The application of Heat Capacity Mapping Mission (HCMM) thermal data to snow hydrology

The application of HCMM thermal infrared data to snow hydrology and the prediction of snowmelt runoff was evaluated. Data for the Salt Verde watershed in central Arizona and the southern Sierra Nevada in California were analyzed and compared to LANDSAT and NOAA satellite data, U-2 thermal data, and other correlative data. It was determined that HCMM thermal imagery provides data as accurate for snow mapping as does visible imagery, and that in comparison with the reslution of other satellite imagery, it may be the most useful. Data from the HCMM thermal channel, with careful calibration, provides useful snow surface temperature data for hydrological purposes. An approach to an automated method of analysis is presented

Permafrost hydrology in changing climatic conditions: seasonal variability of stable isotope composition in rivers in discontinuous permafrost

Role of changing climatic conditions on permafrost degradation and hydrology was investigated in the transition zone between the tundra and forest ecotones at the boundary of continuous and discontinuous permafrost of the lower Yenisei River. Three watersheds of various sizes were chosen to represent the characteristics of the regional landscape conditions. Samples of river flow, precipitation, snow cover, and permafrost ground ice were collected over the watersheds to determine isotopic composition of potential sources of water in a river flow over a two year period. Increases in air temperature over the last forty years have resulted in permafrost degradation and a decrease in the seasonal frost which is evident from soil temperature measurements, permafrost and active-layer monitoring, and analysis of satellite imagery. The lowering of the permafrost table has led to an increased storage capacity of permafrost affected soils and a higher contribution of ground water to river discharge during winter months. A progressive decrease in the thickness of the layer of seasonal freezing allows more water storage and pathways for water during the winter low period making winter discharge dependent on the timing and amount of late summer precipitation. There is a substantial seasonal variability of stable isotopic composition of river flow. Spring flooding corresponds to the isotopic composition of snow cover prior to the snowmelt. Isotopic composition of river flow during the summer period follows the variability of precipitation in smaller creeks, while the water flow of larger watersheds is influenced by the secondary evaporation of water temporarily stored in thermokarst lakes and bogs. Late summer precipitation determines the isotopic composition of texture ice within the active layer in tundra landscapes and the seasonal freezing layer in forested landscapes as well as the composition of the water flow during winter months

Using the space-borne NASA scatterometer (NSCAT) to determine the frozen and thawed seasons

We hypothesize that the strong sensitivity of radar backscatter to surface dielectric properties, and hence to the phase (solid or liquid) of any water near the surface should make space-borne radar observations a powerful tool for large-scale spatial monitoring of the freeze/thaw state of the land surface, and thus ecosystem growing season length. We analyzed the NASA scatterometer (NSCAT) backscatter from September 1996 to June 1997, along with temperature and snow depth observations and ecosystem modeling, for three BOREAS sites in central Canada. Because of its short wavelength (2.14 cm), NSCAT was sensitive to canopy and surface water. NSCAT had 25 km spatial resolution and approximately twice-daily temporal coverage at the BOREAS latitude. At the northern site the NSCAT signal showed strong seasonality, with backscatter around −8 dB in winter and −12 dB in early summer and fall. The NSCAT signal for the southern sites had less seasonality. At all three sites there was a strong decrease in backscatter during spring thaw (4–6 dB). At the southern deciduous site, NSCAT backscatter rose from −11 to −9.2 dB during spring leaf-out. All sites showed 1–2 dB backscatter shifts corresponding to changes in landscape water state coincident with brief midwinter thaws, snowfall, and extreme cold (Tmax\u3c−25°C). Freeze/thaw detection algorithms developed for other radar instruments gave reasonable results for the northern site but were not successful at the two southern sites. We developed a change detection algorithm based on first differences of 5-day smoothed NSCAT backscatter measurements. This algorithm had some success in identifying the arrival of freezing conditions in the autumn and the beginning of thaw in the spring. Changes in surface freeze/thaw state generally coincided with the arrival and departure of the seasonal snow cover and with simulated shifts in the directions of net carbon exchange at each of the study sites

Snow Cover Monitoring from Remote-Sensing Satellites: Possibilities for Drought Assessment

