Optimising hydropower operations to balance economic profitability and support functioning ecosystem services is integral to river management policy. In this article, we propose a multi-objective optimization framework for small hydropower plants (SHPs) to evaluate trade-offs among environmental flow scenarios. Specifically, we examine the balance between short-term losses in hydropower generation and the potential for compensatory benefits in the form of revenue from recreational ecosystem services, irrespective of the direct beneficiary. Our framework integrates a fish habitat model, a hydropower optimization model, and a recreational ecosystem service model to evaluate each environmental flow scenario. The optimisation process gives three outflow release scenarios, informed by previous streamflow realisations (dam inflow), and designed environmental flow constraints. The framework is applied and tested for the river Kuusinkijoki in North-eastern Finland, which is a habitat for migratory brown trout and grayling populations. We show that the revenue loss due to the environmental flow constraints arises through a reduction in revenue per generated energy unit and through a reduction in turbine efficiency. Additionally, the simulation results reveal that all the designed environmental flow constraints cannot be met simultaneously. Under the environmental flow scenario with both minimum flow and flow ramping rate constraints, the annual hydropower revenue decreases by 16.5%. An annual increase of 8% in recreational fishing visits offsets the revenue loss. The developed framework provides knowledge of the costs and benefits of hydropower environmental flow constraints and guides the prioritizing process of environmental measures.

Heterogeneous chemical cycles of pyrogenic nitrogen and halides influence tropospheric ozone and affect the stratosphere during extreme pyrocumulonimbus (PyroCB) events. We report field-derived N2O5 uptake coefficients, γ(N2O5), and ClNO2 yields, φ(ClNO2), from two aircraft campaigns observing fresh smoke in the lower and mid troposphere and processed/aged smoke in the upper troposphere and lower stratosphere (UTLS). Derived φ(ClNO2) varied across the full 0–1 range but was typically < 0.5 and smallest in a PyroCB (< 0.05). Derived γ(N2O5) was low in agricultural smoke (0.2–3.6 ×10-3), extremely low in mid-tropospheric wildfire smoke (0.1 × 10-3), but larger in PyroCB processed smoke (0.7–5.0 × 10–3). Aged BB aerosol in the UTLS had a higher median γ(N2O5) of 17 × 10–3 that increased with sulfate and liquid water, but that was nevertheless 1–2 orders of magnitude lower than values for aqueous sulfuric aerosol used in stratospheric models.

A document by Liguang Jiang. Click on the document to view its contents.

To help meet emission standards, hydrogen sulfide (H2S) from geothermal production may be injected back into the subsurface, where basalt offers, in theory, the capacity to mineralize H2S into pyrite. Ensuring the viability of this pollution mitigation technology requires information on how much H2S is mineralized, at what rate and where. To date, monitoring efforts of field-scale H2S reinjection have mostly occurred via mass balance calculations, typically capturing less than 5\% of the injected fluid. While these studies, along with laboratory experiments and geochemical models, conclude effective H2S mineralization, their extrapolation to quantify mineralization and its persistence over time leads to considerable uncertainty. Here, a geophysical methodology, using time-domain induced polarization (TDIP) logging in two of the injection wells (NN3 and NN4), is developed to follow the fate of H2S re-injected at Nesjavellir geothermal site in south-west Iceland. Results show a strong chargeability increase at +40 days, corresponding to precipitation of up to 1\% in NN4 and 2\% in NN3 according to laboratory-based relationships. A uniform increase is observed along NN4, whereas it is localized below 450 in NN3. Changes are more pronounced with the larger electrode spacing, indicating that pyrite precipitation takes place away from the wells. Furthermore, a chargeability decrease is observed at later monitoring rounds in both wells, suggesting that pyrite is either passivated or re-dissolved after precipitating. These results highlight the ability of TDIP logging to monitor pyrite mineralization and have implications for understanding the fate of H2S upon subsurface storage in basaltic environments.

