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The Red River of the North basin (RRB) is vulnerable to spring snowmelt flooding because of its flat terrain, low permeability soils, and the presence of river ice jams resulting from the river's northward flow direction. The onset and magnitude of major flood events in the RRB have been very difficult to forecast, in part due to limited field observations of snow water equivalent (SWE). Coarse-resolution (25-km) passive microwave observations from satellite instruments are well suited for the monitoring of SWE. Despite routine use in the Earth sciences community to document the impacts of climate change, the use of passive microwave observations in operational flood forecasting is rare. This paper compares daily satellite passive microwave SWE observations from special sensor microwave/imager (SSM/I) and special sensor microwave imager/sounder (SSMIS), advanced microwave scanning radiometer for earth observing system (AMSR-E), and advanced microwave scanning radiometer 2 (AMSR2) from 2003 to 2016 to modeled output from the SNOw Data Assimilation System (SNODAS) and Global Snow Monitoring for Climate Research -2 (GlobSnow-2) in the RRB to determine the differences between the remotely sensed SWE estimates and the model products currently used in flood forecasting. Results show statistically significant differences between the satellite observations and SNODAS in the northern region of the basin that were not evident in the southern region. Satellite estimates of peak SWE in the forecast subbasins in the northern region were 40–125% higher than the model results which points to the lack of ground observations used to constrain the model simulations. This paper recommends that satellite SWE observations should be considered for improving operational snowmelt forecasting in the RRB.

Wetland emissions remain one of the principal sources of uncertainty in the global atmospheric methane (CH4) budget, largely due to poorly constrained process controls on CH4 production in waterlogged soils. Process-based estimates of global wetland CH4 emissions and their associated uncertainties can provide crucial prior information for model-based top-down CH4 emission estimates. Here we construct a global wetland CH4 emission model ensemble for use in atmospheric chemical transport models (WetCHARTs version 1.0). Our 0.5° × 0.5° resolution model ensemble is based on satellite-derived surface water extent and precipitation reanalyses, nine heterotrophic respiration simulations (eight carbon cycle models and a data-constrained terrestrial carbon cycle analysis) and three temperature dependence parameterizations for the period 2009–2010; an extended ensemble subset based solely on precipitation and the data-constrained terrestrial carbon cycle analysis is derived for the period 2001–2015. We incorporate the mean of the full and extended model ensembles into GEOS-Chem and compare the model against surface measurements of atmospheric CH4; the model performance (site-level and zonal mean anomaly residuals) compares favourably against published wetland CH4 emissions scenarios. We find that uncertainties in carbon decomposition rates and the wetland extent together account for more than 80 % of the dominant uncertainty in the timing, magnitude and seasonal variability in wetland CH4 emissions, although uncertainty in the temperature CH4 : C dependence is a significant contributor to seasonal variations in mid-latitude wetland CH4 emissions. The combination of satellite, carbon cycle models and temperature dependence parameterizations provides a physically informed structural a priori uncertainty that is critical for top-down estimates of wetland CH4 fluxes. Specifically, our ensemble can provide enhanced information on the prior CH4 emission uncertainty and the error covariance structure, as well as a means for using posterior flux estimates and their uncertainties to quantitatively constrain the biogeochemical process controls of global wetland CH4 emissions.

We analyzed 4-years of Advanced Scatterometer (ASCAT) incidence angle normalized C-band backscatter, Next-Generation Radar (NEXRAD) precipitation and United States Department of Agriculture (USDA) crop yield data over the contiguous United States (U.S.). Large negative anomalies in backscatter are evident over the U.S. during the 2011 and 2012 growing seasons, coincident with two instances of severe drought. The backscatter anomalies were correlated with diminished growing season precipitation and with reduced crop yields reflecting where drought-induced impacts on above-ground biomass production occurred. During periods of acute drought, differences in diurnal backscatter were reduced and in some cases reversed reflecting diminished nocturnal leave rehydration. The results indicate that the C-band, VV-polarized ASCAT backscatter data is sensitive to interannual variability in agricultural productivity rather than soil moisture, and can offer an important geophysical monitoring tool capable of identifying and assessing drought-related vegetation responses independent of and complementary to satellite optical/near-infrared remote sensing data. This technique is flexible and adaptable, providing a capability to address region-specific needs. The twice-daily, weather independent and near-real time global monitoring capabilities of ASCAT and similar microwave instruments (e.g. RapidSCAT, Sentinel-1) can help improve drought preparedness and mitigation planning allowing improved water resource management for agriculture on the local as well as the state level.

