The NOAA Physical Sciences Laboratory produces the Global Ensemble Forecasting System (GEFS) which comprises 11 ensemble members (1 control and 10 perturbation runs) for over a 36-year period (December 1984 to present), with forecasts initialized every day for the next 16 days (first 8-day forecasts obtained from a high-resolution grid and the next 8-day forecasts from a low-resolution grid). The system provides 36 variables related to a wide range of hydrometeorological processes. In this study, we assess the predictability of precipitation within the context of statistical downscaling using a minimum set of predictor variables (precipitation and temperature). We use feedforward backpropagation neural networks with a suite of training algorithms to determine which variables (features) are of most relevance at different forecast lead times. The outcome of this study will significantly benefit short-term flood forecasting using GEFS data.

Satellite data reveal widespread changes in Earth’s vegetation cover. Regions intensively attended to by humans are mostly greening due to land management. Natural vegetation, on the other hand, is exhibiting patterns of both greening and browning in all continents. Factors linked to anthropogenic carbon emissions, such as CO2 fertilization, climate change, and consequent disturbances such as fires and droughts, are hypothesized to be key drivers of changes in natural vegetation. A rigorous regional attribution at the biome level that can be scaled to a global picture of what is behind the observed changes is currently lacking. Here we analyze different datasets of decades-long satellite observations of global leaf area index (LAI, 1981–2017) as well as other proxies for vegetation changes and identify several clusters of significant long-term changes. Using process-based model simulations (Earth system and land surface models), we disentangle the effects of anthropogenic carbon emissions on LAI in a probabilistic setting applying causal counterfactual theory. The analysis prominently indicates the effects of climate change on many biomes – warming in northern ecosystems (greening) and rainfall anomalies in tropical biomes (browning). The probabilistic attribution method clearly identifies the CO2 fertilization effect as the dominant driver in only two biomes, the temperate forests and cool grasslands, challenging the view of a dominant global-scale effect. Altogether, our analysis reveals a slowing down of greening and strengthening of browning trends, particularly in the last 2 decades. Most models substantially underestimate the emerging vegetation browning, especially in the tropical rainforests. Leaf area loss in these productive ecosystems could be an early indicator of a slowdown in the terrestrial carbon sink. Models need to account for this effect to realize plausible climate projections of the 21st century.

This article is composed of one integrated commentary about the state of ICON principles (Goldman et al., 2021) in natural hazards and a discussion on the opportunities and challenges of adopting them. Natural hazards pose risks to society, infrastructure, and the environment. Hazard interactions and their cascading phenomena in space and time can further intensify the impacts. Natural hazards’ risks are expected to increase in the future due to environmental, demographic, and socioeconomic changes. It is important to quantify and effectively communicate risks to inform the design and implementation of risk mitigation and adaptation strategies. Multihazard multisector risk management poses several nontrivial challenges, including: i) integrated risk assessment, ii) Earth system data-model fusion, iii) uncertainty quantification and communication, and iv) crossing traditional disciplinary boundaries. Here, we review these challenges, highlight current research and operational endeavors, and underscore diverse research opportunities. We emphasize the need for integrated approaches, coordinated processes, open science, and networked efforts (ICON) for multihazard multisector risk management.

