10 yrs of Improved Groundwater Table Estimates in Northern Peatlands Through Assimilation of Passive Microwave Observations into PEATCLSM

2020 ◽  
Author(s):  
Michel Bechtold ◽  
Gabrielle De Lannoy ◽  
Rolf H Reichle ◽  
Dirk Roose ◽  
Nicole Balliston ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Land Surface Model ◽  
Open Loop ◽  
Groundwater Table ◽  
Surface Model ◽  
Simulation Experiments ◽  
Peatland Hydrology ◽  
Groundwater Table Depth ◽  
L Band

<p>Groundwater table depth and peat moisture, exert a first order control on a range of biogeochemical and -physical peatland processes, and the susceptibility to peat fires. Therefore, one of the first critical measures to identify “peatlands under pressure” is the change of hydrological conditions, e.g. due to changing climatic conditions or direct “hydraulic” human influence. In this presentation, we introduce a new opportunity for the global-scale monitoring of moisture conditions in peatlands. We assimilate L-band brightness temperature (Tb) data from the Soil Moisture Ocean Salinity (SMOS) into the Catchment land surface model (CLSM) to improve the simulation of Northern peatland hydrology from 2010 through 2019. We compare four simulation experiments: two open loop and two data assimilation simulations, either using the default CLSM or a recently-developed peatland-specific adaptation of it (PEATCLSM, Bechtold et al. 2019). The assimilation system uses a spatially distributed ensemble Kalman filter to update soil moisture and groundwater table depth. The simulation experiments are evaluated against an in-situ dataset of groundwater table depth in about 20 natural and semi-natural peatlands that are large enough to be dominant in the corresponding 81-km<sup>2</sup> model grid cells. For PEATCLSM, Tb data assimilation increases the temporal Pearson correlation (R) and anomaly correlation (aR) between simulated and measured groundwater table from 0.53 and 0.38 (open-loop) to 0.58 and 0.45 (analysis), respectively. Time series comparison at monitoring sites demonstrates how the assimilation effectively corrects for remaining deficiencies in model physics and/or errors of the global meteorological data forcing the model. The generally lower coefficients of 0.30 (R) and 0.09 (aR) for the default CLSM also improve after Tb data assimilation to values of 0.39 (R) and 0.28 (aR). However, even with Tb data assimilation, the skill of CLSM remains inferior to that of PEATCLSM. The more realistic model physics of PEATCLSM are also supported by a reduction of the Tb misfits (observed Tb – forecasted Tb) over 94 % of the Northern peatland area. The temporal variance of Tb misfits is reduced by 20 % on average and is largest over the large peatland areas of the Western Siberian (25 %) and Hudson Bay Lowlands (40 %). This study demonstrates, for the first time, an improved estimation of the peatland hydrological dynamics by the assimilation of SMOS L-band brightness data into a global land surface model and suggests a new route of research focusing on the incorporation of additional satellite observations into peatland-specific modeling schemes.</p><p>Bechtold, M., De Lannoy, G.J M., Koster, R.D., Reichle, R.H., et al. (2019). PEAT-CLSM: A Specific Treatment of Peatland Hydrology in the NASA Catchment Land Surface Model. JOURNAL OF ADVANCES IN MODELING EARTH SYSTEMS, 11 (7), 2130-2162. doi: 10.1029/2018MS001574.</p>

The Role of Different Sources of Uncertainty in Data Assimilation with Coupled Land Surface - Subsurface Models

2021 ◽  
Author(s):  
Eduardo Emilio Sanchez-Leon ◽  
Natascha Brandhorst ◽  
Bastian Waldowski ◽  
Ching Pui Hung ◽  
Insa Neuweiler ◽  
...  
Keyword(s):  
Plant Physiology ◽  
Data Assimilation ◽  
Land Surface ◽  
Reference Model ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Model Parameters ◽  
Area Index ◽  
Sources Of Uncertainty