Knowledge of snow water equivalent (SWE) magnitude and spatial distribution are keys to improving snowmelt flood predictions. Since the 1980s, the operational National Oceanic and Atmospheric Administration's (NOAA) airborne gamma radiation soil moisture (SM) and SWE survey has provided over 20,000 SWE observations to regional National Weather Service (NWS) River Forecast Centers (RFCs). Because the gamma SWE algorithm is based on the difference in natural gamma emission measurements from the soil between bare (fall) and snow-covered (winter) conditions, it requires a baseline fall SM for each flight line. The operational approach assumes the fall SM remains constant throughout that winter's SWE survey. However, early-winter snowmelt and rainfall events after the fall SM surveys have the potential to introduce large biases into airborne gamma SWE estimates. In this study, operational airborne gamma radiation SWE measurements were improved by updating the baseline fall SM with Soil Moisture Active Passive (SMAP) enhanced SM measurements immediately prior to winter onset over the north-central and eastern United States and southern Canada from September 2015 to April 2018. The operational airborne gamma SM had strong agreement with the SMAP SM (Pearson's correlation coefficient, R = 0.69, unbiased root mean square difference, ubRMSD = 0.057 m3/m3), compared to the Advanced Microwave Scanning Radiometer 2 (AMSR2) SM (R = 0.45, ubRMSD = 0.072 m3/m3) and the North American Land Data Assimilation System Phase 2 (NLDAS-2) Mosaic SM products (R = 0.53, ubRMSD = 0.069 m3/m3) in non-forested regions. The SMAP-enhanced gamma SWE was evaluated with satellite-based SWE (R = 0.57, ubRMSD = 34 mm) from the Special Sensor Microwave Imager Sounder (SSMIS) and in-situ SWE (R = 0.71–0.96) from the Soil Climate Analysis Network and United States Army Corps of Engineer (USACE) St. Paul District, which had better agreement than the operational gamma SWE (R = 0.48, ubRMSD = 36 mm for SSMIS and R = 0.65–0.75 for in-situ SWE). The results contribute to improving snowmelt flood predictions as well as the accuracy of the NOAA SNOw Data Assimilation System.

Shortly after the launch of ALOS PALSAR L-band SAR by the Japan Space Exploration Agency (JAXA), a program to develop an Earth Science Data Record (ESDR) for inundated wetlands was funded by NASA. Using established methodologies, extensive multi-temporal L-band ALOS ScanSAR data acquired bi-monthly by the PALSAR instrument onboard ALOS were used to classify the inundation state for South America for delivery as a component of this Inundated Wetlands ESDR (IW-ESDR) and in collaboration with JAXA’s ALOS Kyoto and Carbon Initiative science programme. We describe these methodologies and the final classification of the inundation state, then compared this with results derived from dual-season data acquired by the JERS-1 L-band SAR mission in 1995 and 1996, as well as with estimates of surface water extent measured globally every 10 days by coarser resolution sensors. Good correspondence was found when comparing open water extent classified from multi-temporal ALOS ScanSAR data with surface water fraction identified from coarse resolution sensors, except in those regions where there may be differences in sensitivity to widespread and shallow seasonal flooding event, or in areas that could be excluded through use of a continental-scale inundatable mask. It was found that the ALOS ScanSAR classification of inundated vegetation was relatively insensitive to inundated herbaceous vegetation. Inundation dynamics were examined using the multi-temporal ALOS ScanSAR acquisitions over the Pacaya-Samiria and surrounding areas in the Peruvian Amazon.

