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LamaH-CE: LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe – files
<p><strong>Version 1.0 - This version is the final revised one.</strong></p> <p>This is the LamaH-CE dataset accompanying the paper: Klingler et al., LamaH-CE | LArge-SaMple DAta for Hydrology and Environmental Sciences for Central Europe, published at Earth System Science Data (ESSD), 2021 (<a href="https://doi.org/10.5194/essd-13-4529-2021">https://doi.org/10.5194/essd-13-4529-2021</a>).</p> <p>LamaH-CE contains a collection of runoff and meteorological time series as well as various (catchment) attributes for 859 gauged basins. The hydrometeorological time series are provided with daily and hourly time resolution including quality flags. All meteorological and the majority of runoff time series cover a span of over 35 years, which enables long-term analyses with high temporal resolution.<br> LamaH is in its basics quite sililar to the well-known CAMELS datasets for the contiguous United States (<a href="https://doi.org/10.5194/hess-21-5293-2017">https://doi.org/10.5194/hess-21-5293-2017</a>), Chile (<a href="https://doi.org/10.5194/hess-22-5817-2018">https://doi.org/10.5194/hess-22-5817-2018</a>), Brazil (<a href="https://doi.org/10.5194/essd-12-2075-2020">https://doi.org/10.5194/essd-12-2075-2020</a>), Great Britain (<a href="https://doi.org/10.5194/essd-12-2459-2020">https://doi.org/10.5194/essd-12-2459-2020</a>) and Australia (<a href="https://doi.org/10.5194/essd-13-3847-2021">https://doi.org/10.5194/essd-13-3847-2021</a>), but new features like additional basin delineations (intermediate catchments) and attributes allow to consider the hydrological network and river topology in further applications.</p> <p>We provide two different files to download: 1) Hydrometeorological time series with daily and hourly resolution, which requires decompressed about 70 GB of free disk space. 2) Hydrometeorological time series only with daily resolution, which requires 5 GB. Beyond the temporal resolution of the time series, there are no differences.</p> <p><strong>Note: </strong>It is recommended to read the supplementary info file before using the dataset. For example, it clarifies the time conventions and that <strong>NAs</strong> are indicated by the number<strong> -999</strong> in the <strong>runoff time series</strong>.</p> <p><strong>Disclaimer:</strong> We have created LamaH with care and checked the outputs for plausibility. By downloading the dataset, you agree that we nor the provider of the used source datasets (e.g. runoff time series) cannot be liable for the data provided. The runoff time series of the German federal states Bavaria and Baden-Württemberg are retrospective checked and updated by the hydrographic services. Therefore, it might be appropriate to obtain more up-to-date runoff data from Bavaria (<a href="https://www.gkd.bayern.de/en/rivers/discharge/tables">https://www.gkd.bayern.de/en/rivers/discharge/tables</a>) and Baden-Württemberg (<a href="https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer">https://udo.lubw.baden-wuerttemberg.de/public/p/pegel_messwerte_leer</a>). Runoff data from the Czech Republic may not be used to set up operational warning systems (<a href="https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf">https://www.chmi.cz/files/portal/docs/hydro/denni_data/Podminky_uziti.pdf</a>).</p> <p><strong>License: </strong>This work is licensed with CC BY-SA 4.0 (<a href="https://creativecommons.org/licenses/by-sa/4.0/">https://creativecommons.org/licenses/by-sa/4.0/</a>). This means that you may freely use and modify the data (even for commercial purposes). But you have to give appropriate credit (associated ESSD paper, version of dataset and all sources which are declared in the folder "Info"), indicate if and what changes were made and distribute your work under the same public license as the original.</p> <p><strong>Additional references: </strong>We ask kindly for compliance in citing the following references when using LamaH, as an agreement to cite was usually a condition of sharing the data: BAFU (2020), CHMI (2020), GKD (2020), HZB (2020), LUBW (2020), BMLFUW (2013), Broxton et al. (2014), CORINE (2012), EEA (2019), ESDB (2004), Farr et al. (2007), Friedl and Sulla-Menashe (2019), Gleeson et al. (2014), HAO (2007), Hartmann and Moosdorf (2012), Hiederer (2013a, b), Linke et al. (2019), Muñoz Sabater et al. (2021), Muñoz Sabater (2019a), Myneni et al. (2015), Pelletier et al. (2016), Toth et al. (2017), Trabucco and Zomer (2019), and Vermote (2015). These references are listed in detail in the accompanying <a href="https://doi.org/10.5194/essd-13-4529-2021">paper</a>.