cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.db4f64e38ae44889857746613d9da5fc
title: Global Hydropower Simulation - 20th century

description: This resource contains the 20th century time series data for 1,593 hydropower dams, which collectively represent more than half of the world’s existing installed hydropower capacity (in 2016 when we conducted the study). The time series were generated by forcing a detailed dam model with monthly-resolution, 20th century (1906-2000) inflows—the model includes plant specifications, storage dynamics and realistic operating schemes.

Start exploring the data by downloading the Rdata together with the open_file.R script. You will be able to find monthly-resolution time series outputs of our simulation, including the hydropower production, storage level, and water releases for each dam. 

Reference: 
[1] Ng, J. Y., Turner, S. W., & Galelli, S. (2017). Influence of El Niño Southern Oscillation on global hydropower production. Environmental Research Letters, 12(3), 034010.
DOI: 10.4211/hs.90628426e37c4c83b8e45527cece6fdd
title: Global Hydropower Simulation - 21st century

description: This resource contains the 21st century time series data for 1,593 hydropower dams, which collectively represent more than half of the world’s existing installed hydropower capacity (in 2016 when we conducted the study). The time series were generated by forcing a detailed dam model with monthly-resolution, 21st century (2001-2100) inflows—the model includes plant specifications, storage dynamics and realistic operating schemes.

We used inflows simulated by a Global Hydrological Model (GHM) forced by climate projections derived from three General Circulation  Models (GCMs). The GCMs are CNRM-CM3 (Centre National de Recherches Météorologiques), ECHAM5/MPIOM (Max Planck Institute of Meteorology) and LMDZ-4 (Institute Pierre Simon Laplace) (denoted CNRM, ECHAM and IPSL). These models belong to the World Climate Research Programme CMIP3 multi-model dataset and were selected to represent a range in projected precipitation change. For each GCM, two emissions scenarios were considered – IPCC SRES scenarios A2 and B1. 

Start exploring the data by downloading the Rdata (each corresponding to a different GCM) together with the open_file.R script. You will be able to find monthly-resolution time series outputs of our simulation, including the hydropower production, storage level, and water releases for each dam. 

Reference:
[1] Turner, S. W., Ng, J. Y., & Galelli, S. (2017). Examining global electricity supply vulnerability to climate change using a high-fidelity hydropower dam model. Science of the Total Environment, 590, 663-675.
DOI: 10.4211/hs.2b38eceeb5ba40c1a88a242e8c18fafe
title: Global Hydropower Simulation - Forecast

description: This resource contains the hydropower time series for 1,593 hydropower dams operating under 3 different schemes – control rules, forecast-informed operations with perfect forecast, and forecast informed operations with deterministic forecast. The deterministic streamflow forecasts depend on six drivers, that is, four large scale climate drivers— El Niño Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO)—and two variables accounting for local processes—lagged inflow and soil moisture.

Start exploring the data by downloading the Rdata together with the open_file.R script. You will be able to find monthly-resolution time series outputs of our simulation, including the hydropower production, storage level, and water releases for each dam.
DOI: 10.4211/hs.94a5baa2cc774cd48ba3174bc9a71f22
title: Python tool to modify SWAT model input files for the Spatial Factor Substitution (SFS) method

description: This site provides the Python code we used to modify SWAT model input files for the Spatial Factor Substitution (SFS) method of Jepsen and Harmon (in press). In the SFS method, categorical factors are transferred from a "source" subbasin to a "target" subbasin of a watershed according to a prescribed space-for-time substitution scenario. Categorical factors handled in this Python code are precipitation time series, air temperature time series, land cover, soil type, and slope. We developed the SFS method to resolve landscape contributions to an elevational gradient in long-term ET, and the influence of the "covariance-stationarity" assumption of a typical space-for-time substitution model of climate warming (Jepsen and Harmon, in press).
DOI: 10.4211/hs.394a6900a0bd4911b642f9ba94046780
title: Data from Malenda et al. (2019), Floodplain hydrostratigraphy from sedimentology, geophysics, and remote sensing

description: This file includes the data published in: Malenda, H.F., Sutfin, N.A., Stauffer, S., Guryan. G., Rowland, J.C., Williams, K.H., and Singha, K. (2019). From Grain to Floodplain: Evaluating heterogeneity of floodplain hydrostatigraphy using sedimentology, geophysics, and remote sensing. Earth Surface and Planetary Landforms, doi:10.1002/esp.4613.

