The purpose of this study was to evaluate variations of the Natural Resources Conservation Service (NRCS) curve number (CN) method for estimating near real-time runoff for naturalized flow, using high resolution radar rainfall data for watersheds in various agro-climatic regions of Texas. The CN method is an empirical method for calculating surface runoff which has been tested on various systems over a period of several years. Many of the findings of previous studies indicate the need to develop variations of this method to account for regional and seasonal changes in weather patterns and land cover that might affect runoff. This study seeks to address these issues, as well as the inherent spatial variability of rainfall, in order to develop a means of predicting runoff in near real-time for water resource management. In the past, raingauge networks have provided data for hydrologic models. However, these networks are generally unable to provide data in real-time or capture the spatial variability associated with rainfall. Radar networks, such as the Next Generation Weather Radar (NEXRAD) of the National Weather Service (NWS), which are widely available and continue to improve in quality and resolution, can accomplish these tasks. In general, a statistical comparison of the raingauge and NEXRAD data, where both were available, shows that the radar data is as representative of observed rainfall as raingauge data. In this study, watersheds of mostly homogenous land cover and naturalized flow were used as study areas. Findings indicate that the use of a dry antecedent moisture condition CN value and an initial abstraction (Ia) coefficient of 0.1 produced statistically significant results for eight out of the ten watersheds tested. The urban watershed used in this study produced more significant results with the use of the traditional 0.2 Ia coefficient. The predicted results before and during the growing season, in general, more closely agreed with the observed runoff than those after the growing season. The overall results can be further improved by altering the CN values to account for seasonal vegetation changes, conducting field verification of land cover condition, and using bias-corrected NEXRAD rainfall data.

The purpose of this study was to evaluate variations of the Natural Resources Conservation Service (NRCS) curve number (CN) method for estimating near real-time runoff for naturalized flow, using high resolution radar rainfall data for watersheds in various agro-climatic regions of Texas. The CN method is an empirical method for calculating surface runoff which has been tested on various systems over a period of several years. Many of the findings of previous studies indicate the need to develop variations of this method to account for regional and seasonal changes in weather patterns and land cover that might affect runoff. This study seeks to address these issues, as well as the inherent spatial variability of rainfall, in order to develop a means of predicting runoff in near real-time for water resource management. In the past, raingauge networks have provided data for hydrologic models. However, these networks are generally unable to provide data in real-time or capture the spatial variability associated with rainfall. Radar networks, such as the Next Generation Weather Radar (NEXRAD) of the National Weather Service (NWS), which are widely available and continue to improve in quality and resolution, can accomplish these tasks. In general, a statistical comparison of the raingauge and NEXRAD data, where both were available, shows that the radar data is as representative of observed rainfall as raingauge data. In this study, watersheds of mostly homogenous land cover and naturalized flow were used as study areas. Findings indicate that the use of a dry antecedent moisture condition CN value and an initial abstraction (Ia) coefficient of 0.1 produced statistically significant results for eight out of the ten watersheds tested. The urban watershed used in this study produced more significant results with the use of the traditional 0.2 Ia coefficient. The predicted results before and during the growing season, in general, more closely agreed with the observed runoff than those after the growing season. The overall results can be further improved by altering the CN values to account for seasonal vegetation changes, conducting field verification of land cover condition, and using bias-corrected NEXRAD rainfall data.

Pluvial flood is a natural hazard that severely threatens people’s property and safety. With the development of algorithms and computer technologies, numerical modeling has emerged as an effective tool for predicting the impacts of floods. Despite being one of the most costly types of floods in West Africa, pluvial flooding has not been studied as extensively as riverine flooding, probably because modeling runoff across urban areas remains a challenge. Recently, a module based on the SCS Curve Number Method is incorporated in the open-source software TELEAMC-2D, which provides a possibility to model the infiltration process dynamically. TELEMAC-2D is one of the first hydraulic models to consider hydrologic parameters. Although the update is expected to increase the suitability of TELAMC-2D in pluvial flood modeling, the infiltration routine has not yet been tested in a real situation in a semi-arid area. This study aims to investigate the capability of TELEMAC-2D in simulating the rainfall-runoff process during a pluvial flood event in a semi-arid urban area, Niamey city in west Africa. Due to the lack of calibration data, a hydrological model SWAT is used to evaluate the performance of TELEMAC-2D. Through the comparison between the runoffs generated by the two models, it is found that TELEMAC-2D has a similar trend with SWAT in runoff simulation. However, TELEMAC-2D significantly overestimates the runoff magnitude despite having the same SCS values as SWAT. The reason for the overestimation is TELEMAC-2D that does not properly consider evaporation. Two suggestions are made to improve pluvial floods simulations using TELEMAC-2D in semi-arid areas: 1) couple TELEMAC-2D with a hydrologic model, and use net rainfall generated by the hydrologic model as precipitation input; 2) provide functions in infiltration subroutine that calculate rainfall abstractions by other hydrologic phenomena in addition to the infiltration process.