<p>The success of data assimilation systems strongly depends on the suitability of the generated ensembles. While in theory data assimilation should correct the states of an ensemble of models, especially if model parameters are included in the update, its effectiveness will depend on many factors, such as ensemble size, ensemble spread, and the proximity of the prior ensemble simulations to the data. In a previous study, we generated an ensemble-based data-assimilation framework to update model states and parameters of a coupled land surface-subsurface model. As simulation system we used the Terrestrial Systems Modeling Platform TerrSysMP, with the community land-surface model (CLM) coupled to the subsurface model Parflow. In this work, we used the previously generated ensemble to assess the effect of uncertain input forcings (i.e. precipitation), unknown subsurface parameterization, and/or plant physiology in data assimilation. The model domain covers a rectangular area of 1×5km<sup>2</sup>, with a uniform depth of 50m. The subsurface material is divided into four units, and the top soil layers consist of three different soil types with different vegetation. Streams are defined along three of the four boundaries of the domain. For data assimilation, we used the TerrsysMP PDAF framework. We defined a series of data assimilation experiments in which sources of uncertainty were considered individually, and all additional settings of the ensemble members matched those of the reference. To evaluate the effect of all sources of uncertainty combined, we designed an additional test in which the input forcings, subsurface parameters, and the leaf area index of the ensemble were all perturbed. In all these tests, the reference model had homogenous subsurface units and the same grid resolution as all models of the ensemble. We used point measurements of soil moisture in all data assimilation experiments. We concluded that precipitation dominates the dynamics of the simulations, and perturbing the precipitation fields for the ensemble have a major impact in the performance of the assimilation. Still, considerable improvements are observed compared to open-loop simulations. In contrast, the effect of variable plant physiology was minimal, with no visible improvement in relevant fluxes such as evapotranspiration. As expected, improved ensemble predictions are propagated longer in time when parameters are included in the update.</p>

scholarly journals Assimilation of Satellite-Derived Skin Temperature Observations into Land Surface Models

2010 ◽  
Vol 11 (5) ◽  
pp. 1103-1122 ◽  
Author(s):  
Rolf H. Reichle ◽  
Sujay V. Kumar ◽  
Sarith P. P. Mahanama ◽  
Randal D. Koster ◽  
Q. Liu
Keyword(s):  
Heat Flux ◽  
Data Assimilation ◽  
Skin Temperature ◽  
Land Surface ◽  
Sensible Heat Flux ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Ground Heat Flux ◽  
Assimilation System

Abstract Land surface (or “skin”) temperature (LST) lies at the heart of the surface energy balance and is a key variable in weather and climate models. In this research LST retrievals from the International Satellite Cloud Climatology Project (ISCCP) are assimilated into the Noah land surface model and Catchment land surface model (CLSM) using an ensemble-based, offline land data assimilation system. LST is described very differently in the two models. A priori scaling and dynamic bias estimation approaches are applied because satellite and model LSTs typically exhibit different mean values and variabilities. Performance is measured against 27 months of in situ measurements from the Coordinated Energy and Water Cycle Observations Project at 48 stations. LST estimates from Noah and CLSM without data assimilation (“open loop”) are comparable to each other and superior to ISCCP retrievals. For LST, the RMSE values are 4.9 K (CLSM), 5.5 K (Noah), and 7.6 K (ISCCP), and the anomaly correlation coefficients (R) are 0.61 (CLSM), 0.63 (Noah), and 0.52 (ISCCP). Assimilation of ISCCP retrievals provides modest yet statistically significant improvements (over an open loop, as indicated by nonoverlapping 95% confidence intervals) of up to 0.7 K in RMSE and 0.05 in the anomaly R. The skill of the latent and sensible heat flux estimates from the assimilation integrations is essentially identical to the corresponding open loop skill. Noah assimilation estimates of ground heat flux, however, can be significantly worse than open loop estimates. Provided the assimilation system is properly adapted to each land model, the benefits from the assimilation of LST retrievals are comparable for both models.

Where are the peatlands? Comparing two global peatland maps through L-band brightness temperature simulations

2021 ◽  
Author(s):  
Michel Bechtold ◽  
Sarith P. Mahanama ◽  
Rolf H. Reichle ◽  
Randal D. Koster ◽  
Gabrielle J. M. De Lannoy
Keyword(s):  
Soil Moisture ◽  
Brightness Temperature ◽  
Land Surface ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Earth System ◽  
System Models ◽  
L Band ◽  
Earth System Models

