A first attempt to model global hydrology at hyper-resolution DOI Creative Commons
Barry van Jaarsveld, Niko Wanders, Edwin H. Sutanudjaja

et al.

Earth System Dynamics, Journal Year: 2025, Volume and Issue: 16(1), P. 29 - 54

Published: Jan. 7, 2025

Abstract. Global hydrological models are one of the key tools that can help meet needs stakeholders and policy makers when water management strategies policies developed. The primary objective this paper is therefore to establish a first-of-its-kind, truly global hyper-resolution model spans multiple-decade period (1985–2019). To achieve this, two limitations addressed, namely lack high-resolution meteorological data insufficient representation lateral movement snow ice. Thus, novel downscaling procedure better incorporates fine-scale topographic climate drivers incorporated, module capable frozen resembling glaciers, avalanches, wind included. We compare 30 arcsec version PCR-GLOBWB (PCR – Water Balance) previously published 5 arcmin versions by evaluating simulated river discharge, cover, soil moisture, land surface evaporation, total storage against observations. show provides more accurate simulation in particular for smaller catchments. highlight modeling possible with current computational resources results realistic representations cycle. However, our also suggest still incorporate cover heterogeneity relevant processes at sub-kilometer scale provide estimates moisture evaporation fluxes.

Language: Английский

A planetary boundary for green water DOI
Lan Wang‐Erlandsson, Arne Tobian, Ruud van der Ent

et al.

Nature Reviews Earth & Environment, Journal Year: 2022, Volume and Issue: 3(6), P. 380 - 392

Published: April 26, 2022

Language: Английский

Citations

266
Soil hydrology in the Earth system DOI
Harry Vereecken, Wulf Amelung, Sara L. Bauke

et al.

Nature Reviews Earth & Environment, Journal Year: 2022, Volume and Issue: 3(9), P. 573 - 587

Published: Aug. 2, 2022

Language: Английский

Citations

212
Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives DOI
Yassine Himeur, Bhagawat Rimal, Abhishek Tiwary

et al.

Information Fusion, Journal Year: 2022, Volume and Issue: 86-87, P. 44 - 75

Published: June 25, 2022

Language: Английский

Citations

139
Soil Moisture Measuring Techniques and Factors Affecting the Moisture Dynamics: A Comprehensive Review DOI Open Access
Muhammad Waseem Rasheed, Jialiang Tang, Abid Sarwar

et al.

Sustainability, Journal Year: 2022, Volume and Issue: 14(18), P. 11538 - 11538

Published: Sept. 14, 2022

The amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land processes. It affects critical land–atmospheric phenomena, including the division energy and water (infiltration, runoff, evaporation), impacts effectiveness agricultural output (sensible latent heat fluxes air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, interpreting SSM at high spatial temporal resolutions. current study critically reviews methods procedures for calculating variables influencing measurement accuracy applicability under different fields, climates, operational conditions. For laboratory field this divides estimate strategies into (i) direct (ii) indirect procedures. technique depends on environment resources hand. Comparative research geographically restricted, although economical—direct measuring techniques like gravimetric method time-consuming destructive. In contrast, more expensive do not produce measurements scale but data scale. While across significant regions, ground-penetrating radar remote sensing susceptible to errors caused by overlapping atmospheric factors. On other hand, soft computing machine/deep learning quite handy estimating without any technical or laborious We determine factors, e.g., topography, type, vegetation, climate change, groundwater level, depth soil, etc., primarily influence measurements. Different have been put practice various practical situations, comparisons between them available frequently publications. Each offers unique set potential advantages disadvantages. most accurate way identifying best value selection (VSM). neutron probe preferable FDR TDR sensor moisture. Remote filled need large-scale, highly spatiotemporal monitoring. Through self-learning capabilities data-scarce areas, approaches facilitate prediction.

Language: Английский

Citations

112
A new SMAP soil moisture and vegetation optical depth product (SMAP-IB): Algorithm, assessment and inter-comparison DOI Creative Commons
Xiaojun Li, Jean‐Pierre Wigneron, Lei Fan

et al.

