cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.4211/hs.547308e5f90d4ff499e67c37f7cdd621
title: Assessing the Performance of Parametric and Non-Parametric Tests for Trend Detection in Partial Duration Time Series

description: The detection of non-stationarities in partial duration time series (or peak-over-threshold, POT) depends on a number of factors, including the length of the time series, the selected statistical test, and the heaviness of the tail of the distribution. Because of the more limited attention received in the literature when compared to the trend detection on block maxima variables, we perform a Monte Carlo simulation study to evaluate the performance of different approaches (Spearman’s rho (SP), Mann-Kendall test (MK), Ordinary Least Squared Regression (OLS), Sen’s slope estimator (SEN), and the non-stationary Generalized Pareto distribution fit (GPD_NS)) to identify the presence of trends in POT records characterized by different sample sizes (n), shape parameter and degrees of non-stationarity. We also estimate the probability of occurrence of Type S errors when using the OLS and SEN to determine the magnitude of trends. The results point to a power gain for all tests by increasing sample size and degree of non-stationarity. The same increased detection is noted when reducing the shape parameter (i.e., going from unbounded to bounded distributions). While the GPD_NS has the best performance overall, the OLS performs well when detecting trends for low or negative shape values. On the other hand, the use of a non-parametric test is recommended in samples with a high positive skew. Furthermore, the use of sampling rates greater than 1 (i.e., selecting more than just one event per year on average) to increase the POT sample size is encouraged, especially when dealing with small records. In this case, gains in power of detection and a reduction in the probability of type S error occurrence are observed, especially when the sampling rate ≤ 0 (i.e., unbounded distribution). Moreover, the use of SEN to estimate the magnitude of a trend is preferable over OLS due to its slightly smaller probability of occurrence of type S error when the shape parameter is positive.
DOI: 10.48597/ck4c-3egp
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/eehx-n8jg
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.4211/hs.d1537789fea8421aa3fe1cd2ec155b57
title: Hydrologic modeling and field data for studying the role of subsurface critical zone structure on hydrological partitioning

description: This dataset contains the codes and data used in the manuscript “Influence of Subsurface Critical Zone Structure on Hydrological Partitioning in Mountainous Headwater Catchments” submitted to Geophysical Research Letters. The software requirement are summarized in requirement.txt; hydrologic modeling input data are in the folder TLnewtest2sfb2; the observation data used in the simulation are indicated as comments in the python scripts. Note that the hydrologic modeling was run in HPC (Linux system) with parallel computing.

Below are the abstract of the manuscript:
“Headwater catchments play a vital role in regional water supply and ecohydrology, and a quantitative understanding of the hydrological partitioning in these catchments is critically needed, particularly under a changing climate. Recent studies have highlighted the importance of subsurface critical zone (CZ) structure in modulating the partitioning of precipitation in mountainous catchments; however, few existing studies have explicitly taken into account the 3D subsurface CZ structure. In this study, we designed realistic synthetic catchment models based on seismic velocity-estimated 3D subsurface CZ structures. Integrated hydrologic modeling is then used to study the effect of the shape of the weathered bedrock bottom on various hydrologic fluxes and storages in mountainous headwater catchments. Numerical results show that the shape of the weathered bedrock bottom not only affects the magnitude but also the peak time of both streamflow and subsurface dynamic storage.”
DOI: 10.48597/mp8c-x676
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/xpu6-r4nk
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/4nx8-ks9e
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.4211/hs.977dd5ff16eb4c589783f6fd857897fb
title: Microbial assemblages and functional predictions in organic-rich subterranean estuaries

description: Microbial communities in subterranean estuaries (STE) mediate biogeochemical reactions of coastal groundwater discharging into the oceans; however, studies on their response to abrupt environmental changes caused by climate and land use alterations are still limited. In this study, we conducted a controlled laboratory study using combined geochemical and metagenomic approaches to investigate microbial structures and their metabolic pathways under different nitrate (NO3-) inputs, saline solutions, and incubation times. This is a supplementary data to manuscript "Microbial assemblages and functional predictions in organic-rich subterranean estuaries: impact of climate and land use changes" submitted to Journal of Geophysical Research: Biogeosciences.
DOI: 10.48597/557a-dmva
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/7cty-4n7t
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.4211/hs.0d123f789d3944f7a32aedc1fd4ea2e5
title: Deposition and River paired timeseries

description: The data provided here is used in the submitted manuscript "Synchrony of nitrogen wet deposition inputs and watershed nitrogen outputs using information theory" by Desneiges S. Murray1, Edom Moges2, Laurel Larsen2, Michelle D. Shattuck1, William H. McDowell1, and Adam S. Wymore1 (1Department of Natural Resources and the Environment, University of New Hampshire, Durham NH, USA; 2Department of Geography, University of California, Berkeley California, USA. Corresponding author: Desneiges Murray (desneiges.murray@unh.edu) to Water Resources Research.

Weekly, year-round, paired (e.g., < 1 km apart) wet deposition and river nitrogen (TDN, NO3, NH4, DON) time series from 2003 to 2020 from the Lamprey River Hydrological Observatory in New Hampshire, USA. Discharge data from the paired USGS site with river chemistry sampling site is also provided. This data is input to the associated Python Script, and is cleaned, paired, and weekly anomalies are calculated for input to information theory (IT) algorithms.
DOI: 10.48597/97as-62ny
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/pgk7-4dnt
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/ph5p-j9v7
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.4211/hs.fcc917c3ab8d4aa5b69943a2292daff0
title: Quilca watershed-scale hydrologic model

description: The Quilca watershed-scale hydrologic model comprehends watersheds delineated by landuse and ANA subbasins (ANA is the Peruvian Water National authority). The model was simulated with PRMS, and sub-divided into multiple subbasins. HRUs in the simulation represent a geospatial merge between subbasins and landuses. Soil geometries were not considered due to the high uncertainty of the datasets and the lack of measured data in this regional scale simulated. Watersheds and subbasins were re-delineated and some may not contain the entire watershed dimension because only subbasins with monitored data were considered.
Raw data from automatic and conventional weather stations was used and supported with the PISCO gridded dataset developed by SENAMHI. The model comprehends streamflow, diversions, and reservoir operations up to 2016. Reservoir volumes were calibrated, and different water uses, and climate change scenarios were evaluated.
In total, 5 Peruvian watersheds located in the region of Arequipa were evaluated: Yauca, Ocona, Camana Quilca, and Tambo.
DOI: 10.48597/8zfm-ytxf
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.4211/hs.23704797bc07428e8f3ec09b49a8ffbe
title: Camana watershed-scale hydrologic model

description: The Camana watershed-scale hydrologic model comprehends watersheds delineated by landuse and ANA subbasins (ANA is the Peruvian Water National authority). The model was simulated with PRMS, and sub-divided into multiple subbasins. HRUs in the simulation represent a geospatial merge between subbasins and landuses. Soil geometries were not considered due to the high uncertainty of the datasets and the lack of measured data in this regional scale simulated. Watersheds and subbasins were re-delineated and some may not contain the entire watershed dimension because only subbasins with monitored data were considered.
Raw data from automatic and conventional weather stations was used and supported with the PISCO gridded dataset developed by SENAMHI. The model comprehends streamflow, diversions, and reservoir operations up to 2016. Reservoir volumes were calibrated, and different water uses, and climate change scenarios were evaluated.
In total, 5 Peruvian watersheds located in the region of Arequipa were evaluated: Yauca, Ocona, Camana Quilca, and Tambo.
DOI: 10.48597/jhe8-srme
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/jzu8-ahr9
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/m9dn-77ru
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.4211/hs.ddea4aebcedd4e43a35a6ffd4334521f
title: Ocoña watershed-scale hydrologic model

description: The Ocoña watershed-scale hydrologic model comprehends watersheds delineated by landuse and ANA subbasins (ANA is the Peruvian Water National authority). The model was simulated with PRMS, and sub-divided into multiple subbasins. HRUs in the simulation represent a geospatial merge between subbasins and landuses. Soil geometries were not considered due to the high uncertainty of the datasets and the lack of measured data in this regional scale simulated. Watersheds and subbasins were re-delineated and some may not contain the entire watershed dimension because only subbasins with monitored data were considered.
Raw data from automatic and conventional weather stations was used and supported with the PISCO gridded dataset developed by SENAMHI. The model comprehends streamflow, diversions, and reservoir operations up to 2016. Reservoir volumes were calibrated, and different water uses, and climate change scenarios were evaluated.
In total, 5 Peruvian watersheds located in the region of Arequipa were evaluated: Yauca, Ocona, Camana Quilca, and Tambo.
DOI: 10.48597/q9tg-9ch7
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/ykvz-gnsd
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.4211/hs.d487937f530c4a52bd3e223a16c086e1
title: Tambo watershed-scale hydrologic model

