E. Podest

Analysis of the Arctic System for Freshwater Cycle Intensification: Observations and Expectations

Abstract Hydrologic cycle intensification is an expected manifestation of a warming climate. Although positive trends in several global average quantities have been reported, no previous studies have documented broad intensification across elements of the Arctic freshwater cycle (FWC). In this study, the authors examine the character and quantitative significance of changes in annual precipitation, evapotranspiration, and river discharge across the terrestrial pan-Arctic over the past several decades from observations and a suite of coupled general circulation models (GCMs). Trends in freshwater flux and storage derived from observations across the Arctic Ocean and surrounding seas are also described. With few exceptions, precipitation, evapotranspiration, and river discharge fluxes from observations and the GCMs exhibit positive trends. Significant positive trends above the 90% confidence level, however, are not present for all of the observations. Greater confidence in the GCM trends arises through lowe...

Methane emissions from western Siberian wetlands: heterogeneity and sensitivity to climate change

The prediction of methane emissions from high-latitude wetlands is important given concerns about their sensitivity to a warming climate. As a basis for the prediction of wetland methane emissions at regional scales, we coupled the variable infiltration capacity macroscale hydrological model (VIC) with the biosphere–energy-transfer–hydrology terrestrial ecosystem model (BETHY) and a wetland methane emissions model to make large-scale estimates of methane emissions as a function of soil temperature, water table depth, and net primary productivity (NPP), with a parameterization of the sub-grid heterogeneity of the water table depth based on TOPMODEL. We simulated the methane emissions from a 100 km × 100 km region of western Siberia surrounding the Bakchar Bog, for a retrospective baseline period of 1980–1999 and have evaluated their sensitivity to increases in temperature of 0–5 °C and increases in precipitation of 0–15%. The interactions of temperature and precipitation, through their effects on the water table depth, played an important role in determining methane emissions from these wetlands. The balance between these effects varied spatially, and their net effect depended in part on sub-grid topographic heterogeneity. Higher temperatures alone increased methane production in saturated areas, but caused those saturated areas to shrink in extent, resulting in a net reduction in methane emissions. Higher precipitation alone raised water tables and expanded the saturated area, resulting in a net increase in methane emissions. Combining a temperature increase of 3 °C and an increase of 10% in precipitation to represent climate conditions that may pertain in western Siberia at the end of this century resulted in roughly a doubling in annual emissions.

Mapping vegetated wetlands of Alaska using L-band radar satellite imagery.

Wetlands act as major sinks and sources of important atmospheric greenhouse gases and can switch between atmospheric sink and source in response to climatic and anthropogenic forces in ways that are poorly understood. Despite their importance in the carbon cycle, the locations, types, and extents of northern wetlands are not accurately known. We have used two seasons of L-band synthetic aperture radar (SAR) imagery to produce a thematic map of wetlands throughout Alaska. The classification is developed using the Random Forests decision tree algorithm with training and testing data compiled from the National Wetlands Inventory (NWI) and the Alaska Geospatial Data Clearinghouse (AGDC). Mosaics of summer and winter Japanese Earth Resources Satellite 1 (JERS-1) SAR imagery were employed together with other inputs and ancillary datasets, including the SAR backscatter texture map, slope and elevation maps from a digital elevation model (DEM), an open-water map, a map of proximity to water, data collection dates, and geographic latitude. The accuracy of the resulting thematic map was quantified using extensive ground reference data. This approach distinguished as many as nine different wetlands classes, which were aggregated into four vegetated wetland classes. The per-class average error rate for aggregate wetlands classes ranged between 5.0% and 30.5%, and the total aggregate accuracy calculated based on all classified pixels was 89.5%. As the first high-resolution large-scale synoptic wetlands map of Alaska, this product provides an initial basis for improved characterization of land-atmosphere CH4 and CO2 fluxes and climate change impacts associated with thawing soils and changes in extent and drying of wetland ecosystems.

On the causes of the shrinking of Lake Chad

Over the last 40 years, Lake Chad, once the sixth largest lake in the world, has decreased by more than 90% in area. In this study, we use a hydrological model coupled with a lake/wetland algorithm to simulate the effects of lake bathymetry, human water use, and decadal climate variability on the lakes level, surface area, and water storage. In addition to the effects of persistent droughts and increasing irrigation withdrawals on the shrinking, we find that the lakes unique bathymetry—which allows its division into two smaller lakes—has made it more vulnerable to water loss. Unfortunately the lakes split is favored by the 1952–2006 climatology. Failure of the lake to remerge with renewed rainfall in the 1990s following the drought years of the 1970s and 1980s is a consequence of irrigation withdrawals. Under current climate and water use, a full recovery of the lake is unlikely without an inter-basin water transfer. Breaching the barrier separating the north and south lakes would reduce the amount of supplemental water needed for recovery.

