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Using simulation models is a key strategy in agricultural water management and an effective way to predict the impact of irrigation management and water quality on crop yield. This study aimed to evaluate the performance of the SWAP and AquaCrop models in simulating the growth and biomass production of three maize varieties under the conditions of using saline water for irrigation with a drip irrigation system at the research farm of Tehran University of Agriculture and Natural Resources Campus. In order to calibrate and validate the models, field data obtained from a factorial experiment with two factors of maize variety (SC704, 400, and 260) and irrigation water salinity (0.7, 3, and 5 dS/m) were used. In the validation stage of the AquaCrop model, the R², RMSE, and NRMSE statistics for the canopy cover (CC) showed a high level of agreement between the measured and simulated data, with values of 0.953, 5.69, and 8% respectively. For the SWAP model, the calculated statistics for the leaf area index (LAI) were 0.477, 1.610, and 54.2%, respectively. Despite the poor performance of the SWAP model in estimating LAI, both the SWAP and AquaCrop models effectively simulated the biomass of maize cultivars under different treatments. In the calibration and validation stage, RMSE and NRMSE of both models were less than 0.5 ton/ha, with 3% (calibration) and 1 ton/ha, 7% (validation), respectively. In general, both models can be used in various studies for different maize cultivars under irrigation water and soil salinity conditions. [ABSTRACT FROM AUTHOR]

Abstract Introduction: Rainfall-runoff modeling is considered one of the important methods in the study of hydrology and environmental management, especially in urban areas, in which sudden floods lead to significant financial and human losses. Thereupon, various models have been developed for urban flood simulation, among which selecting the best, is of significant consideration in the literature. Methods: Runoff peak flow and hydroghs measured in three nodes of an urban drainage network were applied for both spatial and temporal evaluation of the SWMM hydrological model in EPA SWMM, ASSA and Bently SewerGEMS V8i softwares. Other rainfall-runoff models also evaluated were SCS TR-55, SCS TR-20, Rational, Dekalb Rational, Santabarbara UH models (in ASSA software) and SCS UH model (in SewerGEMS V8i software). The models were calibrated considering the measurements during three precipitation events, and then validated by the measured data of three other events. The Nash-Sutcliffe coefficient along with the BIAS coefficient, the Coefficient of Determination and the Root Mean Square Error were used as the efficiency indicators. Findings: The measured runoff peak flow and hydrograph were most compatible with those simulated by SWMM models of EPA SWMM, SewerGEMS V8i and ASSA softwares, respectively. Regarding the results of the partial parameter and the Spearman correlation coefficient methods, model outputs were most sensitive to the percentage of impervious areas, equivalent width, roughness coefficient of impervious areas, the depth of depression of impervious and pervious areas, the percentage of impervious areas without surface storage and the curve number, respectively. The model was not sensitive to the roughness of the permeable areas. The results suggest EPA SWMM as the software with more reliable simulation results for runoff management projects in the study area and urban basins alike.

Introduction Removing the gap between the yield that is currently obtained by farmers and the yield that can be obtained by using the best water, soil, and crop management practices (yield gap) is the key solution to overcoming the nutritional challenge of the growing world population. Wheat has played an important role in the economy and food security of the world over the history. Considering the importance of the wheat crop in food security and human feeding, as well as the importance of Lorestan province in producing wheat crop in Iran, the current research was carried out using the modeling method in order to estimate the yield gap of irrigated wheat agro-ecosystems of Lorestan province.Materials and MethodsThe current research was conducted in 6 locations in Lorestan province, Iran (Aleshtar, Aligudarz, Borujerd, Khorramabad, Kuhdasht, and Pol-e Dokhtar). To evaluate the APSIM-wheat model for the Chamran cultivar, some independent field experiments were used under different treatments, including planting date, irrigation, and nitrogen regimes. nRMSE, d-index, and MBE indices were used to evaluate the crop model. Then, the management, soil, and climate data of the studied locations were collected. To find the most optimal sowing date, an initial simulation experiment set was performed. After obtaining the optimal sowing date for different locations, the attainable yield (85% of the potential yield) was simulated. Finally, from the difference between attainable yield and actual yield, the total yield gap for the locations was obtained. Also, the contribution of different management practices, including nitrogen, irrigation, and other reducing and limiting factors, was calculated from the total yield gap. In the current research, OriginPro software was used for all statistical analysis and drawing of figures.Results and DiscussionThe results of the model calibration showed that the APSIM-wheat model was able to simulate the days to flowing, biomass, and grain yield accurately. The normalized root mean square error for days to flowering, biomass, and grain yield was 5.13, 5.29, and 7.87%, respectively. The model validation results of the model also showed that the model simulates the grain yield well (10.3%). In the initial simulation, the best production potential (8433 kg ha-1) was related to the October 15 sowing date. Attainable, water-limited, and nitrogen-limited yields were equal to 7179.2, 6302.7, and 4212.5 kg ha-1 in Lorestan province. Across different locations, the water-limited yield ranged from 1.4722 to 2.7448 kg ha-1, followed by attainable yield (from 6537.5 to 7982.7 kg ha-1) and nitrogen-limited (from 2.2 3850 to 4414.5 kg ha-1). The total yield gap of irrigated wheat in Lorestan province was equal to 4177.5 kg ha-1. The results also showed that, in general, throughout the studied locations and years, the contribution of nitrogen, water, and other reducing and limiting factors in Lorestan province was equal to 69, 22, and 10% of the total yield gap. There were many changes among different locations in terms of the total yield gap in Lorestan province so it varied from 2661.8 kg ha-1 (Pol-e Dokhtar) to 5608.4 kg ha-1 (Aligudarz). In terms of yield gap due to water limitation, the highest amount was related to Pol-e Dokhtar (31%), and the lowest value was related to Khorramabad city (14%). For the yield gap caused by nitrogen limitation, Aligudarz had the largest share (77%), while the lowest share of this limitation was obtained for Khorramabad (59%). The maximum share of the yield gap caused by other limiting and reducing factors was also recorded in Khorramabad (27%), while its lowest value was calculated in Aligudarz and Aleshtar (without restrictions).ConclusionIn general, the total yield gap of irrigated wheat in agro-ecosystems of Lorestan province is equal to 59%. Also, the results revealed that the largest share of the total yield gap is related to the lack of optimal application of nitrogen management (time of nitrogen application), and if the optimal management of nitrogen fertilizer is applied, the total yield gap will be significantly reduced at all studied locations in Lorestan province.