Hydrologic and water quality models are often used to understand and simulate non-point source nutrient inputs to receiving waterbodies afflicted by eutrophication. The most widely used hydrologic-water quality model for estimating non-point source nutrient loads from agricultural uplands is the Soil and Water Assessment Tool (SWAT). SWAT uses the QUAL-2E 1-dimensional steady-state model to simulate in-stream processes that govern the transport of nutrients through channels and rivers. However, the instream-solute transport routine within SWAT is limited in predicting phosphorus cycling and algal dynamics. In this study, we improve the in-stream module of SWAT+, a restructured version of SWAT. We apply the modified SWAT+ to the Western Lake Erie Basin to examine how improved representation of the in-stream module influences nutrient dynamics from the edge-of-field through streams and to the watershed outlet. Our source code modifications focus on improving the representation of phosphorus exchange between the stream bed and the water column. This phosphorus exchange is governed by the equilibrium phosphorus concentration (EPC), which determines whether the stream bed is a phosphorus source or sink, and a phosphorus transformation coefficient which determines the rate of P exchange. These improvements to the in-stream routine within SWAT+ will aid decision-makers in understanding the time lags and management levers needed to achieve water quality targets for large basins.
Large rivers can retain a substantial amount of nitrogen (N), particularly in submerged aquatic vegetation (SAV) meadows that may act as disproportionate control points for N retention in rivers. However, the temporal variation of N retention remains unknown since past measurements were snapshots in time. Using high frequency measurements over the summers 2012-2017, we investigated how climate variation influenced N retention in a SAV meadow at the confluence zone of two agricultural tributaries entering the St. Lawrence River. Distinctive combinations of water temperature and level were recorded between years, ranging from extreme hot-low (2012) and cold-high (2017) summers (2 ˚C and 1.4 m interannual range). Using an indicator of SAV biomass, we found that these extreme hot-low and cold-high years had reduced biomass compared to hot summers with intermediate levels. In addition, change in main stem water levels were asynchronous with the tributary discharges that controlled NO3- inputs at the confluence. We estimated daily N uptake rates from a moored NO3- sensor, and partitioned these into assimilatory and dissimilatory pathways. Measured rates were variable but among the highest reported in rivers (median 576 mg N m-2 d-1;, range 60 – 3893 mg N m-2 d-1) and SAV biomass promoted greater proportional retention and permanent N loss through denitrification. We estimated that the SAV meadow could retain up to 0.8 kt N per year and 87% of N inputs, but this valuable ecosystem service is contingent on how climate variations modulate both N loads and SAV biomass.
Dryland play a major role in the global carbon cycle. The US Southwest is experiencing fewer, larger precipitation events and longer dry intervals between rainfalls. These longer droughts are likely driving physiological, phenological, morphological, and community-level responses of dryland vegetation with unknown feedbacks to atmospheric CO2. It remains unclear how seasonal drought intensity and duration affect the magnitude, duration, and direction of dryland vegetation carbon cycling and atmospheric feedbacks. To address this question, we integrated the measurements of soil hydrology, plant community, and carbon fluxes from a new rainfall manipulation experiment site (RainManSR) in the Santa Rita Experimental Range of Southeast Arizona, US into the Community Land Model (CLM5). This field experiment imposed four precipitation treatments (S1–S4), each with the same summer growing season total rainfall (205 mm) but packaged into a range of many/small to few/large rainfall events. This experiment enabled a comprehensive evaluation and parameterization of drought tolerance of semiarid grassland plant functional types (i.e. deep-rooted perennials and shallow-rooted annuals) and their effects on climate extreme-carbon cycles feedbacks. The ability of the improved CLM model to capture dryland productivity and carbon fluxes was then validated at larger scales with observed carbon fluxes from closeby AmeriFlux sites in the US Southwest, such as the semi-arid Kendall grassland site (US-WKG). Applying this model in the Arizona grassland sites indicated that high tolerances of dryland plants to relatively low soil water potential maintains the growing season length of the dryland ecosystem under drought conditions, whereas the acclimation of carbon assimilation and root dynamics to drought mitigate drought effects on vegetation productivity and interannual variability of carbon exchange.
