Validation of a soil water balance model using soil water content and pressure head data     

Martin Wegehenkel 《水文研究》2005,19(6):1139-1164

The validation of soil water balance models and the evaluation of the quality of the model predictions at field‐scale require time‐series of in situ measured model outputs. In our study, we have validated such a model using a 6‐year period with time‐series of automatically recorded, daily volumetric soil water contents measured with the time‐domain reflectometry with intelligent microelements (TRIME) method and daily pressure heads measured with tensiometers. The comparisons of simulated with measured soil water contents and pressure heads were analysed using the modelling efficiency index (IA) and the square root of the mean square error (RMSE) in order to evaluate the prediction quality of the model. In our study, IA and RMSE, obtained either from the comparison of simulated with measured soil water contents or the comparison of calculated with observed pressure heads, in some cases lead to different results regarding the evaluation of the simulation quality of the soil water balance model. For example, a good fit between simulated and observed soil water contents does not necessarily result in a comparably good fit between the corresponding calculated and measured pressure heads. Therefore, a combined use of both measurement techniques, which takes into account their respective advantages and disadvantages, gives a more complete overview on the simulation quality of the soil water balance model than the single use of one of those techniques. Copyright © 2004 John Wiley & Sons, Ltd.  相似文献   

Understanding of the Common Land Model performance for water and energy fluxes in a farmland during the growing season in Korea   总被引：1，自引：0，他引：1  

Minha Choi  Seung Oh Lee  Hyojung Kwon 《水文研究》2010,24(8):1063-1071

The Common Land Model (CLM) is one of the most widely used land surface models (LSMs) due to the practicality of its simple parameterization scheme and its versatility in embracing a variety of field datasets. The improved assessment of land surface water and energy fluxes using CLM can be an alternative approach for understanding the complex land–atmosphere interactions in data‐limited regions. The understanding of water and energy cycles in a farmland is crucial because it is a dominant land feature in Korea and Asia. However, the applications of CLM to farmland in Korea are in paucity. The simulations of water and energy fluxes by CLM were conducted against those from the tower‐based measurements during the growing season of 2006 at the Haenam site (a farmland site) in Korea without optimization. According to the International Geosphere–Biosphere Programme (IGBP) land cover classification, a homogeneous cropland was selected initially for this study. Although the simulated soil moisture had a similar pattern to that of the observed, the former was relatively drier (at 0·1 m³ m^?3) than the latter. The simulated net radiation showed good agreement with the observed, with a root mean squared error (RMSE) of 41 W m^?2, whereas relatively large discrepancies between the simulation and observation were found in sensible (RMSE of 66 W m^?2) and latent (RMSE of 60 W m^?2) heat fluxes. On the basis of the sensitivity analysis, soil moisture was more receptive to land cover and soil texture parameterizations when compared to soil temperature and turbulent fluxes. Despite the uncertainty in the predictive capability of CLM employed without optimization, the initial performance of CLM suggests usefulness in a data‐limited heterogeneous farmland in Korea. Further studies are required to identify the controls on water and energy fluxes with an improved parameterization. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

Evolution of Vertical Moist Thermodynamic Structure Associated with the Indian Summer Monsoon 2010 in a Regional Climate Model     

A. Raju  Anant Parekh  C. Gnanaseelan 《Pure and Applied Geophysics》2014,171(7):1499-1518

The 2010 boreal summer marked a worldwide abnormal climate. An unprecedented heat wave struck East Asia in July and August 2010. In addition to this, the tropical Indian Ocean was abnormally warm during the summer of 2010. Several heavy rainfall events and associated floods were also reported in the Indian monsoon region. During the season, the monsoon trough (an east–west elongated area of low pressure) was mostly located south of its normal position and monsoon low pressure systems moved south of their normal tracks. This resulted in an uneven spatial distribution with above-normal rainfall over peninsular and Northwest India, and deficient rainfall over central and northeastern parts of India, thus prediction (and simulation) of such anomalous climatic summer season is important. In this context, evolution of vertical moist thermodynamic structure associated with Indian summer monsoon 2010 is studied using regional climate model, reanalysis and satellite observations. This synergised approach is the first of its kind to the best of our knowledge. The model-simulated fields (pressure, temperature, winds and precipitation) are comparable with the respective in situ and reanalysis fields, both in intensity and geographical distribution. The correlation coefficient between model and observed precipitation is 0.5 and the root-mean-square error (RMSE) is 4.8 mm day^?1. Inter-comparison of model-simulated fields with satellite observations reveals that the midtropospheric temperature [Water vapour mixing ratio (WVMR)] has RMSE of 0.5 K (1.6 g kg^?1), whereas the surface temperature (WVMR) has RMSE of 3.4 K (2.2 g kg^?1). Similarly, temporal evolution of vertical structure of temperature with rainfall over central Indian region reveals that the baroclinic nature of monsoon is simulated by the model. The midtropospheric warming associated with rainfall is captured by the model, whereas the model failed to capture the surface response to high and low rainfall events. The model has strong water vapour loading in the whole troposphere, but weaker coherent response with rainfall compared to observations. Thus, strong water vapour loading and overestimation of rainfall are reported in the model. This study put forward that the discrepancy in the model-simulated structure may be reduced by assimilation of satellite observations.  相似文献   

