Challenges for sustainable water management

Water is a precious resource essential for all forms of life. It is abundant in nature but has significant temporal and spatial variability. With increasing population, the per capita share of water on earth is decreasing, and in some regions has reached levels where communities face water stress and water scarcity. Whereas lack of safe drinking water is a major problem for close to a billion inhabitants of the earth, too much water also bring about misery, agony and destruction to many people, places and infrastructure. The former may be attributed to the physical lack of water, pollution or unaffordability whereas the latter is attributed mainly to population growth, urbanization and livelihood issues. Optimal management and use of the available water in the 21st century needs a paradigm shift to a holistic approach since all aspects of water as well as social and economic infrastructures of the world are now more interdependent than ever before. Coping with water problems in the 21st century therefore poses many challenges to water managers. Drinking water security, food security, energy security, climate change, water-related disaster management and maintaining acceptable environmental quality in a sustainable manner are among the major challenges at present and in the foreseeable futur

Generation and forecasting of monsoon rainfall data

Generation and forecasting of monsoon rainfall dat

Probabilistic hydrological post-processing at scale: Why and how to apply machine-learning quantile regression algorithms

We conduct a large-scale benchmark experiment aiming to advance the use of machinelearning 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 G\ue9nie Rural \ue0 4 param\ue8tres 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 andconditional upon thismagnitude. Key aspects of thedevelopedmethodological 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 fromtheir 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