In the past two decades several activities in the field of water resources management have been enhanced and intensified. This . rise had at least two independent reasons. The first and main one was the constantly increasing water demand for agriculture and industry on one side and the concern about the deteriorating environment on the other. While this last concern was lately overshadowed by deterioration of national economies, the quantity of available water resources has certainly not increased with the growing scarcity of funds for its development and protection. Furthermore, the standard of living, which raised across the world, even in India and China, countries which concentrate more than a third of the world population, has made people and their governments more aware of natural disasters caused by weather. Since a large percentage of losses in human life and material damage from weather-related disasters are caused by water, either by its excess or scarcity, the concern about water has been increasingly associated with these disasters. The second reason for intensified water resources management is man's spectacular technological advance in electronics, computers and use of satellites. The Koran says that two things cannot be predicted: the sex of the child in its mother's womb and the quantity of water that falls from the sky and flows in rivers. Technological progress has disproved both of these caveats.

This book describes recent developments in hydrometeorological forecasting techniques for a range of timescales, from short term to seasonal and longer terms. It conveniently brings together both meteorological and hydrological aspects in a single volume.

The efficiency of a probabilistic hydrological forecasting system with weather radar and the Probability distributed hydrological model (PDM) was evaluated at the Brue catchment; south-western England. The ability of the radar to measure gauged precipitation in 2007 (regarded as the ground truth) was evaluated using Normalized Bias (NB) and Normalized Error (NE) statistics as the objective function of evaluation. The radar overestimated precipitation measurements by average gauges with NB value of 0.41 and a considerably low NE of 0.68. Furthermore, the effectiveness of a Deterministic nowcasting system (DNS) to forecast radar measured precipitation at 132 forecast time series of 6hrs forecast lead time was assessed. The DNS overestimated the radar measured precipitation with a NB value of 87% and recorded an accumulated NE of 146%. Moreover, the efficiencies of 10 ensemble precipitation forecats generated from a Stochastic nowcasting system (SNS) over the singular deterministic forecasts from the DNS was evaluated at 3 major hydrological events. Some of the ensembles significantly performed better than the deterministic forecast and brilliantly captured the radar measured precipitation at most of the forecast time series. Furthermore, the efficiencies of these sources of precipitation measurement to simulate flows with the PDM at the Brue catchment were also assessed by integrating the radar-based forecasts with measurements from average gauges. The PDM performed satisfactorily well in simulating the flows of 17th January 2007 with an average Nash–Sutcliffe Efficiency Index (NSE) of 0.65 and the model was judged insensitive to the significantly high precipitation inputs for the hydrological event of 27th of May 2007. However, the PDM performed poorly in simulating flows for the historical storms of 20th of July 2007; with the model under estimating flows with bias value of over 250 cumecs for an event popular for its devastating flooding in the Southwest of England. The model inadequacies was however associated to poor radar precipitation measurements and forecasts on which flow simulation was based. This work therefore emphasis the need for developments in hydrological modeling as well as advancement in weather radar technology to effectively correct radar errors due to radar calibration, signal attenuation, clutter and anomalous propagation, vertical variation of reflectivity, range effects, Z-R relationships, variations of drop size distributions, vertical air motions, beam overshooting the shallow precipitation and sampling issues, that has been identified to affect radar measurements.

Hydrometeorological prediction involves the forecasting of the state and variation of hydrometeorological elements -- including precipitation, temperature, humidity, soil moisture, river discharge, groundwater, etc.-- at different space and time scales. Such forecasts form an important scientific basis for informing public of natural hazards such as cyclones, heat waves, frosts, droughts and floods. Traditionally, and at most currently operational centers, hydrometeorological forecasts are deterministic, “single-valued” outlooks: i.e., the weather and hydrological models provide a single best guess of the magnitude and timing of the impending events. These forecasts suffer the obvious drawback of lacking uncertainty information that would help decision-makers assess the risks of forecast use. Recently, hydrometeorological ensemble forecast approaches have begun to be developed and used by operational collection of hydrometeorological services. In contrast to deterministic forecasts, ensemble forecasts are a multiple forecasts of the same events. The ensemble forecasts are generated by perturbing uncertain factors such as model forcings, initial conditions, and/or model physics. Ensemble techniques are attractive because they not only offer an estimate of the most probable future state of the hydrometeorological system, but also quantify the predictive uncertainty of a catastrophic hydrometeorological event occurring. The Hydrological Ensemble Prediction Experiment (HEPEX), initiated in 2004, has signaled a new era of collaboration toward the development of hydrometeorological ensemble forecasts. By bringing meteorologists, hydrologists and hydrometeorological forecast users together, HEPEX aims to improve operational hydrometeorological forecast approaches to a standard that can be used with confidence by emergencies and water resources managers. HEPEX advocates a hydrometeorological ensemble prediction system (HEPS) framework that consists of several basic building blocks. These components include:(a) an approach (typically statistical) for addressing uncertainty in meteorological inputs and generating statistically consistent space/time meteorological inputs for hydrological applications; (b) a land data assimilation approach for leveraging observation to reduce uncertainties in the initial and boundary conditions of the hydrological system; (c) approaches that address uncertainty in model parameters (also called ‘calibration’); (d) a hydrologic model or other approach for converting meteorological inputs into hydrological outputs; and finally (e) approaches for characterizing hydrological model output uncertainty. Also integral to HEPS is a verification system that can be used to evaluate the performance of all of its components. HEPS frameworks are being increasingly adopted by operational hydrometeorological agencies around the world to support risk management related to flash flooding, river and coastal flooding, drought, and water management. Real benefits of ensemble forecasts have been demonstrated in water emergence management decision making, optimization of reservoir operation, and other applications.

This book presents the main hydrological methods and techniques used in the design and operation of hydraulic projects and the management of water resources and associated natural risks. It covers the key topics of water resources engineering, from the estimation of runoff volumes and unit hydrographs to the routing of flows along a river and through lakes, reservoirs, and hydraulic structures. It deals with questions regarding basic hydrological data, hydrological modeling and the prediction and forecasting of low flows and flood discharges.

The impacts of climate change on water resource management, as well as increasingly severe natural disasters over the last decades, have caught global attention. Reliable and accurate hydrological forecasts are essential for efficient water resource management and the mitigation of natural disasters. While the notorious nonlinear hydrological processes make accurate forecasts a very challenging task, it requires advanced techniques to build accurate forecast models and reliable management systems. One of the newest techniques for modeling complex systems is artificial intelligence (AI). AI can replicate the way humans learn and has great capability to efficiently extract crucial information from large amounts of data to solve complex problems. The fourteen research papers published in this Special Issue contribute significantly to the uncertainty assessment of operational hydrologic forecasting under changing environmental conditions and the promotion of water resources management by using the latest advanced techniques, such as AI techniques. The fourteen contributions across four major research areas: (1) machine learning approaches to hydrologic forecasting; (2) uncertainty analysis and assessment on hydrological modeling under changing environments; (3) AI techniques for optimizing multi-objective reservoir operation; (4) adaption strategies of extreme hydrological events for hazard mitigation. The papers published in this issue will not only advance water sciences but also help policymakers to achieve more sustainable and effective water resource management.