A 24-hour 2D time-lapse electrical resistivity imaging (ERI) survey was conducted in an altered mangrove forest on a barrier island in southeast Florida, USA, to (1) assess the method’s utility in hypersaline conditions and (2) understand how trees respond to hypersaline conditions. ERI measurements serve as a proxy for pore water salinity and saturation. Here, resistivity changes suggest a lag between the tidal cycle and changes in ground resistivity. ERI data show that overall changes within 24 hours are very small, but there is more variability in resistivity in the root zone of mangroves than in open salt flat portions along a fixed transect. Two to three hours after sunset, root zone resistivity increased from initial, midday conditions. Overnight, the root zone was less resistive than midday. By sunrise, root zone resistivity was once again higher than initial conditions. Measurements from the salt flat where roots are absent remained generally constant throughout the survey. Thus, changes in resistivity over time are inferred to reflect mangrove tree physiological influences related to diel water use. A mechanistic explanation for the decreased resistivity two hours after sunset from the re-distribution of salts to the soil around the roots is the Cohesion-Tension Theory, which suggests that trees continue water uptake after sunset to balance the pressure after leaf stomates have closed. The corresponding overnight drop in ground resistivity just prior to sunrise may be explained by redistribution of freshwater from the tree to the soil that was delayed until the early morning hours. The limited period of data acquisition limits definitive data interpretations, but the study illustrates the monitoring potential of ERI in hypersaline environments such as a mangrove forest.

Understanding past changes in precipitation extremes could help us predict their dynamics under future conditions. We present a novel approach for analyzing trends in extremes and attributing them to changes in the local precipitation regime. The approach relies on the separation between intensity distribution and occurrence frequency of storms. We examine the relevant case of the eastern Italian Alps, where significant trends in annual maximum precipitation over the past decades were observed. The model is able to reproduce observed trends at all durations between 15 minutes and 24 hours, and allows to quantify trends in extreme return levels. Despite the significant increase in storms occurrence and typical intensity, the observed trends can be only explained considering changes in the tail heaviness of the intensity distribution, that is the proportion between heavy and mild events. Our results suggest these are caused by an increased proportion of summer convective storms.

Monitoring the hydraulic properties within subsurface fractures is vitally important in the contexts of geoengineering developments and earthquakes. Geophysical observations are promising tools for remote determination of subsurface hydraulic properties; however, quantitative interpretations are hampered by the paucity of relevant geophysical data for fractured rock masses. This study explored simultaneous changes in hydraulic and geophysical properties of natural rock fractures with increasing normal stress and correlated these property changes through coupling experiments and digital fracture simulations. We show that electrical resistivity is linked with permeability and flow area regardless of fracture roughness, whereas elastic wave velocity is roughness dependent. We also are able to categorize fracture flow patterns as aperture-dependent, aperture-independent, or disconnected flows, with transitions at specific stress levels. Elastic wave velocity offers potential for detecting the transition between aperture-dependent flow and aperture-independent flow, and resistivity is sensitive to detect the connection/disconnection of the fracture flow.

We aim to identify the relative importance of vapour pressure deficit (VPD), soil water content (SWC) and photosynthetic photon flux density (PPFD) as drivers of tree canopy conductance, which is a key source of uncertainty for modelling vegetation responses under climate change. We use sap flow time series of 1858 trees in 122 sites from the SAPFLUXNET global database to obtain whole-tree canopy conductance (G). The coupling, defined as the percentage of variance (R2) of G explained by the three main hydrometeorological drivers (VPD, SWC and PPFD), was evaluated using linear mixed models. For each hydrometeorological driver we assess differences in coupling among biomes, and use multiple linear regression to explain R2 by climate, soil and vegetation structure. We found that in most areas tree canopy conductance is better explained by VPD than by SWC or PPFD. We also found that sites in drylands are less coupled to all three hydrometeorological drivers than those in other biomes. Climate, soil and vegetation structure were common controls of all three hydrometeorological couplings with G, with wetter climates, fine textured soils and tall vegetation being associated to tighter coupling. Differences across sites in the hydrometeorological coupling of tree canopy conductance may affect predictions of ecosystem dynamics under future climates, and should be accounted for explicitly in models.

