cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.ace98c3bc72b44f1834a58ec8b3af310
title: GOTM water temperature simulations forced at different frequency in the meteorological inputs in Lake Erken (Sweden)

description: The 1-D hydrodynamic lake model GOTM was used to test the ability to simulate Lake Erken water temperature using different meteorological forcing inputs: 1) hourly measured data, 2) daily average data derived from the first data set, and 3) synthetic hourly data created from the daily data set, where air temperature (⁰C), short-wave radiation (W m-2), relative humidity (%) and wind speed (m s-1)were created from Generalized Regression Artificial Neuronal Networks (GRNN) methods, daily values were considered for the cloud cover (0-1) and air pressure (hPa) and an uniform distribution of the daily total for the precipitation (mm).
Resources:
1.	GOTM meteorological input files for calibration (2006-2014) and validation (2015-2016):
1.1.	Hourly data set:
1.1.1.	Erken_DailyPrecip_2006-2014.dat
1.1.2.	Erken_HourlyMeteo_2006-2014.dat
1.1.3.	Erken_HourlySWR_2006-2014.dat
1.1.4.	Erken_DailyPrecip_2015-2016.dat
1.1.5.	Erken_HourlyMeteo_2015-2016.dat
1.1.6.	Erken_HourlySWR_2015-2016
1.2.	Daily data set:
1.2.1.	Erken_DailyPrecip_2006-2014.dat
1.2.2.	Erken_DailyMeteo_2006-2014.dat
1.2.3.	Erken_DailySWR_2006-2014.dat
1.2.4.	Erken_DailyPrecip_2015-2016.dat
1.2.5.	Erken_DailyMeteo_2015-2016.dat
1.2.6.	Erken_DailySWR_2015-2016.dat
1.3.	Synthetic hourly data set:
1.3.1.	Erken_syntheticPh_2006-2016.dat
1.3.2.	Erken_syntheticMETh_2006-2016.dat
1.3.3.	Erken_syntheticSWRh_2006-2016.dat 
2.	GOTM water temperature input file for calibration (2006-2014) and validation (2015-2016):
2.1.	Erken_DailyTemp_2006-2014_NoWinter.dat
2.2.	Erken_DailyTemp_2015-2016_NoWinter.dat
3.	GOTM model configuration files:
3.1.	Hourly data set:
3.1.1.	erken_Hourly_2006-2014.dat
3.1.2.	erken_Hourly_2015-2016.dat
3.2.	Daily data set:
3.2.1.	erken_Daily _2006-2014.dat
3.2.2.	erken_Daily _2015-2016.dat
3.3.	Synthetic hourly data set:
3.3.1.	erken_SyntheticHourly_2006-2014.dat
3.3.2.	erken_SyntheticHourly_2015-2016.dat
4.	Calibration files:
4.1.	config_acpy.xml
4.2.	Erken_DailyTemp_2006-2014_NoWinter. obs 
5.	GOTM outputs (water temperature and depth profiles files):
5.1.	Hourly data set:
5.1.1.	ErkenSimTemp24h_temp_Hourly_2006-2014.txt
5.1.2.	ErkenSimTemp24h_z_Hourly_2006-2014.txt
5.1.3.	ErkenSimTemp24h_temp_Hourly_2015-2016.txt
5.1.4.	ErkenSimTemp24h_z_Hourly_2015-2016.txt
5.2.	Daily data set:
5.2.1.	ErkenSimTemp24h_temp_Daily_2006-2014.txt
5.2.2.	ErkenSimTemp24h_z_Daily_2006-2014.txt
5.2.3.	ErkenSimTemp24h_temp_Daily_2015-2016.txt
5.2.4.	ErkenSimTemp24h_z_Daily_2015-2016.txt
5.3.	Synthetic hourly data set:
5.3.1.	ErkenSimTemp24h_temp_SyntheticHourly_2006-2014.txt
5.3.2.	ErkenSimTemp24h_z_SyntheticHourly_2006-2014.txt
5.3.3.	ErkenSimTemp24h_temp_SyntheticHourly_2015-2016.txt
5.3.4.	ErkenSimTemp24h_z_SyntheticHourly_2015-2016.txt
DOI: 10.4211/hs.f83fc11d18dc4bd8951513eaa23c0b25
title: Data of L. Schoemaker; The source of the southern tributary of the Ruebisbaach

