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

Climate change and urbanization causes the increasing challenges of flooding in urban watersheds. Even the rivers identified as non-vulnerable are causing catastrophic damage due to heavy flooding. So, several satellite and radar-based precipitation data are considered to study the watersheds with no gauge station or need recent precipitation data. Weather Radar (NEXRAD)arch, the accuracy of satellite-based precipitation data, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks - Climate Data Record (PERSIANN-CDR), and radar-based precipitation data, Next Generation Weather Radar (NEXRAD), is evaluated in rainfall-runoff simulation considering Hydrological Engineering Centre-Hydrologic Modeling System (HEC-HMS) and Personal Computer Storm Water Management Model (PCSWMM), respectively. The primary research proposes a framework for modeling the rainfall-runoff process using PERSIANN-CDR and a floodplain map in an ungauged urban watershed. The one-dimensional Hydrologic Engineering Centre-River Analysis System (HEC-RAS) model generates a flood inundation map for the pertinent flooding occurrences from the acquired peak hydrograph, providing a quantifiable display of the inundation extent percentage. The second research uses the PCSWMMs to show the extent of flooding. It also employs the compromise programming method (CPM) to rank the most critical sub-catchments based on three parameters: slope, surface area, and impervious area. Three low-impact development (LID) strategies over the watershed determine the best flood management option. Therefore, the overall study presents a comprehensive framework for flood management in urban watersheds that integrates satellite precipitation data, hydrologic modeling, and LID strategies. The framework can provide an accurate flood-prone zone and help prioritize critical sub-catchments for flood management options. The study proposes using HEC-HMS and PCSWMM models to simulate and analyze interactions between rainfall, runoff, and the extent of the flood zone. Furthermore, LID can be applied to reduce flooding in urban watersheds. Overall, the framework can be helpful for policymakers and system managers to build the watershed's resilience during catastrophic flooding events caused by climate change and urbanization.

Africa is highly vulnerable to the impacts of climate change. In particular shortage of fresh water is expected to be the dominant water problem for West and Northwest Africa of the 21th century. In order to solve present and projected future problems concerning fresh water supply, a highly interdisciplinary approach is used in the book. Strategies are offered for a sustainable and future-oriented water management. Based on different scenarios, a range of management options is suggested with the aid of Information Systems and Spatial Decision Support Systems for two river catchments in Northwest and West Africa: the wadi Drâa in south-eastern Morocco and the Ouémé basin in Benin. The selected catchments are representative in the sense: "what can be learnt from these catchments for other similar catchments?

Beginning with an overview of data and concepts developed in the EU-project HABIT-CHANGE, this book addresses the need for sharing knowledge and experience in the field of biodiversity conservation and climate change. There is an urgent need to build capacity in protected areas to monitor, assess, manage and report the effects of climate change and their interaction with other pressures. The contributors identify barriers to the adaptation of conservation management, such as the mismatch between planning reality and the decision context at site level. Short and vivid descriptions of case studies, drawn from investigation areas all over Central and Eastern Europe, illustrate both the local impacts of climate change and their consequences for future management. These focus on ecosystems most vulnerable to changes in climatic conditions, including alpine areas, wetlands, forests, lowland grasslands and coastal areas. The case studies demonstrate the application of adaptation strategies in protected areas like National Parks, Biosphere Reserves and Natural Parks, and reflect the potential benefits as well as existing obstacles. A general section provides the necessary background information on climate trends and their effects on abiotic and biotic components. Often, the parties to policy change and conservation management, including managers, land users and stakeholders, lack both expertise and incentives to undertake adaptation activities. The authors recognise that achieving the needed changes in behavior – habit – is as much a social learning process as a matter of science-based procedure. They describe the implementation of modeling, impact assessment and monitoring of climate conditions, and show how the results can support efforts to increase stakeholder involvement in local adaptation strategies. The book concludes by pointing out the need for more work to communicate the cross-sectoral nature of biodiversity protection, the value of well-informed planning in the long-term process of adaptation, the definition of acceptable change, and the motivational value of exchanging experience and examples of good practice.

This important monograph is based on the results of a study on the identification of conceptual lumped rainfall-runoff models for gauged and ungauged catchments. The task of model identification remains difficult despite decades of research. A detailed problem analysis and an extensive review form the basis for the development of a Matlab? modelling toolkit consisting of two components: a Rainfall-Runoff Modelling Toolbox (RRMT) and a Monte Carlo Analysis Toolbox (MCAT). These are subsequently applied to study the tasks of model identification and evaluation. A novel dynamic identifiability approach has been developed for the gauged catchment case. The theory underlying the application of rainfall-runoff models for predictions in ungauged catchments is studied, problems are highlighted and promising ways to move forward are investigated. Modelling frameworks for both gauged and ungauged cases are developed. This book presents the first extensive treatment of rainfall-runoff model identification in gauged and ungauged catchments.

Accurate estimates of precipitation at the global scale are vital for a variety of hydrometeorological applications. Quantification of the error sources along with characterization of the error propagation in hydrological simulations are required for promoting use of satellite and reanalysis precipitation estimates in hydrological applications.In this study we address the remotely-sensed precipitation products uncertainty characterization based ona machine learning tree-based model, Quantile Regression Forests (QRF). We first apply the model to satellitepassive microwave estimates from the TRMM satellite. Reference precipitation was based on high-resolution (5 min/1 km) rainfall fields derived from the NOAA/National Severe Storms Laboratory multi-radar multi-sensor system. The model was evaluated using a K-fold validation experiment using systematic and random error statistics of the model-adjusted TRMM passive microwave rainfall point estimates, and ensemble verification statistics of the corresponding prediction intervals. Then, this framework was utilized to combine dynamic and static land surface variables together with multiple global precipitation sources to stochastically generate improved precipitation ensembles (combined product) over complex terrain. Input to the model included multiple global satellite precipitation products; an atmospheric reanalysis precipitation product; and other auxiliary variables including a daily soil moisture dataset, specific humidity and a terrain elevation dataset. The model performance was demonstrated over three mountainous study areas (Peruvian and Colombian Andes and the Blue Nile in East Africa) based on 13 years (2000-2012) ofreference rainfall data derived from in situ rain gauge networks. Results showed that the proposed blending framework could significantly reduce the error andadequately characterize the uncertainty of the combined product. In the last section of this study we investigate the impact of the combined product in hydrological simulations. The Iberian Peninsula was chosen as the study area, which has precipitation and climate variability due to complex orography influenced by both Atlantic and Mediterranean climates.Comparisons of the precipitation product-driven hydrological simulations by a distributed hydrological model against reference-driven streamflow simulations by the same model showed that the magnitude of systematic and random errors for the combined product was significantly lower than those for the individual precipitation products. Moreover, this blending framework rendered a detailed investigation of the precipitation error propagation into multi-hydrologic model simulations, which was accomplished using four global-scale land surface models (JULES, ORCHIDEE, HTESSEL and SURFEX) and one global hydrologic model (WaterGAP3). Through this analysis we investigated the error characteristics of different precipitation forcing datasets (satellite, reanalysis, and combined product) and their error propagation in different hydrologic variables (surface/subsurface runoff, evapotranspiration).