{"type": "FeatureCollection", "features": [{"id": "10.1029/2021gc009904", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:17:35Z", "type": "Journal Article", "created": "2021-10-26", "title": "Quantifying Non\u2010Thermal Silicate Weathering Using Ge/Si and Si Isotopes in Rivers Draining the Yellowstone Plateau Volcanic Field, USA", "description": "Abstract

In active volcanic regions, high\uffe2\uff80\uff90temperature chemical reactions in the hydrothermal system consume CO2 sourced from magma or from the deep crust, whereas reactions with silicates at shallow depths mainly consume atmospheric CO2. Numerous studies have quantified the load of dissolved solids in rivers that drain volcanic regions to determine chemical weathering rates and atmospheric CO2 consumption rates. However, the balance between thermal and non\uffe2\uff80\uff90thermal components to riverine fluxes in these areas remains poorly constrained, hindering accurate estimates of atmospheric CO2 consumption rates. Here we use the Ge/Si ratio and the stable silicon isotopes (\uffce\uffb430Si) as tracers for quantifying non\uffe2\uff80\uff90thermal silicon contributions in rivers draining the Yellowstone Plateau Volcanic Field, USA. The Ge/Si ratio (\uffc2\uffb5mol.mol\uffe2\uff88\uff921) was determined for seven thermal water samples (183\uffc2\uffa0\uffc2\uffb1\uffc2\uffa022), eight rivers (35\uffc2\uffa0\uffc2\uffb1\uffc2\uffa023) and six creeks flowing into Yellowstone Lake (5\uffc2\uffa0\uffc2\uffb1\uffc2\uffa03) during base flow and during peak water discharge following snowmelt. The \uffce\uffb430Si value (\uffe2\uff80\uffb0) was determined for thermal waters (\uffe2\uff88\uff920.09\uffc2\uffa0\uffc2\uffb1\uffc2\uffa00.04), Yellowstone River at Yellowstone Lake outlet (1.91\uffc2\uffa0\uffc2\uffb1\uffc2\uffa00.23) and creek samples (0.82\uffc2\uffa0\uffc2\uffb1\uffc2\uffa00.29). The calculated atmospheric CO2 consumption associated with non\uffe2\uff80\uff90thermal waters flowing through Yellowstone's rivers during peak discharge is \uffe2\uff88\uffbc3.03 ton.km\uffe2\uff88\uff922.yr\uffe2\uff88\uff921, which is \uffe2\uff88\uffbc2% of the annual mean atmospheric CO2 consumption in other volcanic regions. This study highlights the significance of quantifying seasonal variations in chemical weathering rates for improving estimates of atmospheric CO2 consumption rates in active volcanic regions.In subsurface environments, incomplete mixing at the pore scale limits reaction rates, rendering their prediction by Darcy\uffe2\uff80\uff90scale models challenging. Such pore scale concentration gradients are enhanced by the deformation of solute fronts and decay under the action of molecular diffusion and solute filament merging. It is currently unclear how these processes govern concentration gradient dynamics under different flow rates. We measure experimentally pore scale concentrations in solute fronts transported in a two\uffe2\uff80\uff90dimensional porous micromodel over an extensive range of flow rates. We demonstrate that pore\uffe2\uff80\uff90scale shear flow increases concentration gradients up to a time predicted by the lamellar mixing theory in shear flow. However, the flow rate\uffe2\uff80\uff90dependency of the mean concentration gradient at this so\uffe2\uff80\uff90called mixing time is weaker than predicted theoretically, a discrepancy which we explain quantitatively by accounting for lamellae aggregation. These findings shed new light on the pore\uffe2\uff80\uff90scale mechanisms driving mixing dynamics in porous media.Droughts affect ecosystems at multiple time scales, but their sub\uffe2\uff80\uff90seasonal legacy effects on vegetation activity remain unclear. Combining the satellite\uffe2\uff80\uff90based enhanced vegetation index MODIS EVI with a novel location\uffe2\uff80\uff90specific definition of the growing season, we quantify drought impacts on sub\uffe2\uff80\uff90seasonal vegetation activity and the subsequent recovery in the Northern Hemisphere. Drought legacy effects are quantified as changes in post\uffe2\uff80\uff90drought greenness and sensitivity to climate. We find that greenness losses under severe drought are partially compensated by a \uffe2\uff88\uffbc+5% greening within 2\uffe2\uff80\uff936 growing\uffe2\uff80\uff90season months following the droughts, both in woody and herbaceous vegetation but at different timings. In addition, post\uffe2\uff80\uff90drought sensitivity of herbaceous vegetation to hydrological conditions increases noticeably at high latitudes compared with the local normal conditions, regardless of the choice of drought time scales. In general, the legacy effects on sensitivity are larger in herbaceous vegetation than in woody vegetation.Branching river networks are prominent features of the Earth's surface, but the mechanisms that create branching river networks patterns remain elusive. Recent studies have suggested that climate, tectonics, and lithology may control both longitudinal profiles of channel incision and the planform geometry of stream networks. Here we show, by analyzing almost 