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opencc-by-4.0Nov 2019View details →

Roles of irrigation and reservoir operations in modulating terrestrial water and energy budgets in the Indian sub-continental river basins

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: 1). No irrigation and reservoir (VIC-NATURAL) 2). Free irrigation and no reservoir (VIC-FREE) 3). Reservoir and restricted irrigation (VIC-MANAGED) Here, we have shared results in below folders. Fig1: Annual precipitation (P) and reservoir locations used in study. Fig2: Satellite (MODIS and GLEAM) based annual evapotranspiration (ET) and VIC-MANAGED simulated annual ET. Fig3: Annual land surface temperature (LST) from MODIS, AATSR and VIC-MANAGED. Fig4: Mean monthly observed and simulated reservoir storage. Fig5: P, ET, total runoff (TR) and LST from one grid. Fig6: Annual ET change between VIC-NATURAL and VIC-MANAGED run. Fig7: Same as Fig6 but for TR. Fig8: Same as Fig6 but for LST. Fig9: Annual ET change between VIC-FREE and VIC-MANAGED run. Fig10: Annual latent heat flux and sensible heat flux change between VIC-NATURAL and VIC-MANAGED run. 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 Harsh Shah

opencc-by-4.0Sep 2024View details →

High-resolution water budget estimates over the Po basin: progress towards digital replicas (OL, DAg(), DAs(), DAgs())

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). 8 experiments forced with ERA5 meteorology po_ol_hymap_noirr: (OL) open loop simulation, no irrigation modeling --> 10.5281/zenodo.13768739 po_ol_hymap_irr: (OL*) open loop simulation, with irrigation modeling po_da_hymap_gamma_noirr: (DAg) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, no irrigation modeling po_da_hymap_gamma_irr: (DAg*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, with irrigation modeling po_da_hymap_snd_noirr: (DAs) data assimilation of Sentinel-1 snow depth retrievals, no irrigation modeling po_da_hymap_snd_irr: (DAs*) data assimilation of Sentinel-1 snow depth retrievals, with irrigation modeling 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 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 2 experiments forced with MERRA2 meteorology --> 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. po_ol_hymap_noirr_M2: open loop simulation, no irrigation modeling po_ol_hymap_irr_M2: open loop simulation, with irrigation modeling

opencc-by-4.0Sep 2024View details →

High-resolution water budget estimates over the Po basin: progress towards digital replicas (OL): open loop without irrigation

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). 8 experiments forced with ERA5 meteorology po_ol_hymap_noirr: (OL) open loop simulation, no irrigation modeling po_ol_hymap_irr: (OL*) open loop simulation, with irrigation modeling --> 10.5281/zenodo.13754454 po_da_hymap_gamma_noirr: (DAg) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, no irrigation modeling --> 10.5281/zenodo.13754454 po_da_hymap_gamma_irr: (DAg*) data assimilation of Sentinel-1 backscatter (VV) for soil moisture updating, with irrigation modeling --> 10.5281/zenodo.13754454 po_da_hymap_snd_noirr: (DAs) data assimilation of Sentinel-1 snow depth retrievals, no irrigation modeling --> 10.5281/zenodo.13754454 po_da_hymap_snd_irr: (DAs*) data assimilation of Sentinel-1 snow depth retrievals, with irrigation modeling --> 10.5281/zenodo.13754454 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 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 2 experiments forced with MERRA2 meteorology --> 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. po_ol_hymap_noirr_M2: open loop simulation, no irrigation modeling po_ol_hymap_irr_M2: open loop simulation, with irrigation modeling

opencc-by-4.0Sep 2021View details →

Carbon Budget Scenarios for Ireland's Energy System, 2021-50

Carbon Budget Scenarios for Ireland's Energy System, 2021-50, calculated with the TIMES-Ireland model.

opencc-by-4.0Aug 2022View details →

Regional Sea-level Budget from 1993-2016

This repository contains supporting data for Camargo et al.: 'Regionalizing Sea-level Budget with Machine Learning Techniques', Ocean Sciences (2022), https://egusphere.copernicus.org/preprints/2022/egusphere-2022-876/. **Please note that the time series of the GRD component is flipped in the latitude axis (ordered South-North, instead of North-South as the other datasets). So before using, it should be flipped. In order to avoid creating a new DOI for this dataset, we have added just a warning, instead of updating the file. ** This has no impact on the results of the manuscript, as the 'axis error' occurred only when organising the files to be published.  ** Please cite the appropriate papers when using this data ** Please cite 'Regionalizing Sea-level Budget with Machine Learning Techniques' when using this data set. However, most of the data heavily relies on previous work and data sets by many authors, so please acknowledge that work by citing the original sources of the data (which can be found in the main text of 'Regionalizing Sea-level Budget with Machine Learning Techniques'). ** please check this carefully!** This repository contains the following files: budget_components_ENS.nc Regional (1x1 degree) trend, uncertainty and time series of the ensemble mean of each of the budget components: total sea-level change (from altimetry) and the drivers (steric, GRD and dynamic). Please note that the time series of the GRD component is flipped in the latitude axis (ordered South-North, instead of North-South as the other datasets). So before using, it should be flipped. In order to avoid creating a new DOI for this dataset, we have added just a warning, instead of updating the file. If required the individual data sets used for the ensemble, please contact the author.  masks.nc netcdf containing land-ocean mask, as well as the domains maps (SOM and delta-MAPS). We refer to the manuscript for more information of how the regional domains were acquired. dmaps_trend.pkl (and .xlsx) Trend and uncertainties of each of the budget components for each delta-MAPS domains. Available as an excel table (.xlsx) and as pickle file (.pkl) som_trend.pkl (and .xlsx) Trend and uncertainties of each of the budget components for each SOM domains. Available as an excel table (.xlsx) and as pickle file (.pkl) The code to generate this data and the manuscript figures can be found at https://github.com/carocamargo/SLB Corresponding author: carolina.camargo@nioz.nl

