Evaluation of the snow‐covered area data product from MODIS

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.     

A comparison of MODIS and NOHRSC snow‐cover products for simulating streamflow using the Snowmelt Runoff Model

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.     

An approach to using snow areal depletion curves inferred from MODIS and its application to land surface modelling in Alaska

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.     

Eurasian snow cover and seasonal forecast of Indian summer monsoon rainfall

Abstract

This paper presents the relationship between Indian summer monsoon total rainfall and two parameters from Eurasian snow cover, one being the winter snow cover extent and the other the area of spring snowmelt. Satellite-derived Eurasian snow cover extent and Indian monsoon rainfall data were obtained from the NOAA/NESDIS and the India Meteorological Department (IMD) for the period 1966–1985. Seasonal cyclic variations of snow cover showed a higher swing in both the winter and the spring seasons of the cycle as compared to the remaining seasons of the year in the lower region of the cycle. The established inverse relation between winter snow cover and monsoon rainfall during June to September is further extended. Winter snow cover is very strongly correlated with spring snowmelt over Eurasia. Spring snowmelt area is obtained by subtracting the May snow cover extent from that of the previous February. The variations of spring snowmelt were also compared with Indian total monsoon rainfall. The detected correlation is stronger between snowmelt and monsoon rainfall than between the winter snow cover and the monsoon rainfall. There is also a significant multiple correlation among winter snow cover, spring snowmelt and monsoon rainfall. Lastly, a significant multiple correlation suggested a multiple regression equation which might improve the climatic prediction of monsoon rainfall over India.     

Climate variability,snow, and physiographic controls on storm hydrographs in small forested basins,western Cascades,Oregon

Large floods are often attributed to the melting of snow during a rain event. This study tested how climate variability, snowpack presence, and basin physiography were related to storm hydrograph shape in three small (<1 km²) basins with old‐growth forest in western Oregon. Relationships between hydrograph characteristics and precipitation were tested for approximately 800 storms over a nearly 30‐year period. Analyses controlled for (1) snowpack presence/absence, (2) antecedent soil moisture, and (3) hillslope length and gradient. For small storms (<150 mm precipitation), controlling for precipitation, the presence of a snowpack on near‐saturated soil increased the threshold of precipitation before hydrograph rise, extended the start lag, centroid lag, and duration of storm hydrographs, and increased the peak discharge. The presence of a snowpack on near‐saturated soil sped up and steepened storm hydrographs in a basin with short steep slopes, but delayed storm hydrographs in basins with longer or more gentle slopes. Hydrographs of the largest events, which were extreme regional rain and rain‐on‐snow floods, were not sensitive to landform characteristics or snowpack presence/absence. Although the presence of a snowpack did not increase peak discharge in small, forested basins during large storms, it had contrasting effects on storm timing in small basins, potentially synchronizing small basin contributions to the larger basin hydrograph during large rain‐on‐snow events. By altering the relative timing of hydrographs, snowpack melting could produce extreme floods from precipitation events whose size is not extreme. Further work is needed to examine effects of canopy openings, snowpack, and climate warming on extreme rain‐on‐snow floods at the large basin scale. Copyright © 2008 John Wiley & Sons, Ltd.     

Investigation of the snow‐cover dynamics in the Upper Euphrates Basin of Turkey using remotely sensed snow‐cover products and hydrometeorological data

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.     

Changes to flood peaks of a mountain river: implications for analysis of the 2013 flood in the Upper Bow River,Canada

