scholarly journals Spatiotemporal Downscaling of GRACE Total Water Storage Using Land Surface Model Outputs

2021 ◽  
Vol 13 (5) ◽  
pp. 900
Author(s):  
Detang Zhong ◽  
Shusen Wang ◽  
Junhua Li
Keyword(s):  
Spatial Resolution ◽  
Land Surface ◽  
High Spatial Resolution ◽  
Water Storage ◽  
Land Surface Model ◽  
Surface Model ◽  
Total Water ◽  
Spatiotemporal Resolution ◽  
Spherical Cap ◽  
High Spatiotemporal Resolution

High spatiotemporal resolution of terrestrial total water storage plays a key role in assessing trends and availability of water resources. This study presents a two-step method for downscaling GRACE-derived total water storage anomaly (GRACE TWSA) from its original coarse spatiotemporal resolution (monthly, 3-degree spherical cap/~300 km) to a high resolution (daily, 5 km) through combining land surface model (LSM) simulated high spatiotemporal resolution terrestrial water storage anomaly (LSM TWSA). In the first step, an iterative adjustment method based on the self-calibration variance-component model (SCVCM) is used to spatially downscale the monthly GRACE TWSA to the high spatial resolution of the LSM TWSA. In the second step, the spatially downscaled monthly GRACE TWSA is further downscaled to the daily temporal resolution. By applying the method to downscale the coarse resolution GRACE TWSA from the Jet Propulsion Laboratory (JPL) mascon solution with the daily high spatial resolution (5 km) LSM TWSA from the Ecological Assimilation of Land and Climate Observations (EALCO) model, we evaluated the benefit and effectiveness of the proposed method. The results show that the proposed method is capable to downscale GRACE TWSA spatiotemporally with reduced uncertainty. The downscaled GRACE TWSA are also evaluated through in-situ groundwater monitoring well observations and the results show a certain level agreement between the estimated and observed trends.

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.

River parametrisation of the JULES land surface model for improved runoff routing at the global scale.

2021 ◽  
Author(s):  
Aristeidis Koutroulis ◽  
Manolis Grillakis ◽  
Camilla Mathison ◽  
Eleanor Burke
Keyword(s):  
Spatial Resolution ◽  
Land Surface ◽  
River Flow ◽  
Land Surface Model ◽  
Flow Simulation ◽  
Surface Model ◽  
Routing Scheme ◽  
Basin Scale ◽  
River Flow Simulation ◽  
River Routing

<p>The JULES land surface model has a wide ranging application in studying different processes of the earth system including hydrological modeling [1]. Our aim is to tune the existing configuration of the global river routing scheme at 0.5<sup>o</sup> spatial resolution [2] and improve river flow simulation performance at finer temporal scales. To do so, we develop a factorial experiment of varying effective river velocity and meander coefficient, components of the Total Runoff Integrating Pathways (TRIP) river routing scheme. We test and adjust best performing configurations at the basin scale based on observations from GRDC 230 stations that exhibiting a variety of hydroclimatic and physiographic conditions. The analysis was focused on watersheds of near-natural conditions [3] to avoid potential influences of human management on river flow. The HydroATLAS database [4] was employed to identify basin scale descriptive hydro-environmental indicators that could be associated with the components of the TRIP. These indicators summarize hydrologic and physiographic characteristics of the drainage area of each flow gauge. For each basin we select the best performing set of TRIP parameters per basin resulting to the optimal efficiency of river flow simulation based on the Nash–Sutcliffe and Kling–Gupta efficiency metrics. We find that better performance is driven predominantly by characteristics related to the stream gradient and terrain slope. These indicators can serve as descriptors for extrapolating the adjustment of TRIP parameters for global land configurations at 0.5<sup>o</sup> spatial resolution using regression models.</p><p> </p><p>[1] Papadimitriou et al 2017, Hydrol. Earth Syst. Sci., 21, 4379–4401</p><p>[2] Falloon et al 2007. Hadley Centre Tech. Note 72, 42 pp.</p><p>[3] Fang Zhao et al 2017 Environ. Res. Lett. 12 075003</p><p>[4] Linke et al 2019, Scientific Data 6: 283.</p>

scholarly journals Toward a Surface Soil Moisture Product at High Spatiotemporal Resolution: Temporally Interpolated, Spatially Disaggregated SMOS Data

