The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements

Abstract. In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land cover change. Nevertheless, on a worldwide basis the number of meteorological networks and stations measuring soil moisture, in particular on a continuous basis, is still limited and the data they provide lack standardization of technique and protocol. To overcome many of these limitations, the International Soil Moisture Network (ISMN; http://www.ipf.tuwien.ac.at/insitu) was initiated to serve as a centralized data hosting facility where globally available in situ soil moisture measurements from operational networks and validation campaigns are collected, harmonized, and made available to users. Data collecting networks share their soil moisture datasets with the ISMN on a voluntary and no-cost basis. Incoming soil moisture data are automatically transformed into common volumetric soil moisture units and checked for outliers and implausible values. Apart from soil water measurements from different depths, important metadata and meteorological variables (e.g., precipitation and soil temperature) are stored in the database. These will assist the user in correctly interpreting the soil moisture data. The database is queried through a graphical user interface while output of data selected for download is provided according to common standards for data and metadata. Currently (status May 2011), the ISMN contains data of 19 networks and more than 500 stations located in North America, Europe, Asia, and Australia. The time period spanned by the entire database runs from 1952 until the present, although most datasets have originated during the last decade. The database is rapidly expanding, which means that both the number of stations and the time period covered by the existing stations are still growing. Hence, it will become an increasingly important resource for validating and improving satellite-derived soil moisture products and studying climate related trends. As the ISMN is animated by the scientific community itself, we invite potential networks to enrich the collection by sharing their in situ soil moisture data.

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The International Soil Moisture Network: a data hosting facility for global in situ soil moisture measurements

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-1609-2011 ◽

2011 ◽

Vol 8 (1) ◽

pp. 1609-1663 ◽

Cited By ~ 16

Author(s):

W. A. Dorigo ◽

W. Wagner ◽

R. Hohensinn ◽

S. Hahn ◽

C. Paulik ◽

...

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Water Cycle ◽

Soil Moisture Data ◽

The North ◽

Time Period ◽

Different Depths ◽

Operational Networks

Abstract. In situ measurements of soil moisture are invaluable for calibrating and validating land surface models and satellite-based soil moisture retrievals. In addition, long-term time series of in situ soil moisture measurements themselves can reveal trends in the water cycle related to climate or land cover change. Nevertheless, on a worldwide basis the number of meteorological networks and stations measuring soil moisture, in particular on a continuous basis, is still limited and the data they provide lack standardization of technique and protocol. To overcome many of these limitations, the International Soil Moisture Network (ISMN; http://www.ipf.tuwien.ac.at/insitu) was initiated to serve as a centralized data hosting facility where globally available in situ soil moisture measurements from operational networks and validation campaigns are collected, harmonized, and made available to users. Data collecting networks share their soil moisture datasets with the ISMN on a voluntary and no-cost basis. Incoming soil moisture data are automatically transformed into common volumetric soil moisture units and checked for outliers and implausible values. Apart from soil water measurements from different depths, important metadata and meteorological variables (e.g., precipitation and soil temperature) are stored in the database. These will assist the user in correctly interpreting the soil moisture data. The database is queried through a graphical user interface while output of data selected for download is provided according to common standards for data and metadata. Currently (status January 2011), the ISMN contains data of 16 networks and more than 500 stations located in the North America, Europe, Asia, and Australia. The time period spanned by the entire database runs from 1952 until the present, although most datasets have originated during the last decade. The database is rapidly expanding, which means that both the number of stations and the time period covered by the existing stations are still growing. Hence, it will become an increasingly important resource for validating and improving satellite-derived soil moisture products and studying climate related trends. As the ISMN is animated by the scientific community itself, we invite potential networks to enrich the collection by sharing their in situ soil moisture data.

