Intra- and inter-annual variations in snow–water storage in data sparse desert–mountain regions assessed from remote sensing
Introduction
Assessing snow–water equivalence (SWE; i.e., the amount of water stored in snow) in the mountains is important in determining seasonal water flow in snowmelt-dominated river systems in mountainous watersheds. In these watersheds, snow accumulated in the mountains during the snow season is an important source of water to river networks downslope during late spring to mid-summer, when air temperatures in the mountains exceed the melting point of snow.
Measurements of snow depth, density, and SWE are usually not included in the standard data capture of most climate stations (Braun, 1991). Automated stations have been established to observe snowpack conditions at fine temporal resolutions (hourly to daily) in some parts of the world (e.g., SNOTEL; Fassnacht, Dressler, & Bales, 2003) and some manual measurements have been collected on project bases, but these measurements are usually very difficult to acquire and when acquired, they are usually not very representative of conditions beyond the area in which they were collected. Empirical studies in mountainous regions show very high standard deviations (60–100%) from mean snow cover as a result of the broad range of snow conditions encountered in mountainous environments (Fassnacht et al., 2003, Marchand and Killingtveit, 2005). Moreno, Latron, and Lehmann (2010) analyzed the accuracy of different interpolation methods in predicting snow distribution and found that the grid and sample size greatly affected model accuracy. They found that for a small catchment of 6 km2, model accuracy diminished from 80% to 50% based on the D-index (Willmott, 1982), when sample locations were reduced from 991 to 100, suggesting that interpolation would fail to provide acceptable estimates of SWE for even larger unmonitored mountainous watersheds.
Optical RS has been previously used in the past to generate satisfactory calculations of snow-cover area (SCA; Braun, 1991) in the production of moderate to coarse resolution SWE distributions for input in hydrological models (Martinec, Seidel, Burkart, & Baumann, 1991). Recent introduction of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensors have improved our capabilities to map SCA and SWE for regional and global applications (Kuchment, Romanov, Gelfan, & Demidov, 2010). However, sensor resolution and accuracy has caused some problems with the detection of snow, particularly when snow cover is shallow and discontinuous.
Accurate determination of spatiotemporal patterns in precipitation and surface air temperature is basic to modeling SCA and SWE. Historically, spatially-distributed estimates of precipitation and temperature were acquired by interpolating climate-station observations. Inherent in this approach is the assumption that precipitation and surface air temperature at locations not directly measured are adequately represented by values from surrounding monitoring stations. As a general rule, interpolation is recommended for areas where the number of climate stations is high with respect to target spatial resolution and application scale; whereas, spatial enhancement (statistical downscaling) of remote sensing (RS)-based products is recommended for areas where climate stations are sparse and landform is particularly complex (Lanza, Ramirez, & Todini, 2001).
RS-based assessments of precipitation, unlike assessments of land surface temperature, are usually indirect and are mostly modeled from remote measurements of related variables. Accuracy of RS-based estimates of instantaneous precipitation, thus far has generally been low, but reasonable estimates have been generated at longer timescales (Fiorucci et al., 2001, Hu et al., 2013). For example, Ebert and Manton (1998) compared the results of the evaluation of 50 precipitation algorithms with precipitation data acquired with Doppler radar and concluded that satellite-based estimates of precipitation corresponded reasonably well at monthly timescales, with coefficients of correlation of 0.86–0.90 for geostationary satellites and 0.69–0.86 for AVHRR and SSM/I sensors. Lower accuracies were reported for instantaneous precipitation measurements with coefficients of correlation of 0.43–0.58 and 0.6–0.78 for geostationary satellites and AVHRR or SSM/I sensors, respectively. Tropical Rainfall Measuring Mission (TRMM; i.e., product 3B43), available at 0.25° × 0.25° spatial resolution (~ 26 km × ~ 26 km), provides one of the best possible satellite-based product of total precipitation at broad temporal scales (e.g., monthly or annually; GCMD, 2010, Huffman et al., 1997, Huffman et al., 2007). Validation of TRMM-precipitation for major climatic regions of Africa has shown that the 3B43-data closely matched raingauge data, with r2-values (coefficients of determination) of 0.84–0.97 and normalized root mean squared error (RMSE) < 50% for all regions, except for the arid-lands of northern Africa (Adeyewa & Nakamura, 2003). In another validation exercise (Fleming, Awange, Kuhn, & Featherstone, 2011), 3B43-precipitation data from Australia was compared with gridded monthly data generated from raingauge observations and found strong agreement between the two datasets, with coefficient of correlation of 0.93 (r2 = 0.86).
An important limitation of TRMM-image data resides in the data's coarse spatial resolution. On account of the data's resolution, unrefined TRMM-data are usually not recommended for regional application. Spatial enhancement of TRMM-image products at scales appropriate for regional application is the subject of active research, mostly centered on the usage of a coarse relationship between normalized difference vegetation index (NDVI) and precipitation (Duan and Bastiaanssen, 2013, Immerzeel et al., 2009, Jia et al., 2011). Although the relationship is useful at broader timescales (e.g., annually), the relationship fails to capture a significant portion of the variability at shorter time intervals, including at monthly timesteps (Duan and Bastiaanssen, 2013, Verlinde, 2011). Duan and Bastiaanssen (2013) proposed the generation of 1-km resolution monthly precipitation product from TRMM by initially enhancing the original TRMM-images with a NDVI-to-TRMM-precipitation-type relationship at an annual level and disaggregating annual values into monthly estimates based on proportions derived from the original TRMM-image series.
