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Abstract: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 (R 2 = 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.

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.

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 lake's 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 lake's unique bathymetry-which allows its division into two smaller lakes-has made it more vulnerable to water loss. Unfortunately the lake's 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.

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Despite the growing number of remote-sensing products from satellite sensors, mapping of the combined spatial distribution and temporal variability of inundation in tropical wetlands remains challenging. An emerging innovative approach is offered by Global Navigation Satellite System reflectometry (GNSS-R), a concept that takes advantage of GNSS-transmitting satellites and independent radar receivers to provide bistatic radar observations of Earth's surface with large-scale coverage. The objective of this paper is to assess the capability of spaceborne GNSS reflections to characterize surface inundation dynamics in a complex wetlands environment in the Peruvian Amazon with respect to current state-of-the-art methods. This study examines contemporaneous ALOS2 PALSAR-2 L-band imaging radar, CYGNSS GNSS reflections, and ground measurements to assess associated advantages and challenges to mapping inundation dynamics, particularly in regions under dense tropical forest canopies. Three derivatives of CYGNSS Delay-Doppler maps (1) peak signal-to-noise ratio (SNR), (2) leading edge slope, and (3) trailing edge slope, demonstrated statistically significant logarithmic relationships with estimated flooded area percentages determined from SAR, with SNR exhibiting the strongest association. Aggregated Delay-Doppler maps SNR time series data examined for inundated regions undetected by imaging radar suggests GNSS-R exhibits a potentially greater sensitivity to inundation state beneath dense forest canopies relative to SAR. Results demonstrate the capability for mapping extent and dynamic wetlands ecosystems in complex tropical landscapes, alone or in combination with other remote-sensing techniques such as those based on imaging radar, contributing to enhanced mapping of these regions. However, several aspects of GNSS-R observations such as noise level, spatial resolution, and signal coherence need to be further examined.biogeochemical processes such as the generation of atmospheric methane and the outgassing of carbon dioxide [2,3]. These flooding regimes can be impacted by future changes in rainfall, evapotranspiration and land use. The ability to accurately monitor the current state and changes in inundation extent would enable further examination of potential climatological and anthropogenic tipping points in these wetlands regions.Satellite remote sensing is the only practical approach that can provide insight into the spatial and temporal dynamics of wetlands on a large scale on a continuous basis. Although they can offer high spatial resolution and sensitivity to photochemical properties of vegetation, optical sensors are severely limited in capturing inundation dynamics in tropical wetlands due to frequent cloud cover and difficulties detecting sub-canopy inundation [4,5]. Microwave sensors, on the other hand, are not significantly affected by clouds or changing solar illumination and are able to observe processes below vegetation canopy. However, tradeoffs exist in the use of microwave remote sensing with respect to...

Abstract. We used a process-based model to examine the role of spatial heterogeneity of surface and sub-surface water on the carbon budget of the wetlands of the West Siberian Lowland over the period 1948-2010. We found that, while surface heterogeneity (fractional saturated area) had little overall effect on estimates of the region's carbon fluxes, subsurface heterogeneity (spatial variations in water table depth) played an important role in both the overall magnitude and spatial distribution of estimates of the region's carbon fluxes. In particular, to reproduce the spatial pattern of CH 4 emissions recorded by intensive in situ observations across the domain, in which very little CH 4 is emitted north of 60 • N, it was necessary to (a) account for CH 4 emissions from unsaturated wetlands and (b) use spatially varying methane model parameters that reduced estimated CH 4 emissions in the northern (permafrost) half of the domain (and/or account for lower CH 4 emissions under inundated conditions). Our results suggest that previous estimates of the response of these wetlands to thawing permafrost may have overestimated future increases in methane emissions in the permafrost zone.

The use of global navigation satellite system reflectometry (GNSS-R) measurements for classification of inundated wetlands is presented. With the launch of NASA’s Cyclone Global Navigation Satellite System (CYGNSS) mission, space-borne GNSS-R measurements have become available over ocean and land. CYGNSS covers latitudes between ±38°, providing measurements over tropical ecosystems and benefiting new studies of wetland inundation dynamics. The GNSS-R signal over inundated wetlands is driven mainly by coherent scattering associated with the presence of surface water, producing strong forward scattering and a distinctive bistatic scattering signature. This paper presents a methodology used to classify inundation in tropical wetlands using observables derived from GNSS-R measurements and ancillary data. The methodology employs a multiple decision tree randomized (MDTR) algorithm for classification and wetland inundation maps derived from the phased-array L-band synthetic aperture radar (PALSAR-2) as reference for training and validation. The development of an innovative GNSS-R wetland classification methodology is aimed to advance mapping of global wetland distribution and dynamics, which is critical for improved estimates of natural methane production. The results obtained in this manuscript demonstrate the ability of GNSS-R signals to detect inundation under dense vegetation over the Pacaya-Samiria Natural Reserve, a tropical wetland complex located in the Peruvian Amazon. Classification results report an accuracy of 69% for regions of inundated vegetation, 87% for open water regions, and 99% for non-inundated areas. Misclassification of inundated vegetation, primarily as non-inundated area, is likely related to the combination of two factors: partial inundation within the GNSS-R scattering area, and signal attenuation from dense overstory vegetation, resulting in a low signal.