Considerable interest lies in understanding the hydrologic response to climate change in the upper Deschutes Basin, particularly as it relates to groundwater fed streams. Much of the precipitation occurring in the recharge zone falls as snow. Consequently, the timing of runoff and recharge depend on accumulation and melting of the snowpack. Numerical modeling can provide insights into evolving hydrologic system response for resource management consideration. A daily mass and energy balance model known as the Deep Percolation Model (DPM) was developed for the basin in the 1990s. This model uses spatially distributed data and is driven with daily climate data to calculate both daily and monthly mass and energy balance for the major components of the hydrologic budget across the basin. Previously historical daily climate data from weather stations in the basin was used to drive the model. Now we use the University of Washington Climate Impact Group's 1/16th degree daily downscaled climate data to drive the DPM for forecasting until the end of the 21st century. The downscaled climate data is comprised from the mean of eight GCM simulations well suited to the Pacific Northwest. Furthermore, there are low emission and high emission scenarios associated with each ensemble member leading to two distinct means. For the entire basin progressing into the 21st century, output from the DPM using both emission scenarios as a forcing show changes in the timing of runoff and recharge as well as significant reductions in snowpack. Although the DPM calculated amounts of recharge and runoff varies between the emission scenario of the ensemble under consideration, all model output shows loss of the spring snowmelt runoff / recharge peak as time progresses. The response of the groundwater system to changing in the time and amount of recharge varies spatially. Short flow paths in the upper part of the basin are potentially more sensitive to the change in seasonality. However, geologic controls on the system cause this signal to attenuate as it propagates into the lower portions of the basin. This scale-dependent variation to the response of the groundwater system to changes in seasonality and magnitude of recharge is explored by applying DPM calculated recharge to an existing regional groundwater flow model.

Increasing global mean temperature or global warming has the potential to affect the hydrologic cycle. In the 21st century, according to the UN Intergovernmental Panel on Climate Change (IPCC), alterations in the frequency and magnitude of high intensity rainfall events are very likely. Increasing trend of urbanization across the globe is also noticeable, simultaneously. These changes will have a great impact on water infrastructure as well as environment in urban areas. One of the impacts may be the increase in frequency and extent of flooding. India, in the recent years, has witnessed a number of urban floods that have resulted in huge economic losses, an instance being the flooding of Mumbai in July, 2005. To prevent catastrophic damages due to floods, it has become increasingly important to understand the likely changes in extreme rainfall in future, its effect on the urban drainage system, and the measures that can be taken to prevent or reduce the damage due to floods. Reliable estimation of future design rainfall intensity accounting for uncertainties due to climate change is an important research issue. In this context, rainfall intensity-duration-frequency (IDF) relationships are one of the most extensively used hydrologic tools in planning, design and operation of various drainage related infrastructures in urban areas. There is, thus, a need for a study that investigates the potential effects of climate change on IDF relationships. The main aim of the research reported in this thesis is to investigate the effect of climate change on Intensity-Duration-Frequency relationship in an urban area. The rainfall in Bangalore City is used as a case study to demonstrate the applications of the methodologies developed in the research Ahead of studying the future changes, it is essential to investigate the signature of changes in the observed hydrological and climatological data series. Initially, the yearly mean temperature records are studied to find out the signature of global warming. It is observed that the temperature of Bangalore City shows an evidence of warming trend at a statistical confidence level of 99.9 %, and that warming effect is visible in terms of increase of minimum temperature at a rate higher than that of maximum temperature. Interdependence studies between temperature and extreme rainfall reveal that up to a certain range, increase in temperature intensifies short term rainfall intensities at a rate more than the average rainfall. From these two findings, it is clear that short duration rainfall intensities may intensify in the future due to global warming and urban heat island effect. The possible urbanization signatures in the extreme rainfall in terms of intensification in the evening and weekends are also inferred, although inconclusively. The IDF relationships are developed with historical data and changes in the long term daily rainfall extreme characteristics are studied. Multidecedal oscillations in the daily rainfall extreme series are also examined. Further, non-parametric trend analyses of various indices of extreme rainfall are carried out to confirm that there is a trend of increase in extreme rainfall amount and frequency, and therefore it is essential to the study the effects of climate change on the IDF relationships of the Bangalore City. Estimation of future changes in rainfall at hydrological scale generally relies on simulations of future climate provided by Global Climate Models (GCMs). Due to spatial and temporal resolution mismatch, GCM results need to be downscaled to get the information at station scale and at time resolutions necessary in the context of urban flooding. The downscaling of extreme rainfall characteristics in an urban station scale pose the following challenges: (1) downscaling methodology should be efficient enough to