فهرست مطالب asghar azizian

One of the consequences of climate change is the occurrence of extreme events such as drought, which is important to identify, monitor, evaluate, and inform about the occurrence conditions. In this study, the effects of climate change on the drought situation in the near future (2021-2040), mid-term (2041-2060), and long-term (2061-2080) have been discussed using the Aridity Index estimated by the Copernicus Climate Change Database with the use of GFDL-ESM2M climate model output under RCP4.5 and RCP8.5 scenarios.

In the current research, climate data simulated by the Copernicus database is used to calculate the Aridity Index of different parts in various time horizons. Mann-Kendall's test is applied to investigate the changes of drought indices in different time horizons. Also, the aridity index is used to determine the climatic condition of different parts of Iran in several time periods and its changes are analyzed and investigated.

The results illustrate that in the near future and based on the RCP4.5 scenario, the average annual dryness index has an increasing trend in most parts of the country, and these climate changes lead to a change in the climate of some parts of the north of the country from humid to semi-arid and semi-humid, and parts of the center and the southeast become dry to semi-arid. According to the RCP8.5 scenario, there is a downward trend in parts of the east and the center of the country, and an upward trend in parts of West Azerbaijan, East Azerbaijan, Chaharmahal Bakhtiari, and a part of Kermanshah, and climate change rises the spatial extent of dry climates in the country. Also, in the mid-term and based on the RCP4.5 scenario, a downward trend can be seen in most parts of the country, and climate change will increase the area of dry climate lands in the central and southeastern parts, while according to the RCP8.5 scenario, there is an upward trend in most of the country, and climate change causes a soar in semi-arid and dry climates in parts of the northwest, northeast, and parts of the center and south of the country. Finally, in the long-term and based on the RCP4.5 scenario, the mentioned trends will be almost true and climate change will lead to the expansion of areas with arid and semi-arid climates.

The results obtained in the this research showed that the average drought index in the near future, mid-term and long-term according to the RCP4.5 scenario are 0.5, 2.2 and, 2.3%, respectively and according to the RCP8.5 scenario,  2, 4.2 and, 4.1% have risen compared to the primary period, respectively, and it rises by 1.4 percent compared to the base period, and it is a good indication that the reduction in rainfall and the increase in the temperature of the earth's surface will lead to a soaring in the dryness of different parts of the country. In addition to changing the climate of the region, this issue will have irreparable effects on the condition of water resources, agriculture, environment and urbanization. Therefore, in order to reduce the aforementioned effects, it is recommended that managers and officials consider appropriate measures, correspond to each clime region, to control the amount of water consumption in different parts of the country.

The occurrence of frequent floods in Iran necessitates a flood forecasting and warning system with a suitable lead time. The use of numerical rainfall forecasting models in flood forecasting and warning is one of the important measures taken by researchers in most parts of the world. The TIGGE database includes mid-term precipitation forecasts simulated by global forecast centers. The purpose of this research is to evaluate the efficiency and the degree of uncertainty caused by the rainfall forecasts of four numerical models of the TIGGE database (including CPTEC, ECCC, ECMWF, and KMA) for simulating floods with the HEC-HMS hydrological model.

In this research, the precipitation data of seven meteorological stations were used to evaluate the uncertainty of discharge from TIGGE database precipitation prediction models in the Poldokhtar watershed. Also, three flood events on March 24, 2017, April 6, 2018, and April 15, 2018, were studied. At first, precipitation forecasts were extracted from four centers CPTEC, ECCC, ECMWF, and KMA. Due to the existence of systematic error in the forecasts, a bias correction was done on them, and to correct the bias, the Delta method was used. Processed and raw forecasts of four rainfall forecasting models were entered into the HEC-HMS model for flood forecasting, and in the next step, the flow uncertainty assessment of the HEC-HMS model was performed in all members of the four rainfall forecasting models. In this research, 5 factors P, R, S, T, and RD were used for uncertainty analysis.

The results indicate the significant superiority of the ECMWF model in predicting precipitation events. The use of all 4 rainfall sources led to an acceptable simulation of the flood peak flow in three different events. Also, the predicted peak discharge time had little difference from the observed data. According to the results of the uncertainty analysis, the ECMWF model was considered the best model based on P, R, S, T, and RD factors. The KMA model performed well in severe and very severe floods. The group prediction system of TIGGE models also had an acceptable performance in all events. Also, the hydrological-meteorological prediction model predicted the time of flood occurrence and the probability of occurrence well.

The intended research investigates flood forecasting and warning in the Poldokhtar watershed using the meteorological-hydrological system, based on meteorological forecasts of the TIGGE database and flood simulation using the HEC-HMS hydrological model. The final product of this system is probable discharge and flood forecast. The results reveal the success of the TIGGE database in flood forecasting. The ECMWF model excelled in predicting peak discharge. The upper and lower band calculation method was used to determine the uncertainty, which showed the uncertainty well. This system displayed the time of peak discharge well and with a small time delay, which indicates its good performance. The predicted rainfall from the four centers used in this study (ECMWF, ECCC, CPTEC, and KMA) have significant differences. To reduce these differences, we used a multi-model group forecasting system that had encouraging results.