Krypton and Xenon Radionuclides Monitoring in the Northwest Region of Russia     

Yuri V. Dubasov  Nikolay S. Okunev 《Pure and Applied Geophysics》2010,167(4-5):487-498

Monitoring of Xe and Kr radionuclides was conducted from August 2006 to 30 July 2008 within the framework of ISTC Project #2133. Cherepovets City in Vologda Province and St. Petersburg were chosen as monitoring locations. Kr–Xe concentrate samples were obtained as a result of processing of several thousand m³ of atmospheric air. New results of ⁸⁵Kr monitoring show, that for last 15 years, the ⁸⁵Kr volumetric activity in the atmospheric air of the northwest region of Russia has increased approximately 50% and has achieved a level of 1.5 Bq/m³. This value correlates well with similar data for Western Europe and Japan. The xenon fraction (80–160 cm³ under STP) is adsorbed on charcoal in the ampoule, which is measured in the well of HPGe gamma detector. Minimum detectable concentration (MDC) of ¹³³Xe for this technique is 0.008 mBq/m³, and it is the most sensitive method used today. The ¹³³Xe concentration in the atmospheric air of Cherepovets City varied in the monitoring period ranging from 0.09 to 2.5 mBq/m³. During the period of March 2007–30 July 2008, ¹³³Xe activity concentration in the atmospheric air of St. Petersburg changed from background values (0.2–0.3 mBq/m³) to 185 mBq/m³ and for approximately 20% of the samples ¹³⁵Xe was also measured with the ¹³⁵Xe/¹³³Xe activity ratio varied within the range of 0.03–3.5.  相似文献   

Coupling SEBAL with a new radiation module and MODIS products for better estimation of evapotranspiration     

Chaolei Zheng  Pingheng Li 《水文科学杂志》2013,58(8):1535-1547

ABSTRACT

Evapotranspiration (ET) is an important ecohydrological process especially in arid and semi-arid regions. In this study, a new radiation module based on MODIS data has been coupled with the Surface Energy Balance Algorithms for Land (SEBAL) to better estimate ET. The accuracies of the coupled model for estimating available energy and sensible heat (H) were improved significantly compared with the outputs from the original SEBAL which was based on empirical equations. The coupled SEBAL modelled instantaneous λET agreed much better with observations in the arid land of Central Asia than the original SEBAL, with a bias of ?2.86 W m^-2, root mean square error (RMSE) of 9.75 W m^-2, and normalized RMSE (NRMSE) of 0.13. The accuracy was blurred when scaling ET to a daily or monthly scale, mainly due to the uncertainties associated with temporal upscaling methods that were applied. Sensitivity analysis, which was conducted using numerical variance-based techniques, indicated that the estimated ET is sensitive to the available energy, suggesting the importance of obtaining accurate estimates of net radiation when applying the coupled SEBAL to estimate ET. This study provides a simple and reliable way to utilize MODIS products and contains sensitivity analysis for helping to correctly interpret the outputs, which are both important for large-scale ET estimation.  相似文献   

Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches     

《水文科学杂志》2012,57(15):1824-1842

ABSTRACT

In this research, five hybrid novel machine learning approaches, artificial neural network (ANN)-embedded grey wolf optimizer (ANN-GWO), multi-verse optimizer (ANN-MVO), particle swarm optimizer (ANN-PSO), whale optimization algorithm (ANN-WOA) and ant lion optimizer (ANN-ALO), were applied for modelling monthly reference evapotranspiration (ET_o) at Ranichauri (India) and Dar El Beida (Algeria) stations. The estimates yielded by hybrid machine learning models were compared against three models, Valiantzas-1, 2 and 3 based on root mean square error (RMSE), Nash-Sutcliffe efficiency (NSE), Pearson correlation coefficient (PCC) and Willmott index (WI). The results of comparison show that the ANN-GWO-1 model with five input variables (T_min, T_max, RH, U_s, R_s) provides better estimates at both study stations (RMSE = 0.0592/0.0808, NSE = 0.9972/0.9956, PCC = 0.9986/0.9978, and WI = 0.9993/0.9989). Also, the adopted modelling strategy can build a truthful expert intelligent system for estimating the monthly ET_o at study stations.  相似文献   