</p> <p><strong>Supplements: </strong>We have created additional files after publication (therefore non peer-reviewed):<br> 1) Shapefiles for reservoirs (points) and cross-basin water transfers (lines) including several attributes as well as tables with information about the accumulated storage volume and effective catchment area (considerung artificial in- and outflows) for every runoff gauge.<br> 2) Water quality data (e.g. dissolved oxygen, water temperature, conductivity, NO3-N), which are suitable to the gauges. The data for water quality may not be used for commercial purposes.<br> If you are interessted, just send us an email with your name, affiliation and the intended purpose for the requested files to the address listed below. If you find any errors in the dataset, feel free to send us an email to: christoph.klingler@boku.ac.at</p>
Towards Parameter Estimation in Global Hydrological Models
<p>The provided elementary effects are used in the publication J. Kupzig, R. Reinecke, F. Pianosi, M.Flörke and T. Wagener: Towards Parameter Estimation in Global Hydrological Models (submitted to Environmental Research Letters in Feb 2023).</p> <p>In a large sample study, the Morris Method (Morris 1991) application produces the provided elementary effects using a new lightweight version of the global hydrological model WaterGAP3: WaterGAPLite.</p> <ul> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/elementary_effects.zip?versionId=e980e961-2334-41db-900a-637b2dcec119">elementary_effects.zip </a>: elementary effects for all 50 trajectories and all basins (each trajectory is the result of 18 model runs; used bounds of parameters can be found in the Supplement of the manuscript)</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/results_overview.xlsx?versionId=6f38c60f-9084-4376-907d-579414285506">results_overview.xlsx</a>: parameter ranks for each basin and different evaluation criteria based on the elementary effects.</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/MC_Sample.csv">MC_Sample.csv</a>: normalized parameter samples of the additional Monte-Carlo Simulation (used bounds of parameters are the same as for the Morris method)</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/MC_NSE.csv">MC_NSE.csv</a>: resulting NSE values of the Monte-Carlo simulation</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/better_performing_basins.csv">better_performing_basins.csv</a>: list of basins (using GRDC no.) where minimal NSE is greater than -1 within all Monte-Carlo runs</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/standard_calib.csv">standard_calib.csv</a>: calibrated gamma value for each basin and corresponding evaluation criteria, using the standard calibration for WaterGAP3 (fit to mean discharge)</li> <li><a href="https://zenodo.org/api/files/d2ea2535-b6e2-4ca7-9f15-600a83a01dfb/standard_calib_mod.csv">standard_calib_mod.csv</a>: calibrated gamma value for each basin and corresponding evaluation criteria, using a modified version of the standard calibration for WaterGAP3 (maximizing the NSE)<br> </li> </ul>
Integrated freshwater abundance and connectivity clusters at the Hydrologic Unit 8 scale for the Midwest and Northeast U.S.A. – freshwater metric variables and k-means cluster assignment
This dataset includes integrated freshwater abundance and connectivity cluster output, principal component scores, and lake, wetland, and stream abundance and connectivity metrics measured at the Hydrologic Unit 8 (HU8) scale for 17 U.S. states in the Midwest and Northeast regions (appr. 1,800,000 km2). The intent of the cluster analysis is to characterize the macroscale patterns of the integrated freshwater landscape that includes lakes, wetlands, and streams and their surface connectivity attributes. We define freshwater connectivity as the permanent surface hydrologic connections that link lakes, wetlands, and streams and measure connectivity as the landscape position of systems within stream networks. Geographic data used in the analysis are in LAGOS-NE-GEO database v. 1.03 (Lake multi-scaled geospatial and temporal database), an integrated, multi-thematic geographic database (Soranno et al. 2015). The integrated freshwater clusters were created through a multi-step process as follows: 1) we quantified multiple freshwater connectivity metrics for lakes, streams, and wetlands separately, 2) we performed principal components analysis (PCA) on the connectivity metric values for each freshwater type to reduce collinearity, and 3) we performed k-means cluster analysis to group spatial units with similar freshwater connectivity characteristics. The resulting freshwater clusters are representations of the macroscale patterns of freshwater abundance and connectivity in the landscape.
