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19 results for “Water Budget”
Trout Lake USGS Water, Energy, and Biogeochemical Budgets (WEBB) Stream Data 1975-2013
This data was collected by the United States Geological Survey (USGS) for the Water, Energy, and Biogeochemical Budget Project. The data set is primarily composed of water chemistry variables, and was collected from four USGS stream gauge stations in the Northern Highland Lake District of Wisconsin, near Trout Lake. The four USGS stream gauge stations are Allequash Creek at County Highway M (USGS-05357215), Stevenson Creek at County Highway M (USGS-05357225), North Creek at Trout Lake (USGS-05357230), and the Trout River at Trout Lake (USGS-05357245), all near Boulder Junction, Wisconsin. The project has collected stream water chemistry data for a maximum of 36 different chemical parameters,. and three different physical stream parameters: temperature, discharge, and gauge height. All water chemistry samples are collected as grab samples and sent to the USGS National Water Quality Lab in Denver, Colorado. There is historic data for Stevenson Creek from 1975-1977, and then beginning again in 1991. The Trout Lake WEBB project began during the summer of 1991 and sampling of all four sites continues to date.
Steady state carbon, nitrogen, phosphorus, and water budgets for twelve mature ecosystems ranging from prairie to forest and from the arctic to the tropics
We use the Multiple Element Limitation (MEL) model to examine the responses of twelve ecosystems - from the arctic to the tropics and from grasslands to forests - to elevated carbon dioxide (CO2), warming, and 20% decreases or increases in annual precipitation. The ecosystems we simulated include moist acidic tundra, shrub tundra, and wet sedge tundra near Toolik Lake, Alaska, alpine dry meadow tundra near Niwot Ridge, Colorado, restored tallgrass prairie near Kellogg Biological Station, Michigan, native tallgrass prairie at the Konza Prairie, Kansas, upland and lowland boreal forest near Bonanza Creek, Alaska, temperate coniferous forest in HJ Andrews Experimental Forest, Oregon, a northern hardwood forest in Hubbard Brook Experimental Forest, New Hampshire, a transition oak-maple forest in Harvard Forest, Massachusetts, and lowland tropical rainforest near Caxiuanã National Forest, Pará, Brazil. For each of the twelve sites, we run six 100-year simulations beginning from the calibrated steady state (72 simulations total). The six simulations are: (1) increasing CO2 from 400 to 800 μmol mol-1, (2) warming from current temperatures to current plus 3.5oC, (3) decreasing precipitation from 100% to 80% of the current annual rate, (4) increasing precipitation from 100% to 120% of the current annual rate, (5) doubling of CO2, 3.5oC warming, and 20% decrease in precipitation, and (6) doubling of CO2, 3.5oC warming, and 20% increase in precipitation. The carbon, nitrogen, phosphorus, and water budgets presented here are used to calibrate the MEL model prior to running the climate change simulations. Citations and calculations for the data presented here are described in the individual site html files included in this dataset.
Annual estimates of the water budget for the Ipswich River watershed, 1931 to 2018
Annual estimates of the water budget for the Ipswich River watershed, 1931 to 2018. The water budget includes precipitation, evapotranspiration, stream flows, water withdrawals, sewer export and public water supply imports.
