Towards global coverage of gridded parameterization for CLImate GENerator (CLIGEN)

Stochastic weather generators create time series that reproduce key weather dynamics present in long-term observations. The dataset detailed herein is a large-scale gridded parameterization for CLImate GENerator (CLIGEN) that fills spatial gaps in the coverage of existing regional CLIGEN parameterizations, thereby obtaining near-global availability of combined coverages. This dataset primarily covers countries north of 40° latitude with 0.25° spatial resolution. Various CLIGEN parameters were estimated based on 20-year records from four popular global climate products. Precipitation parameters were statistically downscaled to estimate point-scale values, while point-scale temperature and solar radiation parameters were approximated by direct calculation from high-resolution datasets. Surrogate parameter values were used in some cases, such as with wind parameters. Cross-validation was done to assess the downscaling approach for six precipitation parameters using known point-scale values from ground-based CLIGEN parameterizations. These parameter values were derived from daily accumulation records at 7,281 stations and high temporal resolution records at 609 stations. Two sensitive parameters, monthly average storm accumulation and maximum 30-minute intensity, were shown have RMSE values of 1.48 mm and 4.67 mm hr−1, respectively. Cumulative precipitation and the annual number of days with precipitation occurrence were both within 5% of ground-based parameterizations, effectively improving climate data availability.This article is published Fullhart, Andrew T., Guillermo E. Ponce-Campos, Menberu B. Meles, Ryan P. McGehee, Haiyan Wei, Gerardo Armendariz, Shea Burns, and David C. Goodrich. "Towards global coverage of gridded parameterization for CLImate GENerator (CLIGEN)." Big Earth Data 8, no. 1 (2024): 142-165. doi: https://doi.org/10.1080/20964471.2023.2291215. Works produced by employees of the U.S. Government as part of their official duties are not copyrighted within the U.S. The content of this document is not copyrighted

Ecosystem resilience despite large-scale altered hydroclimatic conditions

Climate change is predicted to increase both drought frequency and duration, and when coupled with substantial warming, will establish a new hydroclimatological model for many regions. Large-scale, warm droughts have recently occurred in North America, Africa, Europe, Amazonia and Australia, resulting in major effects on terrestrial ecosystems, carbon balance and food security. Here we compare the functional response of above-ground net primary production to contrasting hydroclimatic periods in the late twentieth century (1975-1998), and drier, warmer conditions in the early twenty-first century (2000-2009) in the Northern and Southern Hemispheres. We find a common ecosystem water-use efficiency (WUE e: Above-ground net primary production/ evapotranspiration) across biomes ranging from grassland to forest that indicates an intrinsic system sensitivity to water availability across rainfall regimes, regardless of hydroclimatic conditions. We found higher WUE e in drier years that increased significantly with drought to a maximum WUE e across all biomes; and a minimum native state in wetter years that was common across hydroclimatic periods. This indicates biome-scale resilience to the interannual variability associated with the early twenty-first century drought - that is, the capacity to tolerate low, annual precipitation and to respond to subsequent periods of favourable water balance. These findings provide a conceptual model of ecosystem properties at the decadal scale applicable to the widespread altered hydroclimatic conditions that are predicted for later this century. Understanding the hydroclimatic threshold that will break down ecosystem resilience and alter maximum WUE e may allow us to predict land-surface consequences as large regions become more arid, starting with water-limited, low-productivity grasslands. © 2013 Macmillan Publishers Limited. All rights reserved

Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images

Rice is a staple food in East Asia and Southeast Asia—an area that accounts for more than half of the world’s population, and 11% of its cultivated land. Studies on rice monitoring can provide direct or indirect information on food security, and water source management. Remote sensing has proven to be the most effective method for the large-scale monitoring of croplands, by using temporary and spectral information. The Google Earth Engine (GEE) is a cloud-based platform providing access to high-performance computing resources for processing extremely large geospatial datasets. In this study, by leveraging the computational power of GEE and a large pool of satellite and other geophysical data (e.g., forest and water extent maps, with high accuracy at 30 m), we generated the first up-to-date rice extent map with crop intensity, at 10 m resolution in the three provinces with the highest rice production in China (the Heilongjiang, Hunan and Guangxi provinces). Optical and synthetic aperture radar (SAR) data were monthly and metric composited to ensure a sufficient amount of up-to-date data without cloud interference. To remove the common confounding noise in the pixel-based classification results at medium to high resolution, we integrated the pixel-based classification (using a random forest classifier) result with the object-based segmentation (using a simple linear iterative clustering (SLIC) method). This integration resulted in the rice planted area data that most closely resembled official statistics. The overall accuracy was approximately 90%, which was validated by ground crop field points. The F scores reached 87.78% in the Heilongjiang Province for monocropped rice, 89.97% and 80.00% in the Hunan Province for mono- and double-cropped rice, respectively, and 88.24% in the Guangxi Province for double-cropped rice