Climate patterns in the agricultural zones of the Indus basin are predicted to undergo undesirable changes in the hydrological cycle. These changes are a threat to the widespread agricultural activity and associated livelihoods of the underlying population. Livestock, an essential sector for human sustenance in the basin, is also a major source of greenhouse gas emissions thereby contributing towards climate change. However, it is also a recipient of climate impacts, thus introducing feedbacks and uncertainties that are further accentuated by the Water-Energy-Food Nexus. Here we model and simulate the farm-level dairy operations of a single dairy farm by introducing informatics-driven precision measurements of water, energy, food, and carbon emissions in a system dynamics framework. We analyze the simulated trajectories for energy, water, and waste fluxes to under different interventive scenarios to identify actions that enhance productivity and minimize environmental impact. The model is constructed based on data gathered from two dairy farms located in rural Punjab, Pakistan. The farms have a livestock capacity of 300 and 134 animals respectively, with data related to water, energy, food, and climate gathered over a duration of two years. The simulated results may be used to uncover structural changes in dairy-farm operations which improve the economic structure of the farm while remining within the thresholds defined by Sustainable Development Goals (SDG) 3, 7 and 13 set by the United Nations. The model itself also helps in unravelling the complex interactions among water-energy-food flows along with their coupling to land-climate interactions in context of the dairy farm operations. Beyond the climate change adaptation measures extracted from this study, the system dynamics model that we construct in the process, can help develop economic tools that leverage the advantages of water/climate informatics driven data services and decisions under large variabilities to devise sound agricultural policy.
Ocean governance is characterised by social-ecological complexity and divergence in stakeholder values and perspectives. Meeting the challenges set out in the UN Decade of Ocean Science for Sustainable Development will require transdisciplinary approaches that can embrace multiple ways of knowing to develop shared understandings within interdependent communities of practice and ensure they can be applied in interventions that are adaptive, proactive, socially just, critically reflexive and fit to meet the Decade’s challenges. We present the outcomes of an innovative participatory art process, the Exquisite Corpse Project, with the aim of highlighting multiple perspectives, and developing empathy between participants. We will engage a selected group of researchers from the emerging ‘Ocean Art-Ocean Science’ community to explore the topic of marine heatwaves and their impacts based on data collected in the Northeast Pacific by Ocean Networks Canada and other sources. Through a facilitated process, participants will create three pieces of art that will build on each other and will be exchanged between participants. At the end, all created artworks will be reviewed by the full group to explore emerging insights on marine heatwaves and to surface participants’ underlying values and emotions, which is rarely done in scientific circles where the main mode of discourse employs rational dispassionate exchange. By creating a fun, emotionally-engaging process, we aim to show how the Exquisite Corpse project can strengthen interpersonal bonds, build social cohesion, create opportunities to surface people’s values and perspectives, and develop new transdisciplinary insights in a non-confrontational way. This study is part of an ongoing process exploring transdisciplinary approaches for multidirectional art-science collaborations and developing new research methods for including artistic insight and expression within the scientific discovery process. Instead of the conventional ‘outward looking’ strategy of many art-science projects translating scientific outputs to new formats, our approach is primarily ‘inward looking’. We aim to provide an opportunity for scientists to create art, thus allowing them to explore their own emotions, values and experiences through different ways of knowing.
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. We outline specific applications for addressing SES questions, 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 years, 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 research community to further explore interdisciplinary questions and theories that might leverage NEON AOP data, and present a new Research Coordination Network aimed at supporting these efforts.
