Main content area

Spatial analysis of infiltration in agricultural lands in arid areas of Iran

Babaei, Fariba, Zolfaghari, Ali Asghar, Yazdani, Mohammad Reza, Sadeghipour, Ahmad
Catena 2018 v.170 pp. 25-35
agricultural land, arithmetics, geometry, hydrologic cycle, infiltrometers, irrigation, models, prediction, regression analysis, runoff, soil erosion, Iran
Infiltration plays an important role in the hydrologic cycle, runoff generation, soil erosion, as well as irrigation. In the current study, we evaluated a variety of infiltration models, in order to determine the model that is best suited to predict infiltration on agricultural lands in arid areas of the Semnan province in Iran. Additionally, we analyzed spatial variability of the infiltration process using scaling parameters. A number of 60 points were determined for the measurement of water infiltration using the conditioned Latin hypercube sampling (cLHS) method. Each infiltration measurement was carried out with a tension infiltrometer apparatus. Several infiltration models, including Philip, Horton, Kostiakov (KO), Modified Kostiakov (MKO) and Revised Modified Kostiakov (RMKO) models, were fitted to the measured infiltration data. Among the mean coefficient of determination (R2) values, the highest R2 values were associated with the RMKO, MKO and Horton models, respectively. While base on the Akaike criterion (ACI), the MKO model was slightly better than the RMKO model for prediction of cumulative infiltration. Cumulative infiltration was scaled using Sorptivity (αS) and transmissivity (αA) scaling factors. By minimizing the differences of the sum of squares between the scaled and the average infiltration, the optimum scaling factors (αopt) were estimated. Arithmetic, geometric and harmonic means (i.e. αm, αG, αH; respectively) of αS and αA scaling factors were calculated and the infiltration data were scaled utilizing the mentioned scaling factors. Our findings indicated that the best result was yielded by αH. Strong correlations were found for αG (r = 0.86) and αH (r = 0.86) with αopt. For defining the relationship between αopt and αA, αS, αm, αG and αH data, a regression analysis was performed. According to our results, curves reflecting the relationship between αopt and αA, αS, αm, αG and αH were sigmoid. Based on the results of this study, infiltration in agricultural lands in the arid area displays a great spatial variability.