Error characteristics associated with satellite-derived precipitation products are important for atmospheric and hydrological model data assimilation, forecasting, and climate diagnostic applications. This information also aids in the refinement of physical assumptions within algorithms by identifying geographical regions and seasons where existing algorithm physics may be incorrect or incomplete. Examination of relative errors between independent estimates derived from satellite microwave data is particularly important over regions with limited surface-based equipments for measuring rain rate such as the global oceans and tropical continents. In this context, analysis of microwave based satellite datasets from the Tropical Rainfall Measuring Mission (TRMM) enables to not only provide information regarding the inherent uncertainty within the current TRMM products, but also serves as an opportunity to prototype error characterization methodologies for the TRMM follow-on program, the Global Precipitation Measurement (GPM) . Most of the TRMM uncertainty evaluation studies focus on the accuracy of rainfall accumulated over time (e.g., season/year). Evaluation of instantaneous rainfall intensities from TRMM orbital data products is relatively rare. These instantaneous products are known to potentially cause large uncertainties during real time flood forecasting studies at the watershed scale. This is more so over land regions, where the highly varying land surface emissivity offers a myriad of complications, hindering accurate rainfall estimation. The error components of orbital data products also tend to interact nonlinearly with hydrologic modeling uncertainty. Keeping these in mind, the present thesis fosters the development of uncertainty analysis using instantaneous satellite orbital data products (latest version 7 of 1B11, 2A25, 2A23, 2B31, 2A12) derived from the passive and active microwave sensors onboard TRMM satellite, namely TRMM Microwave Imager (TMI) and precipitation radar (PR). The study utilizes 11 years of orbital data from 2002 to 2012 over the Indian subcontinent and examines the influence of various error sources on the convective and stratiform precipitation types. Two approaches are taken up to examine uncertainty. While the first approach analyses independent contribution of error from these orbital data products, the second approach examines their combined effect. Based on the first approach, analysis conducted over the land regions of Mahanadi basin, India investigates three sources of uncertainty in detail. These include 1) errors due to improper delineation of rainfall signature within microwave footprint (rain/no rain classification), 2) uncertainty offered by the transfer function linking rainfall with TMI low frequency channels and 3) sampling errors owing to the narrow swath and infrequent visits of TRMM sensors. The second approach is hinged on evaluating the performance of rainfall estimates from each of these orbital data products by accumulating them within a spatial domain and using error decomposition methodologies. Microwave radiometers have taken unprecedented satellite images of earth’s weather, proving to be a valuable tool for quantitative estimation of precipitation from space. However, as mentioned earlier, with the widespread acceptance of microwave based precipitation products, it has also been recognized that they contain large uncertainties. One such source of uncertainty is contributed by improper detection of rainfall signature within radiometer footprints. To date, the most-advanced passive microwave retrieval algorithms make use of databases constructed by cloud or numerical weather model simulations that associate calculated microwave brightness temperature to physically plausible sample rain events. Delineation of rainfall signature from microwave footprints, also known as rain/norain classification (RNC) is an essential step without which the succeeding retrieval technique (using the database) gets corrupted easily. Although tremendous advances have been made to catapult RNC algorithms from simple empirical relations formulated for computational expedience to elaborate computer intensive schemes which effectively discriminate rainfall, a number of challenges remain to be addressed. Most of the algorithms that are globally developed for land, ocean and coastal regions may not perform well for regional catchments of small areal extent. Motivated by this fact, the present work develops a regional rainfall detection algorithm based on scattering index methodology for the land regions of study area. Performance evaluation of this algorithm, developed using low frequency channels (of 19 GHz, 22 GHz), are statistically tested for individual case study events during 2011 and 2012 Indian summer monsoonal months. Contingency table statistics and performance diagram show superior performance of the algorithm for land regions of the study region with accurate rain detection observed in 95% of the case studies. However, an important limitation of this approach is comparatively poor detection of low intensity stratiform rainfall. The second source of uncertainty which is addressed by the present thesis, involves prediction of overland rainfall using TMI low frequency channels. Land, being a radiometrically warm and highly variable background, offers a myriad of complications for overland rain retrieval using microwave radiometer (like TMI). Hence, land rainfall algorithms of TRMM TMI have traditionally incorporated empirical relations of microwave brightness temperature (Tb) with rain rate, rather than relying on physically based radiative transfer modeling of rainfall (as implemented in TMI ocean algorithm). In the present study, sensitivity analysis is conducted using spearman rank correlation coefficient as the indicator, to estimate the best combination