Significant challenges in water resources management arise because of the ever-increasing pressure on the world’s heavily exploited and limited water resources. These stressors include demographic growth, intensification of agriculture, climate variability, and climate change. These challenges to water resources are usually tackled using a top-down approach, which suffers from many limitations including the use of a limited set of climate change scenarios, the lack of methodology to rank these scenarios, and the lack of credibility, particularly on extremes. The bottom-up approach, the recently introduced approach, reverses the process by assessing vulnerabilities of water resources systems to variations in future climates and determining the prospects of such wide range of changes. While it solves some issues of the top-down approach, several issues remain unaddressed. The current project seeks to provide end-users and the research community with an improved version of the bottom-up framework for streamlining climate variability into water resources management decisions. The improvement issues that are tackled are a) the generation of a sufficient number of climate projections that provide better coverage of the risk space; b) a methodology to quantitatively estimate the plausibility of a future desired or undesired outcome and c) the optimization of the size of the projections pool to achieve the desired precision with the minimum time and computing resources. The results will hopefully help to cope with the present-day and future challenges induced mainly by climate. In the first part of the study, the adequacy of stochastically generated climate time series for water resources systems risk and performance assessment is investigated. A number of stochastic weather generators (SWGs) are first used to generate a large number of realizations (i.e. an ensemble of climate outputs) of precipitation and temperature time series. Each realization of the generated climate time series is then used individually as an input to a hydrological model to obtain streamflow time series. The usefulness of weather generators is evaluated by assessing how the statistical properties of simulated precipitation, temperatures, and streamflow deviate from those of observations. This is achieved by plotting a large ensemble of (1) synthetic precipitation and temperature time series in a Climate Statistics Space (CSS), and (2) hydrological indices using simulated streamflow data in a Risk and Performance Indicators Space (RPIS). The performance of the weather generator is assessed using visual inspection and the Mahalanobis distance between statistics derived from observations and simulations. A case study was carried out using five different weather generators: two versions of WeaGETS, two versions of MulGETS and the k-nearest neighbor weather generator (knn). In the second part of the thesis, the impacts of climate change, on the other hand, was evaluated by generating a large number of representative climate projections. Large ensembles of future series are created by perturbing downscaled regional climate models’ outputs with a stochastic weather generator, then used as inputs to a hydrological model that was calibrated using observed data. Risk indices calculated with the simulated streamflow data are converted into probability distributions using Kernel Density Estimations. The results are dimensional joint probability distributions of risk-relevant indices that provide estimates of the likelihood of unwanted events under a given watershed configuration and management policy. The proposed approach offers a more complete vision of the impacts of climate change and opens the door to a more objective assessment of adaptation strategies. The third part of the thesis deals with the estimation of the optimal size of SWG realizations needed to calculate risk and performance indices. The number of realizations required to reach is investigated utilizing Relative Root Mean Square Error and Relative Error. While results indicate that a single realization is not enough to adequately represent a given stochastic weather generator, results generally indicate that there is no major benefit of generating more than 100 realizations as they are not notably different from results obtained using 1000 realizations. Adopting a smaller but carefully chosen number of realizations can significantly reduce the computational time and resources and therefore benefit a larger audience particularly where high-performance machines are not easily accessible. The application was done in one pilot watershed, the South Nation Watershed in Eastern Ontario, yet the methodology will be of interest for Canada and beyond. Overall, the results contribute to making the bottom-up more objective and less computationally intensive, hence more attractive to practitioners and researchers.

