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1.
Daily swath MODIS Terra Collection 6 fractional snow cover (MOD10_L2) estimates were validated with two‐day Landsat TM/ETM + snow‐covered area estimates across central Idaho and southwestern Montana, USA. Snow cover maps during spring snowmelt for 2000, 2001, 2002, 2003, 2005, 2007, and 2009 were compared between MODIS Terra and Landsat TM/ETM + using least‐squared regression. Strong spatial and temporal map agreement was found between MODIS Terra fractional snow cover and Landsat TM/ETM + snow‐covered area, although map disagreement was observed for two validation dates. High‐altitude cirrus cloud contamination during low snow conditions as well as late season transient snowfall resulted in map disagreement. MODIS Terra's spatial resolution limits retrieval of thin‐patchy snow cover, especially during partially cloudy conditions. Landsat's image acquisition frequency can introduce difficulty when discriminating between transient and resident mountain snow cover. Furthermore, transient snowfall later in the snowmelt season, which is a stochastic accumulation event that does not usually persist beyond the daily timescale, will skew decadal snow‐covered area variability if bi‐monthly climate data record development is the objective. As a quality control step, ground‐based daily snow telemetry snow‐water‐equivalent measurements can be used to verify transient snowfall events. Users of daily MODIS Terra fractional snow products should be aware that local solar illumination and sensor viewing geometry might influence fractional snow cover estimation in mountainous terrain. Cross‐sensor interoperability has been confirmed between MODIS Terra and Landsat TM/ETM + when mapping snow from the visible/infrared spectrum. This relationship is strong and supports operational multi‐sensor snow cover mapping, specifically climate data record development to expand cryosphere, climate, and hydrological science applications. Copyright © 2013 John Wiley & Sons, Ltd.  相似文献   

2.
Four satellite‐based snow products are evaluated over the Tibetan Plateau for the 2007–2010 snow seasons. The Moderate Resolution Imaging Spectroradiometer (MODIS) Terra and Aqua snow cover daily L3 Global 500‐m grid products (MOD10A1 and MYD10A1), the National Oceanic and Atmospheric Administration Interactive Multisensor Snow and Ice Mapping System (IMS) daily Northern Hemisphere snow cover product and the Advanced Microwave Scanning Radiometer – Earth Observing System Daily Snow Water Equivalent were validated against Thematic Mapper (TM) snow cover maps of Landsat‐5 and meteorological station snow depth observations. The overall accuracy of MOD10A1, MYD10A1 and IMS is higher than 91% against stations observations and than 79% against Landsat TM images. In general, the daily MODIS snow cover products show better performance than the multisensor IMS product. However, the IMS snow cover product is suitable for larger scale (~4km) analysis and applications, with the advantage over MODIS to allow for mitigation for cloud cover. The accuracy of the three products decreases with decreasing snow depth. Overestimation errors are most common over forested regions; the IMS and Advanced Microwave Scanning Radiometer – Earth Observing System Snow Water Equivalent products also show poorer performance that the MODIS products over grassland. By identifying weaknesses in the satellite products, this study provides a focus for the improvement of snow products over the Tibetan plateau. The quantitative evaluation of the products proposed here can also be used to assess their relative weight in data assimilation, against other data sources, such as modelling and in situ measurement networks. Copyright © 2015 John Wiley & Sons, Ltd.  相似文献   

3.
Snow cover depletion curves are required for several water management applications of snow hydrology and are often difficult to obtain automatically using optical remote sensing data owing to both frequent cloud cover and temporary snow cover. This study develops a methodology to produce accurate snow cover depletion curves automatically using high temporal resolution optical remote sensing data (e.g. Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua MODIS or National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)) by snow cover change trajectory analysis. The method consists of four major steps. The first is to reclassify both cloud‐obscured land and snow into more distinct subclasses and to determine their snow cover status (seasonal snow cover or not) based on the snow cover change trajectories over the whole snowmelt season. The second step is to derive rules based on the analysis of snow cover change trajectories. These rules are subsequently used to determine for a given date, the snow cover status of a pixel based on snow cover maps from the beginning of the snowmelt season to that given date. The third step is to apply a decision‐tree‐like processing flow based on these rules to determine the snow cover status of a pixel for a given date and to create daily seasonal snow cover maps. The final step is to produce snow cover depletion curves using these maps. A case study using this method based on Terra MODIS snow cover map products (MOD10A1) was conducted in the lower and middle reaches of the Kaidu River Watershed (19 000 km2) in the Chinese Tien Shan, Xinjiang Uygur Autonomous Region, China. High resolution remote sensing data (charge coupled device (CCD) camera data with 19·5 m resolution of the China and Brazil Environmental and Resources Satellite (CBERS) data (19·5 m resolution), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data with 15 m resolution of the Terra) were used to validate the results. The study shows that the seasonal snow cover classification was consistent with that determined using a high spatial resolution dataset, with an accuracy of 87–91%. The snow cover depletion curves clearly reflected the impact of the variation of temperature and the appearance of temporary snow cover on seasonal snow cover. The findings from this case study suggest that the approach is successful in generating accurate snow cover depletion curves automatically under conditions of frequent cloud cover and temporary snow cover using high temporal resolution optical remote sensing data. Copyright © 2007 John Wiley & Sons, Ltd.  相似文献   