Roles of irrigation and reservoir operations in modulating terrestrial water and energy budgets in the Indian sub-continental river basins
<p>We have simulated water budget and energy budget over Indian subcontinental basins, using three scenarios from the Variable Infiltration Capacity (VIC) model by including irrigation and reservoir practices in it:</p> <p>1). No irrigation and reservoir (VIC-NATURAL)<br> 2). Free irrigation and no reservoir (VIC-FREE)<br> 3). Reservoir and restricted irrigation (VIC-MANAGED)</p> <p>Here, we have shared results in below folders.</p> <p>Fig1: Annual precipitation (P) and reservoir locations used in study.<br> Fig2: Satellite (MODIS and GLEAM) based annual evapotranspiration (ET) and VIC-MANAGED simulated annual ET.<br> Fig3: Annual land surface temperature (LST) from MODIS, AATSR and VIC-MANAGED.<br> Fig4: Mean monthly observed and simulated reservoir storage.<br> Fig5: P, ET, total runoff (TR) and LST from one grid.<br> Fig6: Annual ET change between VIC-NATURAL and VIC-MANAGED run.<br> Fig7: Same as Fig6 but for TR.<br> Fig8: Same as Fig6 but for LST.<br> Fig9: Annual ET change between VIC-FREE and VIC-MANAGED run.<br> Fig10: Annual latent heat flux and sensible heat flux change between VIC-NATURAL and VIC-MANAGED run.</p> <p>More detail is available in "Roles of irrigation and reservoir operations in modulating terrestrial water and energy budgets in the Indian sub-continental river basins" paper in JGR-Atmosphere. Or contact at harsh.lovekumar.shah@iitgn.ac.in</p> <p>Harsh Shah</p>
High-resolution water budget estimates over the Po basin: progress towards digital replicas (OL*, DAg(*), DAs(*), DAgs(*))
<p>NASA LIS output with water budget variables at 0.7 km^2 resolution over the Po river basin (Italy) for 2015-2023. Netcdf files for 8 + 2 experiments, described in De Lannoy et al. (2024, JAMES). Because of storage limitations, this upload contains 7 of the 8 experiments with ERA5. The baseline OL experiment without irrigation is on a separate zenodo link (see below).</p> <p><strong>8 experiments forced with ERA5 meteorology</strong></p> <p>po_ol_hymap_noirr: (OL) open loop simulation, no irrigation modeling --> 10.5281/zenodo.13768739<br>po_ol_hymap_irr: (OL*) open loop simulation, with irrigation modeling</p> <p>po_da_hymap_gamma_noirr: (DAg) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, no irrigation modeling<br>po_da_hymap_gamma_irr: (DAg*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, with irrigation modeling</p> <p>po_da_hymap_snd_noirr: (DAs) data assimilation of Sentinel-1 snow depth retrievals, no irrigation modeling<br>po_da_hymap_snd_irr: (DAs*) data assimilation of Sentinel-1 snow depth retrievals, with irrigation modeling</p> <p>po_da_hymap_gamma_snd_noirr: (DAgs) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, no irrigation modeling<br>po_da_hymap_gamma_snd_irr: (DAgs*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, with irrigation modeling</p> <p><strong>2 experiments forced with MERRA2 meteorology </strong></p> <p><strong>--> These are not provided on Zenodo, because we hit the maximum storage limit. Feel free to reach out to the authors and ask for these data.<br></strong></p> <p>po_ol_hymap_noirr_M2: open loop simulation, no irrigation modeling<br>po_ol_hymap_irr_M2: open loop simulation, with irrigation modeling </p>
High-resolution water budget estimates over the Po basin: progress towards digital replicas (OL): open loop without irrigation
<p>NASA LIS output with water budget variables at 0.7 km^2 resolution over the Po river basin (Italy) for 2015-2023. Netcdf files for 8 + 2 experiments, described in De Lannoy et al. (2024, JAMES). Because of storage limitations, this upload only contains the baseline open loop without irrigation (OL), i.e. 1 of the 8 experiments with ERA5. The other 7 experiments are on a separate zenodo link (see below).</p> <p><strong>8 experiments forced with ERA5 meteorology</strong></p> <p>po_ol_hymap_noirr: (OL) open loop simulation, no irrigation modeling <br>po_ol_hymap_irr: (OL*) open loop simulation, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p>po_da_hymap_gamma_noirr: (DAg) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, no irrigation modeling --> 10.5281/zenodo.13754454<br>po_da_hymap_gamma_irr: (DAg*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p>po_da_hymap_snd_noirr: (DAs) data assimilation of Sentinel-1 snow depth retrievals, no irrigation modeling --> 10.5281/zenodo.13754454<br>po_da_hymap_snd_irr: (DAs*) data assimilation of Sentinel-1 snow depth retrievals, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p>po_da_hymap_gamma_snd_noirr: (DAgs) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, no irrigation modeling --> 10.5281/zenodo.13754454<br>po_da_hymap_gamma_snd_irr: (DAgs*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating and assimilation of snow depth retrievals, with irrigation modeling --> 10.5281/zenodo.13754454</p> <p><strong>2 experiments forced with MERRA2 meteorology </strong></p> <p><strong>--> These are not provided on Zenodo, because we hit the maximum storage limit. Feel free to reach out to the authors and ask for these data.<br></strong></p> <p>po_ol_hymap_noirr_M2: open loop simulation, no irrigation modeling<br>po_ol_hymap_irr_M2: open loop simulation, with irrigation modeling</p>