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Global soil moisture data derived through machine learning trained with in-situ measurements

Scientific Data ◽

10.1038/s41597-021-00964-1 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Sungmin O. ◽

Rene Orth

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Large Scale ◽

Short Term Memory ◽

Temporal Dynamics ◽

Soil Moisture Data ◽

Wide Range ◽

Global Soil

AbstractWhile soil moisture information is essential for a wide range of hydrologic and climate applications, spatially-continuous soil moisture data is only available from satellite observations or model simulations. Here we present a global, long-term dataset of soil moisture derived through machine learning trained with in-situ measurements, SoMo.ml. We train a Long Short-Term Memory (LSTM) model to extrapolate daily soil moisture dynamics in space and in time, based on in-situ data collected from more than 1,000 stations across the globe. SoMo.ml provides multi-layer soil moisture data (0–10 cm, 10–30 cm, and 30–50 cm) at 0.25° spatial and daily temporal resolution over the period 2000–2019. The performance of the resulting dataset is evaluated through cross validation and inter-comparison with existing soil moisture datasets. SoMo.ml performs especially well in terms of temporal dynamics, making it particularly useful for applications requiring time-varying soil moisture, such as anomaly detection and memory analyses. SoMo.ml complements the existing suite of modelled and satellite-based datasets given its distinct derivation, to support large-scale hydrological, meteorological, and ecological analyses.

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Using data assimilation to optimize pedotransfer functions using large-scale in-situ soil moisture observations

10.5194/hess-2020-359 ◽

2020 ◽

Author(s):

Elizabeth Cooper ◽

Eleanor Blyth ◽

Hollie Cooper ◽

Rich Ellis ◽

Ewan Pinnington ◽

...

Keyword(s):

Soil Moisture ◽

Data Assimilation ◽

Land Surface ◽

Large Scale ◽

Pedotransfer Functions ◽

Surface Model ◽

Climate Projections ◽

Land Surface Models ◽

Surface Models

Abstract. Soil moisture predictions from land surface models are important in hydrological, ecological and meteorological applications. In recent years the availability of wide-area soil-moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in-situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the JULES land surface model using field scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way can improve the performance of land surface models, leading to the potential for better flood, drought and climate projections.

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Groundwater recharge estimated by land surface models: an evaluation in the conterminous U.S.

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0130.1 ◽

2020 ◽

Author(s):

Bailing Li ◽

Matthew Rodell ◽

Christa Peters-Lidard ◽

Jessica Erlingis ◽

Sujay Kumar ◽

...

Keyword(s):

Soil Moisture ◽

Pacific Northwest ◽

Land Surface ◽

Land Surface Models ◽

Deep Soil ◽

Flow Models ◽

Surface Models ◽

Systematic Biases ◽

Preferential Flows

AbstractEstimating diffuse recharge of precipitation is fundamental to assessing groundwater sustainability. Diffuse recharge is also the process through which climate and climate change directly affect groundwater. In this study, we evaluated diffuse recharge over the conterminous U.S. simulated by a suite of land surface models (LSMs) that were forced using a common set of meteorological input data. Simulated annual recharge exhibited spatial patterns that were similar among the LSMs, with the highest values in the eastern U.S. and Pacific Northwest. However, the magnitudes of annual recharge varied significantly among the models and were associated with differences in simulated ET, runoff and snow. Evaluation against two independent datasets did not answer the question of whether the ensemble mean performs the best, due to inconsistency between those datasets. The amplitude and timing of seasonal maximum recharge differed among the models, influenced strongly by model physics governing deep soil moisture drainage rates and, in cold regions, snowmelt. Evaluation using in situ soil moisture observations suggested that true recharge peaks 1-3 months later than simulated recharge, indicating systematic biases in simulating deep soil moisture. However, recharge from lateral flows and through preferential flows cannot be inferred from soil moisture data, and the seasonal cycle of simulated groundwater storage actually compared well with in situ groundwater observations. Long-term trends in recharge were not consistently correlated with either precipitation trends or temperature trends. This study highlights the need to employ dynamic flow models in LSMs, among other improvements, to enable more accurate simulation of recharge.

