JWSS - Journal of Water and Soil Science

In this study, drought characteristics of Arak, Bandar Anzali, Tabriz, Tehran, Rasht, Zahedan, Shiraz and Kerman stations during the statistical period of 1956 to 2015 were studied by Reconnaissance Drought Index (RDI) and Standardized Precipitation Index. Precipitation and temperature data were needed to calculate RDI. Precipitation data was also required to estimate SPI. In this study, Drinc software was used to calculate RDI, SPI and potential evapotranspiration (PET). The software calculated PET by the Thornthwaite method. One of the main challenges in drought monitoring is to determine the indicator that has a high reliability based on its monitoring purpose. Therefore, in this research, two methods used for selecting the appropriate index based on the minimum rainfall and normal distribution were evaluated. The results of the evaluation of the minimum rainfall method for selecting the appropriate index showed that most drought indices with the occurrence of minimum rainfall level indicated severe or very severe drought situations; in most cases, it could not lead to selecting an exact and unique index. Based on the results of the normal distribution method for the stations of Arak, Tabriz, Rasht, Zahedan, Shiraz and Kerman, SPI index, and for the stations of Bandar Anzali and Tehran, RDI index were selected as the most appropriate ones.

The current study was conducted to compute SPI and SPET drought indices due to their multi-scale concept and their ability to analyze different time-scales for selected meteorological stations in Karoon Basin. Regionalization of SPI and SPEI Drought indices based on clustering analysis was another aim of this study for hydrological homogenizing. Accordingly, to run test through data and determine similar statistical periods, 18 stations were selected. SPI and SPEI values were plotted in the sequence periods graphs and their relationships were analyzed using the correlation coefficient. The results were compared by Pearson correlation coefficient at the significance level of 0.01. The results showed that correlation coefficients (0.5-0.95) were positive and meaningful for all stations and the correlation coefficient between the two indices were increased by enhancing the time-scales. Also, time-scales enhancement decreased the frequency of dry and wet periods and increased their duration. Through regionalization of basin stations based on clustering analysis, the stations were classified into 7 classes. The results of SPEI regionalization showed that the frequency percentage of the normal class was more than those of dry and wet classes.

Exactly estimating of water requirement is essential for water balance studies, design and management of irrigation systems and water resources management. Because of limited soil and water resources in Iran, for optimal use of water resources in the agricultural sector, it is necessary to determine the amount of water requirement by different plants in different climatological conditions. In order to determine the water requirement and crop coefficients of Stevia, six lysimeter numbers were used in three replications for stevia and reference plant (grass). The reference Stevia plant evapotranspiration was measured on a daily basis. The results showed that during the 537 day period of Stevia cultivation, the maximum and minimum water requirement in the first and second year of cultivation was respectively 9.85 and 1.69 mm per day, and for the reference plant was obtained as 6.54 and 1.84 mm per day. In this study, the Kc coefficients in initial, development, intermediate and final stages of growth in 2016 were 0.76, 1.11, 1.46 and 1.05 and in 2017 at growth stages, were 0.76, 1.18, 1.52 and 1.29 respectively. The average of individual Stevia plant growth factors for four growth stages in two years of research was obtained as 0.76, 1.15, 1.49, and 1.17, respectively.

In recent years, a lot of research has been done on the Aquacrop model, the results show that this model simulates the product performance for deficit irrigation conditions. But this model, like other models, is sensitive to values of independent variables (model inputs). In this research, the sensitivity of the Aquacrop model was analyzed for 4 input parameters of reference evapotranspiration, normalized water productivity, initial canopy cover percentage and maximum canopy cover for barley. Irrigation treatments included full irrigation and two deficit irrigation treatments of 80% and 60%, the experiment was done in 2014-15 growing season in the field of Abourihan College. The values of measured biomass were used as the base values for treatments. The Beven’s method (Beven et al., 1979) was used for sensitivity analysis of Aquacrop model. The results showed that the model is most sensitive to the reference crop evapotranspiration, So the sensitivity coefficient for this parameter for full irrigation treatments, 80% full irrigation and 60% full irrigation were -1.1, -1.2 and -2.3 respectively. The negative sign indicates that if the value of reference evapotranspiration input is exceeded the actual value into the model, Yield performance is simulated less than actual value. In the meantime, the higher the degree of deficit irrigation, the greater the sensitivity of the model.

