Investigating Wetland and Nonwetland Soil Methane Emissions and Sinks Across the Contiguous United States Using a Land Surface Model

Abstract. In the global methane budget, the largest natural source is attributed to wetlands that encompass all ecosystems composed of waterlogged or inundated ground, capable of methane production. Among them, northern peatlands that store large amounts of soil organic carbon have been functioning, since the end of the last glaciation period, as long-term sources of methane (CH4) and are one of the most significant methane sources among wetlands. To reduce global methane budget uncertainties, it is of significance to understand processes driving methane production and fluxes in northern peatlands. A methane model that features methane production and transport by plants, ebullition process and diffusion in soil, oxidation to CO2 and CH4 fluxes to the atmosphere has been embedded in the ORCHIDEE-PEAT land surface model which includes an explicit representation of northern peatlands. This model, ORCHIDEE-PCH4 was calibrated and evaluated on 14 peatland sites distributed on both Eurasian and American continents in the northern boreal and temperate regions. Data assimilation approaches were employed to optimized parameters at each site and at all sites simultaneously. Results show that, in ORCHIDEE-PCH4, methanogenesis is sensitive to temperature and substrate availability over the top 75 cm of soil depth. Methane emissions estimated using single site optimization (SSO) of model parameters are underestimated by 9 g CH4 m−2 year−1 on average (i.e. 50 % higher than the site average of yearly methane emissions). While using the multi-sites optimization (MSO), methane emissions are overestimated by 5 g CH4 m−2 year−1 on average across all investigated sites (i.e. 37 % lower than the site average of yearly methane emissions).

Download Full-text

Assimilation of Remotely Sensed Leaf Area Index into the Noah-MP Land Surface Model: Impacts on Water and Carbon Fluxes and States over the Continental United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-18-0237.1 ◽

2019 ◽

Vol 20 (7) ◽

pp. 1359-1377 ◽

Cited By ~ 14

Author(s):

Sujay V. Kumar ◽

David M. Mocko ◽

Shugong Wang ◽

Christa D. Peters-Lidard ◽

Jordan Borak

Keyword(s):

United States ◽

Leaf Area Index ◽

Leaf Area ◽

Land Surface ◽

Land Surface Model ◽

Carbon Fluxes ◽

Surface Model ◽

Vegetation Changes ◽

Area Index ◽

Terrestrial Water

Abstract Accurate representation of vegetation states is required for the modeling of terrestrial water–energy–carbon exchanges and the characterization of the impacts of natural and anthropogenic vegetation changes on the land surface. This study presents a comprehensive evaluation of the impact of assimilating remote sensing–based leaf area index (LAI) retrievals over the continental United States in the Noah-MP land surface model, during a time period of 2000–17. The results demonstrate that the assimilation has a beneficial impact on the simulation of key water budget terms, such as soil moisture, evapotranspiration, snow depth, terrestrial water storage, and streamflow, when compared with a large suite of reference datasets. In addition, the assimilation of LAI is also found to improve the carbon fluxes of gross primary production (GPP) and net ecosystem exchange (NEE). Most prominent improvements in the water and carbon variables are observed over the agricultural areas of the United States, where assimilation improves the representation of vegetation seasonality impacted by cropping schedules. The systematic, added improvements from assimilation in a configuration that employs high-quality boundary conditions highlight the significant utility of LAI data assimilation in capturing the impacts of vegetation changes.

Download Full-text

Simulating the Effects of Irrigation over the United States in a Land Surface Model Based on Satellite-Derived Agricultural Data

Journal of Hydrometeorology ◽

10.1175/2009jhm1116.1 ◽

2010 ◽

Vol 11 (1) ◽

pp. 171-184 ◽

Cited By ~ 149

Author(s):

Mutlu Ozdogan ◽

Matthew Rodell ◽

Hiroko Kato Beaudoing ◽

David L. Toll

Keyword(s):

United States ◽

Heat Flux ◽

Land Surface ◽

Agricultural Land ◽

Weather Prediction ◽

Land Surface Model ◽

The United States ◽

Surface Model ◽

Irrigation Scheme ◽

Ground Heat Flux

Abstract A novel method is introduced for integrating satellite-derived irrigation data and high-resolution crop-type information into a land surface model (LSM). The objective is to improve the simulation of land surface states and fluxes through better representation of agricultural land use. Ultimately, this scheme could enable numerical weather prediction (NWP) models to capture land–atmosphere feedbacks in managed lands more accurately and thus improve forecast skill. Here, it is shown that the application of the new irrigation scheme over the continental United States significantly influences the surface water and energy balances by modulating the partitioning of water between the surface and the atmosphere. In this experiment, irrigation caused a 12% increase in evapotranspiration (QLE) and an equivalent reduction in the sensible heat flux (QH) averaged over all irrigated areas in the continental United States during the 2003 growing season. Local effects were more extreme: irrigation shifted more than 100 W m−2 from QH to QLE in many locations in California, eastern Idaho, southern Washington, and southern Colorado during peak crop growth. In these cases, the changes in ground heat flux (QG), net radiation (RNET), evapotranspiration (ET), runoff (R), and soil moisture (SM) were more than 3 W m−2, 20 W m−2, 5 mm day−1, 0.3 mm day−1, and 100 mm, respectively. These results are highly relevant to continental-to-global-scale water and energy cycle studies that, to date, have struggled to quantify the effects of agricultural management practices such as irrigation. On the basis of the results presented here, it is expected that better representation of managed lands will lead to improved weather and climate forecasting skill when the new irrigation scheme is incorporated into NWP models such as NOAA’s Global Forecast System (GFS).