Landscape Dynamics in an Iconic Watershed of Northwestern Mexico: Vegetation Condition Insights Using Landsat and PlanetScope Data

Natural vegetation in arid and semi-arid environments of Northwestern Mexico has been subject to transformation due to extensive and intensive human occupation related mostly to primary activities. Keystone habitats such as riparian ecosystems are extremely sensitive to land use changes that occur in their surrounding landscape. In this study, we developed remote sensing-based land cover classifications and post-classification fragmentation analysis, by using data from Landsat's moderate resolution sensors Thematic Mapper and Operational Land Imager (TM and OLI) to assess land use changes and the shift in landscape configuration in a riparian corridor of a dynamic watershed in central Sonora during the last 30 years. In addition, we derived a high spatial resolution classification (using PlanetScope-PS2 imagery) to assess the "recent state" of the riparian corridor. According to our results, riparian vegetation has increased by 40%, although only 9% of this coverage corresponds to obligate riparian species. Scrub area shows a declining trend, with a loss of more than 17,000 ha due to the expansion of mesquite and buffelgrass-dominated areas. The use of moderate resolution Landsat data was essential to register changes in vegetation cover through time, however, higher resolution PlanetScope data were fundamental for the detection of limited aerial extent classes such as obligate riparian vegetation. The unregulated development of anthropogenic activities is suggested to be the main driver of land cover change processes for arid ecosystems in this region. These results highlight the urgent need for alternative management and restoration projects in an area where there is almost a total lack of protection regulations or conservation efforts.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]

Towards global coverage of gridded parameterization for CLImate GENerator (CLIGEN)

ABSTRACTStochastic weather generators create time series that reproduce key weather dynamics present in long-term observations. The dataset detailed herein is a large-scale gridded parameterization for CLImate GENerator (CLIGEN) that fills spatial gaps in the coverage of existing regional CLIGEN parameterizations, thereby obtaining near-global availability of combined coverages. This dataset primarily covers countries north of 40° latitude with 0.25° spatial resolution. Various CLIGEN parameters were estimated based on 20-year records from four popular global climate products. Precipitation parameters were statistically downscaled to estimate point-scale values, while point-scale temperature and solar radiation parameters were approximated by direct calculation from high-resolution datasets. Surrogate parameter values were used in some cases, such as with wind parameters. Cross-validation was done to assess the downscaling approach for six precipitation parameters using known point-scale values from ground-based CLIGEN parameterizations. These parameter values were derived from daily accumulation records at 7,281 stations and high temporal resolution records at 609 stations. Two sensitive parameters, monthly average storm accumulation and maximum 30-minute intensity, were shown have RMSE values of 1.48 mm and 4.67 mm hr−1, respectively. Cumulative precipitation and the annual number of days with precipitation occurrence were both within 5% of ground-based parameterizations, effectively improving climate data availability

Vegetation productivity responds to sub-annual climate conditions across semiarid biomes

In the southwest United States, the current prolonged warm drought is similar to the predicted future climate change scenarios for the region. This study aimed to determine patterns in vegetation response to the early 21st century drought across multiple biomes. We hypothesized that different biomes (forests, shrublands, and grasslands) would have different relative sensitivities to both climate drivers (precipitation and temperature) and legacy effects (previous-year's productivity). We tested this hypothesis at eight Ameriflux sites in various Southwest biomes using NASA Moderate-resolution Imaging Spectroradiometer Enhanced Vegetation Index (EVI) from 2001 to 2013. All sites experienced prolonged dry conditions during the study period. The impact of combined precipitation and temperature on Southwest ecosystems at both annual and sub-annual timescales was tested using Standardized Precipitation Evapotranspiration Index (SPEI). All biomes studied had critical sub-annual climate periods during which precipitation and temperature influenced production. In forests, annual peak greenness (EVImax) was best predicted by 9-month SPEI calculated in July (i.e., January-July). In shrublands and grasslands, EVImax was best predicted by SPEI in July through September, with little effect of the previous year's EVImax. Daily gross ecosystem production (GEP) derived from flux tower data yielded further insights into the complex interplay between precipitation and temperature. In forests, GEP was driven by cool-season precipitation and constrained by warm-season maximum temperature. GEP in both shrublands and grasslands was driven by summer precipitation and constrained by high daily summer maximum temperatures. In grasslands, there was a negative relationship between temperature and GEP in July, but no relationship in August and September. Consideration of sub-annual climate conditions and the inclusion of the effect of temperature on the water balance allowed us to generalize the functional responses of vegetation to predicted future climate conditions. We conclude that across biomes, drought conditions during critical sub-annual climate periods could have a strong negative impact on vegetation production in the southwestern United States.NASA SMAP Science Definition Team [08-SMAPSDT08-0042]; NASA SMAP Science Team [NNH14AX72I]; U.S. Department of Energy's Office of ScienceThis 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]

