University of BirjandWater Harvesting Research2476-69767220240901Comparative Analysis of the Performance of Gridded Precipitation Products Over Iran175193304810.22077/jwhr.2024.7930.1146ENAli GorjizadehAssistant Professor, Department of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, IranAli MoridiAssociate Professor, Department of Water and Environmental Engineering, Faculty of Civil, Shahid Beheshti University, Tehran,Iran.Journal Article20240722Precipitation provides the most crucial input for hydrological modeling. Rainfall Estimation from rain gauges is the most common and traditional method have been used widely to measure rainfall at catchment scales. In many developing countries, a dense rain-gauge grid is generally unavailable, suffering from a sparse station distribution at high altitudes or in rural areas. Recent advances in remote sensing technologies have provided precipitation data with high spatial and temporal resolution. Accurate information on the benefits and deficits of these datasets is often lacking, especially over Iran. This study aims to provides a comprehensive evaluation of a good variety of state-of-the art precipitation datasets against 41 synoptic gauge observations, as a reference in the period of 2013 to 2020 over Iran. In particular, the performance of ERA5 as reanalysis, PERSIANN as satellite based, CHIRPS and PERSIANN-CDR as satellite-gauge precipitation products at daily, monthly and annual scale has been assessed. Statistical metrics, precipitation detection capability and false alarm ratio have been used to measure the accuracy of each product over spatial and time scales. The result show that over annual and daily scale PERSIANN-CDR product outperforms, and over daily scale PERSIANN-CDR and CHIRPS products perform well compared to ERA5 and PERSIANN products. The CHIRPS and PERSIANN-CDR products deliver reliable and useful ability of precipitation detection comparing to other products. https://jwhr.birjand.ac.ir/article_3048_4448ce3ffa48f3a9a817548d454c5320.pdfUniversity of BirjandWater Harvesting Research2476-69767220240901Comparative Analysis of Transfer Function Method with Advanced Flood Prediction Techniques194209305010.22077/jwhr.2024.7966.1147ENJafar ChabokpourAssociate Professor, Department of Civil Engineering, University of Maragheh, Maragheh, Iran.Journal Article20240726In this paper, the evaluation of the performance of five flood prediction models in the Simineh-Rood River, Lake Urmia basin, Iran, is discussed in detail. To this purpose, the performance of Transfer Function, Saint-Venant equations, Artificial Neural Network, Adaptive Neuro-Fuzzy Inference System, and Support Vector Machine models are evaluated for 2018 and 2019 flood data. Specifically, the models are rated according to their accuracy, computational efficiency, and robustness under different flow regimes and at various forecast times. This now leads to a maximum Nash-Sutcliffe Efficiency of 0.91 for the Saint-Venant equations during the 2019 flood event, followed by ANN with 0.89, ANFIS with 0.87, SVM with 0.85, and lastly, Transfer Function with 0.78. The same is the case for peak flow discharge, which was best predicted by the Saint-Venant model to be 193.80 m³/s while the observed value was 200.83 m³/s. This model maintained its consistency with respect to low, medium, and high flows, where the values of NSE were 0.89, 0.92, and 0.91, respectively. However, compared to the other models, which took 0.5–8 s, it had a much larger computational time, 120 s for a 72-h simulation. The sensitivity analysis returned variable model responses to the quality of the input data; an input variation of 20% reduced the NSE of the Saint-Venant model to 0.73 and that of the Transfer Function to 0.44. This study provides quantitative insight into the choice of flood prediction methods in a semi-arid region, with respect to required accuracy, computational resources, and forecast lead-time.https://jwhr.birjand.ac.ir/article_3050_9a4b2e52daf0b9557db51d6dcc765c4b.pdfUniversity of BirjandWater Harvesting Research2476-69767220240901Effects of Biochar, Foliar Application of Amino Acid, and Drought Stress on Physiological and Morphological Traits, Yield Componenets, and Water Use Efficiency in Spinacia Oleracea210219312510.22077/jwhr.2024.8139.1153ENMatineh RajabneiaM.Sc. Student, Faculty of Agriculture and Natural Resources, Gonbad Kavous University, Gonbad Kavous, Iran.Masumeh