Remote Sensing of Environment, Journal Year: 2022, Volume and Issue: 271, P. 112921 - 112921

Published: Feb. 2, 2022

Passive microwave remote sensing at L-band (1.4 GHz) provides an unprecedented opportunity to estimate global surface soil moisture (SM) and vegetation water content (via the optical depth, VOD), which are essential monitor Earth carbon cycles. Currently, only two space-borne radiometer missions operating: Soil Moisture Ocean Salinity (SMOS) Active (SMAP) in orbit since 2009 2015, respectively. This study presents a new mono-angle retrieval algorithm (called SMAP-INRAE-BORDEAUX, hereafter SMAP-IB) of SM VOD (L-VOD) from dual-channel SMAP radiometric observations. The retrievals based on L-MEB (L-band Microwave Emission Biosphere) model is forward SMOS-IC official SMOS algorithms. SMAP-IB product aims providing good performances for both L-VOD while remaining independent auxiliary data: neither modelled data nor indices used as input algorithm. Inter-comparison with other products (i.e., MT-DCA, SMOS-IC, versions DCA SCA-V extracted passive Level 3 product) suggested that performed well L-VOD. In particular, presented higher scores (R = 0.74) capturing temporal trends in-situ observations ISMN (International Network) during April 2015–March 2019, followed by MT-DCA 0.71). While lowest ubRMSD value was obtained version (0.056 m3/m3), best R, (~ 0.058 m3/m3) bias (0.002 when considering (e.g., NDVI). SMAP-IB, were correlated (spatially) aboveground biomass tree height, spatial R values ~0.88 ~ 0.90, All three exhibited smooth non-linear density distribution linear relationship especially high levels, datasets incorporating information algorithms DCA) showed obvious saturation effects. It expected this can facilitate fusion obtain long-term continuous earth observation products.

Language: Английский

Citations

91
COSMOS-Europe: a European network of cosmic-ray neutron soil moisture sensors DOI Creative Commons
Heye Bogena, Martin Schrön, Jannis Jakobi

et al.

Earth system science data, Journal Year: 2022, Volume and Issue: 14(3), P. 1125 - 1151

Published: March 11, 2022

Abstract. Climate change increases the occurrence and severity of droughts due to increasing temperatures, altered circulation patterns, reduced snow occurrence. While Europe has suffered from drought events in last decade unlike ever seen since beginning weather recordings, harmonized long-term datasets across continent are needed monitor support predictions. Here we present soil moisture data 66 cosmic-ray neutron sensors (CRNSs) (COSMOS-Europe for short) covering recent events. The CRNS sites distributed cover all major land use types climate zones Europe. raw count stations were provided by 24 research institutions processed using state-of-the-art methods. processing included correction counts a methodology conversion into based on available situ information. In addition, uncertainty estimate is with dataset, information that particularly useful remote sensing modeling applications. This paper presents current spatiotemporal coverage describes protocols measurements consistent products. presented COSMOS-Europe network open up manifold potential applications environmental research, such as validation, trend analysis, or model assimilation. dataset could be particular importance analysis extreme climatic at continental scale. Due its timely relevance scope years, demonstrate this application brief variability. entitled “Dataset COSMOS-Europe: A European Cosmic-Ray Neutron Soil Moisture Sensors”, shared via Forschungszentrum Jülich: https://doi.org/10.34731/x9s3-kr48 (Bogena Ney, 2021).

Language: Английский

Citations

87
Global long term daily 1 km surface soil moisture dataset with physics informed machine learning DOI Creative Commons

Qianqian Han,

Yijian Zeng, Lijie Zhang

et al.