The mountain headwater Bow River at Banff, Alberta, Canada, was subject to a large flood in June 2013, over which considerable debate has ensued regarding its probability of occurrence. It is therefore instructive to consider what information long‐term streamflow discharge records provide about environmental change in the Upper Bow River basin above Banff. Though protected as part of Banff National Park, since 1885, the basin has experienced considerable climate and land cover changes, each of which has the potential to impact observations, and hence the interpretations of flood probability. The Bow River at Banff hydrometric station is one of Canada's longest‐operating reference hydrological basin network stations and so has great value for assessing changes in flow regime over time. Furthermore, the station measures a river that provides an extremely important water supply for Calgary and irrigation district downstream and so is of great interest for assessing regional water security. These records were examined for changes in several flood attributes and to determine whether flow changes may have been related to landscape change within the basin as caused by forest fires, conversion from grasslands to forest with fire suppression, and regional climate variations and/or trends. Floods in the Upper Bow River are generated by both snowmelt and rain‐on‐snow (ROS) events, the latter type which include flood events generated by spatially and temporally large storms such as occurred in 2013. The two types of floods also have different frequency characteristics. Snowmelt and ROS flood attributes were not correlated significantly with any climate index or with burned area except that snowmelt event duration correlated negatively to the Pacific Decadal Oscillation. While there is a significant negative trend in all floods over the past 100 years, when separated based on generating process, neither snowmelt floods nor large ROS floods associated with mesoscale storms show any trends over time. Despite extensive changes to the landscape of the basin and in within the climate system, the flood regime remains unchanged, something identified at smaller scales in the region but never at larger scales. Copyright © 2016 John Wiley & Sons, Ltd.     

Automatic mapping of snow cover depletion curves using optical remote sensing data under conditions of frequent cloud cover and temporary snow

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.     

Modelling the spatial–temporal variability of spring snowmelt in an arctic catchment

Arctic spring landscapes are usually characterized by a mosaic of coexisting snow‐covered and bare ground patches. This phenomenon has major implications for hydrological processes, including meltwater production and runoff. Furthermore, as indicated by aircraft observations, it affects land‐surface–atmosphere exchanges, leading to a high degree of variability in surface energy terms during melt. The heterogeneity and related differences when certain parts of the landscape become snow free also affects the length of the growing season and the carbon cycle. Small‐scale variability in arctic snowmelt is addressed here by combining a spatially distributed end‐of‐winter snow cover with simulations of variable snowmelt energy balance factors for the small arctic catchment of Trail Valley Creek (63 km²). Throughout the winter, snow in arctic tundra basins is redistributed by frequent blowing snow events. Areas of above‐ or below‐average end‐of‐winter snow water equivalents were determined from land‐cover classifications, topography, land‐cover‐based snow surveys, and distributed surface wind‐field simulations. Topographic influences on major snowmelt energy balance factors (solar radiation and turbulent fluxes of sensible and latent heat) were modelled on a small‐scale (40 m) basis. A spatially variable complete snowmelt energy balance was subsequently computed and applied to the distributed snow cover, allowing the simulation of the progress of melt throughout the basin. The emerging patterns compared very well visually to snow cover observations from satellite images and aerial photographs. Results show the relative importance of variable end‐of‐winter snow cover, spatially distributed melt energy fluxes, and local advection processes for the development of a patchy snow cover. This illustrates that the consideration of these processes is crucial for an accurate determination of snow‐covered areas, as well as the location, timing, and amount of meltwater release from arctic catchments, and should, therefore, be included in hydrological models. Furthermore, the study shows the need for a subgrid parameterization of these factors in the land surface schemes of larger scale climate models. Copyright © 2005 John Wiley & Sons, Ltd.     

An analysis of snow cover patterns in a small alpine catchment

Snow cover patterns in a 9.4 km² basin in the Austrian Alps are examined during spring and summer 1989. Digital mono-plotting from oblique aerophotographs is used for mapping. on the basis of a square grid with 25 m spacing, snow cover as mapped during nine surveys is analysed as a function of elevation and slope. During winter conditions the snow cover is found to be much better related to these terrain features than during the late ablation period.     

