To cope with complex environmental impacts in a changing climate, researchers are increasingly being asked to produce science that can directly support policy and decision making. To achieve such societal impact, scientists are using climate services to engage directly with stakeholders to better understand their needs and inform knowledge production. However, the wide variety of climate-services outcomes—ranging from establishing collegial relationships with stakeholders to obtaining specific information for inclusion into a pre-existing decision process—do not directly connect to traditional methods of measuring scientific impact (e.g., publication citations, journal impact factor). In this paper, we describe how concepts from the discipline of evaluation can be used to examine the societal impacts of climate services. We also present a case study from climate impacts and adaptation research to test a scalable evaluation approach. Those who conduct research for the purposes of climate services and those who fund applied climate research would benefit from evaluation from the beginning of project development. Doing so will help ensure that the approach, data collection, and data analysis are appropriately conceived and executed.
We developed a framework to estimate high-resolution spatiotemporal soil moisture (monthly, annual, and seasonal) and temperature-moisture regimes. Our approach uses the Newhall simulation model (NSM) which we fully describe in the Larger Citation. For our analyses, we developed and used open-source software (spatial_nsm) relying on Python^TM^ that was translated from jNSM software (v. 1.6.1; U.S. Department of Agriculture 2016)---a java implementation of the NSM relies on aspatial climate stations. Our software allows for spatial estimates, supports additional parameters to inform the model, and improves upon elements of the originating software. Briefly, the NSM is an accounting system of water movement in a vertical soil profile and characterizes the soil moisture and soil temperature conditions (Newhall 1972, Van Wambeke 1982, Newhall and Berdanier 1996, Van Wambeke 2000) using monthly precipitation (total), monthly air temperature (mean), and available water capacity (AWC; characteristic of soil properties defining potential to retain water) as data inputs. The model uses the Thornthwaite-Matter-Sellers PET equation (Thornthwaite 1948, Thornthwaite and Mather 1955, Sellers 1965) to reflect energy available for extracting moisture (see Larger Citation; supplemental S8), but other methods can be exchanged within spatial_nsm. The daily, enumerated simulation of soil moisture movement (NSM) is based on a two-dimensional diagram of the soil profile (Van Wambeke 2000; supplemental S9). The profile extends from the ground surface to a maximum depth of 200 cm (depth defined and characterized by the range of depth corresponding to AWC estimates). For example, clay materials can support an AWC of 200 mm with a soil depth of 80 cm, while sandy loam with an AWC of 200 mm might be as deep as 200 cm (National Soil Survey Center 1998, Van Wambeke 2000). The original NSM estimates soil temperature and moisture regimes (STMRs) to characterize the soil-climate for plant productivity. The STMR classifications described by Natural Resources Conservation Service (NRCS) in Soil Taxonomy (Soil Survey Staff 1999) and Keys to Soil Taxonomy (Soil Survey Staff 2014) were the foundation for classifying soil-climate regimes. The spatial_nsm is a spatial implementation (data inputs and outputs reflect raster surfaces) of the Newhall model. We modified several components and provided additional tools to assess trends and seasonality. These modifications broadly included the following: 1) implementing a dichotomous approach of the classification (Paetzold 1990) that can reliably produce soil temperature-moisture classifications; 2) providing methods for including air-soil monthly temperature offsets; 3) modularizing spatial_nsm that allows for integration of alternative potential evapotranspiration (PET) methods; 4) accounting for timing and redistribution of snowmelt (given data availability), which improves estimates for when and where water can infiltrate soil; and 5) calculating soil moisture, trends, and seasonality (see Larger Citation for details). For our study, we applied the model across the western United States using monthly climate averages (1981 -- 2010) to understand whether soil-climate can enhance our understanding of ecological potential and risk. We demonstrated that soil moisture or soil moisture trends correlated significantly with vegetation patterns, including sagebrush, annual herbaceous plant cover, bare ground, and fire occurrence. Because our framework has the flexibility to assess dynamic climate conditions (historical, contemporary, or projected), we can begin to improve our knowledge of changing spatiotemporal biotic patterns. These spatial resources are intended to provide tools to managers and researchers for assessing risk (invasives and fire), improving estimates of vegetation patterns, and informing prioritization of habitat management and expected restoration outcomes. Developing a spatially explicit soil-climate model involved several steps: 1) identifying and processing spatial input data, 2) processing and collecting data to enhance model, 3) executing models on a high-performance cluster, and 4) identifying key post-analyses products and evaluating soil-climate estimates (Figure 1). For step one, we acquired spatial data representing monthly precipitation and temperature (Prism Climate Group 2015), daily snow deposition (National Operational Hydrologic Remote Sensing Center 2004), soil physical properties (Polaris; Chaney et al. 2016, Chaney et al. 2019), monthly averages from climate stations (Arguez et al. 2010, Arguez et al. 2012), and daily soil conditions from the soil climate analysis network (SCAN; U.S. Department of Agriculture 2020). See the Larger Citation for descriptions of data sources in supplemental (S1; Table S1). Importantly, users of our software are not restricted to these data sources, which are generically described in Data Dependencies. For step two, we used additional data to enhance our model, including the SCAN database to define monthly ambient-temperature offsets, snow cover to account for attenuation of potential evapotranspiration (PET) and offset soil temperature, and organic matter to inform the classification of temperature regimes.