Summary Remote sensing techniques offer an opportunity to improve biodiversity modelling and prediction world-wide. Yet, to date, the weather station-based WorldClim data set has been the primary source of temperature and precipitation information used in correlative species distribution models. WorldClim consists of grids interpolated from in situ station data recorded primarily from 1960 to 1990. Those data sets suffer from uneven geographic coverage, with many areas of Earth poorly represented. Here, we compare two remote sensing data sources for the purposes of biodiversity prediction: MERRA climate reanalysis data and AMSR-E, a pure remote sensing data source. We use these data to generate novel temperature-based bioclimatic information and to model the distributions of 20 species of vertebrates endemic to four regions of South America: Amazonia, the Atlantic Forest, the Cerrado and Patagonia. We compare the bioclimatic data sets derived from MERRA and AMSR-E information with in situ station data and contrast species distribution models based on these two products to models built with WorldClim. Surface temperature estimates provided by MERRA and AMSR-E showed warm temperature biases relative to the in situ data fields, but the reliability of these data sets varied in geographic space. Species distribution models derived from the MERRA data performed equally well (in Cerrado, Amazonia and Patagonia) or better (Atlantic Forest) than models built with the WorldClim data. In contrast, the performance of models constructed with the AMSR-E data was similar to (Amazonia, Atlantic Forest, Cerrado) or worse than (Patagonia) that of models built with WorldClim data. Whereas this initial comparison assessed only temperature fields, efforts to estimate precipitation from remote sensing information hold great promise; furthermore, other environmental data sets with higher spatial and temporal fidelity may improve upon these results.

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, sub-surface 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 CH4 emissions recorded by intensive in situ observations across the domain, in which very little CH4 is emitted north of 60° N, it was necessary to (a) account for CH4 emissions from unsaturated wetlands and (b) use spatially varying methane model parameters that reduced estimated CH4 emissions in the northern (permafrost) half of the domain (and/or account for lower CH4 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 global methane (CH4) budget is becoming an increasingly important component for managing realistic pathways to mitigate climate change. This relevance, due to a shorter atmospheric lifetime and a stronger warming potential than carbon dioxide, is challenged by the still unexplained changes of atmospheric CH4 over the past decade. Emissions and concentrations of CH4 are continuing to increase, making CH4 the second most important human-induced greenhouse gas after carbon dioxide. Two major difficulties in reducing uncertainties come from the large variety of diffusive CH4 sources that overlap geographically, and from the destruction of CH4 by the very short-lived hydroxyl radical (OH). To address these difficulties, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to synthesize and stimulate research on the methane cycle, and producing regular (similar to biennial) updates of the global methane budget. This consortium includes atmospheric physicists and chemists, biogeochemists of surface and marine emissions, and socio-economists who study anthropogenic emissions. Following Kirschke et al. (2013), we propose here the first version of a living review paper that integrates results of top-down studies (exploiting atmospheric observations within an atmospheric inverse-modelling framework) and bottom-up models, inventories and data-driven approaches (including process-based models for estimating land surface emissions and atmospheric chemistry, and inventories for anthropogenic emissions, data-driven extrapolations). For the 2003-2012 decade, global methane emissions are estimated by top-down inversions at 558 TgCH(4) yr(-1), range 540-568. About 60% of global emissions are anthropogenic (range 50-65 %). Since 2010, the bottom-up global emission inventories have been closer to methane emissions in the most carbon-intensive Representative Concentrations Pathway (RCP8.5) and higher than all other RCP scenarios. Bottom-up approaches suggest larger global emissions (736 TgCH(4) yr(-1), range 596-884) mostly because of larger natural emissions from individual sources such as inland waters, natural wetlands and geological sources. Considering the atmospheric constraints on the top-down budget, it is likely that some of the individual emissions reported by the bottom-up approaches are overestimated, leading to too large global emissions. Latitudinal data from top-down emissions indicate a predominance of tropical emissions (similar to 64% of the global budget, amp;lt;30 degrees N) as compared to mid (similar to 32 %, 30-60 degrees N) and high northern latitudes (similar to 4 %, 60-90 degrees N). Top-down inversions consistently infer lower emissions in China (similar to 58 TgCH(4) yr(-1), range 51-72, -14 %) and higher emissions in Africa (86 TgCH(4) yr(-1), range 73-108, + 19 %) than bottom-up values used as prior estimates. Overall, uncertainties for anthropogenic emissions appear smaller than those from natural sources, and the uncertainties on source categories appear larger for top-down inversions than for bottom-up inventories and models. The most important source of uncertainty on the methane budget is attributable to emissions from wetland and other inland waters. We show that the wetland extent could contribute 30-40% on the estimated range for wetland emissions. Other priorities for improving the methane budget include the following: (i) the development of process-based models for inland-water emissions, (ii) the intensification of methane observations at local scale (flux measurements) to constrain bottom-up land surface models, and at regional scale (surface networks and satellites) to constrain top-down inversions, (iii) improvements in the estimation of atmospheric loss by OH, and (iv) improvements of the transport models integrated in top-down inversions. The data presented here can be downloaded from the Carbon Dioxide Information Analysis Center (http://doi.org/10.3334/CDIAC/GLOBAL_METHANE_BUDGET_2016_V1.1) and the Global Carbon Project. Funding Agencies|Swiss National Science Foundation; NASA [NNX14AF93G, NNX14AO73G]; National Environmental Science Program - Earth Systems and Climate Change Hub; European Commission [283576, 633080]; ESA Climate Change Initiative Greenhouse Gases Phase 2 project; US Department of Energy, BER [DE-AC02-05CH11231]; FAO member countries; Environment Research and Technology Development Fund of the Ministry of the Environment, Japan [2-1502]; ERC [322998]; NERC [NE/J00748X/1]; Swedish Research Council VR; Research Council of Norway [244074]; NSF [1243232, 1243220]; National Science and Engineering Research Council of Canada (NSERC); Chinas QianRen Program; CSIRO Australia; Australian Bureau of Meteorology; Australian Institute of Marine Science; Australian Antarctic Division; NOAA USA; Meteorological Service of Canada; National Aeronautic and Space Administration (NASA) [NAG5-12669, NNX07AE89G, NNX11AF17G, NNX07AE87G, NNX07AF09G, NNX11AF15G, NNX11AF16G]; Department of Energy and Climate Change (DECC, UK) [GA01081]; Commonwealth Scientific and Industrial Research Organization (CSIRO Australia); Bureau of Meteorology (Australia); Joint DECC/Defra Met Office Hadley Centre Climate Programme [GA01101]

