cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.d4cea45dfb3a4da1b026ec79dab3128e
title: Evaluation of CMIP6 HighResMIP for hydrologic modeling manuscript code

description: This repository contains codes for a study titled "Evaluation of CMIP6 HighResMIP for hydrologic modeling of annual maximum discharge in Iowa" submitted to Water Resources Research (Article DOI: 10.1029/2022WR034166) by Alexander T. Michalek, Gabriele Villarini, Taereem Kim, Felipe Quintero, Witold F. Krajewski, and Enrico Scoccimarro. 

The resources include R codes for data analysis, figures, and precipitation bias-correction and downscaling. Additionally, codes are provided related to the setup of the hydrologic model (HLM) utilized in the study and found at https://asynch.readthedocs.io/en/latest/index.html. Finally, a subset of data from the simulations is provided for which the analysis is conducted. Full simulation datasets and CMIP6 forcings are not provided as they are too large to store and can be provided upon reasonable request. 

Abstract:
The High-Resolution Model Intercomparison Project (HighResMIP) experiments from the Coupled Model Intercomparison Phase 6 (CMIP6) represent a broad effort to improve the resolution, and performance of climate models. The HighResMIP suite provides high spatial resolution (i.e., 25- and 50-km) forcings that have been shown to improve the representation of climate processes. However, little is known about their suitability for hydrologic applications. We use outputs from the HighResMIP suite to simulate annual maximum discharge with the Hillslope-Link Model (HLM) at ~1000 river communities across Iowa. First, we assess whether the runoff from the climate models can be directly routed through the river network model in HLM to estimate annual maximum discharge. Runoff-based simulations can capture the empirical distribution of flood peaks in five of the ten models/members assessed. Next, we force the HLM with precipitation, temperature, and potential evapotranspiration from HighResMIP models to simulate flood peaks, finding all models/members produce empirical distributions similar to our reference. However, significant biases exist in the model/member forcings as correct flood response is being generated for the wrong reason. To improve their suitability for community-level assessment, we use nine statistical approaches to bias-correct and downscale HighResMIP precipitation to a 4-km resolution. The bias-correction and downscaling of climate model precipitation performs well for all models/members. Furthermore, we do not find significant changes in the magnitude flood peak projections for Iowa based on the HLM forced with HighResMIP outputs, or based on routed runoff, while there are indications that the variability in flood peaks is projected to increase across the state.
DOI: 10.4211/hs.d2c6e67f8b574908842d67f418de9ea5
title: Surface Charge Estimation from SIP Data with AFM Comparison (Matlab script)

description: These data are from a lab experiment comparing spectral induced polarization measurements performed on glass beads and silica gel for treatments that include etching (glass beads only), iron oxide deposition, and calcite precipitation.  The data are associated with a paper by Na Hao (reference: Hao, Moysey, and Dean, Electrical Responses of Modified Mineral Surfaces as Observed with Spectral Induced Polarization and Atomic Force Microscopy, JGR-Solid Earth, in press).
DOI: 10.4211/hs.ebd3c73f946344d8adf0431f20576d1c
title: A decision support framework for pollution source detection via coupled forward-inverse optimization and multi-information fusion

description: Supporting Information (SI) for Research Article "A decision support framework for pollution source detection via coupled forward-inverse optimization and multi-information fusion", which contains
Text S1. Extended methodology: the ANOVA (analysis of variance).
Figure S1. The sensor data shown in pollutant concentration distribution in Case-T1.
Figure S2. The COD sensor data shown in pollutant concentration distribution and decomposition in Case-T2.
Figure S3. The NH sensor data shown in pollutant concentration distribution and decomposition in Case-T2.
Table S1. Standard limit of COD, TP and NH in GB 3838-2002.
Table S2. The non-uniqueness candidate solutions n Case-T2.