Implications of 2007’s Earthquake Activity in Eğirdir Lake (SW Anatolia) Based on Moment Tensor Solutions and Inversion of Stress State   总被引：1，自引：0，他引：1  

Selda Altuncu Poyraz  Alİ Pinar  Süha Özden  Mustafa Kemal Tunçer 《Pure and Applied Geophysics》2014,171(7):1299-1309

We analyzed the waveforms of the small- to moderate-sized earthquakes that took place in the northern part of the inner Isparta Angle (IA) to retrieve their source parameters and combine these results with the focal mechanism solutions of the larger events that occurred in 2007 in E?irdir Lake at the apex of IA. In total, source mechanisms of 20 earthquakes within the magnitude range 3.5 < M < 5.0 were calculated using a regional moment tensor inversion technique. The inversion of the focal mechanisms yields an extensional regime with a NNE–SSW (N38°E) trending σ ₃ axis. Inversion results are related to a mainly WNW–ESE oriented normal fault beneath E?irdir Lake. The R value of a NNE–SSW extensional regime is 0.562 showing a triaxial stress state in the region. The current stress regime results from complex subduction processes such as slab pull, slab break-off, roll-back and/or retreating mechanism along the Hellenic and Cyprus arcs and the southwestward extrusion of the Anatolian block since the early Pliocene.  相似文献   

Performance evaluation of satellite-based approaches for the estimation of daily air temperature and reference evapotranspiration     

H. R. Shwetha 《水文科学杂志》2018,63(9):1347-1367

Different satellite-based radiation (Makkink) and temperature (Hargreaves-Samani, Penman-Monteith temperature, PMT) reference evapotranspiration (ET_o) models were compared with the FAO56-PM method over the Cauvery basin, India. Maximum air temperature (T_max) required in the ET_o models was estimated using the temperature–vegetation index (TVX) and an advanced statistical approach (ASA), and evaluated with observed T_max obtained from automatic weather stations. Minimum air temperature (T_min) was estimated using ASA. Land surface temperature was employed in the ET_o models in place of air temperature (T_a) to check the potency of its applicability. The results suggest that the PMT model with T_a as input performed better than the other ET_o models, with correlation coefficient (r), averaged root mean square error (RMSE) and mean bias error (MBE) of 0.77, 0.80 mm d^?1 and ?0.69 for all land cover classes. The ASA yielded better T_max and T_min values (r and RMSE of 0.87 and 2.17°C, and 0.87 and 2.27°C, respectively).  相似文献   

Data-based modelling approach for variable density flow and solute transport simulation in a coastal aquifer   总被引：1，自引：1，他引：0  

Basant Yadav  Shashi Mathur  Sudheer Ch  Brijesh Kumar Yadav 《水文科学杂志》2018,63(2):210-226

Data-based models, namely artificial neural network (ANN), support vector machine (SVM), genetic programming (GP) and extreme learning machine (ELM), were developed to approximate three-dimensional, density-dependent flow and transport processes in a coastal aquifer. A simulation model, SEAWAT, was used to generate data required for the training and testing of the data-based models. Statistical analysis of the simulation results obtained by the four models show that the data-based models could simulate the complex salt water intrusion process successfully. The selected models were also compared based on their computational ability, and the results show that the ELM is the fastest technique, taking just 0.5 s to simulate the dataset; however, the SVM is the most accurate, with a Nash-Sutcliffe efficiency (NSE) ≥ 0.95 and correlation coefficient R ≥ 0.92 for all the wells. The root mean square error (RMSE) for the SVM is also significantly less, ranging from 12.28 to 77.61 mg/L.  相似文献   

Implementation of a hybrid MLP-FFA model for water level prediction of Lake Egirdir,Turkey     

Mohammad?Ali?GhorbaniEmail author  Ravinesh?C.?Deo  Vahid?Karimi  Zaher?Mundher?Yaseen  Ozlem?Terzi 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(6):1683-1697

