Les systèmes de prévision des crues décrivent les transformations pluie-débit en se basant sur des représentations simplifiées. Ces représentations modélisent les processus physiques impliqués avec des descriptions empiriques, ou basées sur des équations de la mécanique classique. Les performances des modèles actuels de prévision des crues sont affectées par différentes incertitudes liées aux approximations et aux paramètres du modèle, aux données d’entrée et aux conditions initiales du bassin versant. La connaissance de ces incertitudes permet aux décideurs de mieux interpréter les prévisions et constitue une aide à la décision lors de la gestion de crue. L’analyse d’incertitudes dans les modèles hydrologiques existants repose le plus souvent sur des simulations de Monte-Carlo (MC). La mise en œuvre de ce type de techniques requiert un grand nombre de simulations et donc un temps de calcul potentiellement important. L'estimation des incertitudes liées à la modélisation hydrologique en temps réel reste donc une gageure. Dans ce projet de thèse, nous développons une méthodologie de prévision des crues basée sur les réseaux Bayésiens (RB). Les RBs sont des graphes acycliques dans lesquels les nœuds correspondent aux variables caractéristiques du système modélisé et les arcs représentent les dépendances probabilistes entre ces variables. La méthodologie présentée propose de construire les RBs à partir des principaux facteurs hydrologiques contrôlant la génération des crues, en utilisant à la fois les observations disponibles de la réponse du système et les équations déterministes décrivant les processus concernés. Elle est conçue pour prendre en compte la variabilité temporelle des différentes variables impliquées. Les dépendances probabilistes entre les variables (paramètres) peuvent être spécifiées en utilisant des données observées, des modèles déterministes existants ou des avis d’experts. Grâce à leurs algorithmes d’inférence, les RBs sont capables de propager rapidement, à travers le graphe, différentes sources d'incertitudes pour estimer leurs effets sur la sortie du modèle (ex. débit d'une rivière). Plusieurs cas d’études sont testés. Le premier cas d’étude concerne le bassin versant du Salat au sud-ouest de la France : un RB est utilisé pour simuler le débit de la rivière à une station donnée à partir des observations de 3 stations hydrométriques localisées en amont. Le modèle présente de bonnes performances pour l'estimation du débit à l’exutoire. Utilisé comme méthode inverse, le modèle affiche également de bons résultats quant à la caractérisation de débits d’une station en amont par propagation d’observations de débit sur des stations en aval. Le deuxième cas d’étude concerne le bassin versant de la Sagelva situé en Norvège, pour lequel un RB est utilisé afin de modéliser l'évolution du contenu en eau de la neige en fonction des données météorologiques disponibles. Les performances du modèle sont conditionnées par les données d’apprentissage utilisées pour spécifier les paramètres du modèle. En l'absence de données d'observation pertinentes pour l’apprentissage, une méthodologie est proposée et testée pour estimer les paramètres du RB à partir d’un modèle déterministe. Le RB résultant peut être utilisé pour effectuer des analyses d’incertitudes sans recours aux simulations de Monte-Carlo. Au regard des résultats enregistrés sur les différents cas d’études, les RBs se révèlent utiles et performants pour une utilisation en support d’un processus d'aide à la décision dans le cadre de la gestion du risque de crue. / Flood forecasting describes the rainfall-runoff transformation using simplified representations. These representations are based on either empirical descriptions, or on equations of classical mechanics of the involved physical processes. The performances of the existing flood predictions are affected by several sources of uncertainties coming not only from the approximations involved but also from imperfect knowledge of input data, initial conditions of the river basin, and model parameters. Quantifying these uncertainties enables the decision maker to better interpret the predictions and constitute a valuable decision-making tool for flood risk management. Uncertainty analysis on existing rainfall-runoff models are often performed using Monte Carlo (MC)- simulations. The implementation of this type of techniques requires a large number of simulations and consequently a potentially important calculation time. Therefore, quantifying uncertainties of real-time hydrological models is challenging. In this project, we develop a methodology for flood prediction based on Bayesian networks (BNs). BNs are directed acyclic graphs where the nodes correspond to the variables characterizing the modelled system and the arcs represent the probabilistic dependencies between these variables. The presented methodology suggests to build the RBs from the main hydrological factors controlling the flood generation, using both the available observations of the system response and the deterministic equations describing the processes involved. It is, thus, designed to take into account the time variability of different involved variables. The conditional probability tables (parameters), can be specified using observed data, existing hydrological models or expert opinion. Thanks to their inference algorithms, BN are able to rapidly propagate, through the graph, different sources of uncertainty in order to estimate their effect on the model output (e.g. riverflow). Several case studies are tested. The first case study is the Salat river basin, located in the south-west of France, where a BN is used to simulate the discharge at a given station from the streamflow observations at 3 hydrometric stations located upstream. The model showed good performances estimating the discharge at the outlet. Used in a reverse way, the model showed also satisfactory results when characterising the discharges at an upstream station by propagating back discharge observations of some downstream stations. The second case study is the Sagelva basin, located in Norway, where a BN is used to simulate the accumulation of snow water equivalent (SWE) given available weather data observations. The performances of the model are affected by the learning dataset used to train the BN parameters. In the absence of relevant observation data for learning, a methodology for learning the BN-parameters from deterministic models is proposed and tested. The resulted BN can be used to perform uncertainty analysis without any MC-simulations to be performed in real-time. From these case studies, it appears that BNs are a relevant decisionsupport tool for flood risk management.