<p>Mapping the global peatland distribution is important for embedding peatland processes into Earth System Models. Peatland maps are typically compiled from nation-specific soil or ecosystem maps or based on machine learning tools trained on such data. Here, we evaluate the performance of a land surface model with two different peatland map inputs in providing critical land surface estimates (soil moisture, temperature) to a Radiative Transfer Model (RTM) for L-band brightness temperature (Tb). We hypothesize that an improved performance of the land surface model in Tb space indicates a better spatial peatland distribution input within the footprint of Tb observations (~40 km).</p><p>We employ the NASA Catchment Land Surface Model (CLSM) with a recently added module for peatland hydrology (PEATCLSM modules). We run this model at a 9-km EASEv2 resolution over the Northern Hemisphere for two soil maps that differ in their peatland distributions. The applied soil distributions are: (MAP1) a combination of the Harmonized World Soil Database and the State Soil Geographic Database, also used to generate the Soil Moisture Active Passive (SMAP) Level-4 soil moisture product, and (MAP2) a hybrid of HWSD-STATSGO and the ‘PEATMAP’ product, which is mainly compiled from national peatland maps. MAP2 indicates ~30 % more peatland area over the Northern Hemisphere. For both peat distributions, CLSM is run and parameters of the RTM are calibrated with 10 years of multi-angular L-band Tb observations from the Soil Moisture and Ocean Salinity SMOS mission. Afterwards, CLSM is run together with the calibrated RTM within a data assimilation system, with and without (open-loop) assimilating SMAP Tb observations, for the period 2015-2020. Our results demonstrate that Tb misfits (in both the open-loop and assimilation runs) are reduced in the areas with the largest differences in peat distribution, thus indicating a basic validity of assuming a peatland-like hydrological dynamics for the larger peat extent of MAP2. Results will be discussed in the context of how peatlands are defined in global peatland maps and the question of what is typically modeled as a peatland in Earth System Models. We propose the evaluation of future releases of peatland maps in Tb space as a tool to evaluate their suitability for implementation into Earth System Models.</p>

scholarly journals Generating Observation-Based Snow Depletion Curves for Use in Snow Cover Data Assimilation

Geosciences ◽  
2018 ◽  
Vol 8 (12) ◽  
pp. 484 ◽  
Author(s):  
Kristi Arsenault ◽  
Paul Houser
Keyword(s):  
Data Assimilation ◽  
Snow Cover ◽  
Land Surface ◽  
Snow Depth ◽  
Snow Water Equivalent ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Snow Water ◽  
Logarithmic Functions

Snow depletion curves (SDC) are functions that are used to show the relationship between snow covered area and snow depth or water equivalent. Previous snow cover data assimilation (DA) studies have used theoretical SDC models as observation operators to map snow depth to snow cover fraction (SCF). In this study, a new approach is introduced that uses snow water equivalent (SWE) observations and satellite-based SCF retrievals to derive SDC relationships for use in an Ensemble Kalman filter (EnKF) to assimilate snow cover estimates. A histogram analysis is used to bin the SWE observations, which the corresponding SCF observations are then averaged within, helping to constrain the amount of data dispersion across different temporal and regional conditions. Logarithmic functions are linearly regressed with the binned average values, for two U.S. mountainous states: Colorado and Washington. The SDC-based logarithmic functions are used as EnKF observation operators, and the satellite-based SCF estimates are assimilated into a land surface model. Assimilating satellite-based SCF estimates with the observation-based SDC shows a reduction in SWE-related RMSE values compared to the model-based SDC functions. In addition, observation-based SDC functions were derived for different intra-annual and physiographic conditions, and landcover and elevation bands. Lower SWE-based RMSE values are also found with many of these categorical observation-based SDC EnKF experiments. All assimilation experiments perform better than the open-loop runs, except for the Washington region’s 2004–2005 snow season, which was a major drought year that was difficult to capture with the ensembles and observations.

scholarly journals A Dynamic Approach to Addressing Observation-Minus-Forecast Bias in a Land Surface Skin Temperature Data Assimilation System

2015 ◽  
Vol 16 (1) ◽  
pp. 449-464 ◽  
Author(s):  
Clara Draper ◽  
Rolf Reichle ◽  
Gabrielle De Lannoy ◽  
Benjamin Scarino
Keyword(s):  
Data Assimilation ◽  
Skin Temperature ◽  
Land Surface ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Two Stage ◽  
Mean Square Difference ◽  
Mean Differences ◽  
One Year

Abstract In land data assimilation, bias in the observation-minus-forecast (O − F) residuals is typically removed from the observations prior to assimilation by rescaling the observations to have the same long-term mean (and higher-order moments) as the corresponding model forecasts. Such observation rescaling approaches require a long record of observed and forecast estimates and an assumption that the O − F residuals are stationary. A two-stage observation bias and state estimation filter is presented here, as an alternative to observation rescaling that does not require a long data record or assume stationary O − F residuals. The two-stage filter removes dynamic (nonstationary) estimates of the seasonal-scale mean O − F difference from the assimilated observations, allowing the assimilation to correct the model for subseasonal-scale errors without adverse effects from observation biases. The two-stage filter is demonstrated by assimilating geostationary skin temperature Tskin observations into the Catchment land surface model. Global maps of the estimated O − F biases are presented, and the two-stage filter is evaluated for one year over the Americas. The two-stage filter effectively removed the Tskin O − F mean differences, for example, the Geostationary Operational Environmental Satellite (GOES)-West O − F mean difference at 2100 UTC was reduced from 5.1 K for a bias-blind assimilation to 0.3 K. Compared to independent in situ and remotely sensed Tskin observations, the two-stage assimilation reduced the unbiased root-mean-square difference (ubRMSD) of the modeled Tskin by 10% of the open-loop values.

