Journal of Hydrosciences and Environment
Volume:2 Issue: 4, Dec 2018

This paper analyzes the temperature and rainfall data series collected by Dezful stations in a 31-year period (1986 to 2017) in order to evaluate the magnitude of these changes statistically and to forecast their behavior for the 2018-2020 period using SARIMA models. The Mann-Kendall test was used to analyze climate change in the past and future. The results show that rainfall has a decreasing trend and minimum and maximum temperatures have increasing trends. The results of the SARIMA model show that the coefficient of correlation (r) between the observed and forecasted values was 0.95, 0.9 and 0.58 for rainfall, minimum temperature, and maximum temperature and the mean absolute error (MAE) was 1.24, 1.45 and 20.24 for them, respectively. The results of trend analysis reveal that Mann-Kendall's statistics (Z-value) for the data on minimum temperature, maximum temperature and rainfall are 3.81, 1.78 and -2.71, respectively implying a descending trend for temperature and an ascending trend for rainfall. Minimum and maximum temperatures have been rising at the rates of 0.07 and 0.04°C per year, but they are forecasted to have increased by 0.084 and 0.06°C by 2020, respectively. The rate of rainfall variation will decrease from 4.4 mm to 4.85 mm per year. Improved understanding of recent climate change helps to elucidate the impacts and vulnerability of the local population in order to implement the most appropriate practices to cope with climate change and manage the changing situation in a better way.

It is crucial for environmental planning, land management, and sustainable development to be aware of the quantitative and qualitative characteristics of land changes. The use of vegetation maps is one of the important pillars of generating information for macro and micro planning. The present study employed the time and place of vegetation in Fars province. The data were derived from Landsat satellite data of OLI and ETM sensors for a 30-year period from 1986 to 2017, and the NDVI index was calculated. Moreover, quantitative values were classified for qualitative changes in vegetation. The index was classified into three groups: rich, poor, and vegetation-free. Temperature changes at the ground level were calculated using MODIS imagery for the studied period. The results revealed that quantitative and qualitative changes of vegetation over the studied 30 years was significant so that the vegetation-free areas were increased by 107.49, the areas with poor vegetation were decreased by 366.56 hectares, and the rich vegetation cover was decreased by 455.55 ha. The largest reduction in the area was related to the lands with rich vegetation. Investigating the surface temperature of the province with MODIS imagery demonstrated the rise in the surface temperature. The temperature difference was more than 3° (from -2.8°C to 0.96°C), and the highest temperature drop was observed in the eastern and central areas of the province. Finally, to investigate the relationship between vegetation and LST, the annual contamination lines were plotted along with the difference in NDVI over the studied period. The results revealed that in most areas with lower temperatures, the vegetation cover was denser. The statistical analysis between drought and vegetation indicated a significant relationship between these two factors.

The consumption of households and its structure has an important role in the rate of energy consumption and related air pollutant emissions. This study investigates the effect of changes in the structure of consumption in urban and rural areas on the emission of three main energy-related air pollutants (CO2, SO2, and NOX) in Iran during 2001-2011. To this aim, the environmentally extended input-output tables and structural decomposition analysis (SDA) were used. As the contribution of this study, the consumption of households is decomposed into two factors: total consumption and consumption structure. The results revealed that the changes in consumption structure in rural and urban areas increased the emission of all three air pollutants. The effect of changes in total consumption in both urban and rural areas outweighs the effect of changes in consumption structure. The negative environmental impact of urban households is worse than rural ones. The results at sectoral level showed that the changes in urban consumption structure had the main effect on CO2 emission in “water, gas and electricity” (135.68 Mt CO2), “chemical and plastics industry” (30.13 Mt CO2), and “food, clothing and textiles industry” (6.41 Mt CO2) whilst the changes in rural consumption structure had the main effect on CO2 emission in “water, gas and electricity” (25.39 Mt CO2), “chemical and plastics industry” (19.98 Mt CO2), and “food, clothing and textiles industry” (1.74 Mt CO2).

