Surface water is the most readily accessible water resource and provides an array of ecosystem services, but is stressed by changes in climate, land cover, and population size. Understanding drivers of surface water dynamics in space and time is key to better managing our water resources. However, few studies estimating changes in surface water account for climate and anthropogenic drivers both independently and together. We used 19 years (2000-2018) of the newly developed Dynamic Surface Water Extent Landsat Science Product in concert with time series of precipitation, temperature, land cover, and population size to statistically model maximum seasonal percent surface water area as a function of climate and anthropogenic drivers in the Southeastern U.S. We fitted three statistical models (linear mixed effects, random forests, and mixed effects random forests) and three groups of explanatory variables (climate, anthropogenic, and their combination) to assess the accuracy of estimating percent surface water area at the watershed scale with different drivers. We found that anthropogenic drivers accounted for approximately 37% more of the variance in the percent surface water area than the climate variables. The combination of variables in the mixed effects random forest model produced the smallest mean percent errors (mean -0.17%) and the highest explained variance (R2 0.99). Our results indicate that anthropogenic drivers have greater influence when estimating percent surface water area than climate drivers, suggesting that water management practices and land use policies can be highly effective tools in controlling surface water variations in the Southeastern U.S.

High-school students tested soil, paint, and water for lead (Pb) in a total of 80 houses in their town of Pelham, New York, where blood-Pb data indicate relatively high levels of child exposure. All the samples were tested in the laboratory using established procedures but this was preceded by testing of soil and paint in the field with a kit by the students. The total Pb content of 32 of the 159 soil samples that were collected exceeded 400 ppm, the EPA standard for bare soil in areas where children play. Only 4 of the 118 tap water samples that were collected contained over 15 ppb Pb, with the data showing that flushing for 2 min clearly lowered Pb concentration further across the board. The highest risk of child exposure may be posed by old Pb-paint, however, which was detected in 9 of the 48 samples that were tested. Unfortunately, residents were also the least willing to let the students test or sample their paint. High-school students could help reduce exposure in the many towns where child blood-Pb levels remain high today while doing so learning about environmental science and measurement from this hands-on experience.

Mechanical discontinuity embedded in a material plays an essential role in determining the bulk mechanical, physical, and chemical properties. This paper is a proof-of-concept development and deployment of a reinforcement learning framework to control both the direction and rate of the growth of fatigue crack. The reinforcement learning framework is coupled with an OpenAI-Gym-based environment that implements the mechanistic equations governing the fatigue crack growth. Learning agent does not explicitly know about the underlying physics; nonetheless, the learning agent can infer the control strategy by continuously interacting the numerical environment. The Markov decision process, which includes state, action and reward, is carefully designed to obtain a good control policy. The deep deterministic policy gradient algorithm is implemented for learning the continuous actions required to control the fatigue crack growth. An adaptive reward function involving reward shaping improves the training. The reward is mostly positive to encourage the learning agent to keep accumulating the reward rather than terminate early to avoid receiving high accumulated penalties. An additional high reward is given to the learning agent when the crack tip reaches close enough to the goal point within specific training iterations to encourage the agent to reach the goal points as quickly as possible rather than slowly approaching the goal point to accumulate the positive reward. The reinforcement learning framework can successfully control the fatigue crack propagation in a material despite the complexity of the propagation pathway determined by multiple goal points.

This article is composed of a commentary about the state of ICON principles (Goldman et al. 2021) in Volcanology, Geochemistry, Petrology (VGP) and discussion on the opportunities and challenges of adopting them. VGP encompasses a broad field that addresses volcanic, magmatic, hydrothermal, geomicrobial systems and process investigations that span the physical, geochemical and biological realms, and one that is extensively supported by state-of-the-art research facilities. We suggest that an open, inclusive, collaborative and evolving model of an international coordinated network is critical to answering the most pressing challenges in VGP. In this commentary piece, we begin to discuss the elements of, challenges to, and path forward in developing such a model. For this team, ICON means collaboration, equitable access to data for the entire scientific community, and forging of partnerships that potentially contribute to more innovative ways of coordinating and sharing research. It also means bringing more equity to science, by implementing effective measures which consider access to funding, analytical equipment, resources, and mentors. More importantly, ICON to us means having important conversations around what we value in the advancement of science, perhaps exploring outside the idea of meritocracy and evaluating what individual traits can contribute to science outside what has traditionally been considered the norm.