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Triple Collocation Evaluation of In Situ Soil Moisture Observations from 1200+ Stations as part of the U.S. National Soil Moisture Network

Journal of Hydrometeorology ◽

10.1175/jhm-d-20-0108.1 ◽

2020 ◽

Vol 21 (11) ◽

pp. 2537-2549

Author(s):

Trent W. Ford ◽

Steven M. Quiring ◽

Chen Zhao ◽

Zachary T. Leasor ◽

Christian Landry

Keyword(s):

United States ◽

Soil Moisture ◽

Land Surface ◽

Surface Model ◽

National Scale ◽

Model Soil ◽

Soil Moisture Data ◽

Spatial Coverage ◽

Soil Moisture Monitoring

AbstractSoil moisture is an important variable for numerous scientific disciplines, and therefore provision of accurate and timely soil moisture information is critical. Recent initiatives, such as the National Soil Moisture Network effort, have increased the spatial coverage and quality of soil moisture monitoring infrastructure across the contiguous United States. As a result, the foundation has been laid for a high-resolution, real-time gridded soil moisture product that leverages data from in situ networks, satellite platforms, and land surface models. An important precursor to this development is a comprehensive, national-scale assessment of in situ soil moisture data fidelity. Additionally, evaluation of the United States’s current in situ soil moisture monitoring infrastructure can provide a means toward more informed satellite and model calibration and validation. This study employs a triple collocation approach to evaluate the fidelity of in situ soil moisture observations from over 1200 stations across the contiguous United States. The primary goal of the study is to determine the monitoring stations that are best suited for 1) inclusion in national-scale soil moisture datasets, 2) deriving in situ–informed gridded soil moisture products, and 3) validating and benchmarking satellite and model soil moisture data. We find that 90% of the 1233 stations evaluated exhibit high spatial consistency with satellite remote sensing and land surface model soil moisture datasets. In situ error did not significantly vary by climate, soil type, or sensor technology, but instead was a function of station-specific properties such as land cover and station siting.

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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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A comprehensive approach to analyze discrepancies between land surface models and in-situ measurements: a case study over the US and Illinois with SECHIBA forced by NLDAS

Hydrology and Earth System Sciences ◽

10.5194/hess-16-3973-2012 ◽

2012 ◽

Vol 16 (11) ◽

pp. 3973-3988 ◽

Cited By ~ 2

Author(s):

M. Guimberteau ◽

A. Perrier ◽

K. Laval ◽

J. Polcher

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Field Capacity ◽

Land Surface Models ◽

Data Set ◽

Wetness Index ◽

Surface Models ◽

The Us ◽

Vegetation Parameters

Abstract. The purpose of this study is to test the ability of the Land Surface Model SECHIBA to simulate water budget and particularly soil moisture at two different scales: regional and local. The model is forced by NLDAS data set at 1/8th degree resolution over the 1997–1999 period. SECHIBA gives satisfying results in terms of evapotranspiration and runoff over the US compared with four other land surface models, all forced by NLDAS data set for a common time period. The simulated soil moisture is compared to in-situ data from the Global Soil Moisture Database across Illinois by computing a soil wetness index. A comprehensive approach is performed to test the ability of SECHIBA to simulate soil moisture with a gradual change of the vegetation parameters closely related to the experimental conditions. With default values of vegetation parameters, the model overestimates soil moisture, particularly during summer. Sensitivity tests of the model to the change of vegetation parameters show that the roots extraction parameter has the largest impact on soil moisture, other parameters such as LAI, height or soil resistance having a minor impact. Moreover, a new evapotranspiration computation including bare soil evaporation under vegetation has been introduced into the model. The results point out an improvement of the soil moisture simulation when this effect is taken into account. Finally, soil moisture sensitivity to precipitation variation is addressed and it is shown that soil moisture observations can be rather different, depending on the method of measuring field capacity. When the observed field capacity is deducted from the observed volumetric water profiles, simulated soil wetness index is closer to the observations.