Made available in DSpace on 2014-07-29T15:01:46Z (GMT). No. of bitstreams: 1 Dissertacao_Maira de Carvalho.pdf: 846890 bytes, checksum: 1b876a63defdf3d8fc33aa92bba455c5 (MD5) Previous issue date: 2011-08-29 / CARVALHO, M. Calibration models of urban drainage using multiobjective evolutionary algorithms. 2011. Dissertation (Masters of Environmental Engineering) - Civil Engineering College, Post-Graduation Stricto Sensu Program in Environmental Engineering - Federal University of Goiás, Goiânia, Goiás, Brazil, 2011.. This study proposed to develop and implement a calibration routine hydrological models applied to urban drainage using multiobjective optimization techniques. To make this work possible model was adopted Storm Water Management Model (SWMM) and the computational algorithms developed in MATLAB environment using an evolutionary algorithm. The method was applied to two different levels of detail in representing the Arroyo Cancels basin, located in the urban area of Santa Maria-RS, submitted to the hydrological processes involved in the process of rainfall-runoff transformation in the search for optimal values of hydrological parameters the basin. Objective functions were defined and applied simultaneously in the calibration parameters. Worked with the simulation of events of low and high intensity settings for two discretization of the watershed, and other simple and subdivided into 18 sub-basins. The sensitivity analysis performed made it possible to check that the parameters that most influenced the basin were simple: Percentage of impervious area and outlet width. Regarding the results for the various watershed discretization can be seen that in most cases when working with a more detailed watershed they were better, except for some isolated events. Overall the model showed better results when high-intensity simulated events for the best compromise solutions, thus showing the importance of using a multiobjective model. / CARVALHO, M. Calibração de modelos de drenagem urbana utilizando algoritmos evolucionários multiobjetivo. 2011. Dissertação (Mestrado em Engenharia do Meio Ambiente) Escola de Engenharia Civil, Programa de Pós-Graduação Stricto Sensu em Engenharia do Meio Ambiente, Universidade Federal de Goiás, Goiânia, 2011. O presente trabalho propôs desenvolver e aplicar uma rotina de calibração de modelos hidrológicos aplicados a drenagem urbana empregando técnicas de otimização multiobjetivo. Para tornar possível a realização deste trabalho foi adotado o modelo Storm Water Management Model (SWMM) e as rotinas computacionais desenvolvidas em ambiente MATLAB, utilizando um algoritmo evolucionário. O método foi aplicado a dois diferentes níveis de detalhamento na representação da bacia do Arroio Cancela, localizada na zona urbana do município de Santa Maria-RS, na busca de valores ótimos de parâmetros hidrológicos da bacia. Foram definidas funções objetivo e aplicadas simultaneamente na calibração de parâmetros. Trabalhou-se com a simulação de eventos de baixa e alta intensidade para duas configurações de bacia hidrográfica, sendo simples e outra subdividida em 18 sub-bacias. A análise de sensibilidade realizada possibilitou a verificação de que os parâmetros que mais influenciaram na bacia simples foram: Porcentagem de área impermeável e Largura do escoamento. Em relação aos resultados para as diferentes configurações de discretização da bacia hidrográfica pode-se verificar que na maioria dos casos quando se trabalhou com uma bacia mais detalhada estes foram melhores, salvo alguns eventos isolados. No geral o modelo apresentou melhores resultados quando simulou eventos de alta intensidade para as soluções de melhor compromisso, assim mostrando a importância da utilização de um modelo multiobjetivo.

