The Evaluation of SMAP Enhanced Soil Moisture Products Using High-Resolution Model Simulations and In-Situ Observations on the Tibetan Plateau

Abstract In this study, a microwave-based multisatellite merged product released from the European Space Agency’s Climate Change Initiative (ESA CCI) and two model-based simulations from the Community Land Model 4.5 (CLM4.5) and Global Land Data Assimilation System (GLDAS) were used to investigate interannual variations and trends of soil moisture in China between 1979 and 2010. They were also evaluated using in situ observations from the nationwide agrometeorological network. These three datasets show consistent drying trends for surface soil moisture in northeastern and central China, as well the eastern portion of Inner Mongolia, and wetting trends in the Tibetan Plateau, which are also identified by in situ observations. Trends in the root-zone soil moisture are in line with those of surface soil moisture seen in the CLM4.5 and GLDAS simulations obtained from most areas in China (78%–88%), except for northwestern China and southwest of the Tibetan Plateau. Moreover, the drying trend intensifies with increasing soil depth. Taking the in situ measurements as reference, it is found that ESA CCI has better accuracy in identifying the significant drying trends while CLM4.5 and GLDAS capture wetting trends better. Compared to temperature, precipitation is the primary factor responsible for these trends, which controls the direction of soil moisture changes, while increasing temperatures can also enhance soil drying during periods of decreased precipitation.

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Tuning and assessment of the HYCOM-NORWECOM V2.1 modeling system

Geoscientific Model Development Discussions ◽

10.5194/gmdd-7-8399-2014 ◽

2014 ◽

Vol 7 (6) ◽

pp. 8399-8432 ◽

Cited By ~ 1

Author(s):

A. Samuelsen ◽

C. Hansen ◽

H. Wehde

Keyword(s):

High Resolution ◽

Arctic Ocean ◽

Basic Research ◽

Grazing Rate ◽

The Arctic ◽

Atlantic Sector ◽

In Situ Data ◽

Resolution Model ◽

High Resolution Model

Abstract. The HYCOM-NORWECOM modeling system is used both for basic research and as a part of the forecasting system for the Arctic Marine Forecasting Centre through the MyOcean project. Here we present a revised version of this model. The present model, as well as the sensitivity simulations leading up to this version, has been compared to a dataset of in-situ measurements of nutrient and chlorophyll from the Norwegian Sea and the Atlantic sector of the Arctic Ocean. The revisions having most impact included adding diatoms to the diet of micro-zooplankton, increasing micro-zooplankton grazing rate and decreased silicate-to-nitrate ratio in diatoms. Model runs are performed both with a coarse- (~50 km) and higher-resolution (~15 km) model configuration, both covering the North Atlantic and Arctic Ocean. While the new model formulation improves the results in both the coarse- and high-resolution model, the nutrient bias is smaller in the high-resolution model, probably as a result of the better resolution of the main processes and with that improved circulation. The final revised version delivers satisfactory results for all three nutrients as well as improved result for chlorophyll in terms of the annual cycle amplitude. However, for chlorophyll the correlation with in-situ data remains relatively low. Besides the large uncertainties associated with observational data this is possibly caused by the fact that constant C / N and Chl / N ratios are implemented in the model.

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Development of Observation-based Global Multi-layer Soil Moisture Products for 1970 to 2016

10.5194/essd-2021-84 ◽

2021 ◽

Author(s):

Yaoping Wang ◽

Jiafu Mao ◽

Mingzhou Jin ◽

Forrest M. Hoffman ◽

Xiaoying Shi ◽

...

Keyword(s):

Soil Moisture ◽

Earth System Model ◽

System Model ◽

Surface Model ◽

Earth System ◽

Model Simulations ◽

The Earth ◽

Wide Range ◽

In Situ Observations

Abstract. Soil moisture (SM) datasets are critical to understanding the global water, energy, and biogeochemical cycles and benefit extensive societal applications. However, individual sources of SM data (e.g., in situ and satellite observations, reanalysis, offline land surface model simulations, Earth system model simulations) have source-specific limitations and biases related to the spatiotemporal continuity, resolutions, and modeling/retrieval assumptions. Here, we developed seven global, gap-free, long-term (1970–2016), multi-layer (0–10, 10–30, 30–50, and 50–100 cm) SM products at monthly 0.5° resolution (available at https://doi.org/10.6084/m9.figshare.13661312.v1) by synthesizing a wide range of SM datasets using three statistical methods (unweighted averaging, optimal linear combination, and emergent constraint). The merged products outperformed their source datasets when evaluated with in situ observations and the latest gridded datasets that did not enter merging because of insufficient spatial, temporal, or soil layer coverage. Assessed against in situ observations, the global mean bias of the synthesized SM data ranged from −0.044 to 0.033 m3/m3, root mean squared error from 0.076 to 0.104 m3/m3, and Pearson correlation from 0.35 to 0.67. The merged SM datasets also showed the ability to capture historical large-scale drought events and physically plausible global sensitivities to observed meteorological factors. Three of the new SM products, produced by applying any of the three merging methods onto the source datasets excluding the Earth system models, were finally recommended for future applications because of their better performances than the Earth system model–dependent merged estimates. Despite uncertainties in the raw SM datasets and fusion methods, these hybrid products create added value over existing SM datasets because of the performance improvement and harmonized spatial, temporal, and vertical coverages, and they provide a new foundation for scientific investigation and resource management.