Mangroves cover a large area of the coastal region of Kutch in the state of Gujarat in western India. The Maldharis, the inhabitants of this region, have been using mangrove forests for traditional livelihoods such as the rearing of Kharai camel, which feeds mainly on mangrove leaves. The objective of our interdisciplinary project is to explore direction of changes for the sustainability of this community. Since the linkage between the ecosystem services of the mangrove forests and the traditional pastoralism of this community is one important aspect to consider, a clear description of the historical evolution of this ecosystem is an important step for basic information. In addition to collecting narrative-based information from communities, we are using satellite remote sensing data to develop a quantitative description of mangrove biomass since the 1980s. Using Landsat multispectral imageries, we calculated spatial averages of NDVI for several target forest components. The overall NDVI of mangrove forests in the region has increased from 1988 to the present, suggesting an increase in biomass. However, it has been decreasing from the late 1990s to the early 2000s. The most likely reason is that the low precipitation (drought) in the late 1990s to early 2000s increased the salinity of soil and groundwater, which in turn increased water stress. Contrastingly, the lack of significant changes in NDVI due to a single year of drought suggests that mangrove forests are resilient to drought. On the other hand, it is inferred that several factors were involved in the increase of NDVI since the early 2000s. Two of these factors are the higher precipitation during this period and the fact that the Forest Department has been restricting pastoralists’ access to mangrove forests since 2005. These results suggest that climatic conditions and pastoralism intensity influuence the long-term variation of NDVI values in each forest segment. This also suggests that local pastoralists harvested leaves and branches from the mangrove forests as resources but did not destructively take forest trees. This suggests that the Maldharis base their livelihood on maintaining the mangrove forests.
Continuous observations from geostationary satellites have been utilized to understand land surface seasonal dynamics and fill data gaps caused by clouds. However, the limited spatial resolution of geostationary satellite products constrained the processes of detecting the terrestrial changes in landscape scale, particularly over heterogeneous areas. Moreover, the variation of sun-target-sensor geometry in geostationary satellites results in diurnal changes in surface reflectance products. To overcome the limitations of geostationary satellite products over heterogeneous areas, we conducted the series of processes: 1) characterizing the effect of the solar and viewing geometry in surface reflectance using the bidirectional reflectance distribution function (BRDF), 2) harmonizing the satellite products from different platforms into a seamless product, and 3) fusing the different satellite products to enhance the spatial resolution of geostationary satellite products. Finally, we adopted spatial and temporal gap-filling methods to achieve daily gap-filled surface reflectance products. For robust application, the results from the integrated process were evaluated in both space and time using hyperspectral maps derived from unmanned aerial vehicle (UAV) and in situ tower-based continuous spectral measurements. We expect the geostationary satellite products with high spatial resolution to uncover the cloud-bound areas towards sensing the changes of the Earth in space and time.
Understanding marine soundscapes, including the biological, anthropogenic, and geological sounds, is essential to conserving protected species and their habitats. However, the marine resource managers often do not have a strong science background to interpret complicated soundscape data to facilitate them making decisions. The biological components of soundscapes can be useful to characterize biodiversity and monitor the distribution and behavior of individual species. Anthropogenic sound in the ocean is increasing and has been recognized as a threat to marine mammals for decades. To help the marine resource managers and the general public understand the impacts of ocean noise, we as nine undergraduate students from different majors of study at UC Berkeley’s Fung Fellowship Program utilized Human-Centered Design and created an interactive marine soundscape map (https://calsound.herokuapp.com), focusing on the California Current Ecosystem. Based on 14 interviews we conducted with researchers, policymakers, and environmental lobbyists, we decided to portray spectral soundscape metrics alongside the context of animal and human activities in a map format. We then created a digital hub to easily visualize, analyze, and synthesize marine-sourced soundscape data. Our website displays soundscape data over a range of spatial and temporal scales, acoustic detections of marine mammals, species habitat models, and anthropogenic sound source distributions as heat map layers and graphs. The platform not only displays ocean soundscape data, but also provides an overview of marine soundscape technology, as well as related articles and websites. The website is designed so that users who are not familiar with marine soundscape data, such as coastal managers and the public, can guide themselves through a tutorial and explore on their own to gain a better understanding of oceanographic sound. In the future, we will add more features to the website, such as allowing users to upload their own data to the website to visualize them online. The website will be self-sustainable and continue to serve more people. Our website will facilitate people to visualize and understand marine soundscapes, their impacts and our solutions.
