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Risk, reliability, uncertainty, and robustness of water resources systems
Risk, Reliability, Uncertainty, and Robustness of Water Resource Systems is based on the Third George Kovacs Colloquium organized by the International Hydrological Programme (UNESCO) and the International Association of Hydrological Sciences. Thirty-five leading scientists with international reputations provide state-of-the-art reviews of topical areas of research on water resource systems, including aspects of extreme hydrological events: floods and droughts; water quantity and quality dams; reservoirs and hydraulic structures; evaluating sustainability and climate change impacts. As well as discussing essential challenges and research directions, the book will assist in applying theoretical methods to the solution of practical problems in water resources. The authors are multi-disciplinary, stemming from such areas as: hydrology, geography, civil, environmental and agricultural engineering, forestry, systems sciences, operations research, mathematics, physics and geophysics, ecology and atmospheric sciences. This review volume will be valuable for graduate students, scientists, consultants, administrators, and practising hydrologists and water managers.
eBook
Risk, Reliability, Uncertainty, and Robustness of Water Resource Systems
Risk, Reliability, Uncertainty, and Robustness of Water Resource Systems is based on the Third George Kovacs Colloquium organized by the International Hydrological Programme (UNESCO) and the International Association of Hydrological Sciences. Thirty-five leading scientists with international reputations provide reviews of topical areas of research on water resource systems, including aspects of extreme hydrological events: floods and droughts; water quantity and quality dams; reservoirs and hydraulic structures; evaluating sustainability and climate change impacts. As well as discussing essential challenges and research directions, the book will assist in applying theoretical methods to the solution of practical problems in water resources. The authors are multi-disciplinary, stemming from such areas as: hydrology, geography, civil, environmental and agricultural engineering, forestry, systems sciences, operations research, mathematics, physics and geophysics, ecology and atmospheric sciences. This review volume will be valuable for graduate students, scientists, consultants, administrators, and practising hydrologists and water managers.
eBook
New Uncertainty Concepts in Hydrology and Water Resources
One of the greatest problems hydrology research faces is how to quantify uncertainty, which is inherent in every hydrological process. This overview of uncertainty emphasizes non-orthodox concepts, such as random fields, fractals and fuzziness. This book reviews alternative and conventional methods of risk and uncertainty representation in hydrology and water resources. The water-related applications discussed in the book pertain to areas of strong interest, such as multifractals and climate change impacts. The authors represent a variety of research backgrounds, achieving a broad subject coverage. The material covered provides an important insight into theories of uncertainty related to the field of hydrology. The book is international in its scope, and will be welcomed by researchers and graduate students of hydrology and water resources.
eBook
New uncertainty concepts in hydrology and water resources
One of the greatest problems hydrology research faces is how to quantify uncertainty, which is inherent in every hydrological process. This overview of uncertainty emphasizes non-orthodox concepts, such as random fields, fractals and fuzziness. This book reviews alternative and conventional methods of risk and uncertainty representation in hydrology and water resources. The water-related applications discussed in the book pertain to areas of strong interest, such as multifractals and climate change impacts. The authors represent a variety of research backgrounds, achieving a broad subject coverage. The material covered provides an important insight into theories of uncertainty related to the field of hydrology. The book is international in its scope, and will be welcomed by researchers and graduate students of hydrology and water resources.
eBook
Climate Change Impacts on the Upper Indus Hydrology: Sources, Shifts and Extremes
The Indus basin heavily depends on its upstream mountainous part for the downstream supply of water while downstream demands are high. Since downstream demands will likely continue to increase, accurate hydrological projections for the future supply are important. We use an ensemble of statistically downscaled CMIP5 General Circulation Model outputs for RCP4.5 and RCP8.5 to force a cryospheric-hydrological model and generate transient hydrological projections for the entire 21st century for the upper Indus basin. Three methodological advances are introduced: (i) A new precipitation dataset that corrects for the underestimation of high-altitude precipitation is used. (ii) The model is calibrated using data on river runoff, snow cover and geodetic glacier mass balance. (iii) An advanced statistical downscaling technique is used that accounts for changes in precipitation extremes. The analysis of the results focuses on changes in sources of runoff, seasonality and hydrological extremes. We conclude that the future of the upper Indus basin's water availability is highly uncertain in the long run, mainly due to the large spread in the future precipitation projections. Despite large uncertainties in the future climate and long-term water availability, basin-wide patterns and trends of seasonal shifts in water availability are consistent across climate change scenarios. Most prominent is the attenuation of the annual hydrograph and shift from summer peak flow towards the other seasons for most ensemble members. In addition there are distinct spatial patterns in the response that relate to monsoon influence and the importance of meltwater. Analysis of future hydrological extremes reveals that increases in intensity and frequency of extreme discharges are very likely for most of the upper Indus basin and most ensemble members.