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The International Soil Moisture Network in assistance of EO soil moisture validation products, services and models

10.5194/egusphere-egu2020-16493 ◽

2020 ◽

Author(s):

Daniel Aberer ◽

Irene Himmelbauer ◽

Lukas Schremmer ◽

Ivana Petrakovic ◽

Wouter Dorigo ◽

...

Keyword(s):

Quality Control ◽

Soil Moisture ◽

Real Time ◽

Land Surface ◽

Measurement Techniques ◽

European Space Agency ◽

Web Portal ◽

Soil Moisture Data ◽

Spatial Coverage

The International Soil Moisture Network (ISMN, https://ismn.geo.tuwien.ac.at/) is an international cooperation to establish and maintain a unique centralized global data hosting facility, making in situ soil moisture data easily and freely accessible. This database is an essential means for validating and improving global satellite soil moisture products, land surface -, climate- , and hydrological models.&#160;In situ measurements are crucial to calibrate and validate satellite soil moisture products. For a meaningful comparison with remotely sensed data and reliable validation results, the quality of the reference data is essential. The various independent local and regional in situ networks often do not follow standardized measurement techniques or protocols, collecting their data in different units, at different depths and at various sampling rates. Besides, quality control is rarely applied and accessing the data is often not easy or feasible.The ISMN has been created to address the above-mentioned issues and is building a stable base to assist EO products, services and models. Within the ISMN, in situ soil moisture measurements (surface and sub-surface) are collected, harmonized in terms of units and sampling rates, advanced quality control is applied and the data is then stored in a database and made available online, where users can download it for free.Founded in 2009, the ISMN has grown to a widely used in situ data source including 61 networks with more than 2600 stations distributed on a global scale and a steadily growing user community > 3200 registered users strong. Time series with hourly timestamps from 1952 &#8211; up to near real time are stored in the database and are available through the ISMN web portal, including daily near-real time updates from 6 networks (> 900 stations). With continuous financial support through the European Space Agency (formerly SMOS and IDEAS+ programs, currently QA4EO program), the ISMN evolved into a platform of benchmark data for several operational services such as ESA CCI Soil Moisture, the Copernicus Climate Change (C3S), the Copernicus Global Land Service (CGLS) and the online validation service Quality Assurance for Soil Moisture (QA4SM). In general, ISMN data is widely used in a variety of scientific fields (e.g. climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc.).About 10&#8217;000 datasets are available through the web portal. However, the spatial coverage of in situ observations still needs to be improved. For example, in Africa and South America only sparse data are available. Innovative ideas, such as the inclusion of soil moisture data from low cost sensors (eventually) collected by citizen scientists, holds the potential of closing this gap, thus providing new information and knowledge.In this session, we give an overview of the ISMN, its unique features and its benefits for validating satellite soil moisture products.

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The International Soil Moisture Network: an open-source data hosting facility in support of meteorology and climate science

10.5194/ems2021-368 ◽

2021 ◽

Author(s):

Ivana Petrakovic ◽

Irene Himmelbauer ◽

Daniel Aberer ◽

Lukas Schremmer ◽

Philippe Goryl ◽

...

Keyword(s):

