محمدرضا کوثری

Climate change can lead to changes in the frequency, intensity, and duration of extreme climate events in different parts of the world. The purpose of this research is to investigate temperature and precipitation extremes in Lorestan Province. The data used in this study included precipitation and the maximum and minimum daily temperature of nine synoptic stations in Lorestan Province during a 28-year (1990-2017) common period. The matrices of minimum and maximum temperatures and precipitation daily data for each station were prepared and used to compute the extreme climate indices (26 precipitation and temperature extreme indices based on the recommendation of CLIVAR \ CLL expert group) using the R programming software. The results of studying the trend of cold and hot extreme weather indicators during the period 1990-2017 in the province using the Mann-Kendall trend test showed that for all stations, the hot indices have increased and more cold indices have decreased. In different regions of the province, positive and negative trends of hot and cold indices with different intensities have occurred. The highest upward trend of the warm extreme indices has occurred for the hot night’s index. Among the cold indicators, the greatest decrease occurred for several frost days and cold days indices. The decreasing trend of ice days is significant for 45% of stations at 99% level and the decreasing trend of cold days for 77% of stations at different levels of 90, 95, and 99%. The results of the study of the frequency of occurrence and trend of precipitation extreme indicators in Lorestan province showed that the total rainfall of this province, like many regions of the country, has decreased. In contrast, the occurrence of maximum rainfall in addition to being significant in the province has an increasing trend during the period 2017-1990. These conditions can indicate an increase in the number of occurrences of heavy and short-term rainfall events and, shorten the period of the rainfall season in the region.

Drought is a temporary disorder whose characteristics vary from region to region, therefore, it is not possible to define a complete and absolute definition of drought. Drought is one of the most important natural disasters that can occur in any climate regime. Since drought is unavoidable, it is important to know it in order to optimally manage water resources. Drought prediction can play an important role in managing this phenomenon. In other words, recognizing and predicting this phenomenon is one of the topics of interest for scientists who are interested in solving the problem of water shortage. More than 80% of Iran's area is covered by arid and semi-arid regions and lack of rain is a predominant phenomenon in this region. So far, several methods have been proposed to predict drought. Each method offers different results in specific conditions.  Therefore, identifying the best method for predicting drought in the climatic conditions of central Iran is essential. 

In this research, in order to introduce a suitable method for predicting drought for the next month, four methods of artificial intelligence including Deeplearning (using the Alexnet network, one of the convoluted networks), K nearest neighbor algorithm (KNN), multi-class Support vector machines (SVM-MultiClass) and decision tree have been used. Monthly rainfall data from 11 syntactic stations of Yazd province during the 29-year statistical period (1988 to 2017) were used as experimental data. Standardized precipitation index (SPI) was calculated to indicate drought status in terms of severity and duration on 1, 3, 6, 9, 12 and 24 month time scales. Precipitation data was used as neural network input and SPI classification as network output and 80 percent of the data was used for training and 20 percent for testing the networks.In this study, the Recurrence Plot method was used to interpret the time series to convert these series into images and RG and B pages were created. To convert rainfall data into photos at 1, 3, 6, 9, 12 and 24 month time scales, photo layers were created using a standardized rainfall formula, and by merging these three output layers into colored photos and black and white photos were obtained. Using three pages created in MATLAB software and merging them, the output was in the form of a photo, which was placed as the input of the Alexnet network. Combination of Recurrence Plot to create images and deep learning network for classification of drought data has been used for the first time in this research. To evaluate the effectiveness of the classification strategy, standard criteria of accuracy, micro-F1 and macro-F1 were used.

The results showed that all networks were able to predict drought. However, on short time scales such as 3 and 9 months, the accuracy assessment criteria for some classes are zero and the methods of learning from these classes are practically ignored due to their lack of data. But on a larger time scale, this issue has been addressed and the data of those classes are well categorized. Deep learning network with image input could not predict well in the short term due to lack of data, but in the long term due to increased data has improved its performance and had the best performance. The SVM method at different time scales has shown unreliable and variable behaviors that can not be said to be a suitable method for predicting drought at different time scales. Decision Tree and KNN methods have been able to predict drought better in the short term than in the long term. The two methods have been closely related. .Based on the Deeplearning network macro-f1 evaluation criterion, the one-month time scale with 22.71% was the most inefficient method and the Decision Tree with 64.65% was the most efficient method, But with the increase in time scale, the Deeplearning network improved its performance, so that at the 24-month time scale with 65.35%, the best performance for the Deeplearning network followed by the SVM-MultiClass network with 57.40%. For future research, it is suggested that Decision Tree and KNN methods be used to predict short-term drought. In this study, with increasing the time scale and increasing the data used, these two methods have lost their effectiveness compared to the short term.
 