Comprehending the intricate interplay between atmospheric aerosols and water vapour in subsaturated regions is vital for accurate modelling of aerosol–cloud–radiation–climate dynamics. But the microphysical mechanisms governing these interactions with ambient aerosols remain inadequately understood. Here we report results from high-altitude, relatively pristine site in Western-Ghats of India during monsoon, serving as a baseline for climate processes in one of the world’s most polluted regions. Utilizing a novel quartz crystal microbalance (QCM) approach, we conducted size-resolved sampling to analyse humidity-dependent growth factors, hygroscopicity, deliquescence behaviour, and aerosol liquid water content (ALWC). Fine-mode aerosols (≤2.5 μm) exhibited size-dependent interactions with water vapour, contributing significantly to ALWC. Deliquescence was observed in larger aerosols (>180 nm), influenced by organic species, with deliquescence relative humidity (DRH) lower than that of pure inorganic salts. This research highlights the significance of understanding ambient aerosol-water interactions and hygroscopicity for refining climate models in subsaturated conditions.

The land sink of anthropogenic carbon emissions, a crucial component of mitigating climate change, is primarily attributed to the CO₂ fertilization effect on global gross primary productivity (GPP). However, direct observational evidence of this effect remains scarce, hampered by challenges in disentangling the CO₂ fertilization effect from other long-term drivers, particularly climatic changes. Here, we introduce a novel statistical approach to separate the CO₂ fertilization effect on GPP and daily maximum net ecosystem production (NEPmax) using eddy covariance records across 38 extratropical forest sites. We find the median stimulation rate of GPP and NEPmax to be 16.4 ± 4% and 17.2 ± 4% per 100 ppm increase in atmospheric CO₂ across these sites, respectively. To validate the robustness of our findings, we test our statistical method using factorial simulations of an ensemble of process-based land surface models. We acknowledge that additional factors, including nitrogen deposition and land management, may impact plant productivity, potentially confounding the attribution to the CO₂ fertilization effect. Assuming these site-specific effects offset to some extent across sites as random factors, the estimated median value still reflects the strength of the CO₂ fertilization effect. However, disentanglement of these long-term effects, often inseparable by timescale, requires further causal research. Our study provides direct evidence that the photosynthetic stimulation is maintained under long-term CO₂ fertilization across multiple eddy covariance sites. Such observation-based quantification is key to constraining the long-standing uncertainties in the land carbon cycle under rising CO₂ concentrations.

In celebrating its first decade of existence, a Chilean research center organized an open forum on climate and socio-environmental resilience engaging participants -both speakers and audience- from within and outside the academic community. Held the first week of September 2023, the symposum filled a void of events addressing climate and resilience research and its bi-directional links with society.

In various research fields such as hydrogeology, environmental science and energy engineering, geological formations with fractures are frequently encountered. Accurately characterizing these fractured media is of paramount importance when it comes to tasks that demand precise predictions of liquid flow and the transport of solute and energy within them. Since directly measuring fractured media poses inherent challenges, data assimilation (DA) techniques are typically employed to derive inverse estimates of media properties using observed state variables like hydraulic head, concentration, and temperature. Nonetheless, the considerable difficulties arising from the strong heterogeneity and non-Gaussian nature of fractured media have diminished the effectiveness of existing DA methods. In this study, we formulate a novel DA approach known as PEDL (parameter estimator with deep learning) that harnesses the capabilities of DL to capture nonlinear relationships and extract non-Gaussian features. To evaluate PEDL’s performance, we conduct two numerical case studies with increasing complexity. Our results unequivocally demonstrate that PEDL outperforms three popular DA methods: ensemble smoother with multiple DA (ESMDA), iterative local updating ES (ILUES), and ES with DL-based update (ESDL). Sensitivity analyses confirm PEDL’s validity and adaptability across various ensemble sizes and DL model architectures. Moreover, even in scenarios where structural difference exists between the accurate reference model and the simplified forecast model, PEDL adeptly identifies the primary characteristics of fracture networks.

Unlike the radiative forcing linked to CO2 and its cumulative storage in oceans since the start of the industrial era around two centuries ago, the Sun has heated the Earth for billions of years without accumulation and dramatic temperature drift. To overcome this obviously illogical difference in evolution, we first analyze several reasons showing that the current universally adopted relationship between carbon dioxide and global warming does not respect the fundamentals of Chemistry, Physics, and Thermodynamics. A recently proposed alternative mechanism, based on these hard sciences, is briefly recalled. In this new mechanism, heat on Earth is managed by water and its solid-liquid and liquid-vapor interphases equilibria before radiative elimination in space. Today, anthropogenic heat is increasingly seen as a complement to the solar heating although it is neglected in the universally adopted consensus. Anthropogenic heat releases are generally estimated from global energy consumption. A broader list of sources is established that includes the capture of solar thermal infrared radiations by artificial installations, including those acting as greenhouses. Three qualitative scenarios are proposed in which climate change depends on whether the ratio of anthropogenic heat releases relative to solar thermal contributions remains negligible, is acceptable or becomes so large that it could shorten the time until the next ice age. Currently, global temperature and ocean level are still very low compared to those in distant past. On the other hand, ice disappearance is indisputable, particularly at the levels of glaciers, floating ice, and permafrost. These features fit the scenario in which temperature continued to fluctuate as it did during the last 8,000 years of the current Holocene interglacial plateau while local rains, winds, floodings, droughts, etc., worsen in magnitude and frequency to help ice melt and evaporation manage excess heat. Policymakers should not wait to discover that decreasing atmospheric carbon dioxide has little effect on the worsening of climate events to begin mitigating of anthropogenic heat with the help of hard sciences scientists to work on quantification. Key points • Carbon dioxide-based radiative forcing as source of global warming does not resist to critical analysis based on fundamentals of chemistry, physics and thermodynamics • Thermal properties of water, water interphase exchanges, formation of clouds and radiative elimination to space control heat supplies and climate changes since water is present on Earth • Anthropogenic heat releases should not affect much temperature and ocean levels provided they remain negligible relative to solar heat supplies, but heat-dispersing local climatic vents should increase in strength and frequency

Peatland ecosystems are unsurpassed in their carbon-storing capacity. However, they can be hotspots for emissions of greenhouse gases (GHGs) depending on soil water saturation and oxygen status. Using automated floating chambers, we investigated the spatiotemporal variability of CH4 and CO2 fluxes and their environmental drivers from inundated areas in a temperate, rich fen. We distinguished between two areas: one with continuous inundation, caused by upwelling groundwater and a lower-lying area with periodic inundation by flooding from an adjacent stream. Using hourly measurements, we found mean effluxes of CH4 and CO2 to be 0.16 and 1.23 g C m-2 d-1 between October and May with more than a 10-fold variation between observations. For CO2, efflux were higher in the periodically inundated area compared to the continuously inundated area. In contrast, CH4 fluxes were higher, and dominated by ebullition, at the area with continuous inundation. Both fluxes increased with soil temperature and wind speed. Advective and diffusive fluxes of CH4 and CO2 associated to groundwater upwelling and upwards diffusion of dissolved gases from shallow groundwater (0.5-0.8 meters below ground level) contributed negligibly to the measured fluxes, suggesting that the emitted GHGs were produced close to the terrain. Our data highlight the large spatiotemporal variation of CO2 and CH4 emissions from fens due to variations in hydrology and topography affecting GHG production near the soil surface. Particularly, the temporary dynamics of soil inundation played a major role in controlling the contribution by CO2 and CH4 to wetland GHG release.

