Short Communication Mapping snow characteristics based on snow observation probability

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1 INTERNATIONAL JOURNAL OF CLIMATOLOGY Int. J. Climatol. 27: (27) Published online 22 May 27 in Wiley InterScience ( DOI: 1.12/joc.1494 Short Communication Mapping snow characteristics based on snow observation probability Bahram Saghafian* and Rahman Davtalab Soil Conservation and Watershed Management Research Institute, Tehran, Iran Abstract: Measurement/estimation of snow water equivalent (SWE) is a difficult task in water resources studies of snowy regions. SWE point data is measured at snow courses that are normally operated with low density owing to high costs and great difficulty in reaching the stations in cold seasons. Moreover, snow is known to exhibit high spatial variability, which makes SWE studies based solely on sparse station data more uncertain. Ever-increasing availability of satellite images is a promising tool to overcome some of the difficulties associated with analyzing spatial variability of snow. Although National Oceanic and Atmospheric Administration (NOAA) satellite images have low spatial resolution with approximately 1.1-km pixel size, they are adequate for mapping snow cover at regional scales and enjoy a moderate length of record period. In this paper, rain and snow records of synoptic stations and the time series of NOAA-based snow cover maps were used to map average SWE of a vast area in southwestern Iran. First, monthly and annual snow coefficient (SC) at synoptic stations were determined on the basis of analysis of hourly observation of type and amount of precipitation. Then, two new spatially distributed snow characteristics were introduced, namely, average frequency of snow observation () and monthly frequency of maximum snow observation (FMSO), on the basis of existing satellite snow observations. and monthly FMSO maps were prepared by a geographic information system on the basis of snow map time series. Correlation of these two parameters with SC was studied and spatial distribution of SC was estimated on the basis of the best correlation. Moreover, the distribution of mean annual precipitation was derived by comparing a number of interpolation methods. SWE map was generated by multiplying SC and precipitation maps and its spatial variability in the region was analyzed. Copyright 27 Royal Meteorological Society KEY WORDS snow water equivalent; snow coefficient; frequency of snow observation; snow area; spatial variability; NOAA; GIS; Iran Received 15 April 26; Revised 8 November 26; Accepted 14 December 26 INTRODUCTION In mountainous basins, snowmelt is the main surface water resource in spring and early summer period. Gradual snowmelt also recharges groundwater aquifers and later contributes to the summer runoff flowing in the rivers. Therefore, snow data collection and snow budget analysis are of great importance in the management of surface and groundwater as well as hydropower generation in mountainous basins. Different snow factors such as snow cover area, depth and water equivalent are measured at snow courses, once to four times during the end of snow accumulation season. However, mountainous regions are hardly accessible, especially at the time of maximum snow cover. So, snow data may not be collected at some high altitude stations on a regular time basis. Automatic snow pillows have found broad application in developed countries, not only to minimize accessibility problems, but also to facilitate snow data * Correspondence to: Bahram Saghafian, Soil Conservation and Watershed Management Research Institute, Tehran, Iran. Saghafian@scwmri.ac.ir collection at small time intervals. However, installing such stations is not justified in many developing countries for economic and protection reasons. Although operation of automatic stations brings about a favorable time resolution, spatial distribution of point data over the basin remains a major problem. Usually, in snow budget studies, point snow data collected at a number of stations are used. However, such data do not directly provide snow cover area and the spatial distribution of snow characteristics. Today, use of satellite images has expanded in water resource management and environmental studies. National Oceanic and Atmospheric Administration (NOAA)/AVHRR (National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer) satellite images are particularly suitable in large-scale snow cover mapping at time resolutions as small as 1 day. Many researches have been carried out on snow cover extraction from satellite images, e.g. Dozier and Marks (1987); Simpson et al. (1998) and Porhemmat and Saghafian (23). Coarse spatial resolution of NOAA images limits its application only to vast areas. In Copyright 27 Royal Meteorological Society