Streamflow prediction is a long-standing hydrologic problem. Development of models for streamflow prediction often requires incorporation of catchment physical descriptors to characterize the associated complex hydrological processes. Across different scales of catchments, these physical descriptors also allow models to extrapolate hydrologic information from one catchment to others, a process referred to as “regionalization”. Recently, in gauged basin scenarios, deep learning models have been shown to achieve state of the art regionalization performance by building a global hydrologic model. These models predict streamflow given catchment physical descriptors and weather forcing data. However, these physical descriptors are by their nature uncertain, sometimes incomplete, or even unavailable in certain cases, which limits the applicability of this approach. In this paper, we show that by assigning a vector of random values as a surrogate for catchment physical descriptors, we can achieve robust regionalization performance under a gauged prediction scenario. Our results show that the deep learning model using our proposed random vector approach achieves a predictive performance comparable to that of the model using actual physical descriptors. The random vector approach yields robust performance under different data sparsity scenarios and deep learning model selections. Furthermore, based on the use of random vectors, high-dimensional characterization improves regionalization performance in gauged basin scenario when physical descriptors are uncertain, or insufficient.

The factors driving variability in rainfall stable water isotopes (specifically δ¹⁸O and deuterium excess, d = δ²H - 8 δ¹⁸O) were studied in a 13-year dataset of daily rainfall samples from coastal southwestern Western Australia (SWWA). Backwards dispersion modelling, automatic synoptic type classification, and a statistical model were used to establish causes of variability on a daily scale; and predictions from the model were aggregated to longer temporal scales to discover the cause of variability on multiple timescales. Factors differ between δ¹⁸O and d and differ according to temporal scale. Rainfall intensity, both at the observation site and upwind, was most important for determining δ¹⁸O and this relationship was robust across all time scales (daily, seasonal, and interannual) as well as generalizing to a second observation site. The sensitivity of δ¹⁸O to rainfall intensity makes annual mean values particularly sensitive to the year’s largest events. Projecting the rainfall intensity relationship back through ∼ 100 years of precipitation observations can explain ∼ 0.2-0.4‰ shifts in rainfall δ¹⁸O. Twentieth century speleothem records from the region exhibit signals of a similar magnitude, indicating that rainfall intensity should be taken into account during the interpretation of regional climate archives. For d, humidity during evaporation from the ocean was the most important driver of variability at the daily scale, as well as explaining the seasonal cycle, but source humidity failed to explain the longer-term interannual variability.

Covid- 19 dominantly impacted the Indian agricultural sector. During the period of COVID-19 the southwest monsoon covered a major part of the country, thus resulting in an increase of 9 percent coverage in rainfall than the usual average period. Due to the good amount of rainfall the area under cultivation during the kharif season stood above 4.8% than the previous year. During, the initial lockdown period the agriculture has not been much affected and an increase in migration resulted an increase in people employed in agriculture. Through regression analysis the relationship between the yield and rainfall has been determined. The R2 values have been calculated and the spatial relationship between them has been established. Regions with higher R2 values have been found to be more dominantly affected by Covid-19, though in certain areas strong R2 has shown a weaker spatial relationship owing to certain other factors and policies taken by the Government. Therefore, regression analysis can be used as a suitable method to study the relationship of rainfall and agricultural yield during Covid-19. Keywords: Agriculture, Regression Analysis, Spatial relationship, Rainfall, Covid-19.

The interface between rivers and groundwater is a key driver for the turnover of reactive nitrogen compounds, that cause eutrophication of rivers and endanger drinking-water production from groundwater. Molecular-biological data and omics tools have been used to characterize microorganisms responsible for the turnover of nitrogen compounds. While transcripts of functional genes and enzymes are used as measures of microbial activity it is not yet clear how they quantitatively relate to actual turnover rates under variable environmental conditions. We developed a reactive-transport model for denitrification that simultaneously predicts the distributions of functional-gene transcripts, enzymes and reaction rates. Applying the model, we evaluate the response of transcripts and enzymes at the river–groundwater interface to stable and dynamic hydrogeochemical regimes. While functional-gene transcripts respond to short-term (diurnal) fluctuations of substrate availability and oxygen concentrations, enzyme concentrations are stable over such time scales. The presence of functional-gene transcripts and enzymes globally coincides with the zones of active denitrification. However, transcript and enzyme concentrations do not directly translate into denitrification rates in a quantitative way because of non-linear effects and hysteresis caused by variable substrate availability and oxygen inhibition. Based on our simulations, we suggest that molecular-biological data should be combined with aqueous chemical data, which can typically be obtained at higher spatial and temporal resolution, to parameterize and calibrate reactive-transport models.