Effects of a century of land cover and climate change on the hydrology of the Puget Sound basin

The Puget Sound basin in northwestern Washington, USA has experienced substantial land cover and climate change over the last century. Using a spatially distributed hydrology model (the Distributed Hydrology‐Soil‐Vegetation Model, DHSVM) the concurrent effects of changing climate (primarily temperature) and land cover in the basin are deconvolved, based on land cover maps for 1883 and 2002, and gridded climate data for 1915–2006. It is found that land cover and temperature change effects on streamflow have occurred differently at high and low elevations. In the lowlands, land cover has occurred primarily as conversion of forest to urban or partially urban land use, and here the land cover signal dominates temperature change. In the uplands, both land cover and temperature change have played important roles. Temperature change is especially important at intermediate elevations (so‐called transient snow zone), where the winter snow line is most sensitive to temperature change—notwithstanding the effects of forest harvest over the same part of the basin. Model simulations show that current land cover results in higher fall, winter and early spring streamflow but lower summer flow; higher annual maximum flow and higher annual mean streamflow compared with pre‐development conditions, which is largely consistent with a trend analysis of model residuals. Land cover change effects in urban and partially urban basins have resulted in changes in annual flow, annual maximum flows, fall and summer flows. For the upland portion of the basin, shifts in the seasonal distribution of streamflows (higher spring flow and lower summer flow) are clearly related to rising temperatures, but annual streamflow has not changed much. Copyright © 2009 John Wiley & Sons, Ltd.     

Monitoring over‐winter soil water dynamics in a freezing and snow‐covered environment using a thermally insulated tensiometer

Water potential below a frozen soil layer was continuously monitored over an entire winter period (using thermally insulated tensiometers sheltered in a heated chamber) along with other soil, snow and atmospheric variables. In early winter, the freezing front advanced under a thin snow cover, inducing upward soil water flow in the underlying unfrozen soil. The freezing front started to retreat when the snow cover became thick enough to insulate the soil, resulting in the reversal of the flow direction in the unfrozen zone. These data provide a clear illustration of soil water dynamics, which have rarely been monitored with a tensiometer. Copyright © 2005 John Wiley & Sons, Ltd.     

River flood seasonality in the Northeast United States: Characterization and trends

The New England and Mid‐Atlantic regions of the Northeast United States have experienced climate‐induced increases in both the magnitude and frequency of floods. However, a detailed understanding of flood seasonality across these regions, and how flood seasonality may have changed over the instrumental record, has not been established. The annual timing of river floods reflects the flood‐generating mechanisms operating in a basin, and many aquatic and riparian organisms are adapted to flood seasonality, as are human uses of river channels and flood plains. Changes in flood seasonality may indicate changes in flood‐generating mechanisms, and their interactions, with important implications for habitats, flood plain infrastructure, and human communities. I applied a probabilistic method for identifying flood seasons at a monthly resolution for 90 Northeast U.S. watersheds with natural, or near‐natural, flood‐generating conditions. Historical trends in flood seasonality were also investigated. Analyses were based on peaks‐over‐threshold flood records that have, on average, 85 years of data and three peaks per year—thus providing more information about flood seasonality than annual maximums. The results show rich detail about annual flood timing across the region with each site having a unique pattern of monthly flood occurrence. However, a much smaller number of dominant seasonal patterns emerged when contiguous flood‐rich months were classified into commonly recognized seasons (e.g., Mar–May, spring). The dominant seasonal patterns identified by manual classification were corroborated by unsupervised classification methods (i.e., cluster analyses). Trend analyses indicated that the annual timing of flood‐rich seasons has generally not shifted over the period of record, but 65 sites with data from 1941 to 2013 revealed increased numbers of June–October floods—a trend driving previously documented increases in Northeast U.S. flood counts per year. These months have been historically flood‐poor at the sites examined, so warm‐season flood potential has increased with possible implications for aquatic and riparian organisms.     