4.
Land surface albedo plays an important role in the radiation budget and global climate models. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) provide 16‐day albedo product with 500‐m resolution every 8 days (MCD43A3). Some in‐situ albedo measurements were used as the true surface albedo values to validate the MCD43A3 product. As the 16‐day MODIS albedo retrievals do not include snow observations when there is ephemeral snow on the ground surface in a 16‐day period, comparisons between MCD43A3 and 16 day averages of field data do not agree well. Another reason is that the MODIS cannot detect the snow when the area is covered by clouds. The Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) data are not affected by weather conditions and are a good supplement for optical remote sensing in cloudy weather. When the surface is covered by ephemeral snow, the AMSR‐E data can be used as the additional information to retrieve the snow albedo. In this study, we developed an improved method by using the MODIS products and the AMSR‐E snow water equivalent (SWE) product to improve the MCD43A3 short‐time snow‐covered albedo estimation. The MODIS daily snow products MOD10A1 and MYD10A1 both provide snow and cloud information from observations. In our study region, we updated the MODIS daily snow product by combining MOD10A1 and MYD10A1. Then, the product was combined with the AMSR‐E SWE product to generate new daily snow‐cover and SWE products at a spatial resolution of 500 m. New SWE datasets were integrated into the Noah Land Surface Model snow model to calculate the albedo above a snow surface, and these values were then utilized to improve the MODIS 16‐day albedo product. After comparison of the results with in‐situ albedo measurements, we found that the new corrected 16‐day albedo can show the albedo changes during the short snowfall season. For example, from January 25 to March 14, 2007 at the BJ site, the albedo retrieved from snow‐free observations does not indicate the albedo changes affected by snow; the improved albedo conforms well to the in‐situ measurements. The correlation coefficient of the original MODIS albedo and the in‐situ albedo is 0.42 during the ephemeral snow season, but the correlation coefficient of the improved MODIS albedo and the in‐situ albedo is 0.64. It is concluded that the new method is capable of capturing the snow information from AMSR‐E SWE to improve the short‐time snow‐covered albedo estimation. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

5.
Eleven years of daily 500 m gridded Terra Moderate Resolution Imaging Spectroradiometer (MODIS) (MOD10A1) snow cover fraction (SCF) data are evaluated in terms of snow presence detection in Colorado and Washington states. The SCF detection validation study is performed using in‐situ measurements and expressed in terms of snow and land detection and misclassification frequencies. A major aspect addressed in this study involves the shifting of pixel values in time due to sensor viewing angles and gridding artifacts of MODIS sensor products. To account for this error, 500 m gridded pixels are grouped and aggregated to different‐sized areas to incorporate neighboring pixel information. With pixel aggregation, both the probability of detection (POD) and the false alarm ratios increase for almost all cases. Of the false negative (FN) and false positive values (referred to as the total error when combined), FN estimates dominate most of the total error and are greatly reduced with aggregation. The greatest POD increases and total error reductions occur with going from a single 500 m pixel to 3×3‐pixel averaged areas. Since the MODIS SCF algorithm was developed under ideal conditions, SCF detection is also evaluated for varying conditions of vegetation, elevation, cloud cover and air temperature. Finally, using a direct insertion data assimilation approach, pixel averaged MODIS SCF observations are shown to improve modeled snowpack conditions over the single pixel observations due to the smoothing of more error‐prone observations and more accurately snow‐classified pixels. Copyright © 2012 John Wiley & Sons, Ltd.  相似文献   

