Climate change is expected to affect crop production worldwide, particularly in rain-fed agricultural regions. It still unknown how irrigation water needs will a warmer planet and where freshwater be locally available expand without depleting resources. Here, we identify the cropping systems that hold greatest potential for investment expansion because likely suffice demand. Using projections of renewable availability demand under warming scenarios, target regions may sustain climate change....

Abstract Understanding the impacts of climate change on water resources is crucial for developing effective adaptation strategies. We quantify “water gaps”, or unsustainable use – shortfall where demand exceeds supply, resulting in scarcity. baseline and future gaps using a multi-model analysis that incorporates two plausible warming scenarios. The global gap stands at 457.9 km 3 /yr, with projections indicating an increase 26.5 /yr (+5.8%) 67.4 (+14.7%) under 1.5 °C scenarios, respectively....

Recurrent neural networks have recently proved the state-of-the-art approach in forecasting complex oscillatory time series on a multi-step horizon. Researchers field investigated different machine learning techniques and training approaches dynamical systems with degrees of complexity. Still, these analyses are usually limited to noise-free chaotic series. This paper extends analysis from deterministic noisy environment, by considering both observation structural noise. Observation noise is...

Many environmental variables, in particular, related to air or water quality, are measured a limited number of points and often for time span.This forbids the development accurate models interesting locations with missing insufficient data poses question whether model developed another measurement site can be reliably applied.Such is particularly critical when entirely data-driven, such as neural network.In this context, paper proposes procedure evaluate expected performance an existing...

The problem of forecasting hourly solar irradiance over a multi-step horizon is dealt with by using three kinds predictor structures. Two approaches are introduced: Multi-Model (MM) and Multi-Output (MO). Model parameters identified for two neural networks, namely the traditional feed-forward (FF) class recurrent those long short-term memory (LSTM) hidden neurons, which relatively new radiation forecasting. performances considered rigorously assessed appropriate indices compared standard...

Abstract Water scarcity is a critical issue, expected to worsen with global warming. Tackling water requires strategies both decrease consumption and enhance availability. One promising solution mitigate wastewater reuse, which involves collecting, treating, repurposing used water. By employing balance model in conjunction climate outputs, we quantify the potential of reuse reduce gaps — situations where exceeds renewable availability under baseline two warming scenarios. We find that could...

Convective events pose a significant threat to society due the associated heavy rainfall, large hail, strong winds, and lightning. Location timing determination of convective precipitation is still challenge for modern meteorology. Despite good skills current weather forecasting tools in prediction large-scale environment facilitating onset phenomena, multitude spatial scales involved such makes their characterization, observation, forecast difficult task. The problem further complicated by...

How can we scale local knowledge and sustainable solutions to broader territories without losing their contextual relevance? Land degradation is a pressing issue in the Mediterranean, where diverse environmental socio-economic conditions exacerbate its impacts. While bottom-up approaches excel leveraging existing skills, knowledge, practical problem-solving, results often remain tied specific territorial contexts. The challenge lies generalising extending these insights new regions, enabling...

The relationship between Arctic sea ice and tropical climate variability is a crucial aspect of global dynamics. While numerous studies have explored potential links concentration (SIC) or thickness (SIT) teleconnection indices such as AMO, AO, NAO, ENSO, PDO, these investigations often faced challenges in fully capturing the complexity interactions. For instance, most analyses relied on linear, non-causal methods trend matching (although underlying processes are likely highly nonlinear),...

Precipitation is a key variable for assessing the impacts of climate change across diverse sectors, from hydrology to ecology. However, models frequently overestimate occurrence light precipitation events—days or hours that should be dry are instead assigned low rainfall rate. This pervasive issue, known as “drizzle bias” problem” in science, undermines reliability impact assessments.Traditional bias correction methods, such linear scaling empirical...

Multi-reservoir systems management is complex because of the uncertainty on future events and variety purposes, usually conflicting, involved actors. An efficient these can help improving resource allocation, preventing political crisis reducing conflicts between stakeholders. Bellman stochastic dynamic programming (SDP) most famous among many proposed approaches to solve this optimal control problem. Unfortunately, SDP affected by curse dimensionality: computational effort increases...

Today, many complex multiobjective problems are dealt with using genetic algorithms (GAs). They apply the evolution mechanism of a natural population to “numerical” solutions optimize fitness function. GA implementations must find compromise between breath search (to avoid being trapped into local minima) and its depth prevent rough approximation optimal solution). Most use “elitism”, which allows preserving some current best in successive generations. If initial is randomly selected, as...

Artificial neural networks (ANNs) are universal function approximators, therefore suitable to be trained as predictors of oscillatory time series. Though several ANN architectures have been tested predict both synthetic and real-world series, the universality their predictive power remained unexplored. Here we empirically test this across five well-known chaotic oscillators, limiting analysis simplest architecture, namely multi-layer feed-forward one sampling step ahead. To compare different...

With increasing pressure on water resources availability and dependability constraints due to environmental concerns, the traditional approaches for defining reservoir management rules are often inadequate. In particular, in multireservoir systems, when multiple input variables (e.g., storage of other reservoirs system, demand different districts) must be taken into account, it is almost impossible figure out which shape operating rule(s) could have. For these reasons, neural network (NN)...

Intense convective storms usually produce large rainfall volumes in short time periods, increasing the risk of floods and causing damages to population, buildings, infrastructures. In this paper, we propose a framework couple visual statistical analyses thunderstorms at basin scale, considering both spatial temporal dimensions process. The dataset analyzed paper contains intense events that occurred seven years (2012–2018) Seveso-Olona-Lambro (North Italy). data has been acquired by...

Geolocators are a well-established technology to reconstruct migration routes of animals that too small carry satellite tags (e.g. passerine birds). These devices record environmental light-level data enable the reconstruction daily positions from time twilight. However, all current methods for analysing geolocator require manual pre-processing raw records eliminate twilight events showing unnatural variation in light levels, step is time-consuming and must be accomplished by trained expert....

Residential buildings represent a considerable portion of the energy demand temperate country. Old European regions, where most were often built in periods low prices, have large margin for improvement. The study shows how saving measures can be optimally planned at regional level, taking into account specific features building stock, and what consequences an optimal choice are economic environmental terms.