Showing 2 results for Ground Water Level
S.h. Tabatabaei, M. Ghazali ,
Volume 15, Issue 57 (10-2011)
Abstract
The accuracy and precision of the input data in decision making is important. Error originates from data collection, data entry, storage, retrieval and analysis of the data which consequently result in model error. One of the errors in spatial analysis is interpolation error. The main objective of this research was the suitability assessment of some interpolation methods for estimation of groundwater level in Farsan-Joneghan and Sefiddasht aquifers, located in Beheshtabad catchment, Chaharmahl-Va-Bakhtiyari province, Iran. Cross-validation technique was employed for the determination of each method's error. The RMSE and MAE indices were used for the error comparison. The results show that the modified Shapard's method with an MAE=6 and RMSE=7 was the most accurate for interpolation of groundwtaer level in the Sefiddasht aquifer. The inverse distance power method with MAE=6 and RMSE=9 was the best interpolation method for Farsan-Jonaghan aquifer. The Kriging with MAE=7 and RMSE=12 is the second best method in these aquifers. The moving average, minimum curvature and polynomial regression procedures produce the maximum error in the aquifers (17
H. Zare Abyaneh, H.noori, A.m.liaghat, V.karimi, H.noori,
Volume 15, Issue 57 (10-2011)
Abstract
Fertilizers in agriculture are potential sources of environmental pollution, especially in ground water quality and soil resources. Studying factors effective in water and nutrient transport through soil profile is helpful for nutrient management to minimize adverse impacts on environment and nitrate leaching below the root zone. In this study, the ground water level and nitrate leaching transportation below the root zone were measured in a paddy rice field and the data were simulated with the DRAINMOD-N model. For evaluating DRAINMOD-N software in a paddy rice field under surface drainage in Mazandaran, the ground water level and nitrate transportation were measured during four months (June, July, August and September) in 2008. The DRAINMOD-N model was calibrated by adjusting nitrification and denitrification rate constants to reach the best fit between measured and predicted data. Results indicate that predicted ground water level and nitrate concentration by model were significant at one percent level. The statistical comparison was done by model efficiency (EF) 0.84 for estimation of ground water level and 0.97 for estimation of nitrate concentration, respectively. The DRAINMOD-N model can be used as a tool to manage environmental pollution of nitrate in paddy rice fields.