description: The main objective of the research 'The source of the southern tributary of the Ruebisbaach' is to figure out and understand what sources makes the southern tributary still flowing in a dry period and why it does so compared with the northern tributary. The geographical focus of the research report is the southern catchment of the Ruebisbaach. In the results of the research the southern catchment will be compared with the northern catchment. The results includes measured data during the research proces. These data is originated from various discharge and infiltration methods. All the collected discharge and infiltration data were processed in excel formats and analyzed in a systematic way. To find the right data the excel name is described as ‘Method_Topic_Catchment_Year’. For example if you want to find all Levelstick data it is descripted as ‘Levestick_Discharge_Ruebisbaach_2019’.
DOI: 10.4211/hs.39c1144304a74e8ca5616d12cb6ce3f9
title: Data from B.P.G. Huver. Dynamics of transpiration of different trees in the Kuerbaach Catchment, Luxembourg.

description: For the course "Field course Hydrology" of the Vrije Universiteit (Amsterdam), data was gathered of the Kuerbaach stream in Luxembourg. This study focusses on the transpiration of trees. These files contain data as gathered during the study. I strived for self-explanatory column headers and a well-explained Python script as much as possible. Additonal info can be found in the report.
DOI: 10.4211/hs.2b4cfe0f02bf4375bcd0b62e45c61b19
title: GOTM simulations of future changes in thermal structure of Lake Erken drived by daily and synthetic hourly ISIMIP projections

description: The GOTM one-dimensional hydrodynamic model was used to simulate water temperature when using ISIMIP2b bias-corrected climate model projections as input. The lake model was forced by four climate model projections and three emissions scenarios (historical, RCP 2.6 and RCP 6.0) available for GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR and MIROC5 using their original daily resolution and also at hourly resolution using meteorological disaggregated data developed using GRNN models.
Resources:
Daily simulated water temperature when the GOTM model was forced by daily ISIMIP projections:
1.	GFLD-ESM2M
a.	Historical: GOTM_ISIMIP_Erken_GFDL-ESM2M_historical_24hMeteo.dat
b.	RCP 2.6: GOTM_ISIMIP_Erken_GFDL-ESM2M_rcp26_24hMeteo.dat
c.	RCP6.0: GOTM_ISIMIP_Erken_GFDL-ESM2M_rcp26_24hMeteo.dat
2.	HadGEM2-ES 	
a.	Historical: GOTM_ISIMIP_Erken_GFDL-ESM2M_historical_24hMeteo.dat
b.	RCP 2.6: GOTM_ISIMIP_Erken_GFDL-ESM2M_rcp26_24hMeteo.dat
c.	RCP6.0: GOTM_ISIMIP_Erken_GFDL-ESM2M_rcp26_24hMeteo.dat
3.	IPSL-CM5A-LR
a.	Historical: GOTM_ISIMIP_Erken_ IPSL-CM5A-LR _historical_24hMeteo.dat
b.	RCP 2.6: GOTM_ISIMIP_Erken_ IPSL-CM5A-LR _rcp26_24hMeteo.dat
c.	RCP6.0: GOTM_ISIMIP_Erken_ IPSL-CM5A-LR _rcp26_24hMeteo.dat
4.	MIROC5
a.	Historical: GOTM_ISIMIP_Erken_ MIROC5 _historical_24hMeteo.dat
b.	RCP 2.6: GOTM_ISIMIP_Erken_ MIROC5 _rcp26_24hMeteo.dat
c.	RCP6.0: GOTM_ISIMIP_Erken_ MIROC5 _rcp26_24hMeteo.dat
Daily simulated water temperature when the GOTM model was forced by synthetic hourly ISIMIP projections:
5.	GFLD-ESM2M
d.	Historical: GOTM_ISIMIP_Erken_GFDL-ESM2M_historical_1hMeteo.dat
e.	RCP 2.6: GOTM_ISIMIP_Erken_GFDL-ESM2M_rcp26_1hMeteo.dat
f.	RCP6.0: GOTM_ISIMIP_Erken_GFDL-ESM2M_rcp26_1hMeteo.dat
6.	HadGEM2-ES 	
d.	Historical: GOTM_ISIMIP_Erken_GFDL-ESM2M_historical_1hMeteo.dat
e.	RCP 2.6: GOTM_ISIMIP_Erken_GFDL-ESM2M_rcp26_1hMeteo.dat
f.	RCP6.0: GOTM_ISIMIP_Erken_GFDL-ESM2M_rcp26_1hMeteo.dat
7.	IPSL-CM5A-LR
a.	Historical: GOTM_ISIMIP_Erken_ IPSL-CM5A-LR _historical_1hMeteo.dat
b.	RCP 2.6: GOTM_ISIMIP_Erken_ IPSL-CM5A-LR _rcp26_1hMeteo.dat
c.	RCP6.0: GOTM_ISIMIP_Erken_ IPSL-CM5A-LR _rcp26_1hMeteo.dat
8.	MIROC5
a.	Historical: GOTM_ISIMIP_Erken_ MIROC5 _historical_1hMeteo.dat
b.	RCP 2.6: GOTM_ISIMIP_Erken_ MIROC5 _rcp26_1hMeteo.dat
c.	RCP6.0: GOTM_ISIMIP_Erken_ MIROC5 _rcp26_1hMeteo.dat
DOI: 10.4211/hs.b940b704429244a99f902ff7cb30a31f
title: Gap-Free Global Annual Soil Moisture: 15km Grids for 1991-2016