Floodplain stratigraphy, a major structural element of alluvial aquifers, is a fundamental component of floodplain heterogeneity, hydraulic conductivity, and connectivity. Watershed-scale hydrological models often simplify floodplains by modeling them as largely homogeneous, which inherently overlooks natural floodplain heterogeneity and anisotropy and their effects on hydrologic processes such as groundwater flow and transport and hyporheic exchange. This study, conducted in the East River Basin, Colorado, USA, combines point-, meander-, and floodplain-scale data to explore the importance of detailed field studies and physical representation of alluvial aquifers. We combine sediment core descriptions, hydraulic conductivity estimates from slug tests, ground-penetrating radar (GPR), historical maps of former channels, LiDAR-based elevation and Normalized Difference Vegetation Index data to infer 3-D fluvial stratigraphy. We compare and contrast stratigraphy of two meanders with disparate geometries to explore floodplain heterogeneity and connectivity controls on flow and transport. We identify buried point bars, former channels, and overbank deposits using GPR, corroborated by point sediment descriptions collected during piezometer installment and remotely sensed products. We map heterogeneous structural features that should control resultant flow and transport; orientation and connectivity of these features would control residence times important in hydrologic models.
DOI: 10.4211/hs.4cf4cb5aee814289be5eae13d6448a2e
title: Green River Watershed Data used for Evaluating Ensemble Streamflow Forecasting with the Utah Energy Balance (UEB) Snowmelt Model Coupled to the Research Distributed Hydrologic Model (RDHM) with Snow and Streamflow Assimilation

description: Inputs to Research Distributed Hydrologic Model (RDHM) spatially distributed hydrologic model incorporating the UEB snowmelt model that evaluates the effect of snow and streamflow assimilation on streamflow forecasting.

This is data for the following paper
Gichamo, T. Z., & Tarboton, D. G. (2019). Ensemble streamflow forecasting using an energy balance snowmelt model coupled to a distributed hydrologic model with assimilation of snow and streamflow observations. Water Resources Research, 55. https://doi.org/10.1029/2019WR025472
DOI: 10.4211/hs.82a2ba1a767742cca39d4959f10b2a9e
title: The Utah Energy Balance (UEB) Snowmelt Model Coupled to the Research Distributed Hydrologic Model (RDHM) with Parallel Processing using the CUDA GPU

description: The Utah Energy Balance (UEB) Snowmelt Model Coupled to the Research Distributed Hydrologic Model (RDHM) with Parallel Processing using CUDA GPU.

This is the model used in the following paper
Gichamo, T. Z., & Tarboton, D. G. (2019). Ensemble streamflow forecasting using an energy balance snowmelt model coupled to a distributed hydrologic model with assimilation of snow and streamflow observations. Water Resources Research, 55. https://doi.org/10.1029/2019WR025472
DOI: 10.4211/hs.db63c497b61147ce84c0298a16611125
title: Hydroinformatics Assignment 8

description: This script was created in python that can access a specified USGS site and download discharge and stage data for a specified period. The python script can create a time series after downloading the data. The script is then transferred to jupyter notebook.
This script will download and create discharge and stage time series for USGS site – Missouri River at Sioux City, IA (USGS 06486000). User can change the site and date as he or she pleases.
DOI: 10.4211/hs.ff975179a7654e53a29c4fac8b166b00
title: Hydroinformatics Assignment 8

description: This script was created in python that can access a specified USGS site and download discharge and stage data for a specified period. The python script can create a time series after downloading the data. The script is then transferred to jupyter notebook.
This script will download and create discharge and stage time series for USGS site – Missouri River at Sioux City, IA (USGS 06486000). User can change the site and date as he or she pleases.
DOI: 10.4211/hs.1ed2c313862544eb961f105407df7f00
title: Hydroinformatics Assignment 8

description: This script was created in python that can access a specified USGS site and download discharge and stage data for a specified period. The python script can create a time series after downloading the data. The script is then transferred to jupyter notebook.
This script will download and create discharge and stage time series for USGS site – Missouri River at Sioux City, IA (USGS 06486000). User can change the site and date as he or she pleases.
DOI: 10.4211/hs.9d05c5fd7cb14fe7a149894b68d637bf
title: Hydroinformatics Assignment 8