Freshwater connectivity clusters for lakes, wetlands, and streams at the Hydrologic Unit 12 scale in the Midwest and Northeast U.S.A. – freshwater metric variables and K-means cluster assignment
This dataset includes freshwater connectivity cluster output and principal component scores for lakes, wetlands, and streams measured at the Hydrologic Unit 12 (HU12) scale in 17 U.S. states in the Midwest and Northeast regions (appr. 1,800,000 km2). The intent of the cluster analysis is to characterize the macroscale patterns of freshwater connectivity attributes. We define freshwater connectivity as the permanent surface hydrologic connections that link lakes, wetlands, and streams and measure connectivity as the landscape position of systems within stream networks. Geographic data used in the analysis are in LAGOS-NE-GEO database v. 1.03 (Lake multi-scaled geospatial and temporal database), an integrated, multi-thematic geographic database (Soranno et al. 2015). Freshwater connectivity clusters were created separately for lakes, wetlands, and streams through a multi-step process as follows: 1) we quantified multiple freshwater connectivity metrics, 2) we performed principal components analysis (PCA) on the connectivity metric values for each freshwater type to reduce collinearity, and 3) we performed k-means cluster analysis to group spatial units with similar freshwater connectivity characteristics. The resulting freshwater clusters are representations of the macroscale patterns of lake, wetland, and stream connectivity in the landscape.
Local water years for 4-digit hydrologic unit areas across the conterminous United States
Quantifying and predicting precipitation and water flow, and their influence on ecosystems is challenged by the dynamic relationships between and timing of precipitation and water fluxes. To help with these challenges, scientists use “water year” to examine and predict the impacts of precipitation and relevant extreme climatic and hydrological events on ecosystems. However, traditional water year definitions used in the U.S. have limited considerations of areal variations in climate and hydrology, which need to be considered when studying ecosystems at regional or national scales. We developed local water year (LWY) values that consider spatial variation using existing definitions whereby the water year begins in the month with the lowest or highest average monthly streamflow. We employed a spatial interpolation technique to assign the start and end months of two LWY timeframes to 202 subregions across the conterminous U.S. that range from 4,384 to 134,755 km2. This dataset can be linked with diverse climate, terrestrial, and aquatic data for broad-scale studies.
The Jefferson Project 2021 hydrologic, water quality, and soil quality data from 12 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2021, The Jefferson Project had twelve tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_Sucker, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.
The Jefferson Project 2022 hydrologic, water quality, and soil quality data from 11 Tributary Stations within the Lake George basin, NY, USA.