6.
Abstract

Monitoring snow parameters (e.g. snow-cover area, snow water equivalent) is challenging work. Because of its natural physical properties, snow strongly affects the evolution of weather on a daily basis and climate on a longer time scale. In this paper, the snow recognition product generated from the MSG-SEVIRI images within the framework of the Hydrological Satellite Facility (HSAF) Project of EUMETSAT is presented. Validation of the snow recognition product H10 was done for the snow season (from 1 January to 31 March) of the water year 2009. The MOD10A1 and MOD10C2 snow products were also used in the validation studies. Ground truth of the products was obtained by using 1890 snow depth observations from 20 meteorological stations, which are mainly located in mountainous areas and are distributed across the eastern part of Turkey. The possibility of 37% cloud cover reduction was obtained by merging 15-min observations from MSG-SEVIRI as opposed to using only one daily observation from MODIS. The coarse spatial resolution of the H10 product gave higher commission errors compared to the MOD10A1 product. Snow depletion curves obtained from the HSAF snow recognition product were compared with those derived from the MODIS 8-day snow cover product. The preliminary results show that the HSAF snow recognition product, taking advantage of using high temporal frequency measurement with spectral information required for snow mapping, significantly improves the mapping of regional snow-cover extent over mountainous areas.

Citation Surer, S. and Akyurek, Z., 2012. Evaluating the utility of the EUMETSAT HSAF snow recognition product over mountainous areas of eastern Turkey. Hydrological Sciences Journal, 57 (8), 1–11.  相似文献   

7.
Taking northern Xinjiang, China, as an example, this study first compares the standard MODIS Terra and Aqua snow cover classifications, and then compares the accuracy of the standard MODIS daily and 8‐day snow cover products with the new daily and multi‐day snow cover combination of MODIS Terra and Aqua observations using in situ measurements. Under clear sky in both products, the agreement of land classification from MODIS Terra and Aqua daily and 8‐day snow cover products is close to 100% for a entire water year. In contrast, the agreement of snow classification from MODIS Terra and Aqua is high only in the winter months, decreasing in the rest of the period. The high agreement mainly concentrates in land or snow‐dominated areas, and major disagreements take place in the transitions zones from snow to land. The disagreement (mainly snow–land) in the 8‐day products is higher than that in the daily products. In addition, both MODIS Terra and Aqua cloud masks tend to map more areas in the transition zones as cloud. Under clear sky conditions, the three daily products have similar accuracy of snow and land classification, and the 8‐day standard products and the multi‐day combination product also have similar accuracy of snow and land classification. This further suggests that the algorithm in the combination of Terra and Aqua snow cover products is valid. Moreover, in the actual weather/cloud conditions, the combination products from Terra and Aqua reduce cloud blockage and improve snow classification accuracy against either MODIS Terra or Aqua (51% against 44% and 34% for daily and 92% against 87% and 78% for 8‐day, respectively), although Terra snow product (daily or 8‐day) has slightly better accuracy than the Aqua snow product. The new combination products can provide better mapping of spatiotemporal variation of snow cover/glacier and for snow‐melting modeling. Copyright © 2008 John Wiley & Sons, Ltd.  相似文献   

8.
Describing the spatial variability of heterogeneous snowpacks at a watershed or mountain‐front scale is important for improvements in large‐scale snowmelt modelling. Snowmelt depletion curves, which relate fractional decreases in snow‐covered area (SCA) against normalized decreases in snow water equivalent (SWE), are a common approach to scale‐up snowmelt models. Unfortunately, the kinds of ground‐based observations that are used to develop depletion curves are expensive to gather and impractical for large areas. We describe an approach incorporating remotely sensed fractional SCA (FSCA) data with coinciding daily snowmelt SWE outputs during ablation to quantify the shape of a depletion curve. We joined melt estimates from the Utah Energy Balance Snow Accumulation and Melt Model (UEB) with FSCA data calculated from a normalized difference snow index snow algorithm using NASA's moderate resolution imaging spectroradiometer (MODIS) visible (0·545–0·565 µm) and shortwave infrared (1·628–1·652 µm) reflectance data. We tested the approach at three 500 m2 study sites, one in central Idaho and the other two on the North Slope in the Alaskan arctic. The UEB‐MODIS‐derived depletion curves were evaluated against depletion curves derived from ground‐based snow surveys. Comparisons showed strong agreement between the independent estimates. Copyright © 2010 John Wiley & Sons, Ltd.  相似文献   