Download Full-text

Contribution of Meteorological Downscaling to Skill and Precision of Seasonal Drought Forecasts

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0259.1 ◽

2021 ◽

Author(s):

Ryan A. Zamora ◽

Benjamin F. Zaitchik ◽

Matthew Rodell ◽

Augusto Getirana ◽

Sujay Kumar ◽

...

Keyword(s):

United States ◽

Land Surface ◽

Land Surface Model ◽

Surface Model ◽

Drought Forecasting ◽

Surface Hydrology ◽

Seasonal Drought ◽

Earth Observing System ◽

The North ◽

Land Data Assimilation System

AbstractResearch in meteorological prediction on sub-seasonal to seasonal (S2S) timescales has seen growth in recent years. Concurrent with this, demand for seasonal drought forecasting has risen. While there is obvious synergy between these fields, S2S meteorological forecasting has typically focused on low resolution global models, while the development of drought can be sensitive to the local expression of weather anomalies and their interaction with local surface properties and processes. This suggests that downscaling might play an important role in the application of meteorological S2S forecasts to skillful forecasting of drought. Here, we apply the Generalized Analog Regression Downscaling (GARD) algorithm to downscale meteorological hindcasts from the NASA Goddard Earth Observing System (GEOS) global S2S forecast system. Downscaled meteorological fields are then applied to drive offline simulations with the Catchment Land Surface Model (CLSM) to forecast United States Drought Monitor (USDM) style drought indicators derived from simulated surface hydrology variables. We compare the representation of drought in these downscaled hindcasts to hindcasts that are not downscaled, using the North American Land Data Assimilation System Phase 2 (NLDAS-2) dataset as an observational reference. We find that downscaling using GARD improves hindcasts of temperature and temperature anomalies, but the results for precipitation are mixed and generally small. Overall, GARD downscaling led to improved hindcast skill for total drought across the Contiguous United States (CONUS), and improvements were greatest for extreme (D3) and exceptional (D4) drought categories.

Download Full-text

Joint Modeling of Crop and Irrigation in the central United States Using the Noah‐MP Land Surface Model

Journal of Advances in Modeling Earth Systems ◽

10.1029/2020ms002159 ◽

2020 ◽

Vol 12 (7) ◽

Cited By ~ 1

Author(s):

Zhe Zhang ◽

Michael Barlage ◽

Fei Chen ◽

Yanping Li ◽

Warren Helgason ◽

...

Keyword(s):

United States ◽

Land Surface ◽

Land Surface Model ◽

Joint Modeling ◽

Surface Model ◽

Central United States

Download Full-text

Evaluation of Wetland CH<sub>4</sub> in the JULES Land Surface Model Using Satellite Observations

10.5194/bg-2022-2 ◽

2022 ◽

Author(s):

Robert J. Parker ◽

Chris Wilson ◽

Edward Comyn-Platt ◽

Garry Hayman ◽

Toby R. Marthews ◽

...

Keyword(s):

Land Surface ◽

Seasonal Cycle ◽

Land Surface Model ◽

Correlation Coefficients ◽

Methane Emissions ◽

Satellite Observations ◽

Surface Model ◽

Temperature Dependency ◽

Cycle Amplitude ◽

Wetland Extent

Abstract. Wetlands are the largest natural source of methane. The ability to model the emissions of methane from natural wetlands accurately is critical to our understanding of the global methane budget and how it may change under future climate scenarios. The simulation of wetland methane emissions involves a complicated system of meteorological drivers coupled to hydrological and biogeochemical processes. The Joint UK Land Environment Simulator (JULES) is a process-based land surface model that underpins the UK Earth System Model and is capable of generating estimates of wetland methane emissions. In this study we use GOSAT satellite observations of atmospheric methane along with the TOMCAT global 3-D chemistry transport model to evaluate the performance of JULES in reproducing the seasonal cycle of methane over a wide range of tropical wetlands. By using an ensemble of JULES simulations with differing input data and process configurations, we investigate the relative importance of the meteorological driving data, the vegetation, the temperature dependency of wetland methane production and the wetland extent. We find that JULES typically performs well in replicating the observed methane seasonal cycle. We calculate correlation coefficients to the observed seasonal cycle of between 0.58 to 0.88 for most regions, however the seasonal cycle amplitude is typically underestimated (by between 1.8 ppb and 19.5 ppb). This level of performance is comparable to that typically provided by state-of-the-art data-driven wetland CH4 emission inventories. The meteorological driving data is found to be the most significant factor in determining the ensemble performance, with temperature dependency and vegetation having moderate effects. We find that neither wetland extent configuration out-performs the other but this does lead to poor performance in some regions. We focus in detail on three African wetland regions (Sudd, Southern Africa and Congo) where we find the performance of JULES to be poor and explore the reasons for this in detail. We find that neither wetland extent configuration used is sufficient in representing the wetland distribution in these regions (underestimating the wetland seasonal cycle amplitude by 11.1 ppb, 19.5 ppb and 10.1 ppb respectively, with correlation coefficients of 0.23, 0.01 and 0.31). We employ the CaMa-Flood model to explicitly represent river and floodplain water dynamics and find these JULES-CaMa-Flood simulations are capable of providing wetland extent more consistent with observations in this regions, highlighting this as an important area for future model development.