2018 ◽  
Vol 19 (1) ◽  
pp. 183-200 ◽  
Author(s):  
Y. Malbéteau ◽  
O. Merlin ◽  
G. Balsamo ◽  
S. Er-Raki ◽  
S. Khabba ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temporal Resolution ◽  
Land Surface ◽  
Large Scale ◽  
Surface Soil ◽  
Land Surface Model ◽  
Open Loop ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Spatiotemporal Resolution

Abstract High spatial and temporal resolution surface soil moisture is required for most hydrological and agricultural applications. The recently developed Disaggregation based on Physical and Theoretical Scale Change (DisPATCh) algorithm provides 1-km-resolution surface soil moisture by downscaling the 40-km Soil Moisture Ocean Salinity (SMOS) soil moisture using Moderate Resolution Imaging Spectroradiometer (MODIS) data. However, the temporal resolution of DisPATCh data is constrained by the temporal resolution of SMOS (a global coverage every 3 days) and further limited by gaps in MODIS images due to cloud cover. This paper proposes an approach to overcome these limitations based on the assimilation of the 1-km-resolution DisPATCh data into a simple dynamic soil model forced by (inaccurate) precipitation data. The performance of the approach was assessed using ground measurements of surface soil moisture in the Yanco area in Australia and the Tensift-Haouz region in Morocco during 2014. It was found that the analyzed daily 1-km-resolution surface soil moisture compared slightly better to in situ data for all sites than the original disaggregated soil moisture products. Over the entire year, assimilation increased the correlation coefficient between estimated soil moisture and ground measurements from 0.53 to 0.70, whereas the mean unbiased RMSE (ubRMSE) slightly decreased from 0.07 to 0.06 m3 m−3 compared to the open-loop force–restore model. The proposed assimilation scheme has significant potential for large-scale applications over semiarid areas, since the method is based on data available at the global scale together with a parsimonious land surface model.

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.

scholarly journals Assimilating satellite-based soil moisture observations in a land surface model: The effect of spatial resolution

2021 ◽  
Vol 13 ◽  
pp. 100105
Author(s):  
Tasnuva Rouf ◽  
Manuela Girotto ◽  
Paul Houser ◽  
Viviana Maggioni
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model

An offline reanalysis of land surface variables with LDAS-Monde forced by a kilometric scale NWP system

2020 ◽  
Author(s):  
Bertrand Bonan ◽  
Clément Albergel ◽  
Adrien Napoly ◽  
Yongjun Zheng ◽  
Jean-Christophe Calvet
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Surface Soil ◽  
Weather Prediction ◽  
Land Surface Model ◽  
Research Centre ◽  
Small Scale ◽  
Surface Model ◽  
Surface Soil Moisture

<p>LDAS-Monde is the offline land data assimilation system (LDAS) developed by Météo-France’s research centre (CNRM) aiming to monitor the evolution of land surface variables (LSVs) at various scales, from regional to global. It combines numerical simulations from the multilayer and interactive vegetation ISBA land surface model and satellite-derived observations of surface soil moisture and leaf area index (LAI). LDAS-Monde has been successfully validated over the globe.</p><p>In this work, we study the possibility to set up LDAS-Monde to the context of the kilometric spatial resolution. In this context, we assimilate satellite observations of LAI from the Copernicus Global Land Service (CGLS) into the ISBA land surface model forced with Météo-France’s small scale numerical weather prediction system AROME. We produce a reanalysis of LSVs at 2.5-km spatial resolution over the AROME domain centred on France starting from 2017. The quality of this reanalysis is assessed by comparing the obtained reanalysis with satellite products of LAI and surface soil moisture from e.g. CGLS and in-situ measurements of soil moisture from various networks (SMOSMANIA, …). We also show the ability of our system to monitor the evolution of LSVs in the context of the severe drought that France suffered during the summer 2018. LDAS-Monde at 2.5-km spatial resolution displays a great potential for agricultural monitoring at high resolution. We also plan to adapt our framework to 1.0-km spatial resolution.</p>

scholarly journals Implementing northern peatlands in a global land surface model: description and evaluation in the ORCHIDEE high latitude version model (ORC-HL-PEAT)