description: We provide a set of 26 soil moisture predictions across 15km grids at the global scale. We modeled and predicted the ESA-CCI soil moisture values across 26 years of available data (1991-2016) using a ML based kernel method and multiple terrain parameters (e.g., slope, wetness index) as prediction factors. We used ground information from the International Soil Moisture Network (ISMN, n=13376) for evaluating soil moisture predictions. Our downscaled soil moisture predictions across 15km grids showed a statistical accuracy varying 0.69-0.87% and 0.04 m3/m3 of cross-validated explained variance and root mean squared error (RMSE). We found a negative bias (-0.01 to -0.08 m3/m3 ) underestimating the values of ISMN when comparing with the ESA-CCI and our predictions across the analyzed years and a relatively better performance between 1998 and 2016. We found no significant differences between the ESA-CCI and our predictions, but we found discrepancy between multiple evaluation metrics (e.g., bias vs efficiency) comparing the ESA-CCI with the ISMN. However, the temporal analysis as revealed by a robust trend detection strategy (e.g., Theil-Sen estimator), suggests a decline of soil moisture at the global scale that is consistent in both gridded estimates and field measurements of soil moisture varying from -0.7[-0.77, -0.62]% in the ESA-CCI product, -0.9[-1.01, -0.8]% in the downscaled predictions and -1.6 [-1.7, -1.5]% in the ISMN. These results highlight the large potential of digital terrain parameters for improving the accuracy and spatial detail of satellite soil moisture grids at the global scale. The soil moisture predictions provided here (folder: predicted-2001-2016) could be useful for quantifying long term soil moisture emergent patterns (i.e., trends) across areas with low availability of soil moisture information in the ESA-CCI. To ensure reproducible results of this study, we also provide the R code and (also in R native format *.rds) the topographic prediction factors for soil moisture across 15 km grids (file: topographic_predictors_15km_grids.rds). This site also includes the harmonized ISMN data with the ESA-CCI and the downscaled predictions based on terrain analysis in an annual basis (files: harmonizedISMNvsESACCI.rds and harmonizedISMNvsPREDICTED.rds) that we used for validating our prediction framework. The soil moisture predictions provided here could be useful for quantifying soil moisture spatial and temporal dynamics across areas with low availability of soil moisture information in the original ESA-CCI database.
DOI: 10.4211/hs.938ea95c2fe3498a9a893cf4e63f8b3b
title: Supporting data for "Active layer groundwater flow: the interrelated effects of stratigraphy, thaw, and topography"

description: Data pertaining to the following publication:

