Development of a global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

Abstract. Seasonal and interannual variations in global wetland area is a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary with wetland definition, causing substantial disagreement and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed a global Wetland Area and Dynamics for Methane Modeling (WAD2M) dataset at ~25 km resolution at equator (0.25 arc-degree) at monthly time-step for 2000–2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at coarse resolution (~25 km) with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We exclude all permanent water bodies (e.g. lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0 million km2 (Mkm2), which can be separated into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm2, seasonally inundated wetlands at 4.0 Mkm2 (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm2 (long-term maximum minus mean annual maximum). WAD2M has good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Lowland Basin and West Siberian Lowlands, with high Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetlands products. By evaluating the temporal variation of WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño-Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH4 fluxes for global-scale land surface modeling. The dataset can be found at http://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).

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Development of the global dataset of Wetland Area and Dynamics for Methane Modeling (WAD2M)

Earth System Science Data ◽

10.5194/essd-13-2001-2021 ◽

2021 ◽

Vol 13 (5) ◽

pp. 2001-2023

Author(s):

Zhen Zhang ◽

Etienne Fluet-Chouinard ◽

Katherine Jensen ◽

Kyle McDonald ◽

Gustaf Hugelius ◽

...

Keyword(s):

Land Surface ◽

Large Scale ◽

Amazon Basin ◽

Southern Oscillation ◽

Microwave Remote Sensing ◽

Annual Maximum ◽

Time Step ◽

Wetland Area ◽

Wetland Extent

Abstract. Seasonal and interannual variations in global wetland area are a strong driver of fluctuations in global methane (CH4) emissions. Current maps of global wetland extent vary in their wetland definition, causing substantial disagreement between and large uncertainty in estimates of wetland methane emissions. To reconcile these differences for large-scale wetland CH4 modeling, we developed the global Wetland Area and Dynamics for Methane Modeling (WAD2M) version 1.0 dataset at a ∼ 25 km resolution at the Equator (0.25∘) at a monthly time step for 2000–2018. WAD2M combines a time series of surface inundation based on active and passive microwave remote sensing at a coarse resolution with six static datasets that discriminate inland waters, agriculture, shoreline, and non-inundated wetlands. We excluded all permanent water bodies (e.g., lakes, ponds, rivers, and reservoirs), coastal wetlands (e.g., mangroves and sea grasses), and rice paddies to only represent spatiotemporal patterns of inundated and non-inundated vegetated wetlands. Globally, WAD2M estimates the long-term maximum wetland area at 13.0×106 km2 (13.0 Mkm2), which can be divided into three categories: mean annual minimum of inundated and non-inundated wetlands at 3.5 Mkm2, seasonally inundated wetlands at 4.0 Mkm2 (mean annual maximum minus mean annual minimum), and intermittently inundated wetlands at 5.5 Mkm2 (long-term maximum minus mean annual maximum). WAD2M shows good spatial agreements with independent wetland inventories for major wetland complexes, i.e., the Amazon Basin lowlands and West Siberian lowlands, with Cohen's kappa coefficient of 0.54 and 0.70 respectively among multiple wetland products. By evaluating the temporal variation in WAD2M against modeled prognostic inundation (i.e., TOPMODEL) and satellite observations of inundation and soil moisture, we show that it adequately represents interannual variation as well as the effect of El Niño–Southern Oscillation on global wetland extent. This wetland extent dataset will improve estimates of wetland CH4 fluxes for global-scale land surface modeling. The dataset can be found at https://doi.org/10.5281/zenodo.3998454 (Zhang et al., 2020).

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Spatiotemporal Comparison and Validation of Three Global-Scale Fractional Vegetation Cover Products

Remote Sensing ◽

10.3390/rs11212524 ◽

2019 ◽

Vol 11 (21) ◽

pp. 2524 ◽

Cited By ~ 2

Author(s):

Duanyang Liu ◽

Kun Jia ◽

Xiangqin Wei ◽

Mu Xia ◽

Xiwang Zhang ◽

...