scholarly journals SMOS Neural Network Soil Moisture Data Assimilation in a Land Surface Model and Atmospheric Impact

2019 ◽  
Author(s):  
Nemesio Rodriguez-Fernandez ◽  
Patricia de Rosnay ◽  
Clement Albergel ◽  
Philippe Richaume ◽  
Filipe Aires ◽  
...  
Keyword(s):  
Neural Network ◽  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Positive Impact ◽  
Land Surface Model ◽  
Surface Model ◽  
Northern Asia ◽  
The Impact

The assimilation of Soil Moisture and Ocean Salinity (SMOS) data into the ECMWF (European Centre for Medium Range Weather Forecasts) H-TESSEL (Hydrology revised - Tiled ECMWF Scheme for Surface Exchanges over Land) model is presented. SMOS soil moisture (SM) estimates have been produced specifically by training a neural network with SMOS brightness temperatures as input and H-TESSEL model SM simulations as reference. This can help the assimilation of SMOS information in several ways: (1) the neural network soil moisture (NNSM) data have a similar climatology to the model, (2) no global bias is present with respect to the model even if regional differences can exist. Experiments performing joint data assimilation (DA) of NNSM, 2 metre air temperature and relative humidity or NNSM-only DA are discussed. The resulting SM was evaluated against a large number of in situ measurements of SM obtaining similar results to those of the model with no assimilation, even if significant differences were found from site to site. In addition, atmospheric forecasts initialized with H-TESSEL runs (without DA) or with the analysed SM were compared to measure of the impact of the satellite information. Although, NNSM DA has an overall neutral impact in the forecast in the Tropics, a significant positive impact was found in other areas and periods, especially in regions with limited in situ information. The joint NNSM, T2m and RH2m DA improves the forecast for all the seasons in the Southern Hemisphere. The impact is mostly due to T2m and RH2m, but SMOS NN DA alone also improves the forecast in July- September. In the Northern Hemisphere, the joint NNSM, T2m and RH2m DA improves the forecast in April-September, while NNSM alone has a significant positive effect in July-September. Furthermore, forecasting skill maps show that SMOS NNSM improves the forecast in North America and in Northern Asia for up to 72 hours lead time.

Impact of Incorrect Model Error Assumptions on the Sequential Assimilation of Remotely Sensed Surface Soil Moisture

2006 ◽  
Vol 7 (3) ◽  
pp. 421-432 ◽  
Author(s):  
Wade T. Crow ◽  
Emiel Van Loon
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Surface Soil ◽  
Random Noise ◽  
Land Surface Model ◽  
Model Error ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Incorrect Model

Abstract Data assimilation approaches require some type of state forecast error covariance information in order to optimally merge model predictions with observations. The ensemble Kalman filter (EnKF) dynamically derives such information through a Monte Carlo approach and the introduction of random noise in model states, fluxes, and/or forcing data. However, in land data assimilation, relatively little guidance exists concerning strategies for selecting the appropriate magnitude and/or type of introduced model noise. In addition, little is known about the sensitivity of filter prediction accuracy to (potentially) inappropriate assumptions concerning the source and magnitude of modeling error. Using a series of synthetic identical twin experiments, this analysis explores the consequences of making incorrect assumptions concerning the source and magnitude of model error on the efficiency of assimilating surface soil moisture observations to constrain deeper root-zone soil moisture predictions made by a land surface model. Results suggest that inappropriate model error assumptions can lead to circumstances in which the assimilation of surface soil moisture observations actually degrades the performance of a land surface model (relative to open-loop assimilations that lack a data assimilation component). Prospects for diagnosing such circumstances and adaptively correcting the culpable model error assumptions using filter innovations are discussed. The dual assimilation of both runoff (from streamflow) and surface soil moisture observations appears to offer a more robust assimilation framework where incorrect model error assumptions are more readily diagnosed via filter innovations.

scholarly journals The effect of satellite-derived surface soil moisture and leaf area index land data assimilation on streamflow simulations over France

