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» Caldern Klimastation Eddy Covariance created by: Sebastian Achilles, Maik Dobbermann (last update: 2024-04-15)

» Soil properties along Chilean climate gradient created by: Paulina Grigusova (last update: 2023-06-05)

» Modelled sediment redistribution across climate gradient created by: Paulina Grigusova (last update: 2023-06-05)

» Linden - Wind created by: Boris Thies, Sebastian Achilles, Maik Dobbermann (last update: 2024-04-15)

» Linden - Niederschlag Ott created by: Boris Thies, Sebastian Achilles, Maik Dobbermann (last update: 2024-04-16)

» AWS raw data from Chile - Santa Gracia station created by: Lukas Lehnert, Sebastian Achilles, Jörg Bendix (last update: 2022-09-23)

» AWS raw data from Chile - Pan de Azucar - Remote 2 station created by: Lukas Lehnert, Sebastian Achilles, Jörg Bendix (last update: 2022-09-23)

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Caldern Klimastation Eddy Covariance

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Dataset ID:	535
Last updated:	2024-04-15
Dataset creators:	Sebastian Achilles Maik Dobbermann
Contact:	Maik Dobbermann Sebastian Achilles
Temporal coverage:	2021-07-13 11:25:00 - 2024-04-06 13:55:00 - time zone: Europe/Berlin
Geographic coverage:	Caldern Klima-Messturm Wiese
Abstract:	--- Abstract:
Additional info:	--- Additional info:
Keywords:	\| Climate \| AWS \| campbell \|
Intellectual rights:	LCRS

Entity name:	toa5_9485.data.dat_out
Entity type:	dataTable
Attributes:	Date and Time (datetime) (Unitless) Record number (record) [Count] Battery voltage (BattV) [Volt] Temperature in control cabinet (PTemp_C) [Degrees Celsius] Datalogger Temperature Max (PTemp_C_Max) [Degrees Celsius] Average air temperature (AirTC_Avg) [Degrees Celsius] Relative humidity average (RH) [Percent] shortwave up (CM3Up_Avg) [Watt per square meter] shortwave down (CM3Dn_Avg) [Watt per square meter] longwave up (CG3Up_Avg) [Watt per square meter] longwave down (CG3Dn_Avg) [Watt per square meter] casetemp radiation (CNR1TC_Avg) [Degrees Celsius] Radiometer Temperature (case temp) (CNR1TK_Avg) [Degree Kelvin] Shortware Net Radiation (NetRs_Avg) [Watt per square meter] Infrared Net Radiation (NetRl_Avg) [Watt per square meter] Albedo (Albedo_Avg) [Percent] Incoming Total Radiation (UpTot_Avg) [Watt per square meter] Outgoing Total Radiation (DnTot_Avg) [Watt per square meter] Total Net Radiation (NetTot_Avg) [Watt per square meter] Incoming Temperature Corrected Infrared Radiation (CG3upCo_Avg) [Watt per square meter] Outgoing Temperature Corrected Infrared Radiation (CG3DnCo_Avg) [Watt per square meter] Volumetric water content (Vwc) [Count] Electric conductivity (EC) [Square meter per square meter] Soil temperature (Ts) [Degrees Celsius] Soil Permittivity (SPer) [Unitless] Period (VD) [Unitless] Internal parameter to calculate soil water content and electric conductivity (VR) [Unitless] Rain total (PCP_tot_bucket) [Millimeter] Soil Heat Flux Plate (HFP) [Watt per square meter]

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Soil properties along Chilean climate gradient

