Exploring causes of streamflow alteration in the Medjerda river, Algeria

Study region: The Medjerda is a transboundary catchment located in North-Eastern Algeria and shared with Tunisia. Study focus: In this study, we explore the causes of hydrological alteration of streamflow in a subcatchment of the Medjerda in Algeria. The hydrological alteration was explored through the application of Mann-Kendall test based on possible explanatory factors, namely, precipitation, evapotranspiration, temperature, irrigation, and Normalized Difference of Vegetation Index (NDVI). Furthermore, the causal factors of streamflow variation were addressed using Convergent Cross Mapping (CCM) method. New hydrological insights for the region: Results of the trend analysis yield that the streamflow is altered during the period 1981 2012. This is consistent with the trends of the possible explanatory factors of this alteration. The Convergent Cross Mapping (CCM) method showed that streamflow alteration is unidirectionally caused by changes in patterns of precipitation, temperature, evapotranspiration, irrigation, and NDVI, and that there is little feedback from streamflow alteration to these causing factors. Overall, our assessment showed that the method used to identify the causal relationships in dynamical systems based on the CCM algorithm is suitable for exploring the drivers of the hydrologic alteration in multivariate time series, in particular when nonlinear dynamics determine the system

Improving artificial intelligence models accuracy for monthly streamflow forecasting using grey Wolf optimization (GWO) algorithm

Monthly streamflow forecasting is required for short- and long-term water resources management especially in extreme events such as flood and drought. Therefore, there is need to develop a reliable and precise model for streamflow forecasting. The precision of Artificial Intelligence (AI) models can be improved by using hybrid AI models which consist of coupled models. Therefore, the chief aim of this study is to propose efficient hybrid system by integrating Grey Wolf Optimization (GWO) algorithm with Artificial Intelligence (AI) models. 130 years of monthly historical natural streamflow data will be used to evaluate the performance of the proposed modelling technique. Quantitative performance indicators will be introduced to evaluate the validity of the integrated models; in addition to that, comprehensive analysis will be conducted between the predicted and the observed streamflow. The results show the integrated AI with GWO outperform the standard AI methods and can make better forecasting during training and testing phases for the monthly inflow in all input cases. This finding reveals the superiority of GWO meta-heuristic algorithm in improving the accuracy of the standard AI in forecasting the monthly inflow. © 2019 Elsevier B.V