Journal Article
Deep learning methods for flood mapping: a review of existing applications and future research directions
Deep learning techniques have been increasingly used in flood management to overcome the limitations of accurate, yet slow, numerical models and to improve the results of traditional methods for flood mapping. In this paper, we review 58 recent publications to outline the state of the art of the field, identify knowledge gaps, and propose future research directions. The review focuses on the type of deep learning models used for various flood mapping applications, the flood types considered, the spatial scale of the studied events, and the data used for model development. The results show that models based on convolutional layers are usually more accurate, as they leverage inductive biases to better process the spatial characteristics of the flooding events. Models based on fully connected layers, instead, provide accurate results when coupled with other statistical models. Deep learning models showed increased accuracy when compared to traditional approaches and increased speed when compared to numerical methods. While there exist several applications in flood susceptibility, inundation, and hazard mapping, more work is needed to understand how deep learning can assist in real-time flood warning during an emergency and how it can be employed to estimate flood risk. A major challenge lies in developing deep learning models that can generalize to unseen case studies. Furthermore, all reviewed models and their outputs are deterministic, with limited considerations for uncertainties in outcomes and probabilistic predictions. The authors argue that these identified gaps can be addressed by exploiting recent fundamental advancements in deep learning or by taking inspiration from developments in other applied areas. Models based on graph neural networks and neural operators can work with arbitrarily structured data and thus should be capable of generalizing across different case studies and could account for complex interactions with the natural and built environment. Physics-based deep learning can be used to preserve the underlying physical equations resulting in more reliable speed-up alternatives for numerical models. Similarly, probabilistic models can be built by resorting to deep Gaussian processes or Bayesian neural networks.
Journal Article
Performance Comparison of an LSTM-based Deep Learning Model versus Conventional Machine Learning Algorithms for Streamflow Forecasting
Streamflow forecasting plays a key role in improvement of water resource allocation, management and planning, flood warning and forecasting, and mitigation of flood damages. There are a considerable number of forecasting models and techniques that have been employed in streamflow forecasting and gained importance in hydrological studies in recent decades. In this study, the main objective was to compare the accuracy of four data-driven techniques of Linear Regression (LR), Multilayer Perceptron (MLP), Support Vector Machine (SVM), and Long Short-Term Memory (LSTM) network in daily streamflow forecasting. For this purpose, three scenarios were defined based on historical precipitation and streamflow series for 26 years of the Kentucky River basin located in eastern Kentucky, US. Statistical criteria including the coefficient of correlation (R), Nash-Sutcliff coefficient of efficiency (E), Nash-Sutcliff for High flow (EH), Nash-Sutcliff for Low flow (EL), normalized root mean square error (NRMSE), relative error in estimating maximum flow (REmax), threshold statistics (TS), and average absolute relative error (AARE) were employed to compare the performances of these methods. The results show that the LSTM network outperforms the other models in forecasting daily streamflow with the lowest values of NRMSE and the highest values ofEH,EL, and R under all scenarios. These findings indicated that the LSTM is a robust data-driven technique to characterize the time series behaviors in hydrological modeling applications.
Journal Article
Hybrid forecasting: blending climate predictions with AI models
Hybrid hydroclimatic forecasting systems employ data-driven (statistical or machine learning) methods to harness and integrate a broad variety of predictions from dynamical, physics-based models – such as numerical weather prediction, climate, land, hydrology, and Earth system models – into a final prediction product. They are recognized as a promising way of enhancing the prediction skill of meteorological and hydroclimatic variables and events, including rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. Hybrid forecasting methods are now receiving growing attention due to advances in weather and climate prediction systems at subseasonal to decadal scales, a better appreciation of the strengths of AI, and expanding access to computational resources and methods. Such systems are attractive because they may avoid the need to run a computationally expensive offline land model, can minimize the effect of biases that exist within dynamical outputs, benefit from the strengths of machine learning, and can learn from large datasets, while combining different sources of predictability with varying time horizons. Here we review recent developments in hybrid hydroclimatic forecasting and outline key challenges and opportunities for further research. These include obtaining physically explainable results, assimilating human influences from novel data sources, integrating new ensemble techniques to improve predictive skill, creating seamless prediction schemes that merge short to long lead times, incorporating initial land surface and ocean/ice conditions, acknowledging spatial variability in landscape and atmospheric forcing, and increasing the operational uptake of hybrid prediction schemes.
Journal Article
Estimating propagation probability from meteorological to ecological droughts using a hybrid machine learning copula method
The impact of droughts on vegetation is essentially manifested as the transition of water shortage from the meteorological to ecological stages. Therefore, understanding the mechanism of drought propagation from meteorological to ecological drought is crucial for ecological conservation. This study proposes a method for calculating the probability of meteorological drought to trigger ecological drought at different magnitudes in northwestern China. In this approach, meteorological and ecological drought events during 1982–2020 are identified using the three-dimensional identification method; the propagated drought events are extracted according to a certain spatiotemporal overlap rule, and propagation probability is calculated by coupling the machine learning model and C-vine copula. The results indicate that (1) 46 drought events are successfully paired with 130 meteorological and 184 ecological drought events during 1982–2020, and ecological drought exhibits a longer duration but smaller affected area and severity than meteorological drought; (2) a quadratic discriminant analysis (QDA) classifier performs the best among the 11 commonly used machine learning models which are combined with four-dimensional C-vine copula to construct the drought propagation probability model; and (3) the hybrid method considers more drought characteristics and a more detailed propagation process which addresses the limited applicability of the traditional method to regions with large spatial extent.
Journal Article