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Dataset ID:	523
Last updated:	2023-06-05
Dataset creators:	Paulina Grigusova
Contact:	Paulina Grigusova
Temporal coverage:	2019-09-26 00:00:00 - 2019-11-20 00:00:00 - time zone: America/Santiago
Geographic coverage:	Chile
Abstract:	Soil properties analysed from soil samples extract... Abstract: Soil properties analysed from soil samples extracted along 8 hillside catenas at 4 Chilean research sites.
Additional info:	How were the properties estimated: Water content ... Additional info: How were the properties estimated: Water content in %: GSWC=((Sm-Sd))/Sd*100, where Sm [g] is the mass of moist soil measured directly after the extraction and Sd [g] is the mass of soil dried at 105 °C for at least 24 hours. Bulk density in g cm-3: BD=Sd/Sv, where Sv [cm-3] is the volume of the sample. Porosity %: P=1-dm/Sv, where dm [g] is the dry mass of soil particles Skelettanteil (soil) in %: ratio of particles with a diameter above 2 mm. Skelettanteil (above ground) in %: using unsupervised k-means classification Organic components in %: OM=Sc/Sd, where Sc is the weight [g] of the sample dried at 500 °C for 16 h erodibility: using pedotransfer function by Wösten et al. 1997 Soil texture [%] – clay (< 0.002 mm), coarse, middle and fine silt (0.002 mm to 0.02 mm), and coarse, middle and fine sand (0.02 mm to 2 mm) was estimated using a PARIO method (Durner et al. 2017). Following properties were estimated using inductively coupled plasma mass spectrometry according to Amman 2007: Magnesium, Phosphor, Potassium, Calcium, Sodium, Boron, Zink, Iron, Copper, Nickel, Vanadium, Cobalt, Chrom, Stroncium, Barium.
Keywords:	\| soil samples \| soil properties \|
DOI:	10.5678/wsrb-9f70
Intellectual rights:	LCRS

Entity name:	soil samples
Entity type:	dataTable
Attributes:	Datetime of specific event (eventdatetime) (Unitless) Sampling strategy (sampling_strategy) (Unitless) Weather conditions (weather_conditions) (Unitless) Name of site (sitename) (Unitless) location (location) (Unitless) Geographic latitude (coordinate_latitude) [Degree] Geographic longitude (coordinate_longitude) [Degree] elevation (El) [Meter] Topographic position (topographic_position) (Unitless) Type of vegetation (vegetation_type) (Unitless) land use (Land_use) (Unitless) Light situation (light_situation) (Unitless) Slope aspect (slope_aspect) [Degree] Aspect of slope as cardinal point (slope_aspect_cardinalpoint) (Unitless) Name of sample (sample_name) (Unitless) Sample material (material) (Unitless) Sampling method (sampling_method) (Unitless) Sampling depth in meter from ground level (depth_meter) [Meter] Sampling diameter in cm (diameter_cm) [Centimeter] Unit of sampling quantity (quantity_unit) (Unitless) Initial sampling quantity (quantity_initial) [Count] Current sampling quantity (quantity_current) [Count] Name of laboratory (labname) (Unitless) Name of the storage room (storage_room) (Unitless) Name of the storage container (storage_container) (Unitless) Description of the storage unit (storage_description) (Unitless) Name of the sample collector (collector_name) (Unitless) Affiliation of the sample collector (collector_affiliation) (Unitless) Water content in soil in % (water_content) [Percent] Bulk density in g per cm-3 (bulk_density) [Gram per cubic centimeter] Porosity (porosity) [Percent] Skeletal part (soil) in % (skeletal_soil) [Percent] Skeletal part (above ground) in % (skeletal_aboveground) [Percent] Organic components in % (organic_components) [Percent] Erodibility (erodibility) [Metric units of tonnes per square meter per hour] Soil texture clay content (Clay) [Unitless] Fine silt (Silt_fine) (Unitless) Medium silt (Silt_medium) (Unitless) Coarse silt (Silt_coarse) (Unitless) Fine Sand (Sand_fine) (Unitless) Medium sand (Sand_medium) (Unitless) Coarse sand (Sand_coarse) (Unitless) Soil texture silt content (Silt) [Unitless] Soil texture sand content (Sand) [Unitless] Magnesium parts per million (magnesium_ppm) [Parts per million] Phosphor parts per million (phosphorus_ppm) [Parts per million] Potassium parts per million (potassium_ppm) [Parts per million] Calcium parts per million (calcium_ppm) [Parts per million] Sodium parts per million (sodium_ppm) [Parts per million] Boron parts per million (boron_ppm) [Parts per million] Zink parts per million (zinc_ppm) [Parts per million] Iron parts per million (iron_ppm) [Parts per million] Copper parts per million (copper_ppm) [Parts per million] Nickel parts per million (nickel_ppm) [Parts per million] Vanadium parts per million (vanadium_ppm) [Parts per million] Cobalt parts per million (cobalt_ppm) [Parts per million] Chrom parts per million (chromium_ppm) [Parts per million] Stroncium parts per million (strontium_ppm) [Parts per million] Barium parts per million (barium_ppm) [Parts per million]