Soil moisture response to snowmelt timing in mixed‐conifer subalpine forests

Western US forest ecosystems and downstream water supplies are reliant on seasonal snowmelt. Complex feedbacks govern forest–snow interactions in which forests influence the distribution of snow and the timing of snowmelt but are also sensitive to snow water availability. Notwithstanding, few studies have investigated the influence of forest structure on snow distribution, snowmelt and soil moisture response. Using a multi‐year record from co‐located observations of snow depth and soil moisture, we evaluated the influence of forest‐canopy position on snow accumulation and snow depth depletion, and associated controls on the timing of soil moisture response at Boulder Creek, Colorado, Jemez River Basin, New Mexico, and the Wolverton Basin, California. Forest‐canopy controls on snow accumulation led to 12–42 cm greater peak snow depths in open versus under‐canopy positions. Differences in accumulation and melt across sites resulted in earlier snow disappearance in open positions at Jemez and earlier snow disappearance in under‐canopy positions at Boulder and Wolverton sites. Irrespective of net snow accumulation, we found that peak annual soil moisture was nearly synchronous with the date of snow disappearance at all sites with an average deviation of 12, 3 and 22 days at Jemez, Boulder and Wolverton sites, respectively. Interestingly, sites in the Sierra Nevada showed peak soil moisture prior to snow disappearance at both our intensive study site and the nearby snow telemetry stations. Our results imply that the duration of soil water stress may increase as regional warming or forest disturbance lead to earlier snow disappearance and soil moisture recession in subalpine forests. Copyright © 2014 John Wiley & Sons, Ltd.     

Trends in the timing and magnitude of floods in Canada

This study investigates trends in the timing and magnitude of seasonal maximum flood events across Canada. A new methodology for analyzing trends in the timing of flood events is developed that takes into account the directional character and multi-modality of flood occurrences. The methodology transforms the directional series of flood occurrences into new series by defining a new location of the origin. A test of flood seasonality (multi-modality) is then applied to identify dominant flood seasons. Floods from the dominant seasons are analyzed separately by a seasonal trend analysis. The Mann–Kendall test in conjunction with the method of pre-whitening is used in the trend analysis. Over 160 streamflow records from one common observation period are analyzed in watersheds with relatively pristine and stable land-use conditions. The results show weak signals of climate variability and/or change present in the timing of floods in Canada during the last three decades. Most of the significant trends in the timing of spring snowmelt floods are negative trends (earlier flood occurrence) found in the southern part of Canada. There are no significant trends identified in the timing of fall rainfall floods. However, the significance of the fall, rainfall-dominated flood season has been increasing in several analyzed watersheds. This may indicate increasing intensity of rainfall events during the recent years. Trends in the magnitude of floods are more pronounced than the trends in the timing of floods. Almost one fifth of all the analyzed stations show significant trends in the magnitude of snowmelt floods. Most of the significant trends are negative trends, suggesting decreasing magnitudes of snowmelt floods in Canada over the last three decades. Significant negative trends are found particularly in southern Ontario, northern Saskatchewan, Alberta and British Columbia. There are no significant trends in the magnitude of rainfall floods found in the analyzed streamflow records. The results support the outcomes of previous streamflow trend studies conducted in Canada.     

Effects of mid‐twenty‐first century climate and land cover change on the hydrology of the Puget Sound basin,Washington

The distributed hydrology–soil–vegetation model (DHSVM) was used to study the potential impacts of projected future land cover and climate change on the hydrology of the Puget Sound basin, Washington, in the mid‐twenty‐first century. A 60‐year climate model output, archived for the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report (AR4), was statistically downscaled and used as input to DHSVM. From the DHSVM output, we extracted multi‐decadal averages of seasonal streamflow, annual maximum flow, snow water equivalent (SWE), and evapotranspiration centred around 2030 and 2050. Future land cover was represented by a 2027 projection, which was extended to 2050, and DHSVM was run (with current climate) for these future land cover projections. In general, the climate change signal alone on sub‐basin streamflow was evidenced primarily through changes in the timing of winter and spring runoff, and slight increases in the annual runoff. Runoff changes in the uplands were attributable both to climate (increased winter precipitation, less snow) and land cover change (mostly reduced vegetation maturity). The most climatically sensitive parts of the uplands were in areas where the current winter precipitation is in the rain–snow transition zone. Changes in land cover were generally more important than climate change in the lowlands, where a substantial change to more urbanized land use and increased runoff was predicted. Both the annual total and seasonal distribution of freshwater flux to Puget Sound are more sensitive to climate change impacts than to land cover change, primarily because most of the runoff originates in the uplands. Both climate and land cover change slightly increase the annual freshwater flux to Puget Sound. Changes in the seasonal distribution of freshwater flux are mostly related to climate change, and consist of double‐digit increases in winter flows and decreases in summer and fall flows. Copyright © 2010 John Wiley & Sons, Ltd.     

