Knowledge of storage variations in reservoirs and lakes is important for water resources planning and use. In developing countries where lakes may be poorly gauged and water quantity data sparse or unavailable, a simple and cost-effective method of estimating storage would be useful for reservoir operation and management. In this study, we showed how to estimate reservoir storage by combining hydrological mass balance and remotely-measured lake levels. Water levels measured by ERS/ENVISAT and Topex/Poseidon satellite altimeters in Kainji reservoir, Nigeria, were first compared with groundmeasured levels. The resulting time series plot and high determination coefficient for both data sets (R 2 = 0.93 and 0.95 respectively) showed that altimetric levels can complement gage data for this reservoir. Reservoir storage was then estimated from gage and altimetric lake levels using a storage-level curve generated by performing a simple water balance. The resulting correlation and root-mean-square errors between storage estimated from altimetry and water balance suggest that storage may be directly determined from satellite-measured levels. These results have far-reaching implications for water resources monitoring and quantification in ungaged lakes and the methodology could revolutionize conventional techniques of computing volume changes even in gaged reservoir.

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To increase the united Nation's chance of achieving its Sustainable Development goal of providing clean water and sanitation especially in the developing world, a new approach that combines sustainable water resources management with mass reorientation of rural populations must become a priority. This is important considering the increasing global water stress faced by humans and the occasional knowledge gap in demand management and conservation between developed and developing regions where the majority live in rural areas. contemporary endeavours often focus on providing technology and introducing practices that improve or preserve water quality and quantity. but some of these efforts suffer either from a failure to correctly interface with existing local practices or an inability to adequately address the local knowledge gap. This study addressed this problem by combining locationspecific public enlightenment with access to source protection, storage, and treatment technologies. The study area consists of rural settlements in the central region of Nigeria. Datasets used include population statistics, source types (surface water, groundwater, tap water, etc.), demand and availability, water stress levels, quality and quantity enhancement technology, and access to water education (radio, local health official, etc.). results showed that communities that received properly instituted water quality/quantity enhancement technologies with little to no orientation (or vice versa) experienced inconsistent improvements in water sufficiency across the tested populations and some stagnation afterwards. but in communities that benefitted from continuous sustainability orientation as well as a careful interfacing of water quality/quantity enhancement and protection technologies, dramatic improvements in water sufficiency resulted. In addition to socioeconomic and environmental benefits, insights gained from this study have potential applications in planning/policy-making by stakeholders in similar developing regions in central america and Southeast asia.

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Background: Several models have been developed for inflow forecasting in reservoirs based on local parameters which may not include an implicit system characteristic like seasonality. Autoregressive integrated moving average (ARIMA) models can be developed to cater for the presence of seasonal and non-seasonal behavior of natural water systems. Aims: The present study aims to estimate Volumetric Inflow in a Hydropower Dam using Autoregressive Integrated Moving Average (ARIMA) Modelling and Altimetric Lake Levels. Study Design: The study was conducted in the Kainji reservoir, West Africa located along the Niger River. This study combines satellite-altimetry-based rating curves with reservoir inflow models that capture the seasonality of upstream characteristics. Results and Discussion: Seasonal multiplicative ARIMA models were developed based on 27year inflow records and used to forecast seven subsequent years. Reservoir levels measured by satellite radar altimeters were matched with actual inflows to generate rating curves from which future inflows may then be estimated. The model with the best forecasts relative to actual inflow -a seasonal multiplicative ARIMA (2,1,1) x (2,1,2) 12 model -was adopted.