Significant investments are made in the collection of water quality data. Yet barriers to effective data sharing and reuse hamper the ability to leverage this information to its full potential in research and water management decisions. Because water monitoring data are collected by a wide range of organizations, through programs of varying scope and focus, and often within jurisdictional or institutional silos, it can be difficult to connect this information together in standardized and accessible formats. DataStream is an online open-access platform that was developed by The Gordon Foundation and its partners to address the challenge of water data accessibility in Canada. DataStream is free to use and allows users to query, visualize, and download water quality data aligned with widely-adopted data and metadata standards (e.g., Water Quality eXchange, ISO19115 and schema.org). The path towards DataStream evolving as a collaborative and open data platform has been guided by the FAIR and CARE data principles. To date, over 140 different groups across Canada are using DataStream to publish water monitoring results including watershed groups, Indigenous organizations, researchers and governments at all levels. We will highlight our lessons learned in developing the platform to align with FAIR data principles and the elements we believe have been key to our success including DataStream’s open data schema, clear data licensing and regional partnership model.

The well-investigated aquifer in southern Tunisia have been selected from the literature to test the potential use of uranium isotopic compositions as a groundwater dating method. This is the Senonian carbonate aquifer of the Nefzaoua basin (https://doi.org/10.1016/j.quaint.2020.01.024). For the this aquifer, an increase in U concentrations along the generalized flow path is observed in proportion to an increase in total dissolved solids, which may indicate the predominance of dissolution processes over alpha-recoil processes under oxidizing conditions for uranium. There is also an increase in U concentrations with a decrease in 14C values. For this aquifer, positive results were obtained on uranium-isotope dating. It was found that the groundwater residence time in the aquifer increases from 440 to 11,300 years from the recharge area along the generalized flow path (Figure). Uranium age correction model under oxidizing conditions in the aquifer is given in “Malov, 2018. Evolution of the groundwater chemistry in the coastal aquifers of the south-eastern White Sea area (NW Russia) using 14C and 234U-238U dating. Science of the Total Environment. https://doi.org/10.1016/j.scitotenv.2017.10.197” It is shown that for uranium-isotope dating of groundwater, three main conditions must be met: i) oxidizing conditions for uranium in the aquifer, ii) an increase in uranium concentrations with a decrease in 14C activities, and iii) the homogeneity of the aquifer in terms of hydraulic conductivity and lithological composition. The author understands the reality of the fact that groundwater dating methods are under development and improvement. Nevertheless, analysis of the evolution of the chemical and isotopic composition of groundwater, analysis of the geological and hydrogeological history of the region, hydraulic estimates of groundwater velocities in aquifers, hydrodynamic and balance justifications for the formation of groundwater, and analysis of the composition of stable isotopes can increase the reliability of dating. In the future, it is necessary to continue studies to assess the retardation factor and recoil loss factor in order to improve the uranium-isotope method for dating groundwater under oxidizing conditions for uranium. This work was supported by the RFBR (project no. 20-05-00045_A)

Confined meandering rivers are bounded between valleys and they are limited in their lateral migration compared to free meandering ones. In this study, we designed a model-based analysis to investigate types of confined meandering rivers. To simulate cases, we used the model developed by (Bogoni et al. 2017) based on a semi-analytical solution for flow and bed morphology ((Zolezzi and Seminara 2001). According to the result of (Lewin and Brindle 1977) the categorization of this type of meandering river should be based on the potential of evolution. We defined a confinement ratio as the ratio between the confined valley width and the meander belt width that the river would theoretically reach without geological confinement. Such unconfined meander belt width has been computed through long-term morphodynamic model simulations. This differs from previous work (Camporeale et al. 2005), semi-empirically suggesting an unconfined meander belt being nearly 3 times the modeled, linearly most unstable, intrinsic meander length. Our results indicate the existence of four different types of confined meandering rivers, which represents the most important point of this work. Compared to previous work, our classification adds a new class and reclassifies one of the previous types. Our proposed classes are; “Moderately confined condition” for confinement ratio in the range 0.1-1, “Strongly confined condition” for confinement ratio in the range 0.03-0.1. For a smaller confinement ratio than 0.03 we obtain a low sinuosity, single thread river, and for a confinement ratio greater than 1, we retrieve a free meandering river. Preliminary testing of our model-based classification with data from real confined meandering rivers shows promising results.