Estimating the available water plays a vital role in planning water resources projects. The first step in assessing water availability is to calculate runoff in catchments. Moreover, it is necessary to study the effects of climate change on water components such as runoff since climate change directly affects the hydrological components and water resources. In this study, the status of the inflow to Tashk-Bakhtegan lakes as one of the most important lakes in the country was investigated. Because generation of runoff is a complex concept due to its nonlinear and multidimensional nature, various theoretical and physical models have been developed to predict runoff. The high dependence of these physical models on numerous parameters and maps, practically challenfged their efficiency in basins with limited observations. On the other hand, models based on artificial neural networks and fuzzy inference systems are considered applied tools that can help hydrologists in such circumstances. In this study, FFBPNN and ANFIS models and their combination with PSO and GA metaheuristic algorithms have been investigated in evaluating the runoff in historical conditions and under RCP and SSP climate change scenarios.  The obtained results showed that the FFBPNN and ANFIS models combined with the PSO algorithm (ANFIS_PSO) using precipitation, minimum temperature, and maximum temperature as inputs had better performance compared to other models with different inputs. The values of the correlation coefficient, root mean square error (m3/s), Nash-Sutcliffe coefficient and Kling Gupta were respectively 0.99, 2.07, 0.99 and 0.98 in the training period and 0.94, 3.61, 0.91 and 0.88 in the test period and 0.98, 94. 2, 0.97 and 0.98 in the training period and 0.93, 3.87, 0.85 and 0.88 in the test period. The results of this study can be used in investigating the effects of the above mentioned scenarios on important basins of the country and consequently in planning and managing water resources in the context of climate change.

Investigation of future drought trends and variations under climate change scenarios plays a key role in developing management strategies for minimizing drought's negative societal and economic impacts. Therefore, this study aimed to assess changes in drought characteristics such as the frequency of dry and wet periods, and the trend of drought index based on fuzzy drought index (at time scales of 3, 6, and 12 months) under SSP3 and SSP5 scenarios of CMIP6 during the 21st century. The data of 6 synoptic stations in Karoon River Basin during 1991-2014 have been used in this study. Assessing the reliability of climate models for drought monitoring with fuzzy drought index in the base period showed that the highest correlation coefficient (CC>0.90) and the lowest root mean square error (RMSE<0.14) are found at 3 and 6 month time-scales. In addition, monitoring the drought conditions of the basin under climatic scenarios in future periods revealed an increasing trend (at the 95% confidence level) and the wetness frequency in the northern, northwestern, and western of the basin is more likely to decrease. Over the periods 2046–2072 and 2073-2099 the result of the non-parametric Mann-Kendall test for the scenarios of SSP3_7.0 and SSP5_8.5 was 1.64. Therefore, due to the increasing trend of fuzzy drought index changes to dry conditions in Boroujerd, Safiabad, and Kuhrang stations, the risk of drought during the periods 2073-2099 and 2046-2072 are higher. Accordingly, water managers and farmers should adopt strategies in order to reduce the damages. The results of this research can be valuable in adopting policies and planning for sustainable management of water resources affected by climate change.

Accurate estimation of runoff plays an important role in water resources and hydrological studies. Due to simple structure and minimum data requirements, the conceptual hydrologic models are the best way to estimate runoff. The main objective of this study is to investigate the performance of Sacramento model in runoff simulation and determining the best reanalyzed evapotranspiration dataset for using in the model. In this study, four different datasets including HBV, ORCHIDEE, PCR-GLOBW, WATERGAP3 and W3RA are used in Sacramento model. Also, for estimation of basin-averaged rainfall time seri, the Thiessen method was used based on ground gage observations. Results indicate that using most of the reanalyzed datasets in Sacramento model lead to reliable outputs and the performance of model in simulation of daily stream flow is relatively high. However, the performance of model in the case of using W3RA and WATERGAP3 is better than the other data sources, and in both calibration and verification phases the Nash-Sutcliffe efficiency (NSE) is higher than 0.60 and 0.87, respectively. Moreover, findings show that the W3RA dataset is the best one for estimation of runoff volume, high flows (peak floods) and the time to peak flows. Overall, based on the outputs of this research, the reanalyzed datasets can be considered as an alternative or complementary in data-limited regions for water resources and hydrological studies.

Iran is located in the arid and semi-arid belt of the world, which is characterized by low rainfall, thunderstorms, flood flows, and high evapotranspiration. Therefore, quantitative evaluation of evapotranspiration on a regional scale is necessary for water resources management, crop production, and environmental assessments in irrigated lands. In this study, to estimate ETo in four synoptic stations with arid, semi-arid, humid, and semi-humid climates, meteorological data such as temperature, net radiation, relative humidity, and wind speed were used for 1990-2010. Potational evapotranspiration was calculated using 5 different methods, including Hargreaves-Samani, Blaney-Criddle, Priestly-Tailor, Turc, and Makkink, besides, the standard FAO-56 was used (because there was no Lysimetric regionally data) to evaluate the applied formulas. In order to evaluate these methods, were used the mean absolute error (MAE), root mean squared error (RMSE), mean bias error (MBE), correlation coefficient (r), Nash -Satclif index (N.S). Based on these indices, Blaney-Criddle method, after the FAO-56 method, which was used as the basis, was selected as the superior method for the four climates in Iran.

Floods are a natural disaster that threatens the lives of millions of people every year. Obtaining the flood zone and consequently obtaining the flood zone map for current with a specific return period for a desired basin is one of the important results obtained from different models. Therefore, extraction of flood zone is one of the basic needs in the design of water structures, a basic step for management and planning to reduce economic and social flood damage, use to determine the amount of insurance for residential areas along the river and also identify high risk areas of the river In terms of flood status and flood control measures. Evaluating the performance of the HAND model, which relies solely on topographic features, is one of the objectives of the present project.

In this study, the flood zone is determined using the HAND model with a calibrated coefficient of roughness. Seymareh River has been selected as a study area to challenge the performance of the model in relation to the observational data obtained from satellite images and also to evaluate it with the HEC-RAS hydraulic model. Also, the HAND model in low and high flows has been evaluated with a 1D and 2D hydraulic model to evaluate the performance of the model in different flow conditions.