A framework is developed for the assessment of climate change impacts on water resources systems. The applied techniques include: quantifying global climate model (GCM) skill over a range of time scales; developing future climate scenarios based on GCM data that are found to skillfully represent the observed climate over an historical baseline period; and using the climate scenarios together with hydrologic and water resources models to make assessments of the potential impacts and implications of climate change on water resources systems. A statistical analysis of GCM skill in East Africa shows that temperature is well represented in the GCMs at monthly to annual time scales. Precipitation is found to be much less reliable in the models and shows skill in fewer seasons and nodes than temperature. Eight climate scenarios, stemming from three global climate models and two atmospheric emissions scenarios, project temperature increases between 2 and 5 ° Celsius by the year 2080. Precipitation projections vary widely across models as well as regionally. The scenarios project changes in precipitation from -38% to +42%. The climate change impact methodology is applied to the Nile River Basin. It is shown that, in spite of widely varying precipitation projections, the major sub-basins of the Nile River will experience decreases in watershed runoff under all eight climate scenarios. Detailed water resources models are employed to assess the system wide response to the climate-induced hydrologic changes. The assessments indicate that water supply deficits will emerge by 2030 and continue to grow in frequency and magnitude by 2080. Additional impacts include reservoir depletion and reduced hydropower generation. An assessment of the river system response to basin development projects, including additional water storage and wetlands water conservation, indicates that adverse climate impacts may be mitigated for 30 to 40 years. The assessments demonstrate the relevance of climate change considerations to water resources management and the development of water policy.

Anthropogenic climate change significantly affects water resources. River flows in mountainous regions are driven by snowmelt and are therefore highly sensitive to increases in temperature resulting from climate change. Climate-driven hydrological changes are potentially significant for the fluvial geomorphology of river systems. In unchanging climatic and tectonic conditions, a river's morphology will develop in equilibrium with inputs of water and sediment, but climate change represents a potential forcing on these variables that may push the system into disequilibrium and cause significant changes in river morphology. Geomorphic factors, such as channel geometry, planform, and sediment transport, are major determinants of the value of river systems, including their suitability for threatened and endangered species and for human uses of water. This dissertation research uses a hierarchical modeling approach to investigate potential impacts of anthropogenic climate change on river morphology in the interior Pacific Northwest. The research will address the following theoretical and methodological objectives: 1) Develop downscaled climate change scenarios, based on regional climate-model output, including changes in daily minimum and maximum temperature and precipitation. 2) Estimate how climate change scenarios affect river discharge and suspended-sediment load, using a basin-scale hydrologic model. 3) Examine potential impacts of climate-driven hydrologic changes on stream power and shear stress, bedload sediment transport, and river morphology, including channel geometry and planform. The downscaling approach, based on empirically-estimated local topographic lapse rates, produces high-resolution climate grids with positive forecast skill. The hydrologic modeling results indicate that projected climate change in the study rivers will change the annual cycle of hydrology, with increased winter discharge, a decrease in the magnitude of the spring snowmelt peak, and decreased summer discharge. Geomorphic modeling results suggest that changes in reach-averaged bedload transport are highly sensitive to likely changes in the recurrence interval of the critical discharge needed to mobilize bed sediments. This dissertation research makes an original contribution to the climate-change impacts literature by linking Earth processes across a wide range of spatial scales to project changes in river systems that may be significant for management of these systems for societal and ecological benefits. This dissertation includes unpublished co-authored material.

Thesis advisor: Hilary Palevsky / The ocean plays a key role in global carbon cycling, taking up CO2 from the atmosphere. A fraction of this CO2 is converted into organic carbon through primary production in the surface ocean and sequestered in the deep ocean through a process known as the biological pump. The ability of the biological pump to sequester carbon away from the atmosphere is influenced by the interaction between the annual cycle of ocean mixed layer depth (MLD), primary production, and ecosystem processes that influence export efficiency. Gravitational sinking of particulate organic carbon (POC) is the largest component of the biological pump and the aspect that is best represented in Earth System Models (ESMs). I use ESM data from CESM2, an ESM participating in the Coupled Model Intercomparison Project Phase 6 (CMIP6), to investigate how a high-emissions climate change scenario will impact POC flux globally and regionally over the 21st century. The model simulates a 4.4% decrease in global POC flux at the 100 m depth horizon, from 7.12 Pg C/yr in the short-term (2014-2034) to 6.81 Pg C/yr in the long-term (2079-2099), indicating that the biological pump will become less efficient overall at sequestering carbon. However, the extent of change varies across the globe, including the largest POC flux declines in the North Atlantic, where the maximum annual MLD is projected to shoal immensely. In the future, a multi-model comparison across ESMs will allow for further analysis on the variability of these changes to the biological pump. / Thesis (BS) — Boston College, 2021. / Submitted to: Boston College. College of Arts and Sciences. / Discipline: Departmental Honors. / Discipline: Earth and Environmental Science.