Snow cover is an important earth surface characteristic because it influences partitioning of the surface radiation, energy, and hydrologic budgets. Snow is also an important source of moisture for agricultural crops and water supply in many higher latitude or mountainous areas. For instance, snowmelt provides approximately 50%–80% of the annual runoff in the western United States (Pagano and Garen, 2006) and Canadian Prairies (Gray et al., 1989; Fang and Pomeroy, 2007), which substantially impacts warm season hydrology. Limited soil moisture reserves from the winter period can result in agricultural drought (i.e., severe early growing season vegetation stress if rainfall deficits occur during that period), which can be prolonged or intensified well into the growing season if relatively dry conditions persist. Snow cover deficits can also result in hydrological drought (i.e., severe deficits in surface and subsurface water reserves including soil moisture, streamflow, reservoir and lake levels, and groundwater) since snowmelt runoff is the primary source of moisture to recharge these reserves for a wide range of agricultural, commercial, ecological, and municipal purposes. Semiarid regions that rely on snowmelt are especially vulnerable to winter moisture shortfalls since these areas are more likely to experience frequent droughts. In the Canadian Prairies, more than half the years of three decades (1910–1920, 1930–1939, and 1980–1989) were in drought. Wheaton et al. (2005) reported exceptionally low precipitation and low snow cover in the winter of 2000–2001, with the greatest anomalies of precipitation in Alberta and western Saskatchewan along with near-normal temperature in most of southern Canada. The reduced snowfall led to lower snow accumulation. A loss in agricultural production over Canada by an estimated $3.6 billion in 2001–2002 was attributed to this drought. Fang and Pomeroy (2008) analyzed the impacts of the most recent and severe drought of 1999/2004–2005 for part of the Canadian Prairies on the water supply of a wetland basin by using a physically based cold region hydrologic modeling system. Simulation results showed that much lower winter precipitation, less snow accumulation, and shorter snow cover duration were associated with much lower discharge from snowmelt runoff to the wetland area during much of the drought period of 1999/2004–2005 than during the nondrought period of 2005/2006

Snow Cover Monitoring from Remote-Sensing Satellites: Possibilities for Drought Assessment

Snow cover is an important earth surface characteristic because it influences partitioning of the surface radiation, energy, and hydrologic budgets. Snow is also an important source of moisture for agricultural crops and water supply in many higher latitude or mountainous areas. For instance, snowmelt provides approximately 50%–80% of the annual runoff in the western United States (Pagano and Garen, 2006) and Canadian Prairies (Gray et al., 1989; Fang and Pomeroy, 2007), which substantially impacts warm season hydrology. Limited soil moisture reserves from the winter period can result in agricultural drought (i.e., severe early growing season vegetation stress if rainfall deficits occur during that period), which can be prolonged or intensified well into the growing season if relatively dry conditions persist. Snow cover deficits can also result in hydrological drought (i.e., severe deficits in surface and subsurface water reserves including soil moisture, streamflow, reservoir and lake levels, and groundwater) since snowmelt runoff is the primary source of moisture to recharge these reserves for a wide range of agricultural, commercial, ecological, and municipal purposes. Semiarid regions that rely on snowmelt are especially vulnerable to winter moisture shortfalls since these areas are more likely to experience frequent droughts. In the Canadian Prairies, more than half the years of three decades (1910–1920, 1930–1939, and 1980–1989) were in drought. Wheaton et al. (2005) reported exceptionally low precipitation and low snow cover in the winter of 2000–2001, with the greatest anomalies of precipitation in Alberta and western Saskatchewan along with near-normal temperature in most of southern Canada. The reduced snowfall led to lower snow accumulation. A loss in agricultural production over Canada by an estimated $3.6 billion in 2001–2002 was attributed to this drought. Fang and Pomeroy (2008) analyzed the impacts of the most recent and severe drought of 1999/2004–2005 for part of the Canadian Prairies on the water supply of a wetland basin by using a physically based cold region hydrologic modeling system. Simulation results showed that much lower winter precipitation, less snow accumulation, and shorter snow cover duration were associated with much lower discharge from snowmelt runoff to the wetland area during much of the drought period of 1999/2004–2005 than during the nondrought period of 2005/2006

Reflectance variation in boreal landscape during the snow melting period using airborne imaging spectroscopy

We aim a better understanding of the effect of spring-time snow melt on the remotely sensed scene reflectance by using an extensive amount of optical spectral data obtained from an airborne hyperspectral campaign in Northern Finland. We investigate the behaviour of thin snow reflectance for different land cover types, such as open areas, boreal forests and treeless fells. Our results not only confirm the generally known fact that the reflectance of a melting thin snow layer is considerably lower than that of a thick snow layer, but we also present analyses of the reflectance variation over different land covers and in boreal forests as a function of canopy coverage. According to common knowledge, the highly variating reflectance spectra of partially transparent, most likely also contaminated thin snow pack weakens the performance of snow detection algorithms, in particular in the mapping of Fractional Snow Cover (FSC) during the end of the melting period. The obtained results directly support further development of the SCAmod algorithm for FSC retrieval, and can be likewise applied to develop other algorithms for optical satellite data (e.g. spectral unmixing methods), and to perform accuracy assessments for snow detection algorithms. A useful part of this work is the investigation of the competence of Normalized Difference Snow Index (NDSI) in snow detection in late spring, since it is widely used in snow mapping. We conclude, based on the spectral data analysis, that the NDSI-based snow mapping is more accurate in open areas than in forests. However, at the very end of the snow melting period the behavior of the NDSI becomes more unstable and unpredictable in non-forests with shallow snow, increasing the inaccuracy also in non-forested areas. For instance in peatbogs covered by melting snow layer (snow depth <30 cm) the mean NDSI-0.6 was observed, having coefficient of variation as high as 70%, whereas for deeper snow packs the mean NDSI shows positive values.peerReviewe