2 1278 B. SAGHAFIAN AND R. DAVTALAB comparison, daily MODIS satellite images have a better spatial resolution. However, MODIS archive is limited to a few years, which makes it unsuitable for long-term climatic studies. Some recent snow studies in mountainous regions have integrated satellite images and measured point data. Lee et al. (23) used snow cover maps obtained from MODIS images as input for snowmelt models in order to simulate runoff. In this study, the performance of the flow simulation model was compared in cases of with and without satellite images. The application of satellite images was found to improve annual and daily flow predictions. Akyürek and Şorman (22) used NOAA images to study snowfall characteristics in the mountains of east Turkey. Two methods were applied to extract snow cover from NOAA images. Then the relation between the precipitation and snowmelt and other factors such as altitude, slope, aspect, temperature and wind direction and speed were studied. Results showed that snowmelt is related to these factors and thus they must be considered in snowmelt models such as snowmelt runoff model (SRM) (Snowmelt Runoff Model). Smith et al. (2) used a hydrologic model and remote sensing data to study the trend of hydrologic variables in the Middle East at regional and point scale. The effect of winter temperature on snowmelt, time of peak discharge, snow cover and agricultural land use changes was studied. The results exhibited great variations over the study areas. Bruland (22) used both measured and satellite snow data and other climatic variables to study the seasonal dynamics of snow cover on the Tundra in the vicinity of Ny-Alesund in Svalbard, Norway. The study focused on the annual time interval from the beginning of the snowfall season through the end of snowmelt. A snowmelt model was also calibrated based on energy balance and the snow cover maps. The calibration was based on measurements and observations of climate, snow properties and snow distribution during the period One of the basic requirements in the snow budget studies in mountainous basins is the estimation of spatial distribution of time-average accumulated snow water equivalent (SWE). The SWE map can provide useful information in water budget studies particularly for prioritization of different areas with respect to snow storage. The purpose of this study is therefore to combine station climatic data with the snow maps extracted from the NOAA satellite images in order to estimate the spatial distribution of cumulative annual SWE of four large mountainous basins in southwest of Iran. Specifically, two snow indices based on the frequency of snow observations (s) are introduced and the relationship between these indices and the point scale snow coefficient (SC) are studied. Then, the relations are used to assemble spatial distribution of the time-averaged SWE. STUDY REGION The heights of Zagros, largest continuous snow zone in Iran, are the origin of major rivers flowing in Figure 1. Location of four study basins in Iran (solid lines in the country indicate boundary of major river basins). This figure is available in colour online at Figure 2. Location map of synoptic and rain gage stations. This figure is available in colour online at southwestern Iran. Most of precipitation in this region occurs in the cold season and a good portion is in the form of snow. Therefore, snow plays a key role in surface water supply. Four basins, namely, Karkheh, Dez, Karoun and two subbasins of Maroun are included in the study areas. Dez and Karoun rivers originate from Zagros heights and drain into the Persian Gulf. Karkheh and Maroun rivers end in the Hoor-ol Azim and Shadeghan wetlands, respectively. Maintaining minimum flow for both wetlands is a major environmental concern. Several dams have been operating or are under planning/construction in the basins. Investigation of snow budget is vital for irrigation water supplies and hydropower generation. Karkheh, Dez, Karoun, and Maroun cover a total of , , and 685 km 2 in size, respectively. Figure 1 shows the location of the study area in Iran. This region is limited to the 46 5 and 52 3 Eastern Longitude and the 3 15 and Northern Latitudes. Copyright 27 Royal Meteorological Society Int. J. Climatol. 27: (27) DOI: 1.12/joc