9.
Remote sensing is an important source of snow‐cover extent for input into the Snowmelt Runoff Model (SRM) and other snowmelt models. Since February 2000, daily global snow‐cover maps have been produced from data collected by the Moderate Resolution Imaging Spectroradiometer (MODIS). The usefulness of this snow‐cover product for streamflow prediction is assessed by comparing SRM simulated streamflow using the MODIS snow‐cover product with streamflow simulated using snow maps from the National Operational Hydrologic Remote Sensing Center (NOHRSC). Simulations were conducted for two tributary watersheds of the Upper Rio Grande basin during the 2001 snowmelt season using representative SRM parameter values. Snow depletion curves developed from MODIS and NOHRSC snow maps were generally comparable in both watersheds: satisfactory streamflow simulations were obtained using both snow‐cover products in larger watershed (volume difference: MODIS, 2·6%; NOHRSC, 14·0%) and less satisfactory streamflow simulations in smaller watershed (volume difference: MODIS, −33·1%; NOHRSC, −18·6%). The snow water equivalent (SWE) on 1 April in the third zone of each basin was computed using the modified depletion curve produced by the SRM and was compared with in situ SWE measured at Snowpack Telemetry sites located in the third zone of each basin. The SRM‐calculated SWEs using both snow products agree with the measured SWEs in both watersheds. Based on these results, the MODIS snow‐cover product appears to be of sufficient quality for streamflow prediction using the SRM in the snowmelt‐dominated basins. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

10.
The Euphrates and Tigris rivers serve as the most important water resources in the Middle East. Precipitation in this region falls mostly in the form of snow over the higher elevations of the Euphrates Basin and remains on the ground for nearly half of the year. This snow‐covered area (SCA) is a key element of the hydrological cycle, and monitoring the SCA is crucial for making accurate forecasts of snowmelt discharge, especially for energy production, flood control, irrigation, and reservoir‐operation optimization in the Upper Euphrates (Karasu) Basin. Remote sensing allows the detection of the spatio‐temporal patterns of snow cover across large areas in inaccessible terrain, such as the eastern part of Turkey, which is highly mountainous. In this study, a seasonal evaluation of the snow cover from 2000 to 2009 was performed using 8‐day snow‐cover products (MOD10C2) and the daily snow‐water equivalent (SWE) product. The values of SWE products were obtained using an assimilation process based on the Helsinki University of Technology model using equal area Special Sensor Microwave Imager (SSM/I) Earth‐gridded advanced microwave scanning radiometer—EOS daily brightness‐temperature values. In the Karasu Basin, the SCA percentage for the winter period is 80–90%. The relationship between the SCA and the runoff during the spring period is analysed for the period from 2004 to 2009. An inverse linear relationship between the normalized SCA and the normalized runoff values was obtained (r = 0·74). On the basis of the monthly mean temperature, total precipitation and snow depth observed at meteorological stations in the basin, the decrease in the peak discharges, and early occurrences of the peak discharges in 2008 and 2009 are due to the increase in the mean temperature and the decrease in the precipitation in April. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

11.
Taking the Northern Xinjiang region as an example, we develop a snow depth model by using the Advanced Microwave Scanning Radiometer‐Earth Observing System (AMSR‐E) horizontal and vertical polarization brightness temperature difference data of 18 and 36 GHz bands and in situ snow depth measurements from 20 climatic stations during the snow seasons November–March) of 2002–2005. This article proposes a method to produce new 5‐day snow cover and snow depth images, using Terra and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) daily snow cover products and AMSR‐E snow water equivalent and daily brightness temperature products. The results indicate that (1) the brightness temperature difference (Tb18h–Tb36h) provides the most accurate and precise prediction of snow depth; (2) the snow, land and overall classification accuracies of the new images are separately 89.2%, 77.7% and 87.2% and are much better than those of AMSR‐E or MODIS products (in all weather conditions) alone; (3) the snow classification accuracy increases as snow depth increases; and (4) snow accuracies for different land cover types vary as 88%, 92.3%, 79.7% and 80.1% for cropland, grassland, shrub, and urban and built‐up, respectively. We conclude that the new 5‐day snow cover–snow depth images can provide both accurate cloud‐free snow cover extent and the snow depth dynamics, which would lay a scientific basis for water management and prevention of snow‐related disasters in this dry and cold pastoral area. After validations of the algorithms over other regions with different snow and climate conditions, this method would also be used for monitoring snow cover and snow depth elsewhere in the world. Copyright © 2011 John Wiley & Sons, Ltd.  相似文献   

