فهرست مطالب پوریا محیط اصفهانی

In the present study, the variable of the time interval between the peak flood and the peak of drought in thirteen basins of Karkheh basin was investigated. Since the time interval variable from the flood peak and the peak of previous drought has a discrete nature, eight well-known discrete probability distribution functions were tested for frequency analysis. In general, the results of the research showed that in most of the studied small and fourth-degree basins, with the increase in the intensity of annual droughts, the corresponding peak flood discharges will decrease up to SPI= -1, and after that, as the droughts continue to intensify (SPI <-1), the discharges will decrease. The flood peak will increase with an exponential and quadratic pattern. While in large basins and the third level, with the intensification of drought, the peak discharges of floods will also decrease. The results of the frequency analysis determined that the probability distribution functions of DPO and NBI have the most appropriate fits to the time interval variable between the peak of flood and the peak of drought. Based on the frequency analysis, it was found that the time interval between the peak of flood and the peak of drought is approximately 6 to 8 months. The defined variable reflects the interaction and impact of two extreme events of flood and drought in a basin.

The present study was conducted to investigate NPP changes and its relationship with drought. The monthly time series of NPP for the country's rangelands was prepared from the sum of net photosynthesis production (PSN) of the eight-day MODIS sensor (MOD17A2H) with a spatial scale of 500 m for 2000 to 2022.The monthly rainfall data of 165 synoptic stations were obtained from the National Meteorological Organization to calculate the Standard Precipitation Index (SPI) and monitor droughts in this period. Box plots of precipitation and NPP were prepared and showed that the highest values of NPP with an average of more than 50 gC.m-2month-1 occur during spring and early summer in humid areas. Results showed that NPP is increasing in Iran's rangeland ecosystems, and on average, the NPP of all rangeland ecosystems in the country is about 112.6 ± 13 gC.m-2.year-1  ,in other words, about 76 million tons of carbon per year. It is absorbed by the plants in the rangeland ecosystems of the country. The trend of changes in the monthly series of NPP was not significant due to the influence of seasonal characteristics, but the annual changes of NPP showed a significant trend of increase in all regions.

Introducing reliable regional models to predict the maximum discharge of floods using characteristics of sub-basins has special importance in terms of flood management and designing hydraulic structures in basins that have no hydrometric station. The present study has tried to provide appropriate regional flood models using generalized linear models (GLMs) to estimate 2-, 10-, 50-, and 100-year maximum daily discharges of 62 sub-basins in Great-Karoon and Karkhe basins. According to the results, the sub-basins were categorized into four sub-regions based on some physiographic and climatic characteristics of the study sub-basins. The results showed that regional flood modeling was successful in all sub-regions except sub-region II, which includes very large basins (A̅≈17300 km2). The adjusted R2 of the best models in sub-regions I, III, and IV were estimated at around 82.4, 91.3, and 90.6 percent, and these models have a relative error (RRMSE) of around 9.5, 9.23, and 6.7 percent, respectively. Also, it was found that more frequent floods with 2- and 10-year return periods are influenced by properties such as basin’s length, perimeter, and area, while rare floods with 50- and 100-year return periods are mostly influenced by the river systems characteristics such as the main river length, total lengths of the river system, and slope of the main river. According to the research, it can be stated that the behavior of maximum daily discharges in the study area is extremely influenced by the different climatic and physiographic characteristics of the watersheds. Therefore, the maximum daily discharges can be estimated accurately at ungauged sites by appropriate modeling in gauged catchments.

Investigation of homogeneity regions using univariate characteristics is an important step in the regional frequency analysis method. However, some hydrological phenomena have multivariate characteristics that cannot be studied by univariate methods. Droughts are one of these phenomena their definition as univariate will not be effective for risk assessment, decision-making, and management. Therefore, in this study, the regional frequency analysis of drought was studied in multivariate methods using SEI (Standardized Evapotranspiration Index), SSI (Standardized Soil Moisture Index), and SRI (Standardized Runoff Index) indices in the Karkheh River basin from 1996 to 2019. The indices calculated probabilistic distribution between the variables of evapotranspiration, runoff, and soil moisture using multivariate L-moments method and Copula functions and considered meteorological, agricultural, and hydrological droughts simultaneously. The results of multivariate regional frequency analysis considering the Copula Gumbel as the regional Copula showed that the basin is homogeneous in terms of severity of SEI-SSI combined drought indices and is heterogeneous in terms of severity of SEI-SSI combined drought indices. However, after clustering the basin into four homogeneous areas in terms of characteristics of SPI (Standardized Precipitation Index), the basin is homogeneous in all areas in terms of univariate SEI, SSI, and SRI indices and is heterogeneous in the third and fourth clusters of SRI and SSI drought indices. Pearson Type (III), Pareto, normal, and general logistics distribution functions were found suitable to investigate the characteristics of SEI, SSI, and SRI drought indices in this case. Finally, large estimates of the types of combined droughts and their probability of occurrence showed that the northern and southern parts of the Karkheh River basin will experience short and consecutive droughts in the next years. Droughts in areas without meteorological data can be predicted in terms of joint probability using the multivariate regional frequency analysis method proposed in this study.

