, 0.2 and 0.3 respectively). For the natural data sorting, the CN ∞ varies from 0 to 61. However, for the ordered data sorting, the CN ∞ varies from 29 to 83. The CN ∞ , in the current study, is in the same order of magnitude with similar arid and semi-arid regions, however, CN ∞ is lower when compared with temperate regions, which reflects the effect of transmission losses in the wadi system in mountainous basins. Most of the floodwater is lost during the infiltration process though the wadi bed. It seems misleading to use CN ∞ to estimate runoff in this region since the asymptotic value of CN (CN ∞) has not been reached yet within the range of rainfall data except at λ = 0.01 with the lowest correlation coefficient and highest RMSE. • The standard behavior dominate over all storms in the study area. • There is a general tendency of increasing CN ∞ with increasing the value of λ. • CN ∞ shows high variability in arid regions. • The values of CN in mountainous undeveloped basins are relatively low. • It is recommended to use ordered data for design purposes. [ABSTRACT FROM AUTHOR]

This paper presents a methodology of calculating the water transfer capacity of a dyke pumping station in flood wave conditions in order to improve its functioning, especially with regards to the safety of the areas being drained. The exemplary analysis was carried out for a pumping station situated on a small right-bank tributary of the Odra River in the southwest part of Poland, which, due to the inadequate capacity of its pumps, extensively flooded the surrounding areas in May and June 2010. Hydrological analyses were conducted in order to determine the rate of the designed and control flows using a spatial regression equation, and as a comparison, the rainfall-runoff method was also used. The corresponding flood-wave hydrographs were also determined, which included total precipitation using the German Association For Water Resources and Land Improvement (DVWK) method, effective precipitation using the Natural Resource Conservation Service curve number (NRCS-CN) method, as well as hypothetical waves using the instantaneous unit hydrograph (IUH) method. Flood-wave routing was carried out and alternative solutions for both the output of the required pumps and the retarding reservoir capacity were highlighted on this basis. The paper presents the possibility of a correct pump capacity selection, and in turn, the size of the pumping station retarding reservoir that results from this selection. This will enable pumping station exploitation costs or maintenance costs of the retarding reservoir to be considerably reduced. [ABSTRACT FROM AUTHOR]