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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]

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]

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]