FarasatiAssociate Professor, Faculty of Agriculture and Natural Resources, Gonbad Kavous University, Gonbad Kavous, Iran.Ali Nakhzari MoghaddamAssistant Professor, Faculty of Agriculture and Natural Resources, Gonbad Kavous University, Gonbad Kavous, Iran.Ali HeshmatpourAssistant Professor, Faculty of Agriculture and Natural Resources, Gonbad Kavous University, Gonbad Kavous, Iran.Journal Article20240911In this research, attempts have been made to investigate the effects of drought stress, foliar application of amino acid and biochar fertilizer on the morphological traits of spinach subjected to the conditions of the research greenhouse of Gonbad Kavous University within the timerange of 2021-2022. The experiment was conducted as a factorial in the form of randomized complete blocks with 3 replications. The experiment factors included 5 levels of drought stress of 0, 10, 20, 30 and 40 percent Maximum allowable depletion(MAD), 2 levels of amino acid foliar application (0 and 2 Lit/ha) and 2 levels of biochar fertilizer (0 and 4% weight of soil). The results of data analysis variance indicated that the effect of drought stress is significant at a level of 1% on the length of the longest root (cm), length of the largest leaf (cm), chlorophyll (mg.g<sup>-1</sup> fresh leaf), total water use (mlit) and water use efficiency (g.lit<sup>-1</sup>). Also, the effect of amino acid on the total water use and that of biochar on the length of the largest leaf and chlorophyll content were significant at a level of 0.01. Moreover, the effects of biochar and amino acid in antioxidant (µg.ml<sup>-1</sup>) were significant at a level of 5%. A comparison of mean results showed that the highest value of the length of the largest leaf (cm), dry weight (gr), plant fresh weight (gr) is associated with the biochar application treatment. The highest amount of total use water was also found in the treatment without biochar. The highest and lowest amounts of chlorophyll content were found to be associated with the treatments with (14.52 mg.g<sup>-1</sup> fresh leaf) and without (11.92 mg.g<sup>-1</sup> leaf leaf) biochar application, respectively. Furthermore, the comparison of mean results showed that the highest amounts of chlorophyll, dry weight (gr) and plant fresh weight (gr) are corresponding to the amino acid application treatment.https://jwhr.birjand.ac.ir/article_3125_765cae066768545cb59cb615b5798be8.pdfUniversity of BirjandWater Harvesting Research2476-69767220240901Analysis of Trend and Detection of Change Points in Lake Urmia Level and Climatological Parameters Using R Software220233313010.22077/jwhr.2024.8075.1151ENHanieh MohammadiMSc Student, Department of Water Engineering, Urmia University, Urmia, Iran.Keivan KhaliliAssociate Professor, Department of Water Engineering, Urmia University, Urmia, Iran.Hossein RezaieProfessor, Department of Water Engineering, Urmia University, Urmia, Iran.Amin Amini-RakanPh.D, Department of Water Engineering, Urmia University, Urmia, IranJournal Article20240827In recent decade, there has been a substantial reduction in the water level of Lake Urmia. Understanding the underlying causes and pivotal moments of these changes, always have importance in environmental and water resources studies. This study aims to analyze the water level fluctuation, the climatic parameters trends and their interrelationship in Lake Urmia Basin. The data were collected from Urmia airport synoptic station and over 50 years, from 1973 to 2022 on a monthly and annual scale. For trend analysis, the Mann-Kendall, seasonal Mann-Kendall, and correlated seasonal Mann-Kendall tests and for change detection, Standard Normal Homogeneity Test (SNHT), Pettitt's test, the Buishand range test, and U Buishand test, were used. The results of study showed that, the maximum and average temperatures time series showing an increasing trend and the minimum relative humidity demonstrating a decreasing trend. Also, a significant decreasing trend was observed in the lake's water level during this period. Change point tests identified the years 1999 to 2001 as the turning point for the lake's water level, and for other parameters, change points were in the 1990s on both annual and seasonal scales. Furthermore, the lake's water level exhibited different behaviors before and after the change points. The results of this study, indicates a major climate change in the Lake Urmia catchment since year 2000, resulting water level