Abstract Peatland drainage for forestry, agriculture and peat extraction results in runoff rich in organic matter, sediments and nutrients. This has a significant effect on downstream ecosystems. Therefore, water purification using sedimentation basins and wetlands is required in environmental permits for peat extraction in Finland, to reduce downstream impacts. Due to increasingly strict environmental regulations, more advanced water purification methods need to be developed. Using field measurements, laboratory experiments and hydrological/hydraulic modelling, this thesis sought to develop new methods based on: i) more refined hydrological information related to runoff and pollutant load control and ii) hydraulic design of sedimentation basins used in chemical purification. The hydrology of three peatland forestry and two drained peat extraction areas in northern Finland was studied and simulated using the DRAINMOD 6.1 hydrological model. Watertable depth (WTD) and drainage outflow were recorded continuously during several years and the data were used for model calibration and validation. Despite some under- and over-estimation of certain events, WTD fluctuations were simulated quite accurately for all peatland areas. The results demonstrated that DRAINMOD 6.1 can satisfactorily simulate WTD fluctuations in a cold climate such as northern Finland, but the model did not simulate drainage outflow adequately. Chemical treatment facilities were optimised using 3D computational fluid dynamic (CFD) models. COMSOL Multiphysics 5.1 was employed to evaluate the influence of inlet design on treatment efficiency in commonly used treatment basins without any barrier, and for optimization of barrier design through gravity-driven hydraulic flocculators. The results showed that inlet design had a significant effect on treatment efficiency. Several barrier designs were simulated and the best combination was tested for different distances between barriers, to find a geometry ratio and flow depth producing optimal mixing conditions for the treatment process. / Tiivistelmä Turvemaiden ojitus metsätaloutta, maataloutta ja turvetuotantoa varten lisää orgaanisen aineen, kiintoaineineen ja ravinteiden huuhtoutumista alapuolisiin vesistöihin. Lisääntyneellä kuormituksella voi olla merkittäviä vaikutuksia vesiekosysteemeihin, minkä vuoksi turvetuotannon ympäristöluvissa vaaditaan valumavesien puhdistamista mm. laskeutusaltaiden ja pintavalutuskenttien avulla. Tiukentuneiden vesiensuojelumääräysten vuoksi tarvitaan uusia vesiensuojelumenetelmiä sekä tulee tehostaa jo käytössä olevien menetelmien toimintaa. Tämän työn tavoitteena on suositella uusia menetelmiä perustuen I) entistä tarkempaan hydrologiseen tietoon valunnasta ja vesistökuormituksesta ja II) kemiallisen vesienpuhdistuksen yhteydessä käytettävien laskeutusaltaiden hydrauliseen suunnitteluun. Tämä väitöstyö rakentuu maastossa ja laboratoriossa tehtyjen tutkimusten sekä hydrologisen/hydraulisen mallinnuksen varaan. Valuma-alueiden hydrologiaa tutkittiin ja mallinnettiin kolmella turvemetsäalueella ja kahdella turvetuotantoalueella Pohjois-Suomessa. Ojituksen hydrologisten vaikutusten arviointiin käytettiin DRAINMOD 6.1 ohjelmaa, jonka kalibrointia ja validointia varten kerättiin jatkuvatoimisilla antureilla aineistoa pohjaveden pinnankorkeuksista ja virtaamasta useiden vuosien ajalta. Mallin avulla voitiin pohjaveden pinnan vaihtelut kuvata yleisesti melko hyvin kaikilla tutkimusalueilla yksittäisistä sadanta-valuntatapahtuminen yli- tai aliarvioinneista huolimatta. Saadut tulokset osoittavat, että DRAINMOD 6.1 ohjelmalla voidaan riittävällä tarkkuudella simuloida pohjaveden pinnan vaihteluita kylmässä ilmastossa, kuten Pohjois-Suomessa, mutta malli ei soveltunut hyvin ojitusalueelta lähtevän valunnan tarkkaan määrittämiseen. Kemiallisen vesienpuhdistusrakenteiden optimointiin käytettiin COMSOL Multiphysics 5.1 ohjelmaa, jolla voidaan toteuttaa ja laskea veden virtauksia kolmessa dimensiossa (computational fluid dynamic, CFD, model). Mallilla arvioitiin kemikalointialtaan tuloaukon rakenteen vaikutuksia tyypillisesti kemikaloinnissa käytetyn allasrakenteen puhdistustehokkuuteen. Lisäksi mallilla mitoitettiin virtausesteitä optimaalisen sekoittumisolosuhteiden saamiseksi ja puhdistustehokkuuden parantamiseksi painovoimaisesti toimivissa flokkausaltaissa (hidas sekoitus). Saadut tulokset osoittavat, että laskeutusaltaiden tuloaukon rakenteella on merkittävä vaikutus kemikaloinnissa saavutettuun puhdistustehokkuuteen. Lisäksi työssä esitettiin optimaalisia virtausesteiden mitoituksia (geometria, esteiden välinen etäisyys, virtaussyvyys yms.) puhdistuksen kannalta parhaiden mahdollisten sekoitusolosuhteiden saavuttamiseksi.

