Information about extreme precipitation is of great interest for a variety of purposes, which include dam design and its operation, public safety, engineering projects concerned with river management and drainage as well as rainfall-runoff relations. These require knowledge about the spatial and temporal variability of average rainfall over an area. Design rainfall values are generally expressed in the form of point rainfall intensity values which is the rainfall depth at a location. In order to obtain areal average values for an area, hydrologists and engineers require techniques whereby point rainfall amounts can be transformed to average rainfall amounts over a specified area. This problem of point-to-area rainfall conversion can be addressed using depth??area curves which require the use of areal reduction factors. The derivation of areal reduction factors is a focal issue and has been dealt with in diverse manners. Though the methods of derivation of the areal reduction factors vary, results shown by them are comparable. But all these methods have certain shortcomings in the procedures adopted by them. In this application the analysis is based on radar rainfall values obtained from NEXRAD for the study area of Texas as provided by West Gulf River Forecasting Centre (WGRFC). Using NEXRADradar rainfall data, geographically fixed depth area relationships will be determined. Here the objectives are to develop areal reduction factors using radar data and to identify the potential obstacles that might hinder the use of such data. The values of the factors developed will be finally compared to other studies which have been carried out. This approach aims to mitigate the difficulties faced in the applications of various procedures and the shortcomings of the various techniques used to determine the values of areal reduction factors.

Les précipitations présentent un impact socio-économique très important notamment dans les régions où les ressources hydriques sont rares et où les évènements pluvieux ont un caractère torrentiel. Plusieurs modèles hydrologiques ont vu le jour dans le but de prédire les débits qui sont d'une grande utilité pour la conception des barrages ou pour la prévision des inondations. Or, pour fournir des simulations de débit très proches de la réalité, ces modèles ont besoin de données pluviométriques acquises à grande résolution spatio-temporelle. De ce fait, il est intéressant pour ces modèles hydrologiques de recourir aux pluies estimées par les radars météorologiques satellitaires et au sol. Néanmoins, ces données doivent être validées avant toute utilisation. Le principal problème rencontré dans la validation des données radar météorologiques réside dans la grande différence d'échelle spatio-temporelle entre les données radar et les données fournies par les stations pluviométriques. Cette différence d'échelle ne peut pas être prise en compte par les méthodes conventionnelles de validation qui se limitent à calculer le coefficient de corrélation et à élaborer une relation régulière entre les deux types de données. L'objectif général de ce travail de recherche est la validation des pluies de surface estimées aussi bien par le radar satellitaire de TRMM que par le radar au sol NEXRAD WSR-88D afin d'améliorer l'échelle spatiale des simulations hydrologiques. Les données au sol utilisées pour la validation sont celles issues des stations pluviométriques du CNA ( Comisión Nacional del Agua ) et de la NOAA (National Oceanic and Atmospheric Administration ) localisées dans la région de Rio Escondido au Nord-Est du Mexique. Cette validation est réalisée en calculant les coefficients de corrélation entre les données de précipitation radar et les données au sol, en vérifiant l'existence de la propriété d'invariance d'échelle, et en évaluant la fiabilité des sorties du modèle hydrologique CEQUEAU utilisant les données radar comme entrée. La contribution principale de ce travail est d'utiliser la dimension fractale du champ de pluie comme outil de validation des estimations pluviométriques. Les résultats ont confirmé que les précipitations de surface estimées par le radar du satellite TRMM ne présentent pas les mêmes caractéristiques spatiales que celles des mesures fournies par les pluviomètres. Contrairement aux précipitations estimées par TRMM, les données du radar au sol sont compatibles avec un comportement d'échelle fractal et traduisent la variabilité intrinsèque du champ de pluie. C'est pourquoi elles ont été utilisées comme données d'entrée dans le modèle hydrologique CEQUEAU. Il en résulte des débits simulés avec un coefficient de Nash variant de -2,59 à 0,97. L'intérêt de ce résultat est qu'il montre l'utilité des données radar au sol pour les simulations des modèles hydrologiques et ce, particulièrement dans les zones où les pluies sont convectives, donc fortement variables et où les réseaux de pluviographes peuvent être insuffisants ou mal répartis. Cependant, la qualité de la simulation hydrologique dépend de l'échelle temporelle considérée et de l'évènement pluvieux choisi. Ainsi, ce travail a permis d'appliquer plusieurs méthodes de validation aux estimations radar des pluies de surface et de démontrer la pertinence de considérer les différences d'échelles spatio-temporelles dans la validation de ces données estimées.