12.
Characterization of snow is critical for understanding Earth’s water and energy cycles. Maps of snow from MODIS have seen growing use in investigations of climate, hydrology, and glaciology, but the lack of rigorous validation of different snow mapping methods compromises these studies. We examine three widely used MODIS snow products: the “binary” (i.e., snow yes/no) global snow maps that were among the initial MODIS standard products; a more recent standard MODIS fractional snow product; and another fractional snow product, MODSCAG, based on spectral mixture analysis. We compare them to maps of snow obtained from Landsat ETM+ data, whose 30 m spatial resolution provides nearly 300 samples within a 500 m MODIS nadir pixel. The assessment uses 172 images spanning a range of snow and vegetation conditions, including the Colorado Rocky Mountains, the Upper Rio Grande, California’s Sierra Nevada, and the Nepal Himalaya. MOD10A1 binary and fractional fail to retrieve snow in the transitional periods during accumulation and melt while MODSCAG consistently maintains its retrieval ability during these periods. Averaged over all regions, the RMSE for MOD10A1 fractional is 0.23, whereas the MODSCAG RMSE is 0.10. MODSCAG performs the most consistently through accumulation, mid-winter and melt, with median differences ranging from −0.16 to 0.04 while differences for MOD10A1 fractional range from −0.34 to 0.35. MODSCAG maintains its performance over all land cover classes and throughout a larger range of land surface properties. Characterizing snow cover by spectral mixing is more accurate than empirical methods based on the normalized difference snow index, both for identifying where snow is and is not and for estimating the fractional snow cover within a sensor’s instantaneous field-of-view. Determining the fractional value is particularly important during spring and summer melt in mountainous terrain, where large variations in snow, vegetation and soil occur over small distances and when snow can melt rapidly.  相似文献   

13.
Dennis G. Dye 《水文研究》2002,16(15):3065-3077
This study investigated variability and trends in the annual snow‐cover cycle in regions covering high‐latitude and high‐elevation land areas in the Northern Hemisphere. The annual snow‐cover cycle was examined with respect to the week of the last‐observed snow cover in spring (WLS), the week of the first‐observed snow cover in autumn (WFS), and the duration of the snow‐free period (DSF). The analysis used a 29‐year time‐series (1972–2000) of weekly, visible‐band satellite observations of Northern Hemisphere snow cover from NOAA with corrections applied by D. Robinson of Rutgers University Climate Laboratory. Substantial interannual variability was observed in WLS, WFS and DSF (standard deviations of 0·8–1·1, 0·7–0·9 and 1·0–1·4 weeks, respectively), which is related directly to interannual variability in snow‐cover area in the regions and time periods of snow‐cover transition. Over the nearly three‐decade study period, WLS shifted earlier by 3–5 days/decade as determined by linear regression analysis. The observed shifts in the annual snow‐cover cycle underlie a significant trend toward a longer annual snow‐free period. The DSF increased by 5–6 days/decade over the study period, primarily as a result of earlier snow cover disappearance in spring. The observed trends are consistent with reported trends in the timing and length of the active growing season as determined from satellite observations of vegetation greenness and the atmospheric CO2 record. Copyright © 2002 John Wiley & Sons, Ltd.  相似文献   

14.
The Moderate Resolution Imaging Spectroradiometer (MODIS), flown on board the Terra Earth Observing System (EOS) platform launched in December 1999, produces a snow‐covered area (SCA) product. This product is expected to be of better quality than SCA products based on operational satellites (notably GOES and AVHRR), due both to improved spectral resolution and higher spatial resolution of the MODIS instrument. The gridded MODIS SCA product was compared with the SCA product produced and distributed by the National Weather Service National Operational Hydrologic Remote Sensing Center (NOHRSC) for 46 selected days over the Columbia River basin and 32 days over the Missouri River basin during winter and spring of 2000–01. Snow presence or absence was inferred from ground observations of snow depth at 1330 stations in the Missouri River basin and 762 stations in the Columbia River basin, and was compared with the presence/absence classification for the corresponding pixels in the MODIS and NOHRSC SCA products. On average, the MODIS SCA images classified fewer pixels as cloud than NOHRSC, the effect of which was that 15% more of the Columbia basin area could be classified as to presence–absence of snow, while overall there was a statistically insignificant difference over the Missouri basin. Of the pixels classified as cloud free, MODIS misclassified 4% and 5% fewer overall (for the Columbia and Missouri basins respectively) than did the NOHRSC product. When segregated by vegetation cover, forested areas had the greatest differences in fraction of cloud cover reported by the two SCA products, with MODIS classifying 13% and 17% less of the images as cloud for the Missouri and Columbia basins respectively. These differences are particularly important in the Columbia River basin, 39% of which is forested. The ability of MODIS to classify significantly greater amounts of snow in the presence of cloud in more topographically complex, forested, and snow‐dominated areas of these two basins provides valuable information for hydrologic prediction. Copyright © 2003 John Wiley & Sons, Ltd.  相似文献   