The data The simulated data used in the case study（multi-water quality data）
DOI: 10.4211/hs.cdb5fd97e0644a14b22e58d05299f69b
title: Human Alterations of the Global Floodplains: 1992 to 2019 (version 1)

description: Here we present the first-available global dataset that quantifies human alterations in 15 million sq km floodplains along the world’s 520 major river basins. We developed these data using a comprehensive 27-year (1992-2019) analysis of remotely sensed land use change at 250-m resolution. This new dataset reveals that the world has lost ~600,000 sq km floodplains in 27 years (1992-2019), moving from natural forest, grassland, and wetland conditions to 460,000 sq km of new agricultural and 140,000 sq km of new developed areas.

To ensure the maximum reuse of this dataset, we also developed three web-based semi-automatic programming tools partly supported with data-driven tutorials and step-by-step audiovisual instructions.

(1) Floodplain Mapping Tool
- Web-based Python code that runs in any internet browser using Google's high performance computing resource: https://colab.research.google.com/drive/1xQlARZXKPexmDInYV-EMoJ-HZxmFL-eW?usp=sharing
- A tutorial developed and published through an online data-driven geoscience education platform: https://serc.carleton.edu/hydromodules/steps/246320.html
- A YouTube video with step-by-step instructions: https://youtu.be/TgMbkJdALig

(2) Land Use Change Tool
- Web-based Python code that runs in any internet browser using Google's high performance computing resource: https://colab.research.google.com/drive/1vmIaUCkL66CoTv4rNRIWpJXYXp4TlAKd?usp=sharing
- A tutorial developed and published through an online data-driven geoscience education platform: https://serc.carleton.edu/hydromodules/steps/241489.html
- A YouTube video with step-by-step instructions: https://youtu.be/wH0gif_y15A

(3) Human Alteration Tool
- Web-based Python code that runs in any internet browser using Google's high performance computing resource: https://colab.research.google.com/drive/1r2zNJNpd3aWSuDV2Kc792qSEjvDbFtBy?usp=sharing

Note, the floodplain dataset used in this analysis (GFPLAIN250m; Nardi et al., 2019) does not cover deserts and ice-covered regions. Hence, places like northern Africa, Persian Gulf, Tibetan plateau, and the region above 60 degrees north latitude are not included in this analysis.

This global floodplain alteration dataset is built off our recent work published in the Nature Scientific Data: Rajib et al. (2021). The changing face of floodplains in the Mississippi River Basin detected by a 60-year land use change dataset. https://doi.org/10.1038/s41597-021-01048-w
DOI: 10.4211/hs.6127902b82674b8097ec3c372f14514f
title: Eastern Cascades Forest-Snow Observations 2019-2021

description: Forest thinning and gap creation are being implemented across the western United States of America (USA) to reduce wildfire and forest mortality risk as the climate warms. The Eastern Cascades in Washington, USA, is in a transitional zone between maritime and continental climate conditions and represents a data gap in observations describing the relationship between forest density and snowpack. We collected three years of snow observations across a range of forest densities to characterize how forest management efforts in this region may influence the magnitude and duration of snow storage. Observations indicate that peak snow storage magnitude in small gaps ranges from the same to over twice that observed in unburned forest plots in the Eastern Cascades. However, differences in snow duration are generally small. Across all Eastern Cascade sites and years, we observed a median difference of snow storage lasting 7 days longer in gaps as compared to nearby forest plots. A notable exception to this pattern occurred at one north-facing site, where snow lasted 30 days longer in the gap. These observations of similar snow storage duration in the Eastern Cascades are attributed to minimal differences in canopy snow interception processes between forests and gaps at some sites, and to higher ablation rates that counterbalance the higher snow accumulation in the gaps at other sites. At the north-facing site, more snow accumulated in the gap, and ablation rates in the open gap were similar to the shaded forest due to the aspect of the site.  Thus, snow storage duration was much longer in the gap. Together, these data suggest that prescriptions to reduce forest density through thinning and creating gaps may increase the overall amount of snow storage by reducing loss due to sublimation and melting of canopy-intercepted snow. However, reducing forest density in the Eastern Cascades is unlikely to buffer climate-induced shortening of snow storage duration, with the possible exception of gap creation in north-facing forests. Lastly, these observations fill a spatial and climatic data gap and can be used to support hydrological modeling at spatial and temporal scales that are relevant to forest management decisions.
DOI: 10.4211/hs.12fc1ca42131452294a0149131d4eb5f
title: Results and code for "Spatial Analysis of Future Climate Risk to Stormwater Infrastructure"

