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.

In coral reef studies, mass spectrometry methods are widely applied to determine geochemical proxies in corals as a tool to evaluate seawater changes. As the coral grows, its skeleton forms annual bands similar to the growth rings found in trees. The density of the calcium carbonate skeletons changes as the water temperature, light, and nutrient conditions change. The elements stored within these bands can provide insight into the changing conditions of seawater over the entire lifetime of the coral, and serve as useful environmental records. Corals incorporate trace elements that can be precisely measured using high-resolution techniques, such as Laser Ablation-Inductively Coupled Plasma-Mass Spectrometry (LA-ICP-MS). This analytical tool offers high levels of precision to determine the distribution of trace elements along the annual bands of coral skeletons. This approach can serve to monitor fixed-point time-series for water quality research, as well as large-scale observations in ocean science. Ultimately, this procedure can be applied to reconstruct past climate oscillation episodes and/or to quantify the impacts of marine pollution on coral reefs. The benefits of techno-scientific aspects of new and established mass spectrometry applications in coral reef research hold great promise that may continue to be improved in future studies. Given the current climate crisis, this issue requires accurate measurements to increase our understanding on the impacts that have become more frequent and intense.

The Distributed Hydrology Soil Vegetation Model (DHSVM) code was parallelized for distributed memory computers using the Global Arrays (GA) programming model. To analyze parallel performance, DHSVM was used to simulate the hydrology in two river basins of significant size located in the northwest continental United States and southwest Canada at 90~m resolution: the (1) Clearwater (25,000~km) and (2) Columbia (668,000~km) River basins. Meteorological forcing applied to both basins was dynamically down-scaled from a regional reanalysis using the Weather Research and Forecasting (WRF) model and read into DHSVM as 2D maps for each time step. Parallel code speedup was significant. Run times for 1-year simulations were reduced by an order of magnitude for both test basins. A maximum parallel speedup of 105 was attained with 480 processors while simulating the Columbia River basin. Speedup was limited by input-dominated tasks, particularly the input of meteorological forcing data.

A striking feature of the Earth system is that the Northern and Southern Hemispheres reflect identical amounts of sunlight. This hemispheric albedo symmetry comprises two asymmetries: The Northern Hemisphere is more reflective in clear skies, whereas the Southern Hemisphere is cloudier. The most-cited explanation is that the clear-sky asymmetry is primarily due to the relatively-bright continents being disproportionately located in the Northern Hemisphere. However, it is the atmosphere, not the surface, that contributes most to the clear-sky asymmetry. Here we show that the continent-based component of the clear-sky surface asymmetry is largely offset by greater reflection from the Southern Hemisphere poles, allowing the clear-sky asymmetry to be dominated by aerosol. Climate model simulations suggest that aerosol emissions since the pre-industrial era have driven a large increase in the clear-sky asymmetry that would reverse in future low-emission scenarios. High-emission scenarios also show a decrease in asymmetry, but instead driven by declines in Northern Hemisphere ice and snow cover. Strong clear-sky hemispheric albedo asymmetry is therefore a transient, rather than fixed, feature of Earth’s climate. If all-sky symmetry is maintained despite changes in the clear-sky asymmetry, compensating cloud changes would have uncertain but important implications for Earth’s energy balance and hydrological cycle.

The presence of solute concentration fluctuations at spatial scales much below the scale of resolution is a major challenge for modeling reactive transport in porous media. Overlooking small-scale fluctuations, which is the usual procedure, often results in strong disagreements between field observations and model predictions, including, but not limited to, the overestimation of e˙ective reaction rates. Existing innovative approaches that account for local reactant segregation do not provide a general mathematical formulation for the generation, transport and decay of these fluctuations and their impact on chemical reactions. We propose a Lagrangian formulation based on the random motion of fluid particles carrying solute concentrations whose departure from the local mean is relaxed through multi-rate interaction by exchange with the mean (MRIEM). We derive and analyze the macroscopic description of the local concentration covariance that emerges from the model, showing its potential to simulate the dynamics of mixing-limited processes. The action of hydrodynamic dispersion on coarse-scale concentration gradients is responsible for the production of local concentration covariance, whereas covariance destruction stems from the local mixing process represented by the MRIEM formulation. The temporal evolution of integrated mixing metrics in two simple scenarios shows the trends that characterize fully-resolved physical systems, such as a late-time power-law decay of the relative importance of incomplete mixing with respect to the total mixing. Experimental observations of mixing-limited reactive transport are successfully reproduced by the model.