2017 ◽  
Vol 21 (4) ◽  
pp. 2015-2033 ◽  
Author(s):  
David Fairbairn ◽  
Alina Lavinia Barbu ◽  
Adrien Napoly ◽  
Clément Albergel ◽  
Jean-François Mahfouf ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Land Surface ◽  
River Discharge ◽  
Surface Soil ◽  
Land Surface Model ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Area Index

Abstract. This study evaluates the impact of assimilating surface soil moisture (SSM) and leaf area index (LAI) observations into a land surface model using the SAFRAN–ISBA–MODCOU (SIM) hydrological suite. SIM consists of three stages: (1) an atmospheric reanalysis (SAFRAN) over France, which forces (2) the three-layer ISBA land surface model, which then provides drainage and runoff inputs to (3) the MODCOU hydro-geological model. The drainage and runoff outputs from ISBA are validated by comparing the simulated river discharge from MODCOU with over 500 river-gauge observations over France and with a subset of stations with low-anthropogenic influence, over several years. This study makes use of the A-gs version of ISBA that allows for physiological processes. The atmospheric forcing for the ISBA-A-gs model underestimates direct shortwave and long-wave radiation by approximately 5 % averaged over France. The ISBA-A-gs model also substantially underestimates the grassland LAI compared with satellite retrievals during winter dormancy. These differences result in an underestimation (overestimation) of evapotranspiration (drainage and runoff). The excess runoff flowing into the rivers and aquifers contributes to an overestimation of the SIM river discharge. Two experiments attempted to resolve these problems: (i) a correction of the minimum LAI model parameter for grasslands and (ii) a bias-correction of the model radiative forcing. Two data assimilation experiments were also performed, which are designed to correct random errors in the initial conditions: (iii) the assimilation of LAI observations and (iv) the assimilation of SSM and LAI observations. The data assimilation for (iii) and (iv) was done with a simplified extended Kalman filter (SEKF), which uses finite differences in the observation operator Jacobians to relate the observations to the model variables. Experiments (i) and (ii) improved the median SIM Nash scores by about 9 % and 18 % respectively. Experiment (iii) reduced the LAI phase errors in ISBA-A-gs but had little impact on the discharge Nash efficiency of SIM. In contrast, experiment (iv) resulted in spurious increases in drainage and runoff, which degraded the median discharge Nash efficiency by about 7 %. The poor performance of the SEKF originates from the observation operator Jacobians. These Jacobians are dampened when the soil is saturated and when the vegetation is dormant, which leads to positive biases in drainage and/or runoff and to insufficient corrections during winter, respectively. Possible ways to improve the model are discussed, including a new multi-layer diffusion model and a more realistic response of photosynthesis to temperature in mountainous regions. The data assimilation should be advanced by accounting for model and forcing uncertainties.

scholarly journals Land-surface parameter optimisation using data assimilation techniques: the adJULES system V1.0

2016 ◽  
Vol 9 (8) ◽  
pp. 2833-2852 ◽  
Author(s):  
Nina M. Raoult ◽  
Tim E. Jupp ◽  
Peter M. Cox ◽  
Catherine M. Luke
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Gross Primary Production ◽  
Land Surface Model ◽  
Weather Forecast ◽  
Surface Model ◽  
Land Surface Parameter ◽  
Estimation System ◽  
Using Data ◽  
The Uk

Abstract. Land-surface models (LSMs) are crucial components of the Earth system models (ESMs) that are used to make coupled climate–carbon cycle projections for the 21st century. The Joint UK Land Environment Simulator (JULES) is the land-surface model used in the climate and weather forecast models of the UK Met Office. JULES is also extensively used offline as a land-surface impacts tool, forced with climatologies into the future. In this study, JULES is automatically differentiated with respect to JULES parameters using commercial software from FastOpt, resulting in an analytical gradient, or adjoint, of the model. Using this adjoint, the adJULES parameter estimation system has been developed to search for locally optimum parameters by calibrating against observations. This paper describes adJULES in a data assimilation framework and demonstrates its ability to improve the model–data fit using eddy-covariance measurements of gross primary production (GPP) and latent heat (LE) fluxes. adJULES also has the ability to calibrate over multiple sites simultaneously. This feature is used to define new optimised parameter values for the five plant functional types (PFTs) in JULES. The optimised PFT-specific parameters improve the performance of JULES at over 85 % of the sites used in the study, at both the calibration and evaluation stages. The new improved parameters for JULES are presented along with the associated uncertainties for each parameter.

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