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Quantifying Long-Term Land Surface and Root Zone Soil Moisture over Tibetan Plateau

Remote Sensing ◽

10.3390/rs12030509 ◽

2020 ◽

Vol 12 (3) ◽

pp. 509 ◽

Cited By ~ 6

Author(s):

Ruodan Zhuang ◽

Yijian Zeng ◽

Salvatore Manfreda ◽

Zhongbo Su

Keyword(s):

Soil Moisture ◽

Tibetan Plateau ◽

Land Surface ◽

Root Zone ◽

Cumulative Distribution ◽

Analytical Relationship ◽

The Tibetan Plateau ◽

Climate Systems

It is crucial to monitor the dynamics of soil moisture over the Tibetan Plateau, while considering its important role in understanding the land-atmosphere interactions and their influences on climate systems (e.g., Eastern Asian Summer Monsoon). However, it is very challenging to have both the surface and root zone soil moisture (SSM and RZSM) over this area, especially the study of feedbacks between soil moisture and climate systems requires long-term (e.g., decadal) datasets. In this study, the SSM data from different sources (satellites, land data assimilation, and in-situ measurements) were blended while using triple collocation and least squares method with the constraint of in-situ data climatology. A depth scaling was performed based on the blended SSM product, using Cumulative Distribution Function (CDF) matching approach and simulation with Soil Moisture Analytical Relationship (SMAR) model, to estimate the RZSM. The final product is a set of long-term (~10 yr) consistent SSM and RZSM product. The inter-comparison with other existing SSM and RZSM products demonstrates the credibility of the data blending procedure used in this study and the reliability of the CDF matching method and SMAR model in deriving the RZSM.

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Heat and moisture budgets from airborne measurements and high-resolution model simulations

Meteorology and Atmospheric Physics ◽

10.1007/s00703-012-0188-6 ◽

2012 ◽

Vol 117 (1-2) ◽

pp. 47-61 ◽

Cited By ~ 3

Author(s):

Stefan Zacharias ◽

Mark Reyers ◽

Joaquim G. Pinto ◽

Jan H. Schween ◽

Susanne Crewell ◽

...

Keyword(s):

High Resolution ◽

Model Simulations ◽

Airborne Measurements ◽

Resolution Model ◽

High Resolution Model ◽

Heat And Moisture

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ERA-5 and ERA-Interim driven ISBA land surface model simulations: which one performs better?

Hydrology and Earth System Sciences ◽

10.5194/hess-22-3515-2018 ◽

2018 ◽

Vol 22 (6) ◽

pp. 3515-3532 ◽

Cited By ~ 74

Author(s):

Clement Albergel ◽

Emanuel Dutra ◽

Simon Munier ◽

Jean-Christophe Calvet ◽

Joaquin Munoz-Sabater ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Land Surface ◽

Snow Depth ◽

Land Surface Model ◽

Surface Model ◽

Area Index ◽

Model Simulations ◽

In Situ Observations

Abstract. The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the first 7-year segment of its latest atmospheric reanalysis: ERA-5 over the period 2010–2016. ERA-5 has important changes relative to the former ERA-Interim atmospheric reanalysis including higher spatial and temporal resolutions as well as a more recent model and data assimilation system. ERA-5 is foreseen to replace ERA-Interim reanalysis and one of the main goals of this study is to assess whether ERA-5 can enhance the simulation performances with respect to ERA-Interim when it is used to force a land surface model (LSM). To that end, both ERA-5 and ERA-Interim are used to force the ISBA (Interactions between Soil, Biosphere, and Atmosphere) LSM fully coupled with the Total Runoff Integrating Pathways (TRIP) scheme adapted for the CNRM (Centre National de Recherches Météorologiques) continental hydrological system within the SURFEX (SURFace Externalisée) modelling platform of Météo-France. Simulations cover the 2010–2016 period at half a degree spatial resolution. The ERA-5 impact on ISBA LSM relative to ERA-Interim is evaluated using remote sensing and in situ observations covering a substantial part of the land surface storage and fluxes over the continental US domain. The remote sensing observations include (i) satellite-driven model estimates of land evapotranspiration, (ii) upscaled ground-based observations of gross primary production, (iii) satellite-derived estimates of surface soil moisture and (iv) satellite-derived estimates of leaf area index (LAI). The in situ observations cover (i) soil moisture, (ii) turbulent heat fluxes, (iii) river discharges and (iv) snow depth. ERA-5 leads to a consistent improvement over ERA-Interim as verified by the use of these eight independent observations of different land status and of the model simulations forced by ERA-5 when compared with ERA-Interim. This is particularly evident for the land surface variables linked to the terrestrial hydrological cycle, while variables linked to vegetation are less impacted. Results also indicate that while precipitation provides, to a large extent, improvements in surface fields (e.g. large improvement in the representation of river discharge and snow depth), the other atmospheric variables play an important role, contributing to the overall improvements. These results highlight the importance of enhanced meteorological forcing quality provided by the new ERA-5 reanalysis, which will pave the way for a new generation of land-surface developments and applications.

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