The Chilean central Andes are known for its variety of cryospheric landforms, which have included almost every kind of glacier since their first exploration back in the XIX century. However, there has been a severe reduction of the glacierized area since the 1950s, driven by climate change and enhanced due to the megadrought, which has endured for over a decade in the region. Such decline in glacier volume combined with temperature increasing and precipitation reduction can lead to different types of instabilities. In mountainous regions of high public affluence, glacial instabilities are considered as potential hazards leading to the loss of lives and infrastructure. Here we analyze the Rio Volcan basin (-32.82/-70.00), located 40 km east of Santiago city in the international border with Argentina. The region is known for its closeness to the capital, which favors outdoor activities and hydroelectric power development. Elevation ranges from 3380 to over 6000 m a.s.l. at the San José Volcanic Complex, allowing conditions for coexistence of mountain glaciers, valley glaciers, rock glaciers and glaciarets. According to the public Chilean Glacier Inventory, there are more than 140 mapped cryoforms occupying an area of 57 km2 . Beside snow avalanches, there are multiple factor that provide ideal conditions for cryospheric hazards involving glaciers. Some of those factors are pointed out on the following: The presence of an active volcanic complex sets up the triggering agent for lahars and mixed snow/ice avalanche occurrence. There are three moraine-dammed glacial lakes with a cumulated area of up to 24 hectares in front of the El Morado glacier and two innominates. The lakes are still enlarging along with the glacier shrinkage, conforming three potential glofs in the region. Several debris-free glaciers have a very steep front, steeper than 30 degrees, favoring the occurrence of ice falls and ice avalanches. There is a reported surge event in the Nieves Negras glacier, located at the south face of the San Jose; volcano. The latter would have happened in the late 1940s according to literature. In addition, at least four glaciers showed abnormal advance rates in the early 1990s of up to 100 m/yr, along with the surge-like behavior of the Loma Larga glacier. Providing further knowledge of this complex region is key in order to enhance understanding and hazard management on a day to day basis.

This study deals with the riverbed of the Columbia river in the vicinity of the Hanford 300 Area study site in eastern Washington, where fluctuations in river stage take place both naturally (i.e. seasonally) and in conjunction with hydroelectric power dam operations. These fluctuations create conditions conducive to the influx and transport of fine-grained POM (a biological colloid originating from the river water and/or in situ periphyton production), within near-surface riverbed sediments. Although a great deal is known about dissolved organic matter (DOM) transport and metabolism in hyporheic zone sediments, there is a paucity of quantitative information on POM dynamics and its influence on hyporheic zone biogeochemistry (e.g. dissolved oxygen dynamics). We have developed a hydrobiogeochemical model capable of simulating the transport and metabolism of POM and its impact on dissolved oxygen (DO) distribution within the riverbed as influenced by periodic changes in river stage and fluid flow rate and direction. The model was employed as a tool to interpret the results of in situ measurements of POM intrusion into the riverbed made using “POM traps” emplaced within the upper 20 cm of the riverbed, as well as real-time in situ dissolved oxygen concentrations determined with a novel optical sensor buried directly in the riverbed at 20 cm depth. The simulations reproduced the accumulation of fresh POM within the upper few 5 cm of the riverbed observed in field POM trap deployments. Once sufficient surface POM accumulation takes place, an underlying zone of DO depletion develops as a consequence of variation in the rate of fluid exchange and POM/DOM degradation. The model predicted cyclic, hydrologically-driven variations in near-surface DO that are consistent with the results of the in situ DO probe deployments together with parallel measurements of fluid conductivity and hydrologic pressure. Our results suggest a complex interplay between fluid flow rate/direction and DO distribution that has important implication for riverbed biogeochemical dynamics at a variety of scales, as influenced by hydrological variability as well as the relative intensity of POM input and the availability of oxygen and other electron acceptors for microbial metabolism.