In the first part, the HAND model was evaluated using satellite images, which show the very good performance of the model in determining the flood zone. Further sensitivity analysis of Manning roughness coefficient showed that increasing and decreasing it by 25% had no effect on improving the performance of the HAND model and the roughness coefficient was properly calibrated. Finally, the model was evaluated with 1D and 2D hydraulic model in low and high input flow conditions. The results showed that the HAND model still has a good capability in comparison with hydraulic models in different flow conditions.

The most important results can be summarized as follows:• The results obtained from the HAND model based on the calibration coefficient calibrated in Seimareh river indicate the proper performance of the model in obtaining flood zone. According to calculations, the rate of flood zone adaptation is higher than 92% compared to satellite images. Also, the average difference between the HAND model and satellite images for estimating flood zones during the study period of the river was about 8.5%. The occurrence of turbulence and rotational flows due to improper angle of connection between the channel and the freeway bridges in the future will have a significant impact on the hydraulic flow and sediment of the river, especially in the area of the bridges.• The results in Seimareh River show that increasing and decreasing the inlet flow does not change the performance results of the HAND model and also the model has a good capability compared to hydraulic models. According to calculations, the rate of adaptation of the flood zone in comparison with the hydraulic model in different discharges is always higher than 83%. Also, the average difference between the HAND model and hydraulic models for estimating flood zones during the study period of the river will be less than 13%. • Despite the very good performance of the HAND model in estimating the flood zone, in some intervals there are differences between the HAND model with satellite images and the hydraulic model, which can be done by using topographic maps with high resolution and river path inter Extraction of Rating-curves for each substrate greatly enhances the performance of the HAND model.• Hydraulic model has more error in estimating flood zone than HAND model. The main reason for this can be related to important factors such as: distance between cross sections, computational cell dimensions, numerical parameters used in the 2D hydraulic model (such as θ parameter and currant number), boundary conditions used in the hydraulic model Calibrate the time step and determine the Manning roughness coefficient for each cross section. In general, relatively more factors affect the performance of the hydraulic model and affect its output, while the HAND model experiences relatively better conditions in this regard, so that only by considering a coefficient of roughness for the whole The study interval and extraction of the Rating- curve can be used to obtain the flood zone. while the HAND model experiences relatively better conditions in this regard, so that only by considering a coefficient of roughness for the whole The study interval and extraction of the Rating- curve can be used to obtain the flood zone.

Due to the high costs of land surveying, remotely sensed digital elevation models (DEMs) are a common method used to demonstrate topographic variations of the land surface. Generally, these DEM datasets are freely accessible to engineers and researchers covering most parts of the world in different spatial resolutions. DEMs can be classified into two categories of high (small pixel size) and low (large pixel size) resolution DEMs. Several studies have addressed the vertical accuracy of different digital elevation datasets especially in countries lacking access to high quality ground-based data. Despite the widespread application of these products, vertical accuracy of these datasets in different land uses has not been addressed in Iran and most engineering studies use 1:1000 and 1:2000 topographic maps which are very expensive and time-consuming to obtain. The present study seeks to assess vertical accuracy of different resolution DEM datasets used to estimate elevation in various land uses in two Iranian provinces of Qazvin (urban, agricultural lands, garden, and forest, mountainous areas, plains, and rivers) and Mazandaran (urban, agricultural, forest/mountain, plains, and rivers). 

ASTER and SRTM DEMs with a resolution of 30-meter and SRTM DEM with a resolution of 90 m resolution were collected in the present study to investigate their vertical accuracy in various land uses of Qazvin and Mazandaran provinces. Several topographic maps and GPS based datasets of the study areas were also investigated for a better assessment of these DEM datasets. Finally, common statistical measures such as standard deviation (SD), mean absolute difference (MAD) and root mean square error (RMSE) were used to compare remotely sensed DEMs with ground-based observations. 

Findings indicated that 30m SRTM DEMs showed a better agreement with ground-based observations in both study areas. RMSE of this dataset in Qazvin and Mazandaran provinces equaled 3.8m and 5.8 m, respectively. Results also indicated that in 30m SRTM DEM, 87% of points in Qazvin and 79.7% of points in Mazandaran provinces showed a lower than 5m mean absolute difference (MAD), while in the case of 30m ASTER DEM 79% of points in Qazvin and 53% of points in Mazandaran showed a lower than 5m mean absolute difference (MAD). For 90m STRM DEM, around 29% of points in Qazvin and 74% of points in Mazandaran showed a lower than 5m mean absolute difference (MAD). Although 90m SRTM DEM did not work efficiently in Qazvin province, its result in Mazandaran province was almost as efficient as 30m SRTM dataset. Assessing the vertical accuracy of different elevation datasets in different land uses indicated that 30m SRTM showed an acceptable result in most land uses except for mountainous areas and forests. This was mainly due to forest canopies blocking the radio waves penetrating such areas and low density of points generated by STRM sensors. Moreover, 30m ASTER did not show an acceptable result in most land uses except for plains in Qazvin along with urban and agricultural land uses in Mazandaran. Despite having a lower resolution, 90m SRTM worked better than 30m ASTER. However, 90m SRTM showed considerable errors in mountainous, urban and forest land uses, and therefore it shall not be used in such areas. 

Results indicated that 30m STRM DEM is a valuable resource which makes elevation estimation for areas lacking ground-based information possible. Moreover, the type of land cover has a significant effect on the vertical accuracy of elevation datasets and thus, increased vegetation results in decreased accuracy of DEM datasets. Therefore depending on the land cover type in the study area, ground control points can be used along with remotely sensed DEMs to decrease errors.

