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Every year Africa is hit by extreme floods which, combined with high levels of vulnerability and increasing population exposure, often result in humanitarian crises and population displacement. Impact-based forecasting and early warning for natural hazards is recognized as a step forward in disaster risk reduction, thanks to its focus on people, livelihoods, and assets at risk. Yet, the majority of the African population is not covered by any sort of early warning system. This article describes the setup and the methodological approach of Flood-PROOFS East Africa, an impact-based riverine flood forecasting and early warning system for the Greater Horn of Africa (GHA), with a forecast range of 5 d. The system is based on a modeling cascade relying on distributed hydrological simulations forced by ensemble weather forecasts, link to inundation maps for specific return period, and application of a risk assessment framework to estimate population and assets exposed to upcoming floods. The system is operational and supports the African Union Commission and the Disaster Operation Center of the Intergovernmental Authority on Development (IGAD) in the daily monitoring and early warning from hydro-meteorological disasters in eastern Africa. Results show a first evaluation of the hydrological reanalysis at 78 river gauging stations and a semi-quantitative assessment of the impact forecasts for the catastrophic floods in Sudan and in the Nile River basin in summer 2020. More extensive quantitative evaluation of the system performance is envisaged to provide its users with information on the model reliability in forecasting extreme events and their impacts. [ABSTRACT FROM AUTHOR]

Climate change and water security are among the grand challenges of the 21st century, but literacy on these matters among high-school students is often unsystematic and/or detached from the real world. This study aims to introduce the educational objectives, methods, and early results of "Water and Us", a three-module initiative that can contribute to advancing water education in a warming climate by focusing on the natural and anthropogenic water cycle, climate change, and emerging water conflicts. The method of Water and Us revolves around storytelling to aid understanding and generate new knowledge, learning by doing, a flipped-classroom environment, and a constant link to examples from the real world (such as ongoing droughts across the world or seeds of conflict regarding transnational river basins). Water and Us was established in 2021–2022 and, during that school year, involved ≥200 students as part of a proof of concept to test the complete didactic approach using small-scale experiments. Results from ≥40 h of proof-of-concept events confirmed the effectiveness of this approach with respect to conveying the essential elements of the natural and anthropogenic water cycle, the most commonly recurring concepts related to climate change and water as well as the possible conflicts and solutions related to water scarcity in a warming climate. The Water and Us team remains interested in networking with colleagues and potential recipients to upscale and further develop this work. [ABSTRACT FROM AUTHOR]

The knowledge of snowpack dynamics is of critical importance to several real-time applications especially in mountain basins, such as agricultural production, water resource management, flood prevention, hydropower generation. Since simulations are affected by model biases and forcing data uncertainty, an increasing interest focuses on the assimilation of snow-related observations with the purpose of enhancing predictions on snowpack state. The study aims at investigating the effectiveness of snow multivariable data assimilation (DA) at an Alpine site. The system consists of a snow energy-balance model strengthened by a multivariable DA system. An Ensemble Kalman Filter (EnKF) scheme allows assimilating ground-based and remotely sensed snow observations in order to improve the model simulations. This research aims to investigate and discuss: (1) the limitations and constraints in implementing a multivariate EnKF scheme in the framework of snow modelling, and (2) its performance in consistently updating the snowpack state. The performance of the multivariable DA is shown for the study case of Torgnon station (Aosta Valley, Italy) in the period June 2012 - December 2013. The results of several experiments are discussed with the aim of analyzing system sensitivity to the DA frequency, the ensemble size, and the impact of assimilating different observations.

State-of-the-art snow sensing technologies currently provide an unprecedented amount of data from both remote sensing and ground sensors, but their assimilation into dynamic models is bounded to data quality, which is often low – especially in mountain, high-elevation, and unattended regions where snow is the predominant land-cover feature. To maximize the value of snow-depth measurements, we developed a random forest classifier to automatize the quality assurance and quality control (QA/QC) procedure of near-surface snow-depth measurements collected through ultrasonic sensors, with particular reference to the differentiation of snow cover from grass or bare-ground data and to the detection of random errors (e.g., spikes). The model was trained and validated using a split-sample approach of an already manually classified dataset of 18 years of data from 43 sensors in Aosta Valley (northwestern Italian Alps) and then further validated using 3 years of data from 27 stations across the rest of Italy (with no further training or tuning). The F1 score was used as scoring metric, it being the most suited to describe the performances of a model in the case of a multiclass imbalanced classification problem. The model proved to be both robust and reliable in the classification of snow cover vs. grass/bare ground in Aosta Valley (F1 values above 90 %) yet less reliable in rare random-error detection, mostly due to the dataset imbalance (samples distribution: 46.46 % snow, 49.21 % grass/bare ground, 4.34 % error). No clear correlation with snow-season climatology was found in the training dataset, which further suggests the robustness of our approach. The application across the rest of Italy yielded F1 scores on the order of 90 % for snow and grass/bare ground, thus confirming results from the testing region and corroborating model robustness and reliability, with again a less skillful classification of random errors (values below 5 %). This machine learning algorithm of data quality assessment will provide more reliable snow data, enhancing their use in snow models. [ABSTRACT FROM AUTHOR]