Precipitation and surface runoff producing mechanisms are inherently spatially variable. Many hydrologic runoff models do not account for this spatial variability and instead use "lumped" or spatially averaged parameters. Lumped model parameters often must be developed empirically or through optimization rather than be calculated from field measurements or existing data. Recent advances in geographic information systems (GIS) remote sensing (RS), radar measurement of precipitation, and desktop computing have made it easier for the hydrologist to account for the spatial variability of the hydrologic cycle using distributed models, theoretically improving hydrologic model accuracy. Grid based distributed models assume homogeneity of model parameters within each grid cell, raising the question of optimum grid scale to adequately and efficiently model the process in question. For a grid or raster based hydrologic model, as grid cell size decreases, modeling accuracy typically increases, but data and computational requirements increase as well. There is great interest in determining the optimal grid resolution for hydrologic models as well as the sensitivity of hydrologic model outputs to grid resolution. This research involves the application of a grid based hydrologic runoff model to the Upper Roanoke River watershed (1480km2) to investigate the effects of precipitation resolution and grid cell size on modeled peak flow, time to peak and runoff volume. The gridded NRCS curve number (CN) rainfall excess determination and ModClark runoff transformation of HEC-HMS is used in this modeling study. Model results are evaluated against observed streamflow at seven USGS stream gage locations throughout the watershed. Runoff model inputs and parameters are developed from public domain digital datasets using commonly available GIS tools and public domain modeling software. Watersheds and stream networks are delineated from a USGS DEM using GIS tools. Topographic parameters describing these watersheds and stream channel networks are also derived from the GIS. A gridded representation of the NRCS CN is calculated from the soil survey geographic database of the NRCS and national land cover dataset of the USGS. Spatially distributed precipitation depths derived from WSR-88D next generation radar (NEXRAD) products are used as precipitation inputs. Archives of NEXRAD Stage III data are decoded, spatially and temporally registered, and verified against archived IFLOWS rain gage data. Stage III data are systematically degraded to coarser resolutions to examine model sensitivity to gridded rainfall resolution. The effects of precipitation resolution and grid cell size on model outputs are examined. The performance of the grid based distributed model is compared to a similarly specified and parameterized lumped watershed model. The applicability of public domain digital datasets to hydrologic modeling is also investigated. The HEC-HMS gridded SCS CN rainfall excess calculation and ModClark runoff transformation, as applied to the Upper Roanoke watershed and for the storm events chosen in this study, does not exhibit significant sensitivity to precipitation resolution, grid scale, or spatial distribution of parameters and inputs. Expected trends in peak flow, time to peak and overall runoff volume are observed with changes in precipitation resolution, however the changes in these outputs are small compared with their magnitudes and compared to the discrepancies between modeled and observed values. Significant sensitivity of runoff volume and consequently peak flow, to CN choices and antecedent moisture condition (AMC) was observed. The changes in model outputs between the distributed and lumped versions of the model were also small compared to the magnitudes of model outputs. / Master of Science