One of the most common and effective problems in long-term climate studies is the presence of gaps in the time series of various climatic and hydrological data. Therefore, the present study evaluates the accuracy of methods for infilling missing data of daily, monthly and annual temperature time series in the arid regions of Iran. For this purpose, the observed daily minimum, average and maximum temperature data for the period 1987-2014 measured at 73 synoptic stations distributed all over arid regions of Iran were used. Methods of readjustment used include: Normal ratio method, linear regression, multivariate regression and Inverse Distance Weighting (IDW). In this study, the capability of each mentioned methods for infilling missing data of daily, monthly and annual precipitation time series in the arid regions of the Iran was investigated, while the proportion of missing data varies from 5 to 50% of total data. In order to compare and evaluate the accuracy of the four mentioned methods three statistical indicators, namely the correlation coefficient (R), the Root Mean Square Error (RMSE) and Nash coefficient were used. The results showed that in general, each of the methods mentioned had different functionalities at a special level of readjustment and time scale. On annual and monthly scales, linear regression and normal ratio methods are the most accurate method in readjustment temperature data in the arid region of Iran. The correlation value between the readjustment and observational data at different levels reaches more than 0.95 using these methods. On the daily scale, there is no significant difference between the accuracy of the methods used in the readjustment of temperature data, and almost all four of these methods have appropriate accuracy because in all methods the correlation between readjustment and observed data is more than 90%. However, multivariate regression methods with an average correlation of 0.99 showed the most accurate performance in readjustment daily data at different levels of readjustment. Generally, each method should be used in accordance with the conditions, and therefore it is recommended to develop a software package for infilling missing data.

Missing data are common issue in climate data. Also precipitation is a very important part of the hydrological cycle and meteorological and hydrological studies of watersheds, initially depend on the quantity and quality of recorded rainfall data and its distribution in the area. Complete and reliable sets of climatic and hydrological data are required for planning and design of these projects. Therefore for treatment of precipitation missing data, various methods have been developed and applied. Normal ratio method, linear regression, multivariate regression and inverse distance weighting (IDW) have a wide applications in natural resources study in our country. Therefore, it is necessary to determine the ability of these methods, especially in relation to the precipitation parameter, which plays a crucial role in the study of natural resources. In this study, the capability of each mentioned methods for infilling missing data of daily, monthly and annual precipitation time series in the arid regions of Iran was investigated for varying proportion of missing data from 5 to 50% of total data. In fact, the main purpose of this study is to answer the question of which of the four mentioned methods are more effective for infilling precipitation missing data. The daily data of Iran’s synoptic meteorological stations were used for the present study. Using the Run homogeneity test, the data homogeneity was investigated. Also, using graphical exploring data, and especially boxplot diagrams, outlier data were identified and flagged as missing data. The average annual precipitation and temperature of 400 stations were determined, and then based on these data their de Martonne coefficients were computed. In the next step, stations with de Martonne coefficient less than 10 were selected as arid climate. Among them, 73 stations that had sufficient data from 1986 to 2017 were distinguished. To evaluate each of the data reconstruction methods, part of the actual data was deliberately discarded from the original data and then reconstructed. Due to high volume of calculations, this process was programmed in MATLAB software. The results showed that each method had different functionality according to the conditions. Daily data are not well estimated using the normal ratio method to estimate the missing data less than the actual one. The use of linear regression method showed that in daily time scale, unlike the normal ratio method, the model accuracy in data reconstruction is higher. For linear regression approach, the distance between the fitted line between the observed and estimated data is small at first, and as the precipitation increases, this distance increases, indicating that the model is less accurate in estimating the extreme values. Given that the fitting line is below the 1:1 line, the linear regression method estimates the actual values below normal. The same results can be found for IDW producer. The multivariate regression method is more accurate for daily time series when the proportion of missing data are not considerable, but is generally very sensitive to the proportion of missing data. The normal ratio method is not suitable for reconstructing daily missing values, however it is more stable than other methods when missing data increase. In monthly time series, the performance of the IDW method and then the normal ratio is better. In annual series, linear correlation, normal ratio, and IDW have better performances, respectively. The findings of this study show that in general, the accuracy of reconstructions on annual scales is more than monthly and on monthly scales is higher than daily. This is due to smoother time series in the monthly and annual time series than the daily ones. Also it should be noted that the scale of current studies is in Iran. If the data from the reserved rain-gauge stations of the Meteorological Organization and the Ministry of Energy are added to this data, the accuracy of the methods is expected to increase. As the results of the present study show, the accuracy of the models decreases with increasing incomplete data ratio. Therefore, if new data is included in missing data processing, there is an expectation of better performance of each of these methods. Finally it should be considered that each method should be used in accordance with the given conditions, and therefore it is recommended to develop a software package for infilling missing data in Iran.

