Dissertations / Theses on the topic 'Hydrologic models'

The level of spatial and vertical detail of important hydrologic processes within a watershed that needs to be represented by a conceptual rainfall-runoff (CRR) model in order to accurately simulate the streamflow is not well understood. The paucity of high-resolution hydrologic information in the past guided the direction of model development to more accurately represent processes directly related to the vertical movement of moisture within the watershed rather than the spatial variability of these processes. As a result, many of the CRR models currently available are so complex (vertically), that expert knowledge of the model and watershed system is required to successfully estimate values for model parameters using manual methods. Automatic parameter estimation procedures, developed to reduce the time and effort required with manual methods, do not provide parameter estimates and hydrograph simulations that are considered acceptable by the hydrologists responsible for operational forecasting. Newly available, high-resolution hydrologic information may provide insight to the spatial variability of important rainfall-runoff processes. However, effective and efficient methods to incorporate the data into the current modeling strategies need to be developed. This work describes a new hybrid multicriteria calibration approach that combines the strength of automatic and manual calibration methods. The new approach was used to investigate the benefits of different levels of spatial and vertical representation of important watershed hydrologic variables with conceptual rainfall runoff models.

El principal objectiu d'aquesta investigació és estudiar la dinàmica hidrològica d'una conca Mediterrània<br/>afectada per canvis d'ús del sòl, mitjançant el monitoreig d'aquest i de l'aigua superficial. Aquest<br/>objectiu s'ha treballat a partir mesuraments de components del balanç hídric pels diferents tipus de<br/>cobertura i sòl, amb règims d'humitat i temperatura de transició.<br/>Aquest estudi s'ha realitzat a la conca de la Ribera Salada (Prepirineu meridional Català, al NE<br/>d'Espanya), amb una extensió de 222.5 km2, i un interval altitudinal de 420 a 2385 m i predomini de<br/>pendents entre 12 - 25 % i 25 - 50 %. El substrat consisteix en conglomerats calcaris massius, calcilutites<br/>i llims. La precipitació es de 507 i 763 mm. Amb sòls poc profunds, calcaris i pedregosos, essent<br/>majoritàriament Inceptisòls (Typic Calciusteps, Typic Haploustepts) i Entisòls (Typic Ustifluvents, Typic<br/>Udorthortents). A les zones més elevades de la conca, els sòls són més humits, degut a l'augment de la<br/>precipitació, on es produeixen processos de descarbonatació del sòl. L'ús del sòl és majoritàriament<br/>forestal, amb presència d'ecosistemes de ribera, subalpins i vegetació submediterrània. Algunes àrees es<br/>troben amb cultius de patata, cereal i pastures. Una de les característiques més importants d'aquesta<br/>conca són els canvis d'ús del sòl que ha patit en els últims 50 anys degut a l'abandó dels masos i cultius<br/>tradicionals. Es seleccionaren vuit llocs de mostreig considerant les següents cobertes: Quercus ilex, bosc<br/>de ribera, Pinus sylvestris, pastures, cultius (cereal-patata) i Pinus uncinata. A partir de l'any 1997 fins el<br/>2005, s'han anat monitorejant el contingut d'humitat del sòl, l'escolament i els cabals. Des del 2004 s'han<br/>anat anotant dades de drenatge. Les variables meteorològiques es mesuren a l'estació de Lladurs de la<br/>XAC (Xarxa Agrometeorològica de Catalunya).<br/>Els resultats obtenguts durant tres anys mostren una domini del règim d'humitat ústic (SSS, 2006), o xèric<br/>en aquells anys més secs. En la modelització de règims d'humitat i temperatura del sòl, s'utilitzaren els<br/>models de simulació NSM "Newhall simulation model" (Newhall, 1976) i JSM "Jarauta simulation<br/>model" (Jarauta 1989). NSM (Newhall,1976) tendeix a sobre estimar el règim d'humitat del sòl, però<br/>JSM (Jarauta, 1989) simula correctament el règim d'humitat del sòl (SSS, 2006) de la conca, funcionant<br/>millor en condicions intermitges d'humitat del sòl. Ambdós models simulen correctament el règim de<br/>temperatura dels sòls. Predomina un règim de temperatura mèsic-tèrmic, amb tendència a tèrmic els anys<br/>secs. A petita escala la profunditat del sòl, pendent, pedregositat i una alta porositat del sòl són factores<br/>que varien el règim d'humitat del sòl. La informació de sòl i clima, complementada mitjançant SIG, va<br/>permetre l'obtenció de mapes de règim d'humitat del sòl de la conca, a escala 1:50000, els quals<br/>permeten establir mediante simució els règims d'humitat del sòl en diferents escenaris de canvis<br/>meteorològics.<br/>El model TOPLATS ha sigut utilitzat en l'estimació de l'humitat del sòl en diferents usos del sòl. Aquest<br/>model fou calibrat amb les equacions del filtre Kalman estès (EKF), que deriven de la minimització del<br/>quadrat de la diferència entre els valors reals i els estimats (Goegebeur & Pauwels, 2007). Aquesta<br/>metodologia interrelaciona correctament els valors de pluja, humitat del sòl, escolament i infiltració,<br/>essent els valors d'humitat els que més s'aproximen als reals. Els resultats mostren que aquest filtre és<br/>una eina útil per estimar el volum d'aigua del sòl emmagatzemada en conques a escala puntual,<br/>assegurant una aplicació correcta del model hidrològic.<br/>Per la modelització del comportament de l'humitat del sòl i diferents components del balanç hídric<br/>s'utilitzà el modelo TOPLATS (Famiglietti & Wood, 1994). El model de simulació TOPLATS permite<br/>simulà acceptablement el comportament de l'humitat del sòl. Els resultats de infiltració, escolament,<br/>intercepció, evapotranspiració de referència i temperatura del sòl són correctes. Les diferències existents<br/>entre valors simulats i observats són: l'humitat del sòl no sobrepassa el 5%, la infiltració fluctua entre 4%<br/>i 15%, la diferència entre els valors reals i simulats d'evapotranspiració, depèn de l'estació de l'any,<br/>essent 1mm a l'hivern i 2.7 mm a l'estiu. La temperatura varia entre 0.01ºC i 3.5ºC. El model calibrat<br/>prediu amb precisió el comportament de les diferents components del balanç hídric. Respecte als valors<br/>mesurats d'aigua de drenatge correspon al 11-41 % de la pluja total.<br/>Respecte al balanç d'aigua en el sòl (&#916;SW), els valors són negatius durant cert període de l'any, arribant a<br/>valors crítics els mesos secs. La recuperació de humitat del sòl durant la resta de mesos succeeix de<br/>manera parcial. A la part mitja de la conca, alguns mesos els valors d'humitat del sòl s'acosten a<br/>condicions de punt de marchites (ecosistema submediterrani). A la part alta de la conca el sòl conserva<br/>humitat (ecosistema subalpí). Els valors de cabal trobats corresponen a aportacions per escolament el<br/>cuals són molt baixos. La majoria de les sortides es deuen a evapotranspiració, intercepció, infiltració i<br/>drenatge (en ordre de importància).<br>El principal objetivo de esta investigación es estudiar la dinámica hidrológica de una cuenca Mediterránea<br/>afectada por los cambios de uso del suelo, mediante el monitoreo del suelo y el agua superficial. Dicho objetivo<br/>se ha abordado a partir de la medición de componentes del balance hídrico para diferentes tipos de cobertura y<br/>suelo, considerando regimenes de humedad y temperatura de transición.<br/>Este estudio se ha realizado en la cuenca de la Ribera Salada (Prepirineo meridional Catalán, NE España) de<br/>222.5 km2, con un intervalo altitudinal de 420 a 2385 m y predominio de pendientes entre 12 - 25 % y 25 - 50<br/>%. El sustrato consiste en conglomerados calcáreos masivos, calcilutitas y limos. La precipitación anual es de<br/>507 y 763 mm. Los suelos són poco profundos, calcáreos y pedregosos, siendo en su mayoría Inceptisols<br/>(Typic Calciusteps, Typic Haploustepts) y Entisols (Typic Ustifluvents, Typic Udorthortents). En las partes<br/>altas de la cuenca los suelos son más húmedos, debido al aumento de la precipitación, allí ocurren procesos de<br/>descarbonatación del suelo. Predomina el uso forestal, con ecosistemas de ribera, subalpinos y vegetación<br/>submediterránea. Algunas áreas se dedican al cultivo de patatas, cereal y pastos. Una de las características más<br/>importantes de esta cuenca es los importantes cambios de uso del suelo sufridos en los últimos 50 años, debido<br/>al abandono de las masías y cultivos tradicionales.<br/>Se seleccionaron ocho sitios de muestreo, considerando las siguientes coberturas: Quercus ilex, bosque de<br/>ribera, Pinus sylvestris, pastos, cultivo (cereal-patata) y Pinus uncinata. A partir del año 1997 hasta 2005, se<br/>han venido monitoreando el contenido de humedad del suelo, escorrentía y caudales. Desde 2004 se vienen<br/>tomando datos drenaje. Las variables meteorológicas se miden la estación Lladurs perteneciente a la XAC<br/>(Xarxa Agrometeorológica de Cataluña).