decrease and dry up in this lake.https://jwhr.birjand.ac.ir/article_3130_fa5f6412c73fac70ec6816d84100ec72.pdfUniversity of BirjandWater Harvesting Research2476-69767220240901Canopy Temperature Estimation Using Gene Expression Programming Models and Artificial Neural Networks234243313410.22077/jwhr.2024.8265.1155ENMehri SaeidiniaDepartment of Water Engineering, Faculty of Agriculture, Lorestan University, Khorramabad, Iran.AmirHameh HaghiabiDepartment of Water Engineering, Faculty of Agriculture, Lorestan University, Khorramabad, Iran.Journal Article20241011Canopy temperature<strong> </strong>(T<sub>c</sub>)<strong> </strong>is one of the essential for irrigation scheduling. Measuring canopy temperature is expensive and time-consuming. Simple approaches such as soft computing can be a good tool for this purpose because there has been no documented research in this field. In this study, the ANN (MLP with two hidden layers) and GEP models were used to estimate Tc using limited data such as the dry (T<sub>a</sub>) and wet bulb (T<sub>W</sub>) temperatures, saturation vapor pressure (e<sub>s</sub>), actual vapor pressure (e<sub>a</sub>), and the vapor-pressure deficit (VPD). Six combinations of input variables were investigated. The perfect model was selected based on statistical indices during the training and testing. Results showed that the performance of the models were influenced by the number of the input variables. The MLP models outperformed GEP models during the training and testing processes. The MLP7 (input variables: e<sub>s</sub> and e<sub>a</sub>) with MSE of 1.08 <sup>°</sup>C, RMSE of 1.04 <sup>°</sup>C, and R<sup>2</sup> of 0.92 in the training phase and MSE of 1.02, RMSE of 1.00, and R<sup>2</sup> of 0.95 in the validation phase was selected as the perfect model among MLP models. The GEP11(input variables: T<sub>a</sub>, T<sub>W</sub>, e<sub>s</sub>, e<sub>a</sub>, and VPD) with MSE of 1.32, RMSE of 1.15, and R<sup>2</sup> of 0.89 in the training phase and MSE of 0.91, RMSE of 0.95, and R<sup>2</sup> of 0.95 in the validation phase was also the perfect model among GEP models. Accordingly, the proposed GEP and MLP models can be drawn on as a perfect model for estimating T<sub>C</sub>.https://jwhr.birjand.ac.ir/article_3134_50053b40f9f031f457d5da2c05db11c4.pdfUniversity of BirjandWater Harvesting Research2476-69767220240901Assessment of Re-Forecast Data in the Modeling of Extreme Rainfall-Runoff Events (Case Study: Floods in the Bakhtiari Basin, Iran, March-April 2019)246257314610.22077/jwhr.2024.7445.1157ENAmin EidipourDepartment of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.Mohammad Amin MaddahDepartment of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.Ali Mohammad Akhoond-AliDepartment of Hydrology and Water Resources, Faculty of Water and Environmental Engineering, Shahid Chamran University of Ahvaz, Ahvaz, Iran.Journal Article20241010Predicting inflow into reservoirs is essential for their operation during floods, particularly in mountainous watersheds characterized by snow-rain regimes. The objective of this research is to evaluate the GEFSv12 re-forecast data as an input of the HEC-HMS model for forecasting floods due to the extreme precipitation in March/April 2019 in the reservoir of Bakhtiari dam in southwestern Iran. So, ensemble flood forecasting (control and ensemble members) was conducted using extracted precipitation and temperature data with the lead-time up to 10 days. A sequence of predictions for flood warnings was analyzed when 50% of the members exceeded the threshold inflows of 1000 and 1500 m³/s. The relative volume error values for the control member and the ensemble mean for five days ahead were -15% and -22%, respectively. While previous studies in catchments with snow-rain regimes anticipated challenges in flood forecasting at mid-lead times, this research demonstrated that the proposed probabilistic framework could effectively issue flood warnings for events with a lead time of five days. To address and predict flooding at the Bakhtiari Dam with a threshold of 1500 m³/s, flood warnings are issued with a lead time of 5 to 8 days.https://jwhr.birjand.ac.ir/article_3146_c22786089462c9cac12c941cae5e3e72.pdfUniversity of BirjandWater Harvesting Research2476-69767220240901Bivariate Frequency Analysis of Extreme Sea Level with Rainfall and Temperature in New York City258276318910.22077/jwhr.2024.8502.1159ENAli RazmiPhD Candidate, Department