Journal of Hydrosciences and Environment
Volume:3 Issue: 5, Jun 2019

In general, land and water resources are in decline due to the rapid population growth, urbanization, and industrialization. Indeed, demand for these resources has increased sharply making it essential for sustainable development to make optimal use of water and soil. In this paper, we have tried to study the morphometric characteristics of Tuyserkan Basin. Morphometric parameters for the basin were determined using ASTER images in Geographic Information System. The drainage area of the watershed is 364.82 km2 and constitutes a 5th-order drainage basin. The result indicates that the drainage area is 364.82 km2, the perimeter is 324.23 km, the basin length is 37.68 km, the drainage texture is 0.07, the constant of channel maintenance is 9.72, the stream frequency is 0.06, the drainage density is 0.1, and the length overflow is 0.18. A mean bifurcation ratio of 4.29 to the drainage pattern has not been disturbed by structural disturbance. The form factor of 0.25, the circularity ratio of 0.043, and the elongation ratio of 0.57 indicate that the basin is sub-circular to elongate in shape. The results of the analysis of features of the Tuyserkan basin indicate that the watersheds have a moderate roughness. The dendritic drainage network shows the homogeneity of the texture. This type of basin structure helps to explain various parameters of the earth, such as the nature of the earth, penetration capacity, runoff production, and soil erosion. The results of the drainage density and the flow rate of the average permeability are shown.

Every year, significant amounts of freshwater are disappeared from watersheds by evaporation. Many regions of the world have been struggling with arid and semi-arid climates in recent decades. The problem of water shortage that is induced by water evaporation from lakes and water reservoirs or water use for irrigation and domestic consumption has turned into a grave challenge, especially in summer. The present experimental study focuses on the accurate measurement of evaporation and presents a novel approach to decreasing this phenomenon in the south-east of Iran (Zahedan). For this purpose, four ponds with dimensions of 120x95x70 (length, width, and depth) were constructed in Zahedan’s Meteorological Organization field in order to be compared with the results of induced class-A evaporation pan. Two daily time periods (30 days) were selected to measure hydrologic parameters with respect to the spring and summer conditions as well. After the experimental results were evaluated, six relations were extracted for the accurate estimation of evaporation in arid zones. Finally, a composition type of glass physical covers was fabricated and used to decrease evaporation. The cover was made of waste glass, rubber and some glue for the buoyancy. Solar radiation and air free oxygen were allowed to be in contact with the water surface. The results showed that the rate of evaporation decreased by up to 40 percent using these covers just for the 50 percent of the surface covering.

Groundwater plays an important role in providing water supply especially in arid and semi-arid regions such as Iran. Given globally water crisis, monitoring and analyzing water levels can help water resources managers and planners for sustainable utilization and management of water supplies. On the other hand, groundwater processes exhibit dynamic, temporal and spatial patterns; making groundwater fluctuation modeling a complex and challenging task. Among different modeling methods, artificial neural networks (ANNs) are regularly used for complicated problems due to their distinctive and powerful properties. Qom plain in Iran is an arid region whose groundwater utilization in the last decades has led to downfall in water table. In this study groundwater level fluctuations were investigated in two distinct wells in this region using monthly groundwater level data recorded for 11 years. For modeling, the ground water time series of each studied well were entered as the input and output to the network and Time delay neural networks (TDNN) with various network structures and input delays were used for achieving the best results. The findings of the best modeling structure represented fair fitting for forecasted results in comparison with observed data, hereby underlining the promising application of this method for groundwater level modeling.