Keyword(s):

Vegetation Cover ◽

Land Surface ◽

Large Scale ◽

High Spatial Resolution ◽

Remote Sensing Data ◽

Global Scale ◽

Glass Product ◽

Fractional Vegetation Cover ◽

Temporal Profiles

Fractional vegetation cover (FVC) is an important parameter for many environmental and ecological models. Large-scale and long-term FVC products are critical for various applications. Currently, several global-scale FVC products have been generated with remote sensing data, such as VGT bioGEOphysical product Version 2 (GEOV2), PROBA-V bioGEOphysical product Version 3 (GEOV3) and Global LAnd Surface Satellite (GLASS) FVC products. However, studies comparing and validating these global-scale FVC products are rare. Therefore, in this study, the performances of three global-scale time series FVC products, including the GEOV2, GEOV3, and GLASS FVC products, are investigated to assess their spatial and temporal consistencies. Furthermore, reference FVC data generated from high-spatial-resolution data are used to directly evaluate the accuracy of these FVC products. The results show that these three FVC products achieve general agreements in terms of spatiotemporal consistencies over most regions. In addition, the GLASS and GEOV2 FVC products have reliable spatial and temporal completeness, whereas the GEOV3 FVC product contains much missing data over high-latitude regions, especially during wintertime. Furthermore, the GEOV3 FVC product presents higher FVC values than GEOV2 and GLASS FVC products over the equator. The main differences between the GEOV2 and GLASS FVC products occur over deciduous forests, for which the GLASS product presents slightly higher FVC values than the GEOV2 product during wintertime. Finally, temporal profiles of the GEOV2 and GLASS FVC products show better consistency than the GEOV3 FVC product, and the GLASS FVC product presents more reliable accuracy (R2 = 0.7878, RMSE = 0.1212) compared with the GEOV2 (R2 = 0.5798, RMSE = 0.1921) and GEOV3 (R2 = 0.7744, RMSE = 0.2224) FVC products over these reference FVC data.

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Cities Exacerbate Climate Warming

Urban Science ◽

10.3390/urbansci5010027 ◽

2021 ◽

Vol 5 (1) ◽

pp. 27

Author(s):

Lahouari Bounoua ◽

Kurtis Thome ◽

Joseph Nigro

Keyword(s):

Surface Temperature ◽

Land Surface ◽

Large Scale ◽

Climate Models ◽

Warming Trend ◽

Climate Mitigation ◽

Temperature Changes ◽

Surface Warming ◽

The Us

Urbanization is a complex land transformation not explicitly resolved within large-scale climate models. Long-term timeseries of high-resolution satellite data are essential to characterize urbanization within land surface models and to assess its contribution to surface temperature changes. The potential for additional surface warming from urbanization-induced land use change is investigated and decoupled from that due to change in climate over the continental US using a decadal timescale. We show that, aggregated over the US, the summer mean urban-induced surface temperature increased by 0.15 °C, with a warming of 0.24 °C in cities built in vegetated areas and a cooling of 0.25 °C in cities built in non-vegetated arid areas. This temperature change is comparable in magnitude to the 0.13 °C/decade global warming trend observed over the last 50 years caused by increased CO2. We also show that the effect of urban-induced change on surface temperature is felt above and beyond that of the CO2 effect. Our results suggest that climate mitigation policies must consider urbanization feedback to put a limit on the worldwide mean temperature increase.

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Explaining the trends and variability in the United States tornado records using climate teleconnections and shifts in observational practices

Scientific Reports ◽

10.1038/s41598-021-81143-5 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Niloufar Nouri ◽

Naresh Devineni ◽

Valerie Were ◽

Reza Khanbilvardi

Keyword(s):

Population Density ◽

Large Scale ◽

Southern Oscillation ◽

The United States ◽

Anthropogenic Factors ◽

Climate Variables ◽

Term Trend ◽

Cyclical Variability ◽

Long Term Trend

AbstractThe annual frequency of tornadoes during 1950–2018 across the major tornado-impacted states were examined and modeled using anthropogenic and large-scale climate covariates in a hierarchical Bayesian inference framework. Anthropogenic factors include increases in population density and better detection systems since the mid-1990s. Large-scale climate variables include El Niño Southern Oscillation (ENSO), Southern Oscillation Index (SOI), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), and Atlantic Multi-decadal Oscillation (AMO). The model provides a robust way of estimating the response coefficients by considering pooling of information across groups of states that belong to Tornado Alley, Dixie Alley, and Other States, thereby reducing their uncertainty. The influence of the anthropogenic factors and the large-scale climate variables are modeled in a nested framework to unravel secular trend from cyclical variability. Population density explains the long-term trend in Dixie Alley. The step-increase induced due to the installation of the Doppler Radar systems explains the long-term trend in Tornado Alley. NAO and the interplay between NAO and ENSO explained the interannual to multi-decadal variability in Tornado Alley. PDO and AMO are also contributing to this multi-time scale variability. SOI and AO explain the cyclical variability in Dixie Alley. This improved understanding of the variability and trends in tornadoes should be of immense value to public planners, businesses, and insurance-based risk management agencies.