Of immediate widespread concern is the accelerating transition from Holocene-like weather patterns to unknown, and likely unstable, Anthropocene patterns. A fell example is irreversible Arctic phase change. It is not clear if existing AOGCMs are adequate to model anticipated global impacts in detail; however, the GISS ModelE AOGCM can be used to locally compare and extend the PIOMAS Arctic ocean historical ice-volume dataset into the near future. Arctic Amplification (AA) mechanisms are poorly understood; to enable timely results, a simple linear, Arctic TOA grid-boundary energy-input is used to enforce AA, avoiding the perils of arbitrary modification of relatively well-studied parameterizations (e.g., restriction of cloud-top height to induce local warming). Only PIOMAS springtime/max and fall/min Arctic ice-volume decadal, linear trends were enforced. This temporally-broad grid-boundary modification produces a surprisingly detailed consonance with 10 out of 12 temporal profiles falling within 1-sigma of PIOMAS temporal data for the entire history modeled (2003 to 2021). The data are then integrated to 2050. The result is a zero-ice-volume, summer/fall half-year, beginning ca. 2035 (onset 1-sigma of ± ~5 years), with mean annual Arctic temperatures increasingly trending above freezing. Persistent, Arctic phase change follows this half-year transition about 20 years later. Also present in later stages, the 500 hPa height minimum is no longer nearly-coincident with the pole, suggesting jet stream disruption and its consequences. Hypothesized large clathrate-methane releases likely associated with Arctic temperature and phase change are also examined. A basic assumption is that the Arctic ice (i.e., temperature) must be preserved at all costs. This work establishes a reasonably detailed timeline for the Arctic phase change based on well-studied AOGCM physics, slightly tuned to decades of PIOMAS data. This result also points to the Arctic as a key, near-term site for localized, nondestructive intervention to mitigate Arctic phase change (e.g., Stjern [2018]), thereby slowing the Holocene -> Anthropocene growing-season disruption. Although such an intervention cannot itself accomplish the requirements of the IPCC SP-15 [2018], nor Planetary Boundaries theory, delaying the Arctic phase change will likely extend the time-window for accomplishing those critical tasks and ultimately to at least slow the rate of increase of climate emergencies.

Recent breakthroughs in artificial intelligence (AI), and particularly in deep learning (DL), have created tremendous excitement and opportunities in the earth and environmental sciences communities. To leverage these new ‘data-driven’ technologies, however, one needs to understand the fundamental concepts that give rise to DL and how they differ from ‘process-based’, mechanistic modelling. This paper revisits those fundamentals and addresses 10 questions often posed by earth and environmental scientists with the aid of a real-world modelling experiment. The overarching objective is to contribute to a future of AI-assisted earth and environmental sciences where DL models can (1) embrace the typically ignored knowledge base available, (2) function credibly in ‘true’ out-of-sample prediction, and (3) handle non-stationarity in earth and environmental systems. Comparing and contrasting earth and environmental problems with prominent AI applications, such as playing chess and trading in stock markets, provides critical insights for better directing future research in this field.

Hyporheic zone reaction rates are highest just below the sediment-water interface, in a shallow region called the benthic biolayer. Vertical variability of hyporheic reaction rates leads to unexpected reaction kinetics for stream-borne solutes, compared to classical model predictions. We show that deeper, low-reactivity locations within the hyporheic zone retain solutes for extended periods, which delays reactions and causes solutes to persist at higher concentrations in the stream reach than would be predicted by classical approaches. These behaviors are captured by an upscaled model that reveals the fundamental physical and chemical processes in the hyporheic zone. We show how time scales of transport and reaction within the biolayer control solute retention and transformation at the stream scale, and we demonstrate that accurate assessment of stream-scale reactivity requires methods that integrate over all travel times.

Extensive loss of salt marshes in back-barrier tidal embayments is undergoing worldwide as a consequence of land-use changes, wave-driven lateral marsh erosion, and relative sea-level rise compounded by mineral sediment starvation. However, how salt-marsh loss affects the hydrodynamics of back-barrier systems and feeds back into their morphodynamic evolution is still poorly understood. Here we use a depth-averaged numerical hydrodynamic model to investigate the feedback between salt-marsh erosion and hydrodynamic changes in the Venice Lagoon, a large microtidal back-barrier system in northeastern Italy. Numerical simulations are carried out for past morphological configurations of the lagoon dating back up to 1887, as well as for hypothetical scenarios involving additional marsh erosion relative to the present-day conditions. We demonstrate that the progressive loss of salt marshes significantly impacted the Lagoon hydrodynamics, both directly and indirectly, by amplifying high-tide water levels, promoting the formation of higher and more powerful wind waves, and critically affecting tidal asymmetries across the lagoon. We also argue that further losses of salt marshes, partially prevented by restoration projects and manmade protection of salt-marsh margins against wave erosion, which have been put in place over the past few decades, limited the detrimental effects of marsh loss on the lagoon hydrodynamics, while not substantially changing the risk of flooding in urban lagoon settlements. Compared to previous studies, our analyses suggest that the hydrodynamic response of back-barrier systems to salt-marsh erosion is extremely site-specific, depending closely on the morphological characteristics of the embayment as well as on the external climatic forcings.

In general, calibration of a hydrologic model is essential to better simulate the basin processes and behaviour by fitting the model simulated fluxes to observed fluxes. A major challenge in the calibration process is to choose the appropriate length of the observed data series and spatio-temporal resolution of the model schematization. We present a multi-case calibration approach for determining three pillars of an optimum hydrological model configuration: calibration data length, spin-up period and spatial resolution of the hydrological model. The approach is evaluated for the Moselle River basin using calibration and validation results from the spatially distributed meso-scale Hydrological Model (mHM) for 105 different cases representing the combinations of three calibration data lengths, seven spin-up periods and five spatial model resolutions. A metaheuristic global optimization method, i.e. Dynamically Dimensioned Search (DDS) algorithm, and a well-known hydrological performance metric, i.e. Nash Sutcliffe Efficiency (NSE), are utilized for each of the 105 calibration cases. The results show that a 10-year calibration data length, 2-year spin-up period and a 4 km model resolution are appropriate for the Moselle basin to reduce the computational burden. Analyzing the combined effects further allowed us to understand the interactions of these three usually overlooked pillars in hydrological modeling.

A document by Fatemeh Karandish. Click on the document to view its contents.