One of the most important topics in river engineering is the study of the morphological condition of the river, which deals with the expression of the geometric shape, bed form, longitudinal profile of the canal, cross sections, deformation and displacement of the river over time. In fact, rivers and streams are a completely dynamic system, and their position, shape, and other morphological features are constantly changing over time (Rangzan et al., 2008). So far, several studies have been conducted to evaluate the effects of river engineering and water resources engineering projects on the hydrodynamic status of the river (e.g., Yamani et al., 2007; Arshad et al., 2008; Vaezipour et al., 2020; Asghari Saraskanroud, 2013; etc.). The aim of this study is to evaluate these changes according to the implementation of various projects in recent years in the Karaj River that have imposed significant morphological changes on the river system and will change its behavior and response in the future. In this study, possible morphological changes in the Karaj riverbed due to Karaj river canalization projects, construction of Alborz recreational lake and artificial recharge have been investigated. The models used in this research include one and two dimensional HEC-RAS flow models (4.1 & 5), HEC-RAS sediment model and RVR Meander model for morphological simulation. In these modellings, three hydraulic scenarios were performed to investigate the flow status in the initial state (without designs), within the channelization design and within the artificial recharge project, and modeling of bed changes (sediment) and morphology were performed separately. Scenario 1- According to the results obtained from the implementation of the two-dimensional model, the areas located on the right bank of the river, especially the area located in Chamran Park, as well as residential areas located at the beginning of the study reach due to being located in the river area are significantly flooded. One of the main reasons for this is the low altitude of the areas located on the right bank of the river. Scenario 2 - In this scenario, the hydraulic flow is evaluated during the implementation of the Karaj River channelization and Alborz Lake projects. The results of two-dimensional modeling in the area of the main outlet indicate that the velocity of the current passing through this area is extremely high. According to calculations, the values of velocity along the longitudinal section of the channel vary between 2.5 to 4 meters per second, which is a significant number. Such velocity values can lead to erosion of the riverbed and destruction of the riverbed at the outlet of the waterway. Scenario 3- In this scenario, the effect of constructing lakes of artificial recharge plan of Shahriar plain on the flow pattern and sedimentation process of Karaj river has been evaluated. According to the results, the proposed structures increase the water level, reduce the velocity and provide suitable conditions for water infiltration in the riverbed. Turkey nest (dam in the earth) reduce the velocity of the flow and penetrate more and more into the ground, but it is necessary to mention that due to the significant sedimentation of the river and the presence of sand factories upstream, a lot of fine sediments (caused by washing materials in the mines) will enter the lake of each of these structures. The most important results can be summarized as follows: • Backflow upstream of Alborz Lake due to insufficient dimensions of the lake intake and the constriction caused by the implementation of the channelization project, will cause significant energy losses at the beginning of the project and this will cause water return and flood spread upstream. • The occurrence of turbulence and rotational flows due to improper angle of connection between the channel and the freeway bridges in the future will have a significant impact on the hydraulic flow and sediment of the river, especially in the area of the bridges. • Turkey nest structures reduce the velocity of the flow and penetrate the ground as much as possible, but it is necessary to mention that due to the significant sedimentation of the river and the existence of sand mines in the upstream areas, a lot of fine sediments enter the lakes and it will reduce their permeability and their performance. • Construction of wide structures with only one outflow weir will cause the formation of passive areas in the reservoirs. The existence of such areas, in addition to providing suitable conditions for sedimentation, will also create a wetland state. • Due to the high velocity of the outflow from the lake, the riverbed has suffered significant erosion and this erosion is due to the concentration of flow in the bed and the left bank of the river and will greatly increase the possibility of bank failures. • Sediment modeling showed that in a relatively short period of time, relatively large sediments occurred upstream of the Turkey nests. The results show well that the reservoirs are almost full of sediment to overflow crest height, and in practice the discussion of aquifer recharging will face a major challenge. • Based on the morphological model outputs, the main axis of the river, especially downstream of the Karaj-Tehran freeway bridge, has undergone many changes over time, and this issue poses a major threat to the left bank of the river. • Based on the future morphological simulation of the Karak River, the presence of the Karaj-Tehran metro bridge in the outer arch of the river will cause the river to flow to the right bank in the long run and this will cause the destruction of the right bank wall and this area will need to be protected.

Estimating precipitation plays an important role in different studies, including meteorological, hydrological, flood simulation, and drought monitoring. SM2RAIN-ASCAT is the newest attempt to estimate precipitation using surface soil moisture and inverse solving the soil-water balance equation. This research addressed the performance of the SM2RAIN-ASCAT dataset over diverse climate regions of Iran at daily and monthly time scales in 54 synoptic stations located in Iran (2007-2018). In addition, improving the efficiency of this product using Quantile Mapping bias correction method is another objective of this study. Results showed that the performance of SM2RAIN-ASCAT at the monthly time scale was better than the daily time scale, and in most parts of Iran, the correlation coefficient between observed and estimated datasets was relatively high. At the monthly time step, 67% of the studied stations had a CC value higher than 0.65. Moreover, findings indicated that this product tended to overestimate in Iran's north and west parts. Based on the RMSE value at the monthly time scale, SM2RAIN-ASCAT performed well at extra-arid and arid regions compared to the Mediterranean and humid climate zones. Also, removing bias from the raw dataset increased the efficiency of SM2RAN-ASCAT in most parts of the studied areas. Based on contingency table metrics, the bias-corrected dataset's skills in detecting rainy days and false alarm ratio (FAR) improved significantly. For instance, the FAR metric averagely improved 26 and 45 % at daily and monthly time scales, respectively. Finally, results indicated that SM2RAIN-ASCAT is a valuable dataset for estimating monthly precipitation, especially in arid-desert to extra-arid climates. The accuracy of this dataset increases using bias correction in different climates.