The Jefferson Project at Lake George -- a partnership between Rensselaer Polytechnic Institute, IBM Research, and Lake George Association -- combines Internet of Things technology and powerful analytics with science to create a new model for environmental monitoring and prediction. The project is building a computing platform that captures and analyzes data from a network of sensors tracking water quality and movement. These sensor data are combined with other monitoring and experimental data to create a thorough understanding of the factors that drive the lake's food web, hydrology, and water quality. More information about The Jefferson Project is available at https://jeffersonproject.rpi.edu/ In 2022, The Jefferson Project had eleven tributary monitoring stations around the lake collecting data on water quality, soil quality, and hydrology. These stations are TS_Finkle, TS_Hague, TS_Indian, TS_NorthwestBay, TS_Outlet, TS_PoleHill, TS_English, TS_Sunset, TS_ShelvingRock, TS_East, and TS_West. The stations have a sensor payload that may include some or all of the following sensors: YSI EXO2 Multi-parameter sonde, Campbell Scientific CS451 pressure transducer, SonTek-IQ+ multi-beam acoustic flow meter, Sontek-SL Doppler current meter, YSI WaterLOG® H-3123 submersible pressure transducer, and Stevens HydraProbe soil moisture sensor. The sensors collect data at high-frequency (~1 sample per minute) and the data are transferred in near real-time to off-site databases for monitoring and review by Jefferson Project researchers. The data provided here are level 4 data which underwent data correction and downsampling to an hourly frequency.
Measurements of Coarse Woody Debris %C and %N at the Coweeta LTER Terrestrial Gradient Sites, Coweeta Hydrological Laboratory, Otto, NC.
Coarse woody debris (CWD) plays a critical role in nutrient retention and cycling, including the cycling and retention of carbon and nitrogen. However, comparison studies of CWD in different forest types and elevation gradients in the southern Appalachian Mountains are lacking. We measured CWD in five different forest communities/elevations at Coweeta Hydrologic Lab. A subsample of CWD in each plot was measured for percent C and percent N, as well as for cations.
Coarse Woody Debris Cations Measurements at the Coweeta LTER Terrestrial Gradient Sites, Coweeta Hydrological Laboratory, Otto, NC.
Coarse woody debris (CWD) plays a critical role in nutrient retention and cycling, including the cycling and retention of carbon and nitrogen. However, comparison studies of CWD in different forest types and elevation gradients in the southern Appalachian Mountains are lacking. We measured CWD in five different forest communities/elevations at Coweeta Hydrologic Lab. A subsample of CWD in each plot was measured for percent C and percent N, as well as for cations.
Consequences of non-random tree species loss on litter mass loss, nutrient dynamics, carbon cycling, and decomposer communities across a terrestrial-aquatic interface at Coweeta Hydrologic Lab, Otto, NC
Although litter decomposition is a fundamental ecological process, most of our understanding comes from studies of single-species decay. Recently, litter-mixing studies have tested whether monoculture data can be applied to mixed-litter systems. These studies have mainly attempted to detect non-additive effects of litter mixing, which address potential consequences of random species loss. The focus is not on which species are lost, but the decline in diversity per se. Under global change, species loss is likely to be non-random, with some species more vulnerable to extinction than others. Under such scenarios, the effects of individual species (additivity) as well as of species interactions (non-additivity) on decomposition rates are of interest. To examine potential impacts of non-random species loss on ecosystems, we studied additive and non-additive effects of litter mixing on decomposition. A full-factorial litterbag experiment was conducted using four deciduous leaf species, from which mass loss and nitrogen content were measured. Data were analysed using a statistical approach that first looks for additive identity effects based on the presence or absence of species and then significant species interactions occurring beyond those. It partitions non-additive effects into those caused by richness and or composition.
Plethodon study from removal plots located at the Coweeta Hydrologic Laboratory
Recent research shows Plethodon shermani and Plethodon teyahalee within the hybrid zone at the Coweeta LTER in Otto, North Carolina forage heavily on ants (>50% of all prey items consumed; found in 94% of samples). As most vascular plants in the Southern Appalachians rely on ants for seed dispersal, this significant predation on ants, especially Aphaenogaster, reveals an intriguing and important relationship between these salamanders and the vascular plant abundance and distribution within their ecosystem. Additionally, consumption of ants increases with high temperatures and low relative humidity indicating that, with climate change, the effects of Plethodon foraging behavior on woodland biodiversity will be amplified. Using a paired design, we placed removal plots along an elevational gradient within the plethodon shermani-teyahalee hybrid zone at the Coweeta LTER to observe and quantify the effect of Plethodon foraging on ant communities, seed dispersal, and vascular plant distribution by removing the salamanders from treatment plots. Foraging rates of ants, with a focus on Aphaenogaster, were monitored at treatment and control plots using direct observation/counts of ants visiting tuna bait stations.