Wetland emissions remain one of the principal sources of uncertainty in the global atmospheric methane (CH4) budget, largely due to poorly constrained process controls on CH4 production in waterlogged soils. Process-based estimates of global wetland CH4 emissions and their associated uncertainties can provide crucial prior information for model-based top-down CH4 emission estimates. Here we construct a global wetland CH4 emission model ensemble for use in atmospheric chemistry and transport models. Our 0.5° × 0.5° resolution model ensemble is based on satellite-derived surface water extent and precipitation re-analyses, nine heterotrophic respiration simulations (eight carbon cycle models and a data-constrained terrestrial carbon cycle analysis) and three temperature parameterizations for the period 2009–2010; an extended ensemble subset – based solely on precipitation and the data-constrained terrestrial carbon cycle analysis – is derived for the period 2001–2015. We incorporate the mean of the full and extended model ensembles into GEOS-Chem and compare model against surface measurements of atmospheric CH4; model performance (site-level and zonal mean anomaly residuals) compares favourably against published wetland CH4 emissions scenarios. We find that uncertainties in carbon decomposition rates and wetland extent together account for more than 80 % of the primary uncertainty in the timing, magnitude and seasonal variability of wetland CH4 emissions, although uncertainty in the temperature CH4:C dependence is a significant contributor to seasonal variations in mid-latitude wetland CH4 emissions. The combination of satellite, carbon cycle models and temperature dependence parameterizations provides a physically informed structural a priori uncertainty critical for top-down estimates of wetland CH4 fluxes: specifically, our ensemble can provide enhanced information on the prior CH4 emissions uncertainty and the error covariance structure, as well as a means for using posterior flux estimates and their uncertainties to quantitatively constrain global wetland CH4 emission biogeochemical process controls.

Surface water inundation in the Arctic–Boreal region is dynamic and strongly influences land-atmosphere water, energy and carbon (CO 2 , CH 4 ) fluxes, and potential feedbacks to climate change. Here we report on recent (2003–2010) surface inundation patterns across the Arctic–Boreal region (≥ 50°N) and within major permafrost (PF) zones detected using satellite passive microwave remote sensing retrievals of daily fractional open water ( Fw ) cover from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E). The AMSR-E Fw (25-km resolution) maps reflect strong microwave sensitivity to sub-grid scale open water variability and compare favorably (0.71 ≤ R 2 ≤ 0.84) with alternative, static Fw maps derived from finer scale (30-m to 250-m resolution) Landsat, MODIS and SRTM radar (MOD44W) data. The AMSR-E retrievals show dynamic seasonal and annual variability in surface inundation that is unresolved in the static Fw maps. The AMSR-E Fw record also corresponds strongly (0.71 ≤ R ≤ 0.87) with regional wet/dry cycles inferred from basin discharge records. An AMSR-E algorithm sensitivity analysis shows a conservative estimate of Fw retrieval uncertainty (RMSE) within ± 4.1% for effective resolution of regional inundation patterns and seasonal to annual variability. A regional trend analysis of the 8-year AMSR-E record shows no significant Arctic–Boreal region wide Fw trend for the period, and instead reveals contrasting inundation changes within different PF zones. Widespread Fw wetting is detected within continuous (92% of grid cells with significant trend; p Fw drying trends. These results are consistent with previous studies showing evidence of contrasting regional inundation patterns linked to PF degradation and associated changes to surface hydrology under recent climate warming.