of TMI low frequency channels that are highly sensitive to near surface rainfall rate (NSR) from PR. Results indicate that, the TMI channel combinations not only contain information about rainfall wherein liquid water drops are the dominant hydrometeors, but also aids in surface noise reduction over a predominantly vegetative land surface background. Further, the variations of rainfall signature in these channel combinations were seldom assessed properly due to their inherent uncertainties and highly non linear relationship with rainfall. Copula theory is a powerful tool to characterize dependency between complex hydrological variables as well as aid in uncertainty modeling by ensemble generation. Hence, this work proposes a regional model using Archimedean copulas, to study dependency of TMI channel combinations with respect to precipitation, over the land regions of Mahanadi basin, India, using version 7 orbital data from TMI and PR. Studies conducted for different rainfall regimes over the study area show suitability of Clayton and Gumbel copula for modeling convective and stratiform rainfall types for majority of the intraseasonal months. Further, large ensembles of TMI Tb (from the highly sensitive TMI channel combination) were generated conditional on various quantiles (25th, 50th, 75th, 95th) of both convective and stratiform rainfall types. Comparatively greater ambiguity was observed in modeling extreme values of convective rain type. Finally, the efficiency of the proposed model was tested by comparing the results with traditionally employed linear and quadratic models. Results reveal superior performance of the proposed copula based technique. Another persistent source of uncertainty inherent in low earth orbiting satellites like TRMM arise due to sampling errors of non negligible proportions owing to the narrow swath of satellite sensors coupled with a lack of continuous coverage due to infrequent satellite visits. This study investigates sampling uncertainty of seasonal rainfall estimates from PR, based on 11 years of PR 2A25 data product over the Indian subcontinent. A statistical bootstrap technique is employed to estimate the relative sampling errors using the PR data themselves. Results verify power law scaling characteristics of relative sampling errors with respect to space time scale of measurement. Sampling uncertainty estimates for mean seasonal rainfall was found to exhibit seasonal variations. To give a practical demonstration of the implications of bootstrap technique, PR relative sampling errors over the sub tropical river basin of Mahanadi, India were examined. Results revealed that bootstrap technique incurred relative sampling errors of <30% (for 20 grid), <35% (for 10 grid), <40% (for 0.50 grid) and <50% (for 0.250 grid). With respect to rainfall type, overall sampling uncertainty was found to be dominated by sampling uncertainty due to stratiform rainfall over the basin. In order to study the effect of sampling type on relative sampling uncertainty, the study compares the resulting error estimates with those obtained from latin hypercube sampling. Based on this study, it may be concluded that bootstrap approach can be successfully used for ascertaining relative sampling errors offered by TRMM-like satellites over gauged or ungauged basins lacking in in-situ validation data. One of the important goals of TRMM Ground Validation Program has been to estimate the random and systematic uncertainty associated with TRMM rainfall estimates. Disentangling uncertainty in seasonal rainfall offered by independent observations of TMI and PR enables to identify errors and inconsistencies in the measurements by these instruments. Motivated by this thought, the present work examines the spatial error structure of daily precipitation derived from the version 7 TRMM instantaneous orbital data products through comparison with the APHRODITE data over a subtropical region namely Mahanadi river basin of the Indian subcontinent for the seasonal rainfall of 6 years from June 2002 to September 2007. The instantaneous products examined include TMI and PR data products of 2A12, 2A25 and 2B31 (combined data from PR and TMI). The spatial distribution of uncertainty from these data products was quantified based on the performance metrics derived from the contingency table. For the seasonal daily precipitation over 10x10 grids, the data product of 2A12 showed greater skill in detecting and quantifying the volume of rainfall when compared with 2A25 and 2B31 data products. Error characterization using various error models revealed that random errors from multiplicative error models were homoscedastic and that they better represented rainfall estimates from 2A12 algorithm. Error decomposition technique, performed to disentangle systematic and random errors, testified that the multiplicative error model representing rainfall from 2A12 algorithm, successfully estimated a greater percentage of systematic error than 2A25 or 2B31 algorithms. Results indicate that even though the radiometer derived 2A12 is known to suffer from many sources of uncertainties, spatial and temporal analysis over the case study region testifies that the 2A12 rainfall estimates are in a very good agreement with the reference estimates for the data period considered. These findings clearly document that proper characterization of error structure offered by TMI and PR has wider implications in decision making, prior to incorporating the resulting orbital products for basin scale hydrologic modeling. The current missions of GPM envision a constellation of microwave sensors that can provide instantaneous products with a relatively negligible sampling error at daily or higher time scales. This study due to its simplicity and physical approach offers the ideal basis for future improvements in uncertainty modeling in precipitation.