Due to the impact of climate change on the plant water demand and the availability of water, especially in drylands, it is vital to estimate the evapotranspiration rates accurately. In this study, the vegetation status in the marginal desert areas of Varamin Plain was studied, and the actual evapotranspiration and water demand of intercropped farms were assessed. This study also evaluated the potential relationship between the evapotranspiration of different agricultural lands and their vegetation index using remote sensing techniques. A collection of satellite images from Landsat 7 in consecutive seasons was used to determine the greenness rate of marginal desert areas during 2013 and 2014. ENVI software was used for the image processing, which included geometric corrections and atmospheric corrections, to develop NDVI maps. Also, weather data and crop properties of Varamin Plain were collected, and the actual evapotranspiration rate of plant cover was estimated using CropWat. The correlation between NDVI extracted from satellite images and the evaluated evapotranspiration rate was assessed. The results showed a strong relationship between evapotranspiration of heterogeneous agricultural lands and NDVI. This confirmed that the NDVI derived by remote sensing approach could be a useful index to evaluate vegetation status and water demand of farmlands in the desert borders.

Due to the limited water resources and growing population, food security and environmental protection have become a global problem. Increasing water productivity of agricultural products is one of the main solutions to cope with the difficulties. By optimizing applied water and nitrogen fertilizer, the pollution of groundwater could be deceased and the water productivity could be increased. The aim of this research was to determine the relationships between water productivity (IRWP) and water use efficiency (WUE) and different amounts of applied water (irrigation + rain fed) and nitrogen (applied and residual). This study was conducted on wheat (Triticum aestivum L., cv. Shiraz) in Shiraz University School of Agriculture, based on a split-plot design with three replications, in 2009-2010 and 2010-2011 periods. Irrigation treatments varied from zero to 120% of full irrigation depth, and nitrogen fertilizer treatments varied from zero to 138 kg ha^-1 under basin irrigation system. The experimental data of the first and second years were used for the calibration and validation of the proposed relationships, respectively. The calibrated equations using the dimensionless ratios of irrigation depth plus rainfall, actual evapotranspiration and nitrogen fertilizer plus soil residual nitrogen to their amounts in full irrigation and maximum fertilizer amounts were appropriate for the estimation of water productivity and water use efficiency. The values of the determination coefficient (R²) for water productivity and water use efficiency (0.88 and 0.93, respectively), and the values of their normalized root mean square error (NRMSE) (0.2 and 0.13, respectively) showed a good accuracy for the estimation of IRWP and WUE.

Agricultural water management studies require accurate information on actual evapotranspiration. This information must have sufficient spatial detail to allow analysis on the farm or basin level. The methods used to estimate evapotranspiration are grouped into two main groups, which include direct methods and indirect or computational methods. Basics of the indirect methods are based on the relationship between meteorological parameters, which impedes the use of these data with a lack or impairment. On the other hand, this information is a point specific to meteorological stations, and their regional estimates are another problem of uncertainty of their own. To this end, the use of remote sensing technology can be a suitable approach to address these constraints. Real evapotranspiration can be estimated by satellite imagery that has short and long wavelengths and is estimated using surface energy equations. Examples of such algorithms include SEBAL, METRIC, SEBS. Among the above mentioned algorithms, SEBAL and SEBS have been used. Among the factors of superiority of the SEBAL and SEBS algorithms, in comparison with other remote sensing algorithms, is a satellite imagery analysis algorithm based on physical principles and uses satellite simulation and requires minimum meteorological information from ground measurements or air models. 

