Hydrological Sciences Journal

The study analysed the daily water discharge from the Niger River Basin. It relied on streamflow data collected at the Lokoja discharge gauging station during nine (9) decades and two decades, respectively of the 20th and 21st centuries. Using various statistical and change detection methods, the study elicited insightful information on the trends and periodic changes in the streamflow data. The average daily streamflow recorded during the period (6332 m3 s−1) indicates modest runoff relative to the active basin area (~1.11 x 106 km2). Distinct peaks were observed in the mean water discharge during the 1920s and 1950s, a short-lived peak signal in 2012 as well as a prolonged drought during the 1980s. However, the study failed to establish any statistically significant flow trend during the period. However, the streamflow from the Niger River Basin has manifested distinct cycles of peak and low flows more consistent with shore-term climatic signals.

Hydrological processes of evapotranspiration (ET) and precipitation (P) strongly influence the variability of stable water isotope ratios in the atmosphere, especially over rainforests. We show that the temporal variability of difference in evapotranspiration and precipitation fluxes (ET-P) is strongly correlated with normalized stable water isotope ratios (δD004) over forests of northeast India. Long-term satellite observations of δD004 from Atmospheric Infrared Sounder (AIRS) matched well with Tropospheric Emission Spectrometer (TES) and isotope-enabled Scripps Global Spectral Model (IsoGSM) simulations. Strong positive correlation was observed between ET-P and IsoGSM-modelled δD004 in nudged (r = 0.79) and free (r = 0.77) modes with mean error of ~10–15% of total variability. Similarly, ET-P showed positive correlation with satellite-observations of δD004 from AIRS (r = 0.66) and TES (r = 0.60). We estimate that ET contributes ~37–49% towards precipitation over forests of northeast India. Satellite-based observations of stable water isotopes could provide independent estimates of water fluxes for various hydrological applications

Half a century after only national-scale report on saltwater intrusion (SWI) in Lebanon, the evolution of this hazard is re-examined. SWI proxies from 4000+ field-measurement and chemical sampling are collected from a network of 276 sites. For interpreting this dataset, the coast is considered in 7 large coastal areas subdivided into 14 smaller coastal hydrogeological zones (CHZs) reflecting uniform hydrogeological conditions and properties. Hydrochemical analyses characterized the groundwater types of the coast. Fresh-brackish to brackish-salt NaCl or CaCl water types, attaining elevated salt content are present in several zones and ongoing salinization of variable extents affects 4 CHZs. Porous-unconsolidated aquifers suffered the most spread salinization compared to fractured karstic aquifers. Over-pumping is the main SWI driver in many zones. Comparing to older results fast ascending SWI impacts most coastal groundwater resources. Database from this study is shared to serve urgently needed continuous monitoring of SWI proxies and groundwater resources management.

Water quality assessment is crucial for environmental health and quality of life. This study introduces a novel Water Quality Index (WQI) model for reservoirs, using Wadi Dayqah Dam in Oman as a case study. The model advances water quality assessment using a data-driven approach, reducing reliance on subjective expert opinions. A large dataset of water samples was analyzed using machine learning (ML) to select water quality variables (WQVs). Using a bootstrapping and sub-sampling approach, the proposed WQI was then calculated through sub-indexing, weighting, and aggregating sub-indices. WQV weights were estimated using Gradient Boosting and Rank Order Centroid techniques, while aggregation involved scoring and Data Envelopment Analysis (DEA). The model effectively captures uncertainty, prioritizes WQVs, and provides solutions to issues such as eclipsing, ranking, and dealing with bad variable values. The results were validated through uncertainty and sensitivity analyses, highlighting the model’s potential for enhancing data-driven decision-making in reservoir management.