Terrestrial-Stream Biodiversity Litter Processing Datasets from Watershed 20 within the Coweeta Hydrologic Laboratory
Although litter decomposition is a fundamental ecological process, most of our understandings comes from studies of single-species decay. Recently, litter-mixing studies have tested whether monoculture data can be applied to mixed-litter systems. These studies have mainly attempted to detect non-additive effects of litter mixing, which address potential consequences of random species loss -- the focus is not on which species are lost, but the decline in diversity per se. Under global change, species loss is likely to be non-random, with some species more vulnerable to extinction than others. Under such scenarios, the effects of individual species (additivity) as well as of species interactions (non-additivity) on decomposition rates are of interest. To examine potential impacts of non-random species loss on ecosystems, we studied additive and non-additive effects of litter mixing on decomposition. A full-factorial litterbag experiment was conducted using four deciduous leaf species, from which mass loss and nitrogen content were measured. This study was conducted at the Coweeta Hydrologic Laboratory in Watershed 20 on Ball Creek that drains into Coweeta Creek, a tributary of the Little Tennessee River. Data were analyzed using a statistical approach that first looks for additive identiy effects based on the presence or absence of species and then significant species interactions occurring beyond those. It partitions non-additive effects into those caused by richness and/ or composition. This approach addresses questions key to understanding the potential effects of species loss on ecosystem processes. If additive effects dominate, the consequences for decomposition dynamics will be predictable based on our knowledge of individual species, but not statistically predictable if non-additive effects dominate.
Physical Hydrologic Data for the National Audubon Society's 16 Research Sites in coastal mangrove transition zone of southern Florida, March 1986 - ongoing
Temperature, salinity and depth were continuously collected using Hydrolab/Hach sensors within the coastal mangrove transition zone at 16 sites from southern Biscayne Bay to Cape Sable. Data were collected at 12 sites within the coastal mangrove zone of Everglades National Park, incorporating the Cape Sable, Taylor River and Panhandle region. Data were collected at 4 sites within the coastal mangrove zone of southern Biscayne Bay, incorporating the Manatee Bay, Barnes Sound, and Card Sound regions. Rainfall, pH, and dissolved oxygen were collected at a number of these sites with varying periods of record.
Periphyton, hydrological and environmental data in a coastal freshwater wetland (FCE), Florida Everglades National Park, USA (2014-2015)
The characteristic, calcareous periphyton mats of the Everglades, and particularly their diatom assemblages, provide an ideal community to study the patterns and mechanisms of community assembly along environmental gradients with ecotones. Understanding patterns and mechanisms of diatom community assembly along salinity and P gradients can be incorporated into tools for predicting changes in these gradients, and the location and movement of the "white zone" ecotone, caused by saltwater intrusion and water management outcomes in the Southern Everglades. Patterns of environmental variation and periphytic-diatom community structure along the freshwater-marine gradient of Everglades National Park, FL., USA were examined by sampling along a series of 7 transects extending from oligotrophic, freshwater marshes through the ecotone and down to the northern edge of the fringing mangrove forests. Seven transects spanning the west-east extent of the southeast Everglades, from the Main Park Road in the west to the Model Lands in the east, were sampled once in the dry season (May) and once in the wet season (November) of 2014 and 2015. These data are published in "Mazzei and Gaiser. 2018. Diatoms as tools for inferring ecotone boundaries in a coastal freshwater wetland threatened by saltwater intrusion. Ecological Indicators. 88:190-204."
