JWSS - Journal of Water and Soil Science

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Probable Maximum Precipitation (PMP) is the maximum possible amount of precipitation which could occur in a gauging station, a region, or in a watershed. Probable maximum precipitation is usually estimated by two general methods: the first is synoptic method in which short period (hourly) meteorological parameters such as dew point, wind speed and air pressure are used. The second is statistical method which is based on the statistical analysis of the 24-h maximum precipitations. In this study, the amount of 24-h PMP was estimated by Hershfield, Bethlahmy and modified Bethlahmy methods using date obtained from meteorological and Ministry of Energy over 15 or more years.

 The results showed that there exist large differences between statistical and synoptic methods however, there are rather smaller differences between Bethlahmy and synoptic methods. For modified Bethlahmy method, the results were multiplied by a coefficient of relative humidity. Then the calibrated 24-h PMP values were estimated for all meteorological stations of Iran and a contour map of 24-h PMP for the country was developed.

Results showed that a minimum value of 24-h PMP (110 mm) occurred in the central part of country and a maximum amount (260 mm) was found in both south and north parts of Iran.

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The present study evaluates the influence of the El Ninio Southern Oscillation (ENSO) phenomenon on the cold season precipitation over Isfahan, Fars, Khuzestan, Chaharmahal-Bakhtyari, Bushehr and Kohgiluyeh-Boyerahmad provinces. The results indicate that the occurrence of La Nina events caused a 20% to 50% reduction in precipitation over Bushehr, Chaharmahal-Bakhtyari and southern Fars. The cold event did not change the total precipitation over the other parts of the region. In contrast to La Nina episodes, the occurrence of El Ninio events caused a 20% to 70% increase in rainfall in most of the study area. While the most highly wet conditions are related to the El Ninio events, the occurrence probability of the severe droughts has found to be low during such events. In association with La Nina events, the occurrence probability of severe drought was found to be low. Only in Khuzestan and southern parts of the Fars Provinces, this probability has increased to about 0.5.

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The vast pastures and agricultural development plans for dry farming and irrigated farming in Khuzestan Province depend on rain. This requires availability of annual precipitation prediction models to be used in the management decision-making process. In this research, the long-term daily precipitation data from 15 rain gauge stations in the study area were collected for study and a relationship between the early fall season precipitations of 42.5 mm (t_42.5) and the annual precipitation was obtained. The results showed that the relationship was an inverse one such that the later the fall precipitation occurred, the greater the annual precipitation would be. To increase the coefficient of determination in the models, climatic variables such as Persian Gulf sea surface temperature and geographical characteristics (longitude, latitude, altitude, and long term mean annual precipitation) were used. Except for the long term mean annual precipitation and altitude, other variables did not increase the coefficient of determination. The final simple model found is as follows: P_a=184.787-1.891t_42.5+0.855P_m , R²=0.704 where, P_a is the annual precipitation, t_42.5 is the time from beginning of fall season for 42.5 mm of precipitation, and P_m is the long term mean annual precipitation.

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In Iran, about 75% of national rice production is supplied in Gilan and Mazandaran proviences which have the highest amount of precipitation. Seasonal prediction of rainfall induces significant improvement on yield production and on preventing climate hazardz over these feritle areas. Canonical correlation analysis (CCA) model was carried out evaluates the possibility of the prediction of winter rainfall according to the states of ENSO events. The time series of (southern oscilation index (SOI) and SST (sea surface temperature) over Nino's area (Nino's SST) are used as the predictors, and precipitation in Bandar Anzali and Noushahr are used as the predictands. Emperical orthogonal functions (EOF) were applied for reducing the number of original predictors variables to fewer presumably essential orthogonal variables. Four modes of variations (EOF1, EOF2, EOF3, EOF4) which account for about 92% of total variance in predictors field were retained and the others were considered as noise. Based on the retained EOFs and precipitation time series, the canonical correlation analysis (CCA) was carried out to predict winter precipitation in Noushahr and Bandar Anzali. The results indicated that the predictors considered account for about 45% of total variance in the rainfall time series. The correlation coefficents between the simulated and observed time series were significant at 5% significant level. For 70% of events the anomalies of observed and simulated values have the same sign indicating the ability of the model for reasonable prediction of above or below normal values of precipitation. For rainfall prediction, the role of Nino's SST (Nino4 in particular) was found to be around 10% more influential than SOI. .