Flooding is one of the deadliest natural hazards in the world. Forecasting floods in advance can significantly reduce the socio-economic impacts. An accurate and reliable flood forecasting system is heavily dependent on the input precipitation data. Real-time, spatially, and temporally continuous Radar Quantitative Precipitation Estimates (QPEs) is useful precipitation information source. This research aims to investigate the efficacy of American and Canadian weather radar QPEs on hydrological model calibration and validation for flood forecasting in urban and semi-urban watersheds in Canada. A comprehensive review was conducted on the weather Radar network and its’ hydrological applications, challenges, and potential future research in Canada. First, radar QPEs were evaluated to verify the reliability and accuracy as precipitation input for hydrometeorological models. Then, the radar-gauge merging techniques were assessed to select the best method for urban flood forecasting applications. After that, merged Radar QPEs were used as precipitation input for the hydrological models to assess the impact of radar QPEs on hydrological model calibration and validation. Finally, a framework was developed by integrating hydrological and hydraulic models to produce flood forecasts and inundation maps in urbanized watersheds. Results indicated that dual-polarized radar QPEs could be effectively used as a source of precipitation input to hydrological models. The radar-gauge merging enhances both the accuracy and reliability of Radar QPEs, and therefore, the accuracy of streamflow simulation is also improved. Since flood forecasting agencies usually use hydrological models calibrated and validated using gauge data, it is recommended to use bias-corrected Radar QPEs to run existing hydrological models to simulate streamflow to produce flood extent maps. The hydrological and hydraulic models could be integrated into one framework using bias-corrected Radar QPEs to develop a successful flood forecasting system. / Thesis / Doctor of Science (PhD) / Floods are common and increasing deadly natural hazards in the world. Predicting floods in advance using Flood Early Warning System (FEWS) can facilitate flood mitigation. Radar Quantitative Precipitation Estimates (QPEs) can provide real-time, spatially, and temporally continuous precipitation data. This research focuses on bias-correcting and evaluating radar QPEs for hydrologic forecasting. The corrected QPE are applied into a framework connecting hydrological and hydraulic models for operational flood forecasting in urban watersheds in Canada. The key contributions include: (1) Dual-polarized radar QPEs is a useful precipitation input to calibrate, validate and run hydrological models; (2) Radar-gauge merging enhance accuracy and reliability of radar QPEs; (3) Floods could be more accurately predicted by integrating hydrological and hydraulic models in one framework using bias-corrected Radar QPEs; and (4) Gauge-calibrated hydrological models can be run effectively using the bias-corrected radar QPEs. This research will benefit future applications of real-time radar QPEs in operational FEWS.

The primary goal of this research is to evaluate and identify proper calibration approaches, skillful hydrological models, and suitable weather forecast inputs to improve the accuracy and reliability of hydrological forecasting in different types of watersheds. The research started by formulating an approach that examined single- and multi-site, and single- and multi-objective optimization methods for calibrating an event-based hydrological model to improve flood prediction in a semi-urban catchment. Then it assessed whether reservoir inflow in a large complex watershed could be accurately and reliably forecasted by simple lumped, medium-level distributed, or advanced land-surface based hydrological models. Then it is followed by a comparison of multiple combinations of hydrological models and weather forecast inputs to identify the best possible model-input integration for an enhanced short-range flood forecasting in a semi-urban catchment. In the end, Numerical Weather Predictions (NWPs) with different spatial and temporal resolutions were evaluated across Canada’s varied geographical environments to find candidate precipitation input products for improved flood forecasting. Results indicated that aggregating the objective functions across multiple sites into a single objective function provided better representative parameter sets of a semi-distributed hydrological model for an enhanced peak flow simulation. Proficient lumped hydrological models with proper forecast inputs appeared to show better hydrological forecast performance than distributed and land-surface models in two distinct watersheds. For example, forcing the simple lumped model (SACSMA) with bias-corrected ensemble inputs offered a reliable reservoir inflow forecast in a sizeable complex Prairie watershed; and a combination of the lumped model (MACHBV) with the high-resolution weather forecast input (HRDPS) provided skillful and economically viable short-term flood forecasts in a small semi-urban catchment. The comprehensive verification has identified low-resolution NWPs (GEFSv2 and GFS) over Western and Central parts of Canada and high-resolution NWPs (HRRR and HRDPS) in Southern Ontario regions that have a promising potential for forecasting the timing, intensity, and volume of floods. / Thesis / Doctor of Philosophy (PhD) / Accurate hydrological models and inputs play essential roles in creating a successful flood forecasting and early warning system. The main objective of this research is to identify adequately calibrated hydrological models and skillful weather forecast inputs to improve the accuracy of hydrological forecasting in various watershed landscapes. The key contributions include: (1) A finding that a combination of efficient optimization tools with a series of calibration steps is essential in obtaining representative parameters sets of hydrological models; (2) Simple lumped hydrological models, if used appropriately, can provide accurate and reliable hydrological forecasts in different watershed types, besides being computationally efficient; and (3) Candidate weather forecast products identified in Canada’s diverse geographical regions can be used as inputs to hydrological models for improved flood forecasting. The findings from this thesis are expected to benefit hydrological forecasting centers and researchers working on model and input improvements.