opencc-by-4.0Oct 2023View details →

Dataset associated with the manuscript "A comprehensive assessment of anthropogenic and natural sources and sinks of Australasia's carbon budget" by Villalobos et al. (2023), part of the the second phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2).

Dataset associated with the manuscript  "A comprehensive assessment of anthropogenic and natural sources and sinks of Australasia’s carbon budget" by Villalobos et al. (2023), part of the the second phase of the REgional Carbon Cycle Assessment and Processes (RECCAP-2).

Tanana River Floodplain Dissolved Organic Nitrogen (T30 DON) Budget

Tanana River Floodplain Dissolved Organic Nitrogen (DON) Budget, GPS coordinates for Tanana River Floodplain study plots

Ammonium, Nitrate, and Amino Acid concentrations in .5M K2SO4 extracted T0 Tanana Floodplain soils Ammonium, Nitrate, and Amino Acid concentrations in .5M K2SO4 extracted T30 Tanana Floodplain soils Sodium Bicarbonate Extracted Tanana River Floodplain Soil Protein Concentrations, 2001 Plot locations for the Tanana River Floodplain DON buget study. GPS coordinates for the Tanana River Floodplain Dissolved Organic Nitrogen (DON) Budget Study plots GPS coordinates for the Tanana River Floodplain Dissolved Organic Nitrogen (DON) Budget Study plots. Readings were taken from the centre of each 30 X 30 meter plot (1DW3 is 45 X 20). The GPS unit (Garmin etrex Summit) indicated an accuracy of about +/- 5 meters.

Tanana River Floodplain Dissolved Organic Nitrogen (T0 DON) Budget

Ammonium, Nitrate, and Amino Acid concentrations in .5M K2SO4 extracted T0 Tanana Floodplain soils Ammonium, Nitrate, and Amino Acid concentrations in .5M K2SO4 extracted T30 Tanana Floodplain soils Sodium Bicarbonate Extracted Tanana River Floodplain Soil Protein Concentrations, 2001 Plot locations for the Tanana River Floodplain DON buget study. GPS coordinates for the Tanana River Floodplain Dissolved Organic Nitrogen (DON) Budget Study plots 9999 missing data All values given are in micrograms of nitrogen for the given compound per gram dry weight. AA is leucine equivalents of bulk amino acids. Site designations in parenthesis are specific to the study and represent individual transects, each within a unique stand of the following standtypes: W=willow, A=alder, BP=balsam poplar, 4=white spruce, 5=black spruce. Months: 6=June, 7=July, etc.

Tanana River Floodplain Dissolved Organic Nitrogen (DON) Budget, extracted soil protein content

Ammonium, Nitrate, and Amino Acid concentrations in .5M K2SO4 extracted T0 Tanana Floodplain soils Ammonium, Nitrate, and Amino Acid concentrations in .5M K2SO4 extracted T30 Tanana Floodplain soils Sodium Bicarbonate Extracted Tanana River Floodplain Soil Protein Concentrations, 2001 Plot locations for the Tanana River Floodplain DON buget study. GPS coordinates for the Tanana River Floodplain Dissolved Organic Nitrogen (DON) Budget Study plots All values given are in micrograms of bovine serum albumin equivalents per gram dry weight. Site designations in parenthesis are specific to the study and represent individual transects,each within a unique stand of the following standtypes: W=willow,A=alder,BP=balsam poplar,4=white spruce,5=black spruce. Months: 6=June,7=July,etc.

opencc-by-4.0Sep 2020View details →

Resource allocation decision-making in dementia care with and without budget constraints: a qualitative analysis