description: This script was created in python that can access a specified USGS site and download discharge and stage data for a specified period. The python script can create a time series after downloading the data. The script is then transferred to jupyter notebook.
DOI: 10.4211/hs.e12778e5718b414ab530381e89bf24ed
title: Data from Doughty et al. (2020), Electrical imaging of tracer tests and hyporheic exchange from logjams

description: Data from Doughty, M., Sawyer, A., Wohl, E., and Singha, K. (2020). Mapping increases in hyporheic exchange from channel-spanning logjams, Journal of Hydrology, https://doi.org/10.​1016/​j.​jhydrol.​2020.​124931.

Human impacts such as timber harvesting, channel engineering, beaver removal, and urbanization alter the physical and chemical characteristics of streams. These anthropogenic changes have reduced fallen trees and loose wood that form blockages in streams. Logjams increase hydraulic resistance and create hydraulic head gradients along the streambed that drive groundwater-surface water exchange. Here, we quantify changes in hyporheic exchange flow (HEF) due to a channel-spanning logjam using field measurements and numerical modeling in MODFLOW and MT3DMS. Electrical resistivity (ER) imaging was used to monitor the transport of solutes into the hyporheic zone during a series of in-stream tracer tests supplemented by in-stream monitoring. We conducted experiments in two reaches in Little Beaver Creek, Colorado (USA): one with a single, channel-spanning logjam and the second at a control reach with no logjams. Our results show that 1) higher HEF occurred at the reach with a logjam, 2) logjams create complex HEF pathways that can cause bimodal solute breakthrough behavior downstream, and 3) higher discharge rates associated with spring snowmelt increase the extent and magnitude of HEF. The numerical modeling supports all three field findings, and also suggest that lower flows increase solute retention in streams, although this last conclusion is not supported by field results. This study represents the first use of ER to explore HEF around a naturally occurring logjam over different stream discharges and has implications for understanding how logjams influence the transport of solutes, the health of stream ecosystems, and stream restoration and conservation efforts.
DOI: 10.4211/hs.c47c3d446231436eb18783e6d0d8cad5
title: Data files for "Multi-scale feature-feature interactions control patterns of hyporheic exchange in a simulated headwater mountain stream"

description: Model input data (e.g., streambed topography, water surface) and metrics for particle tracks (e.g., release location, upwelling location, flowpath length and timescale, velocity at release location)  for the article "Multi-scale feature-feature interactions control patterns of hyporheic exchange in a simulated headwater mountain stream" published in Water Resources Research (2019) by Herzog, Ward, and Wondzell.

See Readme files (available as .docx and as .pdf) for more details on the included information.
DOI: 10.4211/hs.02e2e437a6044ea39bee0b95ec83fa1e
title: Data from Harmon, R. (2020), Water table depth and bedrock permeability control magnitude and timing of transpiration-induced diel fluctuations in groundwater.

description: Data from Harmon, R., Barnard, H., and Singha, K. (2020). Water-table depth and bedrock permeability control magnitude and timing of transpiration-induced diel fluctuations in groundwater. Water Resources Research, 56, e2019WR025967. https://doi.org/10.1029/2019WR025967.

The subsurface processes that mediate the connection between evapotranspiration and groundwater within forested hillslopes are poorly defined. Here, we investigate the origin of diel signals in unsaturated soil water, groundwater, and stream stage on three forested hillslopes in the H.J. Andrews Experimental Forest in western Oregon, USA, during the summer of 2017, and assess how the diurnal signal in evapotranspiration (ET) is transferred through the hillslope and into these stores. There was no evidence of diel fluctuations in upslope groundwater wells, suggesting that tree water uptake in upslope areas does not directly contribute to the diel signal observed in near-stream groundwater and streamflow. The water table in upslope areas resided within largely consolidated bedrock, which was overlain by highly fractured unsaturated bedrock. These subsurface characteristics inhibit formation of diel signals in groundwater and impeded the transfer of diel signals in soil moisture to groundwater because (1) the bedrock where the water table resides limited root penetration and (2) the low unsaturated hydraulic conductivity of the highly fractured rock weakened the hydraulic connection between groundwater and soil/rock moisture. Transpiration-driven diel fluctuations in groundwater were limited to near-stream areas but were not ubiquitous in space and time. The depth to the groundwater table and the geologic structure at that depth likely dictated rooting depth and thus controlled where and when the transpiration-driven diel fluctuations were apparent in riparian groundwater. This study outlines the role of hillslope hydrogeology and its influence on the translation of evapotranspiration and soil moisture fluctuations to groundwater and stream fluctuations.
DOI: 10.4211/hs.38e069761d7b4cf4abe3cbcaaac06016
title: Dataset for "Mineralization of organic matter in boreal lake sediments: Rates, pathways and nature of the fermenting substrates" Clayer et al.

