Development of an Open-source, Custom Environmental Data Logger for Spatially Scalable Data Collection

Characterizing the processes that lead to differences in ecosystem productivity and watershed hydrology across complex terrain remains a challenge. This difficulty can be partially attributed to the cost of installing networks of proprietary data loggers that monitor differences in the biophysical factors contributing to vegetation growth or hydrological processes. Studies that aim to compare concurrent time-series data sets across multiple locations must therefore balance the high cost of these data logger systems with the need for spatial resolution in their data. Here, we present the design, implementation, and case study for an open-source “Pinecone” data logger system, released under the GNU General Public License that can be manufactured for under $70 USD per unit. The system was designed to accommodate a wide range of generic and proprietary environmental sensors, and to be inexpensive enough to build and deploy large numbers to a study site. A case study was performed in which 54 data loggers were deployed to North Fork Elk Creek, a mountainous watershed located in Lubrecht Experimental Forest in the Garnet mountain range in Northwest Montana for a one year period. The data loggers were deployed across 6 hillsides in the watershed, representing combinations of differing elevations and aspects, at 9 study locations on each hillslope. At each of these locations we recorded air temperature, vapor pressure, soil water content, sap flow velocity, and tree basal area at 30 minute intervals. We evaluated the reliability of the systems in a case study over an 8 month period in 2016 and 4 month period in 2017. Our results suggest that open-source technologies such as the Pinecone logger can make it possible to develop dependable and spatially distributed sensor network within the confines of a typical research budget

SMALL SCALE VARIABILITY IN SNOW ACCUMULATION AND ABLATION UNDER A HETEROGENEOUS MIXED-CONIFER CANOPY

The spatial patterns of snow accumulation and melt in forested watersheds directly control runoff generation processes and the annual quantity and quality of available water to downstream receiving waters. In the western U.S. nearly three quarters of the annual water input into the hydrologic cycle comes from snow accumulation and melt in forested watersheds. This provision of water is one of the most important forest ecosystem services and is necessary for ecological, economic and social health. Despite our understanding of the coupling of forests and watersheds, the relationship between forest spatial patterns and snow hydrology is poorly understood. Forest canopies exhibit heterogeneity manifested as a mosaic of differing species, spatial arrangements, and canopy densities that differentially intercept incoming precipitation, alter wind patterns, and absorb, trap or reflect radiation; controlling the processes of snow accumulation and ablation. Vegetation patterns have been used as surrogates for processes where we expect that spatially recognizable structures give rise to specific ecological processes and vice versa. We investigated how spatial patterns of snow depth, density, snow water equivalent (SWE), and snow disappearance date (SDD) varied within stands of heterogeneous canopy structure. We collected 780 empirical measurements of snow depth, density, and SWE at peak accumulation on two fully georeferenced, mixed-conifer plots at Lubrecht Experimental Forest in western Montana. Throughout the 49 day melt season, we monitored SDD, snow depth, and SWE every third day with 4900 samples per campaign. In 2014, snow depth, density and SWE ranged from 0.0-67.31 cm, 5.43-49.76%, and 0.75-17.90 cm respectively. A canopy competition index ranged from 0.0-86.8 with non-forested areas averaging 11.5 cm SWE, melting around day 41 compared to mature dense canopy with average SWE of 5.1 cm and a SDD around day 9. This preliminary work suggests a strong linkage between canopy structure and accumulation and snowmelt processes. In the future we seek to link canopy patterns and the specific physical mechanisms that lead to differential snow dynamics in forested landscapes. This understanding is essential for improving process-based models and tools for forest managers to optimize forest water resources in a changing climate

IrrMapper: A Machine Learning Approach for High Resolution Mapping of Irrigated Agriculture Across the Western U.S.

High frequency and spatially explicit irrigated land maps are important for understanding the patterns and impacts of consumptive water use by agriculture. We built annual, 30 m resolution irrigation maps using Google Earth Engine for the years 1986–2018 for 11 western states within the conterminous U.S. Our map classifies lands into four classes: irrigated agriculture, dryland agriculture, uncultivated land, and wetlands. We built an extensive geospatial database of land cover from each class, including over 50,000 human-verified irrigated fields, 38,000 dryland fields, and over 500,000 km2 of uncultivated lands. We used 60,000 point samples from 28 years to extract Landsat satellite imagery, as well as climate, meteorology, and terrain data to train a Random Forest classifier. Using a spatially independent validation dataset of 40,000 points, we found our classifier has an overall binary classification (irrigated vs. unirrigated) accuracy of 97.8%, and a four-class overall accuracy of 90.8%. We compared our results to Census of Agriculture irrigation estimates over the seven years of available data and found good overall agreement between the 2832 county-level estimates (r2 = 0.90), and high agreement when estimates are aggregated to the state level (r2 = 0.94). We analyzed trends over the 33-year study period, finding an increase of 15% (15,000 km2) in irrigated area in our study region. We found notable decreases in irrigated area in developing urban areas and in the southern Central Valley of California and increases in the plains of eastern Colorado, the Columbia River Basin, the Snake River Plain, and northern California