Vignettes and COREQ checklist for Applied Partnership Award project: Resource Allocation, Priority-Setting and Consensus in Dementia Care. See Keogh F, Pierse T, O'Shea E et al. Resource allocation decision-making in dementia care with and without budget constraints: a qualitative analysis [version 1; peer review: awaiting peer review]. HRB Open Res 2020, 3:69 (<a href="https://doi.org/10.12688/hrbopenres.13147.1">https://doi.org/10.12688/hrbopenres.13147.1</a>)

opencc-by-4.0Nov 2015View details →

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

Table S1-1 Quantification of GHG and Nr flow intensities [kg CO2eq (kg product)-1 yr-1] or [g N (kg product)-1 yr-1] 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. Table S2-1 Quantification of the main N budget flows in the EU25 agriculture sector

opencc-by-4.0May 2024View details →

Time-varying global energy budget since 1880 from a reconstruction of ocean warming

This dataset was produced as part of the research presented in the paper "Time-varying global energy budget since 1880 from a reconstruction of ocean warming" published in PNAS. For detailed methodology, analysis, and interpretation of the data, please refer to the original publication: 10.1073/pnas.2408839122.

opencc-by-4.0Dec 2021View details →

ECMWF ERA interim derived atmospheric mass, moisture and energy budget products

As observations and atmospheric reanalyses have improved, the diagnostics that can be computed with confidence also increase. Accordingly, a new formulation of the energetics of the atmosphere is laid out, with a view to advancing diagnostic studies of Earth's energy budget and flows. It is utilized to produce assessments of the vertically integrated divergences in both the atmosphere and ocean. Careful conservation of mass is required, with special attention given to the hydrological cycle and redistribution of mass associated with precipitation and evaporation, and a new method for ensuring this is developed. It guarantees that the atmospheric divergence is associated with moisture and precipitation, unlike previous methods. A new term, identified as associated with the enthalpy of precipitation, is included in a preliminary way. It is sensitive to the formulation, and the use of temperature in degrees Celsius instead of Kelvin greatly reduces errors and produces the extra term with values up to about 65 W/m2. New results for 2000 to 2017 are presented for the vertical-mean and annual-mean diabatic atmospheric heating, atmospheric moistening, and total atmospheric energy divergence. Results for the atmospheric divergence are combined with top-of-atmosphere radiation observations to deduce total surface energy fluxes. These data files are monthly and span from 1979 to 2017, smoothed at T-106 resolution. The data format is NetCDF. A full dataset description is available at https://journals.ametsoc.org/view/journals/clim/31/16/jcli-d-17-0838.1.xml

opencc-by-4.0Nov 2024View details →

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>

opencc-by-4.0Nov 2023View details →

Machine Learning-Based Bridge Maintenance Optimization Model for Maximizing Performance within Available Annual Budgets

Effective maintenance planning for bridges is crucial for maintaining their performance, safety, and minimizing maintenance costs. Timely implementation of interventions can improve the performance of bridges and avoid the need for costly interventions. However, bridge maintenance is often delayed due to inadequate planning and budget allocation, as well as resource constraints such as funding. With availability of historical condition data of bridges in databases such as the National Bridge Inventory (NBI) and National Bridge Elements (NBE), there is an opportunity to use data-driven methods to predict deterioration of bridge elements and optimize their maintenance interventions to maximize performance of bridges. This paper presents the development of a novel system that uses Machine Learning (ML) techniques to predict condition of concrete bridge elements and binary linear programming optimization method to identify the optimal selection of maintenance interventions and their timing to maximize the performance of bridges while complying with available annual budgets. Four ML methods are explored: decision tree, random forest, gradient boosting, and support vector machines. The results of the ML evaluation show that, while the values of the predictive performance metrics varied for different elements, random forest method had the best performance for all elements. A case study of a concrete bridge is analyzed to evaluate the performance of the system and demonstrate its new capabilities. The case study results show that the developed model identifies optimal maintenance interventions for various annual budgets over a 50-year study period. The primary contributions of this research to the body of knowledge are: (1) development of a novel system that integrates machine learning techniques and linear programming for predicting bridge element conditions and optimizing maintenance interventions; (2) modeling and predicting the deterioration of bridge elements based on health index metric; and (3) generating long-term maintenance plans for each of bridge elements to maximize the performance of bridges within available annual budgets. The present system is expected to support decision makers, such as highway agencies, in allocating limited financial resources for bridge maintenance more efficiently and cost-effectively.

opencc-by-4.0Dec 2023View details →

ludemannc/fao_cnb: FAO cropland nutrient budget analysis

Data and code used for creating Cropland Nutrient Budget (1961-2020) related figures and tables in the Ludemann et al Earth Systems Science Data article available at: https://doi.org/10.5194/essd-2023-206.

opencc-by-4.0Sep 2022View details →

An ensemble of 48 physically perturbed model estimates of the 1/8° terrestrial water budget over the conterminous United States, 1980–2015

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. 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). 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).

opencc-by-4.0Apr 2024View details →

Data for the publication "Radiative effects of precipitation on the global energy budget and Arctic amplification"

This dataset includes a set of 15yr simulations using the MIROC6 global aerosol-climate model with 1) diagnostic precipitation, 2) prognostic precipitation without radiative effect of precipitation, and 3) prognostic precipitation with radiative effect of precipitation. The data are used in the manuscript entitled "Radiative effects of precipitation on the global energy budget and Arctic amplification".