Recognizing watershed problems and investigating the causes which arose them is the most fundamental step in watershed management plans, which results in identifying key management responses in each watershed. Therefore, it is necessary to thoroughly analyze the problems and determine management strategies before developing operational plans to solve them. The aim of this study was to analyze the environmental problems of the Gorganrood Basin to determine the main managerial responses. Identifying and analyzing the main environmental problems resulted in developing management responses for the region. To prioritize different components of the DPSIR, a Likert questionnaire filled with expert opinions and the Friedman test were used. The results indicated that four driving forces, namely climate change, population growth, management structure, and rules have caused 25 pressures on the Gorganrood Basin resources. The most important pressures were part-thinking organizations, incorrect agricultural expansion and excessive exploitation of water resources. These pressures, in turn, have caused 10 disturbed states, the most important of which are water resources depletion, flooding and soil erosion. These situations have 13 adverse impacts, the most important of which are flood damages, reduced downstream water rights and reduced public confidence in the government agencies. To improve the situation, 28 management responses were presented, of which the most important are integrated watershed management, land use planning and proper organizational structure. The overall result is that cause and effect analysis using the DPSIR framework provides an appropriate conceptual infrastructure, which is a prerequisite for developing the Basin's operational management plan. Therefore, it is suggested that executive organizations identify suitable responses by using this approach before developing operational plans for each watershed.

Precipitation forecasting has important role in water resource management especially in arid regions of Iran. This study aims to explore the relationships between the seasonal precipitation and weather signals such as NINO’s SST including NINO1+2، NINO3، NINO4 و NINO3.4 and SOI as well as MEI and NAO. The correlation analysis was performed in two states involving the correlation analysis of weather signals with one year lag in seasonal precipitations and the correlation analysis without the lag. Also, precipitation forecasting was performed through using partial least squares regression (PLSR). Results showed that MEI, SOI, NINO1+2، NINO3 and NINO3.4 have the most correlations with winter seasonal precipitation when the one year lag is performed. The most correlation refers to NINO1+2 equal to +0.68. This value for the SOI is -0.61 which exhibited the inverse correlation of winter precipitation with SOI in the past year. The time series without the lag showed the most correlation between the summer and autumn NAO and winter precipitation of the same year. Also, results indicated the acceptable performance of PLSR for precipitation forecasting. With the one year lag the winter, spring, summer and autumn precipitations were estimated with the RMSE equal to 12, 9.9, 0.85 and 6.2 mm, respectively. Also, the Nash–Sutcliffe (NS) model efficiency coefficient for the mentioned seasons is 0.69, 0.22, 0.2 and 0.72, respectively. The R correlation coefficients for these time series were equal to 0.83, 0.46, 0.45 and 0.85, respectively. In general, precipitation was predicted more accurately in the cold seasons. The development and use of such prediction models could make water resource management programs more successful.

Drought is one of the most costly natural hazards and is more dangerous in arid and semiarid regions which suffer from limited water resources. Climate change can also affect drought intensity; therefore, it is essential to analyze the trends of drought, particularly in these regions. This study surveyed the trends of meteorological and agricultural drought in hyper-arid, arid and semiarid regions of the world using the standardized precipitation index (SPI). Short time scales of 1-, 3- and 6-month SPI (moderate to severe and extreme drought) were derived for the period of 1970 to 2014 from the gridded precipitation data provided by CRU. The trends of the time series were computed using the Mann-Kendall nonparametric statistical test. The results showed that non-significant trends were dominant; however, some territories showed significant upward and downward SPI trends in different parts of the study regions. The significant negative SPI trends, which imply increased drought, were observed in the Middle East, central Australia and western North America. An increase in drought severity can increase consumption of water resources, particularly in developing countries.

