The Space team has made the following datasets publicly available.
R scripts presented as Jupyter Notebooks and data to generate load and concentration estimates produced for the journal publication:
McDowell, W. H., McDowell, W. G., Potter, J. D. and Ramírez, A. (2018), Nutrient export and elemental stoichiometry in an urban tropical river. Ecol Appl. Accepted Author Manuscript. doi:10.1002/eap.1839 https://www.hydroshare.org/resource/59ec9745d51646eb8efda5e4a6b08d5f
Hydrologic Extremes and Society
Chair: Hilary McMillan (San Diego State University)
This session focuses on observations, prediction, communication and adaptation to hydrologic extremes. By bringing together ideas from flood and drought research, we analyze similarities and differences in societal impacts and interactions with these two extremes. We explore how providing observations and information about hydrologic extremes can change the way societies understand and react to crisis events.
"Tracking Drought Impacts Across Space, Time, Sectors and Scales"
Speaker: Kelly Smith (University of Nebraska Lincoln)
In the 1990s and early 2000s, drought disaster researchers called for creation of a comprehensive database of drought impacts. But creation of such a database presumes that there is a single perspective from which all impacts will be visible. In fact, drought impacts are like fractals – as you focus on smaller scales, new realms of detail become apparent. An individual farmer’s drought-related loss or the hardship that an agricultural community experiences may be completely lost when drought impacts are aggregated to a national scale. Furthermore, drought impacts occur within specific contexts – a household has to water landscape and garden plants more; a reservoir operator produces less hydropower; fish die because a river dried up; fewer lift tickets are sold when there is no snow; and so on. Decision-makers in each of these sectors may or may not consider drought – an abstraction, often one of many pressures – as causing a separate impact, and they typically describe its effects, nested within a context that includes both long- and short term institutional effects. And many people have the adaptive capacity to foresee and prevent losses – a ski resort may offer hiking opportunities instead – so lack of water does not always translate into a drought impact. While this may seem obvious, it means there is no common framework for identifying, let alone quantifying, drought impacts. Sector and scale both matter. Large-scale commodity crops and hydropower production are some of the easiest drought impacts to quantify. Health effects to individuals and ecosystems are some of the hardest. Data collection requires resources, and in the absence of unlimited resources, we need to determine what data needs to be collected – or analyzed – to manage drought impacts. https://www.hydroshare.org/resource/a28aa1d4c2b64519a03d668a970d0c6f
Part of Stroud Water Research Center’s (SWRC) development modelling project for Open Space Institute's (OSI) Land Protection Impact Assessment (LPIA). Parcel analysis carried out by Model My Watershed's (MMW) Site Storm Model. Analyzing the following:
1) Forest to Open Space Development
2) Forest to Low Density Development
3) Forest to Medium Density Development
4) Forest to High Density Development
5) OSI’s Development Scenario https://www.hydroshare.org/resource/fd1b179a8c2143de8e8c1dc21231c864
Observation discharge data required for DeadRun Green Infrastructure workflow demonstration. https://www.hydroshare.org/resource/7feec694d0b140b5991ce20135c1dcef
Capacitance rod installed in Weir 4 stilling pool, located in watershed 4, midway between USFS road 325 (top of hillslope) and Holcombe's Branch. Measuring Discharge/Runoff via stage (5 min resolution) in stilling pool of 90 degree v-notch weir and USFS rating curve: Q = 2.48*(h(ft))^2.49; Q = discharge in cfs, h = stage in feet. Discharge data converted to L/s. Runoff data, in mm/hr, calculated by normalizing discharge to watershed 4 area (6.9 ha). Capacitance water level meter is TruTrack, WT-HR 1000 (http://www.trutrack.com/WT-HR.html), manually donwloaded every six weeks.
