Analysis of changes in snow reservoirs for planning and management of dehydration (Case Study: Sarab Halilroud Area in Kerman Province)

Subject Areas : Regional Planning

mahla shojaei anari ¹ (MSc student of Environmental Hazards, Shahid Bahonar University of Kerman, Kerman, Iran)
mostafa khabazi ² (Assistant Professor of Geomorphology, Shahid Bahonar University of Kerman, Kerman, Iran)
Sadegh Karimi ³ (Assistant Professor of climatology, Shahid Bahonar University of Kerman, Kerman, Iran)

Received: 2019-08-10 Accepted : 2019-10-18 Published : 2020-01-21

Keywords: Iran, Watershed, NDSI, MOD10A2, Snow Cover,

Abstract :

        Today, in the wake of the global water crisis, all countries seek to identify, control, and utilize freshwater resources. Due to the fact that Iran is located in the desert belt of the earth and the reduction of precipitation has caused water scarcity in Iran and Kerman province, therefore identifying water resources and investigating changes in snow cover is necessary. Due to the harsh physical conditions of mountainous environments, it is not possible to permanently measure the terrain to estimate snow sources and to form a database. Therefore, using satellite imagery is very important for identifying snowy surveys and assessing its changes. In this study, we attempted to study the changes in snow cover reservoirs in Halilroud watershed using MOD10A2 satellite imagery. For this purpose, Normalized Difference Snow Index was used. This index has a value greater than 0.4, meaning a pixel with a NDSI greater than 0.4 is referred to as snow and ice. The snow cover specified in the satellite images in ENVI5.1 software was entered into GIS and was classified into 5 classes from 0 to 5000 and the survey of ​​each class was calculated. Then they entered into SPSS and analyzed. Stepwise multivariate regression showed that during the 8-day interval of February (from 18 to 25 February), the most changes of snow survey had a significant and significant relationship with the snow survey trend in these years. In fact, the explanations for the snow changes in this time interval were related to height 3 (2001-3001) and then 4 (3001-4000), respectively. The 2001–3001 height alone accounts for 98.9% of the snowfall trend in the entire study area. Secondly, if the height of 4000-3001 is added, the two class,s justify 99.2 percent of the snowfall change over the past 20 years.   Key words: Watershed, MOD10A2, NDSI, Snow Cover, Iran.   Extended Abstract Introduction:           According to studies, about 60 percent of surface water and 57 percent of groundwater in the country are located in snowy regions and feed on snow melt water (Najafi et al, 2004: 2). Most of the rainfall in the mountainous areas is snow-covered and inaccessible to the mountainous areas, so it is impossible to study them with high-cost, over-the-top terrain methods, and so on, The use of satellite remote sensing technology would be very useful in these studies. The water resources in the mountainous areas are affected by the amount of snowfall and are often fed by snowmelt waters. And the status of the water balance and the discharge regime of the water resources in such areas depend on the extent and speed of snow melting or its persistence on land and their nutritional basin levels. Today, in the wake of the global water crisis, all countries seek to identify and control freshwater resources and their optimal use. As one of the most important Islamic countries in the Middle East, Iran is in dire need of full growth and development. Given that the country lies in the desert belt of the earth, the identification of these very important water resources equals about one-third of the water required for agricultural and irrigation activities around the globe (Najafzade et al, 2004). : 3). In our country, these highlands can also be considered as a rich source of fresh water. Therefore, today in the process of efficient water resources management, the use of remote sensing data with the objective of obtaining accurate information from snow cover is operationalized. Given that recent droughts and shortages of rainfall have caused severe water shortages in Kerman province, changes in snow cover and the prevailing climate conditions are necessary and urgent for the public and authorities to reduce water resources. Be warned and find ways to prevent this crisis. Hezar, Laleh Zar and Bahr-e Asman Mountains (in the central areas of Kerman province) are suitable for detecting changes in snow cover due to their location and elevation in the face of various climate systems. Therefore, in this study, the changes of snow cover during the winter of the