Models are important tools for understanding the hydrological processes that govern water transport in the landscape and for prediction at times and places where no observations are available. The degree of trust placed on models, however, should not exceed the quality of the data they are fed with. The overall aim of this thesis was to tune the modeling process to account for the uncertainty in the data, by identifying robust parameter values using methods from computational geometry. The methods were developed and tested on data from the Choluteca River basin in Honduras. Quality control of precipitation and discharge data resulted in a rejection of 22% percent of daily raingage data and the complete removal of one out of the seven discharge stations analyzed. The raingage network was not found sufficient to capture the spatial and temporal variability of precipitation in the Choluteca River basin. The temporal variability of discharge was evaluated through a Monte Carlo assessment of the rating-equation parameter values over a moving time window of stage-discharge measurements. Al hydrometric stations showed considerable temporal variability in the stage-discharge relationship, which was largest for low flows, albeit with no common trend. The problem with limited data quality was addressed by identifying robust model parameter values within the set of well-performing (behavioral) parameter-value vectors with computational-geometry methods. The hypothesis that geometrically deep parameter-value vectors within the behavioral set were hydrologically robust was tested, and verified, using two depth functions. Deep parameter-value vectors tended to perform better than shallow ones, were less sensitive to small changes in their values, and were better suited to temporal transfer. Depth functions rank multidimensional data. Methods to visualize the multivariate distribution of behavioral parameters based on the ranked values were developed. It was shown that, by projecting along a common dimension, the multivariate distribution of behavioral parameters for models of varying complexity could be compared using the proposed visualization tools. This has a potential to aid in the selection of an adequate model structure considering the uncertainty in the data. These methods allowed to quantify observational uncertainties. Geometric methods have only recently begun to be used in hydrology. It was shown that they can be used to identify robust parameter values, and some of their potential uses were highlighted. / Modeller är viktiga verktyg för att förstå de hydrologiska processer som bestämmer vattnets transport i landskapet och för prognoser för tider och platser där det saknas mätdata. Graden av tillit till modeller bör emellertid inte överstiga kvaliteten på de data som de matas med. Det övergripande syftet med denna avhandling var att anpassa modelleringsprocessen så att den tar hänsyn till osäkerheten i data och identifierar robusta parametervärden med hjälp av metoder från beräkningsgeometrin. Metoderna var utvecklade och testades på data från Cholutecaflodens avrinningsområde i Honduras. Kvalitetskontrollen i nederbörds- och vattenföringsdata resulterade i att 22 % av de dagliga nederbördsobservationerna måste kasseras liksom alla data från en av sju analyserade vattenföringsstationer. Observationsnätet för nederbörd befanns otillräckligt för att fånga upp den rumsliga och tidsmässiga variabiliteten i den övre delen av Cholutecaflodens avrinningsområde. Vattenföringens tidsvariation utvärderades med en Monte Carlo-skattning av värdet på parametrarna i avbördningskurvan i ett rörligt tidsfönster av vattenföringsmätningar. Alla vattenföringsstationer uppvisade stor tidsvariation i avbördningskurvan som var störst för låga flöden, dock inte med någon gemensam trend. Problemet med den måttliga datakvaliteten bedömdes med hjälp av robusta modellparametervärden som identifierades med hjälp av beräkningsgeometriska metoder. Hypotesen att djupa parametervärdesuppsättningar var robusta testades och verifierades genom två djupfunktioner. Geometriskt djupa parametervärdesuppsättningar verkade ge bättre hydrologiska resultat än ytliga, var mindre känsliga för små ändringar i parametervärden och var bättre lämpade för förflyttning i tiden. Metoder utvecklades för att visualisera multivariata fördelningar av välpresterande parametrar baserade på de rangordnade värdena. Genom att projicera längs en gemensam dimension, kunde multivariata fördelningar av välpresterande parametrar hos modeller med varierande komplexitet jämföras med hjälp av det föreslagna visualiseringsverktyget. Det har alltså potentialen att bistå vid valet av en adekvat modellstruktur som tar hänsyn till osäkerheten i data. Dessa metoder möjliggjorde kvantifiering av observationsosäkerheter. Geometriska metoder har helt nyligen börjat användas inom hydrologin. I studien demonstrerades att de kan användas för att identifiera robusta parametervärdesuppsättningar och några av metodernas potentiella användningsområden belystes.