3 MAPPING SNOW CHARACTERISTICS BASED ON SNOW OBSERVATION PROBABILITY 1279 METEOROLOGICAL STATION NETWORK Two different networks collect climatic data in the region. First, a rain gage network operated by the Power Ministry measures the daily precipitation. Second, a limited number of synoptic stations operated by the Meteorological Organization records snow/rain at 6- h time steps. Figure 2 shows the distribution of both networks over the region. In order to estimate the spatial distribution of precipitation over the region, 95 rain gages of the Power Ministry were considered. The selection of these stations was based on adequate length of time series. The period between 1974 and 1999 (25 years) was considered as the base period. The second type of data was precipitation form (rain and snow) and the amount. The data was extracted from the archive of 11 synoptic stations located above 1- m altitude within the region. Although more stations are currently operating in the region, these 11 stations had a record with no missing data. The common record period to coincide with that of the satellite images was 15 years starting from This archive consists of 3-h precipitation type and 6-h precipitation amount. Therefore, the estimation of SC was carried out on a 6-h timescale. In order to examine the differences between 15-year study period and a longer time period, average annual 15-year (P 15 ) and 25-year precipitation (P 25 ) were computed and the percent difference was determined. The 15 and 25-year annual precipitation corresponded to the data of synoptic and rain gage stations, respectively. The highest difference was 5.4% in Karkheh Basin. The average difference in all basins is 4.3%. Overall, 3 5.5% differences in average annual precipitation are expected when the 15-year data period is used. This difference is inevitable but negligible, as longer periods of synoptic and satellite image series is unavailable. It is worth noting that the duration of synoptic station data coincides with that of the satellite images and therefore, simultaneous analysis of these two types of data should be warranted. Snow coefficient METHODS SC in a given period represents the ratio of accumulated SWE i to the total precipitation (R i ) over the same time Figure 4. Frequency of snow observation () map of the region. Figure 3. A sample snow cover map of the area corresponding to Jan. 28, Figure 5. Frequency of maximum snow observation (FMSO) for the month of February. Copyright 27 Royal Meteorological Society Int. J. Climatol. 27: (27) DOI: 1.12/joc

4 128 B. SAGHAFIAN AND R. DAVTALAB period. For example, monthly SC may be calculated by: SC = M SWE i i=1 1 (1) M R i i=1 where M is the number of days in the month. Similarly, one can determine the annual SC. Regional distribution of monthly and annual precipitation There are different methods for determining the spatial distribution of climatic variables. The differences among the maps produced by different interpolation methods can be substantial. Therefore, one has to choose the most suitable interpolation method with the least error. Weighted moving average (WMA) methods are the most common but, unlike geostatistical methods, they do not honor spatial autocorrelation. In this study, thin plate smoothing splines (TPSS), WMA, kriging, co-kriging and regression with elevation methods were compared. In WMA, the weight of known points is determined on Table I. Annual and monthly SC (%), FMSO and in 11 stations. Station SC(%) FMSO Dec. Jan. Feb. Mar. Apr. Annual Dec. Jan. Feb. Mar. Apr. Aligoodarz Broojen Broojerd Eslam abad Kangavar Kermanshah Khoram abad Koohrang Ravansar Shahr kord Yasouj Figure 6. Annual precipitation map of the region based on co-kriging method. Copyright 27 Royal Meteorological Society Int. J. Climatol. 27: (27) DOI: 1.12/joc

5 MAPPING SNOW CHARACTERISTICS BASED ON SNOW OBSERVATION PROBABILITY 1281 the basis of their distance to the unsampled point. The weights are also controlled by the exponent such that higher exponents decrease the effect of distant points. Exponents 2 through 5 (i.e. WMA-2 WMA-5) were tested. In TPSS, a thin flexible plate passes through data points. Kriging and co-kriging methods were the two geostatistical techniques considered in the study. Two error measures, namely, mean average error (MAE) and mean bias error (MBE) were computed to allow comparison of different methods. The most accurate method was selected on the basis of overall performance with minimum MAE and MBE and was applied to generate monthly and annual precipitation maps. Snow cover extraction from NOAA satellite images Use of satellite images is currently one of the most widely practiced snow cover estimation methods. The application of remote sensing data has significantly increased in snow mapping because of accuracy, ease of access, and