In order to study and evaluate the drought stress indices in surface irrigation by furrow method on grain yield, the yield components and water use efficiency, an experiment was conducted at Behbahan Agricultural Research Station in 2014-16. The experiment was conducted as a split plot in a randomized complete block design with 4 replications. Irrigation at two levels (irrigation after 100 and 200 mm evaporation from Class A pan, respectively) was evaluated as the main factor and corn cultivar was considered at 6 levels as the sub-factor. Comparison of the  mean water use efficiency in irrigation and cultivar interactions showed 100 mm evaporation from Class A pan and cultivars V4 (PH1), V5 (PH3) and V2 (SC Mobin) were ranked the first and foremost, respectively, with the  yields of 1.353, 1.299 and 1.296 kg of corn per kg of water consumed, respectively. The mean water consumed in 2014 of the experiment in 100 and 200 mm evaporation from Class A pan was 521.2 and 462.4 mm, respectively. Pearson correlation coefficient results  also showed that with increasing the  yield components, such as the  number of grains per row and number of rows, the  1000-grain weight was  increased due to  the highly significant correlation coefficient of 1000-grain weight with grain yield (r = 0.8776).  Consequently, grain yield was also increased. The highest values of SSI, STI, MP, TOL, GMP HM and YI indices were calculated in V4 (PH1). The higher values of the above indices in cultivar V4 (PH1) than other cultivars caused this treatment to be introduced as the superior one. The decreasing trend of corn yield, which was caused by water deficit stress, increased SSI, STI, MP, TOL, GMP and YI indices, while it decreased corn yield, leading to incremental changes in the YSI indices.

The upcoming climate change has become a serious concern for the human society. These changes, caused and aggravated by the industrial activities of the international community and the increase in the concentration of greenhouse gases in the atmosphere, are seen as a threat to the food security and environment. Temperature change and precipitation are studied in the form of different probabilistic scenarios in order to have an outlook for the future. The present study was conducted to address the effects of climate changes on temperature and precipitation in Qazvin plain in the form of five AOGCMs including Hadcm3, CSIRO-MK3, GFDL, CGCM3 and MICROC3.2, and 3 greenhouse gas emission scenarios of A1B, A2 and B1, based on different possible scenario combinations in the next 30 years, 2021-2050 and 2051-2080 (near and far future). On basis of the study results, all 4 target stations, on average, will have experienced a change between two ratios of 0.5 and 1.4 of  the observed precipitation period  by the end of 2050, and the mean temperature will have had a change  between -0.1 to 1.6 °C, relative to the observed period.  By the end of 2080,  the  precipitation will also have fluctuated between the two proportions of 0.5 and 1.7 times of the observed precipitation period and the mean temperature will touch an increase between 0.6 and 2.6 °C. Both SPI and SPEI indices suggest the increment in the number of dry periods in the near and far future. However, the total number of negative sequences differed considering the 3, 12 and 24-month intervals at the stations level. Given the SPEI index, as compared to the base period, the total negative sequences of drought and number of dry periods will increase at 3 stations of Avaj, Bagh-Kowsar and Shahid-rajaei-powerhouse and decrease at Qazvin station in the future; however, SPI gives different results, such that  for Bagh-Kowsar, there will be an increase in both total negative sequences of drought and number of dry periods, as  compared to the baseline period; three other stations will have more dry periods, specifically, but less total negative sequences. The results reported that the drought events would become severe, and the wet events would become extreme in the future.

The FAO Penman-Monteith is a baseline method to estimate reference evapotranspiration. In many cases, it is difficult to access all data, so replacing simpler models with ‎lower input data and appropriate accuracy is necessary. ‎ The purpose of this study is to investigate the capability of the experimental ‎models, gene expression programming, stepwise regression, and Bayesian network in estimating ‎reference evapotranspiration.‎ In this research, daily information of the Boroujerd synoptic station in the period of 1996 -2017 was used as model inputs. ‎Based on the correlation between input and output parameters, six input patterns were ‎determined for modeling. The results showed that the Kimberly-Penman model has the ‎best performance among the experimental models.‎ Gene expression programming with fourth pattern ‎‎and Default Model Operators (R² = 0.98 and RMSE = 0.9), Bayesian Network with sixth pattern (R²=0.91 and RMSE = 1.01), and stepwise regression with sixth pattern have the most accurate patterns at R² = 0.91 and RMSE = 0.9 in the ‎training stage.‎ Comparison of the performance of the three models showed that the gene expression ‎programming model was superior to the other two models with the Average Absolute Relative Error (AARE) of 0.12 and the Mean Ratio (MR) of 0.94.‎ The results showed that gene expression programming had an acceptable ability to estimate ‎reference evapotranspiration under the weather conditions of Boroujerd and could be introduced as a ‎suitable model.‎

