Bioclimatic and Soil Moisture Monitoring Across Elevation in a Mountain Watershed: Opportunities for Research and Resource Management

Soil moisture data are critical to understanding biophysical and societal impacts of climate change. However, soil moisture data availability is limited due to sparse in situ monitoring, particularly in mountain regions. Here we present methods, specifications, and initial results from the interactive Roaring Fork Observation Network (iRON), a soil, weather, and ecological monitoring system in the Southern Rocky Mountains of Colorado. Initiated in 2012, the network is currently composed of nine stations, distributed in elevation from 1,890 to 3,680 m, that continually collect and transmit measurements of soil moisture at three depths (5, 20, and 50 cm), soil temperature (20 cm), and meteorological conditions. Time‐lapse cameras for phenological observations, snow depth sensors, and periodic co‐located vegetation surveys complement selected stations. iRON was conceived and designed with the joint purpose of supporting bioclimatic research and resource management objectives in a snow‐dominated watershed. In the short term, iRON data can be applied to assessing the impact of temperature and precipitation on seasonal soil moisture conditions and trends. As more data are collected over time, iRON will help improve understanding of climate‐driven changes to soil, vegetation, and hydrologic conditions. In presenting this network and its initial data, we hope that the network’s elevational gradient will contribute to bioclimatic mountain research, while active collaboration with partners in resource management may provide a model for science‐practice interaction in support of long‐term monitoring.Plain Language SummaryAs climate change drives shifts in temperature and precipitation, researchers and resource managers can benefit from improved monitoring of soil moisture. Understanding the relationship between soil moisture and other system components is crucial to improving water availability projections and understanding ecosystem responses to climate change. Despite their significance, in‐ground soil‐moisture measurements are often not available across multiple elevations within a single watershed. This paper presents a network in the Southern Rocky Mountains intended to help address this data gap and compliment data from other networks. The interactive Roaring Fork Observation Network consists of nine locations across an 1,800‐m change in elevation. Each station measures soil moisture at three depths, soil temperature, air temperature, humidity, and precipitation. Some stations are equipped with cameras or snow depth gauges, and for eight sites vegetation surveys are conducted. The data are available through a simple data portal. The network was established with local resource manager support, and one of its guiding purposes is to support management and restoration planning efforts. Because of the network’s ongoing monitoring across multiple elevations and habitats, interactive Roaring Fork Observation Network will provide researchers and resource managers with access to valuable information about changes in soil conditions in a changing climate.Key PointsSoil moisture is key to understanding and predicting change in hydrology and ecology amid climate variability and changeIn situ soil moisture and weather monitoring data are now available across an 1,800‐m elevation span in a mountain watershedThe network is supported and guided by resource managers and supports both research and resource management goalsPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/149210/1/wrcr23834_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/149210/2/wrcr23834.pd

Melt water input from the Bering Glacier watershed into the Gulf of Alaska

The annual runoff from the melting of large glaciers and snow fields along the northern perimeter of the Gulf of Alaska is a critical component of marine physical and biological systems; yet, most of this freshwater is not measured. Here we show estimates of melt for the watershed that contains the largest and longest glacier in North America, the Bering Glacier. The procedure combines in situ observations of snow and ice melt acquired by a long-term monitoring program, multispectral satellite observations, and nearby temperature measurements. The estimated melt is 40 km3 per melt season, ± 3.0 km3, observed over the decadal period, 2002–2012. As a result of climate change, these estimates could increase to 60 km3/yr by 2050. This technique and the derived melt coefficients can be applied to estimate melt from Alaska to Washington glaciers

North Slope long-term monitoring summary list to support gap analysis

An interactive, online summary of long-term monitoring studies across the North Slope of Alaska have been compiled by the North Slope Science Initiative (NSSI) and are available on the NSSI web portal (www.northslope. org/monitoring/). This summary effort is being conducted by the NSSI to inventory existing long-term monitoring studies on the North Slope in order to identify gaps in monitoring and to support scenario development. Listed studies must meet the criteria of long-term monitoring defi ned as multiple collections of the same variable over a period of 10 years or longer by comparable methodology on the North Slope of Alaska and in adjacent waters. Studies meeting this criterion may be added through the interactive web portal referenced above. Corrections and additions to listed studies are also encouraged. We will present the interactive web portal and the functionality to view individual study details and interactively sort and create summary statistics

Vegetation Density and Greenness Change Index (GCI), Southeast Michigan, 1990-2000-2010

<p>The vegetation density composites in 1990, 2000, and 2010 and greenness change index (GCI) between 2000 and 2010 for the Detroit-area counties of Oakland, Macomb, and Wayne.</p

The Carbon Data Explorer: Demonstration Video

<p>This video demonstrates usage of the Carbon Data Explorer's web client. The source code for the Carbon Data Explorer can be found on Github (https://github.com/MichiganTechResearchInstitute/CarbonDataExplorer). More details are available on the project web site (http://spatial.mtri.org/flux/).</p

Relating big data to local natural hazards: lessons learned from data mining the Twitter API for volunteered geographic information on earthquakes, wildfires, and prescribed fires in the contiguous United States

New media are increasingly used to capture ambient and volunteered geographic information in multiple contexts, from mapping the evolution of the social movements to tracking infectious disease. The social media platform Twitter is popular for these applications; it boasts over 500 million messages (‘tweets’) generated every day from as many total users at an average rate of 5,700 messages per second. In the United States, Japan, and Chile to name a few, Twitter is officially and unofficially used as an emergency notification and response system in the event of earthquakes, wildfires, and prescribed fires. A prototype for operational emergency detections from social media, specifically Twitter, was created using natural language processing and information retrieval techniques. The intent is to identify and locate emergency situations in the contiguous United States, namely prescribed fires, wildfires, and earthquakes, that are often missed by satellite detections. The authors present their methodologies and an evaluation of performance in collecting relevant tweets, extracting metrics such as area affected and geo-locating the events. Lessons learned from data mining Twitter for spatiotemporally-explicit information are included to inform future data mining research and applications