Marzieh Mokarram; Saeed Negahban
Abstract
Extended Abstract
Introduction
Investigating the spatial andTranslation errorInvestigatingiiiii temporal variations of soil salinity plays a major role in managing the watershed and preventing the development of salinity (Mohammadi, 2007). Also, the study of groundwater salinity due to the complexity ...
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Extended Abstract
Introduction
Investigating the spatial andTranslation errorInvestigatingiiiii temporal variations of soil salinity plays a major role in managing the watershed and preventing the development of salinity (Mohammadi, 2007). Also, the study of groundwater salinity due to the complexity of hydrological processes, characteristics of the aquifer, and their variability is a difficult task. However, these problems exacerbate by external factors such as atmospheric conditions and human activities affecting the permeability and hydrological processes (Mirzaee and Hassan-Nia, 2013). Because of the costly nature of experiments involving salinity sampling, as well as the computational models not being calibrated and the complexity of these models in order to overcome these limitations and to determine salinity in the depths of the soil, determination of models consistent with natural behaviors and the use of existing models, Increase day by day. On the other hand, considering the fact that many lands are under cultivation in the northwest of Fars province, it is important to study the chemical properties of the soil and water in the region, including salinity.
There are various methods for studying the salinity of water and soil, for example, Syringes et al. (2006) predicted the salinity of soil profile and the drainage outlet in a research using neural networks in an experimental area in India. Arfin et al. (2003) used an artificial neural network model and linear regression model to predict the soil and water salinity. Topographic index is a measure of the extent of flow accumulation at the given point of the topographic surface. As catchment area increases and slope gradient decreases, topographic index increases. Like other combined morphometric variables, topographic index can be derived from a digital elevation model (DEM) by the sequential application of methods for local and nonlocal morphometric characteristics, followed by an arithmetic combination of the results of these calculations.
Materials & Methods
The studied watershed is located in the west of Shiraz, between the cities of Shiraz and Kazeroon. The most important urban center in this basin is the city of Bayza. The geo-location of the studied area is N 29° 12´to 29° 48´and E 52° 06´ to 52° 36´ (Figure 1). The area of the study region is 623.63 KM2. The highest and lowest altitudes in the study area are 1630 and 3083 meters respectively. The average temperature in the region is 16.8 degrees varying from 4.7 to 29.2. The study area is very rich for cultivating crops. It is also a very rich in terms of topography, geology and biodiversity. Regarding the presence of agricultural lands in this region as well as the significance of irrigation water quality and the type of soil in terms of electrical conductivity (EC), the study of the soil and water characteristics of the region is very important in terms of salinity.
The data used in this research include electrical conductivity of water and soil samples provided by Fars Agricultural Jihad Organization (2013). This region was selected considering the importance of the study region for agriculture. The zoning maps for each of them were prepared in the ArcGIS environment with the help of these sample points which were selected randomly. Then, the EC data of water and soil was homogenized and ranged from 0 to 1 with the help of membership functions. Finally, the relationship of the amount of water and soil salinity with the watershed rough terrain was investigated.
Discussion and Results
According to the interpolation maps, it was determined that the lowest and the highest values for water salinity in the study area were 0.42 and 3.07 respectively, while for soil salinity were 0.87 and 8.75 respectively. According to the salinity zoning map prepared for soil samples in the study area, it is determined that the highest soil salinity is in the southwest of the study area, while the north and center of the study area have lower soil salinity. Also, the results of water salinity obtained by IDW method showed that the highest salinity of water is in the north of the region, while the lowest salinity of water is observed in parts of the south of the study area. The fuzzy map values of the study area are between 0.08 to 0.99, that except for a very small part of the study area located in the southeast, the rest of the area contain saline water. Also, the results of soil salinity fuzzy map of the studied area showed that the soil salinity values were between 0.61 and 0.92. In fact, the soil in the study area has a lot of salinity.
Conclusion
After finalizing the fuzzy map of water and soil salinity by fuzzy method, the final salinity map was classified into four classes. Values less than 0.25, between 0.25 and 0.5, 0.5 to 0.75 and more than 0.75 were classified into inappropriate, moderate, good and very good grades, respectively. (The low values: < 0.25 (inappropriate for drinking), moderate: 0.25 – 0.50, high: 0.50 – 0.75, very high: > 0.75 (a-ppropriate for drinking)). Using fuzzy method for soil salinity, it was determined that 24.31% of the area was in poor class (inappropriate), 11.78 in the moderate class, 25.74 in the good class and 38.16% of the area was in the very good class, while for water salinity, it was found that 36.6% was in the moderate class, 31.69% in the good class and 31.65% was in the very good class. At the end, the relationship between the Landform map and the salinity map of the soil and water in the study area was determined. The results showed that salinity of the water in the valleys is very high, while soil salinity in the upstream drainage has shown the highest values. The results also showed that the minimum salinity of the soil and water are in the plains.
Marzeyeh Mokarram; Ali Darvishi; Saeed Negahban
Abstract
Extended Abstract
Introduction
Watershed is an area of land that surface water of rain and melting snow conduct towards a single point, which is usually out of the basin. Check of watershed is one of the main strategies for integrated management of natural resources and sustainable development. Recently, ...