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Modelled sediment redistribution across climate gradient

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Dataset ID:	521
Last updated:	2023-06-05
Dataset creators:	Paulina Grigusova
Contact:	Paulina Grigusova
Temporal coverage:	2016-03-03 00:00:00 - 2022-12-31 00:00:00 - time zone: America/Santiago
Geographic coverage:	Chile
Abstract:	Animal burrowing activity affects soil texture, bu... Abstract: Animal burrowing activity affects soil texture, bulk density, soil water content and redistribution of nutrients. All of these parameters influence sediment redistribution, which shapes the earth surface. Hence it is important to include bioturbation into erosion models. However, up to present, the inclusion of terrestrial burrowing animals into catchment-wide models has been limited to invertebrates, single species, and animal triggered sediment redistribution. However, an inclusion of all burrows created by vertebrate bioturbators into an erosion model to estimate their impact on rainfall triggered erosion is missing. Here, we included vertebrate bioturbator’ burrows into a soil erosion model and interpreted the impacts of bioturbation on sediment redistribution. To do this, we measured the needed soil properties and location of burrows created by bioturbating animals in four research sites located along the Chilean climate gradient. Then, we parametrized a semi-empirical erosion model by applying machine learning algorithms to upscale soil properties and integrated the burrows into the model. We validated the model using several sediment fences in the field. We estimated the modelled sediment redistribution and surface runoff in all climate zones. Lastly, we identified environmental parameters determining the impact of burrows on sediment accumulation or erosion. The model including bioturbation performed much better (R2 = 0.71, RMSE = 0.63 kg ha-1 year-1) than the model without bioturbation (R2 = 0.17, RMSE = 1.18 kg ha-1 year-1). Burrows increased sediment accumulation by 137.8 % ±16.4 % in the arid zone (3.53 kg ha-1 year-1 vs. 48.79 kg ha-1 year-1), sediment erosion by 6.5 % ±0.7 % in the semi-arid zone (129.16 kg ha-1 year-1 vs. 122.05 kg ha-1 year-1) and sediment erosion by 15.6 % ±0.3 % in the Mediterranean zone (4602.69 kg ha-1 year-1 vs. 3980.96 kg ha-1 year-1). Bioturbating animals seem to play only a negligible role in the humid zone. The fraction of sediment excavated by the animal to sediment redistributed during rainfall events was 128 % in arid, 24 % in semi-arid, 33.5 % in Mediterranean and 5.6 % in humid zone. Bioturbation increases erosion with increasing slope, sink connectivity and topography ruggedness, decreasing vegetation cover and soil wetness. Bioturbation increases sediment accumulation with increasing surface roughness, soil wetness and vegetation cover.
Additional info:	Coordinates: Pda north facing: -70.6187599999... Additional info: Coordinates: Pda north facing: -70.61875999999 -25.97690569047 PdA south-facing: -70.61747800000 -25.98792750527 SG north-facing -71.169226580458 -29.758944299143 SG south-facing -71.167225099000 -29.764107785292 LC north-facing -71.09082499999 -32.94316192996 LC south-facing -71.09315231476 -32.93436314092 NA -73.01619835389 -37.80787731366
Keywords:	\| Chile \| sediment redistribution \|
DOI:	10.5678/32wa-d179
Intellectual rights:	LCRS

Entity name:	model output
Entity type:	otherEntity

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Linden - Wind

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Dataset ID:	511
Last updated:	2024-04-15
Dataset creators:	Boris Thies Sebastian Achilles Maik Dobbermann
Contact:	Maik Dobbermann Sebastian Achilles
Temporal coverage:	2021-05-14 00:00:01 - 2024-04-14 23:59:53 - time zone: Europe/Berlin
Geographic coverage:	Linden
Abstract:	10 seconds values... Abstract: 10 seconds values
Additional info:	Raw data extracted from binary data. Initial visua... Additional info: Raw data extracted from binary data. Initial visual control only. Use data with great care, errors might be included.
Keywords:	\| Linden \| raw \| usa \| Ultraschallanemometer \| usonic \|
Intellectual rights:	LCRS