Every year Africa is hit by extreme floods which, combined with high levels of vulnerability and increasing population exposure, often result in humanitarian crises and population displacement. Impact-based forecasting and early warning for natural hazards is recognized as a step forward in disaster risk reduction, thanks to its focus on people, livelihoods and assets at risk. Yet, the majority of the African population is not covered by any sort of early warning system. This article describes the setup of Flood-PROOFS East Africa, an impact-based riverine flood forecasting and early warning system for the Greater Horn of Africa (GHA), with a forecast range of 5 days. The system is based on a modeling cascade relying on distributed hydrological simulations forced by ensemble weather forecasts, link to inundation maps for specific return period, and application of a risk assessment framework to estimate population and assets exposed to upcoming floods. The system is operational and supports the African Union Commission and the IGAD Disaster Operation Center in the daily monitoring and early warning from hydro-meteorological disasters in Eastern Africa. Results show a first evaluation of the hydrological reanalysis at 78 river gauging stations and a semi-quantitative assessment of the impact forecasts for the catastrophic floods in Sudan and in the Nile River Basin in Summer 2020. [ABSTRACT FROM AUTHOR]

Hydrological models often have issues in simulating streamflow (Q) during droughts, because of hard-to-capture feedback mechanisms across precipitation deficit, actual evapotranspiration (ET), and terrestrial water storage anomalies (TWSA). To gain more insights into these performance drops and move toward more robust hydrological models in the anthropogenic era, we evaluated Q, ET, and TWSA simulations during droughts of different severity and their sensitivity to the climatic conditions of the calibration period. We used the distributed hydrological model Continuum over the heavily human-affected Po river basin (northern Italy, period 2010-2022) and independent ground- and remote sensing-based datasets of Q, ET, and TWSA as benchmarks. Across the 38 study sub-catchments, Continuum simulated Q comparably well during wet years (2014 and 2020) and moderate droughts (2012 and 2017) with mean KGE = 0.59±0.32 during wet years and = 0.55±0.25 during moderate droughts. The model simulated well Q for the outlet section of the basin also for the severe 2022 drought (KGE = 0.82). However, performances for 2022 declined across the other sub-catchments (mean KGE = 0.18±0.69, meaning the model still preserved some skill over a climatological mean). The model properly represented seasonality of Q, ET, and TWSA over the basin, as well as a declining trend in TWSA. We explained the performance drops in 2022 with an increased uncertainty in ET anomalies, in particular in human-affected croplands. Calibrating during a moderate drought (2017) did not improve model performances during the severe 2022 drought (mean KGE = 0.18±0.63), pointing to the fairly unique conditions of this period in terms of hydrological processes and human interference on the hydrological cycle. By highlighting increased uncertainty of hydrological models specifically during severe droughts which are expected to increase in frequency, these findings provide relevant guidelines for assessments of model robustness in a changing climate and so for informing water management, disaster risk reduction, and climate change adaptation strategies. [ABSTRACT FROM AUTHOR]

Climate change and water security are among the grand challenges of the 21st century, but literacy on these matters among high-school students is often unsystematic and/or far from the real world. To contribute advancing education in a warming climate and prepare next generations to play their role in future societies, we designed 'Water and Us', a three-module initiative focusing on the natural and anthropogenic water cycle, climate change, and conflicts. The method of Water and Us resolves around storytelling to aid understanding and generate new knowledge, learning by doing, a flipped classroom environment, and a constant link to the real world – such as the archetypal events of the California snow drought or the seeds of conflicts around transnational river basins. Water and Us was established in 2021, and since then has involved 200+ students in a proof of concept to test the didactic approach in small-scale experiments. Results from 40+ hours of events confirm that students are generally aware of climate change (90 %), but have sparse knowledge of the concrete actions that are in place to mitigate or adapt (up to 20 %). Understanding of the water cycle by students is often anchored to a naturalistic, but fictitious view where human interference is minimal. Our approach conveys key elements of the contemporary, natural/anthropogenic water cycle, how this cycle is challenged by warmer temperatures and declining snowpacks, and how education can contribute to avoiding maladaptation and conflicts. While this initiative is being channelled in awareness projects at various levels, the Water and Us team remains interested in networking with colleagues and potential recipients to scale up and further develop this work. [ABSTRACT FROM AUTHOR]