Airborne LiDAR has become an essential data source for large-scale, high-resolution modeling of forest biomass and carbon stocks, enabling predictions with much higher resolution and accuracy than can be achieved using optical imagery alone. Ground noise filtering – that is, excluding returns from LiDAR point clouds based on simple height thresholds – is a common practice meant to improve the ‘signal’ content of LiDAR returns by preventing ground returns from masking useful information about tree size and condition contained within canopy returns. Although this procedure originated in LiDAR-based estimation of mean tree and canopy height, ground noise filtering has remained prevalent in LiDAR pre-processing, even as modelers have shifted focus to forest aboveground biomass (AGB) and related characteristics for which ground returns may actually contain useful information about stand density and openness. In particular, ground returns may be helpful for making accurate biomass predictions in heterogeneous landscapes that include a patchy mosaic of vegetation heights and land cover types. We applied several ground noise filtering thresholds while mapping two regions within New York State, one a forest-dominated area and the other a mixed-use landscape. We observed that removing ground noise via any height threshold systematically biases many of the LiDAR-derived variables used in AGB modeling. By fitting random forest models to each of these predictor sets, we found that that ground noise filtering yields models of forest AGB with lower accuracy than models trained using predictors derived from unfiltered point clouds. The relative inferiority of AGB models based on filtered LiDAR returns was much greater for the mixed land-cover study area than for the contiguously forested study area. Our results suggest that ground filtering should be avoided when mapping biomass, particularly when mapping heterogeneous and highly patchy landscapes, as ground returns are more likely to represent useful ‘signal’ than extraneous ‘noise’ in these cases.
The Southwest Watershed Research Center (SWRC) of the United States Department of Agriculture-Agricultural Research Service has been conducting arid and semiarid (dryland) watershed research since 1953. This included establishment and continuous operation of the Walnut Gulch Experimental Watershed (WGEW) in southeast Arizona. The 149 km2 ephemeral watershed is one of the most densely instrumented dryland research catchments in the world with a drainage area greater than 10 km2. This instrumentation captures many aspects of the hydrological cycle including how precipitation is partitioned into soil moisture, runoff and evapotranspiration and its subsequent effects on sediment transport, vegetation productivity, carbon sequestration, and groundwater recharge. The long-term, high-resolution record of observations on the WGEW enables understanding of the mean and variability of the hydrological processes, not well characterized with shorter term records, that fail to capture the large variability common to dryland regions. This presentation will highlight trends in temperature, precipitation, and runoff over the WGEW observation period. Additional research findings made by the SWRC and collaborators on erosion; plant productivity and carbon sequestration; the facilitation of soil water redistribution by plant roots; and groundwater recharge will also be presented.
As climate change progresses, hydrological regimes of temporary and perennial water bodies are projected to change, affecting biodiversity and ecosystem functions. Researchers have successfully employed the use of satellite imagery, camera traps and site visits to map these changes in hydrological regimes. Though effective, their use can come with considerable cost at high temporal and spatial resolution. A more affordable measure in mapping hydrological regimes has been the use of data loggers of conductivity, but the use of data loggers of temperature and light intensity is uncommon. Using validated data of 213 days of the aquatic and terrestrial phases of a temporary pond, we show that temperature and light intensity data can be used to discern hydrological state. The aquatic phase had lower measures of both parameters when compared to the terrestrial phase. This was caused by the stability of the aquatic environment. The most powerful measures in discerning hydrological state were diel maximum temperature, diel temperature range, and rate of change of temperature. Greater distinctive power was obtained through the use of multiple measures of the parameters. In addition, key events such as flooding and drying were discernible within the temperature and light intensity data. High-resolution temperature and light intensity data are able to aid in understanding these dynamics of hydrological state and can be used to monitor ecosystem functions amid changes in temporary and perennial water bodies.