description: This resource archives Python code and results supporting the paper "Spatial Analysis of Future Climate Risk to Stormwater Infrastructure".  The abstract for this paper follows:  Climate change is expected to result in more intense precipitation events that will affect the performance and design requirements of stormwater infrastructure. Such changes will vary spatially, and climate models provide a range of estimates of the effects on events of different intensities and recurrence. Infrastructure performance should be evaluated against the expected range of events, not just rare extremes. We present a national-scale, spatially detailed screening assessment of the potential effects of hydroclimatic change on precipitation, stormwater runoff, and potential effects on design requirements. This is accomplished through adjustment relative to multiple future climate scenarios of rainfall intensity-duration-frequency analyses presented in NOAA Atlas 14, which are commonly used in infrastructure design. Future precipitation results are estimated for each Atlas 14 station (these currently do not include the Pacific Northwest). Results are interpolated using a geographically conditioned regression kriging approach to provide information about potential climate change impacts in a format more directly useful to local stormwater managers. The intensity of events with 2-year or greater recurrence is likely to increase in most areas of the U.S. leading to increased runoff and potential need for increased BMP volumes. Changes in more frequent events (e.g., the 90th percentile event) commonly used in design of green infrastructure are relatively less.
DOI: 10.4211/hs.632e550deaea46b080bdae986fd19156
title: AltiMaP v1.0

description: Radar altimetry is useful for monitoring water surface dynamics in an era of satellite remote sensing. One of the uses of satellite altimetry is to evaluate/calibrate the hydrodynamic model or to improve river-related variables by optimization/assimilation methods. However, with the discrete nature of the hydrodynamic models, comparing simulated water surface elevation (WSE) with satellite altimetry is challenging due to the difficulty in the exact matching of representative locations by satellite altimetry virtual station (VS) and model grids. We developed an algorithm to assign the VSs to the river network and labeled each VS with a flag according to the characteristics of the allocation to the river network. We presented an altimetry mapping procedure (AltiMaP) to allocate VS locations given in the HydroWeb database (https://hydroweb.theia-land.fr/) to the MERIT Hydro (http://hydro.iis.u-tokyo.ac.jp/~yamadai/MERIT_Hydro/) river network. Each VS was assigned to the nearest river in the MERIT Hydro considering the geometric distance. During the altimetry mapping, flags were added to each VS depending on the method of allocation. The flags were added in cases if the latitude-longitude of VSs is identified as a single-channel river (Flag 10); as a land pixel and allocated to the nearest single-channel river (Flag 20); as a multi-channel river (Flag 30); and as an ocean pixel and allocated to the nearest river (Flag 40). We allocated more than 12000 VSs in which most of the VSs were allocated as Flag 10 which accounts for 71.7% of all VSs. Moreover, most of the Flags 10 and 20 are located up and mid-streams whereas Flags 30 and 40 were in downstream river reaches. The biased VSs which have a large elevation difference between the mean observed WSE with the model elevation were approximately 0.8% of the total VSs and were mostly seen in narrow rivers at high elevations. The error of simulated WSE by a global river hydrodynamic model was reduced by 10.6% by employing the AltiMaP against the allocation VSs by converting longitude and latitude information to the coarse resolution river network. Therefore, the allocation of VSs using the method developed in this study (AltiMaP) to the river network enhanced the comparability of the simulated WSE by the global hydrodynamic model against satellite altimetry. The AltiMaP algorithm and data will be useful for the global hydrological community.
DOI: 10.4211/hs.674721ff170e4b67aa92a2136a0aa5a4
title: data-A decision support framework for pollution source detection via coupled forward-inverse optimization and multi-information fusion

