OPTIWAM: An Intelligent Tool for Optimizing Irrigation Water Management in Coupled Reservoir–Groundwater Systems

Abstract

An approach based on a real coded Genetic Algorithm (GA) model was used to optimize water allocation from a coupled reservoir-groundwater system. The GA model considered five objectives: satisfying irrigation water demand, safeguarding water storage for the environment and fisheries, maximizing crop water productivity, protecting the downstream ecosystem against elevated soil salinity and hydromorphic issues, and reducing the unit cost of water. The model constraints are based on hydraulic and storage continuity requirements. The objectives and constraints were combined into a fitness function using a weighting factor and the penalty approaches. The decision variable was water allocation for irrigation demand from reservoir and groundwater. The irrigation water demands around the reservoir were estimated using the Food and Agriculture Organization (FAO) Penman-Monteith method in the water evaluation and planning (WEAP) software. The deterministic GA model was coded using Visual Basic 6 and a new tool for irrigation water management optimization (OPTIWAM) was developed. To validate the applicability of the deterministic model for the operation of coupled reservoir-groundwater systems, the Boura reservoir (in the center-west region of Burkina Faso) and the downstream irrigation area were used as a case study. Results show that the proposed methodology and the developed tool are effective and useful for Water Resour Manage determining optimal allocation of irrigation water. Furthermore, the methodology and tool can improve water resources management of coupled reservoir-groundwater systems.

Effect of Water Resources Allocation on Groundwater Environment and Soil Salinity Accumulation under Climate Change

“…In order to ensure food security, groundwater exploitation in the downstream district has become a "New Normal" of local agricultural water resources management [2], the determination of groundwater level in the typical agricultural area indicated that groundwater level decreased year by year in the recent 5 years differing by 0.86 m/a, thus, the management of over exploitation was extremely urgent. Up to now, studies for managing the water-resource problems in well-canal combined irrigation district mainly focused on optimization methods of groundwater under water utilization patterns [3][4][5], effect of water-saving irrigation area on ecological bearing capacity [6], model for conjunctive use of surface and groundwater [7][8][9], variation of groundwater salinity and its influence on crops in irrigation area [10], prediction model of farmland water environment after reconstruction of water-saving in the large-scale irrigation district [11][12][13], adaptation between irrigation intensity and groundwater carrying capacity in North China Plain [14]. However, there was absence of consideration about trend of groundwater hydrochemical and evaluation of environmental effect, meanwhile, there was not completely taken into account combined effect of climate change and water resources management strategies in the above studies.…”

Water Economy Balance of the Almaty City

“…Therefore, about 60 UNESCO states carry out scientific research to update their own data on water resources and exchange the experience on optimal and integrated use of natural waters based on a single program. The principles and guidelines of water balance analysis are universal [Fowe et al, 2015; Rushforth, Adams, Ruddell, 2013; Tsoukalas, Makropoulos, 2015]. However, the studies on the problems of urban hydrology and the procedure of drawing up water balances are contradictory [Paterson et al, 2015;Ruddell et al, 2014].…”

Estimating the Economic Value of Interannual Reservoir Storage in Water Resource Systems

“…It can lead to suboptimal reservoir policies if their results are interpreted too prescriptively (Philbrick & Kitanidis, 1999). Yet their ability to formulate and solve complex water resources problems means perfect foresight has remained attractive (Bharati et al, 2008;Fowe et al, 2015;Mendes et al, 2015;Parehkar et al, 2016;Vieira et al, 2011;Yang & Yang, 2013;Zambon et al, 2012;Zarghami et al, 2015). This approach provides a convenient and rigorous method for integrating interannual uncertainty into existing models without having to reformulate them from scratch.…”