return flow

One of the methods for groundwater monitoring is the calculation of the water balance, which provides important information about the status and changes of aquifers. However, estimating the components of the groundwater balance is challenging, which can reduce the accuracy of the results. This study aims to improve the accuracy of evapotranspiration estimation in the groundwater balance using remote sensing, which leads to better estimation of return flow and infiltration from rainfall. Consequently, the groundwater balance is calculated with higher accuracy. For this purpose, data from observation wells, precipitation, withdrawals, rivers, and aquifers in the Qomsheh study area between 1992 and 2016 were used to evaluate the groundwater balance. A conceptual water balance model and the WaPOR remote sensing product were used to estimate ET. Finally, the observed water balance was compared with the calculated water balance. The results showed that during the study period, 40.54 million cubic meters (MCM) of water infiltrated into the aquifer through precipitation, irrigation, and surface flow. In contrast, 262 MCM of water was withdrawn from the aquifer, of which 105.53 MCM returned to the aquifer. Also, 10.21 MCM of water entered as lateral flow and 0.73 MCM of water exited the aquifer. The groundwater balance was estimated to be 120.02 and 119.09 MCM using the computational and observational methods, respectively. Comparison of these two water balances shows that using WaPOR remote sensing products in estimating the components of the water balance reduces uncertainty and increases the accuracy of calculations in the Qomsheh study area.

This study was carried out in 2012 to determine the total irrigation efficiency and real hydro module of Nazlouchai plain in Urmia, using water balance method. In addition to field experiments, we used data gathered from previous documents and related analytical conclusions. Field experiments conducted in 14 farmer’s fields. The result showed that water application efficiency varied from 55.4 to 78.8%. Water distribution efficiency varied from 75 to 94% and water conveyance efficiency was 66 to 92%. Total efficiency of traditional network estimated 44% and net water requirement of cropping pattern of the plain based on Penman-Monteith method calculated about 4930 m^3/ha, thus gross water requirement of cropping pattern was 11204 m^3/ha. The traditional irrigation network of Nazlouchai plain, with 29050 hectares of land, is located in the lower part of Nazlouchai basin. Annual water withdrawal from Nazlouchai river estimated about 128 Mm^3 and differential of input – output of the plain groundwater estimated about 13 Mm^3 annually. Water balance of Nazlouchai plain was negative with 29 Mm^3/year shortage. Estimation reveald that water consumption of Nazlouchai plain was 170 Mm^3 and irrigation actual hydro module of the plain 5838 m^3/yr/ha. The results showed that water efficiency of Nazlouchai plain in agricultural activities was about 85%. The results of this study showed that the actual rate of agricultural water consumption was lower than what was declared, based on national efficiency; the figures announced for water productivity in agricultural production should be reviewed too.

In recent decades, dramatic global population growth along with other related factors such as economic and industrial development, land use/cover change has contributed to an overall effects on water security and environmental problems around the world. To sustainable water management, quantifying the impacts of human activities on local hydrological cycle and water yield is essential in order to control balance between water demand and supply and increasing water security. This paper aims to offer a better characterization of human activities on groundwater stress and sustainability. Herein, we presented an approach for evaluation of water budget to facilitate understanding of the entire flow system of the aquifer in order to inform sustainable water resources management. We used a simple approach for quantifying direct human-hydrologic interactions by analyzing the aquifers’ water budget including human withdrawals (Hout) and return flows (Hin) according to water-balance equation and then normalized them relative to the net flux (Net Flux). Based on the normalized values of human inflow and outflow, the water-use regimes can be classified as surcharged (S), depleted (D), natural-flow dominated (Nf), and human-flow dominated (Hf). In addition we determine groundwater overdraft as a ratio between human withdrawals (Hout) and Recharge (RT) in order to evaluate the flow condition relative to the human water use. Finally we considered two adaptation scenarios to investigate probability of shift of aquifers that are located to depleted regime area to natural-flow dominated area. The results show that half of the studied aquifers in the Karkheh River Basin are of natural-flow dominated type while the rest are depleted. The Asadabad aquifer shows the highest amount (471 mm/yr) of human withdrawals (Hout) in this basin; actually highest overdraft relative to natural recharge to aquifer. Based on investigation of adaption scenarios, we explored that considering adaptation scenarios and decreasing human activities between 10 to 30 percent can be very useful for some aquifers in this basin.

Return flow is one of the main factors in reduction of water efficiency within the irrigation networks. Hence, an accurate prediction of return flow and providing the managerial strategies to reduce it’s quantity or reuse it in an optimal way could improve the efficiency of irrigation systems. In the present study, a regression tree model is developed to predict daily return flow discharge for Salimeh Irrigation Area, SIA, in Dez Irrigation Network. The daily inflow to the irrigation area, effective rainfall, consumptive water demand, percolation loss and evaporation from the surface canals are taken as predictor variables and return flow is treated as the target variable. Four sub-models of regression tree are evaluated. Model training, validation and testing carried out based on the observed data of 1386 and 1388. The model performance shows a good match between the simulated and the field measured return flow values. Results of statistical analysis indicated that the correlation coefficients are high for all sub-models and it can be concluded that the model performs satisfactorily in simulating the return flow. Accordingly, all tree regression sub-models may be recommended in the monitoring process of the SIA. The sensitivity of the model to the predictor variables is evaluated as; daily inflow> consumptive water demand> percolation loss> effective rainfall> evaporation from the surface canals. The findings demonstrate that the model has also a desirable accuracy in spite of deleting effective rainfall and evaporation from the surface canals as predictor variables.