Entity name:	202301_conv_usa
Entity type:	dataTable
Attributes:	Date and Time (datetime) (Unitless) Record number (record) [Count] Averaging_period in seconds (averaging_period) [Seconds] Measurement rate in Hertz (measurement_rate) [Seconds] Azimuth of the mounting angle (mounting_angle) [Degree] Data quality percentage (dataquality) [Percent] Wind vector x in m/s (windvector_x) [Meter per second] Wind vector y in m/s (windvector_y) [Meter per second] Wind vector z in m/s (windvector_z) [Meter per second] Air temperature (Ta) [Degrees Celsius] Standard deviation of wind vector x in m/s (windvector_x_sig) [Meter per second] Standard deviation of wind vector y in m/s (windvector_y_sig) [Meter per second] Standard deviation of wind vector z in m/s (windvector_z_sig) [Meter per second] Standard deviation of the temperature (Ta_sig) [Degrees Celsius] Covariance of X and Y wind vectors (windvector_xy_cov) [Square meter per square second] Covariance of X and Z wind vectors (windvector_xz_cov) [Square meter per square second] Covariance of X and T (windvector_xta_cov) [Millikelvin per second] Covariance of Y and Z wind vectors (windvector_yz_cov) [Square meter per square second] Covariance of Y and T (windvector_yta_cov) [Millikelvin per second] Covariance of Z and T (windvector_zta_cov) [Millikelvin per second] Standard deviation of the wind component parallel to the mean wind direction (psig) [Meter per second] Standard deviation of the wind component horizontal perpendicular to the mean wind direction (qsig) [Meter per second] Standard deviation of the wind component vertical perpendicular to the mean wind direction (rsig) [Meter per second] Longitudinal turbulence intensity (tp) [Unitless] Transversal turbulence intensity (tq) [Unitless] Vertical turbulence intensity (tr) [Unitless] Shear stress velocity (ustar) [Meter per second] Characteristic temperature (tstar) [Degree Kelvin] Ground friction coefficient (cd) [Unitless] Monin-Obukhov stability parameter (mos) [One per meter] Vertical pulse current (mf) [Kilogram per meter second] Vertical heat flow (hf) [Watt per square meter] Mean wind component in east-west direction (u) [Meter per second] Mean wind component in north-south direction (v) [Meter per second] Mean vertical wind component (w) [Meter per second] Wind speed (vel) [Meter per second] Wind direction (dir) [Degree]

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Linden - Niederschlag Ott

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Dataset ID:	510
Last updated:	2024-04-16
Dataset creators:	Boris Thies Sebastian Achilles Maik Dobbermann
Contact:	Maik Dobbermann Sebastian Achilles
Temporal coverage:	2018-01-01 00:00:00 - 2024-04-15 23:59:00 - time zone: Europe/Berlin
Geographic coverage:	Linden
Abstract:	--- Abstract:
Additional info:	Raw data extracted from binary data. Initial visua... Additional info: Raw data extracted from binary data. Initial visual control only. Use data with great care, errors might be included.
Keywords:	\| Niederschlag \| precipitation \| Linden \|
Intellectual rights:	LCRS

Entity name:	202302_conv_pluvio
Entity type:	dataTable
Attributes:	Date and Time (datetime) (Unitless) Record number (record) [Count] Intensity RT - moving precipitation growth maximum (IntenEZ_Max) [Millimeter per hour] Intensity RT - moving precipitation growth (IntenEZ_Avg) [Millimeter per hour] Accu RT-NRT - Accumulated RT - NRT (MenEZNEZ_Tot) [Millimeter] Accu total NRT - Accumulated total content non real time (MenNEZ_Tot) [Millimeter] Accu total RT - Accumulated total content real time (MenTotEZ) [Millimeter] Bucket RT - Bucket realtime (BehEZ) [Millimeter] Bucket NRT - Bucket non realtime (BehNEZ) [Millimeter] Temperature of load cell (TLoadCel_Avg) [Degrees Celsius] Heater status (StatHeat) (Unitless) Status (Status) (Unitless) Message (Message) (Unitless)

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AWS raw data from Chile - Santa Gracia station

