Deriving SAR Soil Moisture Retrieval Algorithms and Soil Drainage Classification for Boreal Peatlands

2021 ◽  
Author(s):  
Laura Bourgeau-Chavez ◽  
Jeremy Graham ◽  
Andrew Poley ◽  
Dorthea Leisman ◽  
Michael Battaglia
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Land Surface ◽  
Ground Surface ◽  
Terrestrial Ecosystem ◽  
Independent Study ◽  
Additive Models ◽  
Soil Drainage ◽  
Boreal Peatlands ◽  
Retrieval Algorithms

<p>Eighty percent of global peatlands are distributed across the boreal and subarctic regions, storing an estimated 30% of earth’s soil organic carbon (1,016 to 1,105 Gt C) despite representing only about 3% of the global land surface. The accumulation of C in peatlands generally depends on hydrologic conditions that maintain saturated soils and impede rates of decomposition. Boreal Peatlands have provided rich reservoirs of stored C for millennia. However, with climate change, warming and drying patterns across the boreal and arctic are resulting in dramatic changes in ecosystems and putting these systems at risk of changing from a C sink to a source.  Recent changes in climate including earlier springs, longer summers and changes in moisture patterns across the landscape, are affecting wildfire regimes of the boreal region including intensity, severity and frequency of wildfires. This in turn has potential to cause shifts in successional trajectories.  Understanding how these changes in climate are affecting peatlands and their vulnerability to wildfire has been a focus of study of the research team since 2009.  Soil moisture is one variable which can provide information to understand wildfire behavior including the depth of peat consumption in these wildfires but it also has a direct effect on post-fire successional trajectories. Further it is needed to understand methane emissions from peatlands.  To develop the soil moisture retrieval algorithms, we studied a range of boreal peatland sites (bogs and fens) stratified across geographic regions from 2012-2014.  We developed soil moisture retrieval algorithms from polarimetric C-band (5.7 cm wavelength) synthetic aperture radar (SAR) data.  Peatlands have low enough aboveground biomass (<3.0 kg/m<sup>2</sup>) to allow this shorter wavelength SAR to penetrate the canopy to reach the ground surface.  Data from over 60, 4 ha sites were collected over 3 seasons from Alaska and Michigan USA and Alberta Canada.  Both multi-linear regressions and general additive models (GAM) were developed.  Using both polarimetric SAR parameters that are sensitive to vegetation structure and parameters most sensitive to surface soil moisture in the models provided the best results.  GAM models were tested in an independent study area, Northwest Territories (NWT), Canada.  The sites of NWT were sampled in 2016-2019 coincident to Radarsat-2 polarimetric image collections.  The high accuracy results will be presented as well as methods developed to use multidate C-band data from Sentinel-1 to classify soil drainage (well drained to poorly drained) in recently burned peatlands.  These products are being used in a fire effects and emissions model, CanFIRE, as we parameterize it for peatlands; as well as the Functionally-Assembled Terrestrial Ecosystem Simulator <strong>(</strong>FATES) to understand the effects of wildfire and hydrology on peatland ecosystems.  Characterization and quantification of boreal peatlands in global C cycling is critical for proper accounting given that peatlands play a significant role in sequestering and releasing large amounts of C. The ability to retrieve soil moisture from C-band SAR, therefore, provides a means to monitor a key variable in scaling C flux estimates as well as understanding the vulnerability and resiliency of boreal peatlands to climate change.</p><p> </p>

scholarly journals Deriving surface soil moisture from reflected GNSS signal observations from a grassland site in southwestern France

2017 ◽  
Author(s):  
Sibo Zhang ◽  
Jean-Christophe Calvet ◽  
José Darrozes ◽  
Nicolas Roussel ◽  
Frédéric Frappart ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Ground Surface ◽  
Satellite System ◽  
Surface Model ◽  
Global Navigation Satellite ◽  
Grassland Site

Abstract. This work aims to assess the estimation of surface volumetric soil moisture (VSM) using the Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) technique. Year-round observations were acquired from a grassland site in southwestern France using an antenna consecutively placed at two contrasting heights above the ground surface (3.3 or 29.4 m). The VSM retrievals are compared with two independent reference datasets: in situ observations of soil moisture, and numerical simulations of soil moisture and vegetation biomass from the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model. Scaled VSM estimates can be retrieved throughout the year removing vegetation effects by the separation of growth and senescence periods and by the filtering of the GNSS-IR observations that are most affected by vegetation. Antenna height has no significant impact on the quality of VSM estimates. Comparisons between the VSM GNSS-IR retrievals and the in situ VSM observations at a depth of 5 cm show a good agreement (R2 = 0.86 and RMSE = 0.04 m3 m−3). It is shown that the signal is sensitive to the grass litter water content and that this effect triggers differences between VSM retrievals and in situ VSM observations at depths of 1 cm and 5 cm, especially during light rainfall events.