The impacts of global change especially the recent climate-related extremes such as floods and droughts reveal significant vulnerability and exposure of freshwater ecosystems and related human systems to current climate variability. However, the effects of the extreme drought in the Okavango Delta system are not well understood and documented. Therefore, the objective of this use case was to apply the products from Digital Earth Africa namely: the Water Observation from Space (WOfS) derived from Landsat, vegetation cover baseline derived from Sentinel 2 data; and data from the meteorological agencies such as rainfall and measured river discharge data to evaluate the effects of drought in the Okavango Delta wetland system in relation to its upstream areas in Angola. In particular, we used the 2019 drought as a case study to assess inundation extent and vegetation cover dynamics with an emphasis on floodplain and dryland vegetation. Our preliminary results reveal that the Okavango Delta permanent marshes are resilient to drought, whereas seasonal floodplains are susceptible to drought. Further, we discovered that the geospatial location of floodplains has a direct effect on the timing of desiccation, with the western tributaries that flow into Lake Ngami and Thamalakane River being the last to dry out due to drought. In addition, we found that the drought phenomenon in the Cubango-Okavango River Basin region started earlier than 2019 spanning over a period of 5 years; with 2018 as the year when the wetland system reached a minimum threshold for a tipping point triggered by the 2019 drought. In addition, the results contribute to the development of large-scale drought risk information and products for the Cubango- Okavango River Basin with a major focus in the Okavango Delta. Further, this use case provides recent baseline information on the effects of drought on vegetation cover and river flows in the Okavango Delta system at a landscape approach, which are essential elements for making informed science-based decisions on climate risks management and Sustainable Development Goals (SDGs) by relevant authorities in the Okavango Delta and the whole of Cubango-Okavango River Basin. In conclusion, this use case will be upscaled to other transboundary river basins in the Southern Africa Development Community.

Water isotopes measured in Antarctic ice cores enable reconstruction at the first order of the past temperature variations. However, the seasonality of the precipitation and episodic events, including synoptic-scale disturbances, influence the isotopic signals recorded in ice cores. In this study, we adopted an isotope-enabled atmospheric general circulation model from 1981 to 2010 to investigate variations in climatic factors in δ18O of precipitation (δ18Op) at Dome Fuji, East Antarctica. The Southern Annular Mode (SAM), the primary mode of atmospheric circulation in the southern mid-high latitudes, significantly contributes to the isotope signals. Positive δ18Op anomalies, especially in the austral winter, are linked to the negative polarity of the SAM, which weakens westerly winds and increases the southward inflow of water vapor flux. Daily variations in temperature and δ18Op in Dome Fuji are significantly small in the austral summer, and their contribution to the annual signals is limited. The isotope signals driven by the SAM are a locational feature of Dome Fuji, related to the asymmetric component of the large-scale atmospheric pattern.

Minnesota is the U.S. state with the strongest winter warming in the contiguous United States. We performed regional climate projections at 10 km horizontal resolution using the WRF model forced by an ensemble of eight CMIP5 GCMs. The selected GCMs have previously been found to be in relatively good agreement with observations compared to other members of the CMIP5 model ensemble. Our projections suggest ongoing warming in all seasons, especially in winter, as well as shallower snow cover and fewer days with snow cover. On the other hand, we expect significant increases in spring and early summer heavy precipitation events. Our comparisons between different time slices and two different emission scenarios indicate a climate for the state of Minnesota at the end of the 21st century that is significantly different from what has been observed by the end of the 20th century. Winters and summers are expected to be up to 6oC and 4oC warmer, respectively, over northern and central Minnesota and spring precipitation may increase by more than 1 mm d-1 over northern Minnesota. Especially over the central part of the state, winter snow height is suggested to decrease by more than 0.5 meters and the number of days per year with snow height of more than 0.0254 meters (one inch) is expected to decrease by up to 60.