The thesis is concerned with development of efficient regional frequency analysis (RFA) approaches to estimate quantiles of hydrometeorological events. The estimates are necessary for various applications in water resources engineering. The classical approach to estimate quantiles involves fitting frequency distribution to at-site data. However, this approach cannot be used when data at target site are inadequate or unavailable to compute parameters of the frequency distribution. This impediment can be overcome through RFA, in which sites having similar attributes are identified to form a region, and information is pooled from all the sites in the region to estimate the quantiles at target site. The thesis proposes new approaches to RFA of precipitation, meteorological droughts and floods, and demonstrates their effectiveness. The approach proposed for RFA of precipitation overcomes shortcomings of conventional approaches with regard to delineation and validation of homogeneous precipitation regions, and estimation of precipitation quantiles in ungauged and data sparse areas. For the first time in literature, distinction is made between attributes/variables useful to form homogeneous rainfall regions and to validate the regions. Another important issue is that some of the attributes considered for regionalization vary dynamically with time. In conventional approaches, there is no provision to consider dynamic aspects of time varying attributes. This may lead to delineation of ineffective regions. To address this issue, a dynamic fuzzy clustering model (DFCM) is developed. The results obtained from application to Indian summer monsoon and annual rainfall indicated that RFA based on DFCM is more effective than that based on hard and fuzzy clustering models in arriving at rainfall quantile estimates. Errors in quantile estimates for the hard, fuzzy and dynamic fuzzy models based on the proposed approach are shown to be significantly less than those computed for Indian summer monsoon rainfall regions delineated in three previous studies. Overall, RFA based on DFCM and large scale atmospheric variables appeared promising. The performance of DFCM is followed by that of fuzzy and hard clustering models. Next, a new approach is proposed for RFA of meteorological droughts. It is suggested that homogeneous precipitation regions have to be delineated before proceeding to develop drought severity - areal extent - frequency (SAF) curves. Drought SAF curves are constructed at annual and summer monsoon time scales for each of the homogeneous rainfall regions that are newly delineated in India based on the proposed approach. They find use in assessing spatial characteristics and frequency of meteorological droughts. It overcomes shortcomings associated with classical approaches that construct SAF curves for political (e.g., state, country) and physiographic regions (e.g., river basin), based on spatial patterns of at-site values of drought indices in the study area, without testing homogeneity in rainfall. Advantage of the new approach can be noted especially in areas that have significant variations in temporal and spatial distribution of precipitation (possibly due to variations in topography, landscape and climate). The DFCM is extended to RFA of floods, and its effectiveness in prediction of flood quantiles is demonstrated by application to Godavari basin in India, considering precipitation as time varying attribute. Six new homogeneous regions are formed in Godavari basin and errors in quantile estimates based on those regions are shown to be significantly less than those computed based on sub-zones delineated in Godavari basin by Central Water Commission in a previous study.

From the Proceedings of the 1971 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - April 22-23, 1971, Tempe, Arizona / The rotadisk rainulator is a recently developed rainfall simulator utilizing a full-cone-spray type nozzle. Its unique feature is the rotation of disks of various size openings that makes it possible to produce intensities from close to zero up to full nozzle capacity. Disks may be quickly changed, making it possible to study the effects of various intensities on infiltration rates, such as occur in natural storms. For all intensities above 1.0 in/hr, the instrument comes closer to duplicating kinetic energies and momenta of natural rainfall than any other type of rainfall simulator. Little rainfall-runoff data are available on most of the Lower Gila watersheds. Infiltration rates were therefore determined using the rotadisk rainulator on recompacted soil samples from the watershed. The results permitted a ranking of the watersheds on the basis of infiltration rates, which supports an independent flood frequency analysis indicating that the flood threat from subwatersheds along the Gila is much lower than had previously been projected. When the instrument is taken into the field, it should be possible to directly determine the infiltration rates of different soil and vegetation types, which will be of more use to hydrologists than data from recompacted samples

From the Proceedings of the 1973 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - May 4-5, 1973, Tucson, Arizona / Hydrologic data collected from three small urban watersheds and one rural watershed were analyzed for the purpose of investigating the effect of urbanization on runoff. A procedure developed by the Soil Conservation Service was used to explain the relationship between the amount of rainfall and runoff. It was noted that the runoff curve number, a parameter of the method, increased as the percentage of impervious area increased. Also, there was evidence that a linear relationship existed between the runoff volume and its corresponding peak rate.