Good estimates of soil hydraulic parameters and their distribution in a catchment is essential for crop and hydrological models. Measurements of soil properties by experimental methods are expensive and often time consuming, and in order to account for spatial variability of these parameters in the catchment, it becomes necessary to conduct large number of measurements. Estimation of soil parameters by inverse modelling using observations on either surface soil moisture or crop variables has been successfully attempted in many studies, but difficulties to estimate root zone properties arise for heterogeneous layered soils. Although extensive soil data is becoming more and more available at various scales in the form of digital soil maps there is still a large gap between this available information and the input parameters needed for hydrological models. Inverse modeling has been extensively used but the spatial variability of the parameters and insufficient data sets restrict its applicability at the catchment scale. Use of remote sensed soil moisture data to estimate soil properties using the inverse modeling approach received attention in recent years but yielded only an estimate of the surface soil properties. However, in multilayered and heterogeneous soil systems the estimation of soil properties of different layers yielded poor results due to uncertainties in simulating root zone soil moisture from remote sensed surface soil moisture. Surface soil properties can be estimated by inverse approach using surface soil moisture data retrieved from remote sensing data. Since soil moisture retrieved from remote sensing is representative of the top 5 cm only, inversion of models using surface soil moisture cannot give good estimates of soil properties of deeper layers. Crop variables like biomass and leaf area index are sensitive to the deeper layer soil properties. The main focus of this study is to develop a methodology of estimation of root zone soil hydraulic properties in heterogeneous soils by crop model based inversion techniques. Further the usefulness of the radar soil moisture and leaf area index in retrieving soil hydraulic properties using the develop approach is be tested in different soil and crop combinations. A brief introduction about the soil hydraulic properties and their importance in agro-hydrological model is discussed in Chapter 1. Soil water retention parameters are explained in detail in this chapter. A detailed review of the literature is presented in chapter 2 to establish the state of art on the following: (i) estimation of soil hydraulic properties, (ii) role of crop models in estimating soil hydraulic properties, (iii) retrieval of surface soil moisture using water cloud model from SAR data, (iv) retrieval of leaf area index from SAR (synthetic aperture radar) data and (v) modeling of root zone soil moisture and potential recharge. The thesis proposes a methodology for estimating the root zone soil hydraulic properties viz. field capacity, wilting point and soil thickness. To test the methodology developed in this thesis for estimating the soil hydraulic properties and their uncertainty, three synthetic experiments were conducted by inversion of STICS (Simulateur mulTIdiscplinaire pour les Cultures Standard) model for maize crop using the GLUE (Generalized Likelihood Uncertainty Estimation) approach. The estimability of soil hydraulic properties in a layer-wise heterogeneous soil was examined with several sets of likelihood combinations, using leaf area index, surface soil moisture and above ground biomass. The robustness of the approach is tested with parameter estimation (model inversion) in two different meteorological conditions. The details of the numerical experiments and the several likelihood and meteorological cases examined are given in Chapter 3. The likelihood combination of leaf area index and surface soil moisture provided consistently good estimates of soil hydraulic properties for all soil types and different meteorological cases. Relatively wet year provided better estimates of soil hydraulic properties as compared with a dry year. To validate the approach of estimating root zone soil properties and to test the applicability of the approach in several crops and soil types, field measurements were carried out in the Berambadi experimental watershed located in the Kabini river basin in south India. The profile soil measurements were made for every 10 cm upto 1 m depth. Maize, Marigold, Sunflower, Sorghum and Turmeric crops were monitored during the four year period from 2010 to 2013. Crop growth parameters viz. leaf area index, above ground biomass, yield, phenological stages and crop management activities were measured/monitored at 10 day frequency for all the five crops in the study area. The details of the field experiments performed, the data collected and the results of the model inversion using the ground measured data are given in Chapter 4. The likelihood combination of leaf area index and surface soil moisture provided consistently lower root mean square error (1.45 to 2.63 g/g) and uncertainty in the estimation of soil hydraulic properties for all soil crop and meteorological cases. The uncertainty in the estimation of soil hydraulic properties was lower in the likelihood combination of leaf area index and soil moisture. Estimability of depth of root zone showed sensitivity to the rooting depth. Estimating root zone soil properties at field plot scale using SAR data (incidence angle 24o, wave length 5.3 GHz) of RADARSAT-2 is presented in the Chapter 5. In the first step, an approach of estimating leaf area index from radar vegetation index using the parametric growth curve of leaf area index and the retrieval of soil moisture using water cloud model are given in Chapter 5. The parameters of the growth curve and the leaf area index are generated using a time series of RADARSAT-2 for two years 2010-2011 and 2011-12 for the crops (maize, marigold, sunflower, sorghum and turmeric) considered in this study. The surface soil moisture is retrieved using the water cloud model, which is calibrated using the ground measured values of leaf area index and surface soil moisture for different soils and crops in the study area. The calibration and validation of LAI and water cloud models are discussed in this Chapter. Eventually, the retrieved leaf area index and surface soil moisture from RADARSAT-2 data were used to estimate the soil hydraulic properties and their uncertainty in a similar manner as discussed in Chapter 4 for various crop and soil plots and the results are presented in Chapter 5. The mean and uncertainty in the estimation of soil hydraulic properties using inversion of remote sensing data provided results similar to the estimates from inversion of ground data. The estimates of soil hydraulic properties compared well (R2 of 0.7 to 0.80 and RMSE of 2.1 to 3.16 g/g) with the physically measured vales of the parameters. In Chapter 6, root zone soil moisture and potential recharge are modelled using the STICS model and the soil hydraulic parameters estimated using the RADARSAT-2 data. The potential recharge is highly sensitive to the water holding capacity of rooting zone. Variability in the root zone soil moisture for wet and dry years for different soil types on irrigated and non-irrigated crops were investigated. Potential recharge from different crop and soil types were compared. The uncertainty in the estimation of potential recharge due to uncertainty in the estimation of field capacity is quantified. The root zone soil moisture modeled by STICS showed good agreement with the measured root zone soil moisture in all crop and soil cases. This was tested for both dry and wet year and provides similar results. The temporal variability of root zone soil moisture was also modeled well by the STICS model; the model also predicted well the intra-soil variability of soil moisture of root zone. The results of the modeling of root zone soil moisture and potential recharge are presented in Chapter 6. At the end, in Chapter 7, the major conclusions drawn from the various chapters are summarized.