<br/>Los resultados obtenidos par un period de tres años muestran una predominancia del regimen de humedad<br/>ústico (SSS, 2006), o xérico en los años más secos. Se utilizaron los modelos de simulación NSM "Newhall<br/>simulation model" (Newhall, 1976) y JSM "Jarauta simulation model" (Jarauta 1989) en la modelización de<br/>regimenes de humedad y temperatura del suelo. NSM (Newhall,1976) tiende a sobre estimar el régimen de<br/>humedad del suelo. Por contra, JSM (Jarauta, 1989) simula de forma correcta el régimen de humedad del suelo<br/>(SSS, 2006) presente en la cuenca, funcionando mejor bajo condiciones medias de humedad del suelo. Ambos<br/>modelos simulan de forma correcta el régimen de temperatura de los suelos. Predomina un régimen de<br/>temperatura mésico-térmico, con tendencia a térmico para los años secos. A pequeña escala la profundidad del<br/>suelo, pendiente, pedregosidad y alta porosidad del suelo son factores que hacen variar el régimen de humedad<br/>del suelo. La información de suelo y clima, complementada mediante SIG, permitió obtener mapas de régimen<br/>de humedad del suelo para la cuenca, a una escala 1:50000, los cuales permiten establecer mediante simulación<br/>los regimenes de humedad en el suelo bajo diferentes escenarios de cambios meteorológicos.<br/>El modelo TOPLATS ha sido utilizado en la estimación de la humedad en el suelo para diferentes usos del<br/>suelo. Este modelo fue calibrado con las ecuaciones del filtro Kalman extendido (EKF), que se derivan de la<br/>minimización del cuadrado de la diferencia entre los valores reales y los estimados (Goegebeur & Pauwels,<br/>2007). Esta metodología interrelaciona correctamente los valores de lluvia, humedad en el suelo, escorrentía y<br/>infiltración, siendo los valores de humedad los mas ajustados a los valores reales. Los resultados muestran que<br/>este filtro es una herramienta para estimar el volumen de agua en el suelo almacenada en las cuencas a escala<br/>puntual, asegurando una aplicación correcta del modelo hidrológico.<br/>Para la modelización del comportamiento de la humedad del suelo y los diferentes componentes del balance<br/>hídrico se utilizó el modelo TOPLATS (Famiglietti & Wood, 1994). El modelo de simulación TOPLATS<br/>permite simular aceptablemente el comportamiento de la humedad del suelo. Los resultados para infiltración,<br/>escorrentía, intercepción, evapotranspiración de referencia y temperatura del suelo son correctos. Las<br/>diferencias existentes entre valores simulados y observados son: la humedad del suelo no sobrepasa el 5%, la<br/>infiltración fluctúa entre 4% y 15%, la diferencia entre los valores reales y simulados de evapotranspiración,<br/>depende de la estación del año, siendo 1mm en invierno y 2.7 mm en verano, la temperatura varia entre 0.01 ºC<br/>y 3.5ºC. El modelo calibrado predice con precisión el comportamiento de las diferentes componentes del<br/>balance hídrico. Respecto a los valores medidos para agua de drenaje corresponde al 11-41 % de la lluvia total.<br/>Respecto al balance de agua en el suelo (&#916;SW), los valores son negativos para un corto periodo del año,<br/>alcanzando valores críticos en meses secos. La recuperación de humedad del suelo para el resto de los meses<br/>ocurre de manera parcial. En la parte media de la cuenca, para algunos meses los valores de humedad del suelo<br/>son cercanos a condiciones de punto de marchites permanente (ecosistema submediterráneo). En la parte alta<br/>de la cuenca el suelo conserva condiciones intermedias de humedad (ecosistema subalpino). Los valores de<br/>caudal encontrados corresponden a los aportes por escorrentía, los cuales son muy bajos. La mayor parte de las<br/>salidas ocurren por evapotranspiración, intercepción, infiltración y drenaje (en orden de importancia).<br>The main aim of this research is to study the hydrological dynamics of a Mediterranean mountain basin<br/>affected by land use changes, by means of the monitoring of soil and surface water. This aim has been<br/>reached by measuring and simulating hydric balance components of different soils and under different<br/>vegetational types, considering water and temperature transition regimes.<br/>This research was done in Ribera Salada basin (Catalan Pre Pyrenees, NE Spain), with an area of 222.5<br/>km2, altitudes between 420 and 2385 m, with predominance slopes between 12 - 25 % and 25 - 50 %. The<br/>substrate consists of massive calcareous conglomerates, calcilutites and limestones. Main annual<br/>precipitation are 507 to 763 mm. Soils are shallow, calcareous and stony, being most of them Inceptisols<br/>(Typic Calciusteps, Typic Haploustepts) and Entisols (Typic Ustifluvents, Typic Udorthortents). In the<br/>upper and moister part of the basin soil decarbonatation takes place. Forest use is predominant, going<br/>from brook forest environments to subalpine and submediterranean vegetation. Agricultural uses include<br/>mainly the growing of cereals, potatoes and pastures. One of the most important characteristics in this<br/>basin are the significant soil use changes in the last 50 years, due to the abandonment of farms and<br/>traditional crops.<br/>Eight sites were studied, corresponding to soils under Quercus ilex, brook forest, Pinus sylvestris, pasture,<br/>crops (cereal-potatoes) and Pinus uncinata. From 1997 until 2005, soil moisture, run-off, water flow and<br/>interception were monitored. From 2004 on, drainage data has been recorded. Meteorological variables<br/>were measured by means of a complete Lladurs meteorological station, belonging to XAC (Catalan<br/>Agrometeorological Network).<br/>The obtained results to three years show the predominance of ustic moisture regime (SSS, 2006), or xeric<br/>during the driest years. The simulation models NSM "Newhall simulation model" (Newhall, 1976) and<br/>JSM "Jarauta simulation model" (Jarauta 1989) were used to represent soil moisture and temperature<br/>regimes. NSM estimates a higher level of soil moisture regimes than observed. On the contrary, JSM<br/>simulates correctly soil moisture regimes, working better under intermediate soil moisture conditions.<br/>Both models simulate correctly the soil temperature regimes, being mesic-thermic to thermic during the<br/>driest years. At detailed scale (plot observation), soil depth, slope, stone amount and high soil porosity are<br/>factors that affect the soil moisture regimes. Soil and climate information, implemented through a GIS,<br/>allowed us to obtain soil moisture regime maps of the basin at a 1:50000 scale, which are very useful to<br/>simulate soil moisture regimes in different scenarios of meteorological changes.<br/>The TOPLATS model, when used to estimate soil moisture under different cover types, was calibrated<br/>with Extend Kalman filter (EKF) equations derived through a minimization of the square difference<br/>between the true and estimated model state (Goegebeur & Pauwels, 2007). This methodology interrelates<br/>correctly rainfall, soil moisture, runoff and infiltration. Among them, the obtained soil moisture values<br/>corresponded the best to observed data. The results show that it is a useful tool to estimate soil water<br/>volume stored in basins at a point scale, ensuring a correct application of this hydrological model.<br/>To model soil moisture behaviour and the different hydric balance components, the TOPLATS model<br/>(Famiglietti & Wood, 1994) was used. TOPLATS model simulates correctly the soil moisture behaviour.<br/>The differences between observed and simulated values are the following: soil moisture does not surpass<br/>5%; the infiltration fluctuates between 4% to 15%; in evapotraspiration depends on the season being<br/>between 1 mm in winter to 2.7 mm in summer, soil temperature values difference fluctuates between<br/>0.01ºC and 3.5ºC.The calibrated model predicts precisely the behaviour of different hydric balance<br/>components. The measured water drainage amount is 11-41 % of total rain.<br/>The observed and simulated soil water storage in the basin (&#916;SW), has negative values during the driest<br/>months. Soil moisture recovery during the rest of the months is only partial. In the medium part of the<br/>basin, occupied by submediterranean ecosystems, soil moisture values are closer to drought conditions<br/>during some months of the year. In the highest part of the basin (subalpine ecosystems) there are<br/>intermediate soil moisture conditions in dry periods. Most part of water outputs are due to<br/>evapotranspiration, interception, infiltration and drainage, in decreasing order of importance. Run-off<br/>values are very low.