Tribal nations and Indigenous communities are key collaborators on adaptation work within the Climate Adaptation Science Center (CASC) network. The centers have partnered with numerous Tribal and Indigenous communities on projects or activities to better understand the communities’ specific knowledge of and exposure to impacts of climate change, to increase or assist with capacity to support adaptation planning, and to identify and address climate science needs. Projects and activities generated in the various CASC regions have different Tribal and Indigenous stakeholders, climate change contexts, and training needs. Consequently, these projects and activities were neither implemented nor reported consistently throughout the network. Information and materials on the various projects and activities were gathered and are presented in the Tribal and Indigenous Projects Data Sheet (hereafter, Data Sheet) with the goals of reducing inconsistencies between CASCs and benefitting other agencies who plan to implement similar activities. The Data Sheet is complementary to this report, which provides a synthesis of the CASC-led climate-related, capacity-building activities for Tribes and Indigenous communities. The results described in this report provide an analysis of the categorization of projects, activities, and individual trainings to highlight detailed information on the various ways each CASC works with and supports Native and Indigenous communities.
Soil temperature and moisture (soil-climate) affect plant growth and microbial metabolism, providing a mechanistic link between climate and growing conditions. However, spatially explicit soil-climate estimates that can inform management and research are lacking. We developed a framework to estimate spatiotemporal-varying soil moisture (monthly, annual, and seasonal) and temperature-moisture regimes as gridded surfaces by enhancing the Newhall simulation model. Importantly, our approach allows for the substitution of data and parameters, such as climate, snowmelt, soil properties, alternative potential evapotranspiration equations and air-soil temperature offsets. We applied the model across the western United States using monthly climate averages (1981–2010). The resulting data are intended to help improve conservation and habitat management, including but not limited to increasing the understanding of vegetation patterns (restoration effectiveness), the spread of invasive species and wildfire risk. The demonstrated modeled results had significant correlations with vegetation patterns—for example, soil moisture variables predicted sagebrush (R2 = 0.51), annual herbaceous plant cover (R2 = 0.687), exposed soil (R2 = 0.656) and fire occurrence (R2 = 0.343). Using our framework, we have the flexibility to assess dynamic climate conditions (historical, contemporary or projected) that could improve the knowledge of changing spatiotemporal biotic patterns and be applied to other geographic regions.
It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building.
Macrosystem-scale research is supported by many ecological networks of people, infrastructure, and data. However, no network is sufficient to address all macrosystems ecology research questions, and there is much to be gained by conducting research and sharing resources across multiple networks. Unfortunately, conducting macrosystem research across networks is challenging due to the diversity of expertise and skills required, as well as issues related to data discoverability, veracity, and interoperability. The ecological and environmental science community could substantially benefit from networking existing networks to leverage past research investments and spur new collaborations. Here, we describe the need for a “network of networks” (NoN) approach to macrosystems ecological research and articulate both the challenges and potential benefits associated with such an effort. We describe the challenges brought by rapid increases in the volume, velocity, and variety of “big data” ecology and highlight how a NoN could build on the successes and creativity within component networks, while also recognizing and improving upon past failures. We argue that a NoN approach requires careful planning to ensure that it is accessible and inclusive, incorporates multimodal communications and ways to interact, supports the creation, testing, and promulgation of community standards, and ensures individuals and groups receive appropriate credit for their contributions. Additionally, a NoN must recognize important trade-offs in network architecture, including how the degree of centralization of people, infrastructure, and data influence network scalability and creativity. If implemented carefully and thoughtfully, a NoN has the potential to substantially advance our understanding of ecological processes, characteristics, and trajectories across broad spatial and temporal scales in an efficient, inclusive, and equitable manner.
Understanding the paths by which water flows through the landscape is critical for providing fresh water for human use, maintaining ecosystem function, and better predicting how disturbances such as fire or drought may impact water quantity and water quality. Yet projected changes in climate, disturbances, and land use , are likely to alter hydrologic flow paths, and .natural resource managers increasingly require information about projected changes in water flow paths to plan for the future. To meet this need, researchers will conduct a synthesis of changing hydrologic processes in the North Central region, and communicate the identified management options and opportunities to natural resource managers in federal and state agencies. Through this project, a postdoctoral fellow will evaluate: 1) how water flow paths and water quality vary with land-use and disturbance regimes; 2)how shifts in the timing and magnitude of snow and rain inputs alter low flows and stream permanence; and 3) how forest management techniques, such as forest thinning, can mitigate the sensitivity of forests and streamflow to droughts The results of this project can help natural resource managers better understand the future of aquatic flows in the North Central region and will also contribute to a national-scale synthesis on the future of aquatic flows across the United States.
