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Relationship between passive microwave‐derived snowmelt and surface‐measured discharge, Wheaton River, Yukon Territory, Canada
Abstract
Snow volume and melt timing are major factors influencing the water cycle at northern high altitudes and latitudes, yet both are hard to quantify or monitor in remote mountainous regions. Twice-daily special sensor microwave imager (SSM/I) passive microwave observations of seasonal snow melt onset in the Wheaton River basin, Yukon Territory, Canada (∼60 ° 08′05″N, ∼134 ° 53′45″W), are used to test the idea that melt onset date and duration of snowpack melt–refreeze fluctuations control the timing of the early hydrograph peaks with predictable lags. This work uses the SSM/I satellite data from 1988 to 2002 to evaluate the chronology of melt and runoff patterns in the upper Yukon River basin. The Wheaton River is a small (875 km2) tributary to the Yukon, and is a subarctic, partly glacierized heterogeneous basin with near-continuous hydrographic records dating back to 1966. SSM/I pixels are sensitive to melt onset due to the strong increase in snow emissivity, and have a robust signal, in spite of coarse (>25 × 25 km2) pixel resolution and varied terrain. Results show that Wheaton River peak flows closely follow the end of large daily variations in brightness temperature of pixels covering the Wheaton River, but the magnitude of flow is highly variable, as might be expected from interannual snow mass variability. Spring rise in the hydrograph follows the end of high diurnal brightness temperature (Tb) amplitude variations (DAV) by 0 to 5 days approximately 90% of the time for this basin. Subsequent work will compare these findings for a larger (7250 km2), unglacierized tributary, the Ross River, which is farther northeast (∼61 ° 59″40″N, ∼132 ° 22″40″W) in the Yukon Territory. These techniques will also be used to try to determine the improvement in melt detection and runoff prediction from the higher resolution (∼15 × 15 km2) advanced microwave scanning radiometer for EOS (AMSR-E) sensor. Copyright © 2006 John Wiley & Sons, Ltd.
... Higher frequency wavelengths are sensitive to the shallow depths of snowpack while lower frequencies can penetrate deeper. Here the 37 GHz vertically polarized wavelength is used due to its high sensitivity to liquid water in the snowpack (Ramage et al., 2006). Previous studies have shown snow cover distribution and snowmelt timing 20 ...
... are adequately measured by passive microwave sensors daily, in all weather conditions (Hall et al., 1991; Mote et al., 1993; Drobot and Anderson, 2001; Ramage and Isacks, 2002; Wang et al., 2005; Ramage et al., 2006; Apgar et al., 2007; Tedesco, 2007; Tedesco et al., 2009)Discussion Paper | Discussion Paper | Discussion Paper | Discussion Paper | EASE-Grid 25 × 25 km 2 with two observations per day at overpass times around 08:30 and 18:30 PST (Armstrong et al., 1994). For a continuous data record from 1988 to 2010, SSM/I data from DMSP F8, F11, F13, and F17 satellites were combined. ...
... The twice-daily observations enable the calculation of the running difference between the ascending and descending brightness temperature values termed the diurnal amplitude variation or DAV, which is interpreted as a proxy of the dynamism of 25 the snowpack as the liquid water content changes (Ramage and Isacks, 2002). High DAV values, especially for 37 GHz sensitive to the top centimeter of snowpack, indicate when the snowpack is melting during the day and re-freezing at night (Ramage et al., 2006). The end of this melt-refreeze period is of interest because its timing is ...
Spring melt is a significant feature of high latitude snowmelt dominated drainage basins influencing hydrological and ecological processes such as snowmelt runoff and green-up. Melt duration, defined as the transition period from snowmelt onset until the end of the melt-refreeze, is characterized by high diurnal amplitude variations (DAV) where the snowpack is melting during the day and refreezing at night, after which the snowpack melts constantly until depletion. Determining trends for this critical period is necessary for understanding how the Arctic is changing with rising temperatures and provides a baseline from which to assess future change. To study this dynamic period, brightness temperature ( T b) data from the Special Sensor Microwave Imager (SSM/I) 37 V-GHz frequency from 1988 to 2010 were used to assess snowmelt timing trends for the Yukon River Basin, Alaska/Canada. Annual T b and DAV for 1434 Equal-Area Scalable Earth (EASE)-Grid pixels (25 km resolution) were processed to determine melt onset and melt-refreeze dates from T b and DAV thresholds previously established in the region. Temporal and spatial trends in the timing of melt onset and melt-refreeze, and the duration of melt were analyzed for the 13 sub-basins of the Yukon River Basin with three different time interval approaches. Results show a lengthening of the melt period for the majority of the sub-basins with a significant trend toward later end of melt-refreeze after which the snowpack melts day and night leading to snow clearance, peak discharge, and green-up. Earlier melt onset trends were also found in the higher elevations and northernmost sub-basins (Porcupine, Chandalar, and Koyukuk Rivers). Latitude and elevation displayed the dominant controls on melt timing variability and spring solar flux was highly correlated with melt timing in middle (~600–1600 m) elevations.
... Based on these findings and on a manual overview of the data, no correction was deemed necessary given the risk of introduction of new unknown bias. SSM/I data and the technique for detecting snowmelt timing has been previously 25 established in the upper YRB using 37 GHz vertically polarized data (Ramage et al., 2006) and has been found to correlate well with higher resolution Advanced Microwave Scanning Radiometer -EOS (AMSR-E) derived snowmelt onset (Apgar et al., 2007 the ascending and descending brightness temperature values termed the diurnal amplitude variation or DAV, which is a proxy of the dynamism of the snowpack as liquid water content changes (Ramage and Isacks, 2002). High DAV values, especially for 37 GHz sensitive to the top centimeter of snowpack, indicate when the snowpack is melting during the day and re-freezing at night (Ramage et al., 2006). ...
... SSM/I data and the technique for detecting snowmelt timing has been previously 25 established in the upper YRB using 37 GHz vertically polarized data (Ramage et al., 2006) and has been found to correlate well with higher resolution Advanced Microwave Scanning Radiometer -EOS (AMSR-E) derived snowmelt onset (Apgar et al., 2007 the ascending and descending brightness temperature values termed the diurnal amplitude variation or DAV, which is a proxy of the dynamism of the snowpack as liquid water content changes (Ramage and Isacks, 2002). High DAV values, especially for 37 GHz sensitive to the top centimeter of snowpack, indicate when the snowpack is melting during the day and re-freezing at night (Ramage et al., 2006). Snowmelt on-5 set is determined from SSM/I data (37 GHz vertically polarized) when T b is greater than 246 K and DAV are above ±10 K, thresholds previously developed and validated (Ramage and Isacks, 2002;Ramage et al., 2006). ...
... High DAV values, especially for 37 GHz sensitive to the top centimeter of snowpack, indicate when the snowpack is melting during the day and re-freezing at night (Ramage et al., 2006). Snowmelt on-5 set is determined from SSM/I data (37 GHz vertically polarized) when T b is greater than 246 K and DAV are above ±10 K, thresholds previously developed and validated (Ramage and Isacks, 2002;Ramage et al., 2006). Melt onset (and end high DAV/meltrefreeze) are defined as the first date when at least three of five consecutive days meet the T b and DAV thresholds described above. ...
Brightness temperature (Tb) data from the Special Sensor
Microwave Imager (SSM/I) 37 V-GHz frequency provides a time series from
1988 to 2010 that enables the assessment of snowmelt timing trends
(onset, end of melt-refreeze, and duration) for the Yukon River Basin.
Tb and diurnal amplitude variation (DAV) thresholds determine
dates of melt onset and melt-freeze end (end of high DAV), defined as
the first date when thresholds are met for more than three of five
consecutive days. Temporal and spatial trends in melt onset and end of
melt-refreeze date are determined with varying time period intervals and
for each sub-basin and elevation class. Earlier melt onset trends are
found in the highest elevations and northernmost sub-basins (Porcupine,
Chandalar, and Koyukuk Rivers). Significant later (>0.75 d
yr-1) end of melt-refreeze and longer melt duration trends
are found in a majority of the sub-basins. Moving interval trends
suggest interannual variability within the time series and a power
spectrum analysis reveals peak frequencies and periods of 5-7 and ~11
years, possibly related to El Nino- Southern Oscillation and the solar
cycle, respectively. Latitude and elevation display the dominant
controls on timing variance and spring solar flux is highly correlated
with melt timing in middle elevations.
... Higher frequency wavelengths are sensitive to the shallow depths of snowpack while lower frequencies can penetrate deeper. Here the 37 GHz vertically polarized wavelength is used due to its high sensitivity to liquid water in the snowpack (Ramage et al., 2006). Previous studies have shown snow cover distribution and snowmelt timing are adequately measured by passive microwave sensors daily, in all weather conditions (Hall et al., 1991;Mote et al., 1993;Drobot and Anderson, 2001;Ramage and Isacks, 2002;Wang et al., 2005;Ramage et al., 2006;Apgar et al., 2007;Tedesco, 2007;Tedesco et al., 2009). ...