Over recent decades Iran and especially its western regions have suffered from devastating floods. Planning and designing hydraulic structures require information from flood magnitude corresponding to a given occurrence probability. Hence, in this study, we aimed to determine flood homogeneous regions and regional growth curves using L-moments and flood index method for annual maximum flow of 62 sub-basins in Great-Karoon and Karkhe basins. Moreover, the impacts of droughts and hydraulic structures on the stationary and stochastic nature of floods were investigated using Mann-Kendall trend test, Pettitt’s change point detection test (CPD) and graphical interpretations. It was found that there are five hydrological homogeneous regions in the study area. The goodness of fit tests results showed that the Generalized Pareto (GPA) distribution for the first and second regions, the Generalized Normal (GNO) distribution for the third region and the Generalized Logistic (GPA) distribution for the fourth and fifth regions are the most appropriate regional statistical distributions. Also, maximum daily flows corresponding to 2, 5, 10, 25, 50, 100, 500 and 1000-year return periods were estimated for all study sub-basins.  Regional flood frequency analysis revealed that for 100-year return period the fifth region’s sub-basins, with 4060 m3/s discharge on average, have the highest maximum daily flow. The average 100-year maximum daily flows in the regions I, II, III and IV were estimated around 632, 682, 460 and 429 (m3/s), respectively.

During last century, anthropogenic forcing and climate change have influenced the stationarity of the many extremes. Therefore, using of stationary pre-assumption-based Frequency Analysis (FA) techniques for estimating the risk of extremes may not be reliable anymore. In present study we introduced a nonstationary FA method to estimate risk of 24-hour maximum precipitation variable, as one of the major factors for generating floods in West of Iran. Nonstationarity assessments showed that among 53 investigated stations, only 4 stations (i.e., Khorramabad and Poldokhtar stations with increasing linear trends and Izeh and Ramhormuz stations with decreasing linear trends) have significant nonstationarity. Three hydrological homogeneous regions were characterized based on statistical properties of 24-hour maximum precipitation variable at study stations. Results revealed that stations in the Region_II, mostly including the stations in Fars, Chaharmahal and Bakhtiari, Bushehr and Kohgiluyeh and Boyerahmad Provinces, have higher risks for more severe 24-hour maximum precipitation compared to stations in other regions. Also, results showed that if nonstationarity in Khorramabad and Poldokhtar stations is ignored, 100-year quantiles of 24-hour maximum precipitation will underestimate around 10.1% and 13.8% in those stations.

Flood control and management is a fundamental issue for hydrology researchers and managers. Regarding the design and construct of different hydrologic structures such as reservoirs and dams as effective techniques for flood control, the appropriate estimation of the dimension and resilience of these structures needs correct estimation of the magnitude and return periods of flood for which the design flood in different return periods or probability level is applied. Design flood estimation is done through frequency analysis with the key stationary assumption. Nowadays the factors such as land use change, inappropriate management and climate change has influenced stationary conditions of flood peaks. Therefore, in the existence of non-stationary, the estimation based on stationary assumption is not confident and may have large errors. In this regard, this study for non-stationary flood frequency analysis the GAMLESS model for location, scale and shape parameter estimation as well as visual inspection of non-stationary are introduced and developed for quantile estimation in non-stationary conditions. Six hydrometery stations from different provinces in the North of Iran were selected. Frequency analysis with and without stationary assumptions was done for each station. Results indicated that location and scale parameters have trend with the linear and quadratic manner. In addition, results indicated that design flood estimated by non-stationary procedure has increased around 3 times higher than stationary estimation in Nodekhormalo station. Results also demonstrated that in a station with increasing non-stationary trend, return period of big floods is decreasing and for the same return periods, flood quantiles has increased.

Drought, as one of the most complicated natural events, causes many direct and indirect damages each year. Hence, single variable identification and monitoring of drought may not be appropriate enough for decision-making and management. In this study, in order to monitor the meteorological-agricultural drought in Chaharmahal and Bakhtiari province, Multivariate Standardized Drought Index (MSDI) was calculated using precipitation and soil moisture variables. In addition, to evaluate the performance of MSDI in drought identification and monitoring, Standardized Precipitation Index (SPI) and Standardized Soil Moisture Index (SSI) were used for meteorological and agricultural drought monitoring, respectively. MSDI was calculated based on the soil moisture and precipitation joint probabilities. We used the Gringorten probability as an empirical method and Archimedean copulas as the parametric method to calculate the joint probability between soil moisture and precipitation time series. The results indicated that MSDI was twice more capable of detecting drought as SSI and SPI. Furthermore, the MSDI-based drought monitoring results showed Charmahal and Bakhtiari province had experienced severe meteorological-agricultural drought in 2000, 2008, 2011 and 2014.

Today, there are many factors affecting watersheds, consequently changing natural ecosystems succession. The aim of this study was to investigate the effect of drought events on land use changes at Doiraj Watershed of Ilam Province from 1996 to 2015. For this purpose, Landsat satellite images were used for land use change monitoring and Standardized Precipitation Index (SPI) was applied for drought characterization. The results showed that during the two last decades, the forest and rangeland have been continuously destroyed. However, in the first period (1996-2006), land use changes and land degradation have been more intensive than the second period (2006-2015). Generally, in the past twenty years, about a quarter of the forest land and more than half of the rangeland had been destroyed or changed to become other land uses. Drought monitoring results also showed an extremely wet condition in 1996 and normal and near normal conditions for 2006 and 2015. Therefore, we can conclude that in the first period, in addition to human activities, changes in drought condition as a climatic factor could affected  land use change and land degradation.