The research described here is motivated by the need to further investigate the eco-hydrometeorological processes involved in the long term self-support of desert oases in NW China. In earlier studies (e.g., Bourque and Hassan, 2009, Bourque and Mir, 2012, Zhang et al., 2010), precipitation was calculated spatially with Genetic Algorithms trained on 10-years of monthly climate-station data and an assumed point-trend in upper-mountain precipitation. In general, the method reproduced monthly recorded precipitation at lower elevations (i.e., ≤ 3400 m above mean sea level, AMSL) reasonably well (r2 ≥ 0.75; Bourque and Hassan, 2009, Zhang et al., 2010). Greatest discrepancies between monthly values (modeled and recorded) normally occurred in the dry, lowlands of the Shiyang River watershed in the Hexi Corridor of westcentral Gansu (r2 = 0.75), where local precipitation was observed to be lower (~ 115 mm yr− 1) and generally more intermittent because of its convective origin (Bourque and Hassan, 2009, Zhang et al., 2010). With further analysis of generated precipitation surfaces, it was determined that the quantities of precipitation calculated in the mountains may have been too high and, as a result, may have caused snow accumulation and associated calculations of SWE in the mountains to have been overestimated (Bourque & Mir, 2012; their Fig. 6). There is an anticipation that improved estimates of precipitation at a finer spatial resolution may help ameliorate computations of SWE in the mountains.
The specific objectives of this paper are to: (i) develop a new monthly snow accumulation and snowmelt model (“snowmelt model”, hereafter) that addresses the melting of snow as a function of melting degree-days (utilizing surface air temperature; Dou et al., 2011, Li and Williams, 2008, Martinec and Rango, 1986, Wang and Li, 2001) and precipitation inputs based on spatially-enhanced MODIS-temperature and TRMM images; (ii) apply the snowmelt model to calculate SCA and SWE in the Qilian Mountains (NW China) from a decade of monthly data (2000–2009); and (iii) compare modeled outputs of SCA and SWE with their counterparts derived from MODIS and AMSR-E image-products in an analysis of intra- and inter-annual variation in SCA and SWE.
Section snippets
Study area
The study area comprises of the Shiyang and Hei River watersheds in westcentral Gansu Province, NW China (Fig. 1). The Shiyang River originates from the Qilian Mountains and flows northeastward before terminating in the Minqin-lake district (Li, Xiao, He, Chen, & Song, 2007). The watershed area is ≈ 49,500 km2. Elevation in the Shiyang River watershed varies from 1284 to 5161 m AMSL, with an average elevation of 1871 m AMSL. The Hei River watershed, covering ≈ 128,000 km2, is the second largest
Snowmelt model
Separating precipitation into snow and rain is important for snow accumulation and melt modeling. The phase of precipitation depends on many factors, including temperature, thickness of the atmospheric layer, types of cloud, movement of the air column, and relative humidity (Kienzle, 2008). To accommodate all these factors into a single snowmelt model would be highly impractical. To simplify the modeling process, empirical relationship between the snow–rain phase with several meteorological and
Results
Fig. 3 provides comparisons of fine-resolution MODIS-based and climate-station T and Td (representative of 2006–2009) for three climate stations in each of the Shiyang and Hei River watersheds (Fig. 1). Validation shows very high correlation between datasets (Fig. 3), giving r2's and RMSE's of 0.96, 2.56 mm month− 1 and 0.86, 2.39 mm month− 1 for T and Td, respectively.
Correlation analysis (Table 2) shows that TRMM-precipitation is reasonably correlated to RH (r = 0.7), and less so to TPW, H, and EVI (
Discussion
In the past, spatial-enhancement of TRMM-images depended on relating the patterns of vegetation growth and precipitation (Immerzeel et al., 2009, Jia et al., 2011), as a result of the strong positive correlation between variables at annual and seasonal timescales. However, the strength of these correlations usually varied for different vegetation, soil, and topographic and precipitation characteristics (Davenport and Nicholson, 1993, Nicholson et al., 1990, Nightingale and Phinn, 2003). At
Conclusion
This paper developed a methodology to produce spatially-distributed monthly precipitation, SCA, and SWE at moderate spatial resolutions (i.e., 250-m resolution) using remote sensing-based products (Table 1) and a simple snowmelt model. Digitally-enhanced precipitation surfaces after calibration matched climate-station data (at lower elevations) reasonably well, both in terms of their magnitude and seasonality. Discrepancies in magnitude are likely to be greatest in the high mountains of the
Acknowledgment
This study was jointly funded by the: (i) China Meteorological Administration; (ii) Lanzhou Regional Climate Centre of the Gansu Provincial Meteorological Bureau (GMB), Lanzhou, China (Natural Science Foundation of China under Grant 40830957); and (iii) the Faculty of Forestry and Environmental Management, University of New Brunswick (UNB), New Brunswick, Canada. We are grateful to Fang Feng of GMB for his assistance with retrieving the meteorological data used in this paper. We would also like
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