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Extended Abstract
Introduction
Watershed is an area of land that surface water of rain and melting snow conduct towards a single point, which is usually out of the basin. Check of watershed is one of the main strategies for integrated management of natural resources and sustainable development. Recently, the availability of remote sensing (RS) data and Geographical information system (GIS) technologies has allowed for improved understanding of the morphometric properties and surface drainage characteristics of many watersheds in different parts of the world (Parveenet al., 2012; Nayar& Natarajan, 2013). For example, Shrimaliet al. (2001) presented a case study of the 42 km Sukhana lake catchment in the Shiwalik hills for the delineation and prioritization of soil erosion areas. In addition, Srinivasaet al. (2004) used GIS techniques for morphometric analysis of subwatersheds in the Pawagada area, Tumkur district, Karnataka. Nookaratnamet al. (2005) carried out a study on dam positioning through prioritization of microwatersheds using the sediment yield index (SYI) model and morphometric analysis. Khan et al. (2001), used RS and GIS techniques for watershed prioritization in the Guhiya basin and sub-watersheds in Odisha, India respectively.
Materials & Methods
The study area is one of the subwatersheds of the river of Urmia (Nazloochaei) that is located in North West of Iran with an area of 948.75 km2. The study area was selected for detailed morphometric analysis using Geography information system (GIS). The input data for morphometric analysis was DEM with resolution of 30 m from ASTER satellite. The steps of stream extraction consist of:
1. Extraction of drainage networks from the DEM using the flow direction method, which consists of the following steps (O’Callaghan & Mark, 1984):
i. Fill Sinks: A sink is an uncompleted value lower than the values of its neighborhood. To ensure proper drainage mapping, these sinks were filled by increasing elevations of sink points to their lowest outflow point.
ii. Calculate Flow Direction: Using the filled DEM produced in Step1, the flow directions were calculated using the eight-direction flow model, which assigns flow from each grid cell to one of its eight adjacent cells in the direction with the steepest downward slope.
iii. Calculate Flow Accumulation: Using the output flow direction raster created in Step2, the number of upslope cells flowing to a location was computed.
iv. Define Stream Network: The next step is to determine a critical support area that defines the minimum drainage area that is required to initiate a channel using a threshold value.
v. Stream Segmentation: After the extraction of drainage networks, a unique value was given for each section of the network associated with a flow direction.
Morphometric analysis of the study area consist of:
Stream number (Nu)
Nu is number of segments in order U
Stream order (U)
Cumulative length of streams (L), L = ∑Nu, L is calculated as the number of streams in each order and total length of each order is computed at sub-watershed level (Horton, 1945).
Bifurcation ratio (Rb)
Rb=Nu/N (u+1) N (u+1) = Number of segments of the next higher order (Schumms, 1956),
Watershed relief (Bb), Bb = Hmax – Hmin, Bb is defined as the maximum vertical distance between the lowest and the highest points of a sub-watershed. Hmax and Hmin are maximum and minimum elevations respectively (Schumms, 1956)
Drainage density (Dd)
Dd=Lu/A, A=Watershed area (km2), L (u) is total stream length (Horton, 1932)
Stream frequency (Fs), Fs = Nu/A, Fs is computed as the ratio between the total number of streams and area of the watershed (Horton, 1932)
Form factor (Rf)
Rf =A/Lb2, Rf is computed as the ratio between the watershed area and square of the watershed length. 𝐿 is the watershed length (Horton, 1932)
Circularity ratio (Rc)
Rc= 4π*A/P2, P is the watershed perimeter (km)
Elongation ratio (Re)
Re= (2/Lb)*(A/π) 0.5
Results and discussion
The results showed that according to the high number of streams (489 waterways), the existence of first, second and third degree streams, the high length of the streams, the high proportion of length of the streams in relation to the basin area, high coefficient of relief which indicates high elevations and slopes, the area is erodible and requires more management. Also, Landform studies in the studied area showed that with the help of morphometric characteristics, the sensitivity of landforms to erosion can be determined in the area. So, after the mapping of landforms using topographic position index (TPI), and considering the erosion-sensitive areas through morphometric characteristics, erosion-sensitive landforms in the study area were determined, So that the increase in the number of waterways and their length in the watershed indicates an increase in erosion. Comparing the map of the landforms and the map of the streams in the studied area, it was determined that class 4 (U-shaped valleys) and class III (high drainage) landforms have the highest erodibility. The results showed that, with increasing drainage density, the erodibility increases and the highest erodibility was observed in Class 4 (U-shaped valleys) and Class 6 landforms due to the high drainage density.
Conclusion
Ridge landforms such as those in high altitude (landforms in class 9 and 10), had the highest erosion and were therefore the most sensitive landforms. The drainage density features as the most important factor for determination of erosion and its relation to landforms were used. The results showed that by increasing the amount of drainage density the erosion increases which were for landforms Class 4 and Class 6. This study has demonstrated that morphometric characteristics can be used to predict other watershed characteristics.