Field survey and long-term measurements of biological, geomorphic, and hydrologic properties across ecosystem states in a non-tidal, salinizing peat marsh in Everglades National Park, Florida, USA, June 2018 - ongoing
This dataset package details a field survey and long-term measurements of biological, geomorphic, and hydrologic properties across ecosystem states found within a salinizing, non-tidal peat marsh in the coastal Everglades. There are four datasets included: FCE1250_Lamb_AltStableState_HydroGeo includes point measurements of soil surface elevation, bedrock elevation, soil depth, water depth, and porewater salinity across ecosystem states: emergent marsh (sawgrass dominated), submerged marsh (submerged aquatic vegetation), and unvegetated open water. The survey was conducted between November 2019 to January 2020. FCE1250_Lamb_AltStableState_Bio contains plot-scale (1 m^2) biological measurements from emergent marsh (sawgrass). Biological measurements (total sawgrass, average individual sawgrass biomass, and total sawgrass aboveground biomass), are inherently plot-level but geomorphic and hydrologic measurements were averaged (n = 3) for each plot. Standard deviations are provided. FCE1250_Lamb_AltStableState_Bio_LongTerm contains biological plot-scale (1 m^2) measurements at a bi-monthly timestep from Nov 2019 - July 2022 from a subset of plots (n = 9) included in the FCE_1250_Lamb_AltStableState_Bio dataset. Each bi-monthly measurement is the average value across all 9 nine plots. Standard deviations are also included. FCE_1250_Lamb_AltStableState_SETMH contains long-term measurements of soil surface elevation change and vertical accretion from surface elevation change and marker horizon plots from June 2018 - May 2023. Average and standard errors across all plots are included as well as measurements from each plot. Also included is the cumulative number of dry days based on the average soil surface elevation for all plots. Data collection for all datasets is complete, except for FCE_1250_Lamb_AltStableState_SETMH. SET-MH measurements are still ongoing.
Throw trap and electrofishing data collected during 1996–2022 from the Everglades, Florida, United States for the publication "Hydrology-mediated ecological function of a large wetland threatened by an invasive predator"
Asian swamp eels (Monopterus albus/javanensis complex) were first reported from Florida in 1997 and the Everglades in 2007; swamp eels have been established in Taylor Slough of Everglades National Park since 2014. This dataset incorporates plot-level mean densities (# of individuals per square meter) of common aquatic animals collected during 1996–2022 from 24 sites across four regions of the Everglades: Taylor Slough, Shark River Slough, Water Conservation Area 3, and the C-111 Panhandle. Prey species included are the six most common small fishes prior to swamp eel invasion of Taylor Slough (1996–2009) and the three common decapod species (two crayfish species and grass shrimp). An annual index of mean wet season electrofishing catch-per-unit-effort of swamp eels, Mayan cichlids (Mayaheros uruphthalmus), and the three other large 'top predator' fishes (Amia calva, Lepisosteus platyrhincus, Micropterus salmoides) is included for plots where electrofishing was performed from 1997-2021. Hydrologic measures used in analyses are included.
Saddle catchment Distributed Hydrology Soil Vegetation Model Simulation (DHSVM) surface variable outputs (SWE, snowmelt, streamflow, soil moisture), 2 meter, 2000-2019.
The Saddle Catchment of the Niwot Ridge LTER is a densely observed, high elevation site that is ideal for hydrological model simulation and calibration. The files produced are the result of a calibration of the Distributed Hydrology Soil Vegetation model (DHSVM) using observationally based states and forcings. Input state files of vegetation, soil properties, shading, and elevation were generated using ground and satellite observations, which, in the case of coarse-resolution or point scale observations, were then interpolated to match the high resolution of the model (2-meter grid cells). Temporally continuous meteorological forcings at the hourly time-step were used to force the model to produce an hourly simulation of the surface and subsurface hydrology within the Saddle catchment. DHSVM was calibrated to effectively reproduce the annual cycle (r^2) and total volume (percent bias) of observed runoff using observations of streamflow at the outflow pour point of the Saddle Catchment from 2001-2019. Calibrated parameters include the lateral conductivity of soil types, exponential decrease of soil conductivity, snow roughness, the snow melting temperature threshold, and the vertical conductivity of the soils. The resulting simulation generated spatially distributed time series of the snow water equivalent, snow melt, precipitation, total evapotranspiration, potential evapotranspiration, and a time-series of the total runoff generated at the outflow pour-point of the Saddle catchment. This data package contains the spatially distributed time series of snow water equivalent, snow melt, and runoff, as well as the model configuration file. Outputs of precipitation, total evapotranspiration, actual evapotranspiration, as well as model inputs are archived separately on the Environmental Data Initiative.
