Hydrological Forcing Research Articles

ELM‐Wet: Inclusion of a Wet‐Landunit With Sub‐Grid Representation of Eco‐Hydrological Patches and Hydrological Forcing Improves Methane Emission Estimations in the E3SM Land Model (ELM)

AbstractWetlands are the largest emitters of biogenic methane (CH4) and represent the highest source of uncertainty in global CH4 budgets. Here, we aim to improve the realism of wetland representation in the U.S. Department of Energy's Exascale Earth System Model land surface model, ELM, thereby reducing uncertainty of CH4 flux predictions. We develop an updated version, ELM‐Wet, where we activate a separate landunit for wetlands that handles multiple wetland‐specific eco‐hydrological patch functional types. We introduce more realistic hydrological forcing through prescribing site‐level constraints on surface water elevation, which allows resolving different sustained inundation depth for different patches, and if data exists, prescribing inundation depth. We modified the calculation of aerenchyma transport diffusivity based on observed conductance per leaf area for different vegetation types. We use Bayesian Optimization to parameterize CO2 and CH4 fluxes in the developed wet‐landunit. Site‐level simulations of a coastal non‐tidal freshwater wetland in Louisiana were performed with the updated model. Eddy covariance observations of CO2 and CH4 fluxes from 2012 to 2013 were used to train the model and data from 2021 were used for validation. Patch‐specific chamber flux observations and observations of CH4 concentration profiles in the soil porewater from 2021 were used for evaluation of the model performance. Our results show that ELM‐Wet reduces the model's CH4 emission root mean squared error by up to 33% and is able to represent inter‐daily CO2 and CH4 flux variability across the wetland's eco‐hydrological patches, including during periods of extreme dry or wet conditions.

Glacier Surges and Seasonal Speedups Integrated Into a Single, Enthalpy‐Based Model Framework

AbstractGlacier speedups occur on daily to centennial timescales. While basal water and subglacial drainage configuration are thought to drive glacier speedups across these timescales, it remains unclear whether this forcing always occurs through the same mechanisms. Here, we explore whether the enthalpy model of glacier surging can explain speedups over a broader range of timescales if modified to account for seasonality in surface melt and continuous water supply to the glacier bed. We simulate velocity oscillations that range from seasonal to years. Our model results more closely resemble observations of surges than previous model versions because ice flow variability at seasonal and multi‐year timescales is reproduced simultaneously through hydrological forcing. Under favorable conditions, seasonal water delivery to the bed gradually accumulates in a poorly‐connected basal drainage system, priming the glacier to surge. Surges themselves are marked by high water fluxes and enthalpy drainage from the glacier base.

A hybrid statistical–dynamical framework for compound coastal flooding analysis

Abstract Compound coastal flooding due to astronomic, atmospheric, oceanographic, and hydrologic forcings poses severe threats to coastal communities. While physics-driven approaches are able to dynamically simulate temporally and spatially varying compound flooding generated by multiple partially correlated drivers, computational burdens limit their capability to explore the full range of conditions that contribute to compound coastal hazards. Data-driven statistical approaches address some of these computational challenges, however they are also unable to explore all possible forcing combinations due to short observational records, and projections are typically limited to a few locations. This study proposes a hybrid statistical-dynamical framework for compound coastal flooding analysis that integrates a stochastic generator of compound flooding drivers, a hydrodynamic model, and a machine learning-based surrogate model. The framework is demonstrated in San Francisco (SF) Bay over the past 500 years with high accuracy and computational efficiency. The stochastic generator of compound flooding drivers is developed by coupling a sea surface temperature (SST) reconstruction model with a climate emulator, weather generator, and hydrologic model. Using reconstructed SSTs as input, the generator of compound flooding drivers is employed to simulate time series of the forcing factors contributing to compound flooding in SF Bay. A process-based hydrodynamic model is built to predict total water levels (TWLs) varying in time and space throughout SF Bay based on the stochastically generated drivers. A machine learning-based surrogate model is then developed from a relatively small library of hydrodynamic model simulations to efficiently predict water levels (WLs) for compound flooding analysis under the full range of stochastic drivers. This study contributes a hybrid statistical-dynamical framework to better understand the spatial distribution and temporal evolution of compound coastal flooding, along with the relative contributions of the forcing drivers in complex nearshore, estuarine, and river environments for centennial timescales under past, present, and future climates.

Non-linear elasticity, earthquake triggering and seasonal hydrological forcing along the Irpinia fault, Southern Italy

Pump-probe experiments investigate the strain sensitivity of crustal elastic properties, showing nonlinear variations during the strain cycle. In the laboratory, pre-seismic reductions in seismic velocity indicate that asperity contacts within the fault zone begin to fail before the macroscopic frictional sliding. The recognition of such effects in natural seismic-cycles has been challenging. Here we exploit seasonal hydrological strains, performing a natural analogue to a quasi-static laboratory pump-probe experiment to investigate the nonlinear strain sensitivity of crustal rocks and its role in seismic failure along the tectonically-active Irpinia Fault System (Southern Italy). By comparing 14-years-long series of spring discharge, strain, seismic velocity variations and earthquakes rate, we find that seismicity peaks during maximum hydrological forcing and minimum seismic velocity. Seasonal strains of ~10−6 are required for both earthquake triggering and significant nonlinearity effects arising from modulus reduction. We suggest that, for faults in a critical state, cyclical softening may lead to failure and seasonal seismicity.

Drivers of barrier island water-table fluctuations and groundwater salinization

Barrier islands are threatened by climate change as sea-level rise and higher frequency storm surge lead to more flooding and saltwater intrusion. Vegetation plays a vital role in preventing erosion of barