scholarly journals Global Soil Water Estimates as Landslide Predictor: The Effectiveness of SMOS, SMAP, and GRACE Observations, Land Surface Simulations, and Data Assimilation

2021 ◽  
Vol 22 (5) ◽  
pp. 1065-1084
Author(s):  
Anne Felsberg ◽  
Gabriëlle J. M. De Lannoy ◽  
Manuela Girotto ◽  
Jean Poesen ◽  
Rolf H. Reichle ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Soil Water ◽  
Land Surface ◽  
Complex Terrain ◽  
Land Surface Model ◽  
Remote Sensing Data ◽  
Surface Model ◽  
Probability Estimates ◽  
Terrestrial Water

AbstractThis global feasibility study assesses the potential of coarse-scale, gridded soil water estimates for the probabilistic modeling of hydrologically triggered landslides, using Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), and Gravity Recovery and Climate Experiment (GRACE) remote sensing data; Catchment Land Surface Model (CLSM) simulations; and six data products based on the assimilation of SMOS, SMAP, and/or GRACE observations into CLSM. SMOS or SMAP observations (~40-km resolution) are only available for less than 20% of the globally reported landslide events, because they are intermittent and uncertain in regions with complex terrain. GRACE terrestrial water storage estimates include 75% of the reported landslides but have coarse spatial and temporal resolutions (monthly, ~300 km). CLSM soil water simulations have the added advantage of complete spatial and temporal coverage, and are found to be able to distinguish between “stable slope” (no landslide) conditions and landslide-inducing conditions in a probabilistic way. Assimilating SMOS and/or GRACE data increases the landslide probability estimates based on soil water percentiles for the reported landslides, relative to model-only estimates at 36-km resolution for the period 2011–16, unless the CLSM model-only soil water content is already high (≥50th percentile). The SMAP Level 4 data assimilation product (at 9-km resolution, period 2015–19) more generally updates the soil water conditions toward higher landslide probabilities for the reported landslides, but is similar to model-only estimates for the majority of landslides where SMAP data cannot easily be converted to soil moisture owing to complex terrain.

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.

Evapotranspiration and soil moisture indexes derived from remote sensing data to identify and investigate the mechanisms of the spatio-temporal patterns of drought in the Ebro-Basin (NE Spain).

2020 ◽  
Author(s):  
Jaime Gaona ◽  
Pere Quintana-Seguí ◽  
Maria José Escorihuela
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Remote Sensing Data ◽  
Temporal Patterns ◽  
Surface Model ◽  
Ebro Basin ◽  
Sensing Data ◽  
Spatio Temporal

<p>The Mediterranean climate of the Iberian Peninsula defines high spatial and temporal variability of drought at multiple scales. These droughts impact human activities such as water management, agriculture or forestry, and may alter valuable natural ecosystems as well. An accurate understanding and monitoring of drought processes are crucial in this area. The HUMID project (CGL2017-85687-R) is studying how remote sensing data and models (Quintana-Seguí et al., 2019; Barella-Ortiz and Quintana-Seguí, 2019) can improve our current knowledge on Iberian droughts, in general, and in the Ebro basin, more specifically.</p><p>The traditional ground-based monitoring of drought lacks the spatial resolution needed to identify the microclimatic mechanisms of drought at sub-basin scale, particularly when considering relevant variables for drought such as soil moisture and evapotranspiration. In situ data of these two variables is very scarce.</p><p>The increasing availability of remote sensing products such as MODIS16 A2 ET and the high-resolution SMOS 1km facilitates the use of distributed observations for the analysis of drought patterns across scales. The data is used to generate standardized drought indexes: the soil moisture deficit index (SMDI) based on SMOS 1km data (2010-2019) and the evapotranspiration deficit index (ETDI) based on MODIS16 A2 ET 500m. The study aims to identify the spatio-temporal mechanisms of drought generation, propagation and mitigation within the Ebro River basin and sub-basins, located in NE Spain where dynamic Atlantic, Mediterranean and Continental climatic influences dynamically mix, causing a large heterogeneity in climates.</p><p>Droughts in the 10-year period 2010-2019 of study exhibit spatio-temporal patterns at synoptic and mesoscale scales. Mesoscale spatio-temporal patterns prevail for the SMDI while the ETDI ones show primarily synoptic characteristics. The study compares the patterns of drought propagation identified with remote sensing data with the patterns estimated using the land surface model SURFEX-ISBA at 5km.  The comparison provides further insights about the capabilities and limitations of both tools, while emphasizes the value of combining approaches to improve our understanding about the complexity of drought processes across scales.</p><p>Additionally, the periods of quick change of drought indexes comprise valuable information about the response of evapotranspiration to water deficits as well as on the resilience of soil to evaporative stress. The lag analysis ranges from weeks to seasons. Results show lags between the ETDI and SMDI ranging from days to weeks depending on the precedent drought status and the season/month of drought’s generation or mitigation. The comparison of the lags observed on remote sensing data and land surface model data aims at evaluating the adequacy of the data sources and the indexes to represent the nonlinear interaction between soil moisture and evapotranspiration. This aspect is particularly relevant for developing drought monitoring aiming at managing the impact of drought in semi-arid environments and improving the adaptation to drought alterations under climate change.</p>

Analysis of Soil Moisture Changes in Europe during a Single Growing Season in a New ECMWF Soil Moisture Assimilation System

2008 ◽  
Vol 9 (1) ◽  
pp. 116-131 ◽  
Author(s):  
Bart van den Hurk ◽  
Janneke Ettema ◽  
Pedro Viterbo
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Land Surface Model ◽  
Growing Season ◽  
Atmospheric Model ◽  
Surface Model ◽  
Soil Moisture Data ◽  
The Impact ◽  
Assimilation System

Abstract This study aims at stimulating the development of soil moisture data assimilation systems in a direction where they can provide both the necessary control of slow drift in operational NWP applications and support the physical insight in the performance of the land surface component. It addresses four topics concerning the systematic nature of soil moisture data assimilation experiments over Europe during the growing season of 2000 involving the European Centre for Medium-Range Weather Forecasts (ECMWF) model infrastructure. In the first topic the effect of the (spinup related) bias in 40-yr ECMWF Re-Analysis (ERA-40) precipitation on the data assimilation is analyzed. From results averaged over 36 European locations, it appears that about half of the soil moisture increments in the 2000 growing season are attributable to the precipitation bias. A second topic considers a new soil moisture data assimilation system, demonstrated in a coupled single-column model (SCM) setup, where precipitation and radiation are derived from observations instead of from atmospheric model fields. For many of the considered locations in this new system, the accumulated soil moisture increments still exceed the interannual variability estimated from a multiyear offline land surface model run. A third topic examines the soil water budget in response to these systematic increments. For a number of Mediterranean locations the increments successfully increase the surface evaporation, as is expected from the fact that atmospheric moisture deficit information is the key driver of soil moisture adjustment. In many other locations, however, evaporation is constrained by the experimental SCM setup and is hardly affected by the data assimilation. Instead, a major portion of the increments eventually leave the soil as runoff. In the fourth topic observed evaporation is used to evaluate the impact of the data assimilation on the forecast quality. In most cases, the difference between the control and data assimilation runs is considerably smaller than the (positive) difference between any of the simulations and the observations.

scholarly journals Development and evaluation of 0.05° terrestrial water storage estimates using CABLE land surface model and GRACE data assimilation

2021 ◽  
Author(s):  
Natthachet Tangdamrongsub ◽  
Michael F. Jasinski ◽  
Peter Shellito
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Model Parameters ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Terrestrial Water

Abstract. Accurate estimation of terrestrial water storage (TWS) at a meaningful spatiotemporal resolution is important for reliable assessments of regional water resources and climate variability. Individual components of TWS include soil moisture, snow, groundwater, and canopy storage and can be estimated from the Community Atmosphere Biosphere Land Exchange (CABLE) land surface model. The spatial resolution of CABLE is currently limited to 0.5° by the resolution of soil and vegetation datasets that underlie model parameterizations, posing a challenge to using CABLE for hydrological applications at a local scale. This study aims to improve the spatial detail (from 0.5° to 0.05°) and timespan (1981–2012) of CABLE TWS estimates using rederived model parameters and high-resolution meteorological forcing. In addition, TWS observations derived from the Gravity Recovery and Climate Experiment (GRACE) satellite mission are assimilated into CABLE to improve TWS accuracy. The success of the approach is demonstrated in Australia, where multiple ground observation networks are available for validation. The evaluation process is conducted using four different case studies that employ different model spatial resolutions and include or omit GRACE data assimilation (DA). We find that the CABLE 0.05° developed here improves TWS estimates in terms of accuracy, spatial resolution, and long-term water resource assessment reliability. The inclusion of GRACE DA increases the accuracy of groundwater storage (GWS) estimates and has little impact on surface soil moisture or evapotranspiration. The use of improved model parameters and improved state estimations (via GRACE DA) together is recommended to achieve the best GWS accuracy. The workflow elaborated in this paper relies only on publicly accessible global datasets, allowing reproduction of the 0.05° TWS estimates in any study region.

