In recent years, the idea of nebkhas as a degradation index changed to signs of adaption and stability in desert conditions. Plants growth condition is very restricted in desert biomes, but nebkhas ecologically provide a better condition for some plants to grow. Nebkhas also change physical and chemical properties of the soil and also water balance conditions. In this study infiltration rate was compared between nebkhas formed by Ephedra strobilacea and Tamarix ramosissima and soil between nebkhas in Abarkouh area located in Yazd province. In addition, there are many hydrological models that use Green-Ampt or Horton equations for forecasting surface runoff. So this study also evaluated the efficiency of Green-Ampt or Horton equations. At the first, the locations of each nebkhas were recognized and soil of nebkhas and the soil between nebkhas were sampled. Soil samples were then delivered to the laboratory and main physico-chemical characteristics of all samples were measured. Infiltration rate was measured by double ring between nebkhas (bare) and in the nebkhas of the two species. Horton and Green-Ampt constants were also calculated. Infiltration models efficiency were analyzed by calculation of Nash-Sutcliffe coefficient and RMSE-Standard Deviation of Observation (RSR). Final permeability was measured at about 24 cm/hr for Ephedra strobilacea and Tamarix ramosissima nebkhas. Final permeability for between nebkhas (bare) of Tamarix ramosissima and Ephedra strobilacea were measured 3.6 and 7 cm/hr, respectively. Final permeability for Ephedra strobilacea and Tamarix ramosissima nebkhas was 6.7 and 3.4 times more than between nebkhas. It is concluded that nebkha play an important role in water infiltration and water resources in desert areas. Nash Sutcliffe ranges showed Green-Ampt and Horton models are in acceptable levels of performance for predicting infiltration rate of Tamarix ramosissima and Ephedra strobilacea nebkhas. However, the models show less accuracy for soil between nebkhas (bare), because of soil stratifying. The cause of these differences is that Aeolian materials of nebkhas sorted by wind show homogenous texture compared with the layered soil of between nebkhas. Horton and Green -Ampt models assumptions have conformity with homogenous soils.

Soil infiltration is one of the key processes in the design of irrigation systems, water resources management, soil conservation, and erosion control in watershed management. As to the importance of the mentioned subject, infiltration changes and modeling were investigated on the surface soil of geological formations in this study. The double-ring infiltrometer was used to measure the infiltration rate in the surface soil of some geological formations on the Alashtar Watershed due to the flooding potential of the area. The performance of the modified Green-Ampt, Philip, Kostiakov, the US Soil Conservation Service (SCS), and the Horton models were evaluated in estimating the infiltration rate in the surface soils formed or deposited on those formations. The results indicated that the Asmari Formation (Oml) had a higher cumulative infiltration, average infiltration rate, terminal infiltration rate than other formations. This was followed by the Quaternary Formations (Q and Qt). The results indicated that the modified Green-Ampt model had an acceptable accuracy as compared to other models in estimating infiltration in the Pabdeh (EL), the Khami Group (Jk, Mz), the Gachsaran (M), the Aghajari (Muplaj, Mpaj), and the Bakhtyari (Plb) Formations with efficiency coefficients of 93.9, 94.4, 95.9, 97.6, 87.7, and 92.7%, respectively. The Kostiakov model was more suitable for the Q and Oml Formations, and the Horton model was the best for the Qt Formation (C.C= 0.925), which may be used to quantify the amount of infiltrated water and to estimate the runoff volume for the soils formed or deposited on those formations different on the mentioned geological formations.

Runoff is formed by spending some time after rain and significantly depends on rainfall intensity, soil moisture, and slope. One of the fundamental questions about runoff is the time that it starts to create. In this research, the runoff start time in sandy loam soil was evaluated experimentally under different conditions using a precipitation simulator machine. The rainfall intensity parameters of (60, 80, and 100 millimeters per hour) and the slope of (0 and 5 percent) were investigated. The rainfall was created in the three soil treatment types completely dry (Sdry), the dry soil that had been saturated 24 hours before the test (S24hrlag), and the dry soil that had been saturated 48 hours before the test (S48hrlag). Eighteen tests were conducted on this soil. At the end of each test, the soil moisture was measured. The experimental results were compared with the numerical model of Green-Ampt. According to the Kendall and Spearman correlation test results, as the rainfall be intense, the start time of the runoff is lower. Also, the runoff starts at a faster time in the slope of 5 percent for every three types of soil. Also, the results of starting time of the runoff in the soil with a delay of 48 hours in the rain compared to the soil with a delay of 24 hours in the rain are closer in all of the rainfall intensity and slopes compared to the case of dry soil. Therefore, in the experiments related to a delay of 24 hours, the time of the start of runoff decreases. While in tests with a delay of 48 hours, it was not much different from completely dry soil. Also, the Green-Ampt results are close to the experimental results (R2=0.9775), and the maximum difference between the two mentioned methods is 4.8 minutes. Therefore, it can be used with the Green-Ampt method to calculate the start time of runoff in sandy loam soil in different states of rainfall intensity and bed slope.