Several studies have focused on the importance of river bathymetry (channel geometry) in hydrodynamic routing along individual reaches. However, its effect on other watershed processes such as infiltration and surface water (SW) – groundwater (GW) interactions has not been explored across large river networks. Surface and subsurface processes are interdependent, therefore, errors due to inaccurate representation of one watershed process can cascade across other hydraulic or hydrologic processes. This study hypothesizes that accurate bathymetric representation is not only essential for simulating channel hydrodynamics but also affects subsurface processes by impacting SW-GW interactions. Moreover, quantifying the effect of bathymetry on surface and subsurface hydrological processes across a river network can facilitate an improved understanding of how bathymetric characteristics affect these processes across large spatial domains. The study tests this hypothesis by developing physically-based distributed models capable of bidirectional coupling (SW-GW) with four configurations with progressively reduced levels of bathymetric representation. A comparison of hydrologic and hydrodynamic outputs shows that changes in channel geometry across the four configurations has a considerable effect on infiltration, lateral seepage, and location of water table across the entire river network. For example, when using bathymetry with inaccurate channel conveyance capacity but accurate channel depth, peak lateral seepage rate exhibited 58% error. The results from this study provide insights into the level of bathymetric detail required for accurately simulating flooding-related physical processes while also highlighting potential issues with ignoring bathymetry across lower order streams such as spurious backwater flow, inaccurate water table elevations, and incorrect inundation extents.

Modelling of the land surface water-, energy-, and carbon balance provides insight into the behaviour of the Earth System, under current and future conditions. Currently, there exists a substantial variability between model outputs, for a range of model types, whereby differences between model input parameters could be an important reason. For large-scale land surface, hydrological, and crop models, soil hydraulic properties (SHP) are required as inputs, which are estimated from pedotransfer functions (PTFs). To analyse the functional sensitivity of widely used PTFs, the water fluxes for different scenarios using HYDRUS-1D was simulated and predictions compared. The results showed that using different PTFs causes substantial variability in predicted fluxes. In addition, an in-depth analysis of the soil SHPs and derived soil characteristics was performed to analyse why the SHPs estimated from the different PTFs cause the model to behave differently. The results obtained provide guidelines for the selection of PTFs in large scale models. The model performance in terms of numerical stability, time-integrated behaviour of cumulative fluxes, as well as instantaneous fluxes was evaluated, in order to compare the suitability of the PTFs. Based on this, the Rosetta, Wösten, and Tóth PTF seem to be the most robust PTFs for the Mualem van Genuchten SHPs and the PTF of Cosby et al. (1984) for the Brooks Corey functions. Based on our findings, we strongly recommend to harmonize the PTFs used in model inter-comparison studies to avoid artefacts originating from the choice of PTF rather from different model structures.

Excess nitrogen (N) from anthropogenic sources deteriorates freshwater resources. Actions taken to reduce N inputs to the biosphere often show no or only delayed effects in receiving surface waters hinting at large legacy N stores built up in the catchments soils and groundwater. Here, we quantify transport and retention of N in 238 Western European catchments by analyzing a unique data set of long-term N input and output time series. We find that half of the catchments exhibited peak transport times larger than five years with longer times being evident in catchments with high potential evapotranspiration and low precipitation seasonality. On average the catchments retained 72% of the N from diffuse sources with retention efficiency being specifically high in catchments with low discharge and thick, unconsolidated aquifers. The estimated transport time scales do not explain the observed N retention, suggesting a dominant role of biogeochemical legacy in the catchments’ soils rather than a legacy store in the groundwater. Future water quality management should account for the accumulated biogeochemical N legacy to avoid long-term leaching and water quality deteriorations for decades to come.