Supplementary Information S1 - Detailed results of the CAPRI N-LCA and S2 - Quantification of the main N budget flows in the EU25 agriculture sector of Leip, A., Billen, G., Garnier, J., Grizzetti, B., Lassaletta, L., Reis, S., Simpson, D., Sutton, M. a, de Vries, W., Weiss, F., Westhoek, H. (2015). Impacts of European livestock production: nitrogen, sulphur, phosphorus and greenhouse gas emissions, land-use, water eutrophication and biodiversity. Environ. Res. Lett. 10, 115004. doi:10.1088/1748-9326/10/11/115004
<p>Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)<sup>-1</sup> yr<sup>-1</sup>] or [g N (kg product)<sup>-1</sup> yr<sup>-1</sup>] with the CAPRI N-LCA model for six main livestock products (BEEF: beef, PORK: pork, EGGS: eggs, POUM: poultry meat; DAIR: milk and dairy products, SGMP: meat from sheep and goats) and six main vegetable food groups (POTA: potatoes, SUGB: sugar beet before processing, OILP: oil seeds before processing; CERR: cereals, LEGU: leguminous crops) as well as other crops (OCRP) and aggregated livestock (ANIMP) and vegetable (CROPP) food. </p> <p>Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector</p>
StageIV-IRC – A High-resolution Dataset of Extreme Orographic Quantitative Precipitation Estimates (QPE) Constrained to Water Budget Closure for Historical Floods in the Appalachian Mountains
<h2>Quantitative Flood Estimation (QFE) in complex terrain remains a grand challenge in operational hydrology due to the lack of accurate high-resolution Quantitative Precipitation Estimates (QPE) at spatial and temporal resolutions needed to capture the variability of orographic precipitation, and where radar-based QPE are available there are significant biases due to the geometry and constraints of radar operations. Here, we present a high-resolution (i.e. 250m, 5minute-hourly) QPE dataset for the most extreme (flood-producing) events from 2008 to 2024 for 26 gauged basins (in total 215 events) in the Appalachian mountains constrained to meet basin-scale water budget closure through inverse rainfall-runoff modeling to correct the Next Generation Weather Radar (NEXRAD) Stage IV analysis (4km resolution, hourly) using a fully-distributed uncalibrated hydrological model that leverages recent advances in hydrologic modeling in mountainous regions (e.g. improved river routing and initial soil moisture estimation) (Liao and Barros, 2024a and 2024b). The corrected Stage IV analysis is referred to as StageIV-IRC (Inverse Rainfall Correction). Previously, a subset of this dataset informed the construction of a generalized QPE error model (Liao and Barros, 2023), supporting the development of water budget closure constrained QPE and providing physics insights into orographic QPE uncertainties for various radar-based products at high resolution in complex terrain. The unique advantage of the StageIV-IRC QPE is that it achieves water budget closure at the storm-flood event scale within observational uncertainty of streamflow observations, that is the golden standard in hydrological modeling. The QPE dataset is publicly available at: <a href="https://doi.org/10.5281/zenodo.14028867">https://doi.org/10.5281/zenodo.14028867</a></h2> <p><strong> </strong></p>
An ensemble of 48 physically perturbed model estimates of the 1/8° terrestrial water budget over the conterminous United States, 1980–2015
<p>This dataset contains the 1980–2015 monthly terrestrial water budget simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the abbreviation for "terrestrial water budget", the used parameterization, the time scale, and the suffix.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
An ensemble of 48 physically perturbed model estimates of the 1/8° terrestrial water budget over the conterminous United States, 1980–2015