Master of Science / Department of Biological & Agricultural Engineering / Kyle R. Douglas-Mankin / Radar-derived P3 data from Next Generation Radar (NEXRAD) of the National Weather Service (NWS) offer higher spatial resolution than precipitation gauge data, which might improve the accuracy of streamflow simulations using watershed models. The objective of this study was to evaluate the performance of spatially-averaged subwatershed-specific NEXRAD P3 data on streamflow simulations using Soil and Water Assessment Tool (SWAT) model. The SWAT hydrologic model was chosen for this study to simulate the hydrologic processes in North Fork Ninnescah Watershed located in south-central Kansas. A precipitation gauge station for each subwatershed was created using an area-weighted average of NEXRAD P3 precipitation estimates for all HRAP grid cells covering the subwatershed. The SWAT model was calibrated with both NEXRAD P3 data and NCDC precipitation gauge (PG) data from 1 January 2002 to 31 December 2008. The P3-calibrated model was validated using PG data for the same simulation period (2002-2008), and vice versa. The PG-calibrated model yielded slightly higher daily Nash-Sutcliffe efficiency (E(subscript)NS = 0.40) than P3 calibrated model (ENS = 0.35), but the yearly ENS and PBIAS for P3 calibrated model (ENS = 0.80) was much better than PG-calibrated model (ENS = 0.43). The P3-validated model (PG calibration) had yearly ENS = of 0.70, whereas the PGcalibrated model had ENS = 0.43. The daily PBIAS value for P3-calibrated model in 2007 (wet year) was -14.13 and for the P3-calibrated model was -32.83; PG data overestimated the streamflow compared to P3 data in 2007. The P3 data has better agreement with PG data from 2002-2008 period than for 1996-2001 period. The streamflow estimation was better with NEXRAD P3 precipitation data in both calibration and validation runs. Even though the model was calibrated with PG data, the validated model with P3 data has comparatively high ENS. The spatial variation of streamflow response within the watershed was greater compared to the temporal variation in both the calibrated models. The spatial representation of precipitation data by NEXRAD P3 has improved the modeling performance compared to PG data; it is evident that NEXRAD data is an alternative to precipitation gauge measurements.

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.

Assessment of vegetation productivity on rangelands is needed to assist in timely decision making with regard to management of the livestock enterprise as well as to protect the natural resource. Characterization of the vegetation resource over large landscapes can be time consuming, expensive and almost impossible to do on a near real-time basis. The overarching goal of this study was to examine available technologies for implementing near real-time systems to monitor forage biomass available to livestock on a given landscape. The primary objectives were to examine the ability of the Climate Prediction Center Morphing Product (CMORPH) and Next Generation Weather Radar (NEXRAD) rainfall products to detect and estimate rainfall at semi-arid sites in West Texas, to verify the ability of a simulation model (PHYGROW) to predict herbaceous biomass at selected sites (patches) in a semi-arid landscape using NEXRAD rainfall, and to examine the feasibility of using cokriging for integrating simulation model output and satellite greenness imagery (NDVI) for producing landscape maps of forage biomass in Mongolia’s Gobi region. The comparison of the NEXRAD and CMORPH rainfall products to gage collected rainfall revealed that NEXRAD outperformed the CMORPH rainfall with lower estimation bias, lower variability, and higher estimation efficiency. When NEXRAD was used as a driving variable in PHYGROW simulations that were calibrated using gage measured rainfall, model performance for estimating forage biomass was generally poor when compared to biomass measurements at the sites. However, when model simulations were calibrated using NEXRAD rainfall, performance in estimating biomass was substantially better. A suggested reason for the improved performance was that calibration with NEXRAD adjusted the model for the general over or underestimation of rainfall by the NEXRAD product. In the Gobi region of Mongolia, the PHYGROW model performed well in predicting forage biomass except for overestimations in the Forest Steppe zone. Cross-validation revealed that cokriging of PHYGROW output with NDVI as a covariate performed well during the majority of the growing season. Cokriging of simulation model output and NDVI appears to hold promise for producing landscape maps of forage biomass as part of near real-time forage monitoring systems.

