The deficit irrigation strategy is a well-known approach to optimize crop water use through the estimation of crop water use efficiency (CWUE). However, studies that comprehensively reported the prediction of crop evapotranspiration (ET_c) and CWUE under deficit irrigation for improved water resources planning are scarce. The objective of the study is to predict seasonal ET_c and CWUE of maize using multiple linear regression (MLR) and artificial neural network (ANN) models under two scenarios, i.e., (1) when only climatic parameters are considered and (2) when combining crop parameter(s) with climatic data in amended soil. Three consecutive field experimentations were carried out with biochar applied at rates of 0, 3, 6, 10 and 20 t/ha, while inorganic fertilizer was applied at rates of 0 and 300 Kg/ha, under three water regimes: 100% Full Irrigation Treatment (FIT), 80% and 60% FIT. Seasonal ET_c was determined using the soil water balance method, while growth data were monitored weekly. The CWUE under each treatment was also estimated and modelled. The MLR and ANN models were developed, and their evaluations showed that the ANN model was satisfactory for the predictions of both ET_c and CWUE under all soil water conditions and scenarios. However, the MLR model without crop data was poor in predicting CWUE under extreme soil water conditions (60% FIT). The coefficient of determination (R²) increased from 0.03 to 0.67, while root mean-square error (RMSE) decreased from 4.07 to 1.98 mm after the inclusion of crop data. The model evaluation suggests that using a simple model such as MLR, crop water productivity could be accurately predicted under different soil and water management conditions.

Elevated concentrations of metals in rivers can pose significant risks to human health. Consequently, it is essential to monitor metal levels in river water to minimize human exposure, particularly through consumption of harvested fish and other uses. This study examined the water quality of the Ona River in Ibadan, Nigeria. Seven potentially toxic elements (PTEs), namely Fe, Cu, Mn, Zn, Pb, Cd, Cr and four macronutrients: Mg, Na, K, Ca were investigated. Surface and pore water samples were collected from six sampling points during the peak of the dry and the rainy seasons. These were analyzed according to standard procedures and processed using the Simple Fuzzy Classification (SFC) model with four classes. The results revealed that the PTEs: Fe, Pb, Cd, Cr, and Mn were detected above guideline limits in several sampling points, indicating possible contamination from anthropogenic sources. Simple fuzzy classification puts the quality of water in the investigated portion of the river in the range of 67.4–88.5% in the C₁ (pristine) category during the dry season and 61.9–88.6% in C₁ (pristine) during rainy season. However, there were membership functions of up to 58.7 and 63.2; 61.2 and 75.4 as well as 44.3 and 43.5% in C₂, C_3, and C₄ categories (contaminated, polluted and extremely polluted, respectively) during the first and second sampling, respectively. Due to the high concentrations of toxic and bioaccumulative metals in the river, efforts should be made to control the discharge of pollutants into the river. These will prevent human health exposure risks tendencies for various uses, including fish production.

This study presents a novel approach to incorporating technology into an intelligent weather station. In recent years, the nation has experienced a decline in agricultural activities due to the change in climate causing lot of danger in the farm output; this enables the weather forecaster to actually prescribe disaster by employing the use of temperature, humidity and light sensors which senses the temperature and humidity in the atmosphere. A tipping bucket is used to measure the amount of rainfall with an ESP-32 microcontroller which displays results through a Liquid Crystal Display (LCD) and then send it to the registered number and at the same time is been sent to cloud for easy retrieval purpose using the internet which can be retrieved from any location. This system is designed primarily for use in the school meteorological stations, it is also applicable in various weather stations. The primary goal is to create a low-cost intelligent system for storing data obtained by measuring various physical parameters in the atmosphere, such as temperature, humidity, and rainfall. An ESP-32 is used as the technology for storing measured data; it is an advanced and efficient solution for connection to the Internet. The intelligent weather station system is built around the following steps: direct environmental sensing, data measurement and storage, and allowing users to access the results through the cloud. The system is built around a collection of embedded sensors, and a microcontroller serving as the system's core and server, and wireless internet communication protocol. The results were sent to the cloud, where they are displayed in a tabular and graphical format; these results can be retrieved from the cloud into an excel file.