Precipitation forecasting models play important role in the performance of flood and meteorological warning systems. In this research, the efficiency of five numerical weather prediction (NWP) models, which exist in the TIGGE database, are assessed to determine the best temporal resolution of forecasted datasets at distinct climate regions of Iran, during 2014-2018. Findings show that by increasing the lead time the accuracy of all forecasts decreases significantly. Moreover, most of the NWP models, especially the ECMWF and UKMO perform well, based on correlation coefficient (CC) and RMSE metrics, up to lead time of 3 days. Also, results indicate that by removing biases from the raw forecast datasets, the performance of all NWP models in different lead times increases considerably. After bias correction, the RMSE values of ECMWF, JMA, and KMA models in the lead time of 10 days reduces about 70, 65, and 73%, respectively, and, except for JMA, all NWP models perform well in most climate regions. The JMA model in humid climate zones (north and west parts of Iran) has a high level of bias and leads to unreliable forecasts.

Flood is one of the most devastating natural hazards that causes significant losses and damages. Flood inundation maps are initial requirements for river floodplain management and eliminating flood risk in integrated flood management plans.Surveyed topography datasets, together with bathymetry data, were utilized to construct the river terrain for flood inundation mapping, however, such data is rare. Therefore, for tackling this challenging issue, DEMs as an accelerator and free of charge datasets, are widely used in flood modelling. The recent advancement of GIS and RS technologies enabled scientists to benchmark high-accuracy DEMs, such as LIDAR (Light Detection and Ranging), which perform as excellent as driven DEMs from surveyed topographies, for flood modelling. Despite the reliability of these finer datasets, the limited coverage of world and the exorbitant price of provision has forced experts to utilize coarser-resolution DEMs rather than these higher-resolution DEM sources. As a result, the accuracy of open-access and free of charge DEMs used in flood modelling in the data-sparse rivers should be investigated broadly, unless the lower-resolution DEMs hinder flood modelling results by incorrectly reproducing river terrain. In this study, three sets of widely used open-access DEMs’ performance in flood inundation mapping and estimating hydraulic parameters of four various rivers are assessed thoroughly.

In this paper, three sets of free and accessible 30m resolution DEM resources (ALOS, SRTM, ASTER) of four various sorts of rivers in Iran will be utilized as HEC-RAS geometry input file. In short, this study consistsof 5 main steps:1) Creating surveyed topography maps with 1:1000 or 1:2000 scales2) Generating GDEM by interpolating topography maps obtained from the prior step3) Determining channel geometry and extraction of cross-sections by operating GIS-based DEMs and GDEM in HEC-GeoRAS extension on ArcGIS4) Flood modelling of the rivers by geometry input files produced from step 3, plus US and DS boundary conditions and design flow of the rivers5) Assessment of DEMs’ performance in estimating hydraulic parameters based on efficiency measures, such as Root Mean Square Error (RMSE), Mean Absolute Difference (MAD), Mean Absolute Percentage Error (MAPE), Relative Error (RE) and F-statistics.6) Investigating the relation among the accuracy of DEMs and morphology characteristics of various Iran rivers, namely Sojasrud in Zanjan province, Taleghanrud in Alborz province, Gorganrud in Gorgan province and Sarbaz in Sistan-Baluchistan province of Iran.

The river geometry derived from ALOS DEMs were more identical to surveyed cross-sections. According to previous studies, ALOS DEMs captures the more accurate depicting river bathymetry data, the higher performance of this dataset in flood modelling is expected. However, all of these GIS-based DEMs were unable to present the Gorganrud river geometry data.Apart from the better representation of river geometry, the ALOS DEMs dataset is superior to ASTER and SRTM DEMs datasets in terms of fairy prediction of hydraulic parameters (water extents and water surface elevation). For instance, ALOS had the least RMSE (2.3-8.6 m) in predicting flood extent compared to the rest. Moreover, the higher value of F statistics (approximately 80%) proves that this model presented the flood inundation map with the highest agreement. Reversely, the RMSE of ASTER, the least accurate model, in flood extent estimation was from 2.8 to 15m. Identically, the predicted flood pattern map of these rivers using ASTER leads to the more significant discrepancy with the F-statistics of 68% at most. The SRTM performance in generating flood inundation map was better than ASTER and less accurate compared to ALOS (maximum value of 78% in F-statistic was recorded).The accuracy of DEMs in stimulating WSE of the mountainous rivers was similar to each other, and it was higher than predicted WSE of lower-slope rivers. Moreover, the estimated WSE using ALOS led to less disagreement with the benchmark values, whereas the operation of ASTER and SRTM for this purpose resulted in overestimating. Although the Relative Error of these DEMs in predicting Gorganrud river flood WSE was almost 5%, the MAPE of whole DEMs was 1% within all cross-sections in the rest of the study rivers.

The accuracy of flood inundation mapping is highly dependent upon its geometry input file. The high-resolution DEMs, including LIDAR dataset, are often used due to their accuracy, still in most of flood inundation mappings projects, coarser-resolution DEMs are operated owing to their accessibility and free of charge datasets. It is critical to evaluate the influence of these DEMs on hydraulic outputs. In this research, various open-access DEMs configurations were operated in four various rivers located in different regions of Iran. The results reveal that the ALOS DEM dataset is superior to ASTER and SRTM DEM datasets in terms of a better representation of river geometry and fairy prediction of hydraulic parameters (water extents and water surface elevation). For instance, the higher percentage of F statistics (approximately 80%) proves that this model presented the flood inundation map with the highest agreement. However, the maximum values of F-statistics of SRTM and ASTER were nearly 78% and 68%, respectively, showing the flaws of these models in flood extents mapping.The efficiency of all DEMs datasets in estimating WSE of the rivers was excellent, but Gorganrud river is an exemption. The Mean Absolute Percentage Error of these DEMs for estimating WSE, which was under 1% within the majority of cross-sections, was not meaningful.Consequently, ALOS is particularly potent in accurately hydraulic modelling. Additionally, the remote-sensing based DEMs are more applicable in wide and (or) straight river reaches than narrow and meandering rivers. It would be better if DEMs are modified in advance to reduce the level of disparities in flood modelling. In this line, there are several modification methods led to lowering this predictable errors, such as using GCPs (Ground Control Points) or merging cross-sections with DEMs to create an integrated surface.