simulate rainfall at the tail of rainfall distribution (e.g., annual maximum rainfall), (2) downscaling at hourly or up to a few minutes temporal resolution is required, and (3) various uncertainties such as GCM uncertainties, future scenario uncertainties and uncertainties due to various statistical methodologies need to be addressed. For overcoming the first challenge, a stochastic rainfall generator is developed for spatial downscaling of GCM precipitation flux information to station scale to get the daily annual maximum rainfall series (AMRS). Although Regional Climate Models (RCMs) are meant to simulate precipitation at regional scales, they fail to simulate extreme events accurately. Transfer function based methods and weather typing techniques are also generally inefficient in simulating the extreme events. Due to its stochastic nature, rainfall generator is better suited for extreme event generation. An algorithm for stochastic simulation of rainfall, which simulates both the mean and extreme rainfall satisfactorily, is developed in the thesis and used for future projection of rainfall by perturbing the parameters of the rainfall generator for the future time periods. In this study, instead of using the customary two states (rain/dry) Markov chain, a three state hybrid Markov chain is developed. The three states used in the Markov chain are: dry day, moderate rain day and heavy rain day. The model first decides whether a day is dry or rainy, like the traditional weather generator (WGEN) using two transition probabilities, probabilities of a rain day following a dry day (P01), and a rain day following a rain day (P11). Then, the state of a rain day is further classified as a moderate rain day or a heavy rain day. For this purpose, rainfall above 90th percentile value of the non-zero precipitation distribution is termed as a heavy rain day. The state of a day is assigned based on transition probabilities (probabilities of a rain day following a dry day (P01), and a rain day following a rain day (P11)) and a uniform random number. The rainfall amount is generated by Monte Carlo method for the moderate and heavy rain days separately. Two different gamma distributions are fitted for the moderate and heavy rain days. Segregating the rain days into two different classes improves the process of generation of extreme rainfall. For overcoming the second challenge, i.e. requirement of temporal scales, the daily scale IDF ordinates are disaggregated into hourly and sub-hourly durations. Disaggregating continuous rainfall time series at sub-hourly scale requires continuous rainfall data at a fine scale (15 minute), which is not available for most of the Indian rain gauge stations. Hence, scale invariance properties of extreme rainfall time series over various rainfall durations are investigated through scaling behavior of the non-central moments (NCMs) of generalized extreme value (GEV) distribution. The scale invariance properties of extreme rainfall time series are then used to disaggregate the distributional properties of daily rainfall to hourly and sub-hourly scale. Assuming the scaling relationships as stationary, future sub-hourly and hourly IDF relationships are developed. Uncertainties associated with the climate change impacts arise due to existence of several GCMs developed by different institutes across the globe, climate simulations available for different representative concentration pathway (RCP) scenarios, and the diverse statistical techniques available for downscaling. Downscaled output from a single GCM with a single emission scenario represents only a single trajectory of all possible future climate realizations and cannot be representative of the full extent of climate change. Therefore, a comprehensive assessment of future projections should use the collective information from an ensemble of GCM simulations. In this study, 26 different GCMs and 4 RCP scenarios are taken into account to come up with a range of IDF curves at different future time periods. Reliability ensemble averaging (REA) method is used for obtaining weighted average from the ensemble of projections. Scenario uncertainty is not addressed in this study. Two different downscaling techniques (viz., delta change and stochastic rainfall generator) are used to assess the uncertainty due to downscaling techniques. From the results, it can be concluded that the delta change method under-estimated the extreme rainfall compared to the rainfall generator approach. This study also confirms that the delta change method is not suitable for impact studies related to changes in extreme events, similar to some earlier studies. Thus, mean IDF relationships for three different future extreme events, similar to some earlier studies. Thus, mean IDF relationships for three different future periods and four RCP scenarios are simulated using rainfall generator, scaling GEV method, and REA method. The results suggest that the shorter duration rainfall will invigorate more due to climate change. The change is likely to be in the range of 20% to 80%, in the rainfall intensities across all durations. Finally, future projected rainfall intensities are used to investigate the possible impact of climate change in the existing drainage system of the Challaghatta valley in the Bangalore City by running the Storm Water Management Model (SWMM) for historical period, and the best and the worst case scenario for three future time period of 2021–2050, 2051–2080 and 2071–2100. The results indicate that the existing drainage is inadequate for current condition as well as for future scenarios. The number of nodes flooded will increase as the time period increases, and a huge change in runoff volume is projected. The modifications of the drainage system are suggested by providing storage pond for storing the excess high speed runoff in order to restrict the width of the drain The main research contribution of this thesis thus comes from an analysis of trends of extreme rainfall in an urban area followed by projecting changes in the IDF relationships under climate change scenarios and quantifying uncertainties in the projections.