Grade Level: [Suitable grade level, e.g., 6-8]Duration: 50 minutes

Machine-learned uncertainty quantification (ML-UQ) has become a hot topic in environmental science, especially for neural networks.  Scientists foresee the use of ML-UQ to make better decisions and assess the trustworthiness of the ML model.  However, because ML-UQ is a new tool, its limitations are not yet fully appreciated.  For example, some types of uncertainty are fundamentally unresolvable, including uncertainty that arises from data being out of sample, i.e., outside the distribution of the training data.  While it is generally recognized that ML-based point predictions (predictions without UQ) do not extrapolate well out of sample, this awareness does not exist for ML-based uncertainty.  When point predictions have a large error, instead of accounting for this error by producing a wider confidence interval, ML-UQ often fails just as spectacularly.  We demonstrate this problem by training ML with five different UQ methods to predict shortwave radiative transfer.  The ML-UQ models are trained with real data but then tasked with generalizing to perturbed data containing, e.g., fictitious cloud and ozone layers.  We show that ML-UQ completely fails on the perturbed data, which are far outside the training distribution.  We also show that when the training data are lightly perturbed -- so that each basis vector of perturbation has a little variation in the training data -- ML-UQ can extrapolate along the basis vectors with some success, leading to much better (but still somewhat concerning) performance on the validation and testing data.  Overall, we wish to discourage overreliance on ML-UQ, especially in operational environments.

A document by Martin Velez Pardo. Click on the document to view its contents.

Vegetation Gross Primary Productivity (GPP) is the single largest carbon flux of the terrestrial biosphere which, in turn, is responsible for sequestering $25-30\%$ of anthropogenic carbon dioxide emissions. The ability to model GPP is therefore critical for calculating carbon budgets as well as understanding climate feedbacks. Earth System Models (ESMs) have the capability to simulate GPP but vary greatly in their individual estimates, resulting in large uncertainties. We describe a Machine Learning (ML) approach to investigate two key factors responsible for differences in simulated GPP quantities from ESMs: the relative importance of different atmospheric drivers and differences in the representation of land surface processes. We describe the different steps in the development of our interpretable Machine Learning (ML) framework including the choice of algorithms, parameter tuning, training and evaluation. Our results show that ESMs largely agree on the physical climate drivers responsible for GPP as seen in the literature, for instance drought variables in the Mediterranean region or radiation and temperature in the Arctic region. However differences do exist since models don’t necessarily agree on which individual variable is most relevant for GPP. We also explore a distance measure to attribute GPP differences to climate influences versus process differences and provide examples for where our methods work (South Asia, Mediterranean)and where they are inconclusive (Eastern North America).

This paper presents a method to analyze and improve the set of equations constituting a rainfall-runoff model structure based on a combination of a data assimilation algorithm and polynomial updates to the state equations. The method, which we have called “Data Assimilation Informed model Structure Improvement” (DAISI) is generic, modular, and demonstrated with an application to the GR2M model and 201 catchments in South-East Australia. Our results show that the updated model generated with DAISI generally performed better for all metrics considered included KGE, NSE on log transform flow and flow duration curve bias. In addition, the modelled elasticity of runoff to rainfall is higher in the updated model, which suggests that the structural changes could have a significant impact on climate change simulations. Finally, the DAISI diagnostic identified a reduced number of update configurations in the GR2M structure with distinct regional patterns in three sub-regions of the modelling domain (Western Victoria, central region, and Northern New South Wales). These configurations correspond to specific polynomials of the state variables that could be used to improve equations in a revised model. Several potential improvements of DAISI are proposed including the use of additional observed variables such as actual evapotranspiration to better constrain the model internal fluxes.