Precipitation is one of the main components of flood, drought and water resources warning studies, hence, its quantitative prediction is of the great importance. The increasing development of computing and satellite technologies and remote sensing in recent years has led to the development of several meteorological forecasting models, of which the TIGGE database with a large number of powerful forecasting models, is the most important. The aim of this study was to evaluate the performance of all available numerical models in the database to predict daily precipitation in 38 synoptic stations located in different climates of Iran. In addition, removing biases from raw datasets using Quantile Mapping (QM) method is another objective of this study. Results showed that in humid, semi-humid, Mediterranean and Arid climate zones (mostly includes the southwest, northwest and northeast parts of Iran), most of the prediction models are highly correlated with ground observations, while in semi-arid and extra-arid regions the correlation coefficient (CC) between the forecasted and observed datasets is very low. For example, the CC and RMSE values obtained from ECMWF and METEO centers in most parts of the country are higher than 0.6 and lower than 4 mm/day, respectively, while the performance of CMA and CPTEC models is not remarkable and leads to the weak results. Also, evaluation of the corrected precipitation values by QM method indicates that there is a significant improvement in the performance of most prediction systems. Findings in extra-arid, arid, and Mediterranean zones demonstrate an increase in CC value, averagely about 20%. Moreover, the results depicted that by removing biases from the raw datasets, the performance of numerical weather prediction (NWP) models in estimating the low and high precipitation events is improved and this issue further increases the applicability of precipitation forecasting systems in flood warning systems and water resources management.

Accurate estimation of evapotranspiration is one of the most important issues in water balance calculation at the catchment and field scale. The lack of meteorological observations for using empirical methods and the high costs of measuring evapotranspiration using lysimeter restricts the usability of these methods in most cases. To tackle these problems, this research addressed the performance of reanalysis models including W3RA, HBV-SIMREG, PCR-GLOBW, WATER-GAP, and Ensemble for estimation of evapotranspiration at different climate regions of Iran. Besides, the reference evapotranspiration for assessment of the above models obtained using lysimeter measurements. Findings showed that in most cases studies the Ensemble model’s performance with the RMSE value ranges from 3.42 to 7.57 mm/day is the weakest one, while the results of HBV-SIMREG and W3RA show the best agreements with lysimeter dataset. Analyzing the outputs based on mean bias error (MBE) depicted that the estimated evapotranspiration based on W3RA and Ensemble have the minimum and maximum bias, respectively. As an overall conclusion, although results indicate that PCR-GLOBW has the maximum correlation coefficient (CC) with the reference datasets, HBV-SIMREG is the best and reliable model for estimation of evapotranspiration in most climate regions of Iran and can be considered as an alternative dataset, especially in data-limited areas.

Quantifying the uncertainty contribution of important factors on the performance of rainfall-runoff models has always been one of the major challenges for researchers and hydrologists. The main problems of applying these models especially in calibration period are the large number of required parameters and the lack of physical understanding for some of them. This research addressed the uncertainty contribution of different infiltration methods (Green-Ampt, SCS-CN, Exponential, Smith-Parlange, Initial-Constant and Deficit-Constant) on the performance of HEC-HMS model using GLUE algorithm. Results showed that using each of infiltration methods imposes different uncertainty bounds on the simulated flood hydrograph by HEC-HMS. Findings indicate that SCS-CN and Smith-Parlange owing to have the higher P-factor (0.78 and 0.72) and lower ARIL (0.39 and 0.40) values, enforce minimum uncertainty on the model output. In addition, the mentioned infiltration methods have the fewer sensitive parameters and then performs better than other methods. In contrast, the uncertainty of applying Initial-Constant and Deficit-Constant methods for simulation of flood hydrograph is relatively high the smaller percentage of the observed data are within the 95% uncertainty bandwidth. Moreover, sensitivity analysis of the parameters of each of the infiltration methods using the nonparametric Kolmogorov–Smirnov (D) test showed that parameters with the sharp and peaked distributions indicate well-identifiable parameters, while flat and spread distributions indicate uncertain parameters. Overall, the outcomes of this study prove that GLUE algorithm has high ability to determine the optimal range of rainfall-runoff model parameters and the prediction uncertainty bandwidth.

Temperature and rainfall affect the quantity and quality of agricultural products. Therefore, it is important to estimate its spatial-temporal changes. In many region of the country, due to the low density of meteorological stations or the small statistical period of new stations, limited time and space information is available. Therefore, this study aims to use the data of CRU, AgMERRA, AgCFSR and GPCC gridded weather datasets in estimation of yield and water requirement of wheat and comparing with the estimated values with the information of Qazvin Synoptic Station. For this purpose, monthly weather time series of Qazvin synoptic station were extracted from 1980 to 2010 along with the data from the selected gridded datasets extracted from the closest grid cell to the synoptic station (K1), the average of four closest grid cells to the synoptic station (K4), and the average of eight closest grid cells to the synoptic station (K8). The quality of the gridded datasets was assessed with four statistical indices (R2, RMSE, NRMSE, ME) in indirect way (the latter using the outputs of the AquaCrop model). In estimating wheat water requirement, GPCC database with four points (K4) and one point (K1) showed the best performance. Wheat yield simulated with AgMERRA data with K1 and K4 closest grid cells had the highest correlation with the simulated values with data of synoptic station. Results showed that selected gridded datasets can be used to simulated grain yield, but only data from GPCC-CUR would result in reliable estimation of water requirement.