coverage of large areas by satellite images. The selection of suitable satellite images is based on the length of image time series, temporal and spatial resolution, size of the area and cost. Overall, for the region of study, NOAA/AVHRR images are the most appropriate for this study. The common time period between the satellite image archive for Iran and the synoptic data period starts from 1984 and runs for a total of 15 years. The NOAA image resolution is about 1.1 km, which is generally acceptable for extracting snow cover maps in large areas. Snow cover maps had been derived by Iran Space Agency after discarding images with poor quality owing to atmospheric effects or excessive cloud cover. The image processing procedure followed that proposed by Simpson et al. (1998). In each snow map, zero Annual SC (%) y =.22x R 2 =.4621 Dec SC (%) y =.325x R 2 = Jan SC (%) y =.257x R 2 = Feb SC (%) y =.254x R 2 = Mar SC (%) y =.237x R 2 =.293 Apr SC (%) y =.153x R 2 = Figure 7. Elevation versus monthly and annual SC. This figure is available in colour online at Copyright 27 Royal Meteorological Society Int. J. Climatol. 27: (27) DOI: 1.12/joc

6 1282 B. SAGHAFIAN AND R. DAVTALAB represents no-snow and one represents snow-covered. Figure 3 shows a sample snow map in the study area. Seasonal frequency of snow observation from satellite snow cover maps Another useful snow variable presumed to influence snow budget is the for the whole period of observed snow in each year of record. Since the study area receives snow from early December to late February, all snow maps derived from satellite images corresponding to this time period were selected. Then the variable for the i-th pixel was determined by: (i) = T X(i,l) l=1 T (2) Annual SC (%) y = x R 2 =.8732 Des SC (%) y = x R 2 = (a) (b) y = 93.18x R 2 = y = 95.95x R 2 = Jan SC (%) 6 4 Feb SC (%) (c) (d) y = 14.32x R 2 = y = 59.13x R 2 = Mar SC (%) Apr SC (%) (e) (f) Figure 8. versus annual and monthly snow coefficient (dashed lines represent 9% confidence limits). This figure is available in colour online at Copyright 27 Royal Meteorological Society Int. J. Climatol. 27: (27) DOI: 1.12/joc

7 MAPPING SNOW CHARACTERISTICS BASED ON SNOW OBSERVATION PROBABILITY 1283 where T is the total number of snow cover maps over the study period and X is a binary number equal to one if it is snow and zero otherwise. Figure 4 shows the map of for the region where 2% are found in the 3 15-m elevation range. The approaches the 1% mark for small areas over the 45-m elevation range, but remember that is derived for a period starting from November up to the month of April. Frequency of maximum snow observation Since the time interval between consecutive snow maps is not uniform and the focus is on monthly timescales, a monthly maximum snow observation (MSO) was introduced. The MSO bears similarity with how the maximum normalized difference vegetation index (NDVI) maps are assembled. NDVI is an index obtained from the combination of certain satellite image bands and it is affected by the vegetation density and type. A maximum NDVI map is normally constructed by choosing the maximum value of NDVI from the irregularly spaced daily maps over the 1-day or monthly periods. In the MSO map, if a pixel has received snow at least once in a given month, then a value of one is assigned to the pixel. The MSO map essentially indicates the maximum area, which receives snow at least in 1 day of the month, but not necessarily throughout the whole month. Moreover, abrupt changes in the snow cover areas are smoothed in the monthly MSO map. The outcome of this operation will be the Table II. MAE and MBE of SC- relationship. Error measure\ month DEC JAN FEB MAR APR Annual MAE (in %) MBE (in %) time series of monthly MSO maps for the 15-year study period. Frequency of maximum snow observation (FMSO) on monthly basis may be determined as follows: N MSO(i,j,k) k=1 FMSO(i,j) = (3) N where i, j, andk represent the pixel, month, and year numbers, respectively, and N is the total number of years. FMSO values indicate how frequently, on the average in the study period, snow is observed in a given month. Figure 5 shows the February FMSO map, which clearly classifies areas with respect to the in February. Indirect snow water equivalent estimation Correlation between SC and snow variables derived from processing of snow satellite images was studied. This was done for the location of synoptic climate stations Figure 9. Map of average annual snow coefficient. Copyright 27 Royal Meteorological Society Int. J. Climatol. 27: (27) DOI: 1.12/joc