study years are studied in three mentioned peaks. Therefore, the following question and hypothesis is raised: Hypothesis: The percentage of snow cover seems to have decreased over the past 20 years. Q: Has the percentage of snow cover at different altitudes changed over time? Methodology:        Normalized snow cover differential index greater than 0.4 means that pixels with NDSI greater than 0.4 are introduced as snow and ice and obtained using the 5-2 relationship (Hall et al, 1995: 120). (1) NDSI = (MODIS4-MODIS6) / (MODIS4 + MODIS6) Snow and ice are generally determined by having NDSI values larger than other levels. A pixel in a low forest area is called snow or ice when it is 0.4≥ NDSI. While snow and ice cover in forested areas may have NDSI values below 0.4, the combination of NDSI and NDVI (Normalized Vegetation Index) can help to separate snow and ice cover from non-snow and ice in forest areas (Zhang : 2003: 52). The accuracy of the NDSI method is estimated to be 91-95%, which is less accurate in forest areas and in Tondra areas (Hall et al, 1998: 31). Results and discussion:        The specified snow cover satellite images were entered into GIS environment in ENVI5.1 software and were classified into 5 classes from 0 to 5000 and the area of ​​each class was calculated. Then they entered into SPSS environment and analyzed. Stepwise Weiss multivariate regression showed that during the 8-day interval of February (from 18 to 25 February) the most changes of snow area had a significant and significant relationship with the snow area trend in these years and in fact justified. The changes in snow during this time interval were related to altitudes of 3 (2001–1000) and then 4 (4000–3001), respectively. The 2001–2003 altitude alone accounts for 98.9% of the snowfall trend in the entire study area. Secondly, if the altitude of 3,000-4,000 is added, the two altitudes justify 99.2 percent of the variation in snowfall over the past 19 years. Conclusion:        The results of the satellite satellite imagery showed that the MOD10A2 Moderator daily snow product is capable of estimating the snow cover area of ​​the study area. In this research, the snow cover maps prepared in ENVI software were entered into ArcGIS software and the snow cover was identified as the study area. The February snow cover maps were classified into five elevation classes in ArcGIS software: the first floor contains 0-1000 height, the second floor contains 2001-1000 height, the third floor contains 2001-2003 height, fourth floor Includes altitude 3,000-4,000 meters and fifth floor contains altitude 4,000-5001 meters. Then, the snow cover area of ​​each of the elevated floors marked with different colors was calculated. The results of this study include the values ​​of snow cover levels in February as outlined in Table 7. According to the Pearson correlation results, the third floor (2001-2003 m) had the highest average snow area (3293.4 sq km) during the study period. Then the fourth floor (4000-1001 m) in the next row had the highest average snow area (1751.6 sq km) during the study period. Stepwise Weiss multivariate regression showed that during the 8-day interval of February (from 18 to 25 February) the most changes of snow area had a significant and significant relationship with the snow area trend in these few years and in fact justified. Snow variations in this time interval were related to altitude 3 and then 4, respectively. And as the descriptive statistics table observed, the 2001-2003 altitude alone accounted for 98.9% of the snowfall trend in the entire study area. Secondly, if the altitude of 3,000-4,000 is added, the two altitudes justify 99.2 percent of the variation in snowfall over the past 20 years. This means that during this time, snowfall in other elevations has not had much impact on the process of snow changes. In the end, it was determined that the highest snowfall in February (12687.89 sq km) was in 2015. Figure 5 Although the overall snow cover situation in February from 2000 to 2015 showed an upward trend, from 2016 to 2019 this trend declined. Therefore, the crowd has thwarted the whole process.

References:

- Adeli, A., (2005), Climatology of Snowfall in Northwest of Iran, MSc Thesis, GIS Center and Remote Sensing, Tabriz University.

- Alizadeh, Katayoun (2001). The effects of tourism on the environment in the cities of Torqaba and Shandiz, Healthy City Conference - Environment, Neyshabour Islamic Azad University.

- Aggarwal, S.P., Thakur, P.K., Nikam, B.R., Garg, V. (2014). Integrated approach for snowmelt run-off estimation using temperature index model, remote sensing and GIS. Current Science, 106(3), 397-407.