Wildfire is common across the pan-Arctic tundra. Tundra fires exert significant impacts on terrestrial carbon balance and ecosystem functioning. Interactions between fire and climate change can enhance their impacts on the Arctic. However, the driving mechanisms of tundra fire occurrences remain poorly understood. This study focuses on identifying key environmental factors controlling fire occurrence in Arctic tundra of Alaska. Our random forest models, considering ignition source, fuel, fire weather, and topography, have shown a strong predictive capability with an overall accuracy above 91%. We found cloud-to-ground (CG) lightning probability by far the dominant driver controlling tundra fire occurrence. Warmer and drier near-surface weather was required to support burning, while fuel composition and topography have modest impacts on fire occurrence. Our results highlight the critical role of CG lightning in driving tundra fires and that incorporating lightning modeling is essential for fire monitoring, forecasting, and management in the Arctic.
The role of greenhouse gases (GHGs) in global climate change is now well recognised and there is a clear need to measure emissions and verify the efficacy of mitigation measures. To this end, reliable estimates are needed of the GHG balance at national scale and over long time periods, but these estimates are difficult to make accurately. Because measurement techniques are generally restricted to relatively small spatial and temporal scales, there is a fundamental problem in translating these into long-term estimates on a regional scale. The key challenge lies in spatial and temporal upscaling of short-term, point observations to estimate large-scale annual totals, and quantifying the uncertainty associated with this upscaling. Here, we review some approaches to this problem, and synthesise the work in the recent UK Greenhouse Gas Emissions and Feedbacks Programme, which was designed to identify and address these challenges. Approaches to the scaling problem included: instrumentation developments which mean that near-continuous data sets can be produced with larger spatial coverage; geostatistical methods which address the problem of extrapolating to larger domains, using spatial information in the data; more rigorous statistical methods which characterise the uncertainty in extrapolating to longer time scales; analytical approaches to estimating model aggregation error; enhanced estimates of C flux measurement error; and novel uses of remote sensing data to calibrate process models for generating probabilistic regional C flux estimates.
River systems represent important drivers of carbon loading, utilization and storage. However, underlying controls of hydrological transport and biological degradation on fluvial dissolved organic carbon (DOC) have yet to be revealed. Here, we explored spatiotemporal variability of DOC concentrations, components and sources, as well as its biodegradability in a headwater tributary of the Yangtze. We found that temporal rainfall stimulated terrestrial inputs and increased terrigenous DOC abundance. Hydrological transport was accompanied by biological generation and utilization of DOC, resulting in reduced labile components and accumulated recalcitrant components from tributaries to the main stem. Biodegradable DOC (BDOC) notably responded to temperature gradients over a 56-day laboratory incubation. Riverine DOC component, molecular weight and source highly predicted its biodegradation. Particularly, partial refractory (ultraviolet humic-like) fractions contributed to biological degradation of DOC, which was incompletely degraded from high-molecular to low-molecular weight compounds. The findings hope to supplement a new understanding of carbon fate under global change.
The annual area burned due to wildfires in the western United States (WUS) increased by more than 300% between 1984 and 2020. However, accounting for the nonlinear, spatially heterogeneous interactions between climate, vegetation, and human predictors driving the trends in fire frequency and sizes at different spatial scales remains a challenging problem for statistical fire models. Here we introduce a novel stochastic machine learning (ML) framework to model observed fire frequencies and sizes in 12 km x 12 km grid cells across the WUS. This framework is implemented using Mixture Density Networks trained on a wide suite of input predictors. The modeled WUS fire frequency corresponds well with observations at both monthly (r= 0.94) and annual (r= 0.85) timescales, as do the monthly (r= 0.90) and annual (r= 0.88) area burned. Moreover, the annual time series of both fire variables exhibit strong correlations (r >= 0.6) in 16 out of 18 ecoregions. Our ML model captures the interannual variability and the distinct multidecade increases in annual area burned for both forested and non-forested ecoregions. Evaluating predictor importance with Shapley additive explanations, we find that fire month vapor pressure deficit (VPD) is the dominant driver of fire frequencies and sizes across the WUS, followed by 1000-hour dead fuel moisture (FM1000), total monthly precipitation (Prec), mean daily maximum temperature (Tmax), and fraction of grassland cover in a grid cell. Our findings serve as a promising use case of ML techniques for wildfire prediction in particular and extreme event modeling more broadly.