Date Range Comments: Water Years 2015-2016 https://www.hydroshare.org/resource/a22295e88b204d5997faeeaebb4dbe09
This resource contains a WaterML retrieved from the USGS IV service by the Gaugeviewer WaterML application representing observed discharge data for gauge number 10150500, which is located at lat: 40.049678 long: -111.547971 https://www.hydroshare.org/resource/d28685eb86474737ae93cc901f78dbb0
Demographic and legislative boundaries for Puerto Rico. https://www.hydroshare.org/resource/ea01f4863ead453bbc478fa35ac08f92
Land Use Value export for sub-watershed area within the Pequea Creek watershed. https://www.hydroshare.org/resource/bc4ea4ed41ad44c68bcd6650f297c694
Overview:
Land cover mapping represents the coverage of vegetation, bare, wet and built surfaces (developed and natural surfaces) at a given point in time. The existing land cover map was developed by Whatcom County Planning and Development Services (PDS) during spring of 2012 for the Lower Nooksack Water Budget. The dataset represents ground conditions between 2006 and 2010. The project team created the existing condition land cover dataset by combining local and regional datasets to get the most accurate and current data for the U.S. and Canadian portions of WRIA 1. The development of the existing land cover map includes 14 land cover categories; each has a unique impact on the water balance. The agricultural land cover class was further classified into crop types.
Land cover and crop types influence evapotranspiration and infiltration, playing an important role in determining the watershed’s water balance. Land cover data provides information used to parameterize the water movement through the vegetation canopy and water demand of plant evapotranspiration in the estimation of the water budget by the hydrology model.
Land cover changes over time, as exemplified by comparing the existing and historic land cover data in WRIA 1, displayed in Figure 1 and Figure 2. Historic land cover mapping developed by Utah State University (Winkelaar, 2004) as part of the WRIA 1 Watershed Management Project was used to represent land cover/land use for the undepleted flow simulations. This work was done using a suite of studies and ancillary datasets, including turn of the century GLO maps and NRCS soils data. Methods and sources more thoroughly described in Mapping Methodology and Data Sources for Historic Conditions Landuse/ Landcover Within Water Resource Inventory Area 1 (WRIA1) Washington, U.S.A. The historic land cover map includes 10 land cover classes.
Purpose:
Within the Topnet-WM hydrologic model used to estimate the Lower Nooksack Water Budget, the local land cover type is used to parameterize the water movement through the vegetation canopy and water demand for plant evapotranspiration, as described in detail in Chapter 2: Water Budget Model. Water input to the canopy comes from rainfall, snowmelt, and irrigation. The process of some water retention by the canopy is known as interception. Potential evapotranspiration is first satisfied from the canopy interception storage. Water that passes through the canopy to the soil becomes input to the vadose zone soil storage. The vadose zone is the unsaturated soil region above the water table. Potential evapotranspiration not satisfied from the interception storage becomes potential evapotranspiration from the vadose zone soil storage. The model calculates crop evapotranspiration using the Penman-Monteith method. Irrigation requirements are calculated using potential crop evapotranspiration and irrigation efficiency. Land cover mapping also identifies impervious surfaces where water directly runs off, as well as lakes and wetlands where water is stored and evaporates.
This resource is a subset of the Lower Nooksack Water Budget (LNWB) Collection Resource. https://www.hydroshare.org/resource/479ae9daeec34b48885f7645ea0966b4
Multiple cameras installed various locations in Upper and Lower Gordon Gulch.
The linked page will access ALL Cameras.