"O'Connor, M., Cardenas, M., Neilson, B., Nicholaides, K., and Kling, G. (2019). Active layer groundwater flow: the interrelated effects of stratigraphy, thaw, and topography. Water Resources Research, DOI:10.1029/2018WR024636"
DOI: 10.4211/hs.419f2564a43c4f639066edb9594fd0d3
title: Index of Floodplain Integrity in Colorado

description: This resource represents the results of a project that: 1) developed a methodology to assess floodplain integrity using geospatial datasets available for large spatial scales; and 2) used the methodology to evaluate spatial patterns of floodplain integrity in the state of Colorado. To accomplish these objectives,  the critical floodplain functions of attenuating floods, storing groundwater, regulating sediment, providing habitat, and regulating organics and solutes were evaluated. For each floodplain function,  measurable stressors that inhibit the specific function were identified. The density of each stressor variable in the floodplain was quantified using datasets that are publicly available at large spatial scales. The index of integrity for a given floodplain function was then determined using the density of all stressors that inhibit the function. Next, the overall index of floodplain integrity for a given floodplain unit was calculated using a geometric mean of the indices of integrity for each of the five floodplain functions. The index of floodplain integrity methodology was applied in the state of Colorado to analyze the integrity of each of the five floodplain functions and the aggregated overall integrity. This resource contains a table with the resulting numeric index of floodplain integrity for each of the floodplain functions for each floodplain unit segregated by HUC-12 boundaries. It also contains a shapefile of the floodplain-containing HUC-12 units in Colorado with the index of floodplain integrity values as attributes.
DOI: 10.4211/hs.474ecc37e7db45baa425cdb4fc1b61e1
title: CAMELS benchmark models

description: This data set contains the model outputs of different hydrology models calibrated using the same forcing data (Maurer) and the same calibration period for the CAMELS data set. The models are: SAC-SMA, VIC, HBV, FUSE and mHM. All of these models have been calibrated for each basin separately. Additionally, for VIC and mHM, also regionally calibrated model outputs exist. All models have been calibrated using the period 1 October 1999 until 30 September 2008 and were validated in the period 1 October 1989 until 30 September 1999.
DOI: 10.4211/hs.fe3912e234a84478b767eff5db5fb9b5
title: Walker River distributed temperature sensing, thermal infrared imaging, and temperature modeling data

description: Watershed-scale stream temperature models are often one-dimensional because they require fewer data and are more computationally efficient than two- or three-dimensional models.  However, one-dimensional models assume completely mixed reaches and ignore small-scale spatial temperature variability, which may create temperature barriers or refugia for cold-water aquatic species.  Fine spatial and temporal resolution stream temperature monitoring provides information to identify river features with increased thermal variability.  We used distributed temperature sensing (DTS) to observe small-scale stream temperature variability, measured as a temperature range through space and time, within two 400 meter reaches in summer 2015 in Nevada’s East Walker and mainstem Walker Rivers.  Thermal infrared (TIR) aerial imagery collected in summer 2012 quantified the spatial temperature variability throughout the Walker Basin.  We coupled both types of high resolution measured data with simulated stream temperatures to corroborate model results and estimate the spatial distribution of thermal refugia for Lahontan cutthroat trout and other cold-water species.  Temperature model estimates were within the DTS measured temperature ranges 21% and 70% of the time for the East Walker River and mainstem Walker River, respectively, and within TIR measured temperatures 17%, 5%, and 5% of the time for the East Walker, West Walker, and mainstem Walker Rivers, respectively.  DTS, TIR, and modeled stream temperatures in the mainstem Walker River nearly always exceeded the 21°C optimal temperature threshold for adult trout, usually exceeded the 24 °C stress threshold, and could exceed the 28 °C lethal threshold for Lahontan cutthroat trout.  Measured stream temperature ranges bracketed ambient river temperatures by -10.1 to +2.3 °C in agricultural return flows, -1.2 to +4 °C at diversions, -5.1 to +2 °C in beaver dams, -4.2 to 0 °C at seeps.  To better understand the role of these river features on thermal refugia during warm time periods, the respective temperature ranges were added to simulated stream temperatures at each of the identified river features.  Based on this analysis, the average distance between thermal refugia in this system was 2.8 km.  While simulated stream temperatures are often too warm to support Lahontan cutthroat trout and other cold-water species, thermal refugia may exist to improve habitat connectivity and facilitate trout movement between spawning and summer habitats.  Overall, high resolution DTS and TIR measurements quantify temperature ranges of refugia and augment process-based modeling.
DOI: 10.4211/hs.12da8939a96849f5a464b28b910e519f
title: Topographic Indices, NYC