scholarly journals The Influence of Assimilating Leaf Area Index in a Land Surface Model on Global Water Fluxes and Storages

2019 ◽  
Author(s):  
Xinxuan Zhang ◽  
Viviana Maggioni ◽  
Azbina Rahman ◽  
Paul Houser ◽  
Yuan Xue ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Leaf Area Index ◽  
Leaf Area ◽  
Land Surface ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Area Index ◽  
Global Water

Abstract. Vegetation plays a fundamental role not only in the energy and carbon cycle, but also the global water balance by controlling surface evapotranspiration. Thus, accurately estimating vegetation-related variables has the potential to improve our understanding and estimation of the dynamic interactions between the water and carbon cycles. This study aims to assess to what extent a land surface model can be optimized through the assimilation of leaf area index (LAI) observations at the global scale. Two observing system simulation experiments (OSSEs) are performed to evaluate the efficiency of assimilating LAI through an Ensemble Kalman Filter (EnKF) to estimate LAI, evapotranspiration (ET), interception evaporation (CIE), canopy water storage (CWS), surface soil moisture (SSM), and terrestrial water storage (TWS). Results show that the LAI data assimilation framework effectively reduces errors in LAI simulations. LAI assimilation also improves the model estimates of all the water flux and storage variables considered in this study (ET, CIE, CWS, SSM, and TWS), even when the forcing precipitation is strongly positively biased (extremely wet condition). However, it tends to worsen some of the model estimated water-related variables (SSM and TWS) when the forcing precipitation is affected by a dry bias. This is attributed to the fact that the amount of water in the land surface model is conservative and the LAI assimilation introduces more vegetation, which requires more water than what available within the soil. Future work should investigate a multi-variate data assimilation system that concurrently merges both LAI and soil moisture (or TWS) observations.

Improving estimates of UK soil moisture using the JULES land surface model with COSMOS-UK measurements in the LaVEnDAR data assimilation framework

2020 ◽  
Author(s):  
Elizabeth Cooper ◽  
Ewan Pinnington ◽  
Richard Ellis ◽  
Eleanor Blyth ◽  
Simon Dadson ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Soil Texture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Surface Model ◽  
Soil Physics ◽  
Soil Moisture Sensors ◽  
The Uk

<p>Soil moisture predictions are increasingly important in hydrological, ecological and agricultural applications. In recent years the availability of wide-area assessments of current and future soil-moisture states has grown, yet few studies have combined model-based assessments with observations beyond the point scale. Here we use the JULES land surface model together with COSMOS-UK data to evaluate the extent to which data assimilation can improve predictions of soil moisture across the United Kingdom.</p><p>COSMOS-UK is a network of soil moisture sensors run by UKCEH. The network provides soil moisture measurements at around 50 sites throughout the UK using innovative Cosmic Ray Neutron Sensors (CRNS). Half hourly measurements of the meteorological variables that the Joint UK Land Environment Simulator (JULES) requires as driving data are also recorded at COSMOS-UK sites, allowing us to run JULES at observation locations. This provides a unique opportunity to compare soil moisture outputs from JULES with CRNS observations; these measurements have a footprint of up to 12 ha (approx 30 acres) and are therefore better scale matched with JULES outputs than those from point sensors.</p><p>We have used the Land Variational Ensemble Data Assimilation Framework (LaVEnDAR) to combine soil moisture estimates from JULES with daily CRNS observations from one year at a number of COSMOS-UK sites. We show that this results in improved soil moisture predictions from JULES over several years. This has been achieved by optimising parameters in the pedo-transfer function used to derive JULES soil physics parameters from soil texture information. Using data assimilation with LaVEnDAR in this way allows us to explore the relationships between soil moisture estimates, soil physics parameters and soil texture, as well as improving the agreement between JULES model outputs and observations.</p>

scholarly journals Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data

2021 ◽  
Vol 25 (3) ◽  
pp. 1617-1641
Author(s):  
Ewan Pinnington ◽  
Javier Amezcua ◽  
Elizabeth Cooper ◽  
Simon Dadson ◽  
Rich Ellis ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Mean Squared Error ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Pedotransfer Functions ◽  
Surface Model ◽  
Squared Error ◽  
Surface Models

Abstract. Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures, data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure, we find improved estimates of soil moisture and heat flux for the Joint UK Land Environment Simulator (JULES) land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK) and three flux tower sites. The spatial resolution of the COSMOS probes is much more representative of the 1 km model grid than traditional point-based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain, we find an average 22 % reduction in root mean squared error, a 16 % reduction in unbiased root mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.

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