Thesis (Ph. D.)--Ohio State University, 2003.<br>Title from first page of PDF file. Document formatted into pages; contains xvi, 378 p.; also includes graphics (some col.). Includes bibliographical references (p. 229-252). Available online via OhioLINK's ETD Center

<p> Multi-model analysis (MMA) considers multiple model interpretations of a system. MMA provides a more realistic assessment of uncertainty associated with model predictions because both uncertainty of individual models and uncertainty associated with different model structures are considered. Models are evaluated for the strength of evidence that they represent an unknown system using different Information Criteria (IC) equations. IC equations are designed to assess the likelihood that a model in a set of models represents the true but unknown system. IC equations do not include a component which identifies a deficient model. Therefore, inclusion of deficient models in the set of models leads to poor model-averaged results. Evaluation of models to assess whether available observation data sufficiently support the model structure is an important step in MMA. Measures for evaluating models include: 1) failure to reach proper convergence during non-linear regression; 2) unreasonable parameter estimates; 3) unreasonable confidence intervals on parameters or a coefficient of variation greater than ten for one or more parameters; 4) high correlations between parameters; 5) determinant of the correlation matrix less than 1x10^-12; 6) condition number of the Jacobian matrix greater than 2000; and 7) unreasonable confidence intervals on predictions.</p><p> Experiments presented herein are designed to evaluate how components of AIC, AICc, BIC, and KIC rank models and assign model probabilities, and to demonstrate how removing deficient models improves MMA results. Synthetic models are used to represent true but unknown systems in contrast to experimental models that are created to simulate a simplified version of the unknown system based on observation data taken from the synthetic models. AIC, AICc, BIC, and KIC generally assign high probability to deficient models. AICc generally assigns high probability to deficient models if 1) there are many observation data or 2) there are few observation data and the model fits the data well. KIC generally assigns high probability to deficient models because these models have low Fisher Information. AIC and BIC are influenced by the goodness-of-fit and are more likely to assign high probability to more complex models because these models are generally over-fitted. Removing deficient models results in improved MMA results using AIC, AICc, BIC and KIC.</p><p>