The Curve Number (CN) method of estimating the direct runoff response to rainfall events was originally developed in the 1950's primarily for agricultural purposes in the mid-western United States. The accuracy of the CN method is greatly affected by variation of the soil type and land use of the region. Curve Numbers developed for a given region are not appropriate for application in other regions. In order to produce reliable, consistent results, Curve Numbers must be calibrated for the area in which the CN method is to be applied. Calibration is ideally accomplished by direct measurement using several rain and stream gauges within a watershed. Gauged data, however, is not always available or easily obtained. A more feasible method of calibration is therefore necessary for broad application of the CN method. The purpose of this study is to develop a method of CN calibration that can be easily applied to regions where no gauged data is available using the United States Geological Survey (USGS) regression equations. In this study, the peak flow values estimated using the regression equations were used in conjunction with a dimensionless hydrograph to compute runoff volume. The National Oceanic and Atmospheric Administration (NOAA) rainfall grids were used to estimate precipitation. Given the rainfall and runoff, a Curve Number can then calibrated through back-calculation. The method of CN calibration using the USGS regression equations was applied to nearly 60 watersheds in the state of Utah for this research. The calibration results obtained using the regression equations were compared to other CN calibrations developed using gauged data. Calibrations performed through the use of the regression equations were quite consistent with calibrations obtained using measured data. To ensure the validity of the application of this method in other regions, more comparisons to results obtained using measured data should be further pursued.

Planning and design of stormwater facilities (including best management practices and low impact development) involve the calculation of peak flows and runoff volumes. Rainfall-runoff models are frequently utilized to estimate this information. A userriendly rainfall-runoff tool (LIDIA) was developed using Visual Basic for Applications in Microsoft Office Excel. This research showed comprehensive guidelines on how to setup a model in LIDIA and reported the first evaluation of LIDIA using field data. LIDIA hydrologic module was tested using 10-minute rainfall, land cover, soil series, land cover management, and runoff data from two small watersheds in North Mississippi. Eleven storm events, over a period of seven months were used for the one evaluation site and 11 storm events were used for the second case study. Overall the development and results of LIDIA tool showed in this study are positive in keeping the enhancement of the model.

The main goal of this dissertation is to develop a seasonal water balance model for evaporation, runoff and water storage change based on observations from a large number of watersheds, and further to obtain a comprehensive understanding on the dominant physical controls on intra-annual water balance. Meanwhile, the method for estimating evaporation and water storage based on recession analysis is improved by quantifying the seasonal pattern of the partial contributing area and contributing storage to base flow during low flow seasons. A new method for quantifying seasonality is developed in this research. The difference between precipitation and soil water storage change, defined as effective precipitation, is considered as the available water. As an analog to climate aridity index, the ratio between monthly potential evaporation and effective precipitation is defined as a monthly aridity index. Water-limited or energy-limited months are defined based on the threshold of 1. Water-limited or energy-limited seasons are defined by aggregating water-limited or energy-limited months, respectively. Seasonal evaporation is modeled by extending the Budyko hypothesis, which is originally for mean annual water balance; while seasonal surface runoff and base flow are modeled by generalizing the proportionality hypothesis originating from the SCS curve number model for surface runoff at the event scale. The developed seasonal evaporation and runoff models are evaluated based on watersheds across the United States. For the extended Budyko model, 250 out of 277 study watersheds have a Nash-Sutcliff efficiency (NSE) higher than 0.5, and for the seasonal runoff model, 179 out of 203 study watersheds have a NSE higher than 0.5. Furthermore, the connection between the seasonal parameters of the developed model and a variety of physical factors in the study watersheds is investigated. For the extended Budyko model, vegetation is identified as an important physical factor that related to the seasonal model parameters. However, the relationship is only strong in water-limited seasons, due to the seasonality of the vegetation coverage. In the seasonal runoff model, the key controlling factors for wetting capacity and initial wetting are soil hydraulic conductivity and maximum rainfall intensity respectively. As for initial evaporation, vegetation is identified as the strongest controlling factor. Besides long-term climate, this research identifies the key controlling factors on seasonal water balance: the effects of soil water storage, vegetation, soil hydraulic conductivity, and storminess. The developed model is applied to the Chipola River watershed and the Apalachicola River basin in Florida for assessing potential climate change impact on the seasonal water balance. The developed model performance is compared with a physically-based distributed hydrologic model of the Soil Water Assessment Tool, showing a good performance for seasonal runoff, evaporation and storage change.