As a result of increase in global average surface temperature, abnormalities in different hydroclimatic components such as evapotranspiration, stream flow and precipitation have been experienced. So investigation has to be carried out to assess the hidden abnormality subsisting in the hydroclimatological time series in the form of trend. This thesis broadly consists of following four parts. The first part comprises of a detailed review of various trend detection approaches. Approaches incorporating the effect of serial correlation for trend detection and interesting developments concerning various non parametric approaches are focused explicitly. Recent trends in annual, monthly, and seasonl (winter, pre-monsoon, monsoon and post-monsoon) Tmax and Tmin have been analyzed considering three time slots viz. 1901-2003, 1948-2003 and 1970-2003. For this purpose, time series of Tmax and Tmin of India as a whole and for seven homogeneous regions, viz. Western Himalaya (WH), Northwest (NW), Northeast (NE), North Central (NC), East coast (EC), West coast (WC) and Interior Peninsula (IP) were originally considered. During the last three decades significant upward trend in Tmin is found to be present in all regions considered either at annual or seasonal level. Sequential Mann Kendall test revealed that most of the significant upward trends both in Tmax and Tmin began after 1970. The second part discusses about numerous climate models from both Coupled Model Inter comparison Project-5 and 3 (i.e. CMIP5, CMIP3) and their skills in simulating Indian climate and assessing their performance using various evaluation measures. Performances of climate models were evaluated for whole of India and over all the individual grid points covering India. The newly defined metric symbolized as Skill_All is an intersection of the three metrics i.e. Skill_r, Skill_s and Skill_rmse, is used for overall model evaluation analysis. A notable enhancement of Skill_All for CMIP5 over CMIP3 was found. After overall model evaluation study, Compromise Programming, a distance based decision making technique, was employed to rank the GCMs gridwise. Entropy method was employed to obtain weights of the chosen indicators. Group decision making methodology was used to arrive at a consensus based on the ranking pattern obtained by individual grid points. In the third part, a detailed detection and attribution (D&A) analysis is performed to determine the causes of changes in seasonal Tmax and Tmin during the period 1950-2005. This formal D&A exercise helps in providing better insight (than trend detection analysis) into the nature of the observed seasonal temperature changes. It was noticed that the emergence of observed trend was more pronounced in Tmin compared to Tmax. Although observed changes were not solely associated with one specific causative factor, most of the changes in Tmin are above the bounds of natural internal climate variability. Finally in the fourth part, to understand the climate change impact on the hydrological cycle, a spatiotemporal change detection study of potential evapotranspiration (PET) along with Tmax and Tmin over India has been performed. Climatology patterns for PET confirmed a greater PET rate during the month of March, April, May and June. A significant increasing trend in both Tmax and Tmin (Tmin being more) was observed in more number of grid points compared to PET. Significant positive trends in Tmax, Tmin and PET were observed over most of the grid points in the IP region. Heterogeneities existed in the spatiotemporal variability of PET over all India. This spatio-temporal change detection study would be helpful for present and future water resources management.