From the Proceedings of the 1973 Meetings of the Arizona Section - American Water Resources Assn. and the Hydrology Section - Arizona Academy of Science - May 4-5, 1973, Tucson, Arizona / An infiltrometer was designed to more closely simulate natural storm characteristics and still maintain sufficient portability to be used in various test sites in the field. In addition to portability, a relatively large test plot can be used over a relatively long duration. The instrument is designed to produce rainfall intensities of 2 to 6 inches per hour which are comparable to natural storm intensities found in northern Arizona. Capillary tubes produce water drops of equivalent kinetic energy at impact to natural raindrops. Errors due to lateral flow are minimized through peripheral wetting. Mounting the infiltrometer on a four-wheel drive vehicle allows nearly the portability of a hand carried unit with a greater water carrying capacity and allows the equipment to be large enough to test a representative plot.

Flood estimation is often required in hydrologic design and has important economic significance. For example, in Australia, the annual spending on infrastructure requiring flood estimation is of the order of $650 million ARR (I.E. Aust., 1998). Rainfall-based flood estimation techniques are most commonly adopted in practice. These require several inputs to convert design rainfalls to design floods. Of all the inputs, loss is an important one and defined as the amount of precipitation that does not appear as direct runoff. The concept of loss includes moisture intercepted by vegetation, infiltration into the soil, retention on the surface, evaporation and loss through the streambed and banks. As these loss components are dependent on topography, soils, vegetation and climate, the loss exhibits a high degree of temporal and spatial variability during the rainfall event. In design flood estimation, the simplified lumped conceptual loss models were used because of their simplicity and ability to approximate catchment runoff behaviour. In Australia, the most commonly adopted conceptual loss model is the initial losscontinuing loss model. For a specific part of the catchment, the initial loss occurs prior to the commencement of surface runoff, and can be considered to be composed of the interception loss, depression storage and infiltration that occur before the soil surface saturates. ARR (I. E. Aust., 1998) mentioned that the continuing loss is the average rate of loss throughout the remainder of the storm. At present, there is inadequate information on design losses in most parts of Australia and this is one of the greatest weaknesses in Australian flood hydrology. Currently recommended design losses are not compatible with design rainfall information in Australian Rainfall and Runoff. Also design losses for observed storms show a wide variability and it is always difficult to select an appropriate value of loss from this wide range for a particular application. Despite the wide variability of loss values, in the widely used Design Event Approach, a single value of initial and continuing losses is adopted. Because of the non-linearity in the rainfall-runoff process, this is likely to introduce a high degree of uncertainty and possible bias in the resulting flood estimates. In contrast, the Joint Probability Approach can consider probability-distributed losses in flood estimation. In ARR (I. E. Aust., 1998) it is recommended to use a constant continuing loss value in rainfall events. In this research it was observed that the continuing loss values in the rainfall events were not constant, rather than it decays with the duration of the rainfall event. The derived loss values from the 969 rainfall and streamflow events of Queensland catchments would provide better flood estimation than the recommended design loss values in ARR (I. E. Aust., 1998). In this research, both the initial and continuing losses were computed using IL-CL loss model and a single median loss value was used to estimate flood using Design Event Approach. Again both the initial and continuing losses were considered to be random variables and their probability distribution functions were determined. Hence, the research showed that the probability distributed loss values can be used for Queensland catchments in near future for better flood estimate. The research hypothesis tested was whether the new loss value for Queensland catchments provides significant improvement in design flood estimation. A total of 48 catchments, 82 pluviograph stations and 24 daily rainfall stations were selected from all over Queensland to test the research hypothesis. The research improved the recommended design loss values that will result in more precise design flood estimates. This will ultimately save millions of dollars in the construction of hydraulic infrastructures.