Saddle catchment Distributed Hydrology Soil Vegetation Model Simulation (DHSVM) precipitation and transpiration variable outputs (precipitation, total, potential and actual evapotranspiration), 2 meter, 2000-2019.
The Saddle Catchment of the Niwot Ridge LTER is a densely observed, high elevation site that is ideal for hydrological model simulation and calibration. The files produced are the result of a calibration of the Distributed Hydrology Soil Vegetation model (DHSVM) using observationally based states and forcings. Input state files of vegetation, soil properties, shading, and elevation were generated using ground and satellite observations, which, in the case of coarse-resolution or point scale observations, were then interpolated to match the high resolution of the model (2-meter grid cells). Temporally continuous meteorological forcings at the hourly time-step were used to force the model to produce an hourly simulation of the surface and subsurface hydrology within the Saddle catchment. DHSVM was calibrated to effectively reproduce the annual cycle (r^2) and total volume (percent bias) of observed runoff using observations of streamflow at the outflow pour point of the Saddle Catchment from 2001-2019. Calibrated parameters include the lateral conductivity of soil types, exponential decrease of soil conductivity, snow roughness, the snow melting temperature threshold, and the vertical conductivity of the soils. The resulting simulation generated spatially distributed time series of the snow water equivalent, snow melt, precipitation, total evapotranspiration, potential evapotranspiration, and a time-series of the total runoff generated at the outflow pour-point of the Saddle catchment. This data package contains the spatially distributed time series of precipitation, total evapotranspiration and actual evapotranspiration Outputs of snow water equivalent, snow melt, and runoff, as well as the model configuration file, as well as model inputs are archived separately on the Environmental Data Initiative.
Future hydrologic outputs using the Distributed Hydrology Soil Vegetation Model (DHSVM) for the Saddle Catchment, 2001 - 2100.
The Saddle Catchment of the Niwot Ridge LTER is subject to warming in a future climate and thus changes in precipitation phase, precipitation redistribution, and timing and distribution of surface water inputs (the summation of rainfall and snowmelt) as well as changes in atmospheric demand (potential evapotranspiration, PET) and the amount of evapotranspiration (ET). The input warming data were developed to first force a future climate across the Saddle Catchment and evaluate resultant hydrologic outputs using the Distributed Hydrology Soil Vegetation Model (DHSVM). Future forcing data were generated by calculating and implementing delta values between daily average historical data and those generated from end-of-current-century Weather Research Forecasting model data. The variables perturbed in the warming DHSVM simulation were: precipitation, air temperature, relative humidity and longwave radiation. Target outputs included: daily spatially distributed precipitation (historical and future), daily spatially distributed surface water inputs (historical and future), total spatially distributed PET (historical and future), and total spatially distributed ET (historical and future). The precipitation and surface water inputs products are orthorectified (UTM projection) raster products, and the forcing data and PET and ET are CSV files. The forcing data represent catchment averages, which are distributed within DHSVM, and all other files are at the 2 m resolution.
SBC LTER: Land: Hydrology: Stream discharge and associated parameters at Atascadero Creek, Puente St (AT07)
Stream Discharge and water temperature were collected with a Solinst Model 3001 LT Levelogger at Atascadero Creek, Puente St in the Santa Barbara coastal area (site ID: AT07). Data are reported hourly. Stage values were converted to discharge using a rating curve developed with stream channel cross-sections, roughness estimates and the HEC-RAS model.
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.