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A high-resolution global dataset of topographic index values for use in large-scale hydrological modelling

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-11-6139-2014 ◽

2014 ◽

Vol 11 (6) ◽

pp. 6139-6166 ◽

Cited By ~ 2

Author(s):

T. R. Marthews ◽

S. J. Dadson ◽

B. Lehner ◽

S. Abele ◽

N. Gedney

Keyword(s):

High Resolution ◽

Land Surface ◽

Large Scale ◽

Global Scale ◽

Surface Fluxes ◽

Data Sets ◽

Topographic Index ◽

Surface Water Flow ◽

Land Surface Water ◽

Compound Topographic Index

Abstract. Modelling land surface water flow is of critical importance for simulating land-surface fluxes, predicting runoff and water table dynamics and for many other applications of Land Surface Models. Many approaches are based on the popular hydrology model TOPMODEL, and the most important parameter of this model is the well-knowntopographic index. Here we present new, high-resolution parameter maps of the topographic index for all ice-free land pixels calculated from hydrologically-conditioned HydroSHEDS data sets using the GA2 algorithm. At 15 arcsec resolution, these layers are 4× finer than the resolution of the previously best-available topographic index layers, the Compound Topographic Index of HYDRO1k (CTI). In terms of the largest river catchments occurring on each continent, we found that in comparison to our revised values, CTI values were up to 20% higher in e.g. the Amazon. We found the highest catchment means were for the Murray-Darling and Nelson-Saskatchewan rather than for the Amazon and St. Lawrence as found from the CTI. We believe these new index layers represent the most robust existing global-scale topographic index values and hope that they will be widely used in land surface modelling applications in the future.

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Oceanography of the New Zealand subantarctic region

10.26686/wgtn.17009291 ◽

2021 ◽

Author(s):

◽

Aitana Forcén-Vázquez

Keyword(s):

New Zealand ◽

Interannual Variability ◽

Water Column ◽

Large Scale ◽

Southern Oscillation ◽

Complex Structure ◽

Positive Trend ◽

Intermediate Water ◽

Subantarctic Region

Subantarctic New Zealand is an oceanographycally dynamic region with the Subtropical Front (STF) to the north and the Subantarctic Front (SAF) to the south. This thesis investigates the ocean structure of the Campbell Plateau and the surrounding New Zealand subantarctic, including the spatial, seasonal, interannual and longer term variability over the ocean properties, and their connection to atmospheric variability using a combination of in-situ oceanographic measurements and remote sensing data. The spatial and seasonal oceanographic structure in the New Zealand subantarctic region was investigated by analysing ten high resolution Conductivity Temperature and Depth (CTD) datasets, sampled during oceanographic cruises from May 1998 to February 2013. Position of fronts, water mass structure and changes over the seasons show a complex structure around the Campbell Plateau combining the influence of subtropical and subantarctic waters. The spatial and interannual variability on the Campbell Plateau was described by analysing approximately 70 low resolution CTD profiles collected each year in December between 2002 and 2009. Conservative temperature and absolute salinity profiles reveal high variability in the upper 200m of the water column and a homogeneous water column from 200 to 600m depth. Temperature variability of about 0.7 °C, on occasions between consecutive years, is observed down to 900m depth. The presence of Subantarctic Mode Water (SAMW) on the Campbell Plateau is confirmed and Antarctic Intermediate Water (AAIW) reported for the first time in the deeper regions around the edges of the plateau. Long-term trends and variability over the Campbell Plateau were investigated by analysing satellite derived Sea Level Anomalies (SLA) and Sea Surface Temperature (SST) time series. Links to large scale atmospheric processes are also explored through correlation with the Southern Oscillation Index (SOI) and Southern Annular Mode (SAM). SST shows a strong seasonality and interannual variability which is linked to local winds, but no significant trend is found. The SLA over the Campbell Plateau has increased at a rate of 5.2 cm decade⁻¹ in the last two decades. The strong positive trend in SLA appears to be a combination of the response of the ocean to wind stress curl (Ekman pumping), thermal expansion and ocean mass redistribution via advection amongst others. These results suggest that the variability on the Campbell Plateau is influenced by the interaction of the STF and the SAF. The STF influence reaches the limit of the SAF over the western Campbell Plateau and the SAF influence extends all around the plateau. Results also suggest different connections between the plateau with the surrounding oceans, e.g., along the northern edge with the Bounty Trough and via the southwest edge with the SAF. A significant correlation with SOI and little correlation with SAM suggest a stronger response to tropically driven processes in the long-term variability on the Campbell Plateau. The results of this thesis provide a new definitive assessment of the circulation, water masses and variability of the Campbell Plateau on mean, annual, and interannual time scales which will support research in other disciplines such as palaeoceanography, fisheries management and climate.