Within the frame work of the International Water Research Alliance Saxony (IWAS), project “Middle East” a complex integrated water management system is developed and tested in the project region of Middle East (Oman and Saudi-Arabia). Hence, new solutions for a sustainable management of the scarce water resources in (semi-) arid regions are explored within IWAS in the sultanate of Oman on which this study work is carried out. Rainfall runoff models are established to estimate the “water yield” of the catchments in the project region. Modeling is a very important tool that enables hydrologists to make more comprehensive use of rainfall time series. Rainfall-runoff modeling is also useful for water resources assessment as these models can generate a long representative time series of stream flow volumes from which water supply schemes can be designed (D.A. Hughes, 1995). Therefore, this study project mainly focuses on the following main tasks such as data analysis, data processing and statistical evaluation; Model selection and model setup; Model adaptation test and verification. As part of the common modeling protocol, sensitivity analysis of a Rainfall-Runoff Modeling Toolbox (RRMT) is carried out in this study with the aim to identify sensitive model parameters. RRMT has been developed in order to produce parsimonious, lumped model structures with a high level of parameter identifiability. Such identifiability is crucial if relationships between the model parameters representing the system and catchment characteristics are to be established. RRMT is a modular framework that allows its user to implement different model structures to find a suitable balance between model performance and parameter identifiability. The study is carried out in the upper catchment part of Wadi Ma’wil (gauge near to Afi’), Batinah Region of the Sultanate of Oman. Arid and semi-arid zones are characterized by rainfall which is highly variable in space, time, quantity and duration (Noy-Meir, 1973). The Sultanate of Oman is characterized by hyper-arid (<100 mm rainfall), through the arid (100–250 mm rainfall) and semi-arid (250–500 mm rainfall) environments that are experienced in different parts of the country. Furthermore, arid areas have distinctive hydrological features substantially different from those of humid areas. The high temporal and spatial distribution of the rainfall, flash floods, absence of base flow, sparsity of plant cover, high transmission losses, high amounts of evaporation and evapotranspiration and the general climatologies are examples of such differences.:Acknowledgments i Abstract ii List of Figures and Photos v List of Tables and Plots v 1. Description of Study Area 1 1.1 General characteristics of arid regions 1 1.2 Study area (Batinah Region and Ma’awil catchment of gauge ‘Afi’) 2 1.2.1 Overview of Study area 2 1.2.2 Wadi Ma’awil and Gauge near to Afi’ 3 2. Data Processing and Evaluation 6 2.1 Rainfall data 6 2.1.1 Monthly and Annual Mean Rainfall Analyses 6 2.1.2 Estimation of Missing Precipitation Data 6 2.1.3 Annual and monthly average rainfall 6 2.2 Runoff data 9 2.2.1Rainfall-Runoff events – Processing and Analysis 9 2.2.2 Wadi Ma’awil Runoff Analysis 9 2.3 Areal Precipitation 11 2.3.1 Area 11 2.3.2 Summary of Calculated Results of Mean Annual Areal Precipitation 12 2.4 Evapotranspiration 13 2.4.1 Evaporation and Potential Evapotranspiration 13 2.4.2 Calculation of Evapotranspiration by FAO Penman-Monteith Equation 13 2.4.3 Sample Calculation for Daily ET using FAO Penman-Monteith Equation 14 2.4.4 Comparisons of Evapotranspiration Calculation Results 16 3. Rainfall-Runoff Modeling 16 3.1 Modeling approach – selection of modules 16 3.1.1 Basic Principle 16 3.1.2 Classification of models 16 3.1.3 Modeling Process 17 3.2 Rainfall-Runoff Modeling Toolbox 19 3.2.1 Introduction 19 3.2.2 Data Needs and Model Structure 20 3.3 Provision of input data 20 3.4 Calibration and Validation 20 3.4.1 Model Calibration and Validation 21 3.5 Sensitivity Analysis 22 3.6 Discussions of Results 23 3.6.1 Optimization Modules 23 3.6.2 Soil Moisture Accounting (SMA) Modules 24 3.6.3 Routing (R) Modules 25 3.6.4 The objective functions 26 3.6.5 Visualization Modules Results 27 3.7 Conclusions and Recommendations 35 3.7.1 Conclusions 35 3.7.2 Limitations and Recommendations 35 References 37 Appendix 38 Appendix A: Daily extraterrestrial radiation (Ra) for different latitudes for the 15th day of the month 38 Appendix B: Mean daylight hours (N) for different latitudes for the 15th of the month 38 Annexes 39 Annex - A: Mean Rainfall for the Gauge Afi’ from 1995 – 2005 39 Annex A-1: Annual Mean Rainfall for Gauge Afi’ for the time period 1995-2005 39 Annex A-2: Monthly Mean Rainfall for Gauge Afi’ for the time period 1995-2005 39 Annex A-3: Monthly Mean Rainfall for each Rain Gauge within the Wadi Ma’awil Catchment area for the time period 1995-2005 40 Annex - B: Rainfall - Runoff events for the Gauge Afi’ 41 Annex B-1: Annual Rainfall Vs Runoff events for the Gauge Afi’ from 1995 – 2005 42 Annex B-2: Monthly Rainfall Vs Runoff events for the Gauge Afi’ from 1995 – 2005 44 Annex B-3: Daily Rainfall Vs Runoff events for the Gauge Afi’ sample graphs with the time period from 1995to 2005 46