Climate change impacts have been shown to increase the social, economic and ecological vulnerabilities of poor groups in coastal communities of Asian countries. Mangunharjo village in Semarang city, Indonesia, has been identified as vulnerable to sea level rise, coastal erosion, tidal inundation and flooding, and the well-being of residents is threatened due to loss of livelihoods. In order to secure their future, the community has to enhance its resilience to climate change impacts, however, additional factors are undermining thepotential of a resilient and prosperous village. As resilience theory carried out in practice could negatively affect already marginalized people if trade-offs are not identified, a complementing theory is needed. This study develops a novel joint framework of resilience theory and environmental justice for analyzing the potential of enhancin gthe community’s resilience. It also explores what is needed for the village in order to increase its resilience. The framework revealed to be successful in identifying root problems and highlighted deficiencies in current resilience strategies. Moreover, the incorporation of environmental justice broadened the perspective of what could weaken the resilience ofthe village. Hence, an environmental justice perspective complements resilience theory as it identifies potential trade-offs and analyzes whose resilience is enhanced. The framework is argued to be a useful tool to secure resilience of a social-ecological system of various scales, however, further research is needed onthe optimal linkages of the two theories.

abstract: This dissertation advances the capability of water infrastructure utilities to anticipate and adapt to vulnerabilities in their systems from temperature increase and interdependencies with other infrastructure systems. Impact assessment models of increased heat and interdependencies were developed which incorporate probability, spatial, temporal, and operational information. Key findings from the models are that with increased heat the increased likelihood of water quality non-compliances is particularly concerning, the anticipated increases in different hardware components generate different levels of concern starting with iron pipes, then pumps, and then PVC pipes, the effects of temperature increase on hardware components and on service losses are non-linear due to spatial criticality of components, and that modeling spatial and operational complexity helps to identify potential pathways of failure propagation between infrastructure systems. Exploring different parameters of the models allowed for comparison of institutional strategies. Key findings are that either preventative maintenance or repair strategies can completely offset additional outages from increased temperatures though-- improved repair times reduce overall duration of outages more than preventative maintenance, and that coordinated strategies across utilities could be effective for mitigating vulnerability. / Dissertation/Thesis / Doctoral Dissertation Civil, Environmental and Sustainable Engineering 2019

Climate change could aggravate periodic and chronic shortfalls of water, particularly in arid and semi-arid areas of the world (IPCC, 2001). Climate change is likely to accelerate the global hydrological cycle, with increase in temperature, changes in precipitation patterns, and evapotranspiration affecting the water quantity and quality, water availability and demands. The various components of a surface water resources system affected by climate change may include the water availability, irrigation demands, water quality, hydropower generation, ground water recharge, soil moisture etc. It is prudent to examine the anticipated impacts of climate change on these different components individually or combinedly with a view to developing responses to minimize the climate change induced risk in water resources systems. Assessment of climate change impacts on water resources essentially involves downscaling the projections of climatic variables (e.g., temperature, humidity, mean sea level pressure etc.) to hydrologic variables (e.g., precipitation and streamflow), at regional scale. Statistical downscaling methods are generally used in the hydrological impact assessment studies for downscaling climate projections provided by the General Circulation Models (GCMs). GCMs are climate models designed to simulate time series of climate variables globally, accounting for the greenhouse gases in the atmosphere. The statistical techniques used to bridge the spatial and temporal resolution gaps between what GCMs are currently able to provide and what impact assessment studies require are called as statistical downscaling methods. Generally, these methods involve deriving empirical relationships that transform large-scale simulations of climate variables (referred as the predictors) provided by a GCM to