Conselho Nacional de Desenvolvimento Científico e Tecnológico / The soil tillage systems modify its water balance and for the correct irrigation management is fundamental to determining the runoff and effective rainfall, which helps to maximize the use of rainwater and minimizes the use of supplemental irrigation. The objective of this study was to determine, model and estimate the runoff and the effective rainfall during the development cycle of the common black bean and maize in soil with and without straw on the surface, in different land slope and rainfall intensities simulated, using the field experiments, multivariate equations, the Curve Number Method (CN) and the SIMDualKc Model. Two experiments were conducted in the field with crops of black beans and maize, where different intensities of simulated rainfall (35, 70 and 105 mm h-1) were applied at different times of the crop cycle (soil cover of 0, 28, 63 and 100% by the canopy beans; 0, 30, 72 and 100% by canopy of maize) and distinct land slope (1, 5 and 10%) in soil without and with (5 Mg ha-1) of oat straw on the surface. The runoff values observed were compared with those estimated by the CN method, suggesting new values of CN to improve the estimate. From the set of data collected from the field analysis of multiple linear regression to estimate runoff and simulations with SIMDualKc model to estimate runoff and effective rainfall were performed. The start time of the runoff, constant runoff rate, total runoff and the percentage of runoff in relation to the volume of rain were little influenced by the crops of beans and maize. Reductions in runoff were provided by the straw on the soil surface within 45 and 48% for the crops beans and maize, respectively. The CN method for the bean crop underestimated runoff by up to 10% for the soil without straw on the surface, and overestimated by up to 17% for the soil with straw. For maize, the method overestimated the runoff by up 32.4% in soil with straw and 12% in soil without straw. To improve estimation the CN, new values are proposed for CN, considering the crop, the presence or absence of straw on soil surface and intensity rain. The use of multiple linear regression analyzes indicated that the volume of precipitation (R2=0.52) and soil cover by straw (R2=0.18) are the variables with the greatest influence on runoff. Four multiple equations were generated, and the equation 2, whose input parameters are the volume of rain and amount of litter on the soil surface, was presented the best estimate of the runoff of a data set than the one that gave its origin. The SIMDualKc Model requires adjustments to estimate runoff and effective rainfall during the crop cycle of beans and maize, so consider the benefits of straw on the soil surface in reducing runoff. Thus, the suggested value of CN (CN=75) was changed to 71 and 87 to the black bean crop, and 56 and 79 for the maize crop for the soil with and without straw on the surface, respectively. / Os sistemas de manejo do solo modificam o seu balanço hídrico e para o correto manejo da irrigação é de fundamental importância a determinação do escoamento superficial e da chuva efetiva, o que contribui para maximizar o uso da água das chuvas e minimiza a utilização de irrigação suplementar. O objetivo do presente trabalho foi determinar, modelar e estimar o escoamento superficial e a chuva efetiva durante o ciclo de desenvolvimento das culturas do feijão e milho, cultivados em solo com e sem palha na superfície, em diferentes declividade do terreno e intensidades de chuvas simuladas, utilizando experimentos a campo, equações multivariadas, o método Curva Número (CN) e o modelo SIMDualKc. Foram realizados dois experimentos à campo, com as culturas do feijão e milho, em que foram aplicadas diferentes intensidades de chuvas simuladas (35, 70 e 105 mm h-1), em diferentes momentos do ciclo das culturas (cobertura do solo de 0, 28, 63 e 100% pelo dossel vegetativo do feijão; 0, 30, 72 e 100% pelo dossel vegetativo do milho) e distintas declividade do terreno (1, 5 e 10%), em solo sem e com (5 Mg ha-1) palha de aveia preta na superfície. Os valores de escoamento superficial observados foram comparados com os estimados pelo método CN, sugerindo-se novos valores de CN para melhorar a estimativa. A partir do conjunto de dados coletados a campo, foram realizadas análises de regressão linear múltiplas para a estimativa do escoamento superficial e, simulações com o modelo SIMDualKc para estimativa do escoamento superficial e da chuva efetiva. O tempo de início do escoamento, a taxa constante de escoamento, o escoamento total e a porcentagem de escoamento em relação ao volume da chuva foram pouco influenciados pelo crescimento do dossel vegetativo das plantas de feijão e milho. Reduções no escoamento superficial foram proporcionadas pela presença de palha na superfície do solo, em até 45 e 48% para as culturas do feijão e milho, respectivamente. O método CN para a cultura do feijão subestimou o escoamento superficial em até 10% para o solo sem palha na superfície e, superestimou em até 17% para o solo com palha. Para a cultura do milho, o método CN superestimou o escoamento superficial em até 32,4% no solo com palha e 12% no solo sem palha. Para melhorar a estimativa do método CN, foram propostos novos valores de CN, considerando a cultura, a presença ou não de palha na superfície do solo e a intensidade da chuva. A utilização de análises de regressão linear múltiplas indicaram que o volume da chuva (R2=0,52) e a cobertura do solo por palha (R2=0,18) são as variáveis com maior influência sobre o escoamento superficial. Foram geradas quatro equações múltiplas, sendo que a equação 2, cujos parâmetros de entrada são o volume da chuva e quantidade de palha na superfície do solo, foi a que apresentou a melhor estimativa do escoamento superficial de um conjunto de dados diferente daquele que lhe deu origem. O modelo SIMDualKc necessita de ajustes para estimar o escoamento superficial e a chuva efetiva durante o ciclo das culturas de feijão e milho, de modo que considere os benefícios da palha na superfície do solo na redução do escoamento superficial. Desta forma, o valor sugerido de CN (CN=75) foi alterado para 71 e 87 para a cultura do feijão e, 56 e 79 para a cultura do milho, para o solo com e sem palha na superfície, respectivamente.

This thesis is focused to determine water erosion of soil and drain from basin of štefanov stream. The basin of štefanov stream is unobserved with area 15,1 km2. After evaluation of current status is processed integrated design against the adverse effects of surface runoff.