Hydrological drought forecasting is essential for effective water resource management planning. Innovations in computer science and artificial intelligence (AI) have been incorporated into Earth science research domains to improve predictive performance for water resource planning and disaster management. Forecasting of future hydrological drought can assist with mitigation strategies for various stakeholders. This study uses the transformer deep learning model to forecast hydrological drought, with a benchmark comparison with the long short-term memory (LSTM) model. These models were applied to the Apalachicola River, Florida, with two gauging stations located at Chattahoochee and Blountstown. Daily stage-height data from the period 1928–2022 were collected from these two stations. The two deep learning models were used to predict stage data for five different time steps: 30, 60, 90, 120, and 180 days. A drought series was created from the forecasted values using a monthly fixed threshold of the 75th percentile (75Q). The transformer model outperformed the LSTM model for all of the timescales at both locations when considering the following averages: $MSE=0.11$, $MAE=0.21$, $RSME=0.31$, and $R^2=0.92$ for the Chattahoochee station, and $MSE=0.06$, $MAE=0.19$, $RSME=0.23$, and $R^2=0.93$ for the Blountstown station. The transformer model exhibited greater accuracy in generating the same drought series as the observed data after applying the 75Q threshold, with few exceptions. Considering the evaluation criteria, the transformer deep learning model accurately forecasts hydrological drought in the Apalachicola River, which could be helpful for drought planning and mitigation in this area of contested water resources, and likely has broad applicability elsewhere.

Groundwater is the main source of water for domestic use in Nigeria because it is perceived to be clean. The presence of geogenic contaminants (arsenic and fluoride), and the level of awareness of their presence in groundwater in Ibadan, Nigeria was examined in this study. A total of one hundred and twenty groundwater samples were collected from hand dug wells which tap into shallow aquifers and their location taken with the aid of a GPS. The concentration of arsenic was determined by Atomic Absorption Spectrophotometry (AAS) while concentration of fluoride was determined by single beam spectrophotometer. Three hundred and fifty semi structured questionnaires were also administered within the study area to determine the level of awareness of contamination problem. Simple summary statistics including mean (m) standard deviation (s) and minimum-maximum values of the hydro-chemical data was used in the data analyses, while spatial concentrations were mapped using ArcGIS. The results showed arsenic concentration exceeding the WHO (2011) recommended concentration for drinking water in 98% and 100% of the dry and wet season samples. Concentration of Fluoride exceeded the recommended limits in 13% and 100% of the dry and wet season samples. Questionnaire analyses revealed that 85% of respondents have never tested their wells, 55% have no knowledge of geogenic contamination, while 92% never heard of arsenic or fluoride (52%). The study recommends enlightenment on geogenic contamination and testing of wells for remediation purposes.

Climate change-induced precipitation variability is the leading cause of rainfall erosivity that leads to excessive soil losses in most countries of the world. In this paper, four global climate models (GCMs) were used to characterize the spatiotemporal prediction of rainfall erosivity and assess the effect of variations of rainfall erosivity in Central Asia. The GCMs (BCCCSM1-1, IPSLCM5BLR, MIROC5, and MPIESMLR) were statistically downscaled using the delta method under Representative Concentration Pathways (RCPs) 2.6 and 8.5 for two time periods: “Near” and “Far” future (2030s and 2070s). These GCMs data were used to estimate rainfall erosivity and its projected changes over Central Asia. WorldClim data was used as the present baseline precipitation scenario for the study area. The rainfall erosivity (R) factor of the Revised Universal Soil Loss Equation (RUSLE) was used to determine rainfall erosivity. The results show an increase in the future periods of the annual rainfall erosivity compared to the baseline. For all GCMs, with an average change in rainfall erosivity of about 5.6% (424.49 MJ mm ha⁻¹ h⁻¹ year⁻¹) in 2030s and 9.6% (440.57 MJ mm ha⁻¹ h⁻¹ year⁻¹) in 2070s as compared to the baseline of 402 MJ mm ha⁻¹ h⁻¹ year⁻¹. The magnitude of the change varies with the GCMs, with the largest change being 26.6% (508.85 MJ mm ha⁻¹ h⁻¹ year⁻¹), occurring in the MIROC-5 RCP8.5 scenario in the 2070s. Although annual rainfall erosivity shows a steady increase, IPSLCM5ALR (both RCPs and periods) shows a decrease in the average erosivity. Higher rainfall amounts were the prime causes of increasing spatial-temporal rainfall erosivity.

Tropical cyclones are incredibly destructive and deadly, inflicting immense losses to coastal properties and infrastructure. Hurricane-induced coastal floods are often the biggest threat to life and the coastal environment. A quick and accurate estimation of coastal flood extent is urgently required for disaster rescue and emergency response. In this study, a combined Digital Elevation Model (DEM) based water fraction (DWF) method was implemented to simulate coastal floods during Hurricane Harvey on the South Texas coast. Water fraction values were calculated to create a 15 km flood map from multiple channels of the Advanced Technology Microwave Sound dataset. Based on hydrological inundation mechanism and topographic information, the coarse-resolution flood map derived from water fraction values was then downscaled to a high spatial resolution of 10 m. To evaluate the DWF result, Storm Surge Hindcast product and flood-reported high-water-mark observations were used. The results indicated a high overlapping area between the DWF map and buffered flood-reported high-water-marks (HWMs), with a percentage of more than 85%. Furthermore, the correlation coefficient between the DWF map and CERA SSH product was 0.91, which demonstrates a strong linear relationship between these two maps. The DWF model has a promising capacity to create high-resolution flood maps over large areas that can aid in emergency response. The result generated here can also be useful for flood risk management, especially through risk communication.