regional scale hydrologic variables (referred as the predictands). This general methodology is characterized by various uncertainties such as GCM and scenario uncertainty, uncertainty due to initial conditions of the GCMs, uncertainty due to downscaling methods, uncertainty due to hydrological model used for impact assessment and uncertainty resulting from multiple stake holders in a water resources system. The research reported in this thesis contributes towards (i) development of methodologies for climate change impact assessment of various components of a water resources system, such as water quality, water availability, irrigation and reservoir operation, and (ii) quantification of GCM and scenario uncertainties in hydrologic impacts of climate change. Further, an integrated reservoir operation model is developed to derive optimal operating policies under the projected scenarios of water availability, irrigation water demands, and water quality due to climate change accounting for various sources of uncertainties. Hydropower generation is also one of the objectives in the reservoir operation. The possible climate change impact on river water quality is initially analyzed with respect to hypothetical scenarios of temperature and streamflow, which are affected by changes in precipitation and air temperature respectively. These possible hypothetical scenarios are constructed for the streamflow and river water temperature based on recent changes in the observed data. The water quality response is simulated, both for the present conditions and for conditions resulting from the hypothetical scenarios, using the water quality simulation model, QUAL2K. A Fuzzy Waste Load Allocation Model (FWLAM) is used as a river water quality management model to derive optimal treatment levels for the dischargers in response to the hypothetical scenarios of streamflow and water temperature. The scenarios considered for possible changes in air temperature (+1 oC and +2 oC) and streamflow (-0%, -10%, -20%) resulted in a substantial decrease in the Dissolved Oxygen (DO) levels, increase in Biochemical Oxygen Demand (BOD) and river water temperature for the case study of the Tunga-Bhadra River, India. The river water quality indicators are analyzed for the hypothetical scenarios when the BOD of the effluent discharges is at safe permissible level set by Pollution Control Boards (PCBs). A significant impairment in the water quality is observed for the case study, under the hypothetical scenarios considered. A multi-variable statistical downscaling model based on Canonical Correlation Analysis (CCA) is then developed to downscale future projections of hydro¬meteorological variables to be used in the impact assessment study of river water quality. The CCA downscaling model is used to relate the surface-based observations and atmospheric variables to obtain the simultaneous projection of hydrometeorological variables. Statistical relationships in terms of canonical regression equations are obtained for each of the hydro-meteorological predictands using the reanalysis data and surface observations. The reanalysis data provided by National Center for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) are used for the purpose. The regression equations are applied to the simulated GCM output to model future projections of hydro-meteorological predictands. An advantage of the CCA methodology in the context of downscaling is that the relationships between climate variables and the surface hydrologic variables are simultaneously expressed, by retaining the explained variance between the two sets. The CCA method is used to model the monthly hydro-meteorological variables in the Tunga-Bhadra river basin for water quality impact assessment study. A modeling framework of risk assessment is developed to integrate the hydro¬meteorological projections downscaled from CCA model with a river water quality management model to quantify the future expected risk of low water quality under climate change. A Multiple Logistic Regression (MLR) is used to quantify the risk of Low Water Quality (LWQ) corresponding to a threshold DO level, by considering the streamflow and water temperature as explanatory variables. An Imprecise Fuzzy Waste Load Allocation Model (IFWLAM) is adopted to evaluate the future fractional removal policies for each of the dischargers by including the predicted future risk levels. The hydro-meteorological projections of streamflow, air temperature, relative humidity and wind speed are modeled using MIROC 3.2 GCM simulations with A1B scenario. The river water temperature is modeled by using an analytical temperature model that includes the downscaled hydro-meteorological variables. The river water temperature is projected to increase under climate change, for the scenario considered. The IFWLAM uses the downscaled projections of streamflow, simulated river water temperature