1 million river junctions and over 4.2 million groundwater wells across the contiguous United States, that stream network branching angles vary systematically with the degree to which streams lose water to, or gain water from, nearby groundwater aquifers. Streams whose surfaces lie above nearby groundwater levels, and thus are likely to be losing flow to underlying aquifers, tend to have narrower branching angles than streams that lie below nearby groundwater levels, and thus are likely to gain flow from groundwater. This systematic relationship persists across several stream orders, and across a wide range in channel gradients.Branching river networks are prominent features of the Earth's surface, but the mechanisms that create branching river networks patterns remain elusive. Recent studies have suggested that climate, tectonics, and lithology may control both longitudinal profiles of channel incision and the planform geometry of stream networks. Here we show, by analyzing almost 1 million river junctions and over 4.2 million groundwater wells across the contiguous United States, that stream network branching angles vary systematically with the degree to which streams lose water to, or gain water from, nearby groundwater aquifers. Streams whose surfaces lie above nearby groundwater levels, and thus are likely to be losing flow to underlying aquifers, tend to have narrower branching angles than streams that lie below nearby groundwater levels, and thus are likely to gain flow from groundwater. This systematic relationship persists across several stream orders, and across a wide range in channel gradients.Branching river networks are prominent features of the Earth's surface, but the mechanisms that create branching river networks patterns remain elusive. Recent studies have suggested that climate, tectonics, and lithology may control both longitudinal profiles of channel incision and the planform geometry of stream networks. Here we show, by analyzing almost 1 million river junctions and over 4.2 million groundwater wells across the contiguous United States, that stream network branching angles vary systematically with the degree to which streams lose water to, or gain water from, nearby groundwater aquifers. Streams whose surfaces lie above nearby groundwater levels, and thus are likely to be losing flow to underlying aquifers, tend to have narrower branching angles than streams that lie below nearby groundwater levels, and thus are likely to gain flow from groundwater. This systematic relationship persists across several stream orders, and across a wide range in channel gradients.Abstract. Citizen science, scientific work and data collection conducted by or with non-experts, is rapidly growing. Although the potential of citizen science activities to generate enormous amounts of data otherwise not feasible is widely recognized, the obtained data are often treated with caution and scepticism. Their quality and reliability is not fully trusted since they are obtained by non-experts using low-cost instruments or scientifically non-verified methods. In this study, we evaluate the performance of Parrot's Flower Power soil moisture sensor used within the European citizen science project the GROW Observatory (GROW; https://growobservatory.org, last access: 30\uffc2\uffa0March\uffc2\uffa02020). The aim of GROW is to enable scientists to validate satellite-based soil moisture products at an unprecedented high spatial resolution through crowdsourced data. To this end, it has mobilized thousands of citizens across Europe in science and climate actions, including hundreds who have been empowered to monitor soil moisture and other environmental variables within 24 high-density clusters around Europe covering different climate and soil conditions. Clearly, to serve as reference dataset, the quality of ground observations is crucial, especially if obtained from low-cost sensors. To investigate the accuracy of such measurements, the Flower Power sensors were evaluated in the lab and field. For the field trials, they were installed alongside professional soil moisture probes in the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria. We assessed the skill of the low-cost sensors against the professional probes using various methods. Apart from common statistical metrics like correlation, bias, and root-mean-square difference, we investigated and compared the temporal stability, soil moisture memory, and the flagging statistics based on the International Soil Moisture Network (ISMN) quality indicators. We found a low intersensor variation in the lab and a high temporal agreement with the professional sensors in the field. The results of soil moisture memory and the ISMN quality flags analysis are in a comparable range for the low-cost and professional probes; only the temporal stability analysis shows a contrasting outcome. We demonstrate that low-cost sensors can be used to generate a dataset valuable for environmental monitoring and satellite validation and thus provide the basis for citizen-based soil moisture science.

