JWSS - Journal of Water and Soil Science

Advance velocity is an important factor in surface irrigation system design and simulation. Volume balance is a simple model based on continuity equation used in surface irrigation design and management. In the past volume balance models, it is generally assumed that the upstream depth of surface water is constant and equal to normal depth. This initial assumption may cause significant errors in computing advance flow. In this paper, a modified volume balance (MVB) model is developed to predict the advance curve in furrow irrigation. In the suggested method the upstream surface, water depth is actual depth and variable in time. Predicted advance distance of VB, VB-ZI and MVB was compared to the observed data obtained for the three furrow lengths of 60, 80 and 90m. Evaluation indexes indicated that the modified volume balance equation is more accurate than the previous equations by RMSE 9.26, 7.37 and 6.76 respectively. Sensitivity analysis showed that the inlet discharge has the greatest effect on the model and the model is more sensitive to decreasing the discharge amount than to increasing it

One of the most essential and appropriate groundwater model components is accurate information of the recharge values among input data often introduced to the model as the percentage of rainfall of aquifers. The recharge values are influenced by many temporal and spatial factors. Firoozabad plain is one of the suitable plains for agriculture in the Fars province in which utilization of groundwater resources has been banned since 23 September 2002, due to the declining water level and negative balance. The main purpose of this study was to estimate the recharge values of groundwater aquifer by using SWAT in the MODFLOW model. Firstly, surface water was simulated via SWAT model, and sensitivity analysis, calibration, validation and uncertainty analysis of results were performed by SWAT-CUP software. After extraction of aquifer recharge values from the calibrated model, the groundwater of basin was simulated via MODFLOW model in both steady and unsteady conditions. Following the model calibration, the hydrodynamic coefficients of plain were determined and sensitivity of model was checked in terms of hydraulic conductivity and discharge rate of pumping wells. As for the confidence, the model was revalidated, which proved in simulating the behavior of the aquifer very well.

The aim of this paper is to adapt a water quality index for individual samples and to compare the results with that of the original GIS-based approach. Thirteen water quality parameters observed in 97 wells from the Shahrekord aquifer were used. In GIS-based method, quality parameters maps are difference-normalized, ranked and GWQI map is drawn. In derived method, observations from individual wells were separately and similarly treated to obtain WQI for each well. Both GWQI maps displayed similar trends and were highly correlated (R=0.91). While the minimum and mean GWQI for both methods were identical (respectively 81 and 84) the derived method estimated the maximum GWQI slightly lower (7%) and showed up to 6% difference in water quality class coverage. Overall, the derived method GWQI is more correlated with observations and performs better than the GIS-based method, and therefore, can be used for determining the overall quality of individual water samples and without the requirement of samples being spatially distributed.

This paper aimed to assess the severity of desertification in Segzi plain located in the eastern part of Isfahan city, focusing on groundwater quality criteria used in MEDALUS model. Bayesian Belief networks (BBNs) were also used to convert MEDALUS model into a predictive, cause and effects model. Different techniques such as Kriging and IDW were applied to water quality data of 12 groundwater wells to map continuous variations of the CL, SAR, EC, TDS, pH and decline in water table indices in GIS environment. The effects of measured water quality indicators on desertification severity levels were assessed using sensitivity and scenario analysis in BBNs model. According to the results of the MEDALUS, the desertification of the study area was classified as severe class due to its low quality of groundwater. Sensitivity analysis by the both models showed that decline in waater table, water chloride content and electrical conductivity were the most important parameters responsible for desertification in the region from ground water condition standpoint. The determination coefficient between the outputs of the MEDALUS and BBNs models (R2>0.63) indicated that the results of both models were significantly correlated (α=5 %). These results indicate that the application of BBNs model in desertification assessment can appropriately accommodate the uncertainty of desertification methods and can help managers to make better decision for upcoming land management projects.