Natural hazards such as coastal and river floods, tornadoes, droughts, heatwaves, wildfires, and landslides cause significant economic losses (e.g., agriculture and property damage) and notable counts of fatalities. While natural hazards are often considered to be caused by a single climatic driver (e.g., coastal flood from storm surge only), they can be associated with the combined occurrence of multiple drivers (e.g., coastal flood driven by storm surge and precipitation). Defining whether the climatic drivers (e.g., precipitation, temperature, or wind) are extreme enough to turn the hazards into disasters is crucial for estimating disaster risks. To date, extreme events are often defined using the block maxima or peaks over threshold methods ignoring the effects of the built environment and socio-economic conditions. However, a hazard with the same magnitude can cause very different impacts in regions with varying built environments and socio-economic conditions. Additionally, when multiple climatic drivers are involved, traditional methods of defining extreme events (block maxima and peaks over threshold) are challenging to apply. In this research, we employ an impact-based approach and define critical thresholds of climatic drivers for 12 different hazards, across the US southeast coast. We use the SHELDUS database (CEMHS, 2020) to identify historical hazard events that caused socio-economic losses (property and crop damage) and identify corresponding magnitudes of climatic drivers from historical in-situ observations and reanalysis datasets from 1979 to 2019. We then identify thresholds of climatic drivers for impact events where only one or multiple drivers were involved. These impact-based thresholds can be used, for example, to backfill loss databases (where impact information was not available) and to project potential impacts into the future using projections of the different climatic drivers.

Land ecosystems offer an effective nature-based solution to climate change mitigation by absorbing approximately 30% of anthropogenically emitted carbon. This estimated absorption is primarily based on constraints from atmospheric and oceanic measurements while quantification from direct studies of the land carbon cycle themselves displays great uncertainty. The latter hinders prediction of the future fate of the land carbon sink. This talk will present a matrix approach, which will be shown to unify land carbon cycle models, help diagnose model performance with new analytics, accelerate computational efficiency for spin-up, enable data assimilation with complex models, and guide carbon cycle research with a new theoretical framework. The unified framework can be used to evaluate relative importance of various processes, identify sources of uncertainty in model predictions, and improve accuracy of quantification of land carbon sequestration.

We present a regional study of the Pampean loess in South America based on a detailed analysis of three sections across the core of the Pampas. High-resolution luminescence dating resulted in a new chronology that covers a period from Marine Isotope Stage 3 to the early Holocene. Reliable estimations of mass accumulation rates (MARs) for loess were used to infer the temporal dust flux variation during the last glacial/interglacial transition in southern South America (SSA). Minimum MARs in each section were identified for the Last Glacial Maximum (LGM), contrasting with high dust fluxes observed in more distal Southern Atlantic Ocean (SAO) and East Antarctica. We hypothesize that the power of the Pampean loess as a sink of dust was reduced during the LGM, allowing long-range transport of SSA dust to SAO and East Antarctica. This hypothesis is consistent with proxy data and models suggesting drier conditions in the Pampas during the LGM, which would have shut down loess accumulation. It is also consistent with isotopic evidence that points to northern Patagonia and southern central-western Argentina as main contributors of dust to East Antarctica during glacials, given that the prevailing regional wind system implies that dust emitted from these regions would have necessarily passed through the Pampas in its way to the SAO and East Antarctica. Forthcoming Nd, Sr, and Pb isotope results for the Pampean loess will allow further testing of this hypothesis.

Physical processes involved in the ascent of naturally seeped oil from the seafloor and its persistence as a slick are considered. Simplified, physics-based models are developed, drawing in part from the extensive literature concerned with anthropogenic releases of oil at sea. The first model calculates the ascent of oil droplets or oil-coated gas bubbles as they ascend to the sea surface from the seep source. The second model calculates slick longevity as a function of the effect of wind-driven breaking waves. Both models have simplified inputs and algorithms making them suitable for Monte Carlo-type analysis. Using the oil ascent model, we find that slicks from shallower seeps are offset farther relative to their water depth than those from deeper sources. The slick longevity model reveals four growth modes for seepage slicks: persistent (low wind speeds), ephemeral (high wind speeds), reset (all slicks are cleared from an area by high wind speeds), and aging (slick growth after a reset). A year’s worth of modeled winds from the Gulf of Mexico indicate average slick ages of ~ 12 hours. Taking account of the expected oil release duration implied by slick recurrences yields average slick longevities for high recurrence seeps of ~6.5 hours and ~ 5 hours for low recurrence seeps. Seep flux estimates that include the length of individual slicks and the constraints of local currents and wind implicitly take into account the impact of wind-speed history. Those that assume a slick age should be re-evaluated in light of the current findings.