The changing climate is a concern to sustainable water resources. This study examined climate change impacts on river discharge seasonality in two West African river basins; the Niger river basin and the Hadejia-Jama’are Komadugu-Yobe Basin (HJKYB). The basins have their gauges located within Nigeria and cover the major climatic settings. Here, we set up and validated the hyper resolution global hydrological model PCR-GLOBWB for these rivers. Time series plots as well five performance evaluation metrics such as Kling–Gupta efficiency (KGE),); the ratio of RMSE-observations standard deviation (RSR); per cent bias (PBIAS); the Nash–Sutcliffe Efficiency criteria (NSE); and, the coefficient of determination (r2), were employed to verify the PCR-GLOBWB simulation capability. The validation results showed from satisfactory to very good on individual rivers as specified by PBIAS (−25 to 0.8), NSE (from 0.6 to 0.8), RSR (from 0.62 to 0.4), r2 (from 0.62 to 0.88), and KGE (from 0.69 to 0.88) respectively. The impact assessment was performed by driving the model with climate projections from five global climate models for the representative concentration pathways (RCPs) 4.5 and 8.5. We examined the median and range of expected changes in seasonal discharge in the far future (2070–2099). Our results show that the impacts of climate change cause a reduction in discharge volume at the beginning of the high flow period and an increase in discharge towards the ending of the high flow period relative to the historical period across the selected rivers. In the Niger river basin, at the Lokoja gauge, projected decreases added up to 512 m3/s under RCP 4.5 (June to July) and 3652 m3/s under RCP 8.5 (June to August). The three chosen gauges at the HJKYB also showed similar impacts. At the Gashua gauge, discharge volume increased by 371 m3/s (RCP8.5) and 191 m3/s (RCP4.5) from August to November. At the Bunga gauge, a reduction/increase of -91 m3/s/+84 m3/s (RCP 8.5) and -40 m3/s/+31 m3/s/(RCP 4.5) from June to July/August to October was simulated. While at the Wudil gauge, a reduction/increase in discharge volumes of −39/+133 m3/s (RCP8.5) and −40/133 m3/s (RCP 4.5) from June to August/September to December is projected. This decrease is explained by a delayed start of the rainy season. In all four rivers, projected river discharge seasonality is amplified under the high-end emission scenario (RCP8.5). This finding supports the potential advantages of reduced greenhouse gas emissions for the seasonal river discharge regime. Our study is anticipated to provide useful information to policymakers and river basin development authorities, leading to improved water management schemes within the context of changing climate and increasing need for agricultural expansion.

Drought persistence in the Sudan–Sahel transition zone of Northern Nigeria poses a substantial risk to agricultural productivity. This study develops a Hybrid Prophet–Long Short-Term Memory (LSTM) architecture to address the existing research gap in near-term predictive capacity for non-stationary hydroclimatic time series. Utilizing Kano State as a case study, the Prophet algorithm was employed to extract deterministic trends from the Standardized Precipitation Evapotranspiration Index (SPEI-12) derived from CRU TS v4.09 data (1980–2024), while an integrated LSTM network modeled the stochastic residuals. Diagnostic results indicate a statistically significant trend toward moisture recovery (p < 0.0001). Comparative analysis demonstrated that the hybrid model significantly outperformed standalone baselines, achieving a Nash–Sutcliffe Efficiency (NSE) exceeding 0.87 and a 67.2% reduction in Root Mean Square Error (RMSE). Furthermore, the framework accurately simulated hydroclimatic transitions with a directional accuracy exceeding 87%, confirming high predictive reliability. Projections for the 2025–2030 period indicate a continued positive moisture shift of approximately 0.9 SPEI units. These findings underscore the technical necessity of decoupling non-linear noise from deterministic signals to resolve complex drought dynamics. Consequently, the proposed framework serves as a robust tool for near-term climate prediction. Scaling this methodology across diverse agroecological zones is recommended to enhance national drought early warning systems and regional climate resilience strategies.