Isoprene is the dominant non-methane organic compound emitted to the atmosphere, where it drives ozone and aerosol production, modulates atmospheric oxidation, and interacts with the global nitrogen cycle. Isoprene emissions are highly variable and uncertain, as is the non-linear chemistry coupling isoprene and its primary sink, the hydroxyl radical (OH). Space-based isoprene measurements can help close the gap on these uncertainties, and when combined with concurrent formaldehyde data provide a new constraint on atmospheric oxidation regimes. Here we present a next-generation machine-learning isoprene retrieval for the Cross-track Infrared Sounder (CrIS) that provides improved sensitivity, lower noise, and thus higher space-time resolution than earlier approaches. The Retrieval of Organics with CrIS Radiances (ROCR) isoprene measurements compare well with previous space-based retrievals as well as with the first-ever ground-based isoprene column measurements, with 20-50% discrepancies that reflect differing sources of systematic uncertainty. An ensemble of sensitivity tests points to the spectral background and isoprene profile specification as the most relevant uncertainty sources in the ROCR framework. We apply the ROCR isoprene algorithm to the full CrIS record from 2012-2020, showing that it can resolve fine-scale spatial gradients at daily resolution over the world’s isoprene hotspots. Results over North America and Amazonia highlight emergent connections between isoprene abundance and daily-to-interannual variations in temperature, nitrogen oxides, and drought stress.
Crop yield is sensitive to climate change and has been projected to be negatively affected by future climate. To reduce yield loss and ensure food security in the context of climate change, it is critical to understand how climate variables interact with crop growth in agroecosystems. One important and widely used tool to study yield responses to climate is process-based modeling. However, using process-based models to simulate the climate impacts on crops is becoming challengeable as the future climate is characterized by more and more frequent extreme events, such as heatwaves, unpredictable rainfall, and droughts. Most existing crop models may not be capable of characterizing the impacts of such extreme events on crops simply because they usually do not simulate some critical processes that climate variables directly affect crop growth such as photosynthesis. Instead, they use a simplified approach–radiation-use efficiency (RUE) which is a coefficient to describe empirical relationships between intercepted radiation and biomass. The usage of RUE has simplified computation but also limited our understanding of interactions between climate variables (e.g., temperature, CO2, rainfall) and crop growth. Thus, we developed a module combining processes of radiative transfer and photosynthesis (RP) within the canopy to account for the impacts of climate variables on crop growth dynamically. Then, we integrated the RP module into a popular agricultural system model—the Environmental Policy Integrated Climate (EPIC) to assess its performance. The results show that its capabilities of predicting crop yield are comparable to the traditional RUE method. The correlation between observed and simulated biomass is 0.77 for the RUE method, while 0.76 for the RP method. But the RP method could show responses of biomass accumulation to changes in climate factors, which is almost overwhelming for RUE. For instance, the RP module could simulate how extremely high temperatures (which usually last several hours during a day) affect crop growth and also allow the EPIC to distinguish elevated CO2 impacts on C3 and C4 crops, while the default RUE method could not. Therefore, the RP module is promising to improve capabilities and extend functionalities of current process-based models, which is not only beneficial to the community of crop modeling but also enhances our ability to evaluate the impacts of climate change on the agroecosystem.