scholarly journals Improving soil moisture and runoff simulations over Europe using a high-resolution data-assimilation modeling framework

2018 ◽  
Author(s):  
Bibi S. Naz ◽  
Wolfgang Kurtz ◽  
Carsten Montzka ◽  
Wendy Sharples ◽  
Klaus Goergen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Water Resources ◽  
Data Assimilation ◽  
Open Loop ◽  
Modeling Framework ◽  
High Resolution Data ◽  
Continental Scale ◽  
Community Land Model ◽  
Assimilation Experiment

Abstract. Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of observations at large scales and the uncertainties of model simulations due to errors in model structure and inputs (e.g. hydrologic parameters and atmospheric forcings). In this study, we assimilated ESA CCI soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The assimilation experiment was conducted over a time period from 2000 to 2006 with the Community Land Model, version 3.5 (CLM3.5) integrated with the Parallel Data Assimilation Framework (PDAF) at spatial resolution of 0.0275° (~ 3 km) over Europe. The model was forced with the high-resolution reanalysis COSMO-REA6 from the Hans-Ertel Centre for Weather Research (HErZ). Our results show that estimates of soil moisture have improved, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. On average, the mean bias in soil moisture was reduced from 0.1 mm3/mm3 in open-loop simulations to 0.004 mm3/mm3 with SM assimilation. The assimilation experiment also shows overall improvements in runoff, particularly during peak runoff. The results demonstrate the potential of assimilating satellite soil moisture observations to improve high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring.

scholarly journals Improving soil moisture and runoff simulations at 3 km over Europe using land surface data assimilation

2019 ◽  
Vol 23 (1) ◽  
pp. 277-301 ◽  
Author(s):  
Bibi S. Naz ◽  
Wolfgang Kurtz ◽  
Carsten Montzka ◽  
Wendy Sharples ◽  
Klaus Goergen ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Water Resources ◽  
Data Assimilation ◽  
Land Surface ◽  
European Space Agency ◽  
Loop Model ◽  
Continental Scale ◽  
Community Land Model ◽  
Assimilation Experiment

Abstract. Accurate and reliable hydrologic simulations are important for many applications such as water resources management, future water availability projections and predictions of extreme events. However, the accuracy of water balance estimates is limited by the lack of large-scale observations, model simulation uncertainties and biases related to errors in model structure and uncertain inputs (e.g., hydrologic parameters and atmospheric forcings). The availability of long-term and global remotely sensed soil moisture offers the opportunity to improve model estimates through data assimilation with complete spatiotemporal coverage. In this study, we assimilated the European Space Agency (ESA) Climate Change Initiative (CCI) derived soil moisture (SM) information to improve the estimation of continental-scale soil moisture and runoff. The assimilation experiment was conducted over a time period 2000–2006 with the Community Land Model, version 3.5 (CLM3.5), integrated with the Parallel Data Assimilation Framework (PDAF) at a spatial resolution of 0.0275∘ (∼3 km) over Europe. The model was forced with the high-resolution reanalysis COSMO-REA6 from the Hans Ertel Centre for Weather Research (HErZ). The performance of assimilation was assessed against open-loop model simulations and cross-validated with independent ESA CCI-derived soil moisture (CCI-SM) and gridded runoff observations. Our results showed improved estimates of soil moisture, particularly in the summer and autumn seasons when cross-validated with independent CCI-SM observations. The assimilation experiment results also showed overall improvements in runoff, although some regions were degraded, especially in central Europe. The results demonstrated the potential of assimilating satellite soil moisture observations to produce downscaled and improved high-resolution soil moisture and runoff simulations at the continental scale, which is useful for water resources assessment and monitoring.

scholarly journals Recent Progress of the NHM-4DVAR towards a Super-High Resolution Data Assimilation

SOLA ◽  
2014 ◽  
Vol 10 (0) ◽  
pp. 145-149 ◽  
Author(s):  
Takuya Kawabata ◽  
Kosuke Ito ◽  
Kazuo Saito
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Recent Progress ◽  
High Resolution Data ◽  
Resolution Data

scholarly journals Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation

Sensors ◽  
2019 ◽  
Vol 19 (14) ◽  
pp. 3161 ◽  
Author(s):  
Haizhu Pan ◽  
Zhongxin Chen ◽  
Allard de Wit ◽  
Jianqiang Ren
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Winter Wheat ◽  
Crop Yield ◽  
Wheat Yield ◽  
Crop Growth ◽  
Open Loop ◽  
Yield Estimation ◽  
Spatiotemporal Resolution ◽  
Sentinel 2

It is well known that timely crop growth monitoring and accurate crop yield estimation at a fine scale is of vital importance for agricultural monitoring and crop management. Crop growth models have been widely used for crop growth process description and yield prediction. In particular, the accurate simulation of important state variables, such as leaf area index (LAI) and root zone soil moisture (SM), is of great importance for yield estimation. Data assimilation is a useful tool that combines a crop model and external observations (often derived from remote sensing data) to improve the simulated crop state variables and consequently model outputs like crop total biomass, water use and grain yield. In spite of its effectiveness, applying data assimilation for monitoring crop growth at the regional scale in China remains challenging, due to the lack of high spatiotemporal resolution satellite data that can match the small field sizes which are typical for agriculture in China. With the accessibility of freely available images acquired by Sentinel satellites, it becomes possible to acquire data at high spatiotemporal resolution (10–30 m, 5–6 days), which offers attractive opportunities to characterize crop growth. In this study, we assimilated remotely sensed LAI and SM into the Word Food Studies (WOFOST) model to estimate winter wheat yield using an ensemble Kalman filter (EnKF) algorithm. The LAI was calculated from Sentinel-2 using a lookup table method, and the SM was calculated from Sentinel-1 and Sentinel-2 based on a change detection approach. Through validation with field data, the inverse error was 10% and 35% for LAI and SM, respectively. The open-loop wheat yield estimation, independent assimilations of LAI and SM, and a joint assimilation of LAI + SM were tested and validated using field measurement observation in the city of Hengshui, China, during the 2016–2017 winter wheat growing season. The results indicated that the accuracy of wheat yield simulated by WOFOST was significantly improved after joint assimilation at the field scale. Compared to the open-loop estimation, the yield root mean square error (RMSE) with field observations was decreased by 69 kg/ha for the LAI assimilation, 39 kg/ha for the SM assimilation and 167 kg/ha for the joint LAI + SM assimilation. Yield coefficients of determination (R2) of 0.41, 0.65, 0.50, and 0.76 and mean relative errors (MRE) of 4.87%, 4.32%, 4.45% and 3.17% were obtained for open-loop, LAI assimilation alone, SM assimilation alone and joint LAI + SM assimilation, respectively. The results suggest that LAI was the first-choice variable for crop data assimilation over SM, and when both LAI and SM satellite data are available, the joint data assimilation has a better performance because LAI and SM have interacting effects. Hence, joint assimilation of LAI and SM from Sentinel-1 and Sentinel-2 at a 20 m resolution into the WOFOST provides a robust method to improve crop yield estimations. However, there is still bias between the key soil moisture in the root zone and the Sentinel-1 C band retrieved SM, especially when the vegetation cover is high. By active and passive microwave data fusion, it may be possible to offer a higher accuracy SM for crop yield prediction.

scholarly journals Estimating cardiac contraction through high resolution data assimilation of a personalized mechanical model

2018 ◽  
Vol 24 ◽  
pp. 85-90 ◽  
Author(s):  
Henrik Finsberg ◽  
Gabriel Balaban ◽  
Stian Ross ◽  
Trine F. Håland ◽  
Hans Henrik Odland ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Mechanical Model ◽  
Cardiac Contraction ◽  
High Resolution Data ◽  
Resolution Data

High-resolution pan-European multi-model simulations of hydrologic states and fluxes

2020 ◽  
Author(s):  
Bibi S Naz ◽  
Wendy Sharples ◽  
Klaus Goergen ◽  
Stefan Kollet
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Groundwater Flow ◽  
Hydrologic Model ◽  
Hydrological Processes ◽  
Model Parameters ◽  
Time Step ◽  
European Continent ◽  
Model Simulations ◽  
Community Land Model

<p> <span>High-resolution large-scale predictions of hydrologic states and fluxes are important for many regional-scale applications and water resource management. However, because of uncertainties related to forcing data, model structural errors arising from simplified representations of hydrological processes or uncertain model parameters, model simulations remain uncertain. To quantify this uncertainty, multi-model simulations were performed at 3km resolution over the European continent using the Community Land Model (CLM3.5) and the ParFlow hydrologic model. While Parflow uses a similar approach as CLM in simulating the snow, vegetation and land-atmosphere exchange processes, it simulates three-dimensional variably saturated groundwater flow solving Richards equation and overland flow with a two-dimensional kinematic wave approximation. </span><span>The </span><span>CLM</span><span>3.5</span><span> uses a simple groundwater model to account for groundwater recharge and discharge processes. Both models were driven with the COSMO-REA6 reanalysis dataset at 6km resolution for the time period from 2000 to 2006 at an hourly time step, and both used the same datasets for the static input variables (such as topography, vegetation and soil properties). The performance of both models was analyzed through comparisons with independent observations including satellite-derived and in-situ soil moisture, evapotranspiration, river discharge, water table depth and total water storage datasets. Overall, both models capture the interannual variability in the hydrologic states and fluxes well, however differences in performance between models showed the uncertainty associated with the representation of hydrological processes, such as groundwater flow and soil moisture and its control on latent and sensible heat fluxes at the surface.</span></p>

Modeling of a forested study site with the Community Land Model version 5 using climate projections for the 21st century.

2020 ◽  
Author(s):  
Lukas Strebel ◽  
Klaus Goergen ◽  
Bibi S. Naz ◽  
Heye Bogena ◽  
Harry Vereecken ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Water Content ◽  
Soil Water ◽  
Soil Water Content ◽  
Land Surface ◽  
Model Parameters ◽  
Climate Projections ◽  
Modeling Framework ◽  
Area Index ◽  
Community Land Model

<p>Modeling forest ecosystems is important to facilitate adaptations in forest management approaches necessary to address the challenges of climate change, particularly of interest are ecohydrological states and fluxes such as soil water content, biomass, leaf area index, and evapotranspiration.</p><p>The community land model in its current version 5 (CLM5) simulates a broad collection of important land-surface processes; from moisture and energy partitioning, through biogeophysical processes, to surface and subsurface runoff. Additionally, CLM5 contains a biogeochemistry model (CLM5-BGC) which includes prognostic computation of vegetation states and carbon and nitrogen pools. However, CLM5 predictions are affected by uncertainty related to uncertain model forcings and parameters. Here, we use data assimilation methods to improve model performance by assimilating soil water content observations into CLM5 using the parallel data assimilation framework (PDAF).</p><p> </p><p>The coupled modeling framework was applied to the small (38.5 ha) forested catchment Wüstebach located in the Eifel National Park near the German-Belgian border. As part of the terrestrial environmental observatories (TERENO) network, the SoilNet sensors at the study site provide soil water content and soil temperature measurements since 2009.</p><p>CLM5 simulations for the period 2009-2100 were made, using local atmospheric observations for the period of 2009-2018 and an ensemble of regional climate model projections for 2019-2100. Simulations illustrate that data assimilation of soil water content improves the characterization of past model states, and that estimated model parameters and default model parameters result in different trajectories of ecohydrological states for 2019-2100. The simulations also illustrate that this site is hardly affected by increased water stress in the future.</p><p>The developed framework will be extended and applied for both ecosystem reanalysis as well as further simulations using climate projections across forested sites over Europe.</p>

scholarly journals Evaluation of 18 satellite- and model-based soil moisture products using in situ measurements from 826 sensors

2021 ◽  
Vol 25 (1) ◽  
pp. 17-40
Author(s):  
Hylke E. Beck ◽  
Ming Pan ◽  
Diego G. Miralles ◽  
Rolf H. Reichle ◽  
Wouter A. Dorigo ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Satellite Data ◽  
Irrigation Scheduling ◽  
Open Loop ◽  
Spatiotemporal Variability ◽  
Near Surface ◽  
Satellite Retrievals ◽  
Performance Ranking

Abstract. Information about the spatiotemporal variability of soil moisture is critical for many purposes, including monitoring of hydrologic extremes, irrigation scheduling, and prediction of agricultural yields. We evaluated the temporal dynamics of 18 state-of-the-art (quasi-)global near-surface soil moisture products, including six based on satellite retrievals, six based on models without satellite data assimilation (referred to hereafter as “open-loop” models), and six based on models that assimilate satellite soil moisture or brightness temperature data. Seven of the products are introduced for the first time in this study: one multi-sensor merged satellite product called MeMo (Merged soil Moisture) and six estimates from the HBV (Hydrologiska Byråns Vattenbalansavdelning) model with three precipitation inputs (ERA5, IMERG, and MSWEP) with and without assimilation of SMAPL3E satellite retrievals, respectively. As reference, we used in situ soil moisture measurements between 2015 and 2019 at 5 cm depth from 826 sensors, located primarily in the USA and Europe. The 3-hourly Pearson correlation (R) was chosen as the primary performance metric. We found that application of the Soil Wetness Index (SWI) smoothing filter resulted in improved performance for all satellite products. The best-to-worst performance ranking of the four single-sensor satellite products was SMAPL3ESWI, SMOSSWI, AMSR2SWI, and ASCATSWI, with the L-band-based SMAPL3ESWI (median R of 0.72) outperforming the others at 50 % of the sites. Among the two multi-sensor satellite products (MeMo and ESA-CCISWI), MeMo performed better on average (median R of 0.72 versus 0.67), probably due to the inclusion of SMAPL3ESWI. The best-to-worst performance ranking of the six open-loop models was HBV-MSWEP, HBV-ERA5, ERA5-Land, HBV-IMERG, VIC-PGF, and GLDAS-Noah. This ranking largely reflects the quality of the precipitation forcing. HBV-MSWEP (median R of 0.78) performed best not just among the open-loop models but among all products. The calibration of HBV improved the median R by +0.12 on average compared to random parameters, highlighting the importance of model calibration. The best-to-worst performance ranking of the six models with satellite data assimilation was HBV-MSWEP+SMAPL3E, HBV-ERA5+SMAPL3E, GLEAM, SMAPL4, HBV-IMERG+SMAPL3E, and ERA5. The assimilation of SMAPL3E retrievals into HBV-IMERG improved the median R by +0.06, suggesting that data assimilation yields significant benefits at the global scale.

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