Given the entrance of oil to the northwest of the Persian Gulf from the oilfields in this ecosystem,an investigation was carried out in 2017 to evaluate the historical records of hydrocarbons, source identification, and chemical fingerprinting. To these purposes, sediment core samples were collected from the northwest of the Persian Gulf. Then, compounds (n-alkanes, PAHs, hopane, and sterane) were extracted with a Soxhlet system and two-step column chromatography and were analyzed by a GC-MS instrument in a laboratory. Also, oil samples from five oilfields in the study area were analyzed for the chemical fingerprinting goal. Overall, the results showed petrogenetic sources in recent years in the Persian Gulf. Common petrogenic hydrocarbon source was strongly suggested in all layers by the presence of unresolved compounds resolved (UCM), lower molecular weight/higher molecular weight (LMW/HMW) and carbon preference index (CPI) ratios of n-Alkanes and PAHs in source identification can be effective in precisely specifying hydrocarbon sources in complicated mixtures ecosystems. Furthermore, multivariate analysis and chemical fingerprinting of n-alkanes, PAHs, hopanes, and sterane confirmed that the Hendijan crude oil can be a source of the sediment pollution in the study site.

Erosion phenomenon and sediment transition are among the most complex hydrodynamics problems, so their governing equations cannot be determined easily. This paper used the Imperialist Competitive Algorithm (ICA) to predict the daily suspended sediment loads (SSLs) of the Zarinehrood River. The input was composed of SSL of the sedimentary station located in the Zarinehrood River during 2005-2015 (10 statistics years) with the flow discharge variable during this month. The results of implementing ICA showed the high accuracy of the ICA showing R2 = 0.89 and RMSE = 235 mg/L. In general, the results obtained from the ICA used in this paper showed the high capability and accuracy of the ICA in estimating SSL of the Zarinhorod River consumption reforms.

Reduced rainfall, groundwater resource limitations, and undesirable use of it are the main limiting factors of the development of different uses in arid and semi-arid areas. Changes in surface factors have an effective role in groundwater level because some factors such as vegetation and land use cause changes in groundwater balance. In this study, data from the GRACE satellite and the stations were used to estimate the monthly groundwater level changes in the Zayanderud Basin, Iran from 2002 to 2018. In addition, the annual and seasonal storage of groundwater in this basin was estimated and verified using the GRACE data and this trend was compared with the data from previous rainfall. Our results indicate that terrestrial water storage variations from GRACE and GLDAS are in general consistent with one another. These data show that the current groundwater level depends on the amount of rainfall in the past years. When rainfall is on a downtrend, underground water storage fluctuations are more effective than rainfall. In addition to time trend, vegetation pattern in rangelands, woodlands, and agricultural lands depends on the amount of groundwater storage variations in the Zayanderud basin. The lowest amount of that in 2016 coincided with the lowest amount of underground water storage and recharge (27.36 cm). The statistical correlation analysis of the GRACE satellite data and the observed data of wells indicated that RMSE was equal to 2.23 cm on the seasonal scale. Further, GRACE captures characteristics of groundwater drought that occur as a result of complex human activities and natural changes, thus presenting a framework to assess groundwater drought characteristics.

The main purpose of this paper is to identify adaptation strategies and factors governing farmers’ choice and determine their effects on each adaptation strategy in the face of perceived climate change in the Kamfiruz region of Iran. In this sense, we consider common socio-economic and institutional factors, and also the amount of water input for paddy fields. After surveying the impacts of climate change and production losses due to climate change on rice production, we use a binary logit and multivariate probit model to examine different factors affecting adaptation to climate change and how they affect each adaptation practice in paddy fields. According to the results, adjusting farming calendars, changing crop varieties, increasing consumption of groundwater for irrigation, using irrigation control mechanisms, and intercropping are the most important adaptation strategies. Besides, the estimation of the multinomial logit model implies that eight variables account for 51.4% of the possibility of adaptation of rice farmers. Moreover, actual changes in the possibility of adoption of adaptive measures concerning a unit of increase in cultivated water, age, educational, income level, farm size and training are +3.3, -8, +4, +6, +4, and +17%, respectively. However, of these explanatory variables, water and training are the most prominent variables in influencing more adaptation strategies to climatic events. The paper is concluded with suggestions as to some policy interventions to scale up adaptation to climate change in Kamfirouz paddy fields located in Fars province.