Scientific Data, Journal Year: 2023, Volume and Issue: 10(1)

Published: Feb. 17, 2023

Although soil moisture is a key factor of hydrologic and climate applications, global continuous high resolution datasets are still limited. Here we use physics-informed machine learning to generate global, long-term, spatially dataset surface moisture, using International Soil Moisture Network (ISMN), remote sensing meteorological data, guided with the knowledge physical processes impacting dynamics. Global Surface (GSSM1 km) provides (0-5 cm) at 1 km spatial daily temporal over period 2000-2020. The performance GSSM1 evaluated testing validation datasets, via inter-comparisons existing products. root mean square error in set 0.05 cm

Language: Английский

Citations

47
Strategies to Measure Soil Moisture Using Traditional Methods, Automated Sensors, Remote Sensing, and Machine Learning Techniques: Review, Bibliometric Analysis, Applications, Research Findings, and Future Directions DOI Creative Commons
Abhilash Singh, Kumar Gaurav, Gaurav Kailash Sonkar

et al.

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 13605 - 13635

Published: Jan. 1, 2023

This review provides a detailed synthesis of various in-situ, remote sensing, and machine learning approaches to estimate soil moisture. Bibliometric analysis the published literature on moisture shows that Time-Domain Reflectometry (TDR) is most widely used in-situ instrument, while sensing preferred application, random forest applied algorithm simulate surface We have ten models publicly available dataset (in-situ measurement satellite images) predict compared their results. briefly discussed potential using upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission images Finally, this discusses capabilities physics-informed automated (AutoML) at higher spatial temporal resolutions. will assist researchers in investigating applications broad domain earth sciences.

Language: Английский

Citations

46
Land Data Assimilation: Harmonizing Theory and Data in Land Surface Process Studies DOI Creative Commons
Xin Li, Feng Liu, Chunfeng Ma

et al.

Reviews of Geophysics, Journal Year: 2024, Volume and Issue: 62(1)

Published: March 1, 2024

Abstract Data assimilation plays a dual role in advancing the “scientific” understanding and serving as an “engineering tool” for Earth system sciences. Land data (LDA) has evolved into distinct discipline within geophysics, facilitating harmonization of theory allowing land models observations to complement constrain each other. Over recent decades, substantial progress been made theory, methodology, application LDA, necessitating holistic in‐depth exploration its full spectrum. Here, we present thorough review elucidating theoretical methodological developments LDA distinctive features. This encompasses breakthroughs addressing strong nonlinearities surface processes, exploring potential machine learning approaches assimilation, quantifying uncertainties arising from multiscale spatial correlation, simultaneously estimating model states parameters. proven successful enhancing prediction various processes (including soil moisture, snow, evapotranspiration, streamflow, groundwater, irrigation temperature), particularly realms water energy cycles. outlines development global, regional, catchment‐scale systems software platforms, proposing grand challenges generating reanalysis coupled land‒atmosphere DA. We lastly highlight opportunities expand applications pure geophysical natural human by ingesting deluge observation social sensing data. The paper synthesizes current knowledge provides steppingstone future development, promoting driven theory‐data studies.

Language: Английский

Citations

29
Global critical soil moisture thresholds of plant water stress DOI Creative Commons
Zheng Fu, Philippe Ciais, Jean‐Pierre Wigneron

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: June 6, 2024

Abstract During extensive periods without rain, known as dry-downs, decreasing soil moisture (SM) induces plant water stress at the point when it limits evapotranspiration, defining a critical SM threshold (θ crit ). Better quantification of θ is needed for improving future projections climate and resources, food production, ecosystem vulnerability. Here, we combine systematic satellite observations diurnal amplitude land surface temperature (dLST) during corroborated by in-situ data from flux towers, to generate observation-based global map . We find an average 0.19 m 3 /m , varying 0.12 in arid ecosystems 0.26 humid ecosystems. simulated Earth System Models overestimated dry areas underestimated wet areas. The observed pattern reflects adaptation available atmospheric demand. Using explainable machine learning, show that aridity index, leaf area texture are most influential drivers. Moreover, annual fraction days with stress, stays below has increased past four decades. Our results have important implications understanding inception models identifying tipping points.

Language: Английский

Citations

29