This thesis is a combination of two separate studies which examine hydrologic data assimilation techniques: 1) to determine the applicability of assimilation of remotely sensed data in operational models and 2) to compare the effectiveness of assimilation and other calibration techniques. The first study examines the ability of Data Assimilation of remotely sensed microwave radiance data to improve snow water equivalent prediction, and ultimately operational streamflow forecasts. Operational streamflow forecasts in the National Weather Service River Forecast Center are produced with a coupled SNOW17 (snow model) and SACramento Soil Moisture Accounting (SAC-SMA) model. A comparison of two assimilation techniques, the Ensemble Kalman Filter (EnKF) and the Particle Filter (PF), is made using a coupled SNOW17 and the Microwave Emission Model for Layered Snowpack model to assimilate microwave radiance data. Microwave radiance data, in the form of brightness temperature (TB), is gathered from the Advanced Microwave Scanning Radiometer-Earth Observing System at the 36.5GHz channel. SWE prediction is validated in a synthetic experiment. The distribution of snowmelt from an experiment with real data is then used to run the SAC-SMA model. Several scenarios on state or joint state-parameter updating with TB data assimilation to SNOW-17 and SAC-SMA models were analyzed, and the results show potential benefit for operational streamflow forecasting. The second study compares the effectiveness of different calibration techniques in hydrologic modeling. Currently, the most commonly used methods for hydrologic model calibration are global optimization techniques. While these techniques have become very efficient and effective in optimizing the complicated parameter space of hydrologic models, the uncertainty with respect to parameters is ignored. This has led to recent research looking into Bayesian Inference through Monte Carlo methods to analyze the ability to calibrate models and represent the uncertainty in relation to the parameters. Research has recently been performed in filtering and Markov Chain Monte Carlo (MCMC) techniques for optimization of hydrologic models. At this point, a comparison of the effectiveness of global optimization, filtering and MCMC techniques has yet to be reported in the hydrologic modeling community. This study compares global optimization, MCMC, the PF, the Particle Smoother, the EnKF and the Ensemble Kalman Smoother for the purpose of parameter estimation in both the HyMod and SAC-SMA hydrologic models.

Government Island, located in the Columbia River approximately 16 km (10 mi) upstream of the confluence with the Willamette River, is a wetland mitigation site prompted by expansion of the southwest quadrant of Portland International Airport. The purpose of the study is to predict water levels in two enclosed lowland areas, Jewit Lake and Southeast Pond, based on levels of the Columbia River, precipitation, and evapotranspiration. Mitigation is intended to convert 1.13 km2 (237 acres) of seasonally flooded wetland to 1.27 km2 (267 acres) of semi-permanently flooded wetland and seasonally flooded wetland. Flooding of the wetland is most likely to occur December through January and May through early June when Columbia River water levels at Government Island exceed 3.6 m (12 ft) m.s.l. Flooding of Jewit Lake occurs through a channel connecting the wetland to the Columbia River. A groundwater model (MODFLOW) was parameterized to simulate the hydrology of the wetland. Observations of the subsurface stratigraphy in 25 soil pits, bucket auger cores, and during installation of water monitoring devices were used to estimate thickness and lateral extent of a confining unit that overlies an aquifer. Climatological data for 1994 and water levels were entered into MODFLOW to calibrate rates of water movement through the subsurface. Periods of drying for Jewit Lake and Southeast Pond were predicted based on precipitation and actual evapotranspiration rates expected to be present in the study area between June and December. Results of groundwater modeling show that Jewit Lake will maintain surface water above 3.6 m (12 ft) in most years. Southeast Pond is expected to dry annually as mitigation is unlikely to change the hydrology of Southeast Pond. Groundwater modeling predicted the types of wetlands present at different elevations by evaluating periods of drying within the wetland using the U.S. Fish and Wildlife Service classification of wetlands method. Results suggest that Jewit Lake will be converted to semipermanently flooded wetland below 3.6 m (12 ft) in elevation. Southeast Pond will remain a seasonally flooded wetland as a result of mitigation.

Models used as part of quantitative studies of vadose zone processes are becoming increasingly complex. However, even the most elaborate models can not capture the complex interactions between spatially distributed water, plant, and atmospheric components of the unsaturated flow system. These processes will always need to be approximated by relatively simple mathematical expressions with limited parameterization. Because of this, there is an ever increasing awareness among hydrologists of the need to describe and quantify these uncertainties to better understand the utility of model predictions and inform decisions concerning model development and data collection. Significant developments in the most recent generation of parameter estimation codes have facilitated the estimation of parameters and quantification of the associated uncertainty in the parameter estimates and model structure; however, these codes are computationally expensive. To facilitate the proposed analysis of more computationally efficient models are required.Computationally efficient models do not necessarily imply over simplified models In the appropriate context, simplifications are possible that reduce the complexity of the model but do not reduce the complexity of the system being represented by the model. I investigate a series of approaches to reduce the computational load of models, facilitating inverse analysis with readily available computing facilities.In light of the improvements to the methodology of parameter estimation, the success of the analysis still depends on the observed response to which the model is compared; the data and the information contained in the data. Given limited resources (both cost and technology) it is important to identify those data that will provide the greatest information about a system. To this end, the investigations presented here also investigate methods to identify informative data and to extract information from data effectively.

A method by which a Geographic Information System can be integrated with a hydrologic model in a seamless manner is presented. The three dimensional finite difference ground-water flow model, MODFLOW, is used as an example to show GIS model integrations using Common Object Interfaces (COi). The model is integrated with the GIS using a set of required steps needed to identify the nature ofMODFLOW's data requirements and computational structure. This allows alternate methods to be developed that can obtain data from a Geographic Information System or from a Graphic User Interface. Results of the integration are analyzed by running the integrated model on a sample data set and problems that were encountered during the integration proceedure are identified. The results of the integration indicate that the use of Common Object Interfaces can be successfully incorporated into legacy FORTRAN coded hydrology programs. Factors which verify this conclusion are considered, and the benefits and problems with this type of integration are discussed. Recommendations for future integration efforts and issues that need to be addressed are explored.