description: This dataset includes porewater solute concentrations (CH4, DIC, Cl, NO3, SO4, Acetate, Fe, Mn, S(-II), Mg, Ca) and carbon isotopic signatures of CH4 and DIC in the sediment of three boreal lake basins (Lake Tantaré basin A and B, and Lake Bédard) in October 2014 or 2015, as well as for Lake Bédard in October 2003. Detailed information about the methodology can be found in Clayer et al. (2016 and 2018). The content of this resource serves as the data for "Mineralization of organic matter in boreal lake sediments: Rates, pathways and nature of the fermenting substrates" by Clayer et al. 2020, Biogeosciences
DOI: 10.4211/hs.cd1bbacf2eb84c149754d0bb0e31f184
title: Assignment_8

description: Soil temperature is closely related to growth of biological systems and influences the physical, chemical, and microbiological processes that take place in soil, which may control the transport and fate of contaminants in the subsurface environment (YOLCUBAL et al., 2004). Therefore, soil temperature is an important factor deserving attention. Here soil temperature data from January 14, 2014 to April 10, 2019 was explored using Jupyter Notebook so that the trend of soil temperature and the relationship between it and water content can be shown.
DOI: 10.4211/hs.961b2720ba8142fab64d69d6cf5a2d57
title: Delayed Impact Calculator

description: This calculator is an Excel workbook programmed with Visual Basic for Applications macro code to perform finite-difference computations for assessment of attenuation and delay dynamics of stream-aquifer system response to groundwater impulse time series input.  The tool is intended to estimate impact accrual schedules for well-induced stream depletion or for groundwater return flow scenarios. Single impulse, uniform series, variable series, and annual pattern impulse type options facilitate streamlined input and analysis of a diverse range of occurrence and usage patterns, including intermittent pumping.  Segregation of response output by stream reach gives location-specific insight useful to surface water administration.  Tidy secondary output options include cumulative ratio, response ratio, and period ratios of response to impulse.  The workbook (.xlsm) is accompanied by an instruction manual (.pdf) and a version in Spanish (.xlsm).

La calculadora ofrece las mismas habilidades como el Delayed Impact Calculator original en inglés, sino en español.  Es un cuaderno de Excel, programado con código de macro de Básico Visual para Aplicaciones para hacer computaciones de diferencia finita para evaluación de dinámicas de atenuación y demora de respuesta de sistemas río-acuífero a datos de entrada de serie de tiempo de impulso al agua subterránea.  La herramienta tiene intención de aproximar a horarios de llegada al río de impactos de escenarios de merma de río inducido por bombeo de pozos o de flujo de retorno de agua subterránea.  Opciones de tipo de impulso de impulso solo, serie uniforme, serie variable, y horario anual facilitan entrada de datos racionalizada y análisis de diversos horarios de acontecimiento y uso.  Segregación de salida de datos de respuesta por segmento de río da entendimiento e información específica por ubicación.  Opciones ordenados de salida de datos secundarios incluyen  a proporción acumulativa, proporción de respuesta, y proporción de periodo en términos de respuesta contra impulso.  Viene como cuaderno con macro (.xlsm).
DOI: 10.4211/hs.0646196da11d450a9d95c42c6568a25b
title: An Automated Seepage Meter for Soft-Bottom Streams and Lakes - Supplemental Information