Satellite microwave remote sensing of North Eurasian inundation dynamics: development of coarse-resolution products and comparison with high-resolution synthetic aperture radar data

Wetlands are not only primary producers of atmospheric greenhouse gases but also possess unique features that are favourable for application of satellite microwave remote sensing to monitoring their status and trend. In this study we apply combined passive and active microwave remote sensing data sets from the NASA sensors AMSR-E and QuikSCAT to map surface water dynamics over Northern Eurasia. We demonstrate our method on the evolution of large wetland complexes for two consecutive years from January 2006 to December 2007. We apply river discharge measurements from the Ob River along with land surface runoff simulations derived from the Pan-Arctic Water Balance Model during and after snowmelt in 2006 and 2007 to interpret the abundance of widespread flooding along the River Ob in early summer of 2007 observed in the remote sensing products. The coarse-resolution, 25 km, surface water product is compared to a high-resolution, 30 m, inundation map derived from ALOS PALSAR (Advanced Land Observation Satellite phased array L-band synthetic aperture radar) imagery acquired for 11 July 2006, and extending along a transect in the central Western Siberian Plain. We found that the surface water fraction derived from the combined AMSR-E/QuikSCAT data sets closely tracks the inundation mapped using higher-resolution ALOS PALSAR data.

Development and Evaluation of a Multi-Year Fractional Surface Water Data Set Derived from Active/Passive Microwave Remote Sensing Data

The sensitivity of Earth’s wetlands to observed shifts in global precipitation and temperature patterns and their ability to produce large quantities of methane gas are key global change questions. We present a microwave satellite-based approach for mapping fractional surface water (FW) globally at 25-km resolution. The approach employs a land cover-supported, atmospherically-corrected dynamic mixture model applied to 20+ years (1992–2013) of combined, daily, passive/active microwave remote sensing data. The resulting product, known as Surface WAter Microwave Product Series (SWAMPS), shows strong microwave sensitivity to sub-grid scale open water and inundated wetlands comprising open plant canopies. SWAMPS’ FW compares favorably (R2 = 91%–94%) with higher-resolution, global-scale maps of open water from MODIS and SRTM-MOD44W. Correspondence of SWAMPS with open water and wetland products from satellite SAR in Alaska and the Amazon deteriorates when exposed wetlands or inundated forests captured by the SAR products were added to the open water fraction reflecting SWAMPS’ inability to detect water underneath the soil surface or beneath closed forest canopies. Except for a brief period of drying during the first 4 years of observation, the inundation extent for the global domain excluding the coast was largely stable. Regionally, inundation in North America is advancing while inundation is on the retreat in Tropical Africa and North Eurasia. SWAMPS provides a consistent and long-term global record of daily FW dynamics, with documented accuracies suitable for hydrologic assessment and global change-related investigations.

Feasibility of Characterizing Snowpack and the Freeze–Thaw State of Underlying Soil Using Multifrequency Active/Passive Microwave Data

An ensemble-based data assimilation approach is developed to characterize the snow water equivalent (SWE) and underlying soil freeze-thaw state (including the soil surface temperature and both soil ice and liquid water content) using multifrequency passive and active microwave remote-sensing measurements. Its feasibility was examined using a synthetic test where passive microwave (1.4, 18.7, and 36.5 GHz) and active microwave [L-band (1.4 GHz), C-band (5.4 GHz), and Ku-band (12 GHz)] measurements at the point scale were individually and simultaneously assimilated to estimate the SWE and soil freeze-thaw state using an Ensemble Batch Smoother framework. The contribution of each channel in retrieving the true SWE, soil surface temperature, soil liquid water and ice content was investigated at the local-scale observation site of the National Aeronautics and Space Administration Cold Land Processes Experiments Field Campaign in northern Colorado during both the snow accumulation (Fall 2002-Winter 2003) and melt (Spring 2003) periods. All of the utilized passive and active measurements were found to contain valuable and complementary information for characterizing the SWE and freeze-thaw state of the underlying soil. L-band measurements were most effective for soil freeze-thaw state estimation, whereas higher frequencies were more effective at SWE characterization. In addition, results from the simultaneous assimilation of passive and active microwave data were compared to those from a modeling approach without assimilating microwave data (open loop). It was found that assimilating both passive and active microwave data decreased the errors that are associated with the open-loop approach. Finally, passive and active measurements were undersampled as expected from the overpasses of current and future satellite platforms. It was observed that the developed method can reliably estimate the soil freeze-thaw state and SWE, even with measurement sequences anticipated from the temporal frequency of existing and future satellites such as the Special Sensor Microwave/Imager, Soil Moisture Active Passive Mission, and Cold Regions Hydrology High-Resolution Observatory.