Dissolved iron (dFe) transported by the Amur River greatly contributes to phytoplankton growth in the Sea of Okhotsk. Nevertheless, there has been little research on the dFe source of rivers, especially in the Amur-Mid Basin which is situated in a sporadic permafrost area. In the Amur-Mid Basin, permafrost generally exists under wetlands in the flat valley, and these permafrost wetlands could be a dFe source of rivers. To asess the importance of the permafrost wetlands as a dFe source, first we made a landcover map with high resolution of 30 m using Landsat-8 data and a machine learning technique (decision tree analysis). As a result of decision tree analysis, three normalized indices (normalized diference vegetation index, normalized difference soil index, and normalized difference water index) and slope enabled us to classify landcovers into three vegetation types: wetland, forest, and grassland. Using this landcover map, we investigated the coverages of the permafrost wetland in the sampled watersheds and examined the correlation with river water chemistry (dFe, dissolved organice carbon: DOC, and electrical conductivity: EC). As a result, dFe and DOC concentrations showed a clear positive correlation (dFe: r2 = 0.66, DOC: r2 = 0.46) with the coverage of permafrost wetlands, while EC showed a negative correlation with those (r2 = 0.45). These findings are the first to demonstrate the direct evidence about the importance of permafrost wetlands to supply dFe and DOC to rivers in the Amur-Mid Basin.

We present a high-resolution continental-scale compound flood modeling system. It aims to quantify inland flooding resulting from the composite effects of riverine discharge and surface runoff and storm surge, in the inland-coastal zone during significant riverine and coastal storm events. This is achieved by coupling three continental models: the National Water Model (NWM) for the hydrology component, the Advanced Circulation Ocean Model for the coastal storm surge component, and the WAVEWATCH III model for the surface wave component with a detailed inland-coastal inundation model as the mediator between coastal and inland hydrology module. The inundation model, Delft3D FM, D-Flow Flexible Mesh (D-Flow FM), uses a high quality 2D unstructured grid with high-resolution (~100 m) near coastal features and lower-resolution in other areas to resolve the geometry of the study area. The coastal features are collected from NWM streamlines, National Hydrography Dataset, US medium shorelines and bathymetric features from the United States Army Corps of Engineers . The D-Flow FM model is forced by time-varying water levels and riverine discharges applied at its offshore and inland boundaries, respectively, by spatially- and time-varying wind and pressure fields and incorporates the contributions of surface and subsurface runoff to the total discharge in rivers, channels and streams. We conducted model validations for the following four major flooding events across the US coast: Hurricanes Ike (2008), Sandy (2012), Irma (2017), and Florence (2018). The results highlight the importance of including composite effects of compound flooding to accurately predict water levels during combined river flooding and extreme storm surge events.

Groundwater contamination with geogenic arsenic poses a major health risk to millions of people throughout the world. Among various group of microbes, dissimilatory arsenate reducing bacteria (DARB) are considered to be primarily responsible for arsenic mobilization in anaerobic environments of deep underground aquifer sediments. This group of microbes carries out enzyme catalyzed conversion of more immobilized and less toxic arsenate [As (V)] to more soluble and more toxic arsenite [As (III)]. Aquifers are deep subsurface layers of rocks, sand or soil capable of storing and transmitting water. These are potential environments for arsenic mobilization by anaerobic dissimilatory arsenate reducing bacteria (DARB). Study of these bacteria has been restricted to culture based microcosm studies, which suffers from several drawbacks like inappropriate simulation of ecological factors, exclusion of unculturable members, inappropriate elucidation of community behavior etc. With the recent advent of culture independent molecular analysis, more wholesome analysis of microbial community in diverse ecological habitats has become possible. Anaerobic dissimilatory As(V) reduction is catalyzed by the periplasmic arsenate respiratory reductase (Arr) complex, which consists of a large catalytic subunit (ArrA) and a small subunit (ArrB). arrA gene encoding large subunit of the reductase can be used as a reliable marker for arsenate respiration. Our study is a preliminary attempt to isolate community DNA from aquifer sediments collected from various depths and study the differential distribution of arrA in community genome at various depths. We had successfully isolated humic contaminant free community DNA from aquifer sediments and subjected them to PCR amplification with arrA gene specific primers. The amplicons obtained from community DNA of various depths were subsequently sublected to RFLP analysis by HaeIII and the restriction patterns was compared. The study revealed differential distribution of arrA containing DARB population at various depths of aquifer sediments.