", "keywords": ["QC801-809", "13. Climate action", "0103 physical sciences", "Geophysics. Cosmic physics", "0207 environmental engineering", "02 engineering and technology", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.5194/gi-2019-38"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Instrumentation%2C%20Methods%20and%20Data%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/gi-2019-38", "name": "item", "description": "10.5194/gi-2019-38", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/gi-2019-38"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-11-05T00:00:00Z"}}, {"id": "10.5194/gi-9-117-2020", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:21:48Z", "type": "Journal Article", "created": "2020-04-06", "title": "Evaluating the suitability of the consumer low-cost Parrot Flower Power soil moisture sensor for scientific environmental applications", "description": "

Abstract. Citizen science, scientific work and data collection conducted by or with non-experts, is rapidly growing. Although the potential of citizen science activities to generate enormous amounts of data otherwise not feasible is widely recognized, the obtained data are often treated with caution and scepticism. Their quality and reliability is not fully trusted since they are obtained by non-experts using low-cost instruments or scientifically non-verified methods. In this study, we evaluate the performance of Parrot's Flower Power soil moisture sensor used within the European citizen science project the GROW Observatory (GROW; https://growobservatory.org, last access: 30\u00a0March\u00a02020). The aim of GROW is to enable scientists to validate satellite-based soil moisture products at an unprecedented high spatial resolution through crowdsourced data. To this end, it has mobilized thousands of citizens across Europe in science and climate actions, including hundreds who have been empowered to monitor soil moisture and other environmental variables within 24 high-density clusters around Europe covering different climate and soil conditions. Clearly, to serve as reference dataset, the quality of ground observations is crucial, especially if obtained from low-cost sensors. To investigate the accuracy of such measurements, the Flower Power sensors were evaluated in the lab and field. For the field trials, they were installed alongside professional soil moisture probes in the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria. We assessed the skill of the low-cost sensors against the professional probes using various methods. Apart from common statistical metrics like correlation, bias, and root-mean-square difference, we investigated and compared the temporal stability, soil moisture memory, and the flagging statistics based on the International Soil Moisture Network (ISMN) quality indicators. We found a low intersensor variation in the lab and a high temporal agreement with the professional sensors in the field. The results of soil moisture memory and the ISMN quality flags analysis are in a comparable range for the low-cost and professional probes; only the temporal stability analysis shows a contrasting outcome. We demonstrate that low-cost sensors can be used to generate a dataset valuable for environmental monitoring and satellite validation and thus provide the basis for citizen-based soil moisture science.

