SNOW WATER EQUIVALENT ESTIMATION USING METEOROLOGICAL DATA AND LAND ELEVATION (A CASE STUDY: SARUG-CHAI BASIN)

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ANSARI H.; MAROFI S.

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Journal: Journal of Water and Soil Conservation Year:2016 | Volume:23 | Issue:1 Page(s): 101-118

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Title

SNOW WATER EQUIVALENT ESTIMATION USING METEOROLOGICAL DATA AND LAND ELEVATION (A CASE STUDY: SARUG-CHAI BASIN)

Author(s)

ANSARI H. | MAROFI S. | Issue Writer Certificate

Keywords

SNOW WATER EQUIVALENTQ2

SNOW MEASURING INSTRUMENTSQ1

SARUG-CHAI BASINQ1

GEOSTATISTICSQ2

Abstract

Background and Objectives: SNOW WATER EQUIVALENT (SWE) is a key parameter in hydrological cycle. In Iran, measurement of snow depth and its water equivalent is usually is limited due to lack of automated SNOW MEASURING INSTRUMENTS. According to research conducted in the field of SNOW WATER EQUIVALENT, wind speed, temperature, precipitation and elevation are the factors affecting the amount of SNOW WATER EQUIVALENT. Because values for wind speed, temperature and precipitation can affect the long-term SNOW WATER EQUIVALENT, therefore the aim of this study was using meteorological and geographical parameters to estimate SNOW WATER EQUIVALENT of snow stations in the study area.Materials and Methods: In the current study, based on meteorological data and interpolation method SNOW WATER EQUIVALENT was estimated. In this regard, first, average amounts of precipitation, air temperature and wind speed were computed during periods of 10, 20, 30, 40 and 50 days. Then, binary correlations between SNOW WATER EQUIVALENT and the parameters were estimated. Parameters that had the highest correlation were selected. Then in SPSS software between these parameters and the elevation of the stations, the SNOW WATER EQUIVALENT to a multiple regression was obtained. The regression equation were validated with SNOW WATER EQUIVALENT data measurement in snow stations Results: Based on these results, the average precipitation, 40-days temperature and wind speed of 30-days, showed the highest correlation with SNOW WATER EQUIVALENT, respectively. The best SNOW WATER EQUIVALENT equation was obtained using the relevant parameters. Estimated data was also compared with the observed data, based on the Nash- Sutcliffe criteria (NS=0.83) and regression coefficient (r=0.91). The results showed an acceptable accuracy of the equation on SNOW WATER EQUIVALENT estimation.Conclusion: In this study, due to the lack of meteorological measuring in snow stations, the interpolation methods for estimating the amount of precipitation, wind speed and temperature parameters the location station was used. The results indicated that among the interpolation methods, radial basis functions with model of Inverse Multiquadric for average wind speed of 10 to 50 days, Completely Regularized Spline model to estimate the average temperature of 10 to 50 days and kriging method with Gaussian model for estimating the average precipitation 10 to 50 days, had the high accuracy in the snow stations. Using the parameters that were most correlated with SNOW WATER EQUIVALENT, a regression equation to estimate SNOW WATER EQUIVALENT was obtained. Evaluation showed regression equation can be used to estimate SNOW WATER EQUIVALENT in the respective stations.

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APA: Copy

ANSARI, H., & MAROFI, S.. (2016). SNOW WATER EQUIVALENT ESTIMATION USING METEOROLOGICAL DATA AND LAND ELEVATION (A CASE STUDY: SARUG-CHAI BASIN). JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), 23(1), 101-118. SID. https://sid.ir/paper/156596/en

Vancouver: Copy

ANSARI H., MAROFI S.. SNOW WATER EQUIVALENT ESTIMATION USING METEOROLOGICAL DATA AND LAND ELEVATION (A CASE STUDY: SARUG-CHAI BASIN). JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES)[Internet]. 2016;23(1):101-118. Available from: https://sid.ir/paper/156596/en

IEEE: Copy

H. ANSARI, and S. MAROFI, “SNOW WATER EQUIVALENT ESTIMATION USING METEOROLOGICAL DATA AND LAND ELEVATION (A CASE STUDY: SARUG-CHAI BASIN),” JOURNAL OF WATER AND SOIL CONSERVATION (JOURNAL OF AGRICULTURAL SCIENCES AND NATURAL RESOURCES), vol. 23, no. 1, pp. 101–118, 2016, [Online]. Available: https://sid.ir/paper/156596/en

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