We investigate the characteristics and distribution of pressure ridges in Arctic sea ice using surface height profiles from the Advanced Topographic Laser Altimeter System (ATLAS) on ICESat-2. Applying a new algorithm to ATLAS measurements we derive the frequency and height of individual pressure ridges and map surface roughness and ridging intensity at the basin scale over three winters between 2019 and 2021. Comparisons with near-coincident airborne lidar data show that not only can we detect individual ridges 5.6 m wide, but also measure sail height more accurately than the existing ICESat-2 sea ice height product. We find regional variability in ridge morphology is large while annual variability is low. Ridge characteristics are not only related to their parent ice type but also their geographic location. High-resolution satellite altimetry data are valuable for characterizing sea ice deformation at short length-scales, providing observations that will advance ridge parameterizations in sea ice models.

As Earth's ecological landscape continues to change, it will become increasingly important to understand how it has changed, and how it may change in the future. Freely available multispectral remote sensing datasets, such as the Landsat-8 dataset accessed through the USGS EarthExplorer tool, provide large scale, high resolution satellite imagery that can be leveraged by researchers across scientific disciplines for timeseries index analysis. LINDEX provides an extensible framework that automates Landsat-8 timeseries index analysis, resulting in an average ninety-four percent reduction in overall processing time compared to hands-on methods. Traditionally, in order to make use of this historical data, researchers must acquire the data, uncompress each scene, crop each scene to their region of interest, sort each cropped scene by date and cloud cover, and then either use GIS tools to run index analyses or code the index analyses themselves. This process is time consuming, requires extensive computational knowledge, and is prone to human error. In order to address these challenges, we developed LINDEX, a fully containerized end-to-end extensible processing framework for Landsat-8 timeseries index analysis. LINDEX is open source and leverages open source python packages to automate decompression, cropping, cloud cover detection, sorting by date, and index analysis for Landsat-8 data while also providing a customisable and growing library of eleven ecologically useful indices including NDWI, NDMI, NDSI and NDGI. LINDEX is designed to work synergistically with the EarthExplorer Bulk Download Application as well as QGIS, providing a bridge from download through analysis. The LINDEX framework uses a well defined methodology for incorporating custom indices into your workflow, making LINDEX a useful tool for researchers interested in exploring any Landsat-8 multispectral index while saving time, and reducing error.

A challenge to managing water resources is characterizing the scale-dependent heterogeneity created by the interactions among hydrological, ecological and anthropological processes. It is often difficult to collect sufficient empirical data over the range of scales required to construct mathematical models that facilitate robust bottom-up descriptions or predictions. An alternative is identifying emergent properties of complex systems, whose components self-organize into novel structures or processes via their collective interactions with each other and the environment. A new level of organization and complexity emerges that cannot be predicted from or attributed to the components alone. Emergence offers a number of perspectives from which to interpret, if not predict, the behavior of complex water resource systems. One of these is entropy, which maximizes the options for system components to alter their interactions and, thus, permits variability and adaptability. At the scale of watersheds, increased entropy is pertinent because of its relationship to information (as probability functions), which is transmitted through connected components of a watershed in a manner such that the accrued information gives rise to emergent properties. Hence, analyzing the behaviors of a system according to emergence introduces the possibility of evaluating the information content via its interconnected components. Connectivity then assumes an integral role in a hydrologic system’s response to natural or anthropogenic disturbances (e.g., climate change, land use). Replacing the details of multi-scale heterogeneity and causal mechanisms with the functions that watersheds perform allows processes such as stream flow rate/duration and flood frequency to be construed as emergent spatiotemporal patterns. A reductionist or bottom-up approach to assessing the behavior of aquatic systems shifts to a functional or top-down approach that does not depend upon an understanding of all the physical, chemical or biological mechanisms involved. This latter approach could supplement conventional water resource descriptions and predictions via more comprehensively characterizing watershed or aquatic ecosystem functions.