Thesis (Ph. D.)--West Virginia University, 2006.<br>Title from document title page. Document formatted into pages; contains viii, 95, 55 p. : ill. (some col.), maps (some col.). Includes abstract. Includes bibliographical references.

This research assesses possible methods for extending the Streamflow Prediction Tool from a streamflow forecasting model to a flood extent forecasting model. This new flood extent forecasting model would allow valuable and easy to understand information be disseminated in a timely manner for flood preparation and flood response. The Height Above Nearest Drainage (HAND) method and AutoRoute method were considered for flood extent models but the HAND was the better option for its simple and quick computation as well as its viability on a global scale. Due to the importance of Digital Elevation Models (DEMs) in these flood extent models, an analysis was performed on the sensitivity and response of different DEMs with the HAND method. The HAND method with the differing DEMs was also analyzed using the Streamflow Prediction Tool for model boundary conditions against Sentinel-1 SAR generated flood extent images from August 24, 2017. The MERIT DEM performed the best in this analysis and is recommended for future research in creating a global forecasting flood extent model. The HAND method covered about 25% of the generated flood extent images and more complex flood extent models may need to be considered in areas where HAND underperforms. Finally, a proof of concept flood extent model was created and deployed as a web application for easy accessibility and distribution of flood information. Additional research to consider is flood impact based on affected population or an economic analysis, as well as optimizing model parameters for increased accuracy and performance. Additional research is also needed for HAND DEM analysis in other parts of the world.

Deeper understanding of relationships between flow in river sand various hydrologic elements such as rainfall, land use, and soil type is imperative to solve water related problems like droughts and floods. Advanced computer models are becoming essential in helping us understand such relationships. However, preparing such models requires huge investment of time and resources, much of which are concentrated on acquisition and curation of data. This work introduces agree and open source web Application (web App) that provides researchers with simplified access to hydrological data and modeling functionality. The web App helps in the creation of both hydrologic models, and climatic and geographic data. Free and open source platforms such as Tethys and Hydro Share were used in the development of the web Apia physics based model called TOPographic Kinematic APproximation and Integration (TOPKAPI) was used as the driving use case for which a complete hydrologic modeling service was developed to demonstrate the approach. The final product is a complete modeling system accessible through the web to create hydrologic data and run a hydrologic model for a watershed of interest. An additional model, TOPNET, was incorporated to demonstrate the generality of the approach and capability for adding other models into the framework.

A physically-based, user friendly, hydrologic computer simulation model was developed for pinyon-juniper woodland watersheds. The data requirements are minimum, requiring vegetation conditions, basic soil survey information, and daily values for precipitation and temperature. The model predicts runoff from cleared and uncleared watersheds by simulating hydrologic processes on a daily basis. The model was tested with data from small pinyon-juniper watersheds in central Arizona. A crack-forming vertisol was the dominant soil type, and a special feature for addressing its effects on runoff was included. No significant difference between predicted and observed annual runoff was found at the ninety-five percent confidence level.

Thesis (Ph. D.)--Southern Illinois University Carbondale, 2009.<br>"Environmental Resources and Policy." Keywords: Groundwater modeling, Hydrogeology, Well hydraulics, Hydrologic tests. Includes bibliographical references (p. 67-75). Also available online.

Analytical solutions to well hydraulic problems have restrictive assumptions that often do not match real world conditions. Although numerical models more closely match reality, they either ran too slowly to be practical or lacked accuracy because of coarse grid spacing and large time steps. Advances in computer power over the last few decades now allow for accurate, fast numerical models that handle complex flow systems. The purpose of this dissertation was to develop flexible and accurate numerical modeling codes for the simulation of hydrologic tests. One of these numerical modeling codes, the Slug Test Simulator (STS), was designed for the mechanics of a single well test, or slug test. STS can handle a variety of conditions including unconfined flow, partial penetration, layered heterogeneities, and the presence of a homogeneous well skin like existing codes. This program also extends on the capabilities of earlier codes with its ability to simulate a heterogeneous skin where K can vary in both the radial and vertical directions. STS has a clear user interface, can display graphical results, and allows the user to determine hydraulic conductivity through a trial-and-error curve-matching process. Comparisons of STS to the Cooper-Bredehoeft-Papadopulos analytical solution and the Kansas Geological Survey (KGS) semi-analytical solution produced near-identical curves under a wide variety of conditions. Numerous analytical studies have shown that the well skin is an important factor in the underestimation of hydraulic conductivity in slug tests. STS allows for the exploration of the well skin issue under conditions too complex for analytical models. Model trials revealed two key discoveries: 1) if any layers within the skin have the same hydraulic conductivity as the surrounding formation, flow is concentrated within these conduits and the resultant head response approaches the case when no skin is present; and 2) the two most important properties in determining the overall influence of the skin are specific storage and skin thickness. The first discovery suggests that extensive development activities can essentially eliminate any well skin impacts. Other factors such as partial penetration, the placement of the well screen, and anisotropy play insignificant roles in resultant head responses. Recent research is focusing on alternative direct- push (DP) methodologies to determine hydrologic properties. DP offers advantages over traditional well tests, but may yield inaccurate results if the screen becomes clogged during pushing activities. The Kansas Geological Survey (KGS) developed a new DP technique, the Direct-Push Permeameter (DPP), to overcome this limitation. Existing analytical or numerical models cannot address the specialized nature of DPP tests so a second numerical modeling code, the Direct Push Permeameter Simulator (DPSS), was developed. DPPS was generated by modifying STS so both numerical codes are similar in many ways, particularly with their flexibility and accuracy. The codes differ in how they handle vertical layering, the boundary conditions at the well, and the spreadsheet interfaces. DPPS was able to produce near-identical curves in comparison to the Theis analytical solution. DPPS was also able to reasonably recreate DPP field data conducted at two sites with distinctly different media properties. The GEMS and Nauen sites had an average error of 14.2% and 3.1%, respectively between the field data and DPPS simulations.