Modeling the effects of climate change projections on streamflow in the Nooksack River basin,Northwest Washington

The Nooksack River has its headwaters in the North Cascade Mountains and drains an approximately 2000 km² watershed in northwestern Washington State. The timing and magnitude of streamflow in a snowpack‐dominated drainage basin such as the Nooksack River basin are strongly influenced by temperature and precipitation. Projections of future climate made by general circulation models (GCMs) indicate increases in temperature and variable changes in precipitation for the Nooksack River basin. Understanding the response of the river to climate change is crucial for regional water resources planning because municipalities, tribes, and industry depend on the river for water use and for fish habitat. We combine three different climate scenarios downscaled from GCMs and the Distributed‐Hydrology‐Soil‐Vegetation Model to simulate future changes to timing and magnitude of streamflow in the higher elevations of the Nooksack River. Simulations of future streamflow and snowpack in the basin project a range of magnitudes, which reflects the variable meteorological changes indicated by the three GCM scenarios and the local natural variability employed in the modeling. Simulation results project increased winter flows, decreased summer flows, decreased snowpack, and a shift in timing of the spring melt peak and maximum snow water equivalent. These results are consistent with previous regional studies, but the magnitude of increased winter flows and total annual runoff is higher. Increases in temperature dominate snowpack declines and changes to spring and summer streamflow, whereas a combination of increases in temperature and precipitation control increased winter streamflow. Copyright © 2013 John Wiley & Sons, Ltd.     

MODIS Terra Collection 6 fractional snow cover validation in mountainous terrain during spring snowmelt using Landsat TM and ETM+

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.     

Assessment of methods for mapping snow cover from MODIS

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.     

Reconstructing extreme post‐wildfire floods: a comparison of convective and mesoscale events

In much of western United States destructive floods after wildfire are frequently caused by localized, short‐duration convective thunderstorms; however, little is known about post‐fire flooding from longer‐duration, low‐intensity mesoscale storms. In this study we estimate and compare peak flows from convective and mesoscale floods following the 2012 High Park Fire in the ungaged 15.5 km² Skin Gulch basin in the northcentral Colorado Front Range. The convective storm on 6 July 2012 came just days after the wildfire was contained. Radar data indicated that the total rainfall was 20–47 mm, and the maximum rainfall intensities (upwards of 50 mm h^?1) were concentrated over portions of the watershed that burned at high severity. The mesoscale storm on 9–15 September 2013 produced 220–240 mm of rain but had maximum 15‐min intensities of only 25–32 mm h^?1. Peak flows for each flood were estimated using three independent techniques. Our best estimate using a 2D hydraulic model was 28 m³ s^?1 km^?2 for the flood following the convective storm, placing it among the largest rainfall‐runoff floods per unit area in the United States. In contrast, the flood associated with the mesoscale flood was only 6 m³ s^?1 km^?2, but the long‐duration flood caused extensive channel incision and widening, indicating that this storm was much more geomorphically effective. The peak flow estimates for the 2013 flood had a higher relative uncertainty and this stemmed from whether we used pre‐ or post‐flood channel topography. The results document the extent to which a high and moderate severity forest fire can greatly increase peak flows and alter channel morphology, illustrate how indirect peak flow estimates have larger errors than is generally assumed, and indicate that the magnitude of post‐fire floods and geomorphic change can be affected by the timing, magnitude, duration, and sequence of rainstorms. Copyright © 2017 John Wiley & Sons, Ltd.