Long-running eddy covariance flux towers provide insights into how the terrestrial carbon cycle operates over multiple time scales. Here, we evaluated variation in net ecosystem exchange (NEE) of carbon dioxide (CO2) across the Chequamegon Ecosystem-Atmosphere Study (ChEAS) Ameriflux core site cluster in the upper Great Lakes region of the USA from 1997-2020. The tower network included two mature hardwood forests with differing management regimes (US-WCr and US-Syv), two fen wetlands with varying exposure and vegetation (US-Los and US-ALQ), and a very tall (400 m) landscape-level tower (US-PFa). Together, they provided over 70 site-years of observations. The 19-tower CHEESEHEAD19 campaign centered around US-PFa provided additional information on the spatial variation of NEE. Decadal variability was present in all long-term sites, but cross-site coherence in interannual NEE in the earlier part of the record became decoupled with time. NEE at the tall tower transitioned from carbon source to sink to a more variable period over 24 years. Respiration had a greater effect than photosynthesis on driving variations in NEE at all sites. A declining snowpack offset potential increases in assimilation from warmer springs, as less-insulated soils delayed start of spring green-up. No direct CO2 fertilization trend was noted in gross primary productivity, but influenced maximum net assimilation. Direct upscaling of stand-scale sites led to a larger net sink than the landscape tower. These results highlight the value of clustered, long-term carbon flux observations for understanding the diverse links between carbon and climate and the challenges of upscaling observations.

This study presents a workflow to predict the upscaled absolute permeability of the rock core direct from CT images whose resolution are not sufficient to allow direct pore-scale permeability computation. This workflow exploits the deep learning technique with the data of raw CT images of rocks and their corresponding permeability value obtained by performing flow simulation on high resolution CT images. The permeability map of a much larger region in the rock core is predicted by the trained neural network. Finally, the upscaled permeability of the entire rock core is calculated by the Darcy flow solver, and the results showed a good agreement with the experiment data. This proposed deep-learning based upscaling method allows estimating the permeability of large-scale core samples while preserving the effects of fine-scale pore structure variations due to the local heterogeneity.

The responses of tropical anvil cloud and low-level cloud to a warming climate are among the largest sources of uncertainty in our estimates of climate sensitivity. However, most research on cloud feedbacks relies on either global climate models with parameterized convection, which do not explicitly represent small-scale convective processes, or small-domain models, which cannot directly simulate large-scale circulations. We investigate how self-aggregation, the spontaneous clumping of convection in idealized numerical models, depends on cloud-radiative interactions with different cloud types, sea surface temperatures (SSTs), and stages of aggregation in simulations that form part of RCEMIP (the Radiative-Convective Equilibrium Model Intercomparison Project). Analysis shows that the presence of anvil cloud, which tends to enhance aggregation when collocated with anomalously moist environments, is reduced in nearly all models when SSTs are increased, leading to a corresponding reduction in the aggregating influence of cloud-longwave interactions. We also find that cloud-longwave radiation interactions are stronger in the majority of General Circulation Models (GCMs), typically resulting in faster aggregation compared to Cloud-system Resolving Models (CRMs). GCMs that have stronger cloud-longwave interactions tend to aggregate faster, whereas the influence of circulations is the main factor affecting the aggregation rate in CRMs.

Drought Early Warning Systems (DEWSs) aim to spatially monitor and forecast risk of water shortage to inform early, risk-mitigating interventions. However, due to the scarcity of in-situ monitoring in groundwater-dependent arid zones, spatial drought exposure is inferred using maps of satellite-based indicators such as rainfall anomalies, soil moisture and vegetation indices. On the local scale, these coarse-resolution proxy indicators provide a poor inference of groundwater availability. The improving affordability and technical capability of modern sensors significantly increases the feasibility of taking direct groundwater level measurements in data-scarce, arid regions on a larger scale. Here, we assess the potential of in-situ monitoring to provide a localized index of hydrological drought in Somaliland. We find that calibrating a lumped groundwater model with a short time series of high-frequency groundwater level observations substantially improves the quantification of local water availability when compared to satellite-based indices over the same validation period. By varying the calibration length between 1-30 weeks, we find that data collection beyond 5 weeks adds little to model calibration at all three wells. This suggests that a short monitoring campaign is suitable to improve estimations of local water availability during drought, and provide superior performance compared to regional-scale satellite-based indicators. A short calibration period has practical advantages, as it allows for the relocation of sensors and rapid characterization of a large number of wells. A monitoring system with this contextualized, local information can support earlier financing and better targeting of early actions than regional DEWSs.