Infiltration is one of the most important phenomena which is affected by the soil physical and hydraulic characteristics. Measurement of infiltration needs the high cost and is time consuming, therefore estimation of infiltration is necessary. Also, due to variability of soil moisture, it is important to determine the appropriate model for the estimating the infiltration rate. The present study was carried out for determining the sensitivity of infiltration models including Kostiakov, Philip, Horton, Soil Conservation Service (SCS) and modified Green – Ampt models at different levels of the soil moisture. To consider the homogeneity of soil and slope, this research carried out in Mour Mouri region, of Ilam province. In this study the infiltration rate was measured by using Double Ring Infiltrometer in the prepared plats (1 m2) at four levels of soil moisture, including control (0), 5, 10 and 15 liters. Afterward the best model was selected by some criteria of error assessment, such as percentage of relative error (RE), root mean square error (RMSE), modeling efficiency coefficient (EF) and coefficient of determination (R2). The results showed that generally, the Kostiakov model can estimate the infiltration rate better than other infiltration models with increasing the soil moisture, the precision of Horton model increased.

Infiltration is the process of water penetration from the ground surface into the soil and is an important process in the hydrological cycle by which surface runoff and groundwater recharge can be linked. Over the years، the importance of the infiltration process resulted in the development of several simplified analytical models for predicting infiltration. These infiltration models range from entirely empirical to physically based models. The most serious problem associated with infiltration modelling of a catchment is how to express the spatial soil variability. In the present study، various infiltration models were fitted to the observed infiltration data of 27 double ring infiltrometer tests and the best-fit infiltration model for Darabkola watershed was identified and evaluated. In addition، the spatial variability of the selected infiltration model parameters was analyzed using the geostatistical techniques. Results showed that among of four models، the Green - Ampt model could determine the infiltration rate with smallest values of RMSE. Hence، saturated hydraulic conductivity parameter (KS) and suction head at the wetting front (Sw) were estimated for all the test points. Evaluation of spatial variability of these parameters indicated that parameters KS and Sw had the spatial dependencies of 0. 49 and 0. 25 respectively، showing medium spatial dependencies of both parameters. Also، investigation of interpolation parameter maps showed that in the upland with forest land use، relative to other areas in the watershed، the saturated hydraulic conductivity (KS) and suction head at the wetting front (Sw) have larger (1. 57-2. 69 cm/hr) and smaller values (12. 12-1737 cm)، respectively.

Infiltration is the process of water penetration from the ground surface into the soil and is an important process in the hydrological cycle by which surface runoff and groundwater recharge can be linked. Over the years، the importance of the infiltration process resulted in the development of several simplified analytical models for predicting infiltration. These infiltration models range from entirely empirical to physically based models. The most serious problem associated with infiltration modelling of a catchment is how to express the spatial soil variability. In the present study، various infiltration models were fitted to the observed infiltration data of 27 double ring infiltrometer tests and the best-fit infiltration model for Darabkola watershed was identified and evaluated. In addition، the spatial variability of the selected infiltration model parameters was analyzed using the geostatistical techniques. Results showed that among of four models، the Green - Ampt model could determine the infiltration rate with smallest values of RMSE. Hence، saturated hydraulic conductivity parameter (KS) and suction head at the wetting front (Sw) were estimated for all the test points. Evaluation of spatial variability of these parameters indicated that parameters KS and Sw had the spatial dependencies of 0. 49 and 0. 25 respectively، showing medium spatial dependencies of both parameters. Also، investigation of interpolation parameter maps showed that in the upland with forest land use، relative to other areas in the watershed، the saturated hydraulic conductivity (KS) and suction head at the wetting front (Sw) have larger (1. 57-2. 69 cm/hr) and smaller values (12. 12-1737 cm)، respectively.

