Hydrologic Remote Sensing and Land Surface Data Assimilation

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface?atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation

Assessing the utility of geospatial technologies to investigate environmental change within lake systems

Over 50% of the world's population live within 3. km of rivers and lakes highlighting the on-going importance of freshwater resources to human health and societal well-being. Whilst covering c. 3.5% of the Earth's non-glaciated land mass, trends in the environmental quality of the world's standing waters (natural lakes and reservoirs) are poorly understood, at least in comparison with rivers, and so evaluation of their current condition and sensitivity to change are global priorities. Here it is argued that a geospatial approach harnessing existing global datasets, along with new generation remote sensing products, offers the basis to characterise trajectories of change in lake properties e.g., water quality, physical structure, hydrological regime and ecological behaviour. This approach furthermore provides the evidence base to understand the relative importance of climatic forcing and/or changing catchment processes, e.g. land cover and soil moisture data, which coupled with climate data provide the basis to model regional water balance and runoff estimates over time. Using examples derived primarily from the Danube Basin but also other parts of the World, we demonstrate the power of the approach and its utility to assess the sensitivity of lake systems to environmental change, and hence better manage these key resources in the future

A Review of Global Satellite-Derived Snow Products

Snow cover over the Northern Hemisphere plays a crucial role in the Earth's hydrology and surface energy balance, and modulates feedbacks that control variations of global climate. While many of these variations are associated with exchanges of energy and mass between the land surface and the atmosphere, other expected changes are likely to propagate downstream and affect oceanic processes in coastal zones. For example, a large component of the freshwater flux into the Arctic Ocean comes from snow melt. The timing and magnitude of this flux affects biological and thermodynamic processes in the Arctic Ocean, and potentially across the globe through their impact on North Atlantic Deep Water formation. Several recent global remotely sensed products provide information at unprecedented temporal, spatial, and spectral resolutions. In this article we review the theoretical underpinnings and characteristics of three key products. We also demonstrate the seasonal and spatial patterns of agreement and disagreement amongst them, and discuss current and future directions in their application and development. Though there is general agreement amongst these products, there can be disagreement over certain geographic regions and under conditions of ephemeral, patchy and melting snow

The effects of variation in snow properties on passive microwave snow mass estimation

Estimating snow mass at continental scales is difficult, but important for understanding land-atmosphere interactions, biogeochemical cycles and the hydrology of the Northern latitudes. Remote sensing provides the only consistent global observations, butwith unknown errors. Wetest the theoretical performance of the Chang algorithm for estimating snow mass from passive microwave measurements using the Helsinki University of Technology (HUT) snow microwave emission model. The algorithm's dependence upon assumptions of fixed and uniform snow density and grainsize is determined, and measurements of these properties made at the Cold Land Processes Experiment (CLPX) Colorado field site in 2002–2003 used to quantify the retrieval errors caused by differences between the algorithm assumptions and measurements. Deviation from the Chang algorithm snow density and grainsize assumptions gives rise to an error of a factor of between two and three in calculating snow mass. The possibility that the algorithm performsmore accurately over large areas than at points is tested by simulating emission from a 25 km diameter area of snow with a distribution of properties derived from the snow pitmeasurements, using the Chang algorithm to calculate mean snow-mass from the simulated emission. The snowmass estimation froma site exhibiting the heterogeneity of the CLPX Colorado site proves onlymarginally different than that from a similarly-simulated homogeneous site. The estimation accuracy predictions are tested using the CLPX field measurements of snow mass, and simultaneous SSM/I and AMSR-E measurements

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

PASSIVE MICROWAVE SATELLITE SNOW OBSERVATIONS FOR HYDROLOGIC APPLICATIONS

Melting snow provides an essential source of water in many regions of the world and can also contribute to devastating, wide-scale flooding. The objective of this research was to investigate the potential for passive microwave remotely sensed data to characterize snow water equivalent (SWE) and snowmelt across diverse regions and snow regimes to improve snowmelt runoff estimation. The first step was to evaluate the current, empirically-based passive microwave SWE products compared to NOAA’s operational SWE estimates from SNODAS across 2100 watersheds over eight years. The best agreement was found within basins in which maximum annual SWE is less than 200 mm, and forest fraction is less than 20%. Next, a sensitivity analysis was conducted to evaluate the microwave signal response to spatially distributed wet snow using a loosely-coupled snow-emission model. The results over an area approximately the size of a microwave pixel found a near-linear relationship between the microwave signal response and the percent area with wet snow present. These results were confirmed by evaluating actual wet snow events over a nine year period, and suggest that the microwave response provides the potential basis for disaggregating melting snow within a microwave pixel. Finally, a similar sensitivity analysis conducted in six watersheds with diverse landscapes and snow conditions confirmed the relationship holds at a basin scale. The magnitude of the microwave response to wet snow was compared to the magnitude of subsequent discharge events to determine if an empirical relation exists. While positive increases in brightness temperature (TB) correspond to positive increases in discharge, the magnitude of those changes is poorly correlated in most basins. The exception is in basins where snowmelt runoff typically occurs in one event each spring. In similar basins, the microwave response may provide information on the magnitude of spring runoff. Methods to use these findings to improve current snow and snow melt estimation as well as future research direction are discussed

