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When discharge measurements are not available, design of water structures relies on using frequency analysis of rainfall data and applying a rainfall–runoff model to estimate a hydrograph. The Soil Conservation Service (SCS) method estimates the design hydrograph first through a rainfall–runoff transformation and next by propagating runoff to the basin outlet via the SCS unit hydrograph (UH) method. The method uses two parameters, the Curve Number (CN) and the time of concentration (Tc). However, in data-scarce areas, the calibration of CN andTcfrom nearby gauged watersheds is limited and subject to high uncertainties. Therefore, the inherent uncertainty/variability of the SCS parameters may have considerable ramifications on the safety of design. In this research, a reliability approach is used to evaluate the impact of incorporating the uncertainty of CN andTcin flood design. The sensitivity of the probabilistic outcome against the uncertainty of input parameters is calculated using the First Order Reliability Method (FORM). The results of FORM are compared with the conventional SCS results, taking solely the uncertainty of the rainfall event. The relative importance of the uncertainty of the SCS parameters is also estimated. It is found that the conventional approach, used by many practitioners, might grossly underestimate the risk of failure of water structures, due to neglecting the probabilistic nature of the SCS parameters and especially the Curve Number. The most predominant factors against which the SCS-CN method is highly uncertain are when the average rainfall value is low (less than 20 mm) or its coefficient of variation is not significant (less than 0.5), i.e. when the resulting rainfall at the design return period is low. A case study is presented for Egypt using rainfall data and CN values driven from satellite information, to determine the regions of acceptance of the SCS-CN method.EDITOR D. Koutsoyiannis; ASSOCIATE EDITOR A. Efstratiadis [ABSTRACT FROM AUTHOR]

Summary: A hybrid model based on Artificial Neural Networks (ANNs) and Soil Conservation Service (SCS) Curve Number (CN) was developed to predict the effect of changes in land use/cover (LULC) on daily streamflows. The developed model makes use of LULC, hydrologic soil groups and climatic factors, such as temperature and precipitation, in order to replicate the hydrologic response of a watershed. The model incorporates data from neighboring watersheds for training purposes. Therefore, it can be used to predict daily streamflows in ungauged watersheds as well. The developed ANN model was trained, validated, and tested using streamflow data from 10 small watersheds in western Georgia, USA. One urban and one forested watershed were selected for both validation and testing purposes, while 4 forested, 1 urban, and 1 pastoral watershed were used for training the model. The model was able to predict daily streamflow in test watersheds with good accuracy. A scenario analysis was also performed to explore the potential responses to LULC changes in the two test watersheds. Ten LULC scenarios were developed by changing the LULC percentages in the forested and urban test watersheds. The developed ANN model predicted increases in average flow and flashiness for the urban and pasture dominant scenarios. For the forest dominated scenarios, the model predicted more stable hydrology (less flashy) with lower average flows. [Copyright &y& Elsevier]

The Natural Resources Conservation Service (NRCS) [previously Soil Conservation Service (SCS)] developed the SCS runoff curve-number (CN) method for estimating direct runoff from storm rainfall. The NRCS uses the CN method for designing structures and for evaluating their effectiveness. Structural design is usually based on a single event of a certain probability of occurrence. During the years when many floodwater-retarding watershed projects were planned and constructed (1950-1980), the CN equation was used in a continuous mode to evaluate the projects. To operate CN in a continuous mode, runoff was estimated from a daily rainfall record of approximately 30 years. For each day of recorded rainfall, the five-day antecedent rainfall was used to assign a (dry condition), (average condition), or (wet condition), and runoff was estimated with the appropriate CN. With the development of continuous hydrologic simulation models, CN was related directly to soil water content or estimated using rainfall and potential evapotranspiration (PET) to drive an index. Several methods were attempted and used with different degrees of success over a period of many years. The purpose of this study is to describe the evolution of the continuous CN method and its recent developments. Test results on the basis of the direct-link soil-moisture approach and the revised soil-moisture index method are presented for demonstration purposes. The results indicate that the revised soil-moisture index method is robust and produces realistic runoff estimates over a wide range of soil properties. [ABSTRACT FROM AUTHOR]

The 1936 Omnibus Flood Control Act assigned upstream and downstream missions to the Soil Conservation Service and Army Corps of Engineers. Flood control is a flagrant violation of fundamental physics. In addition, the missions of both agencies fly in the face of the natural role of floods in the aquatic environment. The result is as damaging to human development as the development is to the environment. Human inroads on riverine environments are ramified in economic, policy, and political strategies now and in the future. This paper focuses on the social science issues within an environmental science framework. It discusses economic, historical, political, and sustainability issues. The evidence for what is wrong with the seventy-five year old program from environmental and social science viewpoints is overwhelming. A proposed alternative approach would enable and celebrate natural floods, would manage their hydrological values and not attempt to control them. [ABSTRACT FROM AUTHOR]