[ES] La gestión de los recursos hídricos es de vital importancia para la comprensión de las sequias a largo plazo. En la actualidad, se presentan problemas debido a la disponibilidad y manejo del recurso hídrico. Además, el cambio climático afecta de manera negativa las variables climáticas y la disponibilidad del recurso hídrico. El tomar decisiones en base a información confiable y precisa conlleva un arduo trabajo y es necesario contar con diferentes herramientas que permitan llegar a la gestión de los recursos hídricos. La modelización de las variables climáticas es parte fundamental para determinar la disponibilidad del recurso hídrico. Las más importantes son la precipitación y temperatura o precipitación y evapotranspiración. Los modelos estocásticos se encuentran en un proceso de evolución que permiten reducir la escala de análisis. En esta investigación se ha abordado la modelación de variables climáticas con detalle diario. Se ha planteado una metodología para la generación de series sintéticas de precipitación y temperatura mediante modelización estocástica continua multivariada a escala diaria. Esta metodología también incorpora la corrección del sesgo para precipitación y temperatura de los escenarios de cambio climático con detalle diario. Los resultados de la presente tesis indican que los modelos estocásticos multivariados pueden representar las condiciones espaciales y temporales de las diferentes variables climáticas (precipitación y temperatura). Además, se plantea una metodología para la determinación de la evapotranspiración en función de los datos climáticos disponibles. Por otro lado, los modelos estocásticos multivariados permiten la corrección del sesgo con resultados diarios, mensuales y anuales más realistas que otros métodos de corrección de sesgo. Estos modelos climáticos son una herramienta para pronosticar eventos o escenarios futuros que permiten tomar mejores decisiones de manera anticipada. Estos modelos se programaron en el entorno de MatLab con el objetivo de aplicarlos a diferentes zonas de estudio de manera eficiente y automatizada. Los análisis realizados en la presente tesis se realizaron para la cuenca del Júcar con un buen desempeño para las condiciones de la cuenca. / [CA] La gestió dels recursos hídrics és de vital importància per a la comprensió de les sequeres a llarg termini. En l'actualitat, es presenten problemes a causa de la disponibilitat i maneig del recurs hídric. A més, el canvi climàtic afecta de manera negativa les variables climàtiques i la disponibilitat del recurs hídric. El prendre decisions sobre la base informació de confiança i precisa comporta un ardu treball i és necessari comptar amb diferents eines que permeten arribar a la gestió dels recursos hídrics. La modelització de les variables climàtiques és part fonamental per a determinar la disponibilitat del recurs hídric. Les més importants són la precipitació i temperatura o precipitació i evapotranspiració. Els models estocàstics es troben en un procés d'evolució que permet la incorporació de més detalls reduint l'escala d'anàlisi. En aquesta investigació s'ha abordat el modelatge de variables climàtiques amb detall diari. S'ha plantejat una metodologia per a la generació de sèries sintètiques de precipitació i temperatura mitjançant modelització estocàstica contínua multivariada a escala diària. Aquesta metodologia també incorpora la correcció del biaix per a precipitació i temperatura dels escenaris de canvi climàtic amb detall diari. Els resultats de la present tesi indiquen que els models estocàstics multivariats poden representar les condicions espacials i temporals de les diferents variables climàtiques (precipitació i temperatura). A més es planteja una metodologia per a la determinació de l'evapotranspiració en funció de les dades climàtiques disponibles. D'altra banda, els models estocàstics multivariats permeten la correcció del biaix amb resultats diaris, mensuals i anuals més realistes que altres mètodes de correcció de biaix. Aquests models climàtics són una eina per a pronosticar esdeveniments o escenaris futurs que permeten prendre millors decisions de manera anticipada. Aquests models es van programar a l'entorn de Matlab amb l'objectiu d'aplicar-los a diferents zones d'estudi de manera eficient i automatitzada. Les anàlisis realitzades en la present tesi es van realitzar per a la conca del Xúquer amb un bon acompliment per a les condicions de la conca. / [EN] Management of the water resources is important for understanding long-term droughts. Currently, there are problems due to the availability and management of water resources. Furthermore, climate change negatively affecting climate variables and the availability of water resources. Making decisions based on reliable and accurate information involves hard work and it is necessary to have different tools to achieve the management of water resources. The modeling of the climatic variables is a fundamental part to determine the availability of the water resource. The most important are precipitation and temperature or precipitation and evapotranspiration. Stochastic models are in a process of evolution that allows the incorporation of more details by reducing the scale of analysis. In this research, the modeling of climatic variables has been approached in daily detail. A methodology has been proposed for the generation of synthetic series of precipitation and temperature by means of multivariate continuous stochastic modeling on a daily scale. This methodology also incorporates the bias correction for precipitation and temperature of the climate change scenarios with daily detail. The results of this thesis indicate that multivariate stochastic models can represent the spatial and temporal conditions of the different climatic variables (precipitation and temperature). In addition, a methodology is proposed for the determination of evapotranspiration based on the available climatic data. On the other hand, multivariate stochastic models allow bias correction with more realistic daily, monthly and annual results than other bias correction methods. These climate models are a tool to forecast future events or scenarios that allow better decisions to be made in advance. These models were programmed in the MatLab software with the aim of applying them to different study areas in an efficient and automatically. The work in this thesis was carried out for the Júcar basin with a good performance for the conditions of the basin / Hernández Bedolla, J. (2022). Análisis estocástico de datos climáticos como predictor para la gestión anticipada de sequías en recursos hídricos [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/182095 / TESIS