15.
Snowcover areal depletion curves inferred from the moderate resolution imaging spectroradiometer (MODIS) are validated and then applied in NASA's catchment‐based land surface model (CLSM) for numerical simulations of hydrometeorological processes in the Kuparuk River basin (KRB) of Alaska. The results demonstrate that the MODIS snowcover fraction f derived from a simple relationship in terms of the normalized difference snow index compares well with Landsat values over the range 20 ≤ f ≤ 100%. For f < 20%, however, MODIS 500 m subpixel data underestimate the amount of snow by up to 13% compared with Landsat at spatial resolutions of 30 m binned to equivalent 500 m pixels. After a bias correction, MODIS snow areal depletion curves during the spring transition period of 2002 for the KRB exhibit similar features to those derived from surface‐based observations. These results are applied in the CLSM subgrid‐scale snow parameterization that includes a deep and a shallow snowcover fraction. Simulations of the evolution of the snowpack and of freshwater discharge rates for the KRB over a period of 11 years are then analysed with the inclusion of this feature. It is shown that persistent snowdrifts on the arctic landscape, associated with a secondary plateau in the snow areal depletion curves, are hydrologically important. An automated method is developed to generate the shallow and deep snowcover fractions from MODIS snow areal depletion curves. This provides the means to apply the CLSM subgrid‐scale snow parameterization in all watersheds subject to seasonal snowcovers. Improved simulations and predictions of the global surface energy and water budgets are expected with the incorporation of the MODIS snow data into the CLSM. Copyright © 2005 John Wiley & Sons, Ltd.  相似文献   

16.
In this paper, we addressed a sensitivity analysis of the snow module of the GEOtop2.0 model at point and catchment scale in a small high‐elevation catchment in the Eastern Italian Alps (catchment size: 61 km2). Simulated snow depth and snow water equivalent at the point scale were compared with measured data at four locations from 2009 to 2013. At the catchment scale, simulated snow‐covered area (SCA) was compared with binary snow cover maps derived from moderate‐resolution imaging spectroradiometer (MODIS) and Landsat satellite imagery. Sensitivity analyses were used to assess the effect of different model parameterizations on model performance at both scales and the effect of different thresholds of simulated snow depth on the agreement with MODIS data. Our results at point scale indicated that modifying only the “snow correction factor” resulted in substantial improvements of the snow model and effectively compensated inaccurate winter precipitation by enhancing snow accumulation. SCA inaccuracies at catchment scale during accumulation and melt period were affected little by different snow depth thresholds when using calibrated winter precipitation from point scale. However, inaccuracies were strongly controlled by topographic characteristics and model parameterizations driving snow albedo (“snow ageing coefficient” and “extinction of snow albedo”) during accumulation and melt period. Although highest accuracies (overall accuracy = 1 in 86% of the catchment area) were observed during winter, lower accuracies (overall accuracy < 0.7) occurred during the early accumulation and melt period (in 29% and 23%, respectively), mostly present in areas with grassland and forest, slopes of 20–40°, areas exposed NW or areas with a topographic roughness index of ?0.25 to 0 m. These findings may give recommendations for defining more effective model parameterization strategies and guide future work, in which simulated and MODIS SCA may be combined to generate improved products for SCA monitoring in Alpine catchments.  相似文献   