Multiscale assimilation of Advanced Microwave Scanning Radiometer-EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado

Eight years (2002–2010) of Advanced Microwave Scanning Radiometer–EOS (AMSR-E) snow water equivalent (SWE) retrievals and Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover fraction (SCF) observations are assimilated separately or jointly into the Noah land surface model over a domain in Northern Colorado. A multiscale ensemble Kalman filter (EnKF) is used, supplemented with a rule-based update. The satellite data are either left unscaled or are scaled for anomaly assimilation. The results are validated against in situ observations at 14 high-elevation Snowpack Telemetry (SNOTEL) sites with typically deep snow and at 4 lower-elevation Cooperative Observer Program (COOP) sites. Assimilation of coarse-scale AMSR-E SWE and fine-scale MODIS SCF observations both result in realistic spatial SWE patterns. At COOP sites with shallow snowpacks, AMSR-E SWE and MODIS SCF data assimilation are beneficial separately, and joint SWE and SCF assimilation yields significantly improved root-mean-square error and correlation values for scaled and unscaled data assimilation. In areas of deep snow where the SNOTEL sites are located, however, AMSR-E retrievals are typically biased low and assimilation without prior scaling leads to degraded SWE estimates. Anomaly SWE assimilation could not improve the interannual SWE variations in the assimilation results because the AMSR-E retrievals lack realistic interannual variability in deep snowpacks. SCF assimilation has only a marginal impact at the SNOTEL locations because these sites experience extended periods of near-complete snow cover. Across all sites, SCF assimilation improves the timing of the onset of the snow season but without a net improvement of SWE amounts

Assessing the accuracy of passive microwave estimates of snow water equivalent in data-scarce regions for use in water resource applications

Winter snowpack is a significant contributor to water supply in many regions of the world and accurate estimates of the snow water equivalent (SWE) are necessary for water resource planning. Satellite data is an attractive source of snow information in remote regions with limited ground data. The objective of this study is to assess passive microwave SWE in the Upper Helmand Watershed in Afghanistan where snowmelt is a primary source of water. Passive microwave SWE data were compared over 6 winter seasons, 2004-2009, to an independent estimate of SWE using a snow hydrology model. The snow hydrology model was calibrated to high-resolution snow covered area images and observed reservoir levels. The model was initialized with passive microwave SWE data and found to improve results in years when input precipitation was low. The results showed that passive microwave SWE has potential to provide valuable water resource information in this data-scarce region

Snow stratigraphic heterogeneity within ground-based passive microwave radiometer footprints: implications for emission modeling

Two-dimensional measurements of snowpack properties (stratigraphic layering, density, grain size and temperature) were used as inputs to the multi-layer Helsinki University of Technology (HUT) microwave emission model at a centimeter-scale horizontal resolution, across a 4.5 m transect of ground-based passive microwave radiometer footprints near Churchill, Manitoba, Canada. Snowpack stratigraphy was complex (between six and eight layers) with only three layers extending continuously throughout the length of the transect. Distributions of one-dimensional simulations, accurately representing complex stratigraphic layering, were evaluated using measured brightness temperatures. Large biases (36 to 68 K) between simulated and measured brightness temperatures were minimized (-0.5 to 0.6 K), within measurement accuracy, through application of grain scaling factors (2.6 to 5.3) at different combinations of frequencies, polarizations and model extinction coefficients. Grain scaling factors compensated for uncertainty relating optical SSA to HUT effective grain size inputs and quantified relative differences in scattering and absorption properties of various extinction coefficients. The HUT model required accurate representation of ice lenses, particularly at horizontal polarization, and large grain scaling factors highlighted the need to consider microstructure beyond the size of individual grains. As variability of extinction coefficients was strongly influenced by the proportion of large (hoar) grains in a vertical profile, it is important to consider simulations from distributions of one-dimensional profiles rather than single profiles, especially in sub-Arctic snowpacks where stratigraphic variability can be high. Model sensitivity experiments suggested the level of error in field measurements and the new methodological framework used to apply them in a snow emission model were satisfactory. Layer amalgamation showed a three-layer representation of snowpack stratigraphy reduced the bias of a one-layer representation by about 50%