description: The Tambo watershed-scale hydrologic model comprehends watersheds delineated by landuse and ANA subbasins (ANA is the Peruvian Water National authority). The model was simulated with PRMS, and sub-divided into multiple subbasins. HRUs in the simulation represent a geospatial merge between subbasins and landuses. Soil geometries were not considered due to the high uncertainty of the datasets and the lack of measured data in this regional scale simulated. Watersheds and subbasins were re-delineated and some may not contain the entire watershed dimension because only subbasins with monitored data were considered.
Raw data from automatic and conventional weather stations was used and supported with the PISCO gridded dataset developed by SENAMHI. The model comprehends streamflow, diversions, and reservoir operations up to 2016. Reservoir volumes were calibrated, and different water uses, and climate change scenarios were evaluated.
In total, 5 Peruvian watersheds located in the region of Arequipa were evaluated: Yauca, Ocona, Camana Quilca, and Tambo.
DOI: 10.48597/gn7q-uz4a
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/kp7s-5rvy
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/p9pw-wh4z
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.4211/hs.a4b968cc67244a97b29381c459e059f1
title: Yauca watershed-scale hydrologic model

description: The Yauca watershed-scale hydrologic model comprehends watersheds delineated by landuse and ANA subbasins (ANA is the Peruvian Water National authority). The model was simulated with PRMS, and sub-divided into multiple subbasins. HRUs in the simulation represent a geospatial merge between subbasins and landuses. Soil geometries were not considered due to the high uncertainty of the datasets and the lack of measured data in this regional scale simulated. Watersheds and subbasins were re-delineated and some may not contain the entire watershed dimension because only subbasins with monitored data were considered.
Raw data from automatic and conventional weather stations was used and supported with the PISCO gridded dataset developed by SENAMHI. The model comprehends streamflow, diversions, and reservoir operations up to 2016. Reservoir volumes were calibrated, and different water uses, and climate change scenarios were evaluated.
In total, 5 Peruvian watersheds located in the region of Arequipa were evaluated: Yauca, Ocona, Camana Quilca, and Tambo.
DOI: 10.48597/rw6n-zkda
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/vj89-9gyt
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/w2a9-3h7g
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.4211/hs.0b994c0f13f445ababaa8858ece6e843
title: Algorithm for novel data application & urban flood model calibration

description: The first part of this repository includes a Python file containing two functions that utilize ESRI's ArcGIS arcpy library. Users can input shapefiles (polygons) of sub-catchments and raster files of land use land cover, and soil types. Additionally, another function allows users to input shapefiles (polylines) of the stormwater network, which include data on built material types and the age of infrastructure, to generate grouped categories of sub-catchments and stormwater conduits.

The second part of the repository contains an R file, which includes two algorithms. The first algorithm extracts time series data of nodes' flooding from a one-dimensional SWMM model and overland flood water depth from a one- and two-dimensional coupled version of the SWMM model. It then establishes a statistical relationship between the two models. The second algorithm parameterizes the SWMM 1D version using a "Genetic Algorithm" for single objective optimization in parallel computing nodes.

For details about this work readers are referred to:

1). Shrestha, A., Garcia, M. & Doerry, E. (2024). Leveraging catchment scale automated novel data collection infrastructure to advance urban hydrologic-hydraulic modeling. Environmental Modelling & Software. 
https://doi.org/10.1016/j.envsoft.2024.106046
2). Shrestha, A. (2022). Advances in Urban Flood Management: Addressing Data Uncertainty, Data Gaps and Adaptation Planning (Doctoral dissertation, Arizona State University). https://search.proquest.com/openview/b79c1eb133e93ea0a07b6147fe7feff6/1?pq-origsite=gscholar&cbl=18750&diss=y

For GitHub links to this repository, and any updates, readers are referred to:
1). https://github.com/ashish-shrs/Parameter_grouping_for_hydrologic-hydraulic_model_calibration
2). https://github.com/ashish-shrs/Algorithm_for_novel_data_application_in_hydrologic-hydraulic_model_calibration
DOI: 10.48597/x328-mtas
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/bjbd-fsrk
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/kq5e-2f36
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.4211/hs.090be1e69e734c8991a1c4d8f9b9e21c
title: Colorado River Basin Water Accounts: Provoke discussion about more adaptive operations

description: The purpose of this activity is to provoke collaborators to discuss more adaptive Colorado River operations than existing operations that equalize reservoirs and expire in 2026. Collaborators use web spreadsheets (Google Sheets) during a video conference session. In a model session, up to 6 collaborators immerse in water user roles. Then in each time step, collaborators consume, conserve, and trade water in response to their available water, others choices, and real-time discussion of choices. Collaborators also protect critical levels in Lake Powell and Lake Mead and sustain endangered, native fish of the Grand Canyon. Follow the requirements and start directions in the readme.md file.