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The influence of the Sea Surface Temperatures (SSTs) on the seasonal precipitation over northern and southwestern parts of Iran was investigated. The warm, cold and base phases of the SSTs were defined and the median of precipitation during each of these phases (R_w, R_c and R_b, respectively) was determined. The magnitude of R_w/R_b, R_c/R_b and R_c/R_w were used as criteria for the assessment of the effects of the alternation of SST phases on seasonal precipitation. The results indicate that in association with cold SST phase, winter rainfall is above median over western and central parts of the coastal region, central and southern parts of Fars Province and all the stations studied in Khozestan Province. On the other hand, the prevalence of warm SST phase has caused about 20% decrease in winter precipitation over the Caspian Sea coastal area and northern parts of both Fars and Khozestan provinces. In association with warm SST phase in winter, precipitation during the following spring was found to be above normal for all the stations studied in the coastal region of the Caspian Sea. The highest sensitivity levels were found in Bandar- Anzali and Astara for which spring precipitation has increased by 80% due to the dominance of warm winter phase. However, the occurrence of boreal cold SST events causes shortage of precipitation in the eastern parts of the coastal areas along the Caspian Sea. A Possible Physical mechanisem justifying the influence of the Caspian Sea SST on the Precipitation variability was introduced. According to this mechanisem, temporal and spatial variability of the Siberian High is forced by the fluctuations in these SSTs.

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The Madden–Julian Oscillation (MJO) known as the dominant mode of tropical and extratropical intraseasonal variability has an important role in the coupled ocean-atmosphere system. This study investigates the eastward propagation of the MJO and its impact on monthly (February-April) maximum and minimum precipitation in Fars Province. The positive and negative phases of MJO were categorized for the period 1979-2002. The maximum and minimum values of monthly precipitation was then determined for each phase as well as for the entire length of records. The given results have indicated that, in February, both maximum and minimum precipitation during negative phase were significantly greater than the corresponding values during the positive phase. This implies that the enhanced February precipitation and flood events are associated to the negative MJO phase. On the other hand, severe water shotage in February was linked with prevalence of the positive phase. The results for April were mostly found to be similar to February except that minimum precipitation was not significantly associated to the positive phase. In contrast to February, minimum monthly precipitation in March was found to coincide with the negative MJO phase. Maximum precipitation, however, could coincide with neither of extreme phases of MJO.

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Estimating spatial distribution of precipitation is vital to execute water resources plans, drought, land-use plans environment, watershed management, and agricultural master plans. High variation in amount of precipitation in various parts, lack of measurement stations, and the complexity of relationship between precipitation and parameters affecting it have doubled the importance of developing efficient methods in estimating spatial distribution of precipitation. Artificial neural network has been proved to be efficient as a new way for modeling and predicting the processes for which no solution and explicit relationship has been available in accurately identifying and describing them. The purpose of this study is to investigate the efficiency of artificial neural network in estimating spatial monthly precipitation. To achieve this objective, neural network with multilayer perceptorn topology was employed for preparing model for spatial monthly precipitation in five synoptic and rain-gauge stations located in Kurdistan province. In order to design the topology of the model in each station, as the adjustable parameters (including transfer function, learning rule, amount of momentum, number of hidden layers, number of neurons of the hidden layers, and the number of epochs) changed, different neural networks were made and carried out. In each case, the topology with the minimum amount of root mean square error (RMSE) was selected as the optimal model. Owing to the fact that the selection of each of the variable parameters of neural network necessitated recurring trails and errors, and consequently teaching a large number of networks with various topologies, genetic algorithm method was utilized for finding the optimization of these parameters the efficiency of this method, too, was examined in terms of the optimization of neural network. The findings indicated that neural network enjoys a high degree of accuracy in modeling and estimating spatial distribution of monthly precipitation. In addition, combining it with genetic algorithm method was positively evaluated in optimizing the requirements for executing neural network. In most cases, mixed method proved its superiority over executing neural network without optimization. The most precise model in all of the stations under study was achieved by the use of transfer function, sigmoid, learning rule of Levenberg Marquardt in the selected models, the determination coefficient (R2) observed between the model output amounts and the data observed in station were found to be 0.86 0.89 0.94 0.77 and 0.94.