The relative importance of infrequent, episodic geomorphic events (e.g. floods, landslides, debris flows, earthquakes, tsunamis, etc.) in the evolution of the landscape has been a long-discussed question in the geomorphology community. These events are large in magnitude, but low in frequency, posing the complex question of how effective these events are at shaping the landscape. Unfortunately, the frequencies of these events are so low that it is extremely difficult to observe these events over human time scales. Also, the dangerous nature of these events makes them extremely difficult to observe and measure. However, the last few decades have brought new technology and techniques that provide a way to measure and calculate the magnitudes of these events more accurately and completely. In the present study, we use Next-Generation-Radar (NEXRAD) precipitation products, LiDAR tools, and multiple denudation-rate techniques to approach the magnitude and frequency of episodic events in different ways. Using NEXRAD precipitation products in conjunction with flow-routing algorithms, we were able to improve upon the traditional flood-envelope curves used to estimate the largest possible flood for a given basin area within a region. Improvements included adding frequency and uncertainty information to curves for the Upper and Lower Colorado River Basin, which in turn makes these curves more informative for flood hazard and policy applications. This study allowed us to improve upon a known flood-analysis method for identifying the distribution of the maximum floods with basin area. Both airborne and terrestrial LiDAR methods were used to measure the magnitude and time scale of the post-wildfire erosional response in two watersheds after the Las Conchas fire of 2011 in the Valles Caldera, NM. We found that sediment yield (measured by differencing LiDAR-derived DEMs) decreased exponentially with time in one watershed, while sediment yield in the other watershed decreased in a more complex way with time. Both watersheds had a recovery time (i.e. time interval over which sediment yields recovered to pre-wildfire levels) of one year. LiDAR was also used to understand the complex response of, and the processes on, the piedmonts adjacent to the watersheds. Overall, LiDAR proved to be extremely useful in measuring the magnitude and time scale of post-wildfire geomorphic response and observing the piedmont dynamics associated with elevated sediment yield. To understand the effects of wildfire on the long-term evolution of the landscape, techniques ranging from the relatively simple, traditional techniques (i.e. suspended-sediment-load sampling and paleosurface and modern surface differencing) to more complex and new techniques (i.e. ¹⁰Be and LiDAR) were used to measure the volumes and rates of denudation over multiple time scales in the Valles Caldera, NM. Long-term denudation rates were higher than short-term, non-wildfire-affected denudation rates, but lower than short-term, wildfire-affected denudation rates. Wildfire-affected denudation rates occurring at previously predicted frequencies (occurring<3% of the time interval) were found to account for the majority of long-term denudation, attesting to the importance of these episodic and extreme events in the evolution of the landscape.

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.

Hydrology Modeling using HEC-HMS (Hydrological Engineering Centre-Hydrologic Modeling System) is accepted globally for event-based or continuous simulation of the rainfall-runoff operation. Similarly, Machine learning is a fast-growing discipline that offers numerous alternatives suitable for hydrology research's high demands and limitations. Conventional and process-based models such as HEC-HMS are typically created at specific spatiotemporal scales and do not easily fit the diversified and complex input parameters. Therefore, in this research, the effectiveness of Random Forest, a machine learning model, was compared with HEC-HMS for the rainfall-runoff process. In addition, Point gauge observations have historically been the primary source of the necessary rainfall data for hydrologic models. However, point gauge observation does not provide accurate information on rainfall's spatial and temporal variability, which is vital for hydrological models. Therefore, this study also evaluates the performance of satellite and radar precipitation products for hydrological analysis. The results revealed that integrated Machine Learning and physical-based model could provide more confidence in rainfall-runoff and flood depth prediction. Similarly, the study revealed that radar data performance was superior to the gauging station's rainfall data for the hydrologic analysis in large watersheds. The discussions in this research will encourage researchers and system managers to improve current rainfall-runoff simulation models by application of Machine learning and radar rainfall data.

This dissertation includes two parts. Part 1 develops a geostatistical method to calibrate Texas NexRad rainfall estimates using rain gauge measurements. Part 2 explores the asymptotic joint distribution of sample space-time covariance estimators. The following two paragraphs briefly summarize these two parts, respectively. Rainfall is one of the most important hydrologic model inputs and is considered a random process in time and space. Rain gauges generally provide good quality data; however, they are usually too sparse to capture the spatial variability. Radar estimates provide a better spatial representation of rainfall patterns, but they are subject to substantial biases. Our calibration of radar estimates, using gauge data, takes season, rainfall type and rainfall amount into account, and is accomplished via a combination of threshold estimation, bias reduction, regression techniques and geostatistical procedures. We explore a varying-coefficient model to adapt to the temporal variability of rainfall. The methods are illustrated using Texas rainfall data in 2003, which includes WAR-88D radar-reflectivity data and the corresponding rain gauge measurements. Simulation experiments are carried out to evaluate the accuracy of our methodology. The superiority of the proposed method lies in estimating total rainfall as well as point rainfall amount. We study the asymptotic joint distribution of sample space-time covariance esti-mators of stationary random fields. We do this without any marginal or joint distri-butional assumptions other than mild moment and mixing conditions. We consider several situations depending on whether the observations are regularly or irregularly spaced, and whether one part or the whole domain of interest is fixed or increasing. A simulation experiment illustrates the asymptotic joint normality and the asymp- totic covariance matrix of sample space-time covariance estimators as derived. An extension of this part develops a nonparametric test for full symmetry, separability, Taylor's hypothesis and isotropy of space-time covariances.