CONTENTS:
1) readme.md - Requirements, facilitation directions, publications, file contents, and more.
2) Basin Water Accounts Tool - ColoradoRiverBasinAccounts.xlsx. Move into Google Sheets and follow Facilitation Directions (in readme.md). Or open ReadMe-Directions Worksheet.
3) Lets Start - ColoradoRiverBasinAccounts-LetsStart.pdf. Visual directions.
4) Model Guide - https://github.com/dzeke/ColoradoRiverCoding/blob/main/ModelMusings/Support/ModelGuide/ModelGuide-CombinedLakePowellLakeMead.md. Online. 
5) Manuscript - 3-LessonsInteractImmerseAdaptReservoirOperations.docx. Write up for publication as a journal article. Earlier cite-able version at https://digitalcommons.usu.edu/cee_facpub/3778/.
6) 1-Page Summary - ColoradoRiverBasinWaterAccounts-1PageSummary-v2.pdf.

Please access the final peer-reviewed, published version of the manuscript at:
     + Rosenberg, D. E. (2024). "Lessons from immersive online collaborative modeling to discuss more adaptive reservoir operations." Journal of Water Resources Planning and Management, 150(7). https://doi.org/10.1061/JWRMD5.WRENG-5893.
DOI: 10.48597/nzr4-u4wb
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.4211/hs.5de4d9eb2d224290b13d469f58dc882b
title: King's Creek, Environmental and Water Chemistry (AIMS_GP_KNZ_ENVI)

description: This study was conducted in the South Fork of King’s Creek at Konza Prairie Biological Station, one of the most well-studied prairie streams in the world. At the USGS gage located on the mainstem (06879560; est. 1979), Kings Creek is a 5th order intermittent stream draining 1059-ha of tallgrass prairie in the Kansas Flint Hills.

This synoptic survey was designed in support of the sampling goals of the Aquatic Intermittency effects on Microbiomes in Streams (AIMS) Project. During June, July, and August 2021, a field team co-collected datasets characterizing the hydrology, biogeochemistry, and ecology across 50 locations within a sub-drainage of the South Fork of Kings’ Creek. The 50 sites were selected to balance multiple competing priorities: (i) strategically targeting existing monitoring infrastructure with long-term data (n=14); (ii) including sites near several known springs and tributary junctions (n=9); and (iii) including a range of drainage area and topographic wetness index (TWI) values (n=27), both of which have been correlated with flow permanence elsewhere. Briefly, the sites selected based on drainage area and TWI were chosen by binning drainage area into 10 bins and then binning TWI into quintiles within each drainage area bin. We then randomly selected a point in each bin after accounting for points selected based on existing infrastructure, springs, and tributaries;, and enforcing a minimum spacing of 100 m between locations. We then made minor adjustments to points to account for field conditions, for instance adjusting locations with respect to a road crossing. For a detailed description of the site selection process, please see (Swenson et al., 2023).

This is a collection of site metadata for the June 2021 Konza synoptic, and for the stretch July and August 2021 synoptic sampling campaigns. Most of this data is a collation of data from the different field datasheets. This is supplemented with data from the STIC (stream temperature intermittency conductivity) sensors as well as physical characteristics derived through GIS analysis.
DOI: 10.48597/pg7m-ye55
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/u382-xtmx
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/xh4q-sr4e
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/a9ze-z5s4
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.48597/mbxe-5gxw
title: Measurement of Nitrogen_dioxide at Sniezka

description: Measurement of Nitrogen_dioxide at Sniezka
DOI: 10.13145/bacdive109969.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive109970.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive109971.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive109972.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.15151/esrf-dc-2008703222
title: Temperature dependent Resonant X-ray Inelastic Scattering at Ni L-edge for NaNiO2 and LiNiO2.

description: Ni L3-edge X-ray absorption and Resonnant inelastic X-ray scattering measurement of LiNiO2 and NaNiO2 powder samples. Measurements are performed at different temperatures. 25 K to 520 k for LiNiO2 and 300K to 520 K for NaNiO2.
DOI: 10.13145/bacdive109973.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive109974.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive109975.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.
DOI: 10.13145/bacdive109976.20230509.8.1
title: Streptomyces sp.

description: The range of data encompasses taxonomy, morphology, physiology, sampling and concomitant environmental conditions as well as molecular biology.