The predictive ability of a hybrid model integrating the Firefly Algorithm (FFA), as a heuristic optimization tool with the Multilayer Perceptron (MLP-FFA) algorithm for the prediction of water level in Lake Egirdir, Turkey, is investigated. The accuracy of the hybrid MLP-FFA model is then evaluated against the standalone MLP-based model developed with the Levenberg–Marquadt optimization scheme applied for in the backpropagation-based learning process. To develop and investigate the veracity of the proposed hybrid MLP-FFA model, monthly time scale water level data for 56 years (1961–2016) are applied to train and test the hybrid model. The input combinations of the standalone and the hybrid predictive models are determined in accordance with the Average Mutual Information computed from the historical water level (training) data; generating four statistically significant lagged combinations of historical data to be adopted for the 1-month forecasting of lake water level. The proposed hybrid MLP-FFA model is evaluated with statistical score metrics: Nash–Sutcliffe efficiency, root mean square and mean absolute error, Wilmott’s Index and Taylor diagram developed in the testing phase. The analysis of the results showed that the hybrid MLP–FFA4 model (where 4 months of lagged combinations of lake water level data are utilized) performed more accurately than the standalone MLP4 model. For the fully optimized hybrid (MLP-FFA4) model evaluated in the testing phase, the Willmott’s Index was approximately 0.999 relative to 0.988 (MLP 4) and the root mean square error was approximately 0.029 m and compared to 0.102 m. Moreover, the inter-comparison of the forecasted and the observed data with various other performance metrics (including the Taylor diagram) verified the robustness of the proposed hybrid MLP-FFA4 model over the standalone MLP4 model applied in the problem of forecasting lake water level prediction in the current semi-arid region in Turkey.  相似文献   

Estimating growing-season root zone soil moisture from vegetation index-based evapotranspiration fraction and soil properties in the Northwest Mountain region,USA     

Nawa Raj Pradhan 《水文科学杂志》2019,64(7):771-788

A soil moisture retrieval method is proposed, in the absence of ground-based auxiliary measurements, by deriving the soil moisture content relationship from the satellite vegetation index-based evapotranspiration fraction and soil moisture physical properties of a soil type. A temperature–vegetation dryness index threshold value is also proposed to identify water bodies and underlying saturated areas. Verification of the retrieved growing season soil moisture was performed by comparative analysis of soil moisture obtained by observed conventional in situ point measurements at the 239-km² Reynolds Creek Experimental Watershed, Idaho, USA (2006–2009), and at the US Climate Reference Network (USCRN) soil moisture measurement sites in Sundance, Wyoming (2012–2015), and Lewistown, Montana (2014–2015). The proposed method best represented the effective root zone soil moisture condition, at a depth between 50 and 100 cm, with an overall average R² value of 0.72 and average root mean square error (RMSE) of 0.042.  相似文献   

Test of statistical means for the extrapolation of soil depth point information using overlays of spatial environmental data and bootstrapping techniques     

Helen E. Dahlke  Thorsten Behrens  Jan Seibert  Lotta Andersson 《水文研究》2009,23(21):3017-3029

Hydrological modelling depends highly on the accuracy and uncertainty of model input parameters such as soil properties. Since most of these data are field surveyed, geostatistical techniques such as kriging, classification and regression trees or more sophisticated soil‐landscape models need to be applied to interpolate point information to the area. Most of the existing interpolation techniques require a random or regular distribution of points within the study area but are not adequate to satisfactorily interpolate soil catena or transect data. The soil landscape model presented in this study is predicting soil information from transect or catena point data using a statistical mean (arithmetic, geometric and harmonic mean) to calculate the soil information based on class means of merged spatial explanatory variables. A data set of 226 soil depth measurements covering a range of 0–6·5 m was used to test the model. The point data were sampled along four transects in the Stubbetorp catchment, SE‐Sweden. We overlaid a geomorphology map (8 classes) with digital elevation model‐derived topographic index maps (2–9 classes) to estimate the range of error the model produces with changing sample size and input maps. The accuracy of the soil depth predictions was estimated with the root mean square error (RMSE) based on a testing and training data set. RMSE ranged generally between 0·73 and 0·83 m ± 0·013 m depending on the amount of classes the merged layers had, but were smallest for a map combination with a low number of classes predicted with the harmonic mean (RMSE = 0·46 m). The results show that the prediction accuracy of this method depends on the number of point values in the sample, the value range of the measured attribute and the initial correlations between point values and explanatory variables, but suggests that the model approach is in general scale invariant. Copyright © 2009 John Wiley & Sons, Ltd.  相似文献   