8 1284 B. SAGHAFIAN AND R. DAVTALAB Figure 1. Average annual SWE map. where SC,, and FMSO for different months were available. Simple linear correlations between SC in the one hand and and monthly FMSO s on the other were examined. In case strong correlation coefficient was detected, the selected relationship was used for mapping SC over the region. Spatial distribution of timeaverage SWE may be obtained by multiplying SC and precipitation maps. RESULTS Variation of November through April monthly SCs as well as annual SC values are presented in Table I for the synoptic stations. Monthly SC variation indicates that snowfall in the region normally starts in November and ends in April. November SC is below 1% and is zero for most stations. As also witnessed by the satellite snow maps, large-scale regional snowfall starts in December and terminates in March. We therefore limited our study to the December April period. Results of searching for suitable interpolation method for annual precipitation showed that co-kriging had the least error compared to other methods. The minimum and maximum cross validation MAE values corresponded to co-kriging and WMA-2 methods with 87 and 96 mm, respectively. Likewise, the absolute value of minimum and maximum cross validation MBE corresponded to cokriging and WMA-2 methods with 4.9 (overestimation) and 16.6 mm (underestimation), respectively. Therefore, precipitation map was generated based on the cokriging method (Figure 6). Annual and monthly SC correlation with elevation was further studied in the region (Figure 7). All plots show that SC increases with elevation but the coefficients of determination (COD) are not high. Therefore, use of elevation as a single independent variable to estimate spatial distribution of SC is not warranted. and FMSO are presumed to be directly affected by the SC. We therefore studied whether SC could be predicted by simple linear regressions involving and monthly FMSO as independent variables. Figure 8(a) through 8f show the results for SC- relationships. The COD are generally favorable and all relations Table III. Variation of SWE (in mm) within elevation classes in different basins. Elevation (m) Karkheh Dez Karoun Maroun < > Weighed average: Copyright 27 Royal Meteorological Society Int. J. Climatol. 27: (27) DOI: 1.12/joc

9 MAPPING SNOW CHARACTERISTICS BASED ON SNOW OBSERVATION PROBABILITY 1285 SWE (mm) y =.1256x R 2 =.9734 Dez Basin SWE (mm) y =.925x R 2 =.9647 Karkheh Basin SWE (mm) SWE (mm) y =.1357x R 2 =.9792 Karoun Basin 5 Maroun Basin 4 y =.168x R 2 = Figure 11. Average snow water equivalent (SWE) against elevation in four river basins. This figure is available in colour online at are statistically significant within 1% confidence level. Relatively weak relations were obtained for SC-FMSO in months of December and April due to only a small portion of the ground being under the snow and/or quick snowmelt. SC-FMSO offered stronger relationships in months of higher precipitation. Except for the month of February, was a better predictor than monthly FMSO. This is probably because monthly FMSO maps had to be derived from a relatively small number of maps. On the contrary, the map should be more representative of the overall spatial distribution of average snow accumulation in the region. Variation of with latitude and longitude was also studied in different basins. Raster blocks of 5-min size were overlaid on the map and the pixel average values were plotted against latitude and longitude. No strong relation was found in any of the basins. Since the SC- relations were generally more reliable, they were used to construct the spatial distribution of average SC in the GIS. However, it was decided to perform a jackknife validation on the SC- relationships whereby stations are removed one by one and their SC values are estimated by the developed regressions. The difference between the observed SC value and that predicted by the regression is computed and summed to yield MAE and MBE error measures (Table II). Overall, the SC- regressions performance was found to be acceptable. As an example, Figure 9 shows the map of average annual SC computed based on the simple linear relationship of Figure 8(a). Now, one can estimate the spatial distribution of average cumulative annual and monthly SWE based on maps of SC and average annual precipitation. This is shown in Figure 1 for the cumulative annual SWE which is obtained by crossing the maps in Figures 6 and 9. It is observed that average annual SC and cumulative annual SWE can reach values as high as 8% and 8 mm, respectively, in parts of Dez and Karoun river basins. The estimated accumulated SWE were compared with those measured in a four snow courses. The