Mercury (Hg) is a naturally occurring element that has been greatly enriched in the environment by activities like mining and fossil fuel combustion. Despite commonalities in some CO2 and Hg emission sources, the implications of long-range climate scenarios for anthropogenic Hg emissions have yet to be explored. Here, we present comprehensive projections of anthropogenic Hg emissions (2020-2300) and evaluate impacts on global atmospheric Hg deposition. Projections are based on four shared socioeconomic pathway (SSP) narratives ranging from sustainable reductions in resource and energy intensity to rapid economic growth driven by abundant fossil fuel exploitation. There is a greater than two-fold difference in cumulative anthropogenic Hg emissions between the lower-bound (110 Gg) and upper-bound (230 Gg) scenarios. Hg releases to land and water are approximately six times those of direct emissions to air (600-1470 Gg). At their peak, anthropogenic Hg emissions reach 2200-2600 Mg a-1 sometime between 2010 (baseline) and 2030, depending on the SSP scenario. Coal combustion is the largest determinant of differences in Hg emissions among scenarios. Decoupling of Hg and CO2 emissions sources occurs under low- to mid-range scenarios, though contributions from artisanal and small-scale gold mining remain uncertain. A projected future shift in speciation of Hg emissions toward lower gaseous elemental Hg (Hg0) and higher divalent Hg (HgII) will result in a higher fraction of locally-sourced Hg deposition. Projected re-emissions of previously deposited anthropogenic Hg follow a similar temporal trajectory to primary emissions, amplifying benefits of primary Hg emissions reductions under the most stringent mitigation scenarios.

In the face of a changing climate, the understanding, predictions and projections of natural and human systems are increasingly crucial to prepare and cope with extremes and cascading hazards, determine unexpected feedbacks and potential tipping points, inform long-term adaptation strategies, and guide mitigation approaches. Increasingly complex socio-economic systems require enhanced predictive information to support advanced practices. Such new predictive challenges drive the need to fully capitalize on ambitious scientific and technological opportunities. These include the unrealized potential for very high-resolution modeling of global-to-local Earth system processes across timescales, a reduction of model biases, enhanced integration of human systems and the Earth Systems, better quantification of predictability and uncertainties; expedited science-to-service pathways and co-production of actionable information with stakeholders. Enabling technological opportunities include exascale computing, advanced data storage, novel observations and powerful data analytics, including artificial intelligence and machine learning. Looking to generate community discussions on how to accelerate progress on U.S. climate predictions and projections, representatives of Federally-funded U.S. modeling groups outline here perspectives on a six-pillar national approach grounded in climate science that builds on the strengths of the U.S. modeling community and agency goals. This calls for an unprecedented level of coordination to capitalize on transformative opportunities, augmenting and complementing current modeling center capabilities and plans to support agency missions. Tangible outcomes include projections with horizontal spatial resolutions finer than 10 km, representing extremes and associated risks in greater detail, reduced model errors, better predictability estimates, and more customized projections to support the next generation of climate services.

A document by Yi Yang. Click on the document to view its contents.

Carbon Capture and Storage (CCS) is a pivotal technology for reducing greenhouse gas emissions. While developments have been made in capture and storage capabilities , the planning and development of an optimized transport pipeline network for linking emission sources to storage sites remains understudied. This study aims to extend the capabilities of SimCCS, a widely-used CCS planning tool, to incorporate environmental, social, and cultural considerations alongside economic costs of pipeline networks. Utilizing multi-objective optimization, we introduce an additional objective function that minimizes environmental and social impacts. This function integrates spatial data layers representing critical habitats, protected areas, and other socio-ecological factors. Preliminary results illustrate the model's capacity for multi-objective optimization. The annual expense for maintaining a sample pipeline network increased from $434 million to $622 million, with pipeline lengths of 1986 kilometers and 2878 kilometers, respectively, when shifting focus from cost to environmental and social impacts. This research contributes a more comprehensive framework for the planning of future CCS infrastructure that is both economically and environmentally sustainable.

Accurate forest inventory data are essential for tracking carbon sequestration, estimating carbon emissions from deforestation, assessing plant and animal habitats for biodiversity, and predicting environmental risks such as wildfires. Traditional methods of data collection have faced challenges in either scale or precision. The advent of terrestrial laser scanners addressed some of these issues but faced limitations in cost and mobility. This paper proposes a new approach using mobile LIDAR for forest inventory data collection. By integrating advancements in computer vision, the methodology aims to provide comprehensive individual tree data, including parameters like diameter at breast height, volume estimations, species identification, and temporal tracking of individual trees. This proposed research direction addresses current gaps in the use of LIDAR and camera inference for forestry data where existing work does not generate domain context-aware data by narrowly focusing collection on isolated tree attributes.