<p>This dataset contains the 1980–2015 monthly terrestrial water budget simulated by a 48-member perturbed-physics ensemble configured from the Noah LSM with multi-physics options (Noah‑MP v3.6). Simulation outputs include total evapotranspiration and its constituents (canopy evaporation, soil evaporation, and transpiration), runoff (the surface and subsurface components), as well as terrestrial water storage (snow water equivalent, four-layer soil water content from the surface down to 2 m, and the groundwater storage anomaly). The file name has four parts: the abbreviation for "terrestrial water budget", the used parameterization, the time scale, and the suffix.</p> <p>The 48 physics configurations are generated by combining four runoff parameterizations (run1: SIMGM, run2: SIMTOP, run3: NOAHR, run4: BATS), two parameterizations of stomatal conductance (can1: Ball–Berry, can2: Jarvis), three parameterizations of soil moisture stress factor (btr1:NOAHB, btr2: CLM, btr3: SSiB), and two parameterizations of near-surface atmospheric turbulence (tub1: M-O, tub2: Chen97).</p> <p>The simulation domain covers the all of conterminous United States (25°–53°N, 125°–67°W), which is also called the NLDAS-2 testbed (Xia et al., 2012a, b). The simulations were performed at a spatial resolution of 0.125°, which is the same as for NLDAS-2 models. Details of the simulation settings and spin-up run can be found in Section 2.3 of Zheng et al. (2019) and Section 2.2 of Fei et al. (2021).</p>
Supplementary Materials of "Elementary mathematics helps to shed light on the transpiration budget under water stress"
<p>This directory contains the Jupyter notebook used to do complete analysis from our paper "Elementary mathematics sheds light on the transpiration budget under water stress" submitted to the Ecohydrology Journal at the Special Issues "ECOHYDROLOGY OF INLAND AND COASTAL WATERS in honor of Ignacio Rodriguez-Iturbe".</p> <p>These materials are referenced in the main text and supplemental text of the publication. The purpose of this repository is to facilitate replication of our analysis by any interested parties. </p> <p>Specifically, this directory contains ten files:</p> <ul> <li>From 0 to 5, Jupyter Notebook prepared and used for the analysis (please execute the notebooks in numerical sequence). </li> <li>"Table_S1.xlsx" Data From: Kröber, W., H. Heklau, and H. Bruelheide. 2015. “Leaf Morphology of 40 Evergreen and Deciduous Broadleaved Subtropical Tree Species and Relationships to Functional Ecophysiological Traits.” Plant Biology 17 (2): 373–83. <a href="https://doi.org/10.1111/plb.12250"><span>https://doi.org/10.1111/plb.12250</span></a>.</li> <li>"Richards_VG.csv" contains Van Genuchten Parameters for various soils.</li> <li>"The_Rosetta_Stone_of_the_Darcy_Buckingham_law.pdf" addresses the challenge of converting water flux units between Darcy-like soil and plant descriptions, where hydrologists use "head" units (meters) and plant physiologists use pressure potential (MPa). The aim is to clarify and perform the necessary unit conversions, with detailed explanations available in the relevant section on <a href="https://abouthydrology.blogspot.com/2022/10/my-water-management-in-agricolture.html"><span>this webpage</span></a>.</li> </ul>
Datasets of groundwater level and surface water budget in a central Mediterranean site (June 21, 2017 - October 1, 2022)
<p>The datasets contain data of groundwater levels and surface water budget measured in the Salento University Campus ‘Ecotekne’, in Lecce province, Italy (40°20’ N, 18°06’ E) between June 2017 and October 2022. The groundwater data are obtained from two historic wells (FW and BW) hand dug in the Miocene karst Aquifer of Central-Eastern Salento. They are close (few hundreds of meters) to the micrometeorological station of the CNR-ISAC (National Research Council Institute for Atmospheric Science and Climate), that provided the surface water budget data. </p> <p> </p>
Water budget components and forest structure in burned and unburned longleaf pine stands, the Jones Center at Ichauway, Southwest Georgia, USA, 2015 - 2021
This data package includes data tables used to assemble a stand-level water budget of the longleaf pine-wiregrass ecosystem in southwestern Georgia, USA, between 2015 and 2021. Water budgets were estimated by measuring or modeling each distinct water budget component. Thus, the data tables included in the package are generally divided by stratum (i.e. overstory, midstory, vegetative groundcover, litter layer) and/or process (i.e. evaporation or transpiration). Relevant forest structure data is also included; they are often presented in tables alongside estimates of transpiration. Much of the data were collected across a soil moisture gradient, at both mesic and xeric sites, and in stands treated with different fire regimes: frequent