Multisensor Microwave Sensitivity to Freeze/Thaw Dynamics Across a Complex Boreal Landscape

The annual freeze/thaw (FT) cycle determines the potential growing season in boreal landscapes and is a major factor determining ecosystem productivity and associated exchange of trace gases (CO2, H2O) with the atmosphere. Accurate characterization of these processes can improve regional assessment of seasonal carbon dynamics and climate feedbacks. FT process variations are spatially and temporally complex due to topography, snow depth and wetness, land cover, or local climatic conditions. In this paper, we perform a landscape analysis of multifrequency and multitemporal satellite microwave remote sensing measurements at L-band (JERS-1), C-band (ERS), and Ku-band (QuikSCAT) for characterizing FT dynamics. We first analyze backscatter sensitivity of the three frequencies to FT conditions over selected Alaska temperature sites. We then apply an FT classifier over two study areas (wetland complex and moderate topography) and examine differences in FT timing according to vegetation, elevation, and north/south facing slope. Results show that L-, C-, and Ku-band backscatter are sensitive to landscape FT state transitions, with higher backscatter for nonfrozen than frozen conditions at C- and L-bands but the opposite response at Ku-band. We applied a change detection algorithm to the C-band and L-band data over both study areas and analyzed the FT classifications with land cover information. These results resolve characteristic patterns of earlier spring thawing for south facing slopes, lower elevations, and coniferous vegetation. Our results also inform similar FT algorithm development for the NASA Soil Moisture Active Passive mission by documenting L-band FT sensitivity and heterogeneity over a boreal landscape.

A method for deriving land surface moisture, vegetation optical depth, and open water fraction from AMSR-E

We developed an algorithm to estimate surface soil moisture, vegetation optical depth and fractional open water cover using satellite microwave radiometry. Soil moisture results compare favorably with a simple antecedent site precipitation index, and respond rapidly to precipitation events indicated by TRMM. High optical depth reduces soil moisture sensitivity in forests and croplands during peak biomass, although tundra locations maintain soil moisture sensitivity despite high optical depth. Optical depth varies with characteristic seasonality across vegetation cover types and tracks measures of vegetation canopy cover from MODIS. The algorithm developed in this study is able to monitor the daily variability of several important land surface state variables.

Characterizing Snowpack and the Freeze–Thaw State of Underlying Soil via Assimilation of Multifrequency Passive/Active Microwave Data: A Case Study (NASA CLPX 2003)

Ground-based passive microwave observations at 18.7- and 36.5-GHz frequencies and active microwave observations in L- [1.4 GHz] and Ku- [15.5 GHz] bands are used within an ensemble-based data assimilation (DA) framework to characterize the snow water equivalent (SWE) and the underlying soil freeze-thaw state (including soil surface temperature and both soil ice/liquid water content). The proposed framework is tested at the local-scale observation site of the National Aeronautics and Space Administration (NASA) Cold Land Processes Experiment field campaign during the third intensive observation period (February 18-26, 2003) for which the best set of collocated ground-based passive/active microwave observations, SWE, soil surface temperature, and moisture measurements are available. The DA approach effectively merges an a priori estimate of the soil freeze-thaw state and SWE generated by a land surface model (LSM) with information contained in passive/active microwave observations in order to overcome errors in the forcing data of LSM. Results indicate that the root-mean-square errors of SWE, soil surface temperature, and soil ice+liquid water content after the assimilation of passive (active) observations respectively decrease to 25.4 mm (22.8 mm), 0.61 K (0.52 K), and 0.063 (0.057) from 90.55 mm, 2.17 K, and 0.13 before assimilation, resulting in improvements of 75% (77%), 72% (76%), and 51% (56%). Also, it is found that the simultaneous assimilation of passive and active measurements further improves the estimates of SWE and soil temperature as well as soil ice/liquid water content, suggesting that there is an advantage offered by the synergistic use of passive and active measurements. Overall, the findings show that future studies can take advantage of remotely sensed microwave passive and active measurements from present and upcoming satellites such as Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E), Soil Moisture Active Passive, and COld REgion Hydrology High-resolution Observatory (CoReH2O) for monitoring SWE and the underlying soil freeze-thaw state.