QA4SM – An online tool for satellite soil moisture data validation

2021 ◽  
Author(s):  
Samuel Scherrer ◽  
Wolfgang Preimesberger ◽  
Monika Tercjak ◽  
Zoltan Bakcsa ◽  
Alexander Boresch ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Land Surface ◽  
Global Climate ◽  
European Space Agency ◽  
Land Surface Model ◽  
Surface Model ◽  
Space Applications ◽  
Online Tool ◽  
Soil Moisture Data

<p>To validate satellite soil moisture products and compare their quality with other products, standardized, fully traceable validation methods are required. The QA4SM (Quality Assurance for Soil Moisture; ) free online validation tool provides an easy-to-use implementation of community best practices and requirements set by the Global Climate Observing System and the Committee on Earth Observation Satellites. It sets the basis for a community wide standard for validation studies.</p><p>QA4SM can be used to preprocess, intercompare, store, and visualise validation results. It uses state-of-the-art open-access soil moisture data records such as the European Space Agency’s Climate Change Initiative (ESA CCI) and the Copernicus Climate Change Services (C3S) soil moisture datasets, as well as single-sensor products, e.g. H-SAF Metop-A/B ASCAT surface soil moisture, SMOS-IC, and SMAP L3 soil moisture. Non-satellite data include in-situ data from the International Soil Moisture Network (ISMN: ), as well as land surface model or reanalysis products, e.g. ERA5 soil moisture.</p><p>Users can interactively choose temporal or spatial subsets of the data and apply filters on quality flags. Additionally, validation of anomalies and application of different scaling methods are possible. The tool provides traditional validation metrics for dataset pairs (e.g. correlation, RMSD) as well as triple collocation metrics for dataset triples. All results can be visualised on the webpage, downloaded as figures, or downloaded in NetCDF format for further use. Archiving and publishing features allow users to easily store and share validation results. Published validation results can be cited in reports and publications via DOIs.</p><p>The new version of the service provides support for high-resolution soil moisture products (from Sentinel-1), additional datasets, and improved usability.</p><p>We present an overview and examples of the online tool, new features, and give an outlook on future developments.</p><p><em>Acknowledgements: This work was supported by the QA4SM & QA4SM-HR projects, funded by the Austrian Space Applications Programme (FFG).</em></p>

Drought responses of Amazon forests under climate change: Separating the roles of soil moisture and canopy responses

2020 ◽  
Author(s):  
Hao-wei Wey ◽  
Kim Naudts ◽  
Julia Pongratz ◽  
Julia Nabel ◽  
Lena Boysen
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Land Surface ◽  
Net Primary Production ◽  
Leaf Phenology ◽  
Future Climate Change ◽  
Surface Model ◽  
Litter Production ◽  
Direct Effects ◽  
Climatic Effects

<p>The Amazon forests are one of the largest ecosystem carbon pools on Earth. While more frequent and prolonged droughts have been predicted under future climate change there, the vulnerability of Amazon forests to drought has yet remained largely uncertain, as previous studies have shown that few land surface models succeeded in capturing the vegetation responses to drought. In this study, we present an improved version of the land surface model JSBACH, which incorporates new formulations of leaf phenology and litter production based on intensive field measurement from the artificial drought experiments in the Amazon. Coupling the new JSBACH with the atmospheric model ECHAM, we investigate the drought responses of the Amazon forests and the resulting feedbacks under RCP8.5 scenario. The climatic effects resulted from (1) direct effects including declining soil moisture and stomatal responses, and (2) soil moisture-induced canopy responses are separated to give more insights, as the latter was poorly simulated. Preliminary results show that for net primary production and soil respiration, the direct effects and canopy responses have similar spatial patterns with the magnitude of the latter being 1/5 to 1/3 of the former. In addition, declining soil moisture enhances rainfall in Northern Amazon and suppresses rainfall in the south, while canopy responses have negligible effects on rainfall. Based on our findings, we suggest cautious interpretation of results from previous studies. To address this uncertainty, better strategy in modeling leaf phenology such as implemented in this study should be adopted.</p>

Land-surface feedbacks on temperature and precipitation in CMIP6-LS3MIP projections

2021 ◽  
Author(s):  
Franco Catalano ◽  
Andrea Alessandri ◽  
Wilhelm May ◽  
Thomas Reerink
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Land Surface ◽  
Hydrological Cycle ◽  
Climate Change Signal ◽  
Moisture Change ◽  
Climate Conditions ◽  
Temperature And Precipitation ◽  
Systematic Biases ◽  
The Impact

<p align="justify"><span>The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) aims at diagnosing systematic biases in the land models of CMIP6 Earth System Models and assessing the role of land-atmosphere feedbacks on climate change. Two components of experiments have been designed: the first is devoted to the assessment of the systematic land biases in offline mode (LMIP) while the second component is dedicated to the analysis of the land feedbacks in coupled mode (LFMIP). Here we focus on the LFMIP experiments. In the LFMIP protocol (van den Hurk et al. 2016), which builds upon the GLACE-CMIP configuration, two sets of climate-sensitivity projections have been carried out in amip mode: in the first set (amip-lfmip-pdLC) the land feedbacks to climate change have been disabled by prescribing the soil-moisture states from a climatology derived from “present climate conditions” (1980-2014) while in the second set (amip-lfmip-rmLC) 30-year running mean of land-surface state from the corresponding ScenarioMIP experiment (O’Neill et al., 2016) is prescribed. The two sensitivity simulations span the period 1980-2100 with sea surface temperature and sea-ice conditions prescribed from the first member of historical and ScenarioMIP experiments. Two different scenarios are considered: SSP1-2.6 (f1) and SSP5-8.5 (f2).</span></p><p align="justify"><span>In this analysis, we focus on the differences between amip-lfmip-rmLC and amip-lfmip-pdLC at the end of the 21st Century (2071–2100) in order to isolate the impact of the soil moisture changes on surface climate change. The (2071-2100) minus (1985-2014) temperature change is positive everywhere over land and the climate change signal of precipitation displays a clear intensification of the hydrological cycle in the Northern Hemisphere. Warming and hydrological cycle intensification are larger in SSP5-8.5 scenario. Results show large differences in the feedbacks between wet, transition and semi-arid climates. In particular, over the regions with negative soil moisture change, the 2m-temperature increases significantly while the cooling signal is not significant over all the regions getting wetter. In agreement with Catalano et al. (2016), the larger effects on precipitation due to soil moisture forcing occur mostly over transition zones between dry and wet climates, where evaporation is highly sensitive to soil moisture. The sensitivity of both 2m-temperature and precipitation to soil moisture change is much stronger in the SSP5-8.5 scenario.</span></p>

scholarly journals Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM Soil Moisture Products over the Qinghai-Tibet Plateau and Its Surrounding Areas

2019 ◽  
Vol 11 (7) ◽  
pp. 792 ◽  
Author(s):  
Jin Liu ◽  
Linna Chai ◽  
Zheng Lu ◽  
Shaomin Liu ◽  
Yuquan Qu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Regional Scale ◽  
Temporal Variations ◽  
Comparison Method ◽  
Tibet Plateau ◽  
Quantitative Evidence ◽  
Retrieval Algorithms ◽  
Qinghai Tibet Plateau

