Jean-Emmanuel Paturel

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

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 determining optimal allocation of irrigation water. Furthermore, the methodology and tool can improve water resources management of coupled reservoir-groundwater systems.

Using land cover changes and demographic data to improve hydrological modeling in the Sahel

At the beginning of the drought in the Sahel in the 1970s and 1980s, rainfall decreased markedly, but runoff coefficients and in some cases absolute runoff increased. This situation was due to the conversion of the land cover from natural vegetation with a low annual runoff coefficient, to cropland and bare soils, whose runoff coefficients are higher. Unless they are adapted, hydrological conceptual models such as GR2M, are unable to reproduce this increase in runoff. n nDespite the varying environmental and climatic conditions of the West African Sahel, we show that it is possible to increase the performance of the GR2M model simulations by elaborating a time-varying soil water holding capacity (WHC), and to incorporate this value in the annual maximum amount of water to be stored in reservoir A of the model. n nWe looked for interactions between climate, rural society and the environment. These interactions drive land-cover changes in the Sahel, which in turn drive the distribution of rainfall between infiltration, evaporation and runoff, and hence the water resources, which are vital in this region. We elaborated several time series of key indicators linked to these interactions. n nWe then integrated these changes in the runoff conditions of the GR2M model through the maximum value of the reservoir capacity. We calculated annual values of WHC using the annual values of four classes of land cover, natural vegetation, cultivated area, bare soil and surface water. We then used the hydrological model with and without this time-varying soil value of A, and compared the performances of the model under the two scenarios. n nWhatever the calibration period used, the Nash-Sutcliffe index was always greater in the case of the time-varying A time series. This article is protected by copyright. All rights reserved.