Flood simulation using the hydrological model requires an appropriate rainfall dataset and unfortunately, in most parts of the Iran country the spatiotemporal resolution and density of ground gauges aren’t suitable. Hence, using remotely sensed high spatiotemporal resolution datasets can be useful for filling this gap. The main objective of this research is the assessment of PERSIANN-CCs hourly rainfall dataset for simulation of flood hydrograph using HEC-HMS event-based model at the Asalem river basin (ARB), Iran. Furthermore, to better evaluation of this model, three different spatial scenarios (including lumped, Thiessen and link-lumped) at 1, 3 and 6 hour time steps are used. Findings showed that using of link-lumped scenario at 3 and 6-hour time steps for simulation of flood hydrograph leads to the best results. For example, in this case, the average values of Nash-Sutcliffe efficiency (NSE) and Correlation Coefficient (CC) values for all events are about 0.58 and 0.78 (for Δt= 3 hr) and 0.56 and 0.74 (for Δt= 6 hr), respectively. Moreover, if the main purpose of modeling is the accurate estimation of peak flow, using the second spatial scenario leads to minimum error (with the relative error ranging between 0.2 and 7.6 %) at 1 and 3 hr time steps, while the third spatial scenario(link-lumped) hasn’t the required potential for simulation of peak flow. Also, combing the first spatial scenario and PERSIANN- CCs dataset is the best case for estimation of time to peak (Tp) which is very important in flood warning systems. Findings of this study indicate that in the lack of ground observations, the high spatiotemporal resolution rainfall datasets such as PERSIANN-CCs can be used for flood simulation.

One of the new methods for estimation of rainfall is SM2Rain algorithm which calculates rainfall using soil moisture variations and inverse solution of soil water balance equation. This research addressed the efficiency of SM2Rain algorithm for rainfall estimation over the semi-arid (Khorasan-Razavi) and humid (Mazandaran) climate regions of Iran using ASCAT surface soil moisture dataset during 2006-2013. Findings indicate that the basin-averaged value of correlation coefficient (CC) between the estimated and observed datasets for Khorasan-Razavi and Mazandaran areas is 0.70 and 0.62, respectively. Results in the south and south-west regions of Khorasan-Razavi showed that the SM2Rain algorithm with the CC value of 0.84 and RMSE value of 3.9 mm/day (basin-averaged) performs very well, while in the north parts of the province with the CC value of 0.54 and RMSE value of 7.7 mm/day, the performance of this algorithm is relatively low. Also, the performance of SM2Rain algorithm in most parts of the Mazandaran province, especially in east and central parts, is acceptable and the basin-averaged values of CC and RMSE are 0.72 and 3.9 mm/day, respectively. The results also showed that by adding evapotranspiration term to SM2Rain algorithm, the efficiency of modified algorithm in estimation of rainfall increases about 10-18% in both regions. Furthermore, by using the modified SM2Rain algorithm over the Khorasan-Razavi, the basin-averaged value of relative bias (RBias) decreases from -21.9% to 9.3% and in Mazandaran region, the RBias decreases from -36.9 to 7.9%. The findings of this research indicate that the estimated rainfall with the SM2Rain algorithm can be considered as an alternative or supplementary dataset for ground-based observations, especially in ungauged catchments or data-limited areas.

The lack of required datasets over the catchments impose basic problems for applying hydrologic models. By increasing the satellite-based technologies over the past decades, several water balance components have been developed by using remote sensing and data assimilation techniques. This research addresses the efficiency of evapotranspiration values which are obtained from the reanalysis models (GLEAM, W3RA and HBV) for calibration of VIC-3L hydrologic model over the SefidRood basin (SRB). Results showed that using the evapotranspiration dataset, which is estimated by GLEAM, is the best for calibration of VIC-3L (NS=0.56 and CC=0.80) and simulating the streamflow at the outlet of SRB. Also, using the HBV reanalysis model’s results, in both daily and monthly time scales, with the KGE=0.64 leads to the better performance in simulating streamflow when comparing to the base scenario (calibration of VIC-3L model with the observed streamflow data). Finally, results indicated that using W3RA and GLEAM datasets improved the VIC-3L performance in estimation of runoff volume and the maximum relative error restricted to only 4.0 %.

One of the complicated variables in hydrological and meteorological processes is precipitation. During the past decades several attempts have been carried out to developed and provide precipitation products with different spatio-temporal resolutions. The PERSIANN family products which includes PERSIANN, PERSIANN-CCs and PERSIANN-CDR is one of the most important attempts to estimate rainfall based on remote sensing techniques and cloud thickness. This research assessed the efficiency and performance of PERSIANN family products at the monthly time scale over Iran using 355 synoptic stations. In addition, evaluating the effect of climate and topographic conditions on the performance of these products is another objectives of this study. Results indicated that PERSIANN-CDR, due to using GPCP dataset for removing bias, highly correlated with observed data, while the average correlation coefficient (CC) over Iran for PERSIANN and PERSIANN-CCs is 0.49 and 0.51, respectively. Moreover, all PERSIANN products tend to underestimate rainfall in north and north-west parts of Iran, while in the south and south-east parts of the country the rate of rainfall overestimation increases significantly. Findings on the effect of topographic conditions demonstrate that in the high elevation (between 600m and 2600m) and low elevation (lower than 600m) regions the correlation between ground-gauge observations and PERSIANN family products is high and low, respectively. The performance of these datasets in the Mediterranean, semi-humid and humid climate regions is better than other climate conditions. Also, in per-humid A, per-humid B and extra arid climate regions the relative bias (RB) in rainfall estimation is very high.