- Appel, I., & ,Salomonson, V. V. (2004). Estimating fractional snow cover from MODIS using the normalized difference snow index. Remote sensing of environment, 89(3), 351-360

- Azizi, Ghasem ؛ Rahimi, Mojtaba ؛ Mohammadi, Hossein; Khosh-e Akhlaq, Faramarz, (2017) Spatio-temporal variations of snow cover in the southern slope of central Alborz, Physical Geograph Research Quarterly, Volume 49, Number 3, Autumn 1396, Pp. 381-393.

- Bardsir Watershed Management and Natural Resources Department (2011).

-Blosch, G., Parajka, J., (2008), "The value of MODIS snow covers data in validating and
calibrating conceptual hydrologic models", Journal of Hydrology, 240– 258.

- Bales, RC and Cline, D.(2003).Snow Hydrology and Resources (Western United Sate )

- Burkard, M. B., Whiteley, H. R., Schroeter, H. O., & Donald, J. R. (1991, June). Snow depth/area relationships for various landscape units in southwestern Ontario. In Proceedings of  the Annual Eastern Snow Conference (pp. 5-7).

-Changchun, X., Yaning, Ch., Weihong, L.and Hongtao, Ch. Y.Ge . 2007. Potential impact of climate change on snow cover area in the Tarim River basin. Journal of Environ mental Geology,Vol. 53 , No. 7, p1465-1474.

- Dozier, J.; Painter T.H. (2004), Multispectral and hyperspectral remote sensing of alpine snow properties. Ann. Rev. Earth Planet. Sci. 32, 465–494.

- Fathzadeh, A. Gharai Manesh, Samaneh (2013). Application of artificial intelligence in simulation of spatial distribution of snow density in semi-arid regions: A case studyof upstream regions of Yazd-Ardakan plain, Journal of Geographical Research on Desert Areas, No. 2, pp. 16-1.

- Fathzadeh, Ali; Zare Bidaki, Rafat (2012). Estimating the Distribution of Snow Equivalent Water at the Peak of Snow Accumulation Using the Degree-Day Model of Shemshak Basin, Iranian Journal of Soil and Water Research, No. 3, pp. 177-171.

-Hall, D.K.; Riggs, G.; Salomonson, V.V.; Di Girolamo, N.E.; Bayr, K.J. (2002), MODIS snow-cover products.Remote Sens. Environ. 83, 181-194.

-Hall, D.K.; Riggs G.A.; Salomonson, V.V. (1995), Development of methods for mapping global snowcover using moderate resolution imaging spectroradiometer data. Remote Sens. Environ. 54, 127–140.

-Hall, D.K.; Riggs, G.A. (2007), Accuracy assessment of the MODIS snow-cover products. Hydrol. Process. 21, 1534–1547.

-Hall, D.K.; Foster, J.L.; Verbyla, D.L.; Klein, A.G.; Benson, C.S. (1998), Assessment of snow-covermapping accuracy in a variety of vegetation-cover densities in central Alaska. Remote Sens. Environ. 66, 129–137.

- Hoinkes, H. (1967). "Glaciology in the International Hydrological Decade". IAHS Commission on Snow and Ice: Reports and Discussions. 79:7–16.

- Johansson, E., Majka, J., Burgers, P.M (2001). Structure of DNA polymerase delta from Saccharomyces cerevisiae, Journal Article Research Support, Non-U.S. Gov't, Research Support, U.S. Gov't, P.H.S.

- Khosravi, Mahmoud ؛ Tawassi, Taghi; Raispoor, Kouhzad; Omidi Ghaleh Mohammadi, Mahboubeh, (2017), Survey of Snow Cover Levels in Yellow Mountains of Bakhtiari Using Remote Sensing, Journal of Hydrology, Issue 12, Fall 1396, pp. 25-44.

- Kiani Ghaleh Sard, S. Shahraki, J. Akbari, A. Sardar Shahraki, A. (2018), Planning and Studying the Effects of Climate Change On Iran's Agricultural Development; Application Techniques Positive Mathematical Programming (PMP), Journal of Regional Planning, Year 9, No. 34, p. 3.