High-quality and long time-series soil moisture (SM) data are increasingly required for the Qinghai-Tibet Plateau (QTP) to more accurately and effectively assess climate change. In this study, to evaluate the accuracy and effectiveness of SM data, five passive microwave remotely sensed SM products are collected over the QTP, including those from the soil moisture active passive (SMAP), soil moisture and ocean salinity INRA-CESBIO (SMOS-IC), Fengyun-3B microwave radiation image (FY3B), and two SM products derived from the advanced microwave scanning radiometer 2 (AMSR2). The two AMSR2 products are generated by the land parameter retrieval model (LPRM) and the Japan Aerospace Exploration Agency (JAXA) algorithm, respectively. The SM products are evaluated through a two-stage data comparison method. The first stage is direct validation at the grid scale. Five SM products are compared with corresponding in situ measurements at five in situ networks, including Heihe, Naqu, Pali, Maqu, and Ngari. Another stage is indirect validation at the regional scale, where the uncertainties of the data are quantified by using a three-cornered hat (TCH) method. The results at the regional scale indicate that soil moisture is underestimated by JAXA and overestimated by LPRM, some noise is contained in temporal variations in SMOS-IC, and FY3B has relatively low absolute accuracy. The uncertainty of SMAP is the lowest among the five products over the entire QTP. In the SM map composed by five SM products with the lowest pixel-level uncertainty, 66.64% of the area is covered by SMAP (JAXA: 19.39%, FY3B: 10.83%, LPRM: 2.11%, and SMOS-IC: 1.03%). This study reveals some of the reasons for the different performances of these five SM products, mainly from the perspective of the parameterization schemes of their corresponding retrieval algorithms. Specifically, the parameterization configurations and corresponding input datasets, including the land-surface temperature, the vegetation optical depth, and the soil dielectric mixing model are analyzed and discussed. This study provides quantitative evidence to better understand the uncertainties of SM products and explain errors that originate from the retrieval algorithms.

scholarly journals Copula-Derived Observation Operators for Assimilating TMI and AMSR-E Retrieved Soil Moisture into Land Surface Models

2007 ◽  
Vol 8 (3) ◽  
pp. 413-429 ◽  
Author(s):  
Huilin Gao ◽  
Eric F. Wood ◽  
Matthias Drusch ◽  
Matthew F. McCabe
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Qualitative Difference ◽  
Surface Model ◽  
Land Surface Models ◽  
Satellite Retrieval ◽  
Surface Models ◽  
Model Predictions ◽  
Satellite Retrievals ◽  
Retrieval Algorithms

Abstract Assimilating soil moisture from satellite remote sensing into land surface models (LSMs) has potential for improving model predictions by providing real-time information at large scales. However, the majority of the research demonstrating this potential has been limited to datasets based on either airborne data or synthetic observations. The limited availability of satellite-retrieved soil moisture and the observed qualitative difference between satellite-retrieved and modeled soil moisture has posed challenges in demonstrating the potential over large regions in actual applications. Comparing modeled and satellite-retrieved soil moisture fields shows systematic differences between their mean values and between their dynamic ranges, and these systematic differences vary with satellite sensors, retrieval algorithms, and LSMs. This investigation focuses on generating observation operators for assimilating soil moisture into LSMs using a number of satellite–model combinations. The remotely sensed soil moisture products come from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and the NASA/Earth Observing System (EOS) Advanced Microwave Scanning Radiometer (AMSR-E). The soil moisture model predictions are from the Variable Infiltration Capacity (VIC) hydrological model; the 40-yr European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-40); and the NCEP North American Regional Reanalysis (NARR). For this analysis, the satellite and model data are over the southern Great Plains region from 1998 to 2003 (1998–2002 for ERA-40). Previous work on observation operators used the matching of cumulative distributions to transform satellite-retrieved soil moisture into modeled soil moisture, which implied perfect correlations between the ranked values. In this paper, a bivariate statistical approach, based on copula distributions, is employed for representing the joint distribution between retrieved and modeled soil moisture, allowing for a quantitative estimation of the uncertainty in modeled soil moisture when merged with a satellite retrieval. The conditional probability distribution of model-based soil moisture conditioned on a satellite retrieval forms the basis for the soil moisture observation operator. The variance of these conditional distributions for different retrieval algorithms, LSMs, and locations provides an indication of the information content of satellite retrievals in assimilation. Results show that the operators vary by season and by land surface model, with the satellite retrievals providing more information in summer [July–August (JJA)] and fall [September–November (SON)] than winter [December–February (DJF)] or spring [March–May (MAM)] seasons. Also, the results indicate that the value of satellite-retrieved soil moisture is most useful to VIC, followed by ERA-40 and then NARR.

scholarly journals Deriving surface soil moisture from reflected GNSS signal observations from a grassland site in southwestern France