To potentially better identify variability in sediment yields under a range of forest management and climatic scenarios at the catchment scale, alternative approaches by modelling sediment yields were utilised in this research using: (1) a spatially distributed calibrated algorithm of soil loss coupled with sediment delivery ratio (SDR) functions; (2) linear regression models between sampling suspended sediment concentrations and corresponding instantaneous variables of streamflow and in-stream turbidity; and (3) a physically-based SWAT model. Four catchments in Kangaroo River State forest, northern NSW were assessed for annual changes in sediment yields. Two catchments were selectively logged in 2007 while the two other sites remained undisturbed. Instantaneous changes in streamflow and suspended sediment constituents were systematically monitored between 2001 and 2009 as part of native forest management activities in the forested catchments. In addition, water samples were collected by autosamplers to cover a wide range of water constituent variability. According to the different methods applied in this research, rainfall intensity had the most significant influence on sediment delivery, streamflow and sediment yields at channel outlets in all catchments, with steeply sloping areas contributing large amounts of sediment during moderate and high rainfall years in 2007 and 2009. Slope gradient was found to contribute substantially to the spatial variability of soil loss estimation across the catchments. The impact of vegetation removal was minimal even during high rainfall conditions in the 2007 logging year. It is concluded that the current scenario of single-tree selection logging utilised in the study area is an acceptable and environmentally sound land management strategy for preservation of soil and water resources.

This thesis describes a research project which focuses on improving the accuracy, and extending the capabilities of topographic and hydrologic analysis algorithms. These algorithms can be applied within GIS frameworks for parameterisations of hydrologic models. In this research project, several new algorithms were developed to overcome the observed deficiencies in current algorithms for GIS based analysis of raster Digital Elevation Models (DEMs). These algorithms were used to develop a software product CatchmentSIM which has been made freely available to researchers and practitioners. CatchmentSIM allows for interpolation of a DEM from contour and streamline data, removal of flat and pit cells, catchment delineation, automated catchment break-up, analysis of impervious areas, modelling of urban catchments, and the hydrologic and geomorphologic analysis of subcatchment properties. Following the application of CatchmentSIM to a DEM, a simple internal macro language can be used to automatically create files in any binary or text file format. This allows coupling with a full range of Australian and international hydrologic models, including RAFTS-XP, WBNM, RORB, URBS, DRAINS and HEC-HMS. The algorithims developed during this research were verified by comparative analysis against current approaches, as well as verification in two case studies. CatchmentSIM enables users to build on the increasingly comprehensive information available in todays GIS world, while avoiding the traditional shortcomings of conventional raster GIS techniques, and maintaining tight coupling with existing industry standard modelling approaches.

This report presents a new multiple objective optimization algorithm that is capable of solving for the entire Pareto set in one single optimization run. The multi-objective complex evolution (MOCOM-UA) procedure is based on the following three concepts: (1) population, (2) rank-based selection, and (3) competitive evolution. In the MOCOM-UA algorithm, a population of candidate solutions is evolved in the feasible space to search for the Pareto set. Ranking of the population is accomplished through Pareto ranking, where all points are successively placed on different Pareto fronts. Competitive evolution consists of selecting subsets of points (including all worst points in the population) based on their ranks and moving the worst points toward the Pareto set using the newly developed multi-objective simplex (MOSIM) procedure. Test analysis on the MOCOM-UA algorithm is accomplished on mathematical problems of increasing complexity and based on a bi-criterion measure of performance. The two performance criteria are: (1) efficiency, as measured by the ability of the algorithm to converge quickly, and (2) effectiveness, as measured by the ability of the algorithm to locate the Pareto set. Comparison of the MOCOM-UA algorithm against three multi-objective genetic algorithms (MOGAs) favors the former. In a realistic application, the MOCOM-UA algorithm is used to calibrate the Soil Moisture Accounting model of the National Weather Service River Forecasting Systems (NWSRFS-SMA). Multi-objective calibration of this model is accomplished using two bi-criterion objective functions, namely the Daily Root Mean Square-Heteroscedastic Maximum Likelihood Estimator (DRMS-HMLE) and rising limb /falling limb (RISE/FALL) objective functions. These two multi-objective calibrations provide some interesting insights into the influence of different objectives in the location of final parameter values, as well as limitations in the structure of the NWSRFS-SMA model.

The successful application of a CRR model depends on how well we handle each phase of model calibration. Despite the popularity of CRR models, reports in the literature indicate that it is typically difficult, if not impossible, to obtain a unique set of optimal parameters for a CRR model. Unless the best set of parameters associated with a given calibration data set can be found, it is impossible to determine how sensitive the parameter estimates (and hence the model forecasts) are to factors such as input and output data error, model error, quantity and quality of data, objective function used, and so on. In this dissertation, results that clearly establish the nature of the problem of multiple optima in CRR models are presented. Based on these results it is shown why currently used optimization procedures have little chance of successfully finding the optimal parameter sets. This understanding is then used to develop a new global optimization procedure, the Shuffled Complex Evolution (SCE) method, which can efficiently and effectively identify the optimal values for the model parameters. The efficiency and effectiveness of the SCE method is first demonstrated on some theoretical test functions. It is then used to calibrate a research version of the SMA-NWSRFS model--the SIXPAR model. The SCE method is compared to other available methods used in practice on the theoretical test functions and the SIXPAR model. Finally, the SCE method is applied to the full scale SMA-NWSRFS model using both synthetic data and real data. The test results clearly indicate that the SCE method is superior to other methods tested in this research.

There are several sources of uncertainties in hydrologic modeling studies. Conventional deterministic modeling techniques typically ignore most of these uncertainties. However, there has been a growing need for better quantification of the accuracy and precision of hydrologic model predictions. Bayesian Recursive Estimation (BaRE) is an algorithm being developed towards considering these uncertainties for parameter estimation and prediction within an operational setting. This dissertation work evaluated and improved the current version of the algorithm. The methodology was improved using a progressive re-sampling of the Highest Probability Density (HPD) region of the parameter space, which concentrated the samples in the current HPD region while terminating computations in the nonproductive portions of the parameter space, rather than evaluating feasible parameter space based on the initial set of samples. The covariance structure of the well behaving parameter sets is used to generate new parameter sets, resulting in significant improvements compared to the original BaRE. Further, to reduce the "model/data overconfidence" problem, an entropy term and a data lack-of-confidence factor were introduced into the probability-updating rule. Comparison to batch calibration using the popular Shuffled Complex Evolution (SCE-UA) optimization method indicated that the improved recursive calibration technique is a powerful tool, especially useful where basins are recently gauged and hydrologic data are not well accumulated. The final method is also effective in tracing the temporal variations of parameters as a response to natural or human induced changes in the hydrologic system.