- Klein, A.G.; Barnett, A. (2003), Validation of daily MODIS snow maps of the Upper Rio Grande RiverBasin for the 2000–2001 snow year. Remote Sens. Environ. 86,162–176.

-Klein, A.G.; Hall, D.K.; Riggs, G.A. (1998), Improving snow-cover mapping in forests through the use ofa canopy reflectance model. Hydrol. Process. 12, 1723–1744.

-Lopez, P., Sirguey, P., Arnaud, Y., Pouyaud, B., Chevallier, P (2008). Snow cover monitoring in the Northern Patagonia Icefield using MODIS satellite images (2000–2006), Glibal and Planetary Change, Vol61.

- Maroufi, Safar; Tabari, Hossein; Zare Abianeh, Hamid; Sharifi, Mohammad Reza; Akhundali, Ali Mohammad (2009). Snow equivalent water zoning in one of the Karun mountainous basins using GIS (Case study: Submarine Basin), Journal of Irrigation and Water Engineering, No. 2, pp. 43-31.

-Metcalfea, R.A., Buttle, J.M (1999). Semi-distributed water balance dynamics in a small boreal forest basin,Journal of Hydrology, No226, pp 66–87.

- Najafzadeh, Reza-Abrishami, Ebrahim-Tajrishi, Masoud; Taheri Shahri Aeini, Hamid (2004), Stream Flow with Snowmelt Runoff Modeling Using RS and GIS (Case Study : Pelasjan sub Basin), Journal of Water and Wastewater Volume 15, Number 4; December and December 2004, PP 1-84.

- Nolin, A., Liang, S (2000). Progress in bidirectional reflectance modeling and application for surface particulate media: snow and soil, Remote Sensing Review, No14, pp307-342.

-Rasouli, Ali Akbar-Adhami, Salam (2007). Estimation of Snow Water Equivalent by Processing of MODIS Satellite Imageries, Geography and Development iranian journal, Volume 5, Number 10, pp. 23-36.

- Rangeland Plan, Kerman Department of Natural Resources, (2012).

- Rango, A. and  Shalaby, A.I. (1998). "Operational  Applications of Remote Sensing in Hydrology: Success, Prospects  and Problems Hydrological  Sciences  Journal  "(Journal  Des  Sciences Hydrologiques)43(6):947-965.

- Rango, A. (1993). "Snow  Hydrology  Processes and Remote-Sensing". Hydrological Processes, 7(2):121-138.

-Riggs, G.A., Hall, D.K (2015). MODIS snow products collection 6user guide, pp 9

-Samantha, K.M (2004). Hydrological modeling using MODIS data for snow covered area in the Northern Boreal Forest of Manitoba. University of Calgary.

-Strabala, K (2003). MODIS Cloud Mask User’s Guide, CooperativeInstitute forMeteorological Satellite Studies Website. Last Viewed December 15, 96p.

- Tabari, Hossein Maroufi, Safar; Zare Abianeh, Hamid; Amiri Chayjan, Reza; Sharifi, Mohammad Reza; Akhondali, Ali Mohammad (2009). Comparison of Non-Linear Regression and Computational Intelligence Methods in Estimating Spatial Distribution of Snow Water Equivalent in Karoon Upstream, JWSS - Journal of Water and Soil Science (Journal of Science and Technology of Agriculture and Natural Resources), Volume 13, Number 50, pp. 40-29.

- tamab. 1375. Bulletin of the State of Water of the State, Eighth Year, No. 12, p. 890.

-Thirrel, G.; Notarnicola, C.; Kalas, M.; Zebisch, M.; Schellenberger, T.; Tetzlaff, A.; Duguay, M.;Mölg, N.; Burek, P.; de Roo, (2012), A Assessing the quality of a real-time snow cover area product forhydrological applications. Remote Sens. Environ. 127, 271–287.

-Wang, X.; Xie, H.; Liang, T. (2008), Evaluation of MODIS snow cover and cloud mask and itsapplication in Northern Xinjiang, China. Remote Sens. Environ. 112, 1497-1513.