Water-mediated linkages that connect landscape components are collectively referred to as hydrologic connectivity. In river-floodplain systems, quantifying hydrologic connectivity enables descriptions of hydrologic function that emerge from complex, heterogeneous interactions of underlying geomorphic, climatic and biologic controls. Here, we measure hydrologic connectivity using field indicators and develop a continuous connectivity metric that represents a vector strength between a source along the North St Vrain river to ten surface water target sites within the river-floodplain system. To measure this connectivity strength, we analyzed hydrometric, injected conservative tracers, and natural occurring geochemical and microbial indicators across streamflows in 2018. We developed empirical models of hydrologic connectivity as a function of river stage to predict daily connectivity strength across multiple floodplain sites for five years between May and September of 2016-2020. Three sites were either consistently connected or disconnected to the river, while seven varied across time in their hydrologic connectivity strength. Of the sites with variable connectivity, some disconnected very quickly and others had a prolonged disconnection phase. By scaling site dynamics to the system scale, we found across-system hydrologic connectivity always increased with streamflow while across-system variance in hydrologic connectivity peaked at intermediate streamflow. At sites with intermittent connections to the river, river stage disconnection thresholds were variable (308 to 650 mm) and their connectivity dynamics were sensitive to inter-annual variation in streamflows, suggesting that future connectivity behavior under climate change will depend on how flow durations change across a range of flow states.

Several bills moving through Congress are likely to provide significant funding for expanding research and results in climate change solutions (CCS). This is also a priority of the Biden-Harris Administration. The National Science Foundation (NSF) will be expected to distribute and manage much of this funding through its grant processes. Effective solutions require both a continuation and expansion of research on climate change–to understand and thus plan for potential impacts locally to globally and to continually assess solutions against a changing climate–and rapid adoption and implementation of this science with society at all levels. NSF asked AGU to convene its community to help provide guidance and recommendations for enabling significant and impactful CCS outcomes by 1 June. AGU was asked in particular to address the following: 1. Identify the biggest, more important interdisciplinary/convergent challenges in climate change that can be addressed in the next 2 to 3 years 2. Create 2-year and 3-year roadmaps to address the identified challenges. Indicate partnerships required to deliver on the promise. 3. Provide ideas on the creation of an aggressive outreach/communications plan to inform the public and decision makers on the critical importance of geoscience. 4. Identify information, training, and other resources needed to embed a culture of innovation, entrepreneurialism, and translational research in the geosciences. Given the short time frame for this report, AGU reached out to key leaders, including Council members, members of several committees, journal editors, early career scientists, and also included additional stakeholders from sectors relevant to CCS, including community leaders, planners and architects, business leaders, NGO representatives, and others. Participants were provided a form to submit ideas, and also invited to two workshops. The first was aimed at ideation around broad efforts and activities needed for impactful CCS; the second was aimed at in depth development of several broad efforts at scale. Overall, about 125 people participated; 78 responded to the survey, 82 attended the first workshop, and 28 attended the more-focused second workshop (see contributor list). This report provides a high-level summary of these inputs and recommendations, focusing on guiding principles and several ideas that received broader support at the workshops and post-workshop review. These guiding principles and ideas cover a range of activities and were viewed as having high importance for realizing impactful CCS at the scale of funding anticipated. These cover the major areas of the charge, including research and solutions, education, communication, and training. The participants and full list of ideas and suggestions are provided as an appendix. Many contributed directly to this report; the listed authors are the steering committee.