description: Topographic indices calculated in support of Kelleher and McPhillips (in review).  We calculated two topographic indices - absolute sink depth (m) and topographic wetness index (-) - using TauDEM (v. 5.3) software and the D-infinity flow routing algorithm.

Watersheds include those in Manhattan (CP1, CP2, M1, M2) and Staten Island (SI1, SI2).  Naming convention and sites are shown in the associated manuscript.  Note that processing for Baltimore is limited to the extent of each watershed that overlaps with the Baltimore city limits, though processing occurred for the entire watershed and was masked to this area.

Values were processed based on the LiDAR digital elevation models (DEM) for NYC, linked below in references (note: to make datasets comparable, the NYC DEM was coarsened to 0.91 m resolution).  As presented in the associated manuscript, all topographic index values were extracted for all surfaces (e.g., bare soil, pavement, sidewalks, and vegetated areas) that excluded open water and building footprints (where topographic processing and DEM coverages are less reliable).  Land cover datasets are linked below.

Naming convention for all files first specifies watershed name (NYC: cp1, cp2, m1, m2, si1, si2) followed by topographic index type (twi = topographic wetness index, sink = sink depth).

Descriptions for how each topographic index are calculated are specified in the associated manuscript.  Generally, sink depths were calculated by differencing the filled and unfilled DEMs, and TWI was calculated from topographic slope and accumulated area, both processed within TauDEM (note: when negative slopes were calculated, these were replaced with very small values, e.g., 0.001).
DOI: 10.4211/hs.9afed1aa2aa348fa9c199df30938e9d5
title: Topographic Indices, Baltimore

description: Topographic indices calculated in support of Kelleher and McPhillips (in review).  We calculated two topographic indices - absolute sink depth (m) and topographic wetness index (-) - using TauDEM (v. 5.3) software and the D-infinity flow routing algorithm.

Watersheds include Gwynns Falls [gwynn] and Jones Falls [jones].  Naming convention and sites are shown in the associated manuscript.  Note that processing for Baltimore is limited to the extent of each watershed that overlaps with the Baltimore city limits, though processing occurred for the entire watershed and was masked to this area.

Values were processed based on the LiDAR digital elevation model (DEM) for Baltimore, linked below in references.  As presented in the associated manuscript, all topographic index values were extracted for all surfaces (e.g., bare soil, pavement, sidewalks, and vegetated areas) that excluded open water and building footprints (where topographic processing and DEM coverages are less reliable).  Land cover datasets are linked below.

Naming convention for all files first specifies watershed name (Baltimore; gwynn, jones) followed by topographic index type (twi = topographic wetness index, sink = sink depth).