The ice sheets covering Antarctica and Greenland present the greatest uncertainty in, and largest potential contribution to, future sea level rise. The uncertainty arises from a paucity of suitable observations covering the full range of ice sheet behaviors, incomplete understanding of process influences, and limitations in defining key boundary conditions for the numerical models. To investigate the impact of these uncertainties on ice sheet projections we undertook a structured expert judgement study. Here, we interrogate the findings of that study to identify the dominant drivers of uncertainty in projections and their relative importance as a function of ice sheet and time. We find that for the 21st century, Greenland surface melting, in particular the role of surface albedo effects, and West Antarctic ice dynamics, specifically the role of ice shelf buttressing, dominate the uncertainty. The importance of these effects holds under both a high-end 5°C global warming scenario and another that limits global warming to 2°C. During the 22nd century the dominant drivers of uncertainty shift. Under the 5°C scenario, East Antarctic ice dynamics dominate the uncertainty in projections, driven by the possible role of ice flow instabilities. These dynamic effects only become dominant, however, for a temperature scenario above the Paris Agreement 2°C target and beyond 2100. Our findings identify key processes and factors that need to be addressed in future modeling studies in order to reduce uncertainties in ice sheet projections.

The unusually cold springtime Arctic stratospheres of 2011, 2016 and 2020 generated substantial Polar Stratospheric Clouds (PSCs) activity and a significant ozone hole. These events were accompanied by an unusual presence of precipitating liquid clouds in the high Arctic. Satellite lidar measurements helped to identify a possible mechanistic link between tropospheric cloud formation and the PSCs. The synoptic meteorological context provided by the ERA 5 reanalysis was instrumental in the identification of potential liquid-precipitation formation scenarios related to atmospheric rivers.

Balancing socio-ecological systems among competing water demands is a difficult and complex task. Traditional approaches based on limited, linear growth optimization strategies overseen by command/control have partially failed to account for the inherent unpredictability and irreducible uncertainty affecting most water systems due to climate change. Governments and managers are increasingly faced with understanding driving-factors of major change processes affecting multifunctional systems. In the last decades, the shift to address the integrated management of water resources from a technocratic “top-down” to a more integrated “bottom-up” and participatory approach was motivated by the awareness that water challenges require integrated solutions and a socially legitimate planning process. Assuming water flows as physical, social, political, and symbolic matters, it is necessary to entwining these domains in specific configurations, in which key stakeholders and decision-makers could directly interact through social-learning. The literature on integrated water resources management highlights two important factors to achieve this goal: to deepen stakeholders’ perception and to ensure their participation as a mechanism of co-production of knowledge. Stakeholder Analysis and Governance Modelling approaches are providing useful knowledge about how to integrate social-learning in water management, making the invisible, visible. The first one aims to identify and categorize stakeholders according to competing water demands, while the second one determines interactions, synergies, overlapping discourses, expectations, and influences between stakeholders, including power-relationships. The HydroSocial Cycle (HSC) analysis combines both approaches as a framework to reinforce integrated water management by focusing on stakeholder analysis and collaborative governance. This method considers that water and society are (re)making each other so the nature and competing objectives of stakeholders involved in complex water systems may affect its sustainability and management. Using data collected from a qualitative questionnaire and applying descriptive statistics and matrices, the HSC deepens on interests, expectations, and power-influence relationships between stakeholders by addressing six main issues affecting decision-making processes: relevance, representativeness, recognition, performance, knowledge, and collaboration. The aim of this contribution is to outline this method from both theory and practice perspective by highlighting the benefits of including social sciences approaches in transdisciplinary research collaborations when testing water management strategies affecting competing and dynamic water systems.