and the predicted lower and upper future risk levels to determine the fraction removal policies for each of the dischargers. The results indicate that the optimal fractional removal levels required for the future scenarios will be higher compared to the present levels, even if the effluent loadings remain unchanged. Climate change is likely to impact the agricultural sector directly with changes in rainfall and evapotranspiration. The regional climate change impacts on irrigation water demands are studied by quantifying the crop water demands for the possible changes of rainfall and evapotranspiration. The future projections of various meteorological variables affecting the irrigation demand are downscaled using CCA downscaling model with MIROC 3.2 GCM output for the A1B scenario. The future evapotranspiration is obtained using the Penman-Monteith evapotranspiration model accounting for the projected changes in temperature, relative humidity, solar radiation and wind speed. The monthly irrigation water demands of paddy, sugarcane, permanent garden and semidry crops quantified at nine downscaling locations covering the entire command area of the Bhadra river basin, used as a case study, are projected to increase for the future scenarios of 2020-2044, 2045-2069 and 2070-2095 under the climate change scenario considered. The GCM and scenario uncertainty is modeled combinedly by deriving a multimodel weighted mean by assigning weights to each GCM and scenario. An entropy objective weighting scheme is proposed which exploits the information contained in various GCMs and scenarios in simulating the current and future climatology. Three GCMs, viz., CGCM2 (Meteorological Research Institute, Japan), MIROC3.2 medium resolution (Center for Climate System Research, Japan), and GISS model E20/Russell (NASA Goddard Institute for Space Studies, USA) with three scenarios A1B, A2 and B1 are used for obtaining the hydro-meteorological projections for the Bhadra river basin. Entropy weights are assigned to each GCM and scenario based on the performance of the GCM and scenario in reproducing the present climatology and deviation of each from the projected ensemble average. The proposed entropy weighting method is applied to projections of the hydro-meteorological variables obtained based on CCA downscaling method from outputs of the three GCMs and the three scenarios. The multimodel weighted mean projections are obtained for the future time slice of 2020-2060. Such weighted mean hydro-meteorological projections may be further used into the impact assessment model to address the climate model uncertainty in the water resources systems. An integrated reservoir operation model is developed considering the objectives of irrigation, hydropower and downstream water quality under uncertainty due to climate change, uncertainty introduced by fuzziness in the goals of stakeholders and uncertainty due to the random nature of streamflow. The climate model uncertainty originating from the mismatch between projections from various GCMs under different scenarios is considered as first level of uncertainty, which is modeled by using the weighted mean hydro-meteorological projections. The second level of uncertainty considered is due to the imprecision and conflicting goals of the reservoir users, which is modeled by using fuzzy set theory. A Water Quantity Control Model (WQCM) is developed with fuzzy goals of the reservoir users to obtain water allocations among the different users of the reservoir corresponding to the projected demands. The water allocation model is updated to account for the projected demands in terms of revised fuzzy membership functions under climate change to develop optimal policies of the reservoir for future scenarios. The third level of uncertainty arises from the inherent variability of the reservoir inflow leading to uncertainty due to randomness, which is modeled by considering the reservoir inflow as a stochastic variable. The optimal monthly operating polices are derived using Stochastic Dynamic Programming (SDP), separately for the current and for the future periods of 2020-2040 and 2040-2060 The performance measures for Bhadra reservoir in terms of reliability and deficit ratios for each reservoir user (irrigation, hydropower and downstream water quality) are estimated with optimal SDP policy derived for current and future periods. The reliability with respect to irrigation, downstream water quality and hydropower show a decrease for 2020-2040 and 2040-2060, while deficit ratio increases for these periods. The results reveal that climate change is likely to affect the reservoir performance significantly and changes in the reservoir operation for the future scenarios is unable to restore the past performance levels. Hence, development of adaptive responses to mitigate the effects of climate change is vital to improve the overall reservoir performance.