2017 ◽  
Author(s):  
Chloé Largeron ◽  
Gerhard Krinner ◽  
Philippe Ciais ◽  
Claire Brutel-Vuilmet
Keyword(s):  
Land Surface ◽  
Peat Soil ◽  
Water Storage ◽  
Land Surface Model ◽  
Model Description ◽  
High Water Content ◽  
Surface Model ◽  
Northern Latitudes ◽  
Global Land ◽  
Northern Peatlands

Abstract. Widely present in boreal regions, peatlands contain large carbon stocks because of their hydrologic properties and high water content, making decomposition smaller than primary productivity. We have enhanced the global land surface model ORCHIDEE by introducing northern peatlands. These are considered as a new Plant Functional Types (PFT) in the model, with specific hydrological properties for peat soil. In this paper, we focus on the representation of the hydrology of northern peatlands and on the evaluation of the hydrological impact of this implementation. A prescribed map based on the inventory of Yu et al. (2010) defines peatlands as a fraction of grid cell represented as a PFT comparable to C3 grasses with adaptations to reproduce shallow roots and higher photosynthesis stress. The treatment of peatland hydrology differs from that of other vegetation types by the fact that runoff from other soil types is partially directed towards the peatlands (instead of directly to the river network). The evaluation of this implementation was carried out according to different spatial and temporal scales, from site evaluation to larger scales such as watershed scale and all northern latitudes scale. The simulated net ecosystem exchanges are in agreement with observations from 3 FLUXNET sites. Water table positions were generally close to observations, with some exceptions in winter. Compared to other soils, the simulated peat soil have a reduced seasonal variability of water storage. The seasonal cycle of inundated peatlands area is also compared to flooded area estimated from satellite observations. The model is able to represents more than 89.5 % of the flooded areas located in peatlands areas, where the modelled extent of inundated peatlands reaches 0.83 Mkm2. However, the extent of peatland in northern latitudes is small enough that is does not impact the terrestrial water storage at scale of latitudes over 45° N. Therefore, the inclusion of peatlands has a weak impact on the river discharge located in boreal regions.

scholarly journals Scale Impact of Soil Moisture Observations to Noah-MP Land Surface Model Simulations

2020 ◽  
Vol 12 (7) ◽  
pp. 1169
Author(s):  
Jifu Yin ◽  
Xiwu Zhan
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Land Surface ◽  
Land Surface Model ◽  
Surface Model ◽  
Synthetic Experiment ◽  
Model Simulations ◽  
Coarse Spatial Resolution ◽  
Satellite Microwave ◽  
National Water

Due to the limitations of satellite antenna technology, current operational microwave soil moisture (SM) data products are typically at tens of kilometers spatial resolutions. Many approaches have thus been proposed to generate finer resolution SM data using ancillary information, but it is still unknown if assimilation of the finer spatial resolution SM data has beneficial impacts on model skills. In this paper, a synthetic experiment is thus conducted to identify the benefits of SM observations at a finer spatial resolution on the Noah-MP land surface model. Results of this study show that the performance of the Noah-MP model is significantly improved with the benefits of assimilating 1 km SM observations in comparison with the assimilation of SM data at coarser resolutions. Downscaling satellite microwave SM observations from coarse spatial resolution to 1 km resolution is recommended, and the assimilation of 1 km remotely sensed SM retrievals is suggested for NOAA National Weather Service and National Water Center.

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