Download Full-text

GRACE Improves Seasonal Groundwater Forecast Initialization over the United States

Journal of Hydrometeorology ◽

10.1175/jhm-d-19-0096.1 ◽

2020 ◽

Vol 21 (1) ◽

pp. 59-71 ◽

Cited By ~ 6

Author(s):

Augusto Getirana ◽

Matthew Rodell ◽

Sujay Kumar ◽

Hiroko Kato Beaudoing ◽

Kristi Arsenault ◽

...

Keyword(s):

United States ◽

Data Assimilation ◽

Land Surface ◽

Land Surface Model ◽

The United States ◽

High Plains ◽

Surface Model ◽

Forecast Performance ◽

Terrestrial Water ◽

The Impact

AbstractWe evaluate the impact of Gravity Recovery and Climate Experiment data assimilation (GRACE-DA) on seasonal hydrological forecast initialization over the United States, focusing on groundwater storage. GRACE-based terrestrial water storage (TWS) estimates are assimilated into a land surface model for the 2003–16 period. Three-month hindcast (i.e., forecast of past events) simulations are initialized using states from the reference (no data assimilation) and GRACE-DA runs. Differences between the two initial hydrological condition (IHC) sets are evaluated for two forecast techniques at 305 wells where depth to water table measurements are available. Results show that using GRACE-DA-based IHC improves seasonal groundwater forecast performance in terms of both RMSE and correlation. While most regions show improvement, degradation is common in the High Plains, where withdrawals for irrigation practices affect groundwater variability more strongly than the weather variability, which demonstrates the need for simulating such activities. These findings contribute to recent efforts toward an improved U.S. drought monitoring and forecast system.

Download Full-text

Drought Variability over the Conterminous United States for the Past Century

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0158.1 ◽

2021 ◽

Author(s):

Lu Su ◽

Qian Cao ◽

Mu Xiao ◽

David M. Mocko ◽

Michael Barlage ◽

...

Keyword(s):

United States ◽

Land Surface ◽

Land Surface Model ◽

Area Coverage ◽

Surface Model ◽

Past Century ◽

Drought Duration ◽

Drought Variability ◽

Dead Vegetation ◽

Reconstruction Period

AbstractWe examine the drought variability over the Conterminous United States (CONUS) for 1915-2018 using the Noah-MP land-surface model. We examine different model options on drought reconstruction including optional representation of groundwater and dynamic vegetation phenology. Over our 104-year reconstruction period, we identify 12 great droughts that each covered at least 36% of CONUS and lasted for at least 5 months. The great droughts tend to have smaller areas when groundwater and/or dynamic vegetation are included in the model configuration. We detect a small decreasing trend in dry area coverage over CONUS in all configurations. We identify 45 major droughts in the baseline (with a dry area coverage greater than 23.6% of CONUS) that are, on average, somewhat less severe than great droughts. We find that representation of groundwater tends to increase drought duration for both great and major droughts, primarily by leading to earlier drought onset (some due to short-lived recovery from a previous drought) or later demise (groundwater anomalies lag precipitation anomalies). In contrast, representation of dynamic vegetation tends to shorten major droughts duration, primarily due to earlier drought demise ( closed stoma or dead vegetation reduces ET loss during droughts). On a regional basis, the U.S. Southwest (Southeast) has the longest (shortest) major drought durations. Consistent with earlier work, dry area coverage in all subregions except the Southwest has decreased. The effects of groundwater and dynamic vegetation vary regionally due to differences in groundwater depths (hence connectivity with the surface) and vegetation types.

Download Full-text

Using scientific workflows to calibrate an Australian land surface model (AWRA-L)

Piantadosi, J., Anderssen, R.S. and Boland J. (eds) MODSIM2013, 20th International Congress on Modelling and Simulation ◽

10.36334/modsim.2013.j9.vleeshouwer ◽

2013 ◽

Keyword(s):

Land Surface ◽

Land Surface Model ◽

Scientific Workflows ◽

Surface Model

Download Full-text