This research incorporates streambank erosion and failure processes into a distributed watershed model and evaluates the impacts of climate change on the processes driving streambank sediment mobilization at a watershed scale. Excess sediment and nutrient loading are major water quality concerns for streams and receiving waters. Previous work has established that in addition to surface and road erosion, streambank erosion and failure are primary mechanisms that mobilize sediment and nutrients from the landscape. This mechanism and other hydrological processes driving sediment and nutrient transport are likely to be highly influenced by anticipated changes in climate, particularly extreme precipitation and flow events. This research has two primary goals: to develop a physics-based watershed model with more inclusive representation of sediment by including simulation of streambank erosion and geotechnical failure; and to investigate the impacts of climate change on unstable streams and suspended sediment mobilization by overland erosion, erosion of roads, and the erosion as well as failure of streambanks. This advances mechanistic simulation of suspended sediment mobilization and transport from watersheds, which is particularly valuable for investigating the impacts of climate and land use changes, as well as extreme events. Model development involved coupling two existing physics-based models: the Bank Stability and Toe Erosion Model (BSTEM) and the Distributed Hydrology Soil Vegetation Model (DHSVM). This approach simulates streambank erosion and failure in a spatially explicit environment. The coupled model is applied to the Mad River watershed in central Vermont as a test case. I then use the calibrated Mad River model to predict the response in watershed sediment loading to future climate scenarios that specifically represent local temperature and precipitation trends for the northeastern US, particularly changing trends in the frequency and magnitude of extreme precipitation. Overall the streambank erosion and failure processes are captured in the coupled model approach. Although the presented calibration of the model underestimates suspended sediment concentrations resulting from relatively small storm/flow events, it still improves prediction of cumulative loads and in some cases suspended sediment concentrations during elevated flow events in comparison to model results without including BSTEM. Increases in temperature affect the timing and magnitude of snow melt and spring flows, as well as associated sediment mobilization, in the watershed. Increases in annual precipitation and in extreme precipitation events produce increases in annual as well as peak discharge and sediment loads in the watershed. This research adds to the body of evidence indicating that streambank erosion and failure can be a major source of suspended sediment, and thereby a major source of phosphorus as well. It also shows that local climate trends in the Northeast are likely to result in higher peak discharges and sediment yields from meso-scale, high-gradient watersheds that encompass headwater forested streams and agricultural floodplains. One limitation was that we could not drive the model with meteorological data that represented changes in both temperature and precipitation, highlighting the need for improved climate predictions. This coupled model approach could be parameterized for alternative watersheds and be re-applied to answer various questions related to erosion processes and sediment transport in a watershed. These findings have important implications for resource allocation and targeted watershed management strategies.

Die vorliegende Habilitation beschäftigt sich mit verschiedenen Aspekten des Hochwasserrisikos in Deutschland. In zwölf Artikeln werden neue wissenschaftliche Erkenntnisse über Hochwassergefahren, über Faktoren, die Hochwasserschäden beeinflussen, sowie über effektive private Vorsorgemaßnahmen präsentiert. So wird die jahreszeitliche Verteilung von Hochwasser in ganz Deutschland gezeigt. Weiterhin werden mögliche Auswirkungen des Klimawandels auf Abflussverhältnisse und Häufigkeiten von Hochwasserereignissen am Beispiel des Rhein-Einzugsgebietes abgeschätzt. Ferner wird am Niederrhein simuliert, welche Auswirkungen Deichbrüche haben können. Hochwasserschäden stehen im zweiten Teil der Arbeit im Fokus: Nach dem August-Hochwasser 2002 wurden ca. 1700 Privathaushalte telefonisch befragt. Damit konnten die Einflüsse verschiedener Faktoren, wie der Überflutungsdauer oder der Verunreinigung des Hochwassers mit Öl, auf die Höhe von finanziellen Schäden quantifiziert werden. Daraus ist zum einen ein neues Modell entstanden, mit dem Hochwasserschäden großräumig berechnet werden können. Zum anderen konnten Hinweise für die Verbesserung der privaten Vorsorge abgeleitet