The knowledge of soil moisture is of pronounced importance in various applications e.g. flood control, agricultural production and effective water resources management. These applications require the knowledge of spatial and temporal variation of the soil moisture in the watershed. There are three approaches of estimating/measuring soil moisture namely,(i) in-situ measurements,(ii) remote sensing, and(iii) hydrological modelling. The in situ techniques of measurement provide relatively accurate information at point scale but are not feasible to gather in large numbers relevant for a watershed. The soil moisture can be simulated by hydrological models at the desired spatial and temporal resolution, but these simulations would often be affected by the uncertainties in the model physics, parameters, forcing, initial and boundary conditions. The remote sensing provides an alternative to retrieve the soil moisture of the surface (top few centimeters ) layer, but even this data is limited by the spatial or temporal resolution, which is satellite dependant. Hydrological models could be improved by assimilating remotely sensed soil moisture, which requires a retrieval algorithm. In order to develop a retrieval algorithm the satellite data need to be calibrated/validated with the in-situ ground measurements. The retrieval of surface soil moisture from microwave remote sensing is sensitive to surface conditions, and hence requires calibration/validation specific to a site/region. The improvement in the hydrological variables/fluxes is sensitive to the framework adopted during the assimilation of remotely sensed data. The main focus of the study was to assess the retrieval algorithm for the surface soil moisture from both active (ENVISAT,RADARSAT-2)and passive(AMSR-E) microwave satellites in a semi-arid tropical watershed of South India. Further, the usefulness of these retrieved remotely sensed products for the estimation of recharge was investigated by developing a coupled hydrological model and an assimilation framework. A brief introduction was made in Chapter 1 on the importance of surface soil moisture and evapotranspiration in hydrology, and the feasible options available for the retrieval from microwave remote sensing. A detailed review of the literature is presented in Chapter 2 to establish the state-of-the-art on the following:(i) retrieval algorithms for the surface soil moisture from active and passive microwave remote sensing,(ii) estimation of actual evapotranspiration from optical remote sensing(MODIS),(iii) coupled surface-ground water hydrological models,(iv) estimation of soil hydraulic properties with their uncertainties, and(v) assimilation framework specific to hydrological modelling. To calibrate/validate the retrieval algorithms and to test the coupled model and the assimilation framework developed, field measurements were carried out in the BerambadI experimental watershed located in the Kabini river basin. The surface soil moisture in 50 field plots, profile soil moisture up to 1m depth in 20 field plots, and ground water level in 200 bore wells were measured. Twelve images of ENVISAT, seven teen images of RADARSAT-2, along with AMSR-E and MODIS data were used. These data pertained to different durations during the period 2008 to 2011,the details of which are given in Chapter 3. The approach for the retrieval of surface soil moisture and the associated uncertainty from active and passive microwave remote sensing is given in Chapter 4. Surface soil moisture was retrieved for six vegetation classes using the linear regression model and copulas. Three types of copulas(Clayton, Frank and Gumbel) were investigated. It was found that the ensemble mean simulated using the linear regression model and three copulas was nearly same. The copulas were found to be superior than the linear regression model when comparing the distributions of the mean of the generated ensemble. Among the copulas it was observed that the Clayton copula performed better in the lower and middle ranges of backscatter coefficient, while the Gumbel and Frank copulas were found to be superior in the upper ranges of backscatter coefficients. The range of RMSE was approximatively 4cm3cm−3 indicating that the retrieval from ENVISAT/RADARSAT-2 was good. ACDF based approach was proposed to retrieve the surface soil moisture map for the watershed with a spatial resolution of 100m x 100m ( i.e one hectare). The map of the uncertainty in the retrieved surface soil moisture was also prepared using the Clayton copula. The AMSR-E surface soil moisture product was calibrated for the watershed during the period 2008 to 2011, using the map generated from the ENVISAT/RADARSAT data. They Clayton copula was used to generate the ensemble of the corrected AMSR-E surface soil moisture. The standard deviation of the generated ensemble varied from 0.01 to 0.03cm3cm−3 ,hence the derived surface soil moisture product for Berambadi was found to be good. In the Chapter 5, a one dimensional soil moisture model was developed based on the numerical solution of the Richards’ equation using finite difference method and inverse modeling was carried out using the Generalized Likelihood Uncertainty Estimation(GLUE) approach for estimating the soil hydraulic parameters of the van Genuchten(VG) model and their uncertainty. The parameters were estimated from the two field sites(Berambadi and Wailapally watershed in South India) and from laboratory evaporation experiment for the Wailapally site. It was found that the GLUE approach was able to provide good uncertainty bounds for the soil hydraulic parameters. The uncertainty in the estimates from the field experiment was found to be higher than from the laboratory evaporation experiment for both water retention and hydraulic conductivity curves. The saturated soil moisture(θs )and shape parameter (n) of VG model estimated from the laboratory evaporation and field experiment were found to be the same, and further more they showed a lower uncertainty from both the