The inheritance of historic human-induced disruption and the fierceness of its impact change aquatic ecosystems. This work reviews some of the main stressors on freshwater ecosystems, focusing on their effects, threats, risks, protection, conservation, and management elements. An overview is provided on the water protection linked to freshwater stressors: solar ultraviolet radiation, thermal pollution, nanoparticles, radioactive pollution, salinization, nutrients, sedimentation, drought, extreme floods, fragmentation, pesticides, war and terrorism, algal blooms, invasive aquatic plants, riparian vegetation, and invasive aquatic fish. Altogether, these stressors build an exceptionally composite background of stressors that are continuously changing freshwater ecosystems and diminishing or even destroying their capability to create and maintain ongoing natural healthy products and essential services to humans. Environmental and human civilization sustainability cannot exist without the proper management of freshwater ecosystems all over the planet; this specific management is impossible if the widespread studied stressors are not deeply understood structurally and functionally. Without considering each of these stressors and their synergisms, the Earth’s freshwater is doomed in terms of both quantitative and qualitative aspects.

This paper examines the impacts of three different potential evapotranspiration (PET) models on drought severity and frequencies indicated by the standardized precipitation index (SPEI). The standardized precipitation-evapotranspiration index is a recent approach to operational monitoring and analysis of drought severity. The standardized precipitation-evapotranspiration index combines precipitation and temperature data, quantifying the severity of a drought as the difference in a timestep as the difference between precipitation and PET. The standardized precipitation-evapotranspiration index thus represents the hydrological processes that drive drought events more realistically than the standardized precipitation index at the expense of additional computational complexity and increased data demands. The additional computational complexity is principally due to the need to estimate PET within each time step. The standardized precipitation-evapotranspiration index was originally defined using the Thornthwaite PET model. However, numerous researchers have demonstrated the standardized precipitation-evapotranspiration index is sensitive to the PET model adopted. PET models requiring sparse meteorological inputs, such as the Thornthwaite model, have particular utility for drought monitoring in data scarce environments. The aridity index (AI) investigates the spatiotemporal changes in the hydroclimatic system. It is defined as the ratio between potential evapotranspiration and precipitation. It is used to characterize wet (humid) and dry (arid) regions. In this study, a sensitivity analysis for the standardized precipitation-evapotranspiration and aridity indexes was carried out using three different PET models; namely, the Penman–Monteith model, a temperature-based parametric model and the Thornthwaite model. The analysis was undertaken in six gauge stations in California region where long-term drought events have occurred. Having used the Penman–Monteith model as the PET model for estimating the standardized precipitation-evapotranspiration index, our findings highlight the presence of uncertainty in defining the severity of drought, especially for large timescales (12 months to 48 months), and that the PET parametric model is a preferable model to the Thornthwaite model for both the standardized precipitation-evapotranspiration index and the aridity indexes. The latter outcome is worth further consideration for when climatic studies are under development in data scarce areas where full required meteorological variables for Penman–Monteith assessment are not available.

In February 2017, a failure occurring in Oroville Dam’s main spillway risked causing severe damages downstream. A unique aspect of this incident was the fact that it happened during a flood scenario well within its design and operational procedures, prompting research into its causes and determining methods to prevent similar events from reoccurring. In this study, a hydroclimatic analysis of Oroville Dam’s catchment is conducted, along with a review of related design and operational manuals. The data available allows for the comparison of older flood-frequency analyses to new alternative methods proposed in this paper and relevant literature. Based on summary characteristics of the 2017 floods, possible causes of the incident are outlined, in order to understand which factors contributed more significantly. It turns out that the event was most likely the result of a structural problem in the dam’s main spillway and detrimental geological conditions, but analysis of surface level data also reveals operational issues that were not present during previous larger floods, promoting a discussion about flood control design methods, specifications, and dam inspection procedures, and how these can be improved to prevent a similar event from occurring in the future.

On 18 September 2020, the Karditsa prefecture of Thessaly region (Greece) experienced a catastrophic flood as a consequence of the IANOS hurricane. This intense phenomenon was characterized by rainfall records ranging from 220 mm up to 530 mm, in a time interval of 15 h. Extended public infrastructure was damaged and thousands of houses and commercial properties were flooded, while four casualties were recorded. The aim of this study was to provide forensic research on a reconstruction of the flood event in the vicinity of Karditsa city. First, we performed a statistical analysis of the rainfall. Then, we used two numerical models and observed data, either captured by satellites or mined from social media, in order to simulate the event a posteriori. Specifically, a rainfall–runoff CN-unit hydrograph model was combined with a hydrodynamic model based on 2D-shallow water equations model, through the coupling of the hydrological software HEC-HMS with the hydrodynamic software HEC-RAS. Regarding the observed data, the limited available gauged records led us to use a wide spectrum of remote sensing datasets associated with rainfall, such as NASA GPM–IMREG, and numerous videos posted on social media, such as Facebook, in order to validate the extent of the flood. The overall assessment proved that the exceedance probability of the IANOS flooding event ranged from 1:400 years in the low-lying catchments, to 1:1000 years in the upstream mountainous catchments. Moreover, a good performance for the simulated flooding extent was achieved using the numerical models and by comparing their output with the remote sensing footage provided by SENTINEL satellites images, along with the georeferenced videos posted on social media.

We present and validate a global parametric model of potential evapotranspiration (PET) with two parameters that are estimated through calibration, using as explanatory variables temperature and extraterrestrial radiation. The model is tested over the globe, taking advantage of the Food and Agriculture Organization (FAO CLIMWAT) database that provides monthly averaged values of meteorological inputs at 4300 locations worldwide. A preliminary analysis of these data allows for explaining the major drivers of PET over the globe and across seasons. The model calibration against the given Penman-Monteith values was carried out through an automatic optimization procedure. For the evaluation of the model, we present global maps of optimized model parameters and associated performance metrics, and also contrast its performance against the well-known Hargreaves-Samani method. Also, we use interpolated values of the optimized parameters to validate the predictive capacity of our model against monthly meteorological time series, at several stations worldwide. The results are very encouraging, since even with the use of abstract climatic information for model calibration and the use of interpolated parameters as local predictors, the model generally ensures reliable PET estimations. Exceptions are mainly attributed to irregular interactions between temperature and extraterrestrial radiation, as well as because the associated processes are influenced by additional drivers, e.g., relative humidity and wind speed. However, the analysis of the residuals shows that the model is consistent in terms of parameters estimation and model validation. The parameter maps allow for the direct use of the model wherever in the world, providing PET estimates in case of missing data, that can be further improved even with a short term acquisition of meteorological data.

Over the last decade, Environmental Flow Assessment (EFA) has focused scientific attention around heavily-modified hydrosystems, such as flow regulated releases downstream of dams. In this light, numerous approaches of varying complexity have been developed, the most holistic of which incorporate hydrological, hydraulic, biological and water quality inputs, as well as socioeconomic issues. Finding the optimal flow releases, informing policy and determining an operational framework are often the main focus. This work exhibits a simplification of the DRIFT framework, and is regarded as the first holistic EFA approach, consisting of three modules, namely hydrological, hydraulic and fish quality. A novel conceptual classification for fish quality is proposed, associating fish fauna requirements with hydraulic characteristics, exported by fish survey analyses. The new methodology was applied and validated successfully at three stream sites in Lesotho, where DRIFT was formerly employed.