Soil Moisture ◽

Real Time ◽

International Cooperation ◽

Land Surface ◽

Prediction Models ◽

Weather Prediction ◽

Source Surface ◽

Federal Institute

The International Soil Moisture Network (ISMN, https://ismn.earth) is international cooperation to establish and maintain a unique centralized global data hosting facility, making in-situ soil moisture data easily and freely accessible (Dorigo et al., 2021). Initiated in 2009 as a community effort through international cooperation (ESA, GEWEX, GTN-H, GCOS, TOPC, HSAF, QA4SM, C3S, etc.), the ISMN is an essential means for validating and improving global satellite soil moisture products, land surface-, climate-, and hydrological models.&#160; The ISMN is a widely used, reliable, and consistent in-situ data source (surface and sub-surface) collected by a myriad of data organizations on a voluntary basis. &#160;The in-situ soil moisture measurements are collected, harmonized in terms of units and sampling rates, advanced quality control is applied and the data is then stored in a database and made available online, where users can download it for free. Currently, 71 networks are participating with more than 2800 stations distributed on a global scale and a steadily increasing number of user communities. Long term time series with mainly hourly timestamps from 1952 &#8211; up to near-real-time are stored in the database, including daily near-real-time updates. Besides soil moisture in our database are stored other meteorological variables as well (air temperature, soil temperature, precipitation, snow depth, etc.). The ISMN provides benchmark data for several operational services such as ESA CCI Soil Moisture, the Copernicus Climate Change (C3S) and Global Land Service (CGLS), and the online validation tool QA4SM. ISMN data is widely used in a variety of scientific fields (e.g., climate, water, agriculture, disasters, ecosystems, weather, biodiversity, etc). To validate the land surface representations of meteorological forecasting models soil moisture from the ISMN has often been used. The development of various generations of TESSEL models used both in the Integrated Forecasting Systems and reanalysis products of ECMWF, greatly profited from soil moisture and temperature data from the ISMN. Using ISMN data several studies assessed the soil moisture skill of the Weather Research and Forecasting Model (WRF) and assessed the forecast skill or new implementations of numerical weather prediction models. We greatly acknowledge the financial support provided by ESA through various projects: SMOSnet International Soil Moisture Network, IDEAS+, and QA4EO. To ensure a long-term funding for the ISMN operations, several ideas were perused together with ESA. A partner for this task could be found within the International Center for Water Resources and Global Change (ICWRGC) hosted by the German Federal Institute of Hydrology (BfG).&#160; In this session, we want to give an overview and future outlook of the ISMN, highlighting its unique features and discuss challenges in supporting the hydrological research community in need of freely available, standardized, and quality-controlled datasets.&#160;

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Evaluation and Comparison of the Common Land Model and the Community Land Model by Using In Situ Soil Moisture Observations from the Soil Climate Analysis Network

Land ◽

10.3390/land11010126 ◽

2022 ◽

Vol 11 (1) ◽

pp. 126

Author(s):

Minzhuo Ou ◽

Shupeng Zhang

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Community Land Model ◽

Common Land Model ◽

Surface Models ◽

The Common ◽

Simulation Results ◽

Climate Analysis ◽

Soil Climate

Soil moisture is a key state variable in land surface processes. Since field measurements of soil moisture are generally sparse and remote sensing is limited in terms of observation depth, land surface model simulations are usually used to continuously obtain soil moisture data in time and space. Therefore, it is crucial to evaluate the performance of models that simulate soil moisture under various land surface conditions. In this work, we evaluated and compared two land surface models, the Common Land Model version 2014 (CoLM2014) and the Community Land Model Version 5 (CLM5), using in situ soil moisture observations from the Soil Climate Analysis Network (SCAN). The meteorological and soil attribute data used to drive the models were obtained from SCAN station observations, as were the soil moisture data used to validate the simulation results. The validation results revealed that the correlation coefficients between the simulations by CLM5 (0.38) and observations are generally higher than those by CoLM2014 (0.11), especially in shallow soil (0–0.1016 m). The simulation results by CoLM2014 have smaller bias than those by CLM5 . Both models could simulate diurnal and seasonal variations of soil moisture at seven sites, but we found a large bias, which may be due to the two models’ representation of infiltration and lateral flow processes. The bias of the simulated infiltration rate can affect the soil moisture simulation, and the lack of a lateral flow scheme can affect the models’ division of saturated and unsaturated areas within the soil column. The parameterization schemes in land surface models still need to be improved, especially for soil simulations at small scales.

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