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Global long-term land surface temperature for NOAA AVHRR: extension from 1981-2000 to 1981-2020

10.5194/egusphere-egu21-14373 ◽

2021 ◽

Author(s):

Jin Ma ◽

Ji Zhou

Keyword(s):

Climate Change ◽

Surface Temperature ◽

Land Surface Temperature ◽

Land Surface ◽

Global Scale ◽

Mean Bias Error ◽

Bias Error ◽

Important Indicator ◽

Noaa Avhrr

As an important indicator of land-atmosphere energy interaction, land surface temperature (LST) plays an important role in the research of climate change, hydrology, and various land surface processes. Compared with traditional ground-based observation, satellite remote sensing provides the possibility to retrieve LST more efficiently over a global scale. Since the lack of global LST before, Ma et al., (2020) released a global 0.05 &#215;0.05&#160; long-term (1981-2000) LST based on NOAA-7/9/11/14 AVHRR. The dataset includes three layers: (1) instantaneous LST, a product generated based on an ensemble of several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST at 14:30 solar time; and (3) monthly averages of ODC LST. To meet the requirement of the long-term application, e.g. climate change, the period of the LST is extended from 1981-2000 to 1981-2020 in this study. The LST from 2001 to 2020 are retrieved from NOAA-16/18/19 AVHRR with the same algorithm for NOAA-7/8/11/14 AVHRR. The train and test results based on the simulation data from SeeBor and TIGR atmospheric profiles show that the accuracy of the RF-SWA method for the three sensors is consistent with the previous four sensors, i.e. the mean bias error and standard deviation less than 0.10 K and 1.10 K, respectively, under the assumption that the maximum emissivity and water vapor content uncertainties are 0.04 and 1.0 g/cm2, respectively. The preliminary validation against in-situ LST also shows a similar accuracy, indicating that the accuracy of LST from 1981 to 2020 are consistent with each other. In the generation code, the new LST has been improved in terms of land surface emissivity estimation, identification of cloud pixel, and the ODC method in order to generate a more reliable LST dataset. Up to now, the new version LST product (1981-2020) is under generating and will be released soon in support of the scientific research community.

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Comparison of generalized non-data-driven lake and reservoir routing models for global-scale hydrologic forecasting of reservoir outflow at diurnal time steps

Hydrology and Earth System Sciences ◽

10.5194/hess-24-2711-2020 ◽

2020 ◽

Vol 24 (5) ◽

pp. 2711-2729 ◽

Cited By ~ 2

Author(s):

Joseph L. Gutenson ◽

Ahmad A. Tavakoly ◽

Mark D. Wahl ◽

Michael L. Follum

Keyword(s):

Large Scale ◽

Spatial Scales ◽

Model Performance ◽

Global Scale ◽

Army Corps ◽

Time Step ◽

Large Spatial Scale ◽

Reservoir Routing ◽

Routing Methods ◽

Improved Model

Abstract. Large-scale hydrologic forecasts should account for attenuation through lakes and reservoirs when flow regulation is present. Globally generalized methods for approximating outflow are required but must contend with operational complexity and a dearth of information on dam characteristics at global spatial scales. There is currently no consensus on the best approach for approximating reservoir release rates in large spatial scale hydrologic forecasting, particularly at diurnal time steps. This research compares two parsimonious reservoir routing methods at daily steps: Döll et al. (2003) and Hanasaki et al. (2006). These reservoir routing methods have been previously implemented in large-scale hydrologic modeling applications and have been typically evaluated seasonally. These routing methods are compared across 60 reservoirs operated by the U.S. Army Corps of Engineers. The authors vary empirical coefficients for both reservoir routing methods as part of a sensitivity analysis. The method proposed by Döll et al. (2003) outperformed that presented by Hanasaki et al. (2006) at a daily time step and improved model skill over most run-of-the-river conditions. The temporal resolution of the model influences model performances. The optimal model coefficients varied across the reservoirs in this study and model performance fluctuates between wet years and dry years, and for different configurations such as dams in series. Overall, the method proposed by Döll et al. (2003) could enhance large-scale hydrologic forecasting, but can be subject to instability under certain conditions.