This dissertation presents a new multiple objective optimization algorithm that is capable of solving for the entire Pareto set in one single optimization run. The multi-objective complex evolution (MOCOM-UA) procedure is based on the following three concepts: (1) population, (2) rank-based selection, and (3) competitive evolution. In the MOCOM-UA algorithm, a population of candidate solutions is evolved in the feasible space to search for the Pareto set. Ranking of the population is accomplished through Pareto Ranking, where all points are successively placed on different Pareto fronts. Competitive evolution consists of selecting subsets of points (including all worst points in the population) based on their ranks and moving the worst points toward the Pareto set using the newly developed multi-objective simplex (MOSIM) procedure. Test analysis on the MOCOM-UA algorithm is accomplished on mathematical problems of increasing complexity and based on a bi-criterion measure of performance. The two performance criteria used are (1) efficiency, as measured by the ability of the algorithm to converge quickly and (2) effectiveness, as measured by the ability of the algorithm to locate the Pareto set. Comparison of the MOCOM-UA algorithm against three multi-objective genetic algorithms (MOGAs) favors the former. In a realistic application, the MOCOM-UA algorithm is used to calibrate the Soil Moisture Accounting model of the National Weather Service River Forecasting Systems (NWSRFS-SMA). Multi-objective calibration of this model is accomplished using two bi-criterion objective functions, namely the Daily Root Mean Square-Heteroscedastic Maximum Likelihood Estimator (DRMS, HMLE) and rising limb-falling limb (RISE, FALL) objective functions. These two multi-objective calibrations provide some interesting insights into the influence of different objectives in the location of final parameter values as well as limitations in the structure of the NWSRFS-SMA model.

Reliability and accuracy of the forcing data plays a vital role in the Hydrological Streamflow Prediction. Reliability of the forcing data leads to accurate predictions and ultimately reduction of uncertainty. Currently, Numerical Weather Prediction (NWP) models are developing ensemble forecasts for various temporal and spatial scales. However, it is proven that the raw products of the NWP models may be biased at the basin scale; unlike model grid scale, depending on the size of the catchment. Due to the large space-time variability of precipitation, bias-correcting the ensemble forecasts has proven to be a challenging task. In recent years, Ensemble Pre-Processing (EPP), a statistical approach, has proven to be helpful in reduction of bias and generation of reliable forecast. The procedure is based on the bivariate probability distribution between observation and single-value precipitation forecasts. In the current work, we have applied and evaluated a Bayesian approach, based on the Copula density functions, to develop an ensemble precipitation forecasts from the conditional distribution of the single-value precipitation. Copula functions are the multivariate joint distribution of univariate marginal distributions and are capable of modeling the joint distribution of two variables with any level of correlation and dependency. The advantage of using Copulas, amongst others, includes its capability of modeling the joint distribution independent of the type of marginal distribution. In the present study, we have evaluated the capability of copula-based functions in EPP and comparison is made against an existing and commonly used procedure for same i.e. meta-Gaussian distribution. Monthly precipitation forecast from Climate Forecast System (CFS) and gridded observation from Parameter-elevation Relationships on Independent Slopes Model (PRISM) have been utilized to create ensemble pre-processed precipitation over three sub-basins in the western USA at 0.5-degree spatial resolution. The comparison has been made using both deterministic and probabilistic frameworks of evaluation. Across all the sub-basins and evaluation techniques, copula-based technique shows more reliability and robustness as compared to the meta-Gaussian approach.

The National Water Center (NWC) started using the National Water Model (NWM) in 2016. The NWM delivers state-of-the-science hydrologic forecasts in the nation. The NWM aims at operationally forecasting streamflow in more than 2,000,000 river reaches while currently river forecasts are issued for 4,000. The NWM is a specific configuration of the community WRF-Hydro Land Surface Model (LSM) which has recently been introduced to the hydrologic community. The WRF-Hydro model, itself, uses another newly-developed LSM called Noah-MP as the core hydrologic model. In WRF-Hydro, Noah-MP results (such as soil moisture and runoff) are passed to routing modules. Riverine water level and discharge, among other variables, are outputted by WRF-Hydro. The NWM, WRF-Hydro, and Noah-MP have recently been developed and more research for operational accuracy is required on these models. The overarching goal in this dissertation is improving the ability of these three models in simulating and forecasting hydrological variables such as streamflow and soil moisture. Therefore, data assimilation (DA) is implemented on these models throughout this dissertation. State-of-the art DA is a procedure to integrate observations obtained from in situ gages or remotely sensed products with model output in order to improve the model forecast. In the first chapter, remotely sensed satellite soil moisture data are assimilated into the Noah-MP model in order to improve the model simulations. The performances of two DA techniques are evaluated and compared in this chapter. To tackle the computational burden of DA, Massage Passing Interface protocols are used to augment the computational power. Successful implementation of this algorithm is demonstrated to simulate soil moisture during the Colorado flood of 2013. In the second chapter, the focus is on the WRF-Hydro model. Similarly, the ability of DA techniques in improving the performance of WRF-Hydro in simulating soil moisture and streamflow is investigated. The results of chapter 2 show that the assimilation of soil moisture can significantly improve the performance of WRF-Hydro. The improvement can reach 58% depending on the study location. Also, assimilation of USGS streamflow observations can improve the performance up to 25%. It was also observed that soil moisture assimilation does not affect streamflow. Similarly, streamflow assimilation does not improve soil moisture. Therefore, joint assimilation of soil moisture and streamflow using multivariate DA is suggested. Finally, in chapter 3, the uncertainties associated with flood forecasting are studied. Currently, the only uncertainty source that is taken into account is the meteorological forcings uncertainty. However, the results of the third chapter show that the initial condition uncertainty associated with the land state at the time of forecast is an important factor that has been overlooked in practice. The initial condition uncertainty is quantified using the DA. USGS streamflow observations are assimilated into the WRF-Hydro model for the past ten days before the forecasting date. The results show that short-range forecasts are significantly sensitive to the initial condition and its associated uncertainty. It is shown that quantification of this uncertainty can improve the forecasts by approximately 80%. The findings of this dissertation highlight the importance of DA to extract the information content from the observations and then incorporate this information into the land surface models. The findings could be beneficial for flood forecasting in research and operation.