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Dataset ID:	492
Last updated:	2022-09-23
Dataset creators:	Lukas Lehnert Sebastian Achilles Jörg Bendix
Contact:	Maik Dobbermann Sebastian Achilles
Temporal coverage:	2017-07-30 13:55:00 - 2021-10-22 12:55:00 - time zone: America/Santiago
Geographic coverage:	Chile - Santa Gracia station
Abstract:	--- Abstract:
Additional info:	Second precipitation entity contains rain events i... Additional info: Second precipitation entity contains rain events in 1 minute resolution. Santa Gracia: Fog collector (Fog 3): 0.2x0.42m, second series. m in g = U in mV (measured values from logger table)444.856-29.331 Scale(Scale 6): 0.2x0.2m, second type m in g = U in mV (measured values from logger table)1000.088-96.592
Keywords:	\| Chile \| raw \| AWS \|
Intellectual rights:	LCRS

Entity name:	AWS data
Entity info:	Raw AWS data
Entity type:	dataTable
Attributes:	Date and Time (datetime) (Unitless) Record number (record) [Count] Battery voltage (BattV) [Volt] Solar radiation average (SLR) [Watt per square meter] Total solar Irradiation unit of irradiance (SLRmin_tot) [Megajoules per square meter] Wind direction (WD) [Degree] Wind speed maximum (WSmax) [Meter per second] Wind speed average (WS) [Meter per second] Air temperature average (T) [Degrees Celsius] Maximum temperature (Tmax) [Degrees Celsius] Minimum temperature (Tmin) [Degrees Celsius] Relative humidity average (RH) [Percent] IR Surface Temperature (IRSensor_T_Avg) [Degrees Celsius] IR Surface Temperature Sensor Housing Temperature (IRSensorCan_Temp_Avg) [Degrees Celsius] Fog gauge Signal in mV (fog_mv) [Millivolt] Scale signal in mV (scale_mv) [Millivolt] Soil temperature (Ts) [Degrees Celsius]

Entity name:	Precipitation data
Entity info:	Raw precipitation data from AWS
Entity type:	dataTable
Attributes:	Date and Time (datetime) (Unitless) Record number (record) [Count] Rain rate average bucket (PCP_int_bucket) [Millimeter per hour]

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AWS raw data from Chile - Pan de Azucar - Remote 2 station

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Dataset ID:	491
Last updated:	2022-09-23
Dataset creators:	Lukas Lehnert Sebastian Achilles Jörg Bendix
Contact:	Maik Dobbermann Sebastian Achilles
Temporal coverage:	2017-07-25 17:35:00 - 2019-03-16 14:35:00 - time zone: America/Santiago
Geographic coverage:	Chile - Pan de Azucar - Remote 2 station
Abstract:	--- Abstract:
Additional info:	Remote 2: Fog collector (Fog 4): 0.20.42m, thi... Additional info:* Remote 2: Fog collector (Fog 4): 0.20.42m, third series m in g = U in mV (measured values from logger table)411,557+230,91 Scale (Scale 5):0,2x0,2m, second type. m in g = U in mV (measured values from logger table)*984,301-38,64
Keywords:	\| Chile \| raw \| AWS \|
Intellectual rights:	LCRS

Entity name:	fine.dat_out
Entity type:	dataTable
Attributes:	Date and Time (datetime) (Unitless) Record number (record) [Count] Battery voltage (BattV) [Volt] Solar radiation average (SLR) [Watt per square meter] Total solar Irradiation unit of irradiance (SLRmin_tot) [Megajoules per square meter] Wind direction (WD) [Degree] Wind speed maximum (WSmax) [Meter per second] Wind speed average (WS) [Meter per second] Air temperature average (T) [Degrees Celsius] Maximum temperature (Tmax) [Degrees Celsius] Minimum temperature (Tmin) [Degrees Celsius] Relative humidity average (RH) [Percent] IR Surface Temperature (IRSensor_T_Avg) [Degrees Celsius] IR Surface Temperature Sensor Housing Temperature (IRSensorCan_Temp_Avg) [Degrees Celsius] Fog gauge Signal in mV (fog_mv) [Millivolt] Scale signal in mV (scale_mv) [Millivolt] Soil temperature (Ts) [Degrees Celsius]

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Laboratory for
Climatology and Remote Sensing

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