Monitoring agroecosystem productivity and phenology at a national scale: A metric assessment framework

Effective measurement of seasonal variations in the timing and amount of production is critical to managing spatially heterogeneous agroecosystems in a changing climate. Although numerous technologies for such measurements are available, their relationships to one another at a continental extent are unknown. Using data collected from across the Long-Term Agroecosystem Research (LTAR) network and other networks, we investigated correlations among key metrics representing primary production, phenology, and carbon fluxes in croplands, grazing lands, and crop-grazing integrated systems across the continental U.S. Metrics we examined included gross primary productivity (GPP) estimated from eddy covariance (EC) towers and modelled from the Landsat satellite, Landsat NDVI, and vegetation greenness (Green Chromatic Coordinate, GCC) from tower-mounted PhenoCams for 2017 and 2018. Overall, our analysis compared production dynamics estimated from three independent ground and remote platforms using data for 34 agricultural sites constituting 51 site-years of co-located time series. Pairwise sensor comparisons across all four metrics revealed stronger correlation and lower root mean square error (RMSE) between end of season (EOS) dates (Pearson R ranged from 0.6 to 0.7 and RMSE from 32.5 to 67.8) than start of season (SOS) dates (0.46 to 0.69 and 40.4 to 66.2). Overall, moderate to high correlations between SOS and EOS metrics complemented one another except at some lower productivity grazing land sites where estimating SOS can be challenging. Growing season length estimates derived from 16-day satellite GPP (179.1 days) were significantly longer than those from PhenoCam GCC (70.4 days, padj \u3c 0.0001) and EC GPP (79.6 days, padj \u3c 0.0001). Landscape heterogeneity did not explain differences in SOS and EOS estimates. Annual integrated estimates of productivity from EC GPP and PhenoCam GCC diverged from those estimated by Landsat GPP and NDVI at sites where annual production exceeds 1000 gC/m− 2 yr− 1 . Based on our results, we developed a “metric assessment framework” that articulates where and how metrics from satellite, eddy covariance and PhenoCams complement, diverge from, or are redundant with one another. The framework was designed to optimize instrumentation selection for monitoring, modeling, and forecasting ecosystem functioning with the ultimate goal of informing decision-making by land managers, policy-makers, and industry leaders working at multiple scales

Leveraging the NEON Airborne Observation Platform for socio‐environmental systems research

Abstract During the 21st century, human–environment interactions will increasingly expose both systems to risks, but also yield opportunities for improvement as we gain insight into these complex, coupled systems. Human–environment interactions operate over multiple spatial and temporal scales, requiring large data volumes of multi‐resolution information for analysis. Climate change, land‐use change, urbanization, and wildfires, for example, can affect regions differently depending on ecological and socioeconomic structures. The relative scarcity of data on both humans and natural systems at the relevant extent can be prohibitive when pursuing inquiries into these complex relationships. We explore the value of multitemporal, high‐density, and high‐resolution LiDAR, imaging spectroscopy, and digital camera data from the National Ecological Observatory Network’s Airborne Observation Platform (NEON AOP) for Socio‐Environmental Systems (SES) research. In addition to providing an overview of NEON AOP datasets and outlining specific applications for addressing SES questions, we highlight current challenges and provide recommendations for the SES research community to improve and expand its use of this platform for SES research. The coordinated, nationwide AOP remote sensing data, collected annually over the next 30 yr, offer exciting opportunities for cross‐site analyses and comparison, upscaling metrics derived from LiDAR and hyperspectral datasets across larger spatial extents, and addressing questions across diverse scales. Integrating AOP data with other SES datasets will allow researchers to investigate complex systems and provide urgently needed policy recommendations for socio‐environmental challenges. We urge the SES research community to further explore questions and theories in social and economic disciplines that might leverage NEON AOP data