description: The data The simulated data used in the case study（multi-water quality data）
DOI: 10.34740/kaggle/dsv/14132465
title: Balochi Language to English Translation Dataset
DOI: 10.4211/hs.0b71b6ffeea04179acd9f3d5b13473b0
title: Support Information-Manuscript-2022WR032866

description: Supporting Information (SI) for Research Article "A decision support framework for pollution source detection via coupled forward-inverse optimization and multi-information fusion"
DOI: 10.15468/dl.4aneqc
title: Occurrence Download

description: A dataset containing 176 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Russian Federation, Japan, China)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Salix nipponica"
 ]
}

 The dataset includes 176 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049977-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.8c5b71cf3a7449b0b94c447a3e87e63f
title: Dataset and Supporting Information for Barinas et. al. Floodplain Roughness Study

description: Publication Title: Continental Scale Assessment of Variation in Floodplain Roughness with Vegetation and Flow Characteristics

Quantifying floodplain flows is critical to multiple river management objectives, yet how vegetation within floodplains dissipates flow energy lacks comprehensive characterization. Utilizing over 3.4 million discharge measurements, in conjunction with aboveground biomass and canopy height measurements from NASA’s Global Ecosystem Dynamics Investigation (GEDI), this study characterizes the floodplain roughness coefficient Manning’s n and its determinates across the continental United States. Estimated values of n show that flow resistance in floodplains decreases as flow velocity increases but increases with the fraction of vegetation inundated. A new function (RMSE = 0.024, r2 = 0.74) is proposed for predicting n based of GEDI vegetation characteristics and flow velocity, with GEDI derived n values improving predictions of discharge relative to those based only on land cover. This analysis provides evidence of key hydraulic patterns of energy dissipation in floodplains, and integration of the proposed function into flood and habitat models may reduce uncertainty.
DOI: 10.4211/hs.1d52161761a24e24a38020d9c73bb18e
title: Forming the Future of Agrohydrology

description: These data are linked to the Earth's Future publication "Forming the Future of Agrohydrology" by S.J. Smidt et al. Provided here are the final data used in the manuscript.
DOI: 10.4211/hs.7def95046b9b480c89605e12233059e9
title: Online repository of materials for an all-Africa hydropower atlas (v2.1)

description: This repository accompanies the paper "A spatiotemporal atlas of hydropower in Africa for energy modelling purposes" by Sterl et al. (2021, https://open-research-europe.ec.europa.eu/articles/1-29).
DOI: 10.4211/hs.0a9e7010568c40fe953c9368f0ece748
title: ADVANCEGeo Workplace Climate Survey

description: Exclusionary Behaviors Reinforce Historical Biases and  Contribute to Loss of Talent in the Earth Sciences

accepted, Earth Futures

E. Marin-Spiotta (1), E.J. Diaz-Vallejo (1), R.T. Barnes (2), A. Mattheis (3), B. Schneider (4), A.A. Berhe(5), M.G. Hastings (6), B. Williams (7) and V. Magley (8) 

(1)Department of Geography, University of Wisconsin-Madison, Madison, WI, USA;  (2) Environmental Studies Program, Colorado College, Colorado Springs, CO, USA; (3) Division of Applied and Advanced Studies, California State University, Los Angeles, CA, USA; (4) Kansas Geological Survey, Lawrence, KS, USA; (5) Department of Life and Environmental Sciences, University of California, Merced, Merced, CA, USA; (6)​​ Department of Earth, Environmental, and Planetary Sciences and Institute at Brown for Environment and Society, Brown University, Providence, RI, USA; (7) American Geophysical Union, Washington, D.C., USA, (8) Department of Psychology, University of Connecticut, Storrs, CT, USA. 