This project combined tree-ring based paleo and modern climate and hydrologic research aimed at understanding the primary influences on drought risk and water reliability in basins critical for western U.S. water resources. New paleohydrologic datasets and analyses were developed and applied to contextualize future streamflow projections and address specific water management questions. These questions centered around optimizing future water management protocols for numerous objectives ranging from improving agricultural water allocation during drought while maintaining instream flows for aquatic ecosystem health to the testing of operations across large river systems with complex infrastructure critical for downstream flood control, navigation, and hydropower generation. USGS scientists worked closely with the Bureau of Reclamation (Reclamation) to estimate both past and future drought risk at key management locations throughout the Missouri River basin, the Milk and Saint Mary Rivers system, and across the major managed river systems in the western United States. These efforts provided a roadmap for future water management strategies under changing climate and water supply conditions, which are detailed in Reclamation’s newly completed Missouri Headwaters Basin Study, the 2021 SECURE Water Act Report, and the forthcoming update of the Saint Mary and Milk Rivers Basin Study. Among the major scientific findings to emerge was a new understanding of the long-term (1200-year) history of drought variability for the Missouri River, which highlighted the unusual severity of the early 2000s drought across the Rocky Mountain headwaters and adjacent high plains. By combining the extended drought record with extensive modern and paleoclimate records, we document how warming exacerbates severities of naturally occurring droughts, with recent decades defined by “hot” droughts and the 2000s (2001-2010) drought ranking as the most severe event in 1,200 years. Increasingly severe drought events such as this strain already over-allocated water resources that multiple sectors of society depend heavily upon.
Wildfire occurrence varies among regions and through time due to the long-term impacts of climate on fuel structure and short-term impacts on fuel flammability. Identifying the climatic conditions that trigger extensive fire years at regional scales can enable development of area burned models that are both spatially and temporally robust, which is crucial for understanding the impacts of past and future climate change. We identified region-specific thresholds in fire-season aridity that distinguish years with limited, moderate, and extensive area burned for 11 extensively forested ecoregions in the western United States. We developed a new area burned model using these relationships and demonstrate its application in the Southern Rocky Mountains using climate projections from five global climate models (GCMs) that bracket the range of projected changes in aridity. We used the aridity thresholds to classify each simulation year as having limited, moderate, or extensive area burned and defined fire-size distributions from historical fire records for these categories. We simulated individual fires from a regression relating fire season aridity to the annual number of fires and drew fire sizes from the corresponding fire-size distributions. We project dramatic increases in area burned after 2020 under most GCMs and after 2060 with all GCMs as the frequency of extensive fire years increases. Our adaptable model can readily incorporate new observations (e.g., extreme fire years) to directly address the non-stationarity of fire-climate relationships as climatic conditions diverge from past observations. Our aridity threshold fire model provides a simple yet spatially robust approach to project regional changes in area burned with broad applicability to ecosystem and vegetation simulation models.
Wildfires and housing development have increased since the 1990s, presenting unique challenges for wildfire management. However, it is unclear how the relative influences of housing growth and changing wildfire occurrence have altered risk to homes, or the potential for wildfire to threaten homes. We used a random forests model to predict burn probability in relation to weather variables at 1-km resolution and monthly intervals from 1990 through 2019 in the Southern Rocky Mountains ecoregion. We quantified risk by combining the predicted burn probabilities with decadal housing density. We then compared the predicted burn probabilities and risk across the study area with observed values and quantified trends. Finally, we evaluated how housing growth and changes in burn probability influenced risk individually and combined. Fires burned 9055 km2 and exposed more than 8500 homes from 1990 to 2019. Observed burned area increased 632% from the 1990s to the 2000s, which combined with housing growth, resulted in a 1342% increase in homes exposed. Increases continued in the 2010s but at lower rates; burned area by 65% and exposure by 32%. The random forests model had excellent fit and high correlation with observations (AUC = 0.88 and r = 0.9). Observed values were within the 95% uncertainty interval for all years except 2016 (burned area) and 2000 (exposure). However, our model overpredicted in years with low observed burned area and underpredicted in years with high observed burned area. Overpredictions in risk resulted in lower rates of change in predicted risk compared with change in observed exposure. Increases in risk between the 1990s and 2000s were primarily due to warmer and drier weather conditions and secondarily because of housing growth. However, increases between the 2000s and 2010s were primarily due to housing growth. Our modeling approach identifies spatial and temporal patterns of wildfire potential and risk, which is critical information to guide decision-making. Because the drivers behind risk shift over time, strategies to mitigate risk may need to account for multiple drivers simultaneously.