The estimation of the design peak discharge is crucial for the hydrological design of hydraulic structures. A commonly used approach is to estimate the design storm through the intensity–duration–area–frequency (IDAF) curves and then use it to generate the design discharge through a hydrological model. In ungauged areas, IDAF curves and design discharges are derived throughout regionalization studies, if any exist for the area of interest, or from using the hydrological information of the closest and most similar gauged place. However, many regions around the globe remain ungauged or are very poorly gauged. In this regard, a unique opportunity is provided by satellite precipitation products developed and improved in the last decades. In this paper, we show weaknesses and potentials of satellite data and, for the first time, we evaluate their applicability for design purposes. We employ CMORPH—Climate Prediction Center MORPHing technique satellite precipitation estimates to build IDAF curves and derive the design peak discharges for the Pietrarossa dam catchment in southern Italy. Results are compared with the corresponding one provided by a regionalization study, i.e., VAPI—VAlutazione delle Piene in Italia project, usually used in Italy in ungauged areas. Results show that CMORPH performed well for the estimation of low duration and small return periods storm events, while for high return period storms, further research is still needed.
<p>Drought is a natural phenomenon linked to a temporary but significant reduction in the availability of water resources. Drought usually originates as a deficit in precipitation, with prolonged drought having substantial repercussions on the hydrological, agricultural and socio-economic sectors; making drought one of the most impactful natural hazards modern society faces. The ability to forecast the occurrence of drought events with sufficient lead time, however, allows for the implementation of strategies to reduce drought impacts. Although drought forecasting using both statistical and dynamic techniques has been widely studied, challenges still remain in predicting drought events, especially for sub-seasonal to seasonal forecasts. Because of the increased availability of Earth Observation data, advances in Artificial Intelligence, and progress in computing capabilities in the last decades, drought prediction has received a new impulse. Machine Learning, especially Deep Learning, techniques are now increasingly being used both to improve current weather forecasts and as an alternative to conventional predictions of extreme events.</p><p>In this contribution we explore the use of Machine Learning techniques to improve meteorological drought prediction through post-processing of weather forecast analogues. To this aim, we use both ECMWF extended and long-range forecasts, together with reanalysis data, to build a ML-based model that helps correcting forecasts. We then test the model to explore how much current forecasts can be actually improved with the use of AI-based techniques. We apply the method proposed, in the area of the Rhine Delta in the Netherlands, focussing on 1-month lead time predictions. This work is part of the CLImate INTelligence (CLINT) project, which aims at developing AI-enhanced Climate Services for extreme events detection, causation, and attribution.</p>
Accurate and precise rainfall records are crucial for hydrological applications and water resources management. The accuracy and continuity of ground-based time series rely on the density and distribution of rain gauges over territories. In the context of a decline of rain gauge distribution, how to optimize and design optimal networks is still an unsolved issue. In this work, we present a method to optimize a ground-based rainfall network using satellite-based observations, maximizing the information content of the network. We combine Climate Prediction Center MORPhing technique (CMORPH) observations at ungauged locations with an existing rain gauge network in the Rio das Velhas catchment, in Brazil. We use a greedy ranking algorithm to rank the potential locations to place new sensors, based on their contribution to the joint entropy of the network. Results show that the most informative locations in the catchment correspond to those areas with the highest rainfall variability and that satellite observations can be successfully employed to optimize rainfall monitoring networks.
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