Storage-yield analysis of surface water reservoirs: the role of reliability constraints and operating policies     

S. Jamshid Mousavi  Hosein Alizadeh  Kumaraswamy Ponnambalam 《Stochastic Environmental Research and Risk Assessment (SERRA)》2014,28(8):2051-2061

Stochastic optimization methods are used for optimal design and operation of surface water reservoir systems under uncertainty. Chance-constrained (CC) optimization with linear decision rules (LDRs) is an old approach for determining the minimum reservoir capacity required to meet a specific yield at a target level of reliability. However, this approach has been found to overestimate the reservoir capacity. In this paper, we propose the reason for this overestimation to be the fact that the reliability constraints considered in standard CC LDR models do not have the same meaning as in other models such as reservoir operation simulation models. The simulation models have fulfilled a target reliability level in an average sense (i.e., annually), whereas the standard CC LDR models have met the target reliability level every season of the year. Mixed integer nonlinear programs are presented to clarify the distinction between the two types of reliability constraints and demonstrate that the use of seasonal reliability constraints, rather than an average reliability constraint, leads to 80–150 % and 0–32 % excess capacity for SQ-type and S-type CC LDR models, respectively. Additionally, a modified CC LDR model with an average reliability constraint is proposed to overcome the reservoir capacity overestimation problem. In the second stage, we evaluate different operating policies and show that for the seasonal (average) reliability constraints, open-loop, S-type, standard operating policy, SQ-type, and general SQ-type policies compared to a model not using any operation rule lead to 190–460 % (200–550 %), 100–200 % (80–300 %), 0–90 % (0–60 %), 30–90 % (0–20 %), and 10–90 % (0–10 %) excess capacity, respectively.  相似文献   

Regression models for daily stream temperature simulation: case studies for the river Elbe,Germany     

Hagen Koch  Uwe Grünewald 《水文研究》2010,24(26):3826-3836

Daily stream temperatures are needed in a number of analyses. Such analyses might focus on aquatic organisms or industrial activities. To protect aquatic systems, industrial activities, for example, water withdrawals or discharges, are sometimes restricted. To evaluate where new industrial settings should be placed or if climate change will affect already existing industrial settings, the simulation of stream temperature is needed. Stream temperature models with weekly or monthly time scale might not be sufficient for this kind of analysis. Different regression models to simulate daily stream temperature for the river Elbe (Germany) are developed and their performance is estimated. For the calibration period the Nash–Sutcliffe coefficient (NSC) for the simplest model is 0·97, and the root mean squared error (RMSE) is 1·48 °C. For the most sophisticated model the NSC also is 0·97. However, the RMSE is 1·32 °C. For the validation period the NSC for the simplest model is 0·96, and the RMSE is 1·45 °C. The NSC for the most sophisticated model is 0·97, and the RMSE is 1·25 °C. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

Water quality variations in different climates of Iran: toward modeling total dissolved solid using soft computing techniques     

Majid?Montaseri  Sarvin?Zaman Zad GhavidelEmail author  Hadi?Sanikhani 《Stochastic Environmental Research and Risk Assessment (SERRA)》2018,32(8):2253-2273

In present paper, wavelet analysis of total dissolved solid that monitored at Nazlu Chay (northwest of Iran), Tajan (north of Iran), Zayandeh Rud (central of Iran) and Helleh (south of Iran) basins with various climatic conditions, have been studied. Daubechies wavelet at suitable level (db4) has been calculated for TDS of each selected basins. The performance of artificial neural networks (ANN), two different adaptive-neurofuzzy inference system (ANFIS) including ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), gene expression programming (GEP), wavelet-ANN, wavelet-ANFIS and wavelet-GEP in predicting TDS of mentioned basins were assessed over a period of 20 years at twelve different hydrometric stations. EC (μmhos/cm), Na (meq L^?1) and Cl (meq L^?1) parameters were selected (based on Pearson correlation) as input variables to forecast amount of TDS in four studied basins. To develop hybrid wavelet-AI models, the original observed data series was decomposed into sub-time series using Daubechies wavelets at suitable level for each basin. Based on the statistical criteria of correlation coefficient (R), root mean square error (RMSE) and mean absolute error (MAE), the hybrid wavelet-AI models performance were better than single AI models in all basins. A comparison was made between these artificial intelligence approaches which emphasized the superiority of wavelet-GEP over the other intelligent models with amount of RMSE 18.978, 6.774, 9.639 and 318.363 mg/l, in Nazlu Chay, Tajan, Zayandeh Rud and Helleh basins, respectively.  相似文献   