werden. Beispielsweise zeigte sich, dass versicherte Haushalte schneller und besser entschädigt werden als Nicht-Versicherte. Ebenfalls wurde deutlich, dass verschiedene Bevölkerungsgruppen, wie Mieter und Hauseigentümer, unterschiedliche Möglichkeiten haben, Vorsorge zu betreiben. Dies ist zukünftig in der Risikokommunikation zu berücksichtigen. In den Jahren 2005 und 2006 waren Elbe und Donau wiederum von Hochwasser betroffen. Eine erneute Befragung von Privathaushalten und Behörden ermöglichte, die Verbesserung des Hochwasserrisikomanagement und der Vorsorge am Beispiel der Stadt Dresden zu untersuchen. Viele Methoden und Erkenntnisse dieser Arbeit sind in der wasserwirtschaftlichen Praxis anwendbar und tragen somit zur Verbesserung der Hochwasserrisikoanalyse und des Risikomanagements in Deutschland bei. / This thesis deals with different aspects of flood risk in Germany. In twelve papers new scientific findings about flood hazards, factors that influence flood losses as well as effective private precautionary measures are presented. The seasonal distribution of flooding is shown for the whole of Germany. Furthermore, possible impacts of climate change on discharge and flood frequencies are estimated for the catchment of the river Rhine. Moreover, it is simulated at reaches of the Lower Rhine, which effects may result from levee breaches. Flood losses are the focus of the second part of the thesis: After the flood in August 2002 approximately 1700 households were interviewed by telephone. By this, it was possible to quantify the influence of different factors such as flood duration or the contamination of the flood water with oil on the extent of financial flood damage. On this basis, a new model was derived, by which flood losses can be calculated on a large scale. On the other hand, it was possible to derive recommendations for the improvement of private precaution. For example, the analysis revealed that insured households were compensated more quickly and to a better degree than uninsured. It became also clear that different groups like tenants and homeowners have different capabilities of performing precaution. This is to be considered in future risk communication. In 2005 and 2006, the rivers Elbe and Danube were again affected by flooding. A renewed pool among households and public authorities enabled us to investigate the improvement of flood risk management and the precaution in the City of Dresden. Several methods and finding of this thesis are applicable for water resources management issues and contribute to an improvement of flood risk analysis and management in Germany.

The research presented here attempts to bridge science and policy through the quantification of climate change impacts and the analysis of a science-fed participatory process to face a sustainability challenge in the San Pedro Basin (Arizona). Paper 1 presents an assessment of a collaborative development process of a decision support system model between academia and a multi-stakeholder consortium created to solve water sustainability problems in a local watershed. This study analyzes how science-fed multi-stakeholder participatory processes lead to sustainability learning promoting resilience and adaptation. Paper 2 presents an approach to link an ensemble of global climate model outputs with a hydrological model to quantify climate change impacts in the hydrology of a basin, providing a range of uncertainty in the results. Precipitation projections for the current century from different climate models and IPCC scenarios are used to obtain recharge estimates as inputs to a groundwater model. Quantifying changes in the basin's water budget due to changes in recharge, evapotranspiration (ET) rates are assumed to depend only on groundwater levels. Picking on such assumption, Paper 3 explores the effects of a changing climate on ET. Using experimental eddy covariance data from three riparian sites, it analyzes seasonal controls on ET. An approach to quantify evapotranspiration rates and growing season length under warmer climates is proposed. Results indicate that although atmospheric demand will be greater, increasing pan and reference crop evaporation, ET rates at the studied field sites will remain unchanged due to stomatal regulation. However, the length of the growing season will increase, mainly with an earlier leaf-out and at a lesser level by a delayed growing season end. These findings - implying decreased aquifer recharge, increased riparian water use and a lesser water balance - are very relevant for water management in semi-arid regions. Paper 4, in which I am second author, explores the theory relating changes in area-average and pan evaporation. Using the same experimental data as Paper 3, it corroborates a previous theoretical relationship and discusses the validity of Bouchet's hypothesis.