... Here the 37 GHz vertically polarized wavelength is used due to its high sensitivity to liquid water in the snowpack (Ramage et al., 2006). Previous studies have shown snow cover distribution and snowmelt timing are adequately measured by passive microwave sensors daily, in all weather conditions (Hall et al., 1991;Mote et al., 1993;Drobot and Anderson, 2001;Ramage and Isacks, 2002;Wang et al., 2005;Ramage et al., 2006;Apgar et al., 2007;Tedesco, 2007;Tedesco et al., 2009). SSM/I data provided by the National Snow and Ice Data Center (NSIDC) in the form of Level 3 Equal-Area Scalable Earth (EASE)-Grid Brightness Temperatures gridded data for Northern Hemisphere projection have a resolution of 37 × 28 km 2 gridded to an EASE-Grid 25 × 25 km 2 with two observations per day at overpass times around 08:30 and 18:30 PST (Pacific Standard Time) (Armstrong et al., 1994). ...
... SSM/I data and the technique for detecting snowmelt timing has been previously established and validated in the upper YRB using 37 GHz vertically polarized data (Ramage et al., 2006) and has been found to correlate well with higher resolution Advanced Microwave Scanning Radiometer-EOS (AMSR-E) derived snowmelt onset (Apgar et al., 2007). The twice-daily observations enable the calculation of the running difference between the ascending and descending brightness temperature values termed the diurnal amplitude variation or DAV, which is interpreted as a proxy of the dynamism of the snowpack as the liquid water content changes (Ramage and Isacks, 2002). ...
Spring melt is a significant feature of high latitude
snowmelt dominated drainage basins influencing hydrological
and ecological processes such as snowmelt runoff and
green-up. Melt duration, defined as the transition period from
snowmelt onset until the end of the melt refreeze, is characterized
by high diurnal amplitude variations (DAV) where the
snowpack is melting during the day and refreezing at night,
after which the snowpack melts constantly until depletion.
Determining trends for this critical period is necessary for
understanding how the Arctic is changing with rising temperatures
and provides a baseline from which to assess future
change. To study this dynamic period, brightness temperature
(Tb) data from the Special Sensor Microwave Imager
(SSM/I) 37 V-GHz frequency from 1988 to 2010 were used
to assess snowmelt timing trends for the Yukon River basin,
Alaska/Canada. Annual Tb and DAV for 1434 Equal-Area
Scalable Earth (EASE)-Grid pixels (25 km resolution) were
processed to determine melt onset and melt refreeze dates
from Tb and DAV thresholds previously established in the
region. Temporal and spatial trends in the timing of melt onset
and melt refreeze, and the duration of melt were analyzed
for the 13 sub-basins of the Yukon River basin with three different
time interval approaches. Results show a lengthening
of the melt period for the majority of the sub-basins with a
significant trend toward later end of melt refreeze after which
the snowpack melts day and night leading to snow clearance,
peak discharge, and green-up. Earlier melt onset trends were
also found in the higher elevations and northernmost subbasins
(Porcupine, Chandalar, and Koyukuk rivers). Latitude
and elevation displayed the dominant controls on melt timing
variability and spring solar flux was highly correlated with
melt timing in middle (�600–1600 m) elevations.
... The purpose of this study is to derive the timing of spring snowmelt in the Wheaton River basin with recently acquired AMSR-E observations and to test the sensitivity of this sensor to the dynamic and varied regional snowpack characteristics. A snowmelt onset algorithm developed by Ramage et al. (2006) for use with SSM/I data is modified for use with AMSR-E data to allow for a direct comparison of snowmelt onset timing from both of these passive microwave sensors. In addition, higher-resolution AMSR-E data and elevation data are used to show improvements of this sensor over the SSM/I sensor in detecting snowmelt within heterogeneous terrain. ...
... Derksen et al., 2005; Foster et al., 2005; Goita et al., 2003). The SSM/I sensor has been used in previous studies to establish the timing of the spring melt transition in the upper Yukon River basin (Ramage et al., 2006) as well as in the Juneau Icefield ( Isacks, 2002, 2003). Even though the SSM/I sensor provides twice-daily observations and has been shown to correlate well with ground-based brightness temperature measurements over fairly homogeneous terrain such as the Alaskan North Slope (Kim and England, 2003), the pixel resolution of greater than 25 x 25 km 2 that results from the passive nature of the sensor is a problematic issue in monitoring dynamic changes over heterogeneous terrain. ...
... A rapid increase in emissivity occurs as a result of a small amount (~ 1-2%) of liquid water within the snowpack, causing the T b to increase for wet snow (Ulaby et al., 1986). The T b in the 19 and 37 GHz frequencies associated with the SSM/I sensor are useful in detecting melt on glaciers ( Isacks, 2002, 2003) and on heterogeneous terrain (Ramage et al., 2006) since the T b transition from dry to wet snow occurs as surface temperatures approach 0°C. From the AMSR-E sensor, the T b from the vertically polarized 36.5 GHz frequency (wavelength of 0.82 cm) is comparable to the SSM/I sensor for the detection of snowmelt. ...
The onset of snowmelt in the upper Yukon River basin, Canada, can be derived from brightness temperatures (Tb) obtained by the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) on NASA's Aqua satellite. This sensor, with a resolution of 14 × 8 km2 for the 36·5 GHz frequency, and two to four observations per day, improves upon the twice-daily coverage and 37 × 28 km2 spatial resolution of the Special Sensor Microwave Imager (SSM/I). The onset of melt within a snowpack causes an increase in the average daily 36·5 GHz vertically polarized Tb as well as a shift to high diurnal amplitude variations (DAV) as the snow melts during the day and re-freezes at night. The higher temporal and spatial resolution makes AMSR-E more sensitive to sub-daily Tb oscillations, resulting in DAV that often show a greater daily range compared to SSM/I. Therefore, thresholds of Tb > 246 K and DAV > ± 10 K developed for use with SSM/I have been adjusted for detecting the onset of snowmelt with AMSR-E using ground-based surface temperature and snowpack wetness relationships. Using newly developed thresholds of Tb > 252 K and DAV > ± 18 K, AMSR-E derived snowmelt onset correlates well with SSM/I observations in the small subarctic Wheaton River basin through the 2004 and 2005 winter/spring transition. In addition, the onset of snowmelt derived from AMSR-E data gridded at a higher resolution than the SSM/I data indicates that finer-scale differences in elevation and land cover affect the onset of snowmelt and are detectable with the AMSR-E sensor. On the basis of these observations, the enhanced resolution of AMSR-E is more effective than SSM/I at delineating spatial and temporal snowmelt dynamics in the heterogeneous terrain of the upper Yukon River basin. Copyright © 2007 John Wiley & Sons, Ltd.
... Algorithms have been developed to estimate the melt onset date (MOD) and end of high DAV by counting the number of instances where the DAV and T B exceed specified thresholds in a given time window (Monahan & Ramage, 2010). Studies have also shown that increased stream discharge either coincides with or directly follows the end of the high DAV period in snow-dominated watersheds (Kopczynski et al., 2008;Ramage et al., 2006;Vuyovich & Jacobs, 2011). Other studies have used passive microwave DAV methods for snowmelt detection in Arctic and Antarctic settings (Tedesco, 2007;Tedesco et al., 2007Tedesco et al., , 2009, and there has been some work with passive microwaves and snowmelt detection in mid-latitude environments with seasonal snow cover, such as the Sierra Nevada Mountains, CA (Li et al., 2012). ...
... The end of the high DAV cycles is coincident with or shortly before significant increases in melt generated runoff (timing varies by basin and depends on basin size and characteristics relative to the gauge location) and therefore are close to a hydrologist's definition of melt onset. The relationship is shown in detail for a small, gauged sub-arctic basin in Ramage et al. (2006). Optimal T B and DAV thresholds can vary with region and microwave sensor (Apgar et al., 2007;Li et al., 2012); we are testing whether they vary from previous studies with the new CETB product. ...
Understanding the timing of snowmelt is critical for water resources management in snow‐dominated watersheds. Passive microwave remote sensing can be used to estimate snowmelt events through brightness temperature satellite observations. Previous studies were limited to lower resolution (~25 km or coarser) datasets, making it difficult to quantify snowpack variability in heterogeneous, high‐relief areas. This study investigates the use of recently available Calibrated, Enhanced‐Resolution Passive Microwave Daily EASE‐Grid 2.0 Brightness Temperature ESDR (CETB) to estimate snowmelt timing at much higher spatial resolution (~3‐6 km) than has been previously available. This study investigates the effectiveness of the CETB product for snowmelt detection in several locations in Colorado (North Park, Rabbit Ears, Fraser) that were the sites of previous ground/airborne surveys during the NASA Cold Land Processes Field Experiment (CLPX 2002‐2003), along with data for the Senator Beck Basin from the Center for Snow and Avalanche Studies (CSAS). We compare melt variability with nearby air temperature and stream discharge to show that the new CETB product allows detection of hydrologic processes in mountainous watersheds. We show that the higher resolution CETB product can detect snowmelt in heterogeneous terrain more accurately than the coarser resolution product in terms of the number of winter melt events and seasonal melt onset date. This work lays the foundation for the utilization of higher resolution reprocessed CETB data for snowpack evolution more broadly in a range of environments.