Cryogenic vacuum distillation (CVD) is a widely used technique for extracting plant water from stems for isotopic analysis, but concerns about potential isotopic biases have emerged. Here, we leverage the Cavitron centrifugation technique to extract xylem water and compare its isotopic signature to that of CVD-extracted stem water as well as source water. Conducted under field conditions in tropical northern Australia, our study spans seven tree species naturally experiencing a range of water stress levels. Our findings reveal a significant deuterium bias in CVD-extracted bulk stem water when compared to xylem water (median bias \($-14.9\textperthousand$\)), whereas xylem water closely aligned with source water (median offset \($-1.9\textperthousand$\)). We find substantial variations in deuterium bias among the seven tree species (bias ranging from -19.3 to \($-9.1\textperthousand$\)), but intriguingly, CVD-induced biases were unrelated to environmental factors such as relative stem water content and pre-dawn leaf water potential. These results imply that inter-specific differences may be driven by anatomical traits rather than tree hydraulic functioning. Additionally, our data highlight the potential to use a site-specific deuterium offset, based on the isotopic signature of local source water, for correcting CVD-induced biases.

Understanding drivers of marine-terminating ice sheet behavior is important for constraining ice contributions to global sea-level rise. In part, the stability of marine-terminating ice is influenced by solid-Earth conditions at the grounded-ice margin. While the Cordilleran Ice Sheet (CIS) contributed significantly to global mean sea level during its final post-Last Glacial Maximum (LGM) collapse, the drivers and patterns of retreat are not well constrained. Coastal outcrops in the deglaciated Puget Lowland of Washington state - largely below sea level during glacial maxima, then uplifted above sea level via glacial isostatic adjustment (GIA) - record late Pleistocene history of the CIS. The preservation of LGM glacial and post-LGM deglacial sediments provides a unique opportunity to assess variability in marine ice-sheet behavior of the southernmost CIS. Based on paired stratigraphic and geochronological work with a newly developed marine-reservoir correction for this region, we identify that the late-stage CIS experienced stepwise retreat into a marine environment about 12,000 years before present, placing glacial ice in the region for about 3,000 years longer than previously thought. Stand-still of marine-terminating ice for a millenia, paired with rapid vertical landscape evolution, was followed by continued retreat of ice in a subaerial environment. These results suggest rapid rates of solid Earth uplift and topographic support (e.g., grounding-zone wedges) stabilized the ice-margin, supporting final subaerial ice retreat. This work leads to a better understanding of shallow marine and coastal ice sheet retreat; relevant to sectors of the contemporary Antarctic and Greenland ice sheets and marine-terminating outlet glaciers.

A document by Dr. Anupam Sharma. Click on the document to view its contents.

Seismic ocean thermometry uses sound waves generated by repeating earthquakes to measure temperature change in the deep ocean. In this study, waves generated by earthquakes along the Japan Trench and received at Wake Island are used to constrain temperature variations in the Kuroshio Extension region. This region is characterized by energetic mesoscale eddies and large decadal variability, posing a challenging sampling problem for conventional ocean observations. The seismic measurements are obtained from a hydrophone station off and a seismic station on Wake Island, with the seismic station's digital record reaching back to 1997. These measurements are combined in an inversion for the time and azimuth dependence of the range-averaged deep temperatures, revealing lateral and temporal variations due to Kuroshio Extension meanders, mesoscale eddies, and decadal water mass rearrangements. These results highlight the potential of seismic ocean thermometry for better constraining the variability and trends in deep-ocean temperatures. By overcoming the aliasing problem of point measurements, these measurements complement existing ship- and float-based hydrographic measurements.