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Drought is a natural feature of the climate condition, and its recurrence is inevitable. The main purpose of this research is to evaluate the effects of climatic factors on prediction of drought in different areas of Yazd based on artificial neural networks technique. In most of the meteorological stations located in Yazd area, precipitation is the only measured factor while generally in synoptic meteorological stations in addition to precipitation some other variables including maximum and mean temperature, relative humidity, wind speed, dominant wind direction and the amount of evaporation are also available. In this research it was tried to evaluate the role of the type and number of meteorological factor (as inputs of ANN model) on accuracy of ANN based drought prediction. Research area is a part of Yazd province containing only one synoptic and 13 non-synoptic meteorological stations. Three-year moving average of monthly precipitation was the main input of the models in all stations. The type of ANN used in this study was time lag recurrent network (TLRN), a dynamic architecture which was selected by evaluation of different types of ANN in this research. What was predicted is the three-year moving average of monthly precipitation of the next year, which is the main factor to evaluate drought condition one year before it occurs. For the Yazd synoptic meteorological station, several combinations of input variables was evaluated and tested to find the most relevant type of input variables for prediction of drought. However, for other 13 stations precipitation data was the only variable to use in ANN models for this purpose. Results in all stations were satisfactory, even where only one input (precipitation) was used to the models, although the level prediction accuracy was different from station to station. Result taken from this research, indicates high flexibility of ANN to cope with poor data condition where it is difficult to get acceptable results by most of the methods.

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The Madden Julian oscillation (MJO) is known as the primary mode of large-scale inter-seasonal variability in tropical regions, affectimg equatorial and sub-tropical climates. This study investigated the effects of the MJO on the occurrence of wet and dry spells in Fars province, central southern part of Iran, during November-April. Monthly precipitation data of nine stations spread over various parts of the province was analyzed during 1979-2005. Using two well-known MJO indices: MK and WH, the positive and negative phases of the MJO phases (enhanced and suppressed convective activity over the equatorial Indonesian region, respectively) were identified for monthly and seasonal scales. Precipitation-MJO composites were then constructed for the opposite phases. It was shown that for all the considered stations, seasonal precipitation during negative MJO phase was significantly greater (from about 2.5 to 6.0 folds) than the corresponding values during the positive phase. Moreover, the applied statistical tests proved that the frequency of wet or dry events was related to the prevalence of negative or positive MJO phase, respectively. As the positive MJO phase was engulfed, the probability of dry events varied from 60% to 84%. On the other hand, the probability of wet events was found to vary from 60% to 76% during the MJO negative phase.

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The geographical location of Isfahan province has led the province to be at risk of drought. One of the ways to mitigate drought is evaluation and monitoring of drought based on indices that can determine its intensity and permanence in each region. In this research, for drought and trend analysis standard precipitation index and Mann-Kendall test were used, respectively. Also, monthly precipitation time series of Isfahan province was applied to forecast drought from 1970 to 2009. For this purpose, Box and Jenkins modeling approach (1976) was used which has three main steps, namely model identification, parameter estimation, goodness of fit test or time independency and normal test of residual. The results showed that most of the stations in Isfahan province were faced with severe drought in the year 2000 and this situation was repeated one more time in 2008. Also, the results brought forth multiplicative models in all the stations. ARIMA (1,0,0) (0,1,1) showed the highest correlations between control and forecast data in Isfahan, Meime and Ardestan stations, and the model ARIMA (0,0,1) (0,1,1) displayed the highest correlation between control and forecasted data in Naein, Freydoonshahr, Khansar and Natanz. These models were selected as the best models through which the amount of precipitation was predicted till 2015. The trend of forecast data across Isfahan province showed that in most months the trend is not significant.

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Precipitation is an important element of the hydrologic cycle and lack of this data is one of the most serious problems facing research on hydrological and climatic analysis. On the other hand, using satellite images has been proposed by many researchers as one of practical strategies to estimate precipitation. The present paper aims to evaluate the accuracy of satellite precipitation data, provided by PERSIANN and TRMM-3B42 V7 in Gorganrood basin, Iran. To achieve this aim, two sets of daily precipitation ground-based data, 2003 to 2004 and 2006 to 2007, from six stations of Gorganrood basin, named; “Tamer”, “Ramian”, “Bahalkeh-ye Dashli”, “Gorgan Dam”, “Ghaffar Haji” and “Fazel Abad” have been used in this paper. The evaluation indices have been calculated and analyzed in different time scales, including daily, monthly and seasonal. The results indicated that the two above mentioned satellite models are not accurate in daily scale. However, they showed reasonable accuracy in monthly and seasonal scales. The highest correlations between satellites and recorded data in daily and monthly scales, for TRMM-3B42 V7 in “Gorgan Dam” and “Bahlke Dashlei” stations, are 0.397 and 0.404, respectively. The comparison of measured and satellite data of winter showed better agreement for PERSIANN model. However, TRMM-3B42 V7 shows better correlation in other seasons. The results also indicated that while TRMM-3B42 data displays higher correlation with measured data, PERSIANN provids better results in predicting the number of rainy days. 