The 2D HEC-RAS model uses a new concept called subgrid-variability to include elevation changes within a computational cell and thus has very little dependence on computational cell dimensions. The purpose of this study was to evaluate the effect of the concept of subgrid-variability on the simulated flood area in the Sarbaz River. The other purposes of this study is to investigate the effect of map scale on the performance of 1D and 2D HEC-RAS model. The results showed that with increasing computational cell dimensions, the model error in simulating flood zones was relatively low compared to small cell dimensions. For example, if using a computing grid with dimensions of 500 meters instead of a 20 meters grid, the simulated flood area in different parts of the Sarbaz River would be less than 15 percent. The run time of the 2D model in the cell dimension of 500 meters is approximately 45 times less than the run time of the model in case of using cell dimensions of 20 meters. The findings clearly indicate to what extent the use of the concept of subgrid variability in modeling can be effective when implementing numerical models, especially in large and complex rivers. The calculations also show that the difference between 1D and 2D models in the simulation of hydraulic parameters in small scales is relatively small and by increasing the map scale, the difference between the two models increases.

Application of crop models is essential for numerous purposes including prediction of crop yield and water requirement, evaluation of the impact of climate change, drought and irrigation and agronomic management on crop growth and development. One challenge with the application of these models is the large number of input parameters. Measurement of input parameters can be time-consuming, costly and sometimes practically impossible and they are usually estimated using calibration and inverse modelling. Sensitivity Analysis is a procedure during which the impact of input parameters on target output variables is investigated. During model calibration, sensitive parameters must be measured or estimated with higher accuracy. Analysis of the sensitivity of closed-source models is not as straightforward as it is with open- source models. In this research, the impact of 47 crop parameters on five output variables of AquaCrop, a closed-source crop model, namely soil evaporation, crop transpiration, evapotranspiration, biomass at harvest and grain yield, were studied for wheat and maize in Qazvin Plain and Moghan Pars-Abad in Iran. The sensitivity of the selected parameters was evaluated with the relative Nash–Sutcliffe Efficiency Index. Increase in canopy cover, degree-days from sowing to maturity, degree-days from sowing to start of senescence and maximum canopy cover in fraction of soil cover were identified as sensitive parameters for both crops. Therefore, accurate determination of crop growth stages, in calendar days or degree-days, which are easier to measure than most of the other parameters, is of greater importance. In Qazvin and for all output variables, around half of the selected parameters were ineffective and considered unimportant during calibration. Despite that the methods of local SA are computationally and conceptually simpler than the methods of global SA, the results showed that this method could lead to similar outcomes to previous studies in which global methods were used.

In this paper, direct loss to structures and their properties due to rivers’ floods in urban areas was investigated. Risk evaluation and flood management require an algorithm for evaluating economic loss due to flood. In this research, hydraulic properties such as depth and flow velocity were used to detect the destroyable areas during the flood event. Then the flow depth was used to estimate the economic loss due to floods with different return periods; 2, 5, 10, 25, 50, 100 and 500-year. The economic loss due to floods was calculated using the depth–loss functions of five different models. Furthermore, the costs of expected annual damage (EAD) for structures in the study area were determined for plan floods, of which the critical areas at the region were distinguished. The results showed that using depth-loss function of Arrighi et al. (2013) is more logic in the study area and it can be employed for risk management and flood insurance studies when the proper data are not exist. Finally, the depth-loss functions’ uncertainty were investigated using the First Order Variance Estimation (FOVE) method and the estimated loss confidence interval was determined. The procedure introduced in this paper provides a tool for rapid and acceptable approximation for risk assessment in urban flood-prone areas, especially where detailed information about structures and their pertinent properties cannot be provided.

This study addresses the efficiency of ASCAT surface soil moisture (SSM) for calibration of VIC-3L hydrologic model over the Sefidrood river basin (SRB). Findings on daily time scale indicate that the correlation coefficient (CC) between observed and simulated streamflow, using ASCAT dataset, is equal to 0.75, while in the case of calibration of the VIC-3L model using only with hydrometric station datasets the CC value is equal to 0.80. Moreover, at monthly time scale the performance of model in simulating streamflow is better than daily time scale. The relative error (RE) in simulating runoff volume at daily and monthly time scale is 17.2% and 38.5%, respectively and this shows that using ASCAT dataset at monthly time scale leads to reliable results. In addition, one the advantages of model calibration using satellite-based SSm is the extraction of the spatial pattern of model parameters over the catchment, while in the case of using observed discharge dataset for calibration of the model only leads to the constant values of model’s parameter through the catchment. Based on the results of this research it highly recommended to use satellite-based SSMs for calibration of hydrological model, especially in data limited areas or regions in which ground observations are sparsely-distributed or insufficient.

The present study aimed to determine the chemical composition and in-vitro antibacterial activity of Ziziphora clinopodioides essential oil (ZCEO).
The chemical composition of ZCEO was determined using gas chromatography-mass spectrometry (GC-MS) analysis. Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) were calculated using the microwell dilution assay to assess the antibacterial effects of ZCEO emulsion and nano-emulsion on E. coli O157:H7.
A yield of 1% (w/w) was identified for ZCEO isolation and pulegone (58.78%), menthone (1.15%), and isomenthone (9.91%) as the key components of ZCEO phytochemicals. The MIC values of the ZCEO emulsion and nano-emulsion were 0.8 and 0.025 mg/ml, respectively, and the MBC values were estimated at 1.6 and 0.05, respectively.
It is recommended that the nano-emulsion of ZCEO be applied as a potential source of natural preservatives in food industries.