This study aims to evaluate Spatio-temporal distribution of rainfall through cluster analysis based on Tropical Rainfall Measuring Mission (TRMM) in Iran. To this purpose, the daily-gridded rainfall data were derived for the 1998-2013 period. Then, the seasonal and annual time scale cluster analysis was performed on TRMM-derived rainfall data. The results identified 14 clusters in the annual rainfall. As a high- rainfall zone, Guilan has an average rainfall of 1068 mm per year while Sistan, as a low-rainfall area, has an average rainfall of 106.8 mm annually. Furthermore, the seasonal rainfall distribution indicates high-rainfall zones in Guilan and Mazandaran provinces in spring, summer, and autumn while the areas in the southern range of Alborz Mountain and Persian Gulf are clustered as low-rainfall zone. The rainfall distribution in Iran is seasonal, and the following provinces are among the high-rainfall zones in winter: Kohgiluyeh and Boyer Ahmad, Chaharmahal and Bakhtiari, Khuzestan, and highlands of Lorestan. The low-rainfall areas also include Sistan and Baluchistan, Yazd, and Semnan.

Water consumption has increased in urban areas, so it is important to calculate its tariff. In Iran, Increasing Block Tariffs (IBT) is used to calculate the water tariff. The first disadvantage of this approach is that many hidden subsidies are given to wealthy and high-income people, which consequently leads to an unequal distribution of income. The next drawback is that consumers at the beginning of a higher block should pay their total water prices according to the price of that block. This paper proposes a fuzzy inference system (FIS) to tackle these problems. The results showed that when the FIS was used, the hidden subsidies for low-income and low-consumption households were increased when compared to the IBT method. This increase in subsidies is due to lower water tariffs for low-energy subscribers and tariff increases for high-consumption customers. The monthly water tariff was calculated for 5 m3 of consumption as to be 7,095 and 1,715 IRR (Iranian Rials) using the IBT and FIS, respectively. These values were calculated to be 370040 and 254960 IRR by these methods for a consumption of 40 m3/month, respectively. As it can be seen, the application of FIS indirectly shifts the hidden subsidies to low-cost subscribers. Another important finding is that FIS simplifies computational complexity of calculating water tariffs as compared to IBF and, at the same time, it does not cause a sharp increase in tariffs in a higher-consumption block; besides, the price is calculated more equitably. The results showed that the water tariff accounting system in Iran is more likely to result in the waste of resources and unequal distribution of water subsidies. It is suggested to make reforms in water pricing policies and to raise public awareness of water consumption reforms.

In Iran, water scarcity is one of the major constraints on agricultural activities.  The reuse of industrial and urban wastewater in agriculture can be a sustainable solution to water scarcity. This study is a comparative research work to evaluate the effect of irrigation with industrial (a sugar factory) and urban wastewater on soil chemical elements. It was carried out in a randomized complete block design with five treatments (well water, T1; treated urban wastewater, T2; 33% water + 66% treated wastewater, T3; industrial wastewater, T4; and combined water and wastewater at 1:7 ratio; T5) in three replication. The samples were taken from the soil depth of 0-40 cm in the agricultural land of Bori-Abad in Torbat-Heydarieh, Iran. The studied parameters included nitrogen, potassium and phosphorus contents, acidity, and salinity. The results revealed an increase in N, acidity, and K in soils irrigated with urban wastewater compared to that irrigated with industrial wastewater. However, soil P and salinity were lower in urban wastewater-irrigated soil. It can be concluded that wastewater can increase some elements of soil, contributing to its restoration.

Supply and demand management of Hirmand water resources is one of the most important problems faced by policymakers and they will not be able to manage this sector properly without specifying the future prospects of the Hirmand Area. The main objective of the research is to allocate water resources in the Hirmand Area by using dynamic optimization models in the agricultural and household sectors. The method of this research is based on the applied scientific method. The required statistics and information are obtained by the library method. In this research, the water demand functions in the agricultural and household sectors are achieved. The general objective function for determining the allocation is estimated by the EVIEWS and GAMS software packages. The results showed that for the agricultural sector, water demand is inversely related to water price, so that when the water price increases by 1%, the product will have a negative value decrease by 4.4%. Moreover, since in this model, the demand function for water is only a function of the price, the return to the scale is decreasing and the Isoquant Curve in the agricultural sector has a negative technical substitution rate in all aquatic conditions. In household demand, the results showed that with the increase in water price, consumption decreases and that the rise in price is a necessary, but not sufficient, condition for reducing consumption. The average consumption based on the current trend is 12.42 m3 per month for each household. The reaction in the amount of demand change versus the change in the number of stormy days is positive and equal to 0.39.