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#soilmoisture

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Public
💧🌏 Greg Cocks

Earth's Storage Of Water In Soil, Lakes And Rivers Is Dwindling - And It's Especially Bad For Farming
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phys.org/news/2025-03-earth-st <-- shared technical article
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doi.org/10.1126/science.adq652 <-- shared paper
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#water #hydrology #waterresources #global #management #watersecurity #soil #lake #river #snow #groundwater #agriculture #sealevelerise #SLR #climatechange #farming #overuse #waste #moisture #soilmoisture #ecology #ecosystems #storage #model #modeling #spatialanalysis #spatiotemporal #rainfall #drought #consistency #wateruse #crop #plant #vegetation #irrigation #foodsecurity #evapotranspiration #ET #surfacewater #flux #GIS #spatial #mapping #remotesensing #earthobservation #waterstorage #depletion

Continued thread
Public
CelloMom On Cars

Global #SoilMoisture in ‘permanent’ decline due to #ClimateChange

"The findings, published in Science, suggest the decline is primarily driven by an increasingly thirsty atmosphere as global temperatures rise, as well as shifts in rainfall patterns

The drying out of soil “increases the severity and frequency” of major droughts, with consequences for humans, ecosystems and agriculture."

carbonbrief.org/global-soil-mo
#water #HydrologicalCycle

Carbon Brief · Global soil moisture in 'permanent' decline due to climate change - Carbon BriefA new study warns that global declines in soil moisture over the 21st century could mark a “permanent” shift in the world’s water cycle.
Public
💧🌏 Greg Cocks

Fiber-Optic Seismic Sensing Of Vadose Zone Soil Moisture Dynamics
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doi.org/10.1038/s41467-024-506 <-- shared paper
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[broadly, a 'seismic' listening technique could help researchers map water movement, moisture levels in soil, with these researchers at Caltech have figured out a way to use vibrations from passing cars to see how much water sits directly beneath the ground’s surface…]
#GIS #spatial #mapping #remotesensing #array #survey #soil #regolith #seismic #water #hydrology #waterresources #watersecurity #subsurface #vadose #vadosezone #soilmoisture #moisture #weather #precipitation #rainfall #surfacewater #groundwater #ecology #agriculture #ecosystems #spatiotemporal #model #modeling #spatialanalysis #fiberoptics #fibreoptics #evapotranspiration #insitu #climatechange #drought #extremeweather #watermanagement #semiarid #geophysics

Public
in silico Plants

RT @RemoteSens_MDPI: The Joint Assimilation of Remotely Sensed #LeafAreaIndex and Surface #SoilMoisture into a #Land Surface Model
by Azbina Rahman et al.
Welcome to read the paper: buff.ly/3YUr5tx

MDPIThe Joint Assimilation of Remotely Sensed Leaf Area Index and Surface Soil Moisture into a Land Surface ModelThis work tests the hypothesis that jointly assimilating satellite observations of leaf area index and surface soil moisture into a land surface model improves the estimation of land vegetation and water variables. An Ensemble Kalman Filter is used to test this hypothesis across the Contiguous United States from April 2015 to December 2018. The performance of the proposed methodology is assessed for several modeled vegetation and water variables (evapotranspiration, net ecosystem exchange, and soil moisture) in terms of random errors and anomaly correlation coefficients against a set of independent validation datasets (i.e., Global Land Evaporation Amsterdam Model, FLUXCOM, and International Soil Moisture Network). The results show that the assimilation of the leaf area index mostly improves the estimation of evapotranspiration and net ecosystem exchange, whereas the assimilation of surface soil moisture alone improves surface soil moisture content, especially in the western US, in terms of both root mean squared error and anomaly correlation coefficient. The joint assimilation of vegetation and soil moisture information combines the results of individual vegetation and soil moisture assimilations and reduces errors (and increases correlations with the reference datasets) in evapotranspiration, net ecosystem exchange, and surface soil moisture simulated by the land surface model. However, because soil moisture satellite observations only provide information on the water content in the top 5 cm of the soil column, the impact of the proposed data assimilation technique on root zone soil moisture is limited. This work moves one step forward in the direction of improving our estimation and understanding of land surface interactions using a multivariate data assimilation approach, which can be particularly useful in regions of the world where ground observations are sparse or missing altogether.
Public
VEGAS at U of Stuttgart

We're happy to present at #EGU24! Both presentations are on Monday April 15
@planetwater

Time
ID
Session
Title
Authors
08:55–09:05
EGU24-7899
HS8.1.7
Immobilization of Per- and Polyfluoroalkyl Substances (PFAS) – Experimental and Model-based Analysis of Leaching Behavior
Claus Haslauer, Thomas Bierbaum, Simon Kleinknecht, and Tobias Junginger
15:25–15:35
EGU24-7820
HS3.9
Data-driven surrogate-based Bayesian model calibration for predicting vadose zone temperatures in drinking water supply pipes
Ilja Kröker, Elisabeth Nißler, Sergey Oladyshkin, Wolfgang Nowak, and Claus Haslauer

Ping us or looking forward to meet and chat in Vienna!

#Pfas#Remediation#Soil
Public
gizz

🎉 "Ramsauer & Marzahn present a global, hourly #SoilMoisture estimation based on #NASA's #GPM IMERG data and using a site-specific adjusted antecedent precipitation index (#API)"

🌧️🌍

🔗 doi.org/10.1080/01431161.2022.

get the pdf here: tandfonline.com/eprint/6VHQ7JC
Info:
researchgate.net/publication/3

Taylor & FrancisGlobal Soil Moisture Estimation based on GPM IMERG Data using a Site Specific Adjusted Antecedent Precipitation IndexThis study presents a global, hourly surface soil moisture estimation procedure based on precipitation and temperature data. Information on soil composition further helps to define the local charac...
#soil#soils#moisture
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