Doctor of Philosophy / Department of Biological & Agricultural Engineering / Stacy L. Hutchinson / Precipitation impacts hydrologic structures, agricultural production, water resources management, and recreational activities, all of which significantly affect a state’s economy. Water control structure design is based on the maximum runoff rate resulting from storms with a specific return period and duration. The Rainfall Frequency Atlas (National Weather Service Technical Paper 40, 1961) (TP-40) provided statistical rainfall analysis as the basis for hydrologic structure design until the information was updated for Kansas in February 2013 (National Oceanic and Atmospheric Administration Atlas 14, volume 8) (Atlas-14). With growing concern about the effects of global climate change and predictions of more precipitation and extreme weather events, it is necessary to explore rainfall distribution patterns using the most current and complete data available. In this work, the changes in rainfall patterns were studied using the daily rainfall data from 23 stations in Kansas and 15 stations from adjacent states with daily rainfall data of 1890 through 2012. Analysis showed an increase in extreme precipitation events in Kansas with increase in magnitude from the northwest to southeast part of the state. A comparison of results of the TP-40 analysis to period 1980–2009, showed that approximately 84% of the state had an increase in short-term rainfall event magnitudes. In addition, trend analyzes on the total annual rainfall indicated a gradual increase at 21 out of 23 stations, including eight statistically significant trends. A change-point analysis detected a significant sudden change at twelve stations as early as 1940 and as recently as 1980. The increasing trend, particularly after the significant change-points, is useful in updating water management plans and can assist with agricultural production decisions such as crop selection and new plant variety development. A comparison between 10-yr, 24-hr storms from TP-40 and Atlas-14 indicated a change of -12% to 5% in Kansas. However, the number of exceedances from the 10-yr, 1-, 2-, 3-, 4-, 7-, and 10-day storms demonstrated a tendency towards more exceedances, particularly in the last five decades. Results of this study are useful for hydrologic structure design and water resources management in order to prevent accepting additional risk of failure because of the current changing climate.

Distributed Rainfall-Runoff models are gaining widespread acceptance; yet, a fundamental issue that must be addressed by all users of these models is definition of an acceptable level of watershed discretization (geometric model complexity). The level of geometric model complexity is a function of basin and climatic scales as well as the availability of input and verification data. Equilibrium discharge storage is employed to develop a quantitative methodology to define a level of geometric model complexity commensurate with a specified level of model performance. Equilibrium storage ratios are used to define the transition from overland to channel -dominated flow response. The methodology is tested on four subcatchments in the USDA -ARS Walnut Gulch Experimental Watershed in Southeastern Arizona. The catchments cover a range of basins scales of over three orders of magnitude. This enabled a unique assessment of watershed response behavior as a function of basin scale. High quality, distributed, rainfall -runoff data was used to verify the model (KINEROSR). Excellent calibration and verification results provided confidence in subsequent model interpretations regarding watershed response behavior. An average elementary channel support area of roughly 15% of the total basin area is shown to provide a watershed discretization level that maintains model performance for basins ranging in size from 1.5 to 631 hectares. Detailed examination of infiltration, including the role and impacts of incorporating small scale infiltration variability in a distribution sense, into KINEROSR, over a range of soils and climatic scales was also addressed. The impacts of infiltration and channel losses on runoff response increase with increasing watershed scale as the relative influence of storms is diminished in a semiarid environment such as Walnut Gulch. In this semiarid environment, characterized by ephemeral streams, watershed runoff response does not become more linear with increasing watershed scale but appears to become more nonlinear.

The United States Weather Bureau had published Technical Paper No. 40 (TP-40) in 1961 which provides a rainfall atlas for the United States. These rainfall frequencies have been used by engineers throughout the United States including Jefferson Parish, Louisiana. Rainfall from Audubon and the New Orleans International Airport rain gauge stations were used with the Log Pearson Method to provide rainfall frequency for Jefferson Parish, Louisiana. The results from the frequency rainfall that were developed for this research along with the current Jefferson Parish design storm rainfall were applied to a typical urban development to evaluate the extent of flooding.