A new tool for correcting the spatial and temporal pattern of global precipitation products across mountainous terrain: precipitation and hydrological analysis

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Multi-model hydrological reference dataset over continental Europe and an African basin

Although Essential Climate Variables (ECVs) have been widely adopted as important metrics for guiding scientific and policy decisions, the Earth Observation (EO) and Land Surface and Hydrologic Model (LSM/HM) communities have yet to treat terrestrial ECVs in an integrated manner. To develop consistent terrestrial ECVs at regional and continental scales, greater collaboration between EO and LSM/HM communities is needed. An essential first step is assessing the LSM/HM simulation uncertainty. To that end, we introduce a new hydrological reference dataset that comprises a range of 19 existing LSM/HM simulations that represent the current state-of-the-art of our LSM/HMs. Simulations are provided on a daily time step, covering Europe, notably the Rhine and Po river basins, alongside the Tugela river basin in Africa, and are uniformly formatted to allow comparisons across simulations. Furthermore, simulations are comprehensively evaluated with discharge, evapotranspiration, soil moisture and total water storage anomaly observations. Our dataset provides valuable information to support policy development and serves as a benchmark for generating consistent terrestrial ECVs through the integration of EO products.

The paper was just accepted on Scientific Data ans the preprint can be found clicking on the Figure above. 

The implementation of the GEOframe system in the Po river district – analysis of water availability and scarcity

In recent years, the frequency of extreme events like floods and droughts, which can cause severe environmental, social, and economic damage, has increased due to climate change and environmental alterations. In response to these challenges, the Po River Basin District Authority (AdBPo) initiated the implementation of the GEOframe modelling system across the entire district in 2021, in collaboration with the GCU-M (Gruppo di Coordinamento Unificato-Magre). The goal was to enhance the existing numerical models for water resource management, providing more accurate quantification and forecasting of spatial and temporal water availability across the Po River Basin, thereby improving overall planning and decision-making processes.

Additionally, a historical analysis of water availability was conducted in Valle d’Aosta and Piemonte, showcasing GEOframe's ability to simulate all key components of the water cycle, including evapotranspiration, water storage, snow accumulation, and water discharge. The implementation of GEOframe in these mountainous regions also underscored the critical role of snow and glaciers in determining water availability, particularly in the context of rising temperatures due to climate change. As a result, future developments of GEOframe will prioritize improving the modelling of these elements to better capture their influence on water resources in a warming climate. The short presentation given at IDRA24 can be obtained by clicking on the above figure.  The poster is available here. . 

30-years (1991-2021) Snow Water Equivalent Dataset in the Po River District, Italy

This paper presents a long-term snow water equivalent dataset in the Po River District, Italy, spanning from 1991 to 2021 at daily time step and 500 m spatial resolution partially covering the mountain ranges of Alps and Apennines. The data has been generated using a hybrid modelling approach integrating the hydrological modelling conducted with the physically- based GEOtop model, preprocessing of the meteorological data, and assimilation of in-situ snow measurements and Earth Observation snow products to enhance the quality of the model estimates. 

A rigorous quality assessment of the dataset has been performed at different control points selected based on reliability, quality, and territorial distribution. The point validation between simulated and observed snow depth across control points shows the accuracy of the dataset in simulating the normal and relatively high snow conditions, respectively. Additionally, satellite snow cover maps have been compared with simulated snow depth maps, as a function of elevation and aspect. 2D Validation shows accurate values over time and space, expressed in terms of snowline along the cardinal directions. This paper hase been submitted to Scientific Data.  The preprint and all the indications to get the data is obtained by clicking on the Figure above.