Infiltration is one of the most important soil physical variables and basic data in rainfall–runoff models for water loss determination in watersheds. Its measurement is، however، time consuming and costly. For this reason، soils infiltration rates determination by different models is always a serious concern to researchers. In this research، a single event precipitation was measured using rainfall simulator during a 90-minute rainfall for determination of the best infiltration equation in watershed scale. The parameters of five infiltration models namely Kostiakov، Green-Ampt، Horton، NRCS and Philip، were estimated by sum of squared error optimization for four different land uses and three soil textures of sandy clay loam، loam، and sandy loam. The cumulative infiltration rates estimated from the different models were compared using eight different statistics. The results showed that the Kostiakov and NRCS models had the highest priority score and the most permanent trend for accurate estimating of cumulative infiltration rate in different land uses whereas the Horton model was situated in the next position. Overall، the empirical models (Kostiakov، NRCS and Horton models) had the more realistic efficiency than the physical models (Green-Ampt and Philip models).

Volume of flooding flow volume in ephemeral streams decreases due to evaporation as well as the channel wall and bed penetration. This process is known as transmission losses. In this study, a model is developed to estimate the transmission losses in irregular sections of rivers, in which a finite difference scheme is used to solve Saint-Venant equations. In this model, for estimating the transmission losses, the Green-Ampt equation is coupled with Saint-Venant equations. After verification of model, the transmission losses at Gharesoo river with 18 KM length and 60 cross sections after 65 hours flood event is evaluated. The results showed that the Green-Ampt equation can predict transmission losses with good accuracy. Using model the transmission losses equal to 1400000 cubic meters is computed which is 2.75% of input flood volume to the reach. Also this model is enabled to evaluate the infiltration rate and cumulative infiltration depth.

Soil water infiltration is one of the key properties for designing irrigation systems, hydrological studies, water resources management, drainage projects and soil conservation practices in watershed scale. Also, an accuracy assessment amount of infiltration considering the time for estimation of stored water in the root zone plant is great important in the design and planning irrigation. For this purpose, researchers always drift suitable model for quantity expression of infiltration water to soil. The importance of infiltration has caused to develop different physical and empirical models to estimate the process. In this study, the performance of Green and Ampt, Philip, Horton, Soil Conservation Service (SCS), Kostiakov and Kostiakov-Lewis infiltration models under different land uses were evaluated
Studied soils were in the order of Aridisol, Inseptisol, Entisol and Mollisol and were in the land use of including agronomy, gardens and grassland. The number of measured infiltration in land use of agronomy, garden and grassland were 37, 25 and 20 types of soil, respectively. For this purpose, the infiltration data were obtained by double rings method from 82 point of in different region of Meshkin, Ardabil province. The parameters of these models were then obtained, using least square optimization method. In order to evaluate the accuracy of the models, the root mean square error (RMSE), standard deviation of RMSEs (SDRMSED) and R2 statistics were calculated.
The results showed that compared with other models, estimation of the cumulative infiltration Kostiakov-Lewis model has been consistent trend in all three land use ranked first (R2=0.997, RMSE=0.206 and SDRMSED=0.201). Considering obtained from total evaluation, estimation of cumulative infiltration by infiltration models in this study were as Kostiakov-Lewis and Horton models in first and second rank, Green-Ampt and SCS model in third rank and Kostiakov and Philip in fourth and fifth rank, respectively. So, we can say that the infiltration models examined, Model Kostiakov-Lewis is the best model for quantifying the process of infiltration. One reason for the excellence of Kostiakov-Louis model was great number of parameters than SCS, Kostiakov, Green-Ampt and Philip models and the fitting method is to determine the parameters mentioned models. This feature makes more flexible of this model than other model.

The current investigation was carried out at the Lighavn watershed area, located in East Azerbaijan, North West of Iran, regarding optimizing of the different infiltration models parameters and their evaluations for cumulative infiltration prediction. Applied infiltration models were including Green and Ampt, Kostyakov, modified Kostyakov, Philip, Horton, and Kutilic and Kreja models. To this end, soil sampling and ground based measurements were done at late spring of 2012. Comulative infiltration were also measured at 135 points of the study area using disk infiltrometer. Results, models comparison based on ER criteria, showed that Kutılek and Krejca (ER=4%), Kostyakov (ER=8%), modified Kostyakov (ER=10%), Horton (ER=13%), Green and Ampt (ER=36%), and Philip (ER=37%) models, respectively, were the most to least precise models in cumulative infiltration or infiltration rate prediction. The results also depicted that the Kostyakov and modified Kostyakov models are suitable more models for future uses regarding cumulative infiltration and infiltration rates prediction.