2018 ◽  
Vol 22 (3) ◽  
pp. 1931-1946 ◽  
Author(s):  
Sibo Zhang ◽  
Jean-Christophe Calvet ◽  
José Darrozes ◽  
Nicolas Roussel ◽  
Frédéric Frappart ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Ground Surface ◽  
Satellite System ◽  
Surface Model ◽  
Global Navigation Satellite ◽  
Grassland Site

Abstract. This work assesses the estimation of surface volumetric soil moisture (VSM) using the global navigation satellite system interferometric reflectometry (GNSS-IR) technique. Year-round observations were acquired from a grassland site in southwestern France using an antenna consecutively placed at two contrasting heights above the ground surface (3.3 and 29.4 m). The VSM retrievals are compared with two independent reference datasets: in situ observations of soil moisture, and numerical simulations of soil moisture and vegetation biomass from the ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model. Scaled VSM estimates can be retrieved throughout the year removing vegetation effects by the separation of growth and senescence periods and by the filtering of the GNSS-IR observations that are most affected by vegetation. Antenna height has no significant impact on the quality of VSM estimates. Comparisons between the VSM GNSS-IR retrievals and the in situ VSM observations at a depth of 5 cm show good agreement (R2= 0.86 and RMSE = 0.04 m3 m−3). It is shown that the signal is sensitive to the grass litter water content and that this effect triggers differences between VSM retrievals and in situ VSM observations at depths of 1 and 5 cm, especially during light rainfall events.

ESA CCI and C3S Soil Moisture Products: Generation and Quality Assurance

2021 ◽  
Author(s):  
Tracy Scanlon ◽  
Wouter Dorigo ◽  
Wolfgang Preimesberger ◽  
Robin van der Schalie ◽  
Martin Hirschi ◽  
...  
Keyword(s):  
Climate Change ◽  
Quality Assurance ◽  
Soil Moisture ◽  
Land Surface ◽  
European Space Agency ◽  
Global Scale ◽  
Climate Data ◽  
Change Initiative ◽  
Data Product ◽  
Level 2

<p>Soil moisture Climate Data Records (CDRs) produced from active and passive microwave sensors are valuable for the study of the coupled water, energy and carbon cycles over land on a global scale. As part of the European Space Agency (ESA) Climate Change Initiative (CCI) a multi-decadal CDR is produced by systematically combining Level-2 datasets from separate missions. The combination of individual Level 2 datasets into a single product gives us the opportunity to profit from the advantages of individual missions, and to obtain homogenised CDRs with improved spatial and temporal coverage.<br>The most recent version of the ESA CCI product (v06) provides 3 products: (1978 – 2020), ACTIVE (1991 – 2020) and COMBINED (1978 – 2020). This latest version of the product includes several advances that result in the improved quality of the product. Improvements to the input datasets include updated passive (LPRM – Land Parameter Retrieval Model) data to improve inter-calibration and snow / frozen condition flagging as well as updated ASCAT data from the H-SAF project to improve vegetation correction. <br>Algorithmic improvements include the cross-flagging of snow / frozen conditions to take advantage of the flags provided for each input dataset across all sensors as well as the update of the Signal to Noise Ratio – Vegetation Optical Depth (SNR-VOD) regression used in gap-filling the SNR in locations where retrieval has failed. Additional data is also included through the use of the Global Precipitation Measurement (GPM) mission, the FengYun-3B (FY3B) mission and extending the Tropical Rainfall Measuring Mission (TRMM) dataset used to 2015.<br>An operational product based on the ESA CCI SM product continues to be provided through the EU Copernicus Climate Changes Services (C3S) Climate Data Store (CDS). This operational product provides daily data and decadal (10 daily) aggregates in near-real-time as well as monthly aggregates for the historical dataset. The anomalies derived from this dataset (with a base period of 1991 to 2010) can be seen on the TU Wien data viewer (https://dataviewer.geo.tuwien.ac.at/).<br>The accuracy of each data product is assessed through comparison to in-situ soil moisture observations from the International Soil Moisture Network (ISMN) as well as modelled data from Land Surface Models (LSMs). Such assessments are undertaken each time a new ESA CCI version is generated, and the results compared against previous versions to assess the evolution of the product quality over time. For transparency and traceability, an online portal is provided for the public to perform similar validations (Quality Assurance for Soil Moisture – www.qa4sm.eu). <br>In this study, an overview of the product generation and the updates provided at ESA CCI SM v06 is presented as well as examples of how the data product has been used. The associated quality assurance requirements, assessment procedures and results will also be presented.<br>The development of the ESA CCI products has been supported by ESA’s Climate Change Initiative for Soil Moisture (Contract No. 4000104814/11/I-NB and 4000112226/14/I-NB). Funded by Copernicus Climate Change Service implemented by ECMWF through C3S 312a Lot 7 Soil Moisture service.</p>