17.
Using reflectance values from the seven MODIS “land” bands with 250 or 500 m resolution, along with a corresponding cloud product, we estimate the fraction of each 500 m pixel that snow covers, along with the albedo of that snow. The daily products have data gaps and errors because of cloud cover and sensor viewing geometry. Rather than make users interpolate and filter these patchy daily maps without completely understanding the retrieval algorithm and instrument properties, we use the daily time series to improve the estimate of the measured snow properties for a particular day. We use a combination of noise filtering, snow/cloud discrimination, and interpolation and smoothing to produce our best estimate of the daily snow cover and albedo. We consider two modes: one is the “predictive” mode, whereby we estimate the snow-covered area and albedo on that day using only the data up to that day; the other is the “retrospective” mode, whereby we reconstruct the history of the snow properties for a previous period.  相似文献   

18.
An analysis of the hydrological effects of vegetation changes in the Columbia River basin over the last century was performed using two land cover scenarios. The first was a reconstruction of historical land cover vegetation, c. 1900, as estimated by the federal Interior Columbia Basin Ecosystem Management Project (ICBEMP). The second was current land cover as estimated from remote sensing data for 1990. Simulations were performed using the variable infiltration capacity (VIC) hydrological model, applied at one‐quarter degree spatial resolution (approximately 500 km2 grid cell area) using hydrometeorological data for a 10 year period starting in 1979, and the 1900 and current vegetation scenarios. The model represents surface hydrological fluxes and state variables, including snow accumulation and ablation, evapotranspiration, soil moisture and runoff production. Simulated daily hydrographs of naturalized streamflow (reservoir effects removed) were aggregated to monthly totals and compared for nine selected sub‐basins. The results show that, hydrologically, the most important vegetation‐related change has been a general tendency towards decreased vegetation maturity in the forested areas of the basin. This general trend represents a balance between the effects of logging and fire suppression. In those areas where forest maturity has been reduced as a result of logging, wintertime maximum snow accumulations, and hence snow available for runoff during the spring melt season, have tended to increase, and evapotranspiration has decreased. The reverse has occurred in areas where fire suppression has tended to increase vegetation maturity, although the logging effect appears to dominate for most of the sub‐basins evaluated. Predicted streamflow changes were largest in the Mica and Corralin sub‐basins in the northern and eastern headwaters region; in the Priest Rapids sub‐basin, which drains the east slopes of the Cascade Mountains; and in the Ice Harbor sub‐basin, which receives flows primarily from the Salmon and Clearwater Rivers of Idaho and western Montana. For these sub‐basins, annual average increases in runoff ranged from 4·2 to 10·7% and decreases in evapotranspiration ranged from 3·1 to 12·1%. In comparison with previous studies of individual, smaller sized watersheds, the modelling approach used in this study provides predictions of hydrological fluxes that are spatially continuous throughout the interior Columbia River basin. It thus provides a broad‐scale framework for assessing the vulnerability of watersheds to altered streamflow regimes attributable to changes in land cover that occur over large geographical areas and long time‐frames. Copyright © 2000 John Wiley & Sons, Ltd.  相似文献   

19.
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20.
The Irtysh River is the main water resource of Eastern Kazakhstan and its upper basin is severely affected by spring floods each year, primarily as a result of snowmelt. Knowledge of the large-scale processes that influence the timing of these snow-induced floods is currently lacking, but critical for the management of water resources in the area. In this study, we evaluated the variability in winter–spring snow cover in five major sub-basins of the Upper Irtysh basin between 2000 and 2017 as a possible explanatory factor of spring flood events, assessing the time of peak snow cover depletion rate and snow cover disappearance from the moderate-resolution imaging spectroradiometer (MODIS) MOD10A2 data set. We found that on average, peak snow cover retreat occurs between 22 March and 14 April depending on the basin, with large interannual variations but no clear trend over the MODIS period, while our comparative analysis of longer-term snow cover extent from the National Oceanic and Atmospheric Administration Climate Data Record data set suggests a shift to earlier snow cover disappearance since the 1970s. In contrast, the annual peak snow cover depletion rate displays a weak increasing trend over the study period and exceeded 5,900 km2/day in 2017. The timing of snow disappearance in spring shows significant correlations of up to 0.82 for the largest basin with winter indices of the Arctic Oscillation (AO) over the region. The primary driver is the impact of the large-scale pressure anomalies upon the mean spring (MAM) air temperatures and resultant timing of snow cover disappearance, particularly at elevations 500–2,000 m above sea level. This suggests a lagged effect of this atmospheric circulation pattern in spring snow cover retreat. The winter AO index could therefore be incorporated into long-term runoff forecasts for the Irtysh. Our approach is easily transferable to other similar catchments and could support flood management strategies in Kazakhstan and other countries.  相似文献   

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