of Water and Environmental Engineering, Shahrood University of Technology, Shahrood, Iran.Emad MahjoobiAssistant Professor, Department of Water and Environmental Engineering, Shahrood University of Technology, Shahrood, Iran.Heydar Ali Mardani-FardAssistant Professor, Department of Mathematics, Yasouj University, Yasouj, Iran.Zahra ZahmatkeshPost-Doctoral Fellow, Department of Civil Engineering, McMaster University, Hamilton, Ontario, L8S4L8, Canada.Journal Article20241030Climate change negatively impacts hydrologic patterns, affecting rainfall, temperature extremes, and sea level rise. Long-term averages of these variables may shift over time due to climate change effects. This study conducted trend analysis on rainfall, maximum and minimum temperature, and water level data from Manhattan, Central Park, and Battery Park stations to identify significant changes in means. The Partial Mann-Kendall test was employed for trend analysis. Frequency analysis utilized common probability distribution functions, including Generalized Extreme Value (GEV), normal, log-normal, and Log-Pearson distributions, with goodness-of-fit tests like Kolmogorov-Smirnov to identify the most suitable distributions. While flood frequency analysis typically examines rainfall and water levels separately, their combination can significantly influence floodplain delineation. This study aimed to enhance flood frequency analysis by considering joint probability distributions for rainfall and storm surge. The correlations between variables and joint probabilities of extreme water levels and temperatures were explored to assess the potential impacts of global warming on sea level flooding. Copula functions determined the joint probabilities of water levels with rainfall and temperature across various recurrence intervals. The trend analysis results indicated an increase in long-term averages due to climate change. The GEV distribution emerged as the most appropriate function for extreme climate variables. This joint probability distribution analysis underscored the necessity of incorporating both rainfall and water level data in flood frequency assessments.https://jwhr.birjand.ac.ir/article_3189_aa6ee1c2cb1ede6737c1399b8fc0df28.pdfUniversity of BirjandWater Harvesting Research2476-69767220240901Investigating the Variations in Water Requirement for Main Plants in the Cultivation Pattern (Case Study: Kashmar Plain of Khorasan Razavi)277288319510.22077/jwhr.2024.8291.1156ENHadi DehghanAssistant Professor, Department of Water Engineering, Kashmar Higher Education Institute, Kashmar, Iran.Somayeh GaldaviAssistant Professor, Department of Water Engineering, Kashmar Higher Education Institute, Kashmar, Iran.0000-0002-2218-1186Journal Article20241016The agricultural sector is crucial to Iran's economy, especially in ensuring food security. Climate changes, intense competition for water resources among various sectors, and the declining share of renewable resources in the agricultural sector make managing water consumption in agriculture essential. When doing so, it's important to consider changes in plant water needs. This study investigated the process of changing the water requirement of plants in the cultivation pattern in the Kashmar Plain by calculating evapotranspiration. These plants are including Sunflower, Leafy vegetables, Potato, Apple, Lentils, Walnut, Pears, Wheat, Tomato, Cherries, Peach, Watermelon, Peas, Vegetables, Pistachio, Plum, Pomegranate, Grape, Almonds, Eggplant, Cotton, Onion, Barley, Sugar beet, Melon, Spring cucumber, Autumn cucumber, Fodder corn, Saffron, and Cantaloupe. Firstly, the reference evapotranspiration was calculated on a daily scale over 20 years (1998 to 2017) using the FAO Penman Monteith equation. Then, monthly, seasonal, and annual values were used for calculations, and cultivated plants' evapotranspiration (water requirement) (ET<sub>c</sub>) was determined. The Mann-Kendall test was utilized to examine the changes in Evapotranspiration of plants. The results indicated an increasing trend in the water requirement of plants in the region. The greatest increases were observed in Evapotranspiration for autumn cucumber plants (54.8% increase), sugar beet (41.51% increase), and pistachio (38% increase), while the lowest increase was seen in almonds at 3.07%. After analyzing the data, it was found that pomegranates (19.94% increase) and lentils (20.54% increase) have the lowest increase in