Sub-seasonal soil moisture anomaly forecasting using combinations of deep learning, based on the reanalysis soil moisture records

Sub-seasonal drought forecasting is crucial for early warning in estimating agricultural production and optimizing irrigation management, as forecasting skills are relatively weak during this period. Soil moisture exhibits stronger persistence compared to other climate system quantities, which makes it especially influential in shaping land...

Near-real-time satellite precipitation data ingestion into peak runoff forecasting models

Extreme peak runoff forecasting is still a challenge in hydrology. In fact, the use of traditional physically-based models is limited by the lack of sufficient data and the complexity of the inner hydrological processes. Here, we employ a Machine Learning technique, the Random Forest (RF) together with a combination of Feature Engineering (FE...

Model-Based Assessment of Preventive Drought Management Measures’ Effect on Droughts Severity

Preventive Drought Management Measures (PDMMs) aim to reduce the chance of droughts and minimize drought-associated damages. Selecting PDMMs is not a trivial task, and it can be asserted that actual contributions to drought alleviation still need to be adequately researched. This study evaluates the effects of three potential PDMMs, namely,...

Multivariate regression trees as an “explainable machine learning” approach to explore relationships between hydroclimatic characteristics and agricultural and hydrological drought severity: case of study Cesar River basin

The typical drivers of drought events are lower than normal precipitation and/or higher than normal evaporation. The region's characteristics may enhance or alleviate the severity of these events. Evaluating the combined effect of the multiple factors influencing droughts requires innovative approaches. This study applies hydrological modelling...

Locating Multiple Leaks in Water Distribution Networks Combining Physically Based and Data-Driven Models and High-Performance Computing

Water utilities are urged to decrease their real water losses, not only to reduce costs but also to assure long-term sustainability. Hardware- and software-based techniques have been broadly used to locate leaks; within the latter, previous works that have used data-driven models mostly focused on single leaks. This paper presents a...

St-corabico: A spatiotemporal object-based bias correction method for storm prediction detected by satellite

Advances in near real-time rainstorm prediction using remote sensing have offered important opportunities for effective disaster management. However, this information is subject to several sources of systematic errors that need to be corrected. Temporal and spatial characteristics of both satellite and in-situ data can be combined to enhance...

Large-scale exploratory analysis of the spatiotemporal distribution of climate projections: applying the STRIVIng toolbox

Extreme hydrological events (EHEs), such as droughts and floods, vary spatially and temporally in nature. The increase in the number of events in the last few decades has motivated the research of the spatiotemporal variability of the future extreme precipitation and temperature. To study the consequences on the EHEs due to the uncertainty of...

A Comparison of Spatial–Temporal Scale Between Multiscalar Drought Indices in the South Central Region of Vietnam

Drought indicators are of critical importance in characterization and forecasting. The use of the Standardized Precipitation Index (SPI) has increasingly become the main tool for drought analysis; however, the index lacks hydrological information useful as a proxy for other types of droughts. This study aims at evaluating the SPI against the...

Spatiotemporal Drought Analysis at Country Scale Through the Application of the STAND Toolbox

Understanding, characterizing, and predicting drought is vital for the reduction of its consequences. In the last few decades, many studies have moved drought analysis from the conventional lumped approach to a more spatiotemporal analysis. Two main developments have motivated this: one is global data availability and the other is the number of...

A Spatially Enhanced Data-Driven Multimodel to Improve Semiseasonal Groundwater Forecasts in the High Plains Aquifer, USA

The aim of this paper is to improve semiseasonal forecast of groundwater availability in response to climate variables, surface water availability, groundwater level variations, and human water management using a two-step data-driven modeling approach. First, we implement an ensemble of artificial neural networks (ANNs) for the 300 wells...

Optimal operation of the Eastern Nile System using Genetic Algorithm, and benefits distribution of water resources development

The Eastern Nile Basin is facing a number of transboundary issues, including water resources development, and the associated impacts. The Nile Basin, particularly the Eastern Nile Sub-basin, is considered as one of a few international river systems of potential conflicts between riparian countries. The Eastern Nile is characterized by the...

Impact of water resources development on water availability for hydropower production and irrigated agriculture of the Eastern Nile basin

The Eastern Nile riparian countries are currently developing several reservoir projects to contribute to the needs for energy and food production in the region. In the absence of formal mechanisms for collaboration, the transboundary nature of the Eastern Nile basin makes water resources development challenging. The large seasonal and...

Altitudinal analysis of carbon stocks in the Antisana páramo, Ecuadorian Andes

Aims The importance of quantifying carbon stocks in terrestrial ecosystems is crucial for determining climate change dynamics. However, the present regional assessments of carbon stocks in tropical grasslands are extrapolated to unsampled areas with a high degree of uncertainty and without considering the carbon and nitrogen composition of...

Spatial and temporal variations' of characidae habitat, case study in Abras de Mantequilla wetland, Ecuador

A central component of predictive ecology in wetlands is the analysis of species distribution as a function of their biotic and abiotic environment. This analysis is normally used by decision-makers in biodiversity conservation, species monitoring and environmental planning, among others. Habitat suitability modelling is a major component of the...

Combining semi-distributed process-based and data-driven models in flow simulation: A case study of the Meuse river basin

One of the challenges in river flow simulation modelling is increasing the accuracy of forecasts. This paper explores the complementary use of data-driven models, e.g. artificial neural networks (ANN) to improve the flow simulation accuracy of a semi-distributed process-based model. The IHMS-HBV model of the Meuse river basin is used in this...