Anthropogenic global warming caused by increased atmospheric carbon forcing is expected to cause a decrease in peak snow water equivalent (SWE), shift the timing of snowmelt to earlier in the year, and lead to slower melt rates in the mountains of the Western United States. High-elevation forests in mountainous terrain represent a critical carbon sink. Understanding the ecohydrology of subalpine forests is crucial for assessing the health of these sinks. The Niwot Ridge Long Term Ecological Research station, located at 3000 m amsl in the southern Rocky Mountains of Colorado, receives just over 1 m of annual precipitation mostly as snow, supporting a persistent seasonal snowpack in alpine and subalpine ecosystems. Previous studies show that longer growing season length is correlated with shallower snowpack, earlier spring onset and reduced net CO2 uptake. Co-located sensors provide over 20 years of continuous SWE and eddy covariance (EC) data, allowing for robust direct comparison of snow and carbon phenomena in a high-elevation catchment. Linear regression and time series analysis was performed on snowmelt, meteorological, phenological and ecosystem productivity variables. Peak productivity is correlated with peak SWE (R2=0.54) and further correlated with snowmelt disappearance (R2=0.38) and the timing of spring growth onset (R2=0.30). Timing of both peak productivity and spring growth onset are correlated with snowmelt and meteorological variables. A multivariable regression of meteorological variables, timing of spring growth onset, a temporal trend, and snowmelt rate and explains 94% of interannual variability in the timing of peak forest productivity. These results develop support and introduce new evidence for the existing studies of Niwot Ridge ecohydrology. Future work will investigate the meteorological and hydrological record extending back to 1979 and the long-term trends in snowmelt and forest productivity.

River deltas commonly have a heterogeneous substratum of alternating peat, clay and sand deposits. This has important consequences for the river bed development and in particular for scour hole formation. When the substratum consists of an erosion resistant top layer, erosion is retarded. Upon breaking through a resistant top layer and reaching an underlying layer with higher erodibilty, deep scour holes may form within a short amount of time. The unpredictability and fast development of these scour holes makes them difficult to manage, particularly where the stability of dikes and infrastructure is at stake. In this paper we determine how subsurface lithology controls the bed elevation in net incising river branches, particularly focusing on scour hole initiation, growth rate, and direction. For this, the Rhine-Meuse Estuary forms an ideal study site, as over 100 scour holes have been identified in this area, and over 40 years of bed level data and thousands of core descriptions are available. It is shown that the subsurface lithology plays a crucial role in the emergence, shape, and evolution of scour holes. Although most scour holes follow the characteristic exponential development of fast initial growth and slower final growth, strong temporal variations are observed, with sudden growth rates of several meters per year in depth and tens of meters in extent. In addition, we relate the characteristic build-up of the subsurface lithology to specific geometric characteristics of scour holes, like large elongated expanding scour holes or confined scour holes with steep slopes. As river deltas commonly have a heterogeneous substratum and often face channel bed erosion, the observations likely apply to many delta rivers. These findings call for thorough knowledge of the subsurface lithology, as without it, scour hole development is hard to predict and can lead to sudden failures of nearby infrastructure and flood defence works.

Surrogate models replace computationally expensive simulations of physically-based models to obtain accurate results at a fraction of the time. These surrogate models, also known as metamodels, have been employed for analysis, control, and optimisation of water distribution and urban drainage systems. With the advent of machine learning (ML), water engineers have increasingly resorted to these data-driven techniques to develop metamodels of urban water networks. In this manuscript, we review 31 recent papers on ML-based metamodeling of urban water networks to outline the state-of-the-art of the field, identify outstanding gaps, and propose future research directions. For each paper, we critically examined the purpose of the metamodel, the metamodel characteristics, and the applied case study. The review shows that current metamodels suffer several drawbacks, including i) the curse of dimensionality, hindering implementation for large case studies; ii) black-box deterministic nature, limiting explainability and applicability; and iii) rigid architecture, preventing generalization across multiple case studies. We argue that researchers should tackle these issues by resorting to recent advancements in ML concerning inductive biases, robustness, and transferability. The recently developed Graph Neural Network architecture, which extends deep learning methods to graph data structures, is a preferred candidate for advancing surrogate modelling in urban water networks. Furthermore, we foresee increasing efforts for complex applications where metamodels may play a fundamental role, such as uncertainty analysis and multi-objective optimisation. Lastly, the development and comparison of ML-based metamodel can benefit from the availability of new benchmark datasets for urban drainage systems and realistic complex networks.