The lack of sufficient fresh water resources, climate change and lengthy and severe droughts are of the major today’s world challenges particularly in arid and semi-arid regions. In the current conditions, especially in the case of droughts and in order to water resources planning, it is essential to analyze the changes and trends in drought particularly in hyper-arid, arid and semi-arid regions of the globe. In this study, Mann-Kendall statistics were applied to determine the significant and non-significant positive or negative trends in long term drought time series (9, 12, 18 and 24 monthly time scales) in moderate to severe and extreme classes of droughts derived based on the Standardized Precipitation Index (SPI). Results indicated the both significant positive and negative trends can be found in these regions. By increasing the time scales of droughts time series, the percentage of significant upward or downward trend grow. For example, in the case of decreasing trend of SPI (increasing in the drought), the percentage of 17.8 for 9 monthly SPI increased to 21.9, 28.48 and 31.71 for the 12, 18 and 24 monthly SPIs, respectively. The increasing in long severe and extreme classes of drought can be found in Middle East and west of America. The upward trends of drought particularly in Middle East region which is suffered by expensive tensions among the communities can intensify current critical situations. Therefore, more attention should be paid to this subject particularly in regional and international scales.

Any change in the pattern of precipitation can have a significant impact on the water resources availability, agriculture, and ecosystem. Therefore, knowledge on rainfall trends is an important aspect of water resources management. In this study time series of moving summation with ranks of 1 to 48 months were studied in the 32 national synoptic stations using nonparametric Mann-Kendall method (after removing the correlation value of the first series) and Sen’s slop stimulator. The Results indicated abundance of trends in the long series compared to the short-term series (in total 60% of the obtained trends have significant downward trend, 32% showed a significant upward trend, and 8% were in non-significant trend). Spatial distribution of significant downward trends in most parts of the country is considerable. The present condition can be a serious threat to groundwater resources in most parts of the country since the significant portion of water resources in Iran is dependent on the groundwater resources and these resources are in direct contact with long series of precipitation.

Drought has occurred as a natural disaster and an inevitable phenomenon across many parts of the world, especially in arid areas for a long time. To monitor draughts and to determine their extent and indices, meteorological data, such as those obtained from precipitation, can be of use. In the absence of these data, techniques of remote sensing are useful tools in monitoring droughts. The city of Yazd located in the central plateau of Iran is surrounded by deserts, and the deserts have a great impact on the occurrence of severe droughts in the region. The present study is based on the data obtained from 25 synoptic stations in Yazd province within a 20-year period (1990-2010) to determine the weather SPI and the RDI index in long series in 3, 6 and 18 months. For this purpose, satellite images were used in the form of bands of MODIS images from 2000 to 2010 to assess the remote sensing parameters of VCI and LST. After interpolation of the meteorological indices, the correlation between them was investigated. As the results show, the correlation was relatively low. Therefore, to verify the initial results, the calculation of the correlation indicates was repeated using the station points. The final results showed a good correlation so as for SPI and VCI to be found approximately 0.5 to 61.3 % and for RDI and LST to be between 25.7 and 82.1 respectively. Based on the results, distance sensing indices can be appropriate alternatives for meteorological indices in drought studies.

Estimation of time of concentration has considerable role in physiographic and hydrologic studies of watersheds. Especially, it affects on estimation of peak discharge in hydrological studies. However, there are many various equations for computating of time of concentration, which have not been analyzed by sensitivity analysis. So, in this study, beside of introducing of new straightforward method for sensitivity analysis of simple equations, four common applicable equation of time of concentration in Iran (e.g. Kirpich, California, Bransly Williams and SCS) have been analysed by sensitivity analysis. Results show that very low amount of changes in the slope, especially in low slope reaches, have considerable effects on time of concentration. While, increase in slope of stream, the effects of stream length or curve number (CN) parameters on time of concentration increases. In the other hand, the effect of circularity coefficient on time of concentration is not so considerable. Generally, results show that the measuring or estimating of stream slope, length and curve number need to more attention and correction.

Iran is a part of the world's arid and semi-arid areas and encounters severe lack of water resources. Evapotranspiration is a major component of the hydrological cycle and its precise determination has a high importance in the studies of hydrological balance measurement, design and management of irrigation systems, product yield simulation and design and water resources management. At present, there are various methods for calculating the reference evapotranspiration. According to some researches, FAO-Penman-Monteith method is the most accurate method for the estimating of both dry and wet weather conditions after lysimeter weight method. In this study, the amount of reference evapotranspiration was calculated using FAO-Penman-Monteith method in 29 selected synoptic and climatology stations in Yazd. Then, they were classified in two ways: Firstly, through an establishment of a relationship between evapotranspiration parameters and height, and secondly, through Inverse Distance Weighting (IDW). Finally, variations of reference evapotranspiration in different land cover throughout the province were analyzed. Method Reference evapotranspiration maps show that the amount of evapotranspiration is higher in central, southern and some western areas of the province than in northern and eastern areas.