Water temperature modelling: comparison between the generalized additive model,logistic, residuals regression and linear regression models     

Fatima Laanaya  André St-Hilaire  Erwan Gloaguen 《水文科学杂志》2017,62(7):1078-1093

Water temperature has a significant influence on aquatic organisms, including stenotherm fish such as salmonids. It is thus of prime importance to build reliable tools to forecast water temperature. This study evaluated a statistical scheme to model average water temperature based on daily average air temperature and average discharge at the Sainte-Marguerite River, Northern Canada. The aim was to test a non-parametric water temperature generalized additive model (GAM) and to compare its performance to three previously developed approaches: the logistic, residuals regression and linear regression models. Due to its flexibility, the GAM was able to capture some of the nonlinear response between water temperature and the two explanatory variables (air temperature and flow). The shape of these effects was determined by the trends shown in the collected data. The four models were evaluated annually using a cross-validation technique. Three comparison criteria were calculated: the root mean square error (RMSE), the bias error and the Nash-Sutcliffe coefficient of efficiency (NSC). The goodness of fit of the four models was also compared graphically. The GAM was the best among the four models (RMSE = 1.44°C, bias = ?0.04 and NSC = 0.94).  相似文献   

Topographic accuracy assessment of bare earth lidar-derived unstructured meshes     

《Advances in water resources》2013

This study is focused on the integration of bare earth lidar (Light Detection and Ranging) data into unstructured (triangular) finite element meshes and the implications on simulating storm surge inundation using a shallow water equations model. A methodology is developed to compute root mean square error (RMSE) and the 95th percentile of vertical elevation errors using four different interpolation methods (linear, inverse distance weighted, natural neighbor, and cell averaging) to resample bare earth lidar and lidar-derived digital elevation models (DEMs) onto unstructured meshes at different resolutions. The results are consolidated into a table of optimal interpolation methods that minimize the vertical elevation error of an unstructured mesh for a given mesh node density. The cell area averaging method performed most accurate when DEM grid cells within 0.25 times the ratio of local element size and DEM cell size were averaged. The methodology is applied to simulate inundation extent and maximum water levels in southern Mississippi due to Hurricane Katrina, which illustrates that local changes in topography such as adjusting element size and interpolation method drastically alter simulated storm surge locally and non-locally. The methods and results presented have utility and implications to any modeling application that uses bare earth lidar.  相似文献   

Effects of long-term wastewater irrigation on soil physical properties and performance of selected infiltration models in a semi-arid region     

Soheil Razzaghi  Shoja Ghorbani Dashtaki 《水文科学杂志》2013,58(10):1778-1790

ABSTRACT

In many arid and semi-arid countries, wastewater irrigation is becoming a common practice in agriculture. In this study, the effect of long-term (40 years) wastewater irrigation on selected physical and hydraulic properties of soil in different parts of a landscape was investigated. The performance of some infiltration models, including Philip (Ph), Kostiakov (Kos), Kostiakov-Lewis (Kos-L), Horton (Ho), Huggins and Monke (Hug-M), and linear and nonlinear Smith-Parlange (S-P(L) and S-P(NL)), was compared. This study was performed in the Urmia region, Iran, where flooding wastewater irrigation has been practised for at least 40 years. Five paired sites, each of which contained a measurement location at the wastewater-irrigated (WWI) and adjacent control area were studied. Accuracy of the infiltration models was evaluated using several statistical criteria, including root mean square error (RMSE) and Akaike information criterion (AIC). The models were classified into groups using cluster analysis based on level of similarity in their performance. The cumulative water infiltration into soils after 1 h (I_1h) was calculated using the selected most accurate models and introduced so as to use only one term to compare the infiltration behaviour of soils. Based on RMSE and AIC, the performance of the Ph, Ho, Kos and Kos-L models was considerably better than that of Hug-M, S-P(L) and S-P(NL). The ranking of the models in terms of their AIC values was: Kos-L > Ho > Kos > Ph > S-P(L) > Hug-M > S-P(NL). The models were classified into two distinct groups. The similarity among Ph, Ho, Kos and Kos-L models was more than 80% and for Hug-M, S-P(L), and S-P(NL) models, it was more than 79%. However, the similarity between these two groups of models was less than 58%.