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Since climate change is regarded as a serious threat to different parts of life cycle, separation of factors intensifying this phenomenon seems necessary. This research has investigated the temperature and precipitation trend using the multiple trend test in the upstream Karkheh basin located in west of Iran. For this purpose, two-dimensional graphs of temperature and precipitation anomalies of the CGCM3 Model (1000-year data) were drown for the study area. Then, the attribution of changes in climate variables due to climate internal fluctuations or greenhouse gases affected by human factors were investigated. Based on the findings of this study, in different parts of the study area, the range of natural climate variables for temperature and precipitation changes (95% probability) in the west of the study area are
± 1.4ºC and ±76%, respectively.

The results showed increase and decrease in temperature and precipitation in most of the studied stations, respectively. The variables of temperature and precipitation are affected by climate change and as we approach latest years, especially in the western and central parts of the study area, the impact of greenhouse gases in increasing temperature and reducing precipitation becomes more evident. According to the current results it can be concluded that changes in land use in Iran caused by human interventions can be introduced as a significant factor for the ascending trend of temperature. However, it can be noted that the most important factors of the increased greenhouse gases in recent years are human activities such as land use changes. These changes certainly have affected water resources in the study area.

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Improvement of soils is among the major concerns in civil engineering, therefore a variety of approaches have been employed for different soil types. The annual budget of implementing the projects of this kind in countries clearly implies the importance of the subject. The loose granular soils and sediments have always imposed challenges due to their low strength and bearing capacity. Bio-mediated soil improvement has recently been introduced as a novel link of biotechnology (biotech) and civil engineering for improving the problematic soils, i.e. utilizing some bacteria to precipitate calcite on the soil particles. Bio-grouting is a branch of Bio-mediated soil improvement which is a method based on microbial calcium carbonate precipitation. In this regard, the soil samples were stabilized by injecting the bacterium Sporosarcina pasteurii in the first phase of the process and Urea and Calcium Chloride in the second phase of the process (two-phase injection) as the nutrients into the sandy soil columns and subjected to unconfined compressive strength test. In this research, Taguchi method was utilized for design of experience (DOE). Based on results obtained, the activity of the bacteria caused the precipitation of calcium carbonate in soil samples so that after 21 days, the unconfined compressive strength of the soil increased from 85 kPa in the control sample to 930 kPa at optimum condition.

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In this study, the trends of changes of the standardized precipitation index in a 12-month timescale (SPI-12) and seasonal and annual precipitation were investigated in 21 humid and semi-arid stations of Iran during the 1976-2014 time period. After removing the serial correlation of some series, the trend of precipitation and SPI-12 was detected using the Mann-Kendall nonparametric trend test. The results revealed that the trends of annual precipitation had been declining in all stations over the past 39 years.  The seasonal precipitation trend in winter, spring, autumn and summer was downward in approximately 90, 95, 47 and 37% of the studied stations, respectively. In addition, the descending trend of wintertime precipitation was significant in Sanandaj, Khoy, Urmia, Hamedan, Mashhad, Torbat-e-heydarieh, Nozheh and Qazvin. Also, the temporal trend of SPI-12 was decreasing in all surveyed stations except Shahrekord. Furthermore, SPI-12 showed a significant downward trend only in Sanandaj and Fasa. Moreover, the most severe meteorological drought occurred in the period 1999-2000, in Ramsar, Urmia and Hamedan, and in the period 2008-2009, in Tabriz, Sanandaj, Shiraz, Fasa, Qazvin, Mashhad, Torbat-e-heydarieh, Shahrekord, Gorgan and Kermanshah stations. Overall, the results of this study indicated that the trend of precipitation in most studied sites, particularly in semi-arid parts of the northeast and southwest of Iran, has changed due to the severe and long metrological drought that has occurred in the recent decade (2005-2015).
 

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