evaporation, transpiration, and water requirement. This suggests that changes in the cultivation pattern of the region may be necessary due to the varying water requirements of different plants. The findings of this research could be valuable in making decisions about the cultivation pattern of plants in the region.https://jwhr.birjand.ac.ir/article_3195_5710ad79ae6033f830401b521381c653.pdfUniversity of BirjandWater Harvesting Research2476-69767220240901Selection of the Best Copula Function in Bivariate Analysis of Water Resources Components (Case study: Siminehrood River Basin, Iran)289300320410.22077/jwhr.2024.8508.1160ENFahimeh SharifanPh.D. Student, Department of Water Engineering, University of Birjand, Birjand, Iran.Yousef RamezaniAssociate Professor, Department of Water Engineering, University of Birjand, Birjand, Iran.Mahdi AmirabadizadehAssociate Professor, Department of Water Engineering, University of Birjand, Birjand, Iran.Carlo De MicheleProfessor, Department of Civil and Environmental Engineering, Politecnico di Milano, Milan, Italy.Journal Article20240902The copula function is a joint distribution of correlated random variables that are defined based on univariate marginal distributions. The aim of the present study is to select the best copula function to create joint probability distributions between the pair of parameters of precipitation- river discharge, river discharge- river salinity and river discharge- groundwater level in the Siminehrood River Basin. The necessity of using copula functions is the existence of correlation between the desired pair of parameters. For review the correlation between the pair of parameters, Kendall's tau statistic was used. Correlation between the precipitation- river discharge, river discharge- river salinity and river discharge- groundwater level were obtained 0.43, 0.64 and 0.44, respectively. After correlation evaluation, the marginal distribution of the parameters was investigated. Using the Kolmogorov-Smirnov and Anderson-Darling tests, statistical distribution functions for precipitation, river discharge, river salinity and groundwater level were obtained Lognormal, Gamma, Burr and Lognormal distributions, respectively. Then, by examining the dependence structure and the structure of copulas and using NSE, RMSE and BIAS evaluation criteria, Clayton's copula function was selected for all three pair of parameters, which was used to create a joint probability distribution between the pair of parameters in the Siminehrood River Basin.https://jwhr.birjand.ac.ir/article_3204_8f01742840070001ecaa3f6813fc9766.pdfUniversity of BirjandWater Harvesting Research2476-69767220240901Assessing Annual and Seasonal Precipitation Trends in the Karun River Basin Using Co-Kriging: An Over 60-Year Analysis301313321610.22077/jwhr.2025.8070.1150ENAmin KhoramianAssistant Professor, Department of Hydrology and Water Resources, Shahid Chamran University of Ahvaz, Ahvaz, Iran.Journal Article20240826The Karun River Basin is an important source of water in Iran, located in a semi-arid region that is under increasing pressure due to changing precipitation patterns. In this work, the trends of precipitation from 1955 to 2019 at 163 meteorological and synoptic stations in the Karun River basin were investigated using the Co-Kriging method to generate precipitation maps for the study period. Although there is no trend in annual precipitation from 1955 to 2019, there is a statistically significant decreasing annual trend at the 10% significance level in the period from 1985 to 2019; -6.6 is the negative slope coefficient. This could indicate a shift towards drier conditions in the latter part of the study period, which affects the availability and management of water resources. The seasonal trends are opposite: a possible decrease in winter precipitation with a slope coefficient of -0.102, indicating drier winters, and a possible increase in fall precipitation, where the slope coefficient is positive at 0.131, indicating wetter autumns. Even though these seasonal trends are not statistically significant with p-values of 0.226 and 0.099, respectively, they underline the complex and dynamic nature of precipitation patterns in the catchment. It is therefore very important to understand the trends of current water resource management strategies and possible future changes in the region to avert the associated risks to irrigated agriculture, domestic water supply, and