SMAP L4 Assessment of the US Northern Plains 2017 Flash Drought

A rapidly developing "flash drought" occurred over the US Northern Plains in the summer of 2017, spurred by unusually high temperatures and strong evaporative demand. The impacts of the drought included widespread reductions in rangeland and agricultural productivity that cascaded into significant economic losses. Here, we used satellite information from the NASA Soil Moisture Active Passive (SMAP) mission to clarify the nature and impact of the drought on regional vegetation growth. The model enhanced SMAP Level 4 Soil Moisture (L4SM) and Carbon (L4C) products were used with other ancillary data to examine spatial and seasonal anomalies in surface to root zone soil moisture and vegetation productivity (GPP). We find that the flash drought was triggered by a mid-July heat wave, conditioned by exceptionally low spring rainfall. The drought resulted in anomalous low soil moisture levels and regional GPP collapse, coinciding with severe (D3) to exceptional (D4) drought conditions indicated from the US Drought Monitor. The SMAP L4C GPP anomalies closely tracked reported county-level crop production anomalies for the major regional crop types, indicating generally larger productivity decline in managed croplands than surrounding natural areas. The SMAP L4 global products provide an effective indicator of vegetation growth changes and moisture-related restrictions on ecosystem productivity that are complementary with more traditional drought assessment tools

The Topographic Signature of Ecosystem Climate Sensitivity in the Western United States

It has been suggested that hillslope topography can produce hydrologic refugia, sites where ecosystem productivity is relatively insensitive to climate variation. However, the ecological impacts and spatial distribution of these sites are poorly resolved across gradients in climate. We quantified the response of ecosystem net primary productivity to changes in the annual climatic water balance for 30 years using pixel‐specific linear regression (30‐m resolution) across the western United States. The standardized slopes of these models represent ecosystem climate sensitivity and provide a means to identify drought‐resistant ecosystems. Productive and resistant ecosystems were most frequent in convergent hillslope positions, especially in semiarid climates. Ecosystems in divergent positions were moderately resistant to climate variability, but less productive relative to convergent positions. This topographic effect was significantly dampened in hygric and xeric climates. In aggregate, spatial patterns of ecosystem sensitivity can be implemented for regional planning to maximize conservation in landscapes more resistant to perturbations

Snowpack influences spatial and temporal soil nitrogen dynamics in a western U.S. montane forested watershed

Declines in winter snowpack have increased the severity of summer droughts in western U.S. forests, with the potential to also impact soil available nitrogen (N). To understand how snowpack controls spatiotemporal N availability, we examined seasonal N dynamics across elevation, aspect, and topographic position (hollow vs. slope) in a forested watershed in the northern Rocky Mountains. As expected, peak snow‐water equivalent (SWE) was generally greater at higher elevations and on north‐facing aspects. However, the effects of topographic position and snowdrift led to variability in snow accumulation at smaller spatial scales. Spatial patterns of the snowpack, in turn, influenced soil moisture and temperature, with greater SWE leading to generally higher soil moisture levels during the summer and smaller temperature fluctuations throughout the year. Wetter conditions in spring or fall generally supported greater inorganic N pools, but at the driest locations (low‐elevation slope), pulses of N mineralization in summer may have played important roles in overall N dynamics. More importantly, soil moisture during the summer appeared to be more influenced by antecedent snowpack from the previous year than by current‐year summer rain. Subsequently, N mineralization under snowpack may be strongly influenced by soil moisture and temperature conditions from the previous fall, before snowpack accumulation. Together, our results indicate that snowpack strongly influences N dynamics beyond the current growing season in western coniferous forests through mediation of soil moisture and temperature, and suggest that further decline in winter snowpack may affect these forests through constraints in both water and N availability.Montana EPSCoR Program via the Montana Institute on Ecosystems; USDA, National Institute of Food and Agriculture Grant [2015-67020-23454]Open access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Creating a topoclimatic daily air temperature dataset for the conterminous United States using homogenized station data and remotely sensed land skin temperature

Gridded topoclimatic datasets are increasingly used to drive many ecological and hydrological models and assess climate change impacts. The use of such datasets is ubiquitous, but their inherent limitations are largely unknown or overlooked particularly in regard to spatial uncertainty and climate trends. To address these limitations, we present a statistical framework for producing a 30-arcsec (∼800-m) resolution gridded dataset of daily minimum and maximum temperature and related uncertainty from 1948 to 2012 for the conterminous United States. Like other datasets, we use weather station data and elevation-based predictors of temperature, but also implement a unique spatio-temporal interpolation that incorporates remotely sensed 1-km land skin temperature. The framework is able to capture several complex topoclimatic variations, including minimum temperature inversions, and represent spatial uncertainty in interpolated normal temperatures. Overall mean absolute errors for annual normal minimum and maximum temperature are 0.78 and 0.56 °C, respectively. Homogenization of input station data also allows interpolated temperature trends to be more consistent with US Historical Climate Network trends compared to those of existing interpolated topoclimatic datasets. The framework and resulting temperature data can be an invaluable tool for spatially explicit ecological and hydrological modelling and for facilitating better end-user understanding and community-driven improvement of these widely used datasets