We conduct a large-scale benchmark experiment aiming to advance the use of machine-learning quantile regression algorithms for probabilistic hydrological post-processing “at scale” within operational contexts. The experiment is set up using 34-year-long daily time series of precipitation, temperature, evapotranspiration and streamflow for 511 catchments over the contiguous United States. Point hydrological predictions are obtained using the Génie Rural à 4 paramètres Journalier (GR4J) hydrological model and exploited as predictor variables within quantile regression settings. Six machine-learning quantile regression algorithms and their equal-weight combiner are applied to predict conditional quantiles of the hydrological model errors. The individual algorithms are quantile regression, generalized random forests for quantile regression, generalized random forests for quantile regression emulating quantile regression forests, gradient boosting machine, model-based boosting with linear models as base learners and quantile regression neural networks. The conditional quantiles of the hydrological model errors are transformed to conditional quantiles of daily streamflow, which are finally assessed using proper performance scores and benchmarking. The assessment concerns various levels of predictive quantiles and central prediction intervals, while it is made both independently of the flow magnitude and conditional upon this magnitude. Key aspects of the developed methodological framework are highlighted, and practical recommendations are formulated. In technical hydro-meteorological applications, the algorithms should be applied preferably in a way that maximizes the benefits and reduces the risks from their use. This can be achieved by (i) combining algorithms (e.g., by averaging their predictions) and (ii) integrating algorithms within systematic frameworks (i.e., by using the algorithms according to their identified skills), as our large-scale results point out.

Modeling of the rainfall-runoff process is important in hydrology. Historically, researchers relied on conventional deterministic modeling techniques based either on the physics of the underlying processes, or on the conceptual systems which may or may not mimic the underlying processes. This study investigates the suitability of a conceptual technique along with a data-driven technique, to model the rainfall-runoff process. The conceptual technique used is based on the Xinanjiang model coupled with geographic information system (GIS) for runoff routing and the data-driven model is based on genetic programming (GP), which was used for rainfall-runoff modeling in the recent past. To verify GP’s capability, a simple example with a known relation from fluid mechanics is considered first. For a small, steep-sloped catchment in Hong Kong, it was found that the conceptual model outperformed the data-driven model and provided a better representation of the rainfall-runoff process in general, and better predictio...

The phrase inconvenient truth associated with global warming and climate change has received a great deal of publicity some years back. The objective of this article is to highlight a different kind of inconvenient truth which affects about 29% of the world population. It is about the lack of access to safe drinking water that results in over 1.2 million preventable deaths annually. The first two targets of UN sustainable development goal 6 (SDG6) aim at providing universal, affordable and sustainable access to “water, sanitation and hygiene (WASH)”. Recognizing the right to safe and clean drinking water and sanitation as a basic human right, issues related to this problem as well as possible options to alleviate the problem are discussed.

This chapter is about water and disasters. Disasters can be natural or human-induced. The former type is difficult if not impossible to prevent whereas the latter type is preventable. Water-related disasters include all types of floods and droughts, land and mud slides, storm surges, tsunamis, tidal waves, debris flow, avalanches and all types of cyclones. Human-induced disasters include various types of pollution. This chapter gives brief descriptions of various types of water-related disasters including examples of major floods worldwide, some statistics of deaths caused by droughts worldwide, deadliest landslides of the 21st century and deadliest tsunamis since 1900. The chapter also gives brief descriptions of various international initiatives to reduce disaster risks.

This chapter is about domestic water supply and demand. It describes the different sources of domestic water such as rainwater, surface water from streams and lakes, groundwater and seawater, types of domestic demand for water including biological requirements and requirements demanded by the present-day living habits, collection and storage including associated constraints and problems. Some engineering aspects of impounding reservoirs, their physical characteristics, reservoir yield, reservoir sedimentation and method of their estimation and their control are also briefly discussed in this chapter.

This chapter emphasizes the importance of water, which can be thought of as the blood of the earth. It highlights the inconvenient truth about lack of access to safely managed water for about 29% of the world population including some statistics on access to drinking water. International initiatives to make access to safe drinking water across the world including the international decade for clean drinking water, international decade for action – water for life, sustainable development goals (SDG6 in particular) as well as the global acceleration framework in partnership with 30 UN entities and 40 international organizations with delivery targets by 2030 are highlighted in this chapter. Some statistics about the disparity in preventable deaths due to unsafe water and sanitation, comparison of water crises and GDP and some suggestions for way forward are also included in this chapter.