Sensor array IDs and descriptions for Time Lapse Cameras-
BT_Gully_Camera (BT_Gully), Time-lapse Photography, D-333, Moultrie,
Begin date: 1/7/14 - ongoing
BT_SW_0_Camera (BT_SW_0) Time-lapse Photography
Begin date: 5/28/15 - ongoing
GGL_NF_Met_Camera (GGL_NF_Met), Time-lapse Photography, NA
Begin date: 1/3/14 - ongoing
GGL_NF_SP_4_Camera (GG_NF_SP_4), Time-lapse Photography, Moultrie Gamespy MFHI-65,
Begin date: 4/1/11 - ongoing
GGL_SF_SP_9_Camera GGL_SF_SP_9), Time-lapse Photography, Moultrie Gamespy MFHI-65,
Begin date: 4/1/11 - ongoing
GGL_SW_0_Camera (GGL_SW_0), Time-lapse Photography, Moultrie I-65 Time-Lapse Camera (SN:B0912112900),
Begin date: 2/1/12 - ongoing
GGU_NF_SP_4_Camera (GGU_NF_SP_4), Time-lapse Photography, NA,
Begin date: 3/2/09 - ongoing
GGU_SW_0_Camera (GGU_SW_0), Time-lapse Photography, NA,
Begin date: 3/16/12 - ongoing
GLV_Camera (GLV), Time-lapse Photography Moultrie Game Spy I-65 Time-Lapse Camera,
Begin date:8/28/08 - ongoing https://www.hydroshare.org/resource/8948434b66fb435cb9e09bdc8c12a128
Green roofs were designed by civil engineers to insulate buildings, protect buildings from ultraviolet light, and slow stormwater runoff. However, from a biologist’s perspective they are an untapped resource for growing crops and native plants that support pollinators. Two basic assumptions about green roofs are (1) that they provide more habitat for invertebrates than normal roofs, and (2) that approach the same level of biodiversity as ground level sites. The first assumption is so basic that it has rarely been tested. We compared biodiversity on a green roof composed of plants from a commonly used genus in the green roof industry, sedums, with biodiversity on an asphalt tile roof. To test the second assumption we compared biodiversity on a green roof of plants that contained a mix of native and nonnative plants to ground level sites in the immediate vicinity. Surprisingly, invertebrate biodiversity on a sedum roof was not different from that of an asphalt tile roof containing no vegetation. Biodiversity on the mixed native plant green roof did, however, approach similar levels of biodiversity to nearby ground level sites. We conclude that for green roofs to be functional from both engineering and biological perspectives, they must include a diverse array of plants. We are now testing a variety of native plants from Utah to determine their suitability for green roof installations. The data are limited to 2014 and include two separate sites: the greenroof-asphalt roof paired sites at Southern Utah University in Cedar City, Iron County, Utah, and the greenroof-ground level paired sites at the University of Utah, Salt Lake City, Salt Lake County, Utah. https://www.hydroshare.org/resource/a7821bceca9e42159790d5e1d1441c8c
South-Facing Meteorological Met Station with relative humidity, air temp, incoming shortwave radiation, soil moisture, rain gage and barometric pressure.
GGL_SF_Met is a 2.5 m multi parameter meterological tripod representing south facing aspects of Gordon Gulch.
Query page here: https://bcczo.colorado.edu/query/ggl-sf-met.shtml
See related for GGL_NF_Met (Gordon Gulch Lower South Facing Met Station)
Sensor ID and descriptions-
GGL_Met_SF, Communication, Campbell Scientific CR 1000 s/n 36604, Vaisala Barometer PTB110 s/n D0850005 replaced on 7/12/15 with serial number L2750359 voltage range 0-2.5vdc, Li Cor 200x Pyranometer s/n PY58515, REBS Q-7.1 Net Radiometer s/n Q10011 pos. calibration factor 8.94, negative calibration factor 11.04, replaced 20161110 with Kipp and Zonnen NR-Lite-2 sn# 160888 calibration factor 15.4, calibrated 5/11/2016, due 2 years 5/11/2018, Campbell Scientific T-107 Temperature Probe s/n CZOT_017
Campbell Scientific T-107 Temperature Probe s/n CZOT_013, replaced with Vaisala HMP-60 Temp and RH, serial M3141020 unsure of calibration due date, RMYoung03101 L wind speed s/n CZOwind01, replaced with same instrument, send back for calibration, Campbell Scientific CS616 s/n CZOvw017, replaced 7/7/16 w CS-616 s/n czovw14, Texas Electronics tipping bucket s/n 45911-208,replaced 7/7/16 with Texas Instruments TR-525mm 45910-208, 10 w solar panel, Campbell Scientific RF401a spread spectrum radio serial # 2663, 3-9amp per hour batt., MorningStar Sunsaver 6, Modem Airlink LS300, 9db yagi style antennae, TR-525mm serial 45910-208, remove 22 cm vwc CS-616 no visible serial and replace with CS-616 serial czovw14 ... https://www.hydroshare.org/resource/d66f1f3239a94c7682c71217b1a94e0b
Powered by Clowder (1.22.1#1085 branch:master sha1:f28c203c56b2d4690d32ea0bce5364458de1ec79).