Drought represents one of the most critical natural disasters, exerting profound impacts on agriculture, society, the economy, and water resources. Various indices are used to monitor drought and its effects. This study aims to monitor drought in the Zayandeh-Rud Basin using the Standardized Precipitation Index (SPI), the Standardized Precipitation-Evapotranspiration Index (SPEI), the Evaporative Demand Drought Index (EDDI), the Palmer Drought Severity Index (PDSI), and the Reconnaissance Drought Index (RDI). All these indices are based on potential evapotranspiration, incorporating parameters such as precipitation, temperature, relative humidity, wind speed, and sunshine duration. These five indices were calculated and evaluated during the statistical period of 1993–2023 for meteorological stations in Isfahan, East Isfahan, Kabootarabad, Daran, Shahreza, Najafabad, and Mobarakeh. After calculating the indices and using spatial zoning maps, the studied stations were compared in terms of these indices. The continuity of dry and wet periods, as well as the intensity of droughts and wet spells, was analyzed. Subsequently, drought intensities during different years in these stations were ranked using the TOPSIS model based on factors such as precipitation, potential evapotranspiration, and station elevation. The results showed that in stations with a dry climate (such as Isfahan, East Isfahan, and Shahreza), drought occurrences (as indicated by higher rankings) have been consecutive over multiple years. Comparing the performance of the indices in the studied stations using spatial zoning maps revealed that the intensity of droughts and wet spells in regions with dry and semi-dry climates was not very significant. However, in areas with humid climates, the fluctuations in drought and wet spell intensities were quite substantial. The findings indicate that the PDSI and EDDI indices are more suitable for evaluating drought in dry climates.

Climate change significantly affects the water use efficiency (WUE) and yield of field crops. This study evaluates the impacts of climate change on biological yield, grain yield, water consumption, and WUE of two barley genotypes, Goharan and Reyhan 03, under autumn and spring planting regimes using the CERES-Barley model within the Decision Support System for Agrotechnology Transfer (DSSAT) software. Data provided for model calibration and validation were sourced from the field experiments conducted at the Isfahan University of Technology research farm located in Najafabad, Iran. Meteorological data for the period of 2003 to 2016 were obtained from the Najafabad weather station, while future climate projections for 2020–2050 were generated using the MarkSim weather generator under the Representative Concentration Pathway (RCP) 8.5 scenario. Planting dates were analyzed within a ±35-day window relative to baseline dates of October 22 for autumn and March 3 for spring. The model demonstrated high accuracy in calibrating key traits, including days to anthesis, days to maturity, leaf area index, grain yield, and biological yield. Elevated temperatures associated with climate change reduced grain and biological yields across both planting seasons, with biological yield exhibiting a more pronounced decline, particularly under spring planting. During the 2040–2050 period, water consumption peaked at 387.5 mm for Goharan in autumn planting, while spring planting recorded a minimum of 239 mm for Reyhan 03. Delaying autumn planting by 20–25 days enhanced WUE, while planting earlier in the spring )10–20 days (improved WUE by exploiting cooler temperatures. Evapotranspiration increased by 399 mm in autumn but decreased by 267 mm in spring. The earlier-maturing Reyhan 03 genotype demonstrated smaller yield losses in spring planting due to climate change. The findings of this study suggest that programmed adjustments to planting dates may mitigate the adverse impacts of climate change on barley production, thereby enhancing sustainability.