Descriptions for how each topographic index are calculated are specified in the associated manuscript.  Generally, sink depths were calculated by differencing the filled and unfilled DEMs, and TWI was calculated from topographic slope and accumulated area, both processed within TauDEM (note: when negative slopes were calculated, these were replaced with very small values, e.g., 0.001).
DOI: 10.4211/hs.17c896843cf940339c3c3496d0c1c077
title: CAMELS Extended Maurer Forcing Data

description: This repository contains drop-in replacements for the basin mean Maurer 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 "Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets" and will be merged into the official CAMELS data set in the next update.
The same TERMS OF USE apply as for the original CAMELS data set.
DOI: 10.4211/hs.83ea5312635e44dc824eeb99eda12f06
title: Pretrained models + simulations for our HESSD submission "Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets"

description: Contains all models trained for our publication "Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets", as well as the evaluated model simulations. The set contains 48 runs in total, stemming from 3 different models (trained with 8 repetitions) and two different loss functions.
DOI: 10.4211/hs.1396774b293144689a66080739da8f44
title: Nitrate concentrations in Mississippi River Basin priority watersheds 2000-2015

description: This repository contains data for HUC 8 priority watersheds in the Mississippi River Basin core states (Arkansas, Illinois, Indiana, Iowa, Kentucky, Louisiana, Minnesota, Mississippi, Missouri, Ohio, Tennessee, Wisconsin). Nitrate monitoring data in receiving streams and rivers in each priority watershed from 2000-2015 was collected from Water Quality Portal. Watershed metadata are included in the PriorityWatershed_Metadata file. Results of time series analysis  (Seasonal Mann-Kendall test) for watersheds with sufficient data are detailed in SMK_results, SMKmonth_results, and SMKmonth_Trend_results files. All analyses were conducted in R within the attached R script file.
DOI: 10.4211/hs.8706089581124f308dbe3b9acf8bfdd0
title: Sketches for Driving Water Level Sensor

description: These are modified sketches for driving low-cost water level sensor (Maxbotix-MB7363) as a hands-on style.
DOI: 10.4211/hs.9c57af9b5c1343bb840ba198a49ace1c
title: Nitrate Data for the Holtemme

description: Water Quality Data (only nitrate) of the River Holtemme at the 3 stations: Werbat, Derenburg, Nienhagen
DOI: 10.17026/ar/aslfii
title: PAN-00175449 - pinch lead

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00175449&lt;/p&gt;
description: Modified: 2025-10-29T11:28:45+01:00
DOI: 10.4211/hs.43b277b83b554420b66c53a0d78de7f9
title: Tracking Historical Land-use Cover Change  in Urban Drainage Catchments Using :  Random Forest and Support Vector Machine

description: This is the poster for the 2019 CUAHSI Conference on Hydroinformatics
DOI: 10.4211/hs.72de78ceb81f4117b300639de0908d60
title: Data from Mares et al. (2016), Examining diel patterns of soil and xylem moisture using electrical resistivity imaging

description: This file includes the data published in: Mares, R., Barnard, H.R., Mao, D., Revil, A. and Singha, K. (2016). Examining diel patterns of soil and xylem moisture using electrical resistivity imaging. Journal of Hydrology, https://doi.org/10.1016/j.jhydrol.2016.03.003, 12 p.

The feedbacks among forest transpiration, soil moisture, and subsurface flowpaths are poorly understood. We investigate how soil moisture is affected by daily transpiration using time-lapse electrical resistivity imaging (ERI) on a highly instrumented ponderosa pine and the surrounding soil throughout the growing season. By comparing sap flow measurements to the ERI data, we find that periods of high sap flow within the diel cycle are aligned with decreases in ground electrical conductivity and soil moisture due to drying of the soil during moisture uptake. As sap flow decreases during the night, the ground conductivity increases as the soil moisture is replenished. The mean and variance of the ground conductivity decreases into the summer dry season, indicating drier soil and smaller diel fluctuations in soil moisture as the summer progresses. Sap flow did not significantly decrease through the summer suggesting use of a water source deeper than 60 cm to maintain transpiration during times of shallow soil moisture depletion. ERI captured spatiotemporal variability of soil moisture on daily and seasonal timescales. ERI data on the tree showed a diel cycle of conductivity, interpreted as changes in water content due to transpiration, but changes in sap flow throughout the season could not be interpreted from ERI inversions alone due to daily temperature changes.
DOI: 10.7298/fn0q-v573
title: Data from: Extension and neural operator approximation of the electrical impedance tomography inverse map