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Long-Term Climate and Derived Surface Hydrology and Energy Flux Data for Mexico: 1925–2004

Journal of Climate ◽

10.1175/jcli4086.1 ◽

2007 ◽

Vol 20 (9) ◽

pp. 1936-1946 ◽

Cited By ~ 70

Author(s):

Chunmei Zhu ◽

Dennis P. Lettenmaier

Keyword(s):

Soil Moisture ◽

North American ◽

Land Surface ◽

Surface Fluxes ◽

The Other ◽

Shortwave Radiation ◽

Infiltration Capacity ◽

Time Step ◽

Downward Longwave Radiation

Abstract Studying the role of land surface conditions in the Mexican portion of the North American monsoon system (NAMS) region has been a challenge due to the paucity of long-term observations. A long-term gridded observation-based climate dataset suitable for forcing land surface models, as well as model-derived land surface states and fluxes for a domain consisting of all of Mexico, is described. The datasets span the period of January 1925–October 2004 at 1/8° spatial resolution at a subdaily (3 h) time step. The simulated runoff matches the observations plausibly over most of the 14 small river basins spanning all of Mexico, which suggests that long-term mean evapotranspiration is realistically reproduced. On this basis, and given the physically based model parameterizations of soil moisture and energy fluxes, the other surface fluxes and state variables such as soil moisture should be represented reasonably. In addition, a comparison of the surface fluxes from this study is performed with North American Regional Reanalysis (NARR) data on a seasonal mean basis. The results indicate that downward shortwave radiation is generally smaller than in the NARR data, especially in summer. Net radiation, on the other hand, is somewhat larger in the Variable Infiltration Capacity (VIC) hydrological model than in the NARR data for much of the year over much of the domain. The differences in radiative and turbulent fluxes are attributed to (i) the parameterization used in the VIC forcings for solar and downward longwave radiation, which links them to the daily temperature and temperature range, and (ii) differences in the land surface parameterizations used in VIC and the NCEP–Oregon State University–U.S. Air Force–NWS/Hydrologic Research Lab (Noah) land scheme used in NARR.

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Evaluating model-simulated and satellite-derived SM using in situ observations under different environment conditions in China

10.5194/egusphere-egu2020-2819 ◽

2020 ◽

Author(s):

Yuanyuan Wang ◽

Guicai Li

Keyword(s):

Land Surface ◽

Large Scale ◽

Microwave Remote Sensing ◽

Ground Truth ◽

Original Data ◽

Observation Data ◽

Monitoring Networks ◽

The Future ◽

In Situ Observations

Soil moisture (SM) is a key variable in understanding the climate system through its controls on the land surface energy and water budget. Large scale SM products have become increasingly available thanks to development in microwave remote sensing and land surface modeling. Comprehensive assessments on the reliability of satellite-derived and model-simulated SM products are essential for their improvement and application. In this research, the active, passive and combined Climate Change Initiative (CCI V04.2) SM products and the China Land Data Assimilation System (CLDAS V2.0) SM products were evaluated by comparing with in situ observed data over three networks in China: Hebi, Naqu and Heihe. The three sites have different environmental conditions and sensor densities, providing observations covering more than 2 years. Four statistic scores were calculated: R (considering both original data and anomalies), Bias, RMSE, ubRMSE. TC (Triple Collocation) analysis was also carried out in which uncertainties in observations are taken into account. Results indicate that the performance of the two SM products varies between the monitoring networks. For Naqu site, both products show good performance, with CCI-SM showing slightly higher R and lower ubRMSE. For Hebi site, CLDAS-SM performs better than CCI-SM, whereas for Heihe site, CLDAS-SM performs worse than CCI-SM. The expected uncertainty (0.04 m3/m3) can be achieved in Naqu and Heihe site by CCI-SM, and in Hebi and Naqu site by CLDAS-SM, which is quite encouraging. The two products agree in terms of sign of the Bias value, which is positive in Hebi and negative in Naqu and Heihe. Among all the three networks, Heihe site exhibits the lowest accuracy due to its complicated terrain and heterogeneous land surface. Ranom of CLDAS-SM in Heihe is close to 0, indicating its inability to capture short term variability. TC results reveal that for Naqu site the observation data have quite good qualities, while for Hebi site CLDAS-SM is more approximate to &#8216;ground truth&#8217; than in situ observations, suggesting a refinement of network maybe needed in the future. Overall, the two products are complementary. CLDAS-SM performs better in populated area (e.g., Hebi) where meteorological forcing is more accurate and CCI-SM performs better in remote areas (Naqu, Heihe) where RFI is usually low. More reliable validation networks are needed in the future to comprehensively understand the advantages and disadvantage of the two SM products in China.

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