Understanding the variability of rainfall is important for sustaining rain-dependent agriculture and driving the local economy of Nigeria. Paucity and inadequate rain gauge network across Nigeria has made satellite-based rainfall products (SRPs), which offer a complete spatial and consistent temporal coverage, a better alternative. However, the accuracy of these products must be ascertained before use in water resource developments and planning. In this study, the performances of Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), Precipitation estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR), and Tropical Applications of Meteorology using SATellite data and ground-based observations (TAMSAT), were evaluated to investigate their ability to reproduce long term (1983–2013) observed rainfall characteristics derived from twenty-four (24) gauges in Nigeria. Results show that all products performed well in terms of capturing the observed annual cycle and spatial trends in all selected stations. Statistical evaluation of the SRPs performance show that CHIRPS agree more with observations in all climatic zones by reproducing the local rainfall characteristics. The performance of PERSIANN and TAMSAT, however, varies with season and across the climatic zones. Findings from this study highlight the benefits of using SRPs to augment or fill gaps in the distribution of local rainfall data, which is critical for water resources planning, agricultural development, and policy making.

Changes in runoff trends have caused severe water shortages and ecological problems in agriculture and human well-being in Nigeria. Understanding the long-term (inter-annual to decadal) variations of water availability in river basins is paramount for water resources management and climate change adaptation. Climate change in Northern Nigeria could lead to change of the hydrological cycle and water availability. Moreover, the linkage between climatic changes and streamflow fluctuations is poorly documented in this area. Therefore, this study examined temporal trends in rainfall, temperature and runoff records of Kaduna River basin. Using appropriate statistical tools and participatory survey, trends in streamflow and their linkages with the climate indices were explored to determine their amplifying impacts on water availability and impacts on livelihoods downstream the basin. Analysis indicate variable rainfall trend with significant wet and dry periods. Unlike rainfall, temperature showed annual and seasonal scale statistically increasing trend. Runoff exhibit increasing tendency but only statistically significant on annual scale as investigated with Mann–Kendall trend test. Sen’s estimator values stood in agreement with Mann–Kendall test for all variables. Kendall tau and partial correlation results revealed the influence of climatic variables on runoff. Based on the survey, some of the hydrological implications and current water stress conditions of these fluctuations for the downstream inhabitants were itemized. With increasing risk of climate change and demand for water, we therefore recommend developing adaptive measures in seasonal regime of water availability and future work on modelling of the diverse hydrological characteristics of the entire basin.

Quantifying possible sources of uncertainty in simulations of hydrological extreme events is very important for better risk management in extreme situations and water resource planning. The main objective of this research work is to identify and address the role of input data quality and hydrological parameter sets, and uncertainty propagation in hydrological extremes estimation. This includes identifying and estimating their contribution to flood and low flow magnitude using two objective functions (NSE for flood and LogNSE for low flow), 20,000 Hydrologiska Byråns Vattenbalansavdelning (HBV) hydrological parameter sets, and three frequency distribution models (Log‐Normal, Pearson‐III, and Generalized Extreme Value). The influence of uncertainty on the simulated flow is not uniform across all the selected three catchments due to different flow regimes and runoff generation mechanisms. The result shows that the uncertainty in high flow frequency modeling mainly comes from the input data quality. In the modeling of low flow frequency, the main contributor to the total uncertainty is model parameterization. The total uncertainty of QT90 (extreme peak flow quantile at 90‐year return period) quantile shows that the interaction of input data and hydrological parameter sets have a significant role in the total uncertainty. In contrast, in the QT10 (extreme low flow quantile at 10‐year return period) estimation, the input data quality and hydrological parameters significantly impact the total uncertainty. This implies that the primary factors and their interactions may cause considerable risk in water resources management and flood and drought risk management. Therefore, neglecting these factors and their interaction in disaster risk management, water resource planning, and evaluation of environmental impact assessment is not feasible and may lead to considerable risk. Recommendations for Water Resource Managers The role of hydrological parameters and climate input data is significant in flood and low flow estimations and significantly impacts water resources and extremes management. Input data dominantly controlled flood magnitude and frequency, whilst the low flow magnitude and frequency were dominantly affected by both input data quality and hydrological parameters. It is crucial to consider the main features that cause considerable risk in water resource management and extreme risk management. Neglecting the primary factors and their interaction in disaster risk management, water resource planning, and evaluation of environmental impact assessment is not feasible and may lead to considerable risk. Improved extreme water management through a complex modeling approach to better prepare for the impact of extreme high and low flow changes are the best long‐ and short‐term plans and strategies to combat and minimize the risk in water‐related sectors of the local economy.

Solar radiation (Rs) is an essential input for estimating reference crop evapotranspiration, ETo. An accurate estimate of ETo is the first step involved in determining water demand of field crops. The objective of this study was to assess the accuracy of fifteen empirical solar radiations (Rs) models and determine its effects on ETo estimates for three sites in humid tropical environment (Abakaliki, Nsukka, and Awka). Meteorological data from the archives of NASA (from 1983 to 2005) was used to derive empirical constants (calibration) for the different models at each location while data from 2006 to 2015 was used for validation. The results showed an overall improvement when comparing measured Rs with Rs determined using original constants and Rs using the new constants. After calibration, the Swartman–Ogunlade (R² = 0.97) and Chen 2 models (RMSE = 0.665 MJ∙m⁻²∙day⁻¹) performed best while Chen 1 (R² = 0.66) and Bristow–Campbell models (RMSE = 1.58 MJ∙m⁻²∙day⁻¹) performed least in estimating Rs in Abakaliki. At the Nsukka station, Swartman–Ogunlade (R² = 0.96) and Adeala models (RMSE = 0.785 MJ∙m⁻²∙day⁻¹) performed best while Hargreaves–Samani (R² = 0.64) and Chen 1 models (RMSE = 1.96 MJ∙m⁻²∙day⁻¹) performed least in estimating Rs. Chen 2 (R² = 0.98) and Swartman–Ogunlade models (RMSE = 0.43 MJ∙m⁻²∙day⁻¹) performed best while Hargreaves–Samani (R² = 0.68) and Chen 1 models (RMSE = 1.64 MJ∙m⁻²∙day⁻¹) performed least in estimating Rs in Awka. For estimating ETo, Adeala (R² =0.98) and Swartman–Ogunlade models (RMSE = 0.064 MJ∙m⁻²∙day⁻¹) performed best at the Awka station and Swartman–Ogunlade (R² = 0.98) and Chen 2 models (RMSE = 0.43 MJ∙m⁻²∙day⁻¹) performed best at Abakaliki while Angstrom–Prescott–Page (R² = 0.96) and El-Sebaii models (RMSE = 0.0908 mm∙day⁻¹) performed best at the Nsukka station.

In this study, soil and water assessment tool (SWAT) model was applied to the upper Ebonyi Watershed to simulate streamflow and sediment yield. Results of sensitivity analysis, model calibration and model validation showed that the SWAT model performed reasonably for this study area. Streamflow and sediment discharge predictions were calibrated and validated against the daily flow using observed data. The simulated daily flow fit the observed data for the calibrated period with R