... Snow is ripe and meltwater is released during this phase. Other conditions can impact Tb (e.g., decreased snow crystal grain size, internal snow structures and snowpack heterogeneity, and refrozen snow [Ramage et al., 2006]). Yet the satellite sensor's response to snowmelt geophysically overwhelms the impact of these other factors [Ulaby et al., 1986]. ...
... [8] The DAV approach has been applied over several different regions. Ramage and Isacks [2002] mapped melt over the maritime Juneau Icefield using thresholds A = 246 K and B = 10 K; these same thresholds were used over the boreal forests of the Canadian Yukon Territory [Ramage et al., 2006]. Tedesco [2007] applied this approach over the Greenland Ice Sheet using the 37 V GHz channel (A = 258 K, B = 18 K) and the 19 H GHz channel (A = 245 K, B = 25 K). ...
We advance an approach to use satellite passive microwave observations to track valley glacier snowmelt and predict timing of spring snowmelt-induced floods at the terminus. Using 37 V GHz brightness temperatures (Tb) from the Special Sensor Microwave Imager (SSM/I), we monitor snowmelt onset when both Tb and the difference between the ascending and descending overpasses exceed fixed thresholds established for Matanuska Glacier. Melt is confirmed by ground-measured air temperature and snow-wetness, while glacier hydrologic responses are monitored by a stream gauge, suspended-sediment sensors and terminus ice velocity measurements. Accumulation area snowmelt timing is correlated (R2 = 0.61) to timing of the annual snowmelt flood peak and can be predicted within ±5 days.
... We used T B and diurnal amplitude variation (DAV) to estimate the onset of the main melt and duration of melt-refreeze cycles. To detect melt onset (the presence of liquid water), melt, and occurrences of melt-refreeze, we defined surface wetness of the snowpack as indicated by simultaneous increases in the T B and high DAV values [41]. Snowmelt timing was identified as dates when both thresholds for T B and DAV were met as follows: ...
Caribou (Rangifer tarandus) undergo exceptionally large, annual synchronized migrations of thousands of kilometers, triggered by their shared environmental stimuli. The proximate triggers of those migrations remain mysterious, though snow characteristics play an important role due to their influence on the mechanics of locomotion. We investigate whether the snow melt–refreeze status relates to caribou movement, using previously collected Global Positioning System (GPS) caribou collar data. We analyzed 117 individual female caribou with >30,000 observations between 2007 and 2016 from the Bathurst herd in Northern Canada. We used a hierarchical model to estimate the beginning, duration, and end of spring migration and compared these statistics against snow pack melt characteristics derived from 37 GHz vertically polarized (37V GHz) Calibrated Enhanced-Resolution Brightness Temperatures (CETB) at 3.125 km resolution. The timing of migration for Bathurst caribou generally tracked the snowmelt onset. The start of migration was closely linked to the main melt onset in the wintering areas, occurring on average 2.6 days later (range −1.9 to 8.4, se 0.28, n = 10). The weighted linear regression was also highly significant (p-value = 0.002, R2=0.717). The relationship between migration arrival times and the main melt onset on the calving grounds (R2 = 0.688, p-value = 0.003), however, had a considerably more variable lag (mean 13.3 d, se 0.67, range 3.1–20.4). No migrations ended before the main melt onset at the calving grounds. Thawing conditions may provide a trigger for migration or favorable conditions that increase animal mobility, and suggest that the snow properties are more important than snow presence. Further work is needed to understand how widespread this is and why there is such a relationship.
... Snowmelt runoff onset occurs after the moistening and ripening phases of snowmelt, when the snowpack is ripe and meltwater release begins (Dingman, 2015). This process is particularly important because it indicates the beginning of increased water availability and dictates the rate of spring flow (Ramage et al., 2006). ...
Plain Language Summary
Snowmelt timing is important–knowing when water leaves mountain snowpack is critical for downstream water resource applications like irrigation and hydropower. Snowmelt timing is also affected by regional climate change. However, it is hard to make detailed measurements of when and where snow melts across mountainous regions. We developed an improved method to map snowmelt using satellite based radar data, and we applied this method to study snow on mountains in the Western United States. We documented the detailed relationship between elevation and snowmelt, and how this relationship changed over the past 8 years. In general, at higher elevations and latitudes, snow melts later in the year. We also observed snow melting much earlier than it usually does during the 2015 snow drought, which helps us prepare for future years with low snow accumulation. Finally, from 2016 to 2022, we documented a shift toward snowmelt happening earlier in the year, which means earlier spring flow in rivers. We publicly released interactive, user‐friendly software, so anyone can use our method to study snowmelt timing anywhere on Earth. Collectively, our work will help scientists better understand regional climate change and allow water managers to better manage water resources today and in the future.
... The high temporal resolution of passive microwave data has also been combined with radar and altimetry data over Lake Baikal in Russia (Kouraev et al., 2007). Microwave data has further been used to monitor snow melt processes for Arctic river catchments such as for the Yukon River (Ramage et al., 2006). ...
The timing of ice freeze‐up and break‐up in the Arctic may be responding to climate change. Passive microwave remote sensing is a powerful technique for monitoring this timing. We processed low‐frequency microwave time series from the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission for a set of 31 satellite gauging reaches (SGRs) above 65°N between 2010 and 2020 to determine timing of freeze‐up and break‐up and annual river ice durations. We found indication of progressive ice cover reduction over more than half of the monitored river reaches, with possibly the fastest rate occurring over northeast Russia. Some rivers in high‐latitude North America experienced a slight increase in ice cover. Across the data set, we observed an average 2.2 days shift toward later ice freeze‐up in autumn and an average 0.6 days shift toward earlier ice break‐up in spring, resulting in an average decrease of 3.4 days in ice duration between 2010 and 2020. River reaches with the longest duration of ice cover appeared to have experienced the fastest rate of decrease. A possible reduction of the time lag between air temperature rise or fall and corresponding river ice break‐up and freeze‐up was also observed. Yet results on variability are carefuly interpreted given the short length of the time series (2010–2020) and the low statistical confidence rates calculated for the decadal tendency. Still outcomes are consistent with increases in global and Arctic surface air temperature. Following these time series over the next decade using passive microwave satellite sensors can monitor ice cover duration in the Arctic and will further determine temporal and regional trends.
... Changes in the timing of snowmelt-fed streamflow have great importance for water supply, flood management, and ecological processes, as well as being a common indicator of climate change (Butt & Bilal, 2011;Dettinger & Cayan, 1995;Dudley et al., 2017;Pierson et al., 2013). The rate of spring flow, river break-up processes, and responses of numerous ecosystems to the spring transition are controlled by the temporal change of snow melting onset (Ramage et al., 2006). ...
Changes in timing of snowmelt‐fed streamflow have great importance for water supply, flood management, and ecological processes, as well as being a common indicator of climate change. In this study, snowmelt runoff timing change in the contiguous United States between 1957 and 2016 was investigated by analysing data from 97 streamflow gages. The annual snowmelt runoff timing shift was identified using ‘Center Time (CT)’ and ‘Spring Pulse Onset (SPO)’ methods, jointly with the monthly fractional streamflow (MFS) analysis, conducted between January and June. Since snowmelt‐derived streamflow timing change is mainly induced by regional meteorological factors, such as air temperature and precipitation, their trends and relationship with CT were also examined. Shifts toward earlier snowmelt runoff timing were found by both methods, CT (8.3 days on average) and SPO (8.5 days on average). Although the results of the CT change are stronger than the SPO change, both outcomes are mostly correlated, particularly in the central and northwestern parts of the country. MFS trends support the outcomes of CT and SPO. In January, February, and especially March, a higher number of the stations indicated increasing trends in MFS. In April, May, and June, their number decreased and the number of gages with diminishing trends rose sharply. The timing difference is highly related to temperature change. Annual average temperature and temperature in the melting period increase considerably. The annual average temperature is significantly negatively correlated with CT in the vast majority of the regions. Although precipitation is not as effective as the temperature, its trends have impacts on snowmelt runoff timing change depending on the region and elevation. These results demonstrate the importance of the impacts of snowmelt runoff timing changes due to global warming on the regional and large‐scale hydrology in the contiguous United States.
... If the snow contained liquid water throughout the nighttime, then the DAV method would no longer be triggered, so this method only identifies the transition period of melt-refreeze between completely frozen and persistently wet snow. Subsequent studies used the DAV to relate snow melt-refreeze timing to the timing of river hydrograph peaks (Kopczynski et al., 2008;Ramage et al., 2006) and prevalence of forest fires , extended the DAV method to incorporate T b observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) satellite instrument , compared melt onset timing of locations with different land surface properties , and detected midwinter melt events . Most DAV studies have focused on Canada and Alaska, although variations of the method were also used in the Southern Patagonia Icefield (Monahan & Ramage, 2012), Greenland (Tedesco, 2007), and Antarctica . ...