snow courses are located at high elevations and the measurement is conducted once a year at the end of snowfall season. The snowmelt is expected to be minimal during the cold snowfall period at high elevations. The comparison showed a maximum 1% difference between the estimated and measured SWE. To further study, the variation of annual SWE with elevation, SWE map was crossed with the 5-m classified digital elevation model (DEM) of the basins. The results are reported in Table III. Highest and lowest values of weighted average annual SWE are and 62.1 mm and correspond to Dez and Maroun basins, respectively. Annual SWE appears to exhibit a strong correlation with elevation, as shown in Figure 11 for all four basins. The points in the figure correspond to average SWE values over subsequent 25-m elevation intervals. One may conclude from Figure 11 that elevation can very well Copyright 27 Royal Meteorological Society Int. J. Climatol. 27: (27) DOI: 1.12/joc

10 1286 B. SAGHAFIAN AND R. DAVTALAB explain variation of snow budget in this region. Moreover, the zero SWE elevation line generally increases in northeast southwest direction with values approximately equal to 85, 95, 115, and 14 m, respectively, in Karkheh, Dez, Karoun, and Maroun basins. CONCLUSIONS This study is unique in attempting to integrate remote sensing of regional snow characteristics with point scale snow data to estimate the spatial pattern of snow properties in a mountainous region. In particular, extracted snow cover maps from the NOAA satellite images were arranged to obtain spatial distribution of snow observation over the study period. Two indices based on the s in the time series of snow cover maps were introduced. The indices include the average and the monthly FMSO. The relationships between these indices and the point scale SC were studied. Suitable relationships were applied to assemble spatial distribution of the time-averaged SWE in four large mountainous basins in the southwest of Iran. The important findings of this study are: 1. Two new snow indices based on the snow cover area maps derived from the NOAA satellite images were introduced in this study. The indices are spatially distributed in nature and reflect the over time period. FMSO index was defined on monthly time basis while is a single index for the whole observation period. Using the stronger relationship between FMSO index and the SC at point scale, we could indirectly estimate the spatial distribution of annual and monthly SC and cumulative annual SWE in the region. 2. The time-averaged area-weighed SWE was highest in Dez basin with mm, while Karoun, Karkheh, and Maroun ranked next with 125.9, 75.5, and 62.1 mm, respectively. In all basins, SWE was highly correlated with elevation. 3. The results of studying variation with longitude and latitude did not show a strong correlation. Other specific findings are that the analysis of the satellite images indicates that widespread snowfall occurs over the December March period in the study region. Moreover, the correlation coefficient between the average annual/monthly SCs and the elevation is not particularly strong. Although the relations obtained in this study are site-specific, the methodology and the snow observation indices introduced in this paper could be applied to other regions for studying spatial variation of time-averaged snow characteristics. REFERENCES Akyürek Z, Şorman AU. 22. Monitoring snow-covered areas using NOAA-AVHRR data in the eastern part of Turkey. Hydrological Sciences Journal 47(2): Armstrong RL Snow Properties and data Management, moonstone/cssa91%27.htm. Bruland O. 22. Dynamic of the Seasonal Snow Cover in the Arctica. The Norwegian University of Science and Technology, Doctor Engineer Trondheim: Norway, March 22, no ntnu diva-19.pdf. Dozier J, Marks D Snow mapping and classification from landsat thematic mapper data. Annals of Glaciology 9: 1 7. Lee S, Klein AG, Over TM. 23. In Review, An Assessment of the Suitability of MODIS Snow Products for Simulating Streamflow in the Upper Rio Grande River Basin using the Snowmelt Runoff, Hydrological Processes, review/ hp 23b.pdf. Porhemmat J, Saghafian B. 23. Evaluation of spatial resolution of satellite data on snow cover estimates. In Proceedings of AGU/EGS Joint Assembly, Nice, France. Simpson JJ, Stitt JR, Sienko M Improved estimates of the areal extent of snow cover from AVHRR data. Journal of Hydrology 24: Smith RB, Foster J, Kouchoukos N, Gluhosky PA, Young R, Zhang J. 2. Hydrologic Trends in the Middle East: Modeling and Remote Sensing, nov 2.pdf. Copyright 27 Royal Meteorological Society Int. J. Climatol. 27: (27) DOI: 1.12/joc