Thesis (Ph. D.)--Civil & Environmental Engineering, Georgia Institute of Technology, 2004.<br>Dr. Paul Work, Committee Member ; Dr. Philip Roberts, Committee Member ; Dr. Mustafa Aral, Committee Chair ; Dr. Terry Sturm, Committee Member ; Dr. Turgay Uzer, Committee Member. Vita. Includes bibliographical references (leaves 442-466).

The capability of radar measured rainfall to enhance the simulation of storm hydrographs was assessed. Six rainfall events which occurred in 1986 and 1987 over an 8.13 km$ sp2$ agricultural watershed in south-western Quebec were used in model simulations. Radar measured rainfall rates were calibrated using measurements from a single tipping-bucket raingauge located at the study site.<br>A deterministic, event-based model, HYMO, was used to simulate streamflow using radar and gauge measured rainfall. The model utilized two rainfall abstraction techniques, i.e. the SCS Curve Number method and the Green-Ampt infiltration equation. Simulated streamflow hydrographs were compared with observed storm flows.<br>For short duration, high intensity, simple rainfall events, there were minor improvements in hydrograph simulations when calibrated radar measured rainfalls were input to the model, compared to tipping-bucket raingauge measurements. Complex, low intensity storms were poorly simulated by the model using either rainfall data source. Neither rainfall abstraction method proved consistently superior.

Surface mining activities are known to affect the quantity and quality of stormwater runoff. This can create flooding and water quality degradation of receiving streams. The Surface Mining Control and Reclamation Act (SMCRA) of 1977 provides regulations intended to produce environmentally acceptable results from mining operations. The SMCRA requires that extensive pre-mining monitoring be carried out to assist in determining the probable hydrologic consequences (PHC) of mining. The Finite Element Storm Hydrograph Model (FESHM) was used to demonstrate the utility of hydrologic modeling concepts in simulating runoff volumes and peak flows. Guidelines were proposed for using this methodology to simulate selected pre-mining hydrologic conditions. The use of geographic information system (GIS) technology as a tool for improving data management and modeling efficiency was demonstrated. The required input watershed characteristics were digitized, stored, and manipulated using a computerized GIS. Appropriate software was developed to integrate the GIS with FESHM. The ability of FESHM to simulate runoff events in an ungaged context was evaluated using an experimental watershed. First, simulations were conducted using two separate data bases, "lumped" and "detailed", in order to evaluate the effect of limited data availability, as expected in mining regions, on FESHM's predictive ability. The "lumped" data base produced better simulation results, however, more thorough and detailed research is needed to determine the level of data resolution necessary for a given level of simulation accuracy. Next., significant runoff events from 17-years of the historical record were simulated using data from the "lumped" data base. Statistical analyses were used to make judgments on parameter estimation and model usage. Regression methodology was used to assess expected error and model bias. Simulation bias was found to be related to the input rainfall intensity levels. The results suggest that either spatial variability or parameter values were not adequately defined and that some form of calibration is needed. Two additional drainage basins were used to evaluate FESHM's predictive capabilities in situations considered representative of mining regions. The results indicated that more thorough investigations of watershed characteristics must be made, that calibration procedures should be performed for each watershed, and that FESHM does not adequately model the physical processes involved in forest hydrology.<br>Master of Science

Empirically based lumped hydrologic models have an extensive track record of use where as physically based, multi-dimensional distributed models are evolving for various engineering applications. Despite the availability of high resolution data, better computational resources and robust numerical methods, the usage of distributed models is still limited. The purpose of this research is to establish the credibility and usability of distributed hydrologic modeling tools of the United States Army Corps of Engineers (USACE) in order to promote the extended use of distributed models. Two of the USACE models were used as the modeling tools for the study, with Gridded Surface and Subsurface Hydrologic Analysis (GSSHA) representing a distributed and with Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) representing a lumped model. Watershed Modeling System (WMS) was used as the pre- and post-processing tool. The credibility of distributed models has been established by validating that the distributed models are efficient in solving complex hydrologic problems. The distributed and lumped models in HEC-HMS were compared. Similarly, the capabilities of GSSHA and lumped models in HEC-HMS in simulating land use change scenario were compared. The results of these studies were published in peer-reviewed journals. Similarly, the usability of the distributed models was studied taking GSSHA-WMS modeling as a test case. Some of the major issues in GSSHA-modeling using WMS interface were investigated and solutions were proposed to solve such issues. Personal experience with GSSHA and feedback from the students in a graduate class (CE531) and from participants in the USACE GSSHA training course were used to identify such roadblocks. The project being partly funded by the USACE Engineering Research and Development Center (ERDC) and partly by Aquaveo LLC, the research was motivated in improving GSSHA modeling using the WMS interface.

Green Stormwater Infrastructure (GSI) has become a popular method for flood mitigation as it can prevent runoff from entering streams during heavy precipitation. In this study, a recently developed neighborhood in Gresham, Oregon hosts a comparison of various GSI projects on runoff dynamics. The study site includes dispersed GSI (rain gardens, retention chambers, green streets) and centralized GSI (bioswales, detention ponds, detention pipes). For the 2017-2018 water year, hourly rainfall and observed discharge data is used to calibrate the EPA's Stormwater Management Model to simulate rainfall-runoff dynamics, achieving a Nash-Sutcliffe efficiency of 0.75 and Probability Bias statistic of 3.3%. A synthetic scenario analysis quantifies the impact of the study site GSI and compares dispersed and centralized arrangements. Each test was performed under four precipitation scenarios (of differing intensity and duration) for four metrics: runoff ratio, peak discharge, lag time, and flashiness. Design structure has significant impacts, reducing runoff ratio 10 to 20%, reducing peak discharge 26 to 68%, and reducing flashiness index 56 to 70%. There was a reverse impact on lag time, increasing it to 50 to 80%. Distributed GSI outperform centralized structures for all metrics, reducing runoff ratio 22 to 32%, reducing peak discharge 67 to 69%, increasing lag time 133 to 500%, and reducing flashiness index between 32 and 62%. This research serves as a basis for researchers and stormwater managers to understand potential impact of GSI on reducing runoff and downstream flooding in small urban watersheds with frequent rain.