The mapping of the predominant freeze/thaw state of the landscape is one of the main objectives of the National Aeronautics and Space Administration's proposed Soil Moisture Active Passive (SMAP) mission. This study applies Alaska Ecological Transect (ALECTRA) biophysical network temperature measurements and satellite radar scatterometer data from the Quick Scatterometer (QuikSCAT) to evaluate some of the validation issues regarding the planned SMAP freeze/thaw measurements. Although the QuikSCAT data are acquired at Ku-band frequency, rather than at the L-band frequency of the proposed SMAP instrument, QuikSCAT data do provide a high temporal fidelity over the ALECTRA sites, similar to SMAP. The results of this study show that multiple temperature measurements representative of individual landscape components (soil, snow cover, vegetation, and atmosphere) covering different types of terrain within the satellite field of view are important for understanding the freeze/thaw process and the aggregate radar backscatter response to that process. The backscatter temporal dynamics and relative contribution of the freeze/thaw state of these landscape elements to radar signal vary with land cover, seasonal weather, and climate conditions.

We used a process-based model to examine the roles 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, sub-surface 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 CH4 emissions recorded by intensive in situ observations across the domain, in which very little CH4 is emitted north of 60° N, it was necessary to (a) account for CH4 emissions from unsaturated wetlands and (b) use a methane model parameter set that reduced estimated CH4 emissions in the northern half of the domain. 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 calibration and validation of the freeze/thaw product of NASA's proposed L-band SMAP (Soil Moisture Active and Passive) radar and radiometer mission requires execution of a strategy for characterization of thermal regime of the relevant landscape elements in terms of freeze/thaw state and the associated relationship to the microwave remote sensing signature. The goal of this study is to improve the understanding of the L-band radar backscatter processes over boreal landscapes by comparing ALOS PALSAR high resolution L-band backscatter images with Ku-band backscatter from the SeaWinds QuikSCAT scatterometer and C-, X- and Ka-band brightness temperatures from the Aqua AMSR-E radiometer. The results show that landscape elements driving the L-band backscatter are different from those at higher (Ku-band) frequencies and establishment of an optimal validation strategy for SMAP requires investigation of L-band measurements at spatial scales and temporal fidelity commensurate with landscape freeze/thaw variability.

The mapping of freeze/thaw state of the landscape is one of the main objectives of NASA's upcoming SMAP (Soil Moisture Active and Passive) mission. This study applies ALECTRA (Alaska Ecological Transect) biophysical network and QuikSCAT scatterometer data to evaluate some of the validation issues regarding the SMAP freeze/thaw measurements. Although the QuikSCAT data is at Ku-band frequency, rather than the L-band of the SMAP instrument, the data is utilized due to its uniquely high temporal resolution over the ALECTRA sites. The results show that multiple temperature measurements representative of individual landscape (soil, snow cover, vegetation and atmosphere) elements and spatial heterogeneity within the satellite field-of-view are important for understanding the radar backscatter process and aggregate freeze/thaw signal. The backscatter temporal dynamics and relative contribution of these landscape elements to the freeze-thaw signal varies with land cover type, seasonal weather and climate conditions.

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.

Aggregate annual discharge from the six largest Arctic-draining Eurasian rivers achieved an all-time record high in 2007, accentuating a long-term upward trend that argues for intensification of the Arctic hydrologic cycle. This record discharge was due in part to strong positive anomalies in late winter snow water equivalent across much of northern Eurasia. These anomalies arose in response to an unusual pattern of atmospheric circulation in late 2006 and early 2007, characterized by an extreme northeastward extension of the Icelandic Low and a contraction of the Siberian High. Positive net precipitation anomalies then continued into summer, further contributing to discharge.