Human activities have modified the environment over the years. Urbanization, agriculture lumbering, mining and other land uses have substantially altered the Earth's surface. Land use and the resultant change in land cover have significant effects on ecological, environmental and hydrologic systems and processes. An understanding of past and present land-cover change, together with an analysis of potential future change, is necessary for proper management; thus, the need for models. Hydrologic models are primarily used for hydrologic prediction and for understanding hydrologic processes. With recent technological advances, technological based tools such as GIS are incorporated into hydrologic models for assessing the impacts of various land use/cover. Hydrologic models incorporated with GIS can be used to project future land uses/cover to provide an increased clarity, probability or likelihood of potential consequences on ecosystem services such as biodiversity, water quality and climate. This paper critically examines land use/cover, effects of impacts of land use/cover and the use of hydrologic models to assess the impact of land us/cover on runoff and sediment yield. Hence it calls for their use by watershed managers and decision maker as management tool especially in developing countries.

Understanding the impact of changes in climatic variables on reference evapotranspiration (ETo) is important for predicting possible implications of climate change on the overall hydrology of an area. This study aimed to determine the effects of changes in ETo with respect to changes in climatic variables. In addition, the specific objective was to determine the sensitivity coefficients of ETo in seven different locations in Nigeria with distinct agroecology, namely Maiduguri (Sahel savannah), Sokoto (Sudan savannah), Kaduna (Guinea savannah), Jos (Montane), Enugu (Derived Savannah), Ibadan (tropical rainforest), and Port Harcourt (coastal). The results showed that ETo is most sensitive to changes in maximum temperature (Tmax) in Maiduguri, Sokoto, Kaduna, and Jos. In Enugu and Ibadan, ETo is most sensitive to changes in solar radiation (Rs), while in Port Harcourt, ETo is most sensitive to relative humidity (RH). Overall, based on the average annual sensitivity coefficients (SCs) of the study area, the SC is ranked in the order: RH > Rs > Tmax > U2 > Tmin. Also, the results showed positive SCs of ETo to Rs, Tmax, U2, Tmin, and negative SC for RH. This study can serve as a baseline for sustainable water management in the context of climate change and adapted to areas with a similar climate.

This paper presents a low cost, simple digital soil moisture meter, working on the principle of dielectric. A digital soil moisture meter using the NE555 timer and micro controller as a major electronic component was developed and tested, which display its output in a range of 0.0 to 99% on the 7-segment displayed unit. The digital soil moisture meter developed was compared with gravimetric method for soil moisture determination on fifteen soil samples added different level of water during calibration process. The results revealed a relatively linear relationship between the moisture content process and the digital soil moisture meter. The regression coefficient (R 2) of the digital soil moisture meter calibration was 0.984. Twenty soil samples were used in validation of the calibrated digital soil moisture meter. The regression coefficient (R 2) was found to be 0.964, showing validity of the developed digital soil moisture meter. The developed meter provided up to 96% accuracy in estimating the value of the soil moisture content. The results showed that the developed digital soil moisture meter is more reliable, sensitive, precise and easy to use.

Soil and Water Assessment Tool, (SWAT) model was used to predict the impacts of Climate Change on Ajali River watershed, Aguobu-Umumba, Ezeagu, Enugu State, Nigeria. The model was first used to simulate stream flow using observed data. After model run, parameterization, sensitivity analysis, the monthly coefficients of determination (R 2 ) were 0.5739 and 0.6776 for calibration and validation respectively. Having performed fairly well, the model was thereafter run to simulate climate change impacts on streamflow. Two GCMs -CCCMA and GFDL, were used to generate future climate data and run in SWAT. Total observed streamflow for the baseline (1981-2000) was compared with that predicted (2046 -2064) from the GCMs. The results of the CCCMA models showed an increase of 383.72m 3 /s and 2.1% in the streamflow of the Ajali river watershed when projected to 2046 -2064 as against the historical baseline while GFDL showed 3358.58 m 3 /s and 18.9% respectively. The study, when applied, will help watershed managers and planners in the management of the watershed for effectiveness and efficiency. It will also increase our awareness of the effect of climate change on other water bodies in the hinterlands.

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.

Vegetation has a marked effect on runoff and soil moisture and plays an important the hydrologic cycle. The Watershed Resources Management (WRM) model, a process-based, continuous, distributed parameter simulation model developed for hydrologic and soil erosion studies at the watershed scale lack a crop growth component. As such, this model assumes a constant parameter values for vegetation and hydraulic parameters throughout the duration of hydrologic simulation. A crop growth algorithm based on the original plant growth model used in the Environmental Policy Integrated Climate (EPIC) model was developed for coupling to the WRM model. The developed model was tested for yield simulations using data from a field plot within the Oyun River basin, Ilorin, Nigeria. Model prediction closely matched observed values with R 2 of 0.9 for the years under study. This model will be incorporated into the WRM model in other to improve its representation of vegetation growth stages in a natural basin. This modification will further enhance its capability for accurate hydrologic and crop growth studies.

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