prescribed fire (FF) or fire exclusion (EX). For some data, site or treatment may not be specified if the data were not collected for comparative purposes, but rather as representative baseline data for models that would be applied across sites and treatments. Transpiration data contained within this package include: 1) seasonal averages of daily sap-flux for pines and oaks measured with thermal dissipation probes 2) daily and seasonal transpiration for individual trees 3) seasonal transpiration for functional groups of shrubs and small trees 4) seasonal transpiration of groundcover functional groups Evaporation data contained within this package include: 1) Continuously-measured canopy interception and throughfall 2) overstory stemflow 3) midstory stemflow 3) groundcover interception and throughfall 4) litter moisture content data paired with time since rain (to develop litter drying curves), and 5) litter moisture content data paired with recent rain event depth (to develop litter wetting curves) Forest structure data contained within this package include: 1) Annual measures of tree DBH 2) Biannual estimates of small tree and shrub DBH, by functional group 3) Monthly estimates of groundcover functional group leaf area index 4) Monthly
Taylor Valley Water Budgets
As part of the Long Term Ecological Research (LTER) project in the McMurdo Dry Valleys of Antarctica, a systematic sampling program has been undertaken to monitor the glacial meltwater streams in that region. This table contains data for the total volume of water discharged in various streams and basins of Taylor Valley. The data covers melt periods from 1991-92 through 1996-97.
Data for the publication "The carbon and nitrogen budget of <i>Desmophyllum dianthus</i> – a voracious cold-water coral thriving in an acidified Patagonian fjord"
<p>Raw and supplementary data and detailed statistical results for the publication "The carbon and nitrogen budget of <em>Desmophyllum dianthus</em> – a voracious cold-water coral thriving in an acidified Patagonian fjord" </p>
Data sets used in the manuscript titled "Ecosystem-level energy and water budgets are resilient to canopy mortality in sparse semi-arid biomes"
<p><span>This data set reports water and energy fluxes, soil water content and sap fluxes measured at two adjacent pinon-juniper woodlands in central New Mexico from January 2009 to December 2016. This data set was used for the analysis in the manuscript titled "Ecosystem-level energy and water budgets are resilient to canopy mortality in sparse semi-arid biomes" submitted to JGR-Biogeosciences. </span></p>
Lean mass dynamics in hibernating bats and implications for energy and water budgets
<p>Hibernation requires balancing energy and water demands over several months with no food intake for many species. Many studies have considered the importance of fat for hibernation energy budgets because it is energy dense and can be stored in large quantities. However, protein catabolism in hibernation has received less attention and whole animal changes in lean mass have not previously been considered. We used quantitative magnetic resonance body composition analysis to measure fat and lean mass in two systems of hibernating bats, emphasizing the importance of lean mass for energy and water budgets. For cave myotis ( Myotis velifer ), lean mass represented 38 and 25% (male and female respectively) of pre-hibernation mass gain. In Townsend's big-eared bats ( Corynorhinus townsendii ), lean mass accounted for 18 – 35% of mass change during hibernation, but lean only contributed 3 – 7% of the energy budget. Water is produced from the catabolism of both fat and lean, but net water production is much less than gross water production when accounting for the water required to excrete urea. Although most mammals can't rely on protein catabolism for metabolic water production due to the water cost of excreting urea, we propose a variation on the protein-for-water strategy whereby hibernators could temporally compartmentalize the benefits of protein catabolism to periods of torpor, and the water cost to periodic arousals when free drinking water is typically available. Combined, our analyses demonstrate that lean mass is dynamic in hibernation, with important functional consequences for both energy and water budgets. </p>
Lean mass dynamics in hibernating bats and implications for energy and water budgets
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Data sets used in the manuscript titled "Ecosystem-level energy and water budgets are resilient to canopy mortality in sparse semi-arid biomes"
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