Geosciences remain one of the least diverse fields. Efforts to diversify the discipline need to address the role of hostile work and learning environments, including sexism, racism, ableism, and homophobia. A climate survey distributed to professional organizations illustrates varied experiences of earth and space scientists over a 12-month period (pre-COVID). A majority experienced positive interactions in the workplace. However, scientists of color, women, scientists with disabilities, non-binary and LGBQPA+ scientists more frequently experienced negative interactions, including interpersonal mistreatment, discriminatory language, and sexual harassment. Geoscientists of color were more likely to experience devaluation of their work than white scientists. More than half of women, non-binary, and LGBQPA+ respondents experienced identity-based discriminatory remarks. Disabled geoscientists were more likely to hear negative remarks about race, health, and gender than those who did not disclose a disability. Overall, 14% of all respondents experienced sexual harassment in the previous year. Rates were greatest for historically excluded groups: non-binary (51%), LGBQPA+ (33%), disabled (26%), women (20%), and geoscientists of color (17%). Overall, geoscientists reported avoiding colleagues (51%) and considering leaving their institution (31%) or a career change (30%). Identity matters: historically excluded groups were more likely to report activities with negative professional ramifications. Stereotypes in the geosciences and current organizational structures, policies, and practices maintain hostile workplace environments. To address continued exclusion and low retention in the earth and space sciences, recruitment is not enough. We need to create environments that ensure opportunities for all to thrive.
DOI: 10.5281/zenodo.17912230
title: MARIGOLD: MAchine leaRnIng-Guided Optimisation for caLenDula genotype selection

description: Dataset for the mansucript sent to Computers and Electronics in Agriculture
DOI: 10.5281/zenodo.17912231
title: MARIGOLD: MAchine leaRnIng-Guided Optimisation for caLenDula genotype selection

description: Dataset for the mansucript sent to Computers and Electronics in Agriculture
DOI: 10.4211/hs.ed4530307dda435e9d3dcdb74da86a30
title: Data underlying the paper "Inferring reservoir filling strategies under limited data availability using hydrological modelling and Earth observation: the case of the Grand Ethiopian Renaissance Dam (GERD)"

description: The data include HBV-light parameter sets and (best) simulations at the outlet of the Upper Blue Nile basin using three rainfall products (ARC2, CHIRPS, and PERSIANN-CDR).
DOI: 10.15468/dl.tckv7s
title: Occurrence Download

description: A dataset containing 43429 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Azadirachta indica",
 "HasCoordinate is true",
 "BasisOfRecord is Human Observation"
 ]
}

 The dataset includes 43429 records from 157 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049976-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.4211/hs.d43ec15cf9c64b349ca2f3f36de4686b
title: Assessment of hydrological parameter uncertainty versus climate projection spread on urban streamflow and floods

description: Assessing the uncertainty associated with projections of climate change impacts on hydrological processes can be challenging due to multiple sources of uncertainties within and between climate and hydrological models. Here we compare the effects of parameter uncertainty in a hydrological model to inter-model spread from climate projections on hydrological projections of urban streamflow in response to climate change. Four hourly climate model outputs from the RCP8.5 scenario were used as inputs to a distributed hydrologic model (SWMM) calibrated using a Bayesian approach to summarize uncertainty intervals for both model parameters and streamflow predictions. Continuous simulation of 100 years of  streamflow generated 90% prediction intervals for selected exceedance probabilities and flood frequencies prediction intervals from single climate models were compared to the inter climate model spread resulting from a single calibration of the SWMM model. There will be an increase in future flows with exceedance probabilities of 0.5%-50% and 2-year floods for all climate projections and all 21st century periods. Floods with return periods of ≥5 years increase relative to the historical from mid-century (2046-2070) for most climate projections and parameter sets. Across the four climate models, the 90th percentile increase in flows and floods ranges from 17-108% and 11-63% respectively. Using multiple calibration parameter sets and climate projections helped capture the most likely hydrologic outcomes, as well as upper and lower bounds of future predictions. For this watershed, hydrological model parameter uncertainty was large relative to inter climate model spread, for near term moderate to high flows and for many flood frequencies. The uncertainty quantification and comparison approach developed here may be helpful in decision-making and design of engineering infrastructure in urban watersheds.