", "keywords": ["QC801-809", "13. Climate action", "0103 physical sciences", "Geophysics. Cosmic physics", "0207 environmental engineering", "02 engineering and technology", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/10.5194/gi-9-117-2020"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Instrumentation%2C%20Methods%20and%20Data%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "10.5194/gi-9-117-2020", "name": "item", "description": "10.5194/gi-9-117-2020", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/10.5194/gi-9-117-2020"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-11-05T00:00:00Z"}}, {"id": "20.500.11850/636575", "type": "Feature", "geometry": null, "properties": {"updated": "2026-05-01T16:25:15Z", "type": "Journal Article", "created": "2023-10-06", "title": "Groundwater's Fingerprint in Stream Network Branching Angles", "description": "Abstract

Branching river networks are prominent features of the Earth's surface, but the mechanisms that create branching river networks patterns remain elusive. Recent studies have suggested that climate, tectonics, and lithology may control both longitudinal profiles of channel incision and the planform geometry of stream networks. Here we show, by analyzing almost 1 million river junctions and over 4.2 million groundwater wells across the contiguous United States, that stream network branching angles vary systematically with the degree to which streams lose water to, or gain water from, nearby groundwater aquifers. Streams whose surfaces lie above nearby groundwater levels, and thus are likely to be losing flow to underlying aquifers, tend to have narrower branching angles than streams that lie below nearby groundwater levels, and thus are likely to gain flow from groundwater. This systematic relationship persists across several stream orders, and across a wide range in channel gradients.In active volcanic regions, high\uffe2\uff80\uff90temperature chemical reactions in the hydrothermal system consume CO2 sourced from magma or from the deep crust, whereas reactions with silicates at shallow depths mainly consume atmospheric CO2. Numerous studies have quantified the load of dissolved solids in rivers that drain volcanic regions to determine chemical weathering rates and atmospheric CO2 consumption rates. However, the balance between thermal and non\uffe2\uff80\uff90thermal components to riverine fluxes in these areas remains poorly constrained, hindering accurate estimates of atmospheric CO2 consumption rates. Here we use the Ge/Si ratio and the stable silicon isotopes (\uffce\uffb430Si) as tracers for quantifying non\uffe2\uff80\uff90thermal silicon contributions in rivers draining the Yellowstone Plateau Volcanic Field, USA. The Ge/Si ratio (\uffc2\uffb5mol.mol\uffe2\uff88\uff921) was determined for seven thermal water samples (183\uffc2\uffa0\uffc2\uffb1\uffc2\uffa022), eight rivers (35\uffc2\uffa0\uffc2\uffb1\uffc2\uffa023) and six creeks flowing into Yellowstone Lake (5\uffc2\uffa0\uffc2\uffb1\uffc2\uffa03) during base flow and during peak water discharge following snowmelt. The \uffce\uffb430Si value (\uffe2\uff80\uffb0) was determined for thermal waters (\uffe2\uff88\uff920.09\uffc2\uffa0\uffc2\uffb1\uffc2\uffa00.04), Yellowstone River at Yellowstone Lake outlet (1.91\uffc2\uffa0\uffc2\uffb1\uffc2\uffa00.23) and creek samples (0.82\uffc2\uffa0\uffc2\uffb1\uffc2\uffa00.29). The calculated atmospheric CO2 consumption associated with non\uffe2\uff80\uff90thermal waters flowing through Yellowstone's rivers during peak discharge is \uffe2\uff88\uffbc3.03 ton.km\uffe2\uff88\uff922.yr\uffe2\uff88\uff921, which is \uffe2\uff88\uffbc2% of the annual mean atmospheric CO2 consumption in other volcanic regions. This study highlights the significance of quantifying seasonal variations in chemical weathering rates for improving estimates of atmospheric CO2 consumption rates in active volcanic regions.Droughts affect ecosystems at multiple time scales, but their sub\uffe2\uff80\uff90seasonal legacy effects on vegetation activity remain unclear. Combining the satellite\uffe2\uff80\uff90based enhanced vegetation index MODIS EVI with a novel location\uffe2\uff80\uff90specific definition of the growing season, we quantify drought impacts on sub\uffe2\uff80\uff90seasonal vegetation activity and the subsequent recovery in the Northern Hemisphere. Drought legacy effects are quantified as changes in post\uffe2\uff80\uff90drought greenness and sensitivity to climate. We find that greenness losses under severe drought are partially compensated by a \uffe2\uff88\uffbc+5% greening within 2\uffe2\uff80\uff936 growing\uffe2\uff80\uff90season months following the droughts, both in woody and herbaceous vegetation but at different timings. In addition, post\uffe2\uff80\uff90drought sensitivity of herbaceous vegetation to hydrological conditions increases noticeably at high latitudes compared with the local normal conditions, regardless of the choice of drought time scales. In general, the legacy effects on sensitivity are larger in herbaceous vegetation than in woody vegetation.