experiments. Moreover, the residual soil moisture (θr), inverse of capillary fringe thickness (α) and saturated hydraulic conductivity( KS) showed a relatively higher uncertainty. In the Berambadi watershed ,the inverse modeling was performed in three bare field plots, and it was found that field plots which had higher θs showed a relatively higher actual evapotranspiration (AET) and lower potential recharge. In Chapter 6, the retrieval of profile soil moisture up to 2m by assimilation of surface soil moisture was investigated by performing synthetic experiments on six soil types. The measured surface soil moisture over top 5cm depth was assimilated into the one dimensional soil moisture model to retrieve the profile soil moisture. Even though the assimilation of surface soil moisture helped in improving the profile soil moisture for the six soil types, the bias was observed. To reduce the bias, pseudo observations of profile soil moisture were generated and used in addition to the surface soil moisture in the assimilation altogether. These pseudo observations were generated using the linear relationship existing between the surface and profile soil moisture. A significant bias reduction was found to be feasible by using this method when pseudo observations beyond 75cm depth were used then there was no significant improvement. A coupled surface-ground water model was developed, which had 5 layers for the vadose zone and one layer for the ground water zone, in order to consider the major hydrological processes from ground surface to ground water table in a semi-arid watershed. The details of the coupled model were described in Chapter 7. The major aim of this model was to be able to use remotely sensed data of surface soil moisture and evapotranspiration to simulate recharge. The model was tested by applying in a lumped framework to the field data set in the Berambadi watershed for the year 2010 to 2011. The performance of the model was evaluated with the measured watershed average root zone soil moisture and ground water levels. The watershed average root zone soil moisture was obtained by averaging the field measurements from 20 plots and average ground water level was obtained by averaging the field measurement from 200 bore wells. In order to assimilate the AET into the coupled model, the daily AET at a spatial resolution of 1km was estimated from MODIS data. The AET was validated in one forested and four agricultural sites in the watershed. The validation was based on the comparison with AET simulated from water balance models. For agricultural plots the STICS (crop model) and for the forested site the COMFORT (hydrological) model were used. The AET from the MODIS showed a reasonably good match with both the forested and agricultural plots at the annual scale (for the crop model approximately 4-5 months). Model simulations were carried out with and without assimilating the remotely sensed data and the performance was evaluated. It was found that the assimilation helped in capturing the trends in deeper layer soil moisture and groundwater level. At the end, in Chapter 8 the major conclusions drawn from the various chapters are summarized.

further development of the simple and promising analytical probabilistic approach / Urban stormwater management aims at mitigating the adverse impacts of urbanization. Hydrological models are used in support of stormwater management planning and design. There are three main approaches that can be applied for this modeling purpose: (1) continuous simulation approach which is accurate but time-consuming; (2) design storm approach, which is widely used and its accuracy highly depends on the selected antecedent moisture conditions and temporal distribution of design storms; and (3) the analytical probabilistic approach which is recently developed and still not used in practice. Although it is time-effective and it can produce results as accurate as the other two approaches; the analytical probabilistic approach requires further developments in order to make it more reliable and accurate. For this purpose, three subtopics are investigated in this thesis. (1) Rainfall data analysis as required by the analytical probabilistic approach with emphasis on testing the exponentiality of rainfall event duration, volume and interevent time (i.e., time separating it from its preceding rainfall event). A goodness-of-fit testing procedure that is suitable for this kind of data analysis was proposed. (2) Derivation of new analytical probabilistic models for peak discharge rate incorporating trapezoidal and triangular hydrograph shapes in order to include all possible catchment’s responses. And (3) the infiltration process is assumed to continue until the end of the rainfall event; however, the soil may get saturated earlier and the excess amount would contribute to the runoff volume which may have adverse impact if not taken into consideration. Thus, in addition to the infiltration process, the saturation excess runoff is also included and new models for flood frequencies are developed. All the models developed in this thesis are tested and compared to methods used in practice, reasonable results were obtained. / Thesis / Doctor of Philosophy (PhD) / Urban stormwater management aims at mitigating the adverse impacts of urbanization. Hydrological models are used in support of stormwater management planning and design. The analytical probabilistic stormwater management model (APSWM) is a promising tool for planning and design analysis. The purpose of this thesis is to further develop APSWM in order to make it more reliable and accurate. First, a clear procedure for rainfall data analysis as required by APSWM is provided. Second, a new APSWM is derived incorporating other runoff temporal-distribution patterns. Finally, the possibility of soil layer saturation while it is still raining is added to the model. All the models developed in this thesis are tested and compared to methods used in engineering practice, reasonable results were obtained.