flood control in this important area.https://jwhr.birjand.ac.ir/article_3216_6e3e57749b5d19cad5964ad203891621.pdfUniversity of BirjandWater Harvesting Research2476-69767220240901Assessing Water Governance Gaps with a Four-Layer Governance Model and OECD Principles314333326610.22077/jwhr.2025.8618.1162ENTaghi MahdaviAssistant Professor, Department of Civil Engineering, Maragheh Branch of Islamic Azad University, Maragheh, Iran.Journal Article20241221In this study, water governance in four layers including the contextual layer, institutional layer, relational layer, and performance layer was evaluated using the 12 principles of the Organization of Economic Cooperation in East Azerbaijan Province of Iran. This manuscript has used descriptive cross-sectional research to analyze of water governance gaps. Data were collected using the method of interviewing 36 key experts and farmers and studying documents, including 26 laws, 20 bylaws, 4 canons, and other documents published in the country's water sector. The results of the study showed that there are large gaps in the implementation of laws, policies, and guidelines, especially the achievement of macro goals and accountability and trust in the region. These gaps are mainly in line with Principles 7, 9, and Principle 2. Important obstacles to the implementation of laws and policies are as follows: the short period of management of individuals, low financial and administrative capacity of government departments, monopolization of policy-making by authorities, and as a result lack of role and participation of local stakeholders in the policy-making process, lack of legitimacy of policies at the local level, contradictory laws and contradictory actions of the government and the legislature at various times have led to a loss of trust in the legislature and the government. Therefore, some problems at the local level are tied to problems at the national level, and these problems can be generalized to other provinces, it is necessary to study and evaluate water governance at the national level.https://jwhr.birjand.ac.ir/article_3266_f5562b382de005c163f47df7f518405f.pdfUniversity of BirjandWater Harvesting Research2476-69767220240901Risk Assessment of Water Structure Projects Using Fuzzy Multi-Attribute Decision-Making Methods: Fuzzy OWA and Fuzzy SAW (Case Study: S1 Wellhead Platform in the Salman Oil Field)334348329010.22077/jwhr.2025.8545.1161ENAli KhazaeePh.D. Candidate, Department of Civil Engineering, University of Birjand, Birjand, Iran.Mahdi NaseriAssistant Professor, Department of Civil Engineering, University of Birjand, Birjand, Iran.Journal Article20241210Civil engineering projects, including the construction of oil platforms, are inherently associated with various types of risks from different perspectives. Risk management in large-scale water and marine structure projects, such as the construction of oil platforms, is essential due to the multiple uncertainties and extensive environmental and human factors involved. Identifying, assessing, and prioritizing risks are critical steps in managing these projects effectively. This study aims to identify and rank key risks in the construction of an oil platform using fuzzy multi-attribute decision-making models. In this research, risks in the areas of engineering, execution, passive defense, and the environment were identified through a literature review and expert consultation using brainstorming techniques. Subsequently, a risk management team identified 21 key risks and established 8 evaluation criteria through focused group discussions. To achieve the research objectives, two questionnaires were developed. The first questionnaire was used to form a pairwise comparison matrix and determine the weights of the criteria using the Fuzzy Buckley method, while the second questionnaire assessed the importance of the risks. The collected data were analyzed using the Fuzzy Simple Additive Weighting (SAW) and Ordered Weighted Averaging (OWA) methods. The results indicated that the primary risks were related to the execution phase, highlighting the need for special attention to these risks to improve project outcomes. Unlike many traditional methods, the fuzzy OWA method effectively incorporates the subjective characteristics, risk appetite, and risk aversion of decision-makers, proving to be efficient in risk evaluation.https://jwhr.birjand.ac.ir/article_3290_fa0c154fd7818ceebf712c9ab3dd1916.pdf