Snow melt and refreeze events are important determinants of spring runoff timing, and snowpack stratigraphy and metamorphism. Previous studies have established the utility of differences between twice-daily passive microwave brightness temperature (T b ) observations, called the diurnal amplitude variation (DAV), for identifying snow melt and refreeze. Liquid water in snow leads to a large increase in microwave emissivity compared to a completely frozen snowpack, so phase changes from nighttime freezing and daytime melting result in high DAV values. However, the physical temperature of the land surface also contributes to brightness temperature, independent of the phase of water. Thus, it is important to account for physical temperature change when using T b differences to detect snow melt and refreeze. Here, we use near-surface air temperature (T a ) to approximate the physical temperature of the land surface and compare diurnal T b changes (ΔT b ) from the Advanced Microwave Scanning Radiometer for the Earth Observing System satellite instrument to coincident T a changes. We find that an approximately linear relationship exists between ΔT b and ΔT a for frozen snow and fit this relationship using modal linear regression. Melt and refreeze events are identified as large positive and negative excursions from the regression line, respectively. We demonstrate the method in the Northern Great Plains, USA, and evaluate it using ground-based data from Senator Beck Basin Study Area, Colorado, USA. Melt and refreeze events identified from satellite observations mostly occur after the annual peak snow accumulation and are consistent with snow temperature and snowpack energy balance observations at Senator Beck Basin.
... Further, the bulk permittivity of snow is modified by rain on snow, resulting in a change in T b that can be measured by passive microwaves (Grenfell and Putkonen 2008). Using the 36.5 GHz vertical channel due to its sensitivity to snow wetness (Ramage et al 2006) and relatively higher spatial resolution (14 × 8 km 2 resolution which is gridded as 12.5 km pixels in the NSIDC Equal-Area Scalable Earth (EASE) grid), melt onset was determined as the first date when T b was greater than 252 K and the diurnal amplitude variation (DAV), or difference between the ascending and descending passes, was greater than 18 K for three of five consecutive days. These thresholds have been previously determined and validated in the YRB (Apgar et al 2007). ...
High latitude freshwater systems have and will continue to experience significant change due to warming trends higher than the global average. These systems are directly affected by climate change through alterations in snow melt timing, permafrost extent, and ice cover duration and timing, including shorter ice duration, earlier break-ups in spring and later freeze-ups in fall. Such changes affect the magnitudes and cycles of streamflow, discharge, and flooding, and thus impact the ecosystems in surrounding river basins. Therefore it is critical to be able to understand and model these processes and forecast future trends, especially for ungauged basins with little to no meteorological data. Early melt anomalies, short-lived melt events before melt onset, may be an indicator of climate change and of an area's sensitivity in response to warming. These anomalies are defined here as short melt events where brightness temperatures (Tb) are above the melt threshold for less than three out of five consecutive days, meaning that melt is not sustained. Tb encompasses both physical temperature and emissivity with wet snow easily detected due to its abrupt increase in emissivity. Once melt is sustained, it is deemed melt onset. Advanced Microwave Scanning Radiometer for EOS (AMSR-E) 37GHz vertically polarized data are used to determine when Tb is greater than 252K and when diurnal amplitude variations (DAV) are greater than 18K for the Stewart and Pelly subbasins of the Yukon River Basin for 2003 to 2009. Melt onset and data are used as input into a modified version of SWEHydro (Yan et al. 2009) to determine peak discharge and timing of peak and freshet. This model does not require any meteorological data, an advantage for use in remote northern areas. The number of early melt anomalies are analyzed in relation to the other cryosphere cycle variables. Early melt anomalies are found to correlate strongest with the timing of the end of high DAV later in the year but the direction of the relationship varies from year to year. It is hypothesized that these melt anomalies deplete and change the characteristics of the snowpack, affecting peak discharge and freshet timing. For instance, in 2005 SWEHydro models the peak discharge later than the actual hydrograph, which may be due to the fact that it is not accounting for the large number of early melt anomalies that occurred that year (more than all other years in the time series analyzed). Melt anomalies will be incorporated into SWEHydro to improve its modeling performance. In addition, a longer time series using special sensor microwave imager (SSM/I) satellite data from 1988 to 2009 will be used to evaluate trends in early snow melt anomalies for climate change analysis. It is anticipated that early snow melt anomalies will be a useful predictor of later discharge events and timing, as well as a gauge for the rate and magnitude of climate change in high latitude freshwater systems.
... Further, the bulk permittivity of snow is modified by rain on snow, resulting in a change in T b that can be measured by passive microwaves (Grenfell and Putkonen 2008). Using the 36.5 GHz vertical channel due to its sensitivity to snow wetness (Ramage et al 2006) and relatively higher spatial resolution (14 × 8 km 2 resolution which is gridded as 12.5 km pixels in the NSIDC Equal-Area Scalable Earth (EASE) grid), melt onset was determined as the first date when T b was greater than 252 K and the diurnal amplitude variation (DAV), or difference between the ascending and descending passes, was greater than 18 K for three of five consecutive days. These thresholds have been previously determined and validated in the YRB (Apgar et al 2007). ...
High latitude drainage basins are experiencing higher average temperatures, earlier snowmelt onset in spring, and an increase in rain on snow (ROS) events in winter, trends that climate models project into the future. Snowmelt-dominated basins are most sensitive to winter temperature increases that influence the frequency of ROS events and the timing and duration of snowmelt, resulting in changes to spring runoff. Of specific interest in this study are early melt events that occur in late winter preceding melt onset in the spring. The study focuses on satellite determination and characterization of these early melt events using the Yukon River Basin (Canada/USA) as a test domain. The timing of these events was estimated using data from passive (Advanced Microwave Scanning Radiometer—EOS (AMSR-E)) and active (SeaWinds on Quick Scatterometer (QuikSCAT)) microwave remote sensors, employing detection algorithms for brightness temperature (AMSR-E) and radar backscatter (QuikSCAT). The satellite detected events were validated with ground station meteorological and hydrological data, and the spatial and temporal variability of the events across the entire river basin was characterized. Possible causative factors for the detected events, including ROS, fog, and positive air temperatures, were determined by comparing the timing of the events to parameters from SnowModel and National Centers for Environmental Prediction North American Regional Reanalysis (NARR) outputs, and weather station data. All melt events coincided with above freezing temperatures, while a limited number corresponded to ROS (determined from SnowModel and ground data) and a majority to fog occurrence (determined from NARR). The results underscore the significant influence that warm air intrusions have on melt in some areas and demonstrate the large temporal and spatial variability over years and regions. The study provides a method for melt detection and a baseline from which to assess future change.
... The field of snow monitoring made progress three decades ago using passive microwave remote sensing techniques [e.g., Chang and Gloersen, 1975;Chang et al., 1976Chang et al., , 1987. More recent work has improved detection and analysis of snow extent [e.g., Hall et al., 1991;Armstrong and Brodzik, 2001], SWE [e.g., Derksen et al., 2008], and melt [Ramage et al., 2006;Apgar et al., 2007;Tedesco, 2007]. Existing snowmelt runoff models tend to depend on meteorological information which is scarce in most remote, highlatitude, or alpine basins [e.g., Martinec and Rango, 1986;Schmugge et al., 2002;Woo and Thorne, 2006]. ...
Snowmelt runoff in high latitudes has significant impacts on global climatic and hydrologic systems. Snowmelt timing and snow water equivalent (SWE) from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) are inputs to the new flux-based SWEHydro model to simulate the spring streamflow without meteorological data for high-latitude, snow-dominated drainages. The model was developed for the Ross River (7250 km2) and tested on the Pelly River (49,000 km2), nested tributaries to the Yukon River. The model uses four parameters: snowmelt rate during and after the melt transition (as defined by passive microwave observations), and flow timing during and after the melt transition. A normalized mismatch function was used to calculate the error compared with observed discharge. Curves were ranked by lowest error in freshet timing, peak timing, and magnitude. Melt timing is a good predictor of freshet timing across years and basins. The system is most sensitive to the flow timing after the transition.
... Further, the bulk permittivity of snow is modified by rain on snow, resulting in a change in T b that can be measured by passive microwaves (Grenfell and Putkonen 2008). Using the 36.5 GHz vertical channel due to its sensitivity to snow wetness (Ramage et al 2006) and relatively higher spatial resolution (14 × 8 km 2 resolution which is gridded as 12.5 km pixels in the NSIDC Equal-Area Scalable Earth (EASE) grid), melt onset was determined as the first date when T b was greater than 252 K and the diurnal amplitude variation (DAV), or difference between the ascending and descending passes, was greater than 18 K for three of five consecutive days. These thresholds have been previously determined and validated in the YRB (Apgar et al 2007). ...
High latitude drainage basins are experiencing increases in temperature higher than the global average, with snowmelt dominated basins most sensitive to effects in winter because of the snowpack's integration of these changes over the season. This may influence the timing of snowmelt onset, the melt-refreeze period and snowpack accumulation resulting in changes in spring runoff, associated flooding and drought conditions later in the year, possibly enhancing forest fire potential. Large burned areas cleared of vegetation change discharge dynamics and may affect snowmelt characteristics and discharge in subsequent seasons. Correlations are tested by comparing forest fire occurrence with spring melt onset, the end of the melt-refreeze period (after which snow rapidly depletes) and early snowmelt events. Snow characteristics are derived from brightness temperature (T b) data from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) for 2003–2010. Dates of melt onset, end of melt-refreeze and early melt events are defined with T b and diurnal amplitude variation thresholds. Areas and intensities of forest fires are from the Moderate Resolution Imaging Spectroradiometer (MODIS) thermal anomaly data (MOD14), and all data are mapped to an Equal-Area Scalable Earth Grid to assess spatial correlations. Earlier melt onset and end of melt-refreeze are found in years and areas of high forest fire occurrence by comparing high (2004–2005) and low (2006–2007) fire years in the Porcupine sub-basin of the Yukon River in northeastern Alaska and the Yukon Territory. The burned areas also correlate with relatively later melt onset and later end of melt-refreeze in subsequent low fire years.
... 10 mm. For the TB, the diurnal amplitude variation (DAV) methods were used to select the snowmelt days (Ramage et al., 2006). If the absolute value of TB difference of 37V for SSM/I (36.5V for AMSR-E) between ascending and descending orbits at a given day exceeds 10 K, it is considered as a snowmelt day and is not included in the analysis. ...
Brightness temperatures (TBs) from the special sensor microwave imager (SSM/I) and advanced microwave scanning radiometer (AMSR-E) from 2003 to 2007 are utilized to retrieve and evaluate the snow water equivalent (SWE) over the complex terrain of the Quesnel River Basin (QRB), British Columbia, Canada. Various algorithms including the Environment Canada (EC) algorithms, the spectral polarization difference (SPD) algorithm, and an artificial neural network (ANN) for both SSM/I and AMSR-E are evaluated against in situ SWE observations using several statistical metrics. The results show that the EC algorithms developed specifically for the southern prairies and boreal forest perform poorly across the complex topography and generally deep snow of the QRB. For other frequency combinations of SSM/I and AMSR-E measurements, significant relationships between TB difference and in situ SWE exist only when the snow accumulation is less than a threshold of 250 or 400 mm, which varies at the different in situ stations. Overall, AMSR-E provides better estimates of retrieved SWE than SSM/I. Compared to the algorithms based on TB difference, the ANNs for SSM/I and AMSR-E perform much better. The ANNs trained with all channels of AMSR-E have the best performance in fitting SWE and are able to resolve the temporal variations of SWE at all in situ stations. However, due to the complexity of the topography and vegetation in this mountainous watershed, the ANNs based only on limited in situ stations are not able to retrieve the spatial variations of SWE in this area.
... However, the timing of meltwater release may differ significantly between years. Using satellite observations from 1975 through 2003, Ramage et al. (2006) reported the onset of snow and icemelt in the Yukon River basin occurred between 2 and 26 May, and peak flow occurred anywhere from early May to late June. ...
The physical significance of a negative correlation between a varve record from Mud Lake, British Columbia, and temperature
is discussed in the context of a process-network. The process-network is defined as the system of temporally and spatially
connected processes involved in the transfer of a signal from climate to varved glaciolacustrine sediment. The six systems
defining the network include climate, glacier, fluvial, geomorphic, terrestrial biologic and lacustrine systems to which each
process belongs. A literature review outlines significant variation in the strength and character of correlations between
components of the process-network and highlights that more comprehensive interpretations of varves as a hydroclimatic proxy
require an improved understanding of the process-network. Documenting each process in the network is integral to informing
a more complete model of this system, identifying processes that constitute signal transfer and assessing hydroclimatic proxies
based on linear correlation. Such documentation is of growing importance as varved lacustrine sediments are increasingly used
as a hydroclimatic proxy. The complex nature of the process-network requires greater emphasis on interdisciplinary cooperation
and alternative methods to the linear statistical model.
Monitoring snowmelt dynamics in mountains is crucial to understand water releases downstream. Sentinel-1 (S-1) synthetic-aperture radar (SAR) has become one of the most widely used techniques to achieve this aim due to its high frequency of acquisitions and all-weather capability. This work aims to understand the possibilities of S-1 SAR imagery to capture snowmelt dynamics and related changes in streamflow response in semi-arid mountains. The results proved that S-1 SAR imagery was able not only to capture the final spring melting but also all melting cycles that commonly appear throughout the year in these types of environments. The general change detection approach to identify wet snow was adapted for these regions using as reference the average S-1 SAR image from the previous summer, and a threshold of −3.00 dB, which has been assessed using Landsat images as reference dataset obtaining a general accuracy of 0.79. In addition, four different types of melting-runoff onsets depending on physical snow condition were identified. When translating that at the catchment scale, distributed melting-runoff onset maps were defined to better understand the spatiotemporal evolution of melting dynamics. Finally, a linear connection between melting dynamics and streamflow was found for long-lasting melting cycles, with a determination coefficient (R2) ranging from 0.62 to 0.83 and an average delay between the melting onset and streamflow peak of about 21 days.
Melting snow provides an essential source of water in many regions of the world and can also contribute to devastating, wide-scale flooding. Global datasets of recorded passive microwave emissions provide non-destructive, daily information on snow processes including the presence of liquid water in the snow, which can be an indicator of snowmelt. The objective of this research is to test the sensitivity of the emission signal as it relates to the spatial distribution of liquid water content in the snowpack. This signal response was evaluated over an area approximately the size of a microwave pixel to assess whether a relationship exists between the aerial extent of wet snow and the magnitude of the TB response. A sensitivity analysis was performed using a high-resolution, physically based snow-emission model to simulate microwave emissions. The signal response to wet snow was evaluated given a range of spatially distributed snowpack conditions. Daily snow states were simulated for a 9-year period using a high-resolution (50 m) energy balance snow model over a 34 × 34 km domain. These data were fed into a microwave emission model to simulate brightness temperatures. A near-linear relationship was found between the TB signal response over a spatially heterogeneous snowpack and the percent area with liquid water content (LWC) present. The results were confirmed by evaluating actual wet snow events over a 9-year period. The model output was also compared to AMSR-E passive microwave satellite data and discharge data at a basin outlet within the study area. The results are used to help understand the impact of spatially distributed snowmelt as detected by passive microwave data.
For four decades, satellite-based passive microwave sensors have provided valuable snow water equivalent (SWE) monitoring at a global scale. Before continuous long-term SWE records can be used for scientific or applied purposes, consistency of SWE measurements among different sensors is required. SWE retrievals from two passive sensors currently operating, the Special Sensor Microwave Imager Sounder (SSMIS) and the Advanced Microwave Scanning Radiometer 2 (AMSR2), have not been fully evaluated in comparison to each other and previous instruments. Here, we evaluated consistency between the Special Sensor Microwave/Imager (SSM/I) onboard the F13 Defense Meteorological Satellite Program (DMSP) and SSMIS onboard the F17 DMSP, from November 2002 to April 2011 using the Advanced Microwave Scanning Radiometer for Earth Observing System (AMSR-E) for continuity. Likewise, we evaluated consistency between AMSR-E and AMSR2 SWE retrievals from November 2007 to April 2016, using SSMIS for continuity. The analysis is conducted for 1176 watersheds in the North Central U.S. with consideration of difference among three snow classifications (Warm forest, Prairie, and Maritime). There are notable SWE differences between the SSM/I and SSMIS sensors in the Warm forest class, likely due to the different interpolation methods for brightness temperature (Tb) between the F13 SSM/I and F17 SSMIS sensors. The SWE differences between AMSR2 and AMSR-E are generally smaller than the differences between SSM/I and SSMIS SWE, based on time series comparisons and yearly mean bias. Finally, the spatial bias patterns between AMSR-E and AMSR2 versus SSMIS indicate sufficient spatial consistency to treat the AMSR-E and AMSR2 datasets as one continuous record. Our results provide useful information on systematic differences between recent satellite-based SWE retrievals and suggest subsequent studies to ensure reconciliation between different sensors in long-term SWE records.
This chapter provides evidence for the utility of measuring and monitoring diurnal amplitude variation (DAV) in terrestrial hydrological systems via passive microwave remote sensing techniques, and presents an overview of the development of passive microwave-derived melt, focusing in particular on DAV. Specifically, illustrations of the significance of the DAV measure in relation to other hydrological and ecological processes are provided. In this chapter, passive microwave data from the Special Sensor Microwave/Imager (SSM/I) and the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) are used in conjunction with ground-based observations, modeling results, and optical remote sensing to illustrate the DAV technique and its potential application. During the period of melt-refreeze the surface of the snowpack melts slowly until its minimum temperatures are high enough to have continuous melt until depletion or snow off. The link between melt timing and discharge can be taken a step further and applied to snowmelt runoff modeling.
Over the large ice sheets of Greenland and Antarctica, melting is one of the drivers for mass losses, either through direct runoff or through the impact on ice dynamics. This chapter describes remote sensing tools and techniques for the detection and spatio-temporal analysis of melting snow and ice. It first describes the general considerations concerning techniques for melt detection, using either optical or microwave data. The chapter presents a brief description of the electromagnetic properties of both dry and wet snow. It then shows the techniques and results of remote sensing of melting snow over land, using either optical or microwave techniques. Finally, remote sensing of snow and ice on either the Greenland or Antarctica ice sheets are focused.
Passive microwave estimates of snow water equivalent (SWE) were examined to determine their usefulness for evaluating water resources in the remote Upper Helmand Watershed, central Afghanistan. SWE estimates from the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) and the Special Sensor Microwave/Imager (SSM/I) passive microwave data were analyzed for six winter seasons, 2004–2009. A second, independent estimate of SWE was calculated for these same time periods using a hydrologic model of the watershed with a temperature index snow model driven using the Tropical Rainfall Measuring Mission (TRMM) gridded estimates of precipitation. The results demonstrate that passive microwave SWE values from SSM/I and AMSR-E are comparable. The AMSR-E sensor had improved performance in the early winter and late spring, which suggests that AMSR-E is better at detecting shallow snowpacks than SSM/I. The timing and magnitude of SWE values from the snow model and the passive microwave observations were sometimes similar with a correlation of 0.53 and accuracy between 55 and 62%. However, the modeled SWE was much lower than the AMSR-E SWE during two winter seasons in which TRMM data estimated lower than normal precipitation. Modeled runoff and reservoir storage predictions improved significantly when peak AMSR-E SWE values were used to update the snow model state during these periods. Rapid decreases in passive microwave SWE during precipitation events were also well aligned with flood flows that increased base flows by 170 and 940%. This finding supports previous northern latitude studies which indicate that the passive microwave signal's lack of scattering can be used to detect snow melt. The current study's extension to rain on snow events suggests an opportunity for added value for flood forecasting.
Spring snow melt run-off in high latitude and snow-dominated drainage basins is generally the most significant annual hydrological event. Melt timing, duration, and flow magnitude are highly variable and influence regional climate, geomorphology, and hydrology. Arctic and sub-arctic regions have sparse long-term ground observations and these snow-dominated hydrologic regimes are sensitive to the rapidly warming climate trends that characterize much of the northern latitudes. Passive microwave brightness temperatures are sensitive to changes in the liquid water content of the snow pack and make it possible to detect incipient melt, diurnal melt-refreeze cycles, and the approximate end of snow cover on the ground over large regions. Special Sensor Microwave Imager (SSM/I) and Advanced Microwave Scanning Radiometer for EOS (AMSR-E) passive microwave brightness temperatures (Tb) and diurnal amplitude variations (DAV) are used to investigate the spatial variability of snowmelt onset timing (in two stages, ‘DAV onset’ and ‘melt onset’) and duration for a complex sub-arctic landscape during 2005. The satellites are sensitive to small percentages of liquid water, and therefore represent ‘incipient melt’, a condition somewhat earlier than a traditional definition of a melting snowpack. Incipient melt dates and duration are compared to topography, land cover, and hydrology to investigate the strength and significance of melt timing in heterogeneous landscapes in the Pelly River, a major tributary to the Yukon River. Microwave-derived melt onset in this region in 2005 occurred from late February to late April. Upland areas melt 1–2 weeks later than lowland areas and have shorter transition periods. Melt timing and duration appear to be influenced by pixel elevation, aspect, and uniformity as well as other factors such as weather and snow mass distribution. The end of the transition season is uniform across sensors and across the basin in spite of a wide variety of pixel characteristics. Copyright © 2007 John Wiley & Sons, Ltd.
Salinity stratification is critical to the vertical circulation of the high-latitude ocean. We here examine the control of the vertical circulation in the northern seas, and the potential for altering it, by considering the budgets and storage of fresh water in the Artic Ocean and in the convective regions to the south. We find that the present-day Greenland and Iceland seas, and probably also the Labrador Sea, are rather delicately poised with respect to their ability to sustain convection. Small variations in the fresh water supplied to the convective gyres from the Arctic Ocean via the East Greenland Current can alter or stop the convection in what may be a modern analog to the halocline catastrophes proposed for the distant past. The North Atlantic salinity anomaly of the 1960s and 1970s is a recent example; it must have had its origin in an increased fresh water discharge from the Arctic Ocean. Similarly, the freshing and cooling of the deep North Atlantic in recent years is a likely manifestation of the increased transfer of fresh water from the Artic Ocean into the convective gyres. Finally, we note that because of the temperature dependence of compressibility, a slight salinity stratification in the convective gyres is required to efficiently ventilate the deep ocean.
A methodologically simple approach to estimate snow depth from spaceborne microwave instruments is described. The scattering signal observed in multifrequency passive microwave data is used to detect snow cover. Wet snow, frozen ground, precipitation, and other anomalous scattering signals are screened using established methods. The results from two different approaches (a simple time and continentwide static approach and a space and time dynamic approach) to estimating snow depth were compared. The static approach, based on radiative transfer calculations, assumes a temporally constant grain size and density. The dynamic approach assumes that snowpack properties are spatially and temporally dynamic and requires two simple empirical models of density and snowpack grain radius evolution, plus a dense media radiative transfer model based on the quasicrystalline approximation and sticky particle theory. To test the approaches, a four-year record of daily snow depth measurements at 71 meteorological stations plus passive microwave data from the Special Sensor Microwave Imager, land cover data and a digital elevation model were used. In addition, testing was performed for a global dataset of over 1000 World Meteorological Organization meteorological stations recording snow depth during the 2000-2001 winter season. When compared with the snow depth data, the new algorithm had an average error of 23 cm for the one-year dataset and 21 cm for the four-year dataset (131% and 94% relative error, respectively). More importantly, the dynamic algorithm tended to underestimate the snow depth less than the static algorithm. This approach will be developed further and implemented for use with the Advanced Microwave Scanning Radiometer-Earth Observing System aboard Aqua.
Although the formation and melt of sea ice are primarily functions of the annual radiation cycle, atmospheric sensible-heat forcing does serve to delay or advance the timing of such events. Additionally, if atmospheric conditions in the Arctic were to vary due to climate change it may have significant influence on ice conditions. Therefore, this paper investigates a methodology to determine melt-onset dale distribution, both spatially and temporally, in the Arctic Ocean and surrounding sea-ice covered regions.
Melt determination is made by a threshold technique using the spectral signatures of the horizontal brightness temperatures (19 GHz horizontal channel minus the 37 GHz horizontal channel) obtained from the Special Sensor Microwave Imager (SSM/I) passive-microwave sensor. Passive-microwave observations are used to identify melt because of the large increase in emissivity that occurs when liquid water is present. Emissivity variations are observed in the brightness temperatures due to the different scattering, absorption and penetration depths of the snowpack from the available satellite channels during melt. Monitoring the variations in the brightness temperatures allows the determination of melt-onset dates.
Analysis of daily brightness temperature data allows spatial variations in the date of the snow inch onset for sea ice to be detected. Since the data are gridded on a daily basis, a climatology of daily melt-onset dates can be produced for the Arctic region. From this climatology, progression of melt can be obtained and compared inter-annually.
The extent and duration of surface melting on the Antarctic ice shelves and margins of the Antarctic ice sheet are derived from satellite passive-microwave data for 1978–87. The occurrence of surface melting in daily maps of Tb is indicated by a marked increase in microwave brightness temperature (Tb), which is caused by moisture in the near-surface firn. Tb increases of more than 30 deg above the annual-mean Tb are chosen to indicate melting. Most Antarctic surface melting occurs during December and January. The observed melting is correlated with regional air temperatures, but some melt patterns also appear to be related to katabatic-wind effects. The correlations suggest that the surface melting in Antarctica increases about 3.5 × 106 d km2 per degree of summer temperature increase. The surface-melt index (duration times area of melting) calculated for Antarctica is 24 × 106 d km2, averaged over nine summers. The observed inter-annual and regional variability is large. Surface melting was most extensive during the 1982/83 summer (36 × 106 d km2) and least extensive during the 1985/86 summer (15 × 106d km2). The data indicate a decline in surface melting over the 9 years, but meaningful inferences regarding trends in surface melting are precluded by the large inter-annual variability.
Ablation of snow over sea ice is an important physical process affecting the Arctic surface energy balance. An improved understanding of the spatial and temporal variations in snowmelt onset could be utilized to improve climate simulations in the Arctic, as well as monitor the Arctic for signs of climate change. Utilizing an updated approach for monitoring snowmelt onset over Arctic sea ice, spatial variability in passive microwave derived snowmelt onset dates is examined from 1979 through 1998. The improved technique, termed the advanced horizontal range algorithm (AHRA), utilizes temporal variations in 18/19 GHz and 37 GHz passive microwave horizontal brightness temperatures obtained from the scanning multichannel microwave radiometer (SMMR) and the Special Sensor Microwave/Imager (SSM/I) to identify snowmelt onset. A qualitative assessment of spatial variability in snowmelt onset discusses the 1979 through 1998 mean snowmelt onset pattern, and it also illustrates that there are significant variations in snowmelt onset on an annual basis. Principal component analysis of the snowmelt onset dates suggests snowmelt onset variability is dominated by a zone of abnormally early (late) snowmelt onset near the Siberian coast and another zone of abnormally late (early) snowmelt onset near Baffin Bay. Statistical analysis between the first principal component and March-June monthly averaged Arctic Oscillation values implies that variations in snowmelt onset are related to alterations in the phase of the spring Arctic Oscillation.
Although the formation and melt of sea ice are primarily functions of the annual radiation cycle, atmospheric sensible-heat forcing does serve to delay or advance the timing of such events. Additionally, if atmospheric conditions in the Arctic were to vary due to climate change it may have significant influence on ice conditions. Therefore, this paper investigates a methodology to determine melt-onset dale distribution, both spatially and temporally, in the Arctic Ocean and surrounding sea-ice covered regions.
Melt determination is made by a threshold technique using the spectral signatures of the horizontal brightness temperatures (19 GHz horizontal channel minus the 37 GHz horizontal channel) obtained from the Special Sensor Microwave Imager (SSM/I) passive-microwave sensor. Passive-microwave observations are used to identify melt because of the large increase in emissivity that occurs when liquid water is present. Emissivity variations are observed in the brightness temperatures due to the different scattering, absorption and penetration depths of the snowpack from the available satellite channels during melt. Monitoring the variations in the brightness temperatures allows the determination of melt-onset dates.
Analysis of daily brightness temperature data allows spatial variations in the date of the snow inch onset for sea ice to be detected. Since the data are gridded on a daily basis, a climatology of daily melt-onset dates can be produced for the Arctic region. From this climatology, progression of melt can be obtained and compared inter-annually.
The extent and duration of surface melting on the Antarctic ice shelves and margins of the Antarctic ice sheet are derived from satellite passive-microwave data for 1978-87. The occurrence of surface melting in daily maps of Tb is indicated by a marked increase in microwave brightness temperature (Tb), which is caused by moisture in the near-surface firn. Tb increases of more than 30 deg above the annual-mean Tb are chosen to indicate melting. Most Antarctic surface melting occurs during December and January. The observed melting is correlated with regional air temperatures, but some melt patterns also appear to be related to katabatic-wind effects. The correlations suggest that the surface melting in Antarctica increases about 3.5 × 106 d km2 per degree of summer temperature increase. -from Authors
Snow-cover monitoring using passive microwave remote sensing methods has been shown to be seriously limited under melt conditions when the snowpack becomes wet. A wet snow indicator has been developed using DMSP SSM/I 37 GHz dual-polarization data for the open prairie region of western Canada. The indicator is used to identify areas of wet snow and discriminate them from areas of snow-free land. Validation and testing efforts have illustrated that the addition of the indicator to the current SSM/I snow water equivalent algorithm provides a more accurate representation of spatial snow coverage throughout the winter season for the open prairie region. The improved spatial and temporal information resulting from the use of the indicator enhances both climatological and hydrological analyses of snow-cover conditions using passive microwave data. -from Authors
We have generated consistent sea ice extent and area data records spanning 18.2 years from passive-microwave radiances obtained with the Nimbus 7 scanning multichannel microwave radiometer and with the Defense Meteorological Satellite Program F8, F11, and F13 special sensor microwave/imagers. The goal in the creation of these data was to produce a long-term, consistent set of sea ice extents and areas that provides the means for reliably determining sea ice variability over the 18.2-year period and also serves as a baseline for future measurements. We describe the method used to match the sea ice extents and areas from these four multichannel sensors and summarize the problems encountered when working with radiances from sensors having different frequencies, different footprint sizes, different visit times, and different calibrations. A major obstacle to adjusting for these differences is the lack of a complete year of overlapping data from sequential sensors. Nonetheless, our procedure reduced ice extent differences during periods of sensor overlap to less than 0.05% and ice area differences to 0.6% or less.
A 13 year time series of spaceborne passive microwave radiance measurements over the Antarctic Peninsula ice shelves, reveals a systematic increase in the duration of the summer melt season. Combined with data from meteorological stations on the Antarctic Peninsula, the annual motion and long-term trends of the 0°C isotherm can be monitored.
The melt extent of the snow on the Greenland ice sheet is of considerable importance to the ice sheet's mass and energy balance, as well as Arctic and global climates. By comparing passive microwave satellite data to field observations, variations in melt extent have been detected by establishing melt thresholds in the cross-polarized gradient ratio (XPGR). The XPGR, defined as the normalized difference between the 19-GHz horizontal channel and the 37-GHz vertical channel of the Special Sensor Microwave/Imager (SSM/I), exploits the different effects of snow wetness on different frequencies and polarizations and establishes a distinct melt signal. Using this XPGR melt signal, seasonal and interannual variations in snowmelt extent of the ice sheet are studied. The melt is found to be most extensive on the western side of the ice sheet and peaks in late July. Moreover, there is a notable increasing trend in melt area between the years 1979 and 1991 of 4.4% per year, which came to an abrupt halt in 1992 after the eruption of Mt. Pinatubo. A similar trend is observed in the temperatures at six coastal stations. The relationship between the warming trend and increasing melt trend between 1979 and 1991 suggests that a 1°C temperature rise corresponds to an increase in melt area of 73000 km2, which in general exceeds one standard deviation of the natural melt area variability.
Diurnal observations of the variation of the radar backscattering coefficient σ° and microwave apparent radiometric temperature Tap with snow wetness mv are presented. The results show that σ° decreases and Tap increases with mv and that the magnitude of the sensitivity to mv increases with microwave frequency for both active and passive microwave parameters. Moreover, while the sensitivity of σ° to snow wetness variations increases with angle of incidence, the sensitivity of Tap to snow wetness is approximately angle of incidence independent.
A circumpolar ring of land practically surrounds the Arctic Ocean. The areal extent of the landmass, which transfers flow to this ocean, is substantial and exceeds the ocean area to which it contributes. For example, headwater streams as far south as 46°N in Russia eventually empty into this relatively confined northern basin. As noted by Lewis et al.[1],the total volume and temporal variability of freshwater discharge to the Arctic Ocean (AO) is of critical concern to the ocean freshwater budget (FWB) and subsequently, via atmospheric and oceanographic feedbacks, to global climate. In general, considerable research continues to refine the size and variability of the various components that comprise the AOFWB, some of which are difficult to quantify. Despite the relatively large database for the river-flow component, there exists in the literature considerable variation in the estimates of its contribution. The objectives of this paper were to quantify the magnitude of river flow entering the Arctic Ocean according to the ocean definition of [1], to compare this value with other recent estimates, and to identify reasons for differences in the estimates. A detailed analysis of historical trends in Arctic flow are not included since this is provided by Shiklomanov et al.[2] and Grabs et al.[3], elsewhere in this volume. The potential impact of future climate change on total Arctic river flow, however, is the focus of a final objective. Recommendations for future research conclude the manuscript.
Passive microwave-brightness temperatures over the Greenland ice sheet are examined during the melt season in order to develop a technique for determining surface-melt occurrences. An objective technique is developed to extract melt occurrences from the brightness-temperature time series. Of the two sites with summer melt, the site at the lower elevation had a longer period between the initial and final melt days and had more total days classified as melt during 1988 and 1989. The technique is then applied to the entire Greenland ice sheet for the first major surface-melt event of 1989. The melt-zone signal is mapped from late May to early June to demonstrate the advance and subsequent retreat of one "melt wave'. The use of such a technique to determine melt duration and extent for multiple years may provide an indication of climate change. -from Authors
Twice-daily satellite observations from the Special Sensor Microwave Imager (SSM/I) indicate melt onset and refreeze on southeast-Alaskan icefields. Melt and refreeze are based on 37 GHz vertically polarized brightness temperatures (Tb) and diurnal-amplitude variations (DAV). Two types of melt regime have different summer characteristics. Onset is characterized by increasing average daily Tb and a switch from low- to high-amplitude DAV.Melt timing, calibrated using Juneau Icefield temperatures, correlates well with nearby stream hydrographs. Some pixels maintain high Tb throughout the melt season and return to low-amplitude DAVafter melt onset. Refreeze on these pixels is identified by decrease in Tb and accompanying high-amplitude DAV.Other pixels maintain high DAVthroughout the summer, indicating nocturnal refreeze. Fall refreeze is determined by the end of high-amplitude DAV.Interannual variability in melt timing and ablation-season length is high. Melt onset and refreeze timing show a regional tendency toward earlier glacier-melt onset and longer ablation seasons from 1988-98.
Timing of snowmelt and freeze-up was estimated for glaciers in the Coast and St Elias Ranges of Alaska, U.S.A., and British Columbia, Canada, using twice-daily brightness temperatures (Tb) from the U.S. Defense Meteorological Satellite Program's Special Sensor Microwave/Imager (SSM/I). Melt and freeze-up were determined for a 37 GHz vertically polarized time series using changes in the average daily Tb and high-amplitude Tb diurnal amplitude variations (DAV). DAV are the running difference between the early-morning (usually minimum) and late-afternoon (usually maximum) Tb observations. Year-round temperatures taken at 2 hour intervals on the Juneau Icefield (58°4' N, 134°15' W) validated the microwave response to melt. A bimodal distribution of Tb corresponding to frozen or melting snow helped estimate the Tb at which the transition from frozen to melting snow occurred on pixels without ground observations. Thresholds of Tb (>246 K) and DAV (>±10 K) were used to refine the selection of melt and refreeze timing for southeast Alaska. Melt timing correlates with stream discharge. In general, melt onset occurred progressively earlier and refreeze later in the season between 1988 and 1998. It is not known whether this is related to regional warming or to one of the shorter decadal-scale oscillations in the Gulf of Alaska.
The successful application of passive microwave sensors requires signatures for the unambiguous inversion of the remote sensing data. Due to the large number of object types and large variability of physical properties, the inversion of data from land surfaces is a delicate and often ambiguous task. The present paper is a contribution to the assessment of multi-frequency passive microwave signatures of typical objects on land in winter. We discuss the behaviour of measured emissivities at vertical and horizontal polarization over the frequency range of 5 to 100 GHz (incidence angle of 50 degrees) of water and bare soil surfaces, grass and snowcovers under various conditions. These data and their variabilities lead us toward a classificaion algorithm for some, but not all object classes. Most snowcovers can easily be discriminated from other surfaces, difficulties occur for fresh powder snow if 94 GHz data are not available. The problem of wet snow has found a solution by using a certain combination of observables.In addition to snowcover types we find large differences between frozen and unfrozen bare soil. On the other hand the different situations of grasscovers show all very similar emissivities.For the estimation of physical parameters we propose algorithms for certain object classes. The estimation of surface temperature, especially for snow-free land, seems to be feasible, also the estimation of the snow liquid water content at the surface. For estimating soil moisture lower frequencies (e.g. 1.4 GHz) should be used.For the estimation of the Water Equivalent, WE, we cannot yet find a definitive solution. Certain correlations exist for dry winter snow between WE and observables at frequencies between 10 and 35 GHz. Especially the polarization difference at 10 GHz shows a monotonous increase with increasing WE. Algorithms using higher frequencies are more sensitive to WE, however, they are subject to ambiguities.
Airborne and satellite passive microwave measurements acquired simultaneously with ground measurements of depth, density, and stratigraphy of the snow in central and northern Alaska between March 11 and 19, 1988, are reported. A good correspondence in brightness temperature (TB) trends between the aircraft and satellite data was found. An expected inverse correlation between depth hoar thickness and TB was not found to be strong. A persistent TB minimum in both the aircraft and the satellite data was detected along the northern foothills of the Brooks Range. In an area located at about 68 deg 60 min N, 149 deg 20 min W, the TB as recorded from the aircraft microwave sensor dropped by 55 K. Satellite microwave measurements showed a TB decrease of up to 45 K at approximately the same location. An examination of microwave satellite data from 1978 to 1987 revealed that similar low late-winter values were found in approximately the same locations as those observed in March 1988.
A long-term program of microwave-signature studies at the alpine test site, Weissfluhjoch (2550m altitude), was completed in 1987. Besides passive microwave data at frequencies between 5 and 100 GHz backscatter data at 10 GHz were collected together with dielectric and structural properties as well as classical snow data. The same instrumentation was used from icebreakers to measure snow-covered sea ice. Complementary snow signatures were recently obtained by a new multichannel (1–12 GHz) radiometer-scatterometer at lower-altitude test sites for taking into account possible spatial variability of snow properties, and for extending the spectral coverage to lower frequencies. The results include algorithms for classifying snow types, for mapping snow, for determining the liquid water content of the snow surface, for monitoring melt and refreeze cycles, for estimating energy loss and water equivalent, respectively. We conclude by an updated definition of an optimum snow sensor system.
The feasibility of space-borne microwave radiometers for monitoring the evolution of snow cover in a drainage area is investigated. Four winter sets (1993/94, 1995/96, 1996/97, 1997/98) of SSM/I radiometer observations for the 51,000 km2 River Kemijoki drainage area, Northern Finland, are used for analyses. The Snow Water Equivalent (SWE) of dry snow cover is estimated by employing the Helsinki University of Technology (HUT) Snow Emission Model-based automatic inversion algorithm. For comparison, the SWE estimates are also determined by using a conventional empirical Spectral and Polarization Difference algorithm. The results indicate that the HUT Snow Emission Model-based automatic algorithm can estimate the regional SWE under dry snow conditions with an overall RMSE of about 30 mm without using any training reference data on SWE (e.g., in situ reference values). The retrieval error was found to vary considerably from year to year. At best, the annual SWE retrieval RMSE showed values as low as 20 mm.
The National Oceanic and Atmospheric Administration (NOAA) weekly snow cover dataset (1966–) is the longest available record of snow cover extent (SCE) over the Northern Hemisphere (NH). This dataset has been used extensively to derive trends in continental SCE and in climate-related studies, but it has received only limited validation, particularly in high latitude areas of the NH. This study evaluated spring snow cover depletion in the NOAA dataset over a study area in the Canadian Arctic mainland north of the tree line. The evaluation used four sources of information: (1) surface snow depth and snow survey observations, (2) snow cover extent produced from the Advanced Very High Resolution Radiometer (AVHRR), (3) snow cover extent derived from Special Sensor Microwave/Imager (SSM/I), and (4) Landsat 5 TM browse images. Six spring seasons from the period 1981–2000 with low (1984, 1988, and 1998) and high (1985, 1995, and 1997) spring snow cover extent were evaluated. The evaluation revealed that the NOAA weekly dataset consistently overestimated snow cover extent during the spring melt period, with delays of up to 4 weeks in melt onset. A number of possible reasons for this delay were investigated. The most likely causes for the delayed melt onset were frequent cloud cover in the spring melt period, and the low frequency of data coverage over higher latitudes. The results suggest that caution should be exercised when using this dataset in any studies related to the timing of snowmelt in the high latitudes of the Northern Hemisphere.
Aspects of volume scattering and emission theory are discussed, taking into account a weakly scattering medium, the Born approximation, first-order renormalization, the radiative transfer method, and the matrix-doubling method. Other topics explored are related to scatterometers and probing systems, the passive microwave sensing of the atmosphere, the passive microwave sensing of the ocean, the passive microwave sensing of land, the active microwave sensing of land, and radar remote sensing applications. Attention is given to inversion techniques, atmospheric attenuation and emission, a temperature profile retrieval from ground-based observations, mapping rainfall rates, the apparent temperature of the sea, the emission behavior of bare soil surfaces, the emission behavior of vegetation canopies, the emission behavior of snow, wind-vector radar scatterometry, radar measurements of sea ice, and the back-scattering behavior of cultural vegetation canopies.
Thesis (Ph. D.)--Cornell University, Jan., 2001. Includes bibliographical references.
By comparing data from the Special Sensor Microwave Imager (SSM/I) to field data, a melt threshold of the cross-polarized gradient ratio (XPGR), which is a normalized difference between the 19 GHz horizontally-polarized and 37 GHz vertically polarized brightness temperatures, is determined. This threshold, XPGR = -0.025, is used to classify dry and wet snow. The annual areal extent of melt is mapped for the years 1988 through 1991, and inter-annual variations of melt extent are examined. The results show that the melt extent varied from a low of 38.3% of the ice sheet (1990) to a high of 41.7% (1991) during the years 1988-1991.
The Nimbus-7 satellite launched on October 24, 1978, carries a multifrequency, dual-polarized microwave imager. The instrument is designed to sense the ocean surface, the atmosphere, and land surfaces remotely. From previous ground-based and satellite-based microwave experiments, it is well known, that snow cover over land has a very distinct effect on the microwave signatures of the earth surface. It was the goal of this study to show that the three snow-cover parameters: extent, snow water equivalent, and onset of snow melt can be determined using scanning multichannel microwave radiometer (SMMR) data. Our analysis has shown, that the three snow parameters mentioned above are retrievable with sufficient accuracy to be of great value in climatology, meteorology, and hydrology. Snow extent is determined for dry snow cover with depth ¿5 cm, snow water equivalent can be determined on a regional basis with ¿2 g/cm2 rms accuracy, and the onset of snow melt is clearly visible by the detection of melt and refreeze cycles prior to snow runoff. The algorithms derived are simple enough to be incorporated in fully automated operational data analysis schemes.
Snowmelt onset detection on the North Slope of Alaska using spaceborne passive microwave data
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Baumgras L, Forster RR. 2000. Snowmelt onset detection on the North Slope of Alaska using spaceborne passive microwave data. American Geophysical Union Annual Meeting, San Francisco, CA, 15–19 December
Yukon snow survey bulletin. Environment Yukon Environmental Programs Branch Water Resources Section
- Environment Yukon
Environment Yukon. 2002. Yukon snow survey bulletin. Environment Yukon, Environmental Programs Branch, Water Resources Section
Characterization of snowmelt infiltration scaling parameters within a mountainous subarctic watershed
- Janowicz
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Janowicz JR, Gray DM, Pomeroy JW. 2002. Characterization of snowmelt infiltration scaling parameters within a mountainous subarctic watershed. In Proceedings of the 59th Annual Eastern Snow Conference; 67–81.
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Archived hydrometric data
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Characterization of snowmelt infiltration scaling parameters within a mountainous subarctic watershed. InProceedings of the 59th Annual Eastern Snow Conference
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Snowmelt onset detection on the North Slope of Alaska using spaceborne passive microwave data
- Baumgrasl Forsterrr