Abstract. Citizen science, scientific work and data collection conducted by or with non-experts, is rapidly growing. Although the potential of citizen science activities to generate enormous amounts of data otherwise not feasible is widely recognized, the obtained data are often treated with caution and scepticism. Their quality and reliability is not fully trusted since they are obtained by non-experts using low-cost instruments or scientifically non-verified methods. In this study, we evaluate the performance of Parrot's Flower Power soil moisture sensor used within the European citizen science project the GROW Observatory (GROW; https://growobservatory.org, last access: 30\u00a0March\u00a02020). The aim of GROW is to enable scientists to validate satellite-based soil moisture products at an unprecedented high spatial resolution through crowdsourced data. To this end, it has mobilized thousands of citizens across Europe in science and climate actions, including hundreds who have been empowered to monitor soil moisture and other environmental variables within 24 high-density clusters around Europe covering different climate and soil conditions. Clearly, to serve as reference dataset, the quality of ground observations is crucial, especially if obtained from low-cost sensors. To investigate the accuracy of such measurements, the Flower Power sensors were evaluated in the lab and field. For the field trials, they were installed alongside professional soil moisture probes in the Hydrological Open Air Laboratory (HOAL) in Petzenkirchen, Austria. We assessed the skill of the low-cost sensors against the professional probes using various methods. Apart from common statistical metrics like correlation, bias, and root-mean-square difference, we investigated and compared the temporal stability, soil moisture memory, and the flagging statistics based on the International Soil Moisture Network (ISMN) quality indicators. We found a low intersensor variation in the lab and a high temporal agreement with the professional sensors in the field. The results of soil moisture memory and the ISMN quality flags analysis are in a comparable range for the low-cost and professional probes; only the temporal stability analysis shows a contrasting outcome. We demonstrate that low-cost sensors can be used to generate a dataset valuable for environmental monitoring and satellite validation and thus provide the basis for citizen-based soil moisture science.

", "keywords": ["QC801-809", "13. Climate action", "0103 physical sciences", "Geophysics. Cosmic physics", "0207 environmental engineering", "02 engineering and technology", "15. Life on land", "01 natural sciences", "0105 earth and related environmental sciences"]}, "links": [{"href": "https://doi.org/2983646124"}, {"rel": "related", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/Geoscientific%20Instrumentation%2C%20Methods%20and%20Data%20Systems", "name": "related record", "description": "related record", "type": "application/json"}, {"rel": "self", "type": "application/geo+json", "title": "2983646124", "name": "item", "description": "2983646124", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items/2983646124"}, {"rel": "collection", "type": "application/json", "title": "Collection", "name": "collection", "description": "Collection", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main"}], "time": {"date": "2019-11-05T00:00:00Z"}}], "links": [{"rel": "self", "type": "application/geo+json", "title": "This document as GeoJSON", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=QC801-809&f=json", "hreflang": "en-US"}, {"rel": "alternate", "type": "text/html", "title": "This document as HTML", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=QC801-809&f=html", "hreflang": "en-US"}, {"rel": "collection", "type": "application/json", "title": "Collection URL", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main", "hreflang": "en-US"}, {"type": "application/geo+json", "rel": "first", "title": "items (first)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=QC801-809&", "hreflang": "en-US"}, {"rel": "last", "type": "application/geo+json", "title": "items (last)", "href": "https://repository.soilwise-he.eu/cat/collections/metadata:main/items?keywords=QC801-809&offset=14", "hreflang": "en-US"}], "numberMatched": 14, "numberReturned": 14, "distributedFeatures": [], "timeStamp": "2026-05-02T10:17:13.217385Z"}