description: We describe a new automatic seepage meter for use in soft-bottom streams and lakes.  The meter utilizes a thin-walled tube that is inserted into the streambed or lakebed.  A hole in the side of the tube is fitted with an electric valve.  At the start of a test, the valve is open and the water level inside the tube is the same as the stream or lake level.  The valve then closes and the water level inside the tube changes as it moves toward the equilibrium hydraulic head that exists at the bottom of the tube.  The time rate of change of the water level immediately after the valve closes is a direct measure of the seepage rate.  The meter utilizes a precision linear actuator and a conductance circuit to sense the water level to a precision of about ± 100 m.  The meter can also provide an estimate of Kv if data are collected for at least one characteristic time.  The seepage detection limit depends on the vertical hydraulic gradient, that in turn depends on Kv.  For Kv = 1 m/day, seepage rates on the order of 2 mm/d can be measured.  Testing in a laboratory sand tank and at a field site indicates that seepage rate from the meter is similar to values from Darcian calculations based on independent measurements of Kv and vertical head gradients.  The meter can provide rapid (30 minute) seepage measurements, and compliments other methods for quantifying interactions between groundwater and surface water.
DOI: 10.4211/hs.8f0b5599b871457ebb47f0bac898f156
title: St. Jones Data Compilation 2017

description: This resource contains various hydrological, biological, and geochemical data from St. Jones National Estuarine Research Reserve from the year 2017. These data have been used to assess how net carbon storage is influenced by interactions between crab activity, water movement, and biogeochemistry. Hydraulic conductivity was measured by slug tests; crab burrows were counted manually and casts were created with Plaster of Paris. Redox potential was collected using in-situ, multi-depth redox sensors, and water table elevation was collected using pressure sensors.
DOI: 10.4211/hs.0a68bfd7ddf642a8be9041d60f40868c
title: CAMELS Extended NLDAS Forcing Data

description: This repository contains drop-in replacements for the basin mean NLDAS forcing data files of the CAMELS data set. Compared to the original files contained in the CAMELS data set, these files contain daily minimum and maximum temperature. In the original publications both of those variables contained the daily mean temperature. These files were generated for our HESS manuscript "Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning" and were derived from hourly NLDAS data.
The same TERMS OF USE apply as for the original CAMELS data set.
The same terms of use as of the original CAMELS data set apply here.
DOI: 10.4211/hs.8b5d02c85c714c639d00667d44fb8625
title: Improving hydrological models with the assimilation of data collected by citizens

description: SWAT model for the Boyne River, located in northern Michigan, USA. The model uses citizen science data from CrowdHydrology <http://www.crowdhydrology.com/>. The model was calibrated using the following stations: MI1023. MI1024, MI1025, MI1026, for streamflow, and MI2023. MI2024, MI2025, MI2026, for stream temperature. The SWAT model was built using ArcSWAT Version 2012.10_4.19. The compressed file <swatboyne.zip> is a collection of text files that represent the SWAT model (swatboyne\Scenarios\Default\TxtInOut). 

We acknowledge the Friends of the Boyne River <https://boyneriver.org/> and Michigan Trout Unlimited <http://www.michigantu.org/> for their role in community engagement, support during field visits, and maintenance of the CrowdHydrology network. The authors gratefully acknowledge the active participation of citizens near the Boyne River.
DOI: 10.4211/hs.d5676355cbd94b79a2e58f8089040a40
title: Is The River a Chemostat?

description: Associated data for accepted paper in Geophysical Research Letters , titled "Is the River a Chemostat?: Scale Versus Land Use Controls on Nitrate Concentration-Discharge Dynamics in the Upper Mississippi River Basin".

Assuming package dependencies are met, you will be able to run the R files in order and reproduce the results of the paper. SessionInfo.txt gives R and package version numbers used for the analysis. 

I tried to go through and comment some parts and also delete extraneous code, but there are still some sections with obscure purposes and some dead-end code included. Feel free to contact me if you need clarifications about anything. 

Final publication and DOI issuance will occur once the paper is in press.
DOI: 10.4211/hs.f653335e0de64b2a9a03caf4c752738e
title: Raw Data for Paper # 2020WR027091

description: Raw discharge and water elevation data for 37 watersheds in Switzerland used in the analysis for submitted paper # 2020WR02709. Additional watersheds within the repository are available upon request from the Federal Office for the Environment (FOEN), Bern, Switzerland. Files starting with "P" are water elevation data in meters above mean sea level, while files starting with "Q" are discharge data in cubic meters per second.
DOI: 10.4211/hs.9c541e6a985b490e919d8737c736c2bb
title: Geng and Michael, 2019

description: This includes model files processing codes for nearshore pumping simulations in coastal volcanic aquifers.