scholarly journals LS3MIP (v1.0) contribution to CMIP6: the Land Surface, Snow and Soil moisture Model Intercomparison Project – aims, setup and expected outcome

2016 ◽  
Vol 9 (8) ◽  
pp. 2809-2832 ◽  
Author(s):  
Bart van den Hurk ◽  
Hyungjun Kim ◽  
Gerhard Krinner ◽  
Sonia I. Seneviratne ◽  
Chris Derksen ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Land Surface ◽  
Surface Radiation ◽  
The Arctic ◽  
Model Intercomparison ◽  
Soil Moisture Model ◽  
Surface Snow ◽  
Systematic Biases ◽  
Intercomparison Project

Abstract. The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of land surface, snow and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth system models (ESMs). The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems). The experiments are subdivided in two components, the first addressing systematic land biases in offline mode (“LMIP”, building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework (“LFMIP”, building upon the GLACE-CMIP blueprint).

scholarly journals Contributions of climate change and groundwater extraction to soil moisture trends

2019 ◽  
Vol 10 (3) ◽  
pp. 599-615 ◽  
Author(s):  
Longhuan Wang ◽  
Zhenghui Xie ◽  
Binghao Jia ◽  
Jinbo Xie ◽  
Yan Wang ◽  
...  
Keyword(s):  
Climate Change ◽  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Water Cycle ◽  
Supply And Demand ◽  
Surface Model ◽  
Groundwater Extraction ◽  
Surface Soil Moisture ◽  
Deep Soil

Abstract. Climate change affects water availability for soil, and groundwater extraction influences water redistribution by altering water demand, both of which significantly affect soil moisture. Quantifying their relative contribution to the changes in soil moisture will further our understanding of the mechanisms underlying the global water cycle. In this study, two groups of simulations were conducted with and without groundwater (GW) extraction (estimated based on local water supply and demand) from 1979 to 2010 using the Chinese Academy of Sciences land surface model, CAS-LSM, with four global meteorological forcing datasets (GSWP3, PRINCETON, CRU-NCEP, and WFDEI). To investigate the contribution of climate change and GW extraction, a trajectory-based method was used. Comparing the simulated results with the in situ dataset of the International Soil Moisture Network (ISMN) and the satellite-based soil moisture product of the European Space Agency's Climate Change Initiative (ESA-CCI) indicated that the CAS-LSM reasonably reproduced the distribution of soil moisture and matched the temporal changes well. Globally, our results suggested a significant decreasing trend in surface soil moisture (0–10 cm, 0.98×10-4 mm3 mm−3 yr−1) over the 32-year period tested. The drying trends were mainly observed in arid regions such as the tropical desert regions in North Africa and the Arabian Peninsula, while the wetting trends were primarily in tropical forested areas in South America and northeastern Asia. Climate change contributed 101.2 % and 90.7 % to global drying and wetting trends of surface soil moisture, respectively, while GW extraction accounted for −1.2 % and 9.3 %, respectively. In deep soil, GW extraction contributed 1.37 % and −3.21 % to the drying and wetting trends, respectively. The weak influence of GW extraction may be because this activity occurs in limited areas. GW extraction contributed more than 35 % to the change in surface soil moisture in wetting areas where GW overexploitation occurs. GW is mainly extracted for irrigation to alleviate soil water stress in semiarid regions that receive limited precipitation, thereby slowing the drying trend and accelerating the wetting trend of surface soil. However, GW exploitation weakens the hydraulic connection between the soil and aquifer, leading to deeper soils drying up. Overall, climate change dominated the soil moisture trends, but the effect of GW extraction cannot be ignored.

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