Simulations of snow distribution and hydrology in a mountain basin

We applied a version of the Regional Hydro‐Ecologic Simulation System (RHESSys) that implements snow redistribution, elevation partitioning, and wind‐driven sublimation to Loch Vale Watershed (LVWS), an alpine‐subalpine Rocky Mountain catchment where snow accumulation and ablation dominate the hydrologic cycle. We compared simulated discharge to measured discharge and the simulated snow distribution to photogrammetrically rectified aerial (remotely sensed) images. Snow redistribution was governed by a topographic similarity index. We subdivided each hillslope into elevation bands that had homogeneous climate extrapolated from observed climate. We created a distributed wind speed field that was used in conjunction with daily measured wind speeds to estimate sublimation. Modeling snow redistribution was critical to estimating the timing and magnitude of discharge. Incorporating elevation partitioning improved estimated timing of discharge but did not improve patterns of snow cover since wind was the dominant controller of areal snow patterns. Simulating wind‐driven sublimation was necessary to predict moisture losses

Remote sensing of snow : Factors influencing seasonal snow mapping in boreal forest region

Monitoring of snow cover in northern hemisphere is highly important for climate research and for operational activities, such as those related to hydrology and weather forecasting. The appearance and melting of seasonal snow cover dominate the hydrological and climatic patterns in the boreal and arctic regions. Spatial variability (in particular during the spring and autumn transition months) and long-term trends in global snow cover distribution are strongly interconnected to changes in Earth System (ES). Satellite data based estimates on snow cover extent are utilized e.g. in near-real-time hydrological forecasting, water resource management and to construct long-term Climate Data Records (CDRs) essential for climate research. Information on the quantitative reliability of snow cover monitoring is urgently needed by these different applications as the usefulness of satellite data based results is strongly dependent on the quality of the interpretation. This doctoral dissertation investigates the factors affecting the reliability of snow cover monitoring using optical satellite data and focuses on boreal regions (zone characterized by seasonal snow cover). Based on the analysis of different factors relevant to snow mapping performance, the work introduces a methodology to assess the uncertainty of snow cover extent estimates, focusing on the retrieval of fractional snow cover (within a pixel) during the spring melt period. The results demonstrate that optical remote sensing is well suited for determining snow extent in the melting season and that the characterizing the uncertainty in snow estimates facilitates the improvement of the snow mapping algorithms. The overall message is that using a versatile accuracy analysis it is possible to develop uncertainty estimates for the optical remote sensing of snow cover, which is a considerable advance in remote sensing. The results of this work can also be utilized in the development of other interpretation algorithms. This thesis consists of five articles predominantly dealing with quantitative data analysis, while the summary chapter synthesizes the results mainly in the algorithm accuracy point of view. The first four articles determine the reflectance characteristics essential for the forward and inverse modeling of boreal landscapes (forward model describes the observations as a function of the investigated variable). The effects of snow, snow-free ground and boreal forest canopy on the observed satellite scene reflectance are specified. The effects of all the error components are clarified in the fifth article and a novel experimental method to analyze and quantify the amount of uncertainty is presented. The five articles employ different remote sensing and ground truth data sets measured and/or analyzed for this research, covering the region of Finland and also applied to boreal forest region in northern Europe

Evaluation of ERTS-1 data for certain hydrological uses

The author has identified the following significant results. ERTS-1 MSS data have been used in a variety of hydrologic research including snow-extent mapping; studies of snowmelt, snowmelt runoff, spectral reflectance of snow for assessing snowpack conditions, and snow albedo; lake ice formation, breakup, and migration; lake current measurements; multispectral studies of lake ice; and flood studies. MSS sensing of soil moisture over a well-vegetated test site was unsuccessfully attempted. Although a powerful research tool, ERTS-1 has very limited use as an operational system for hydrologic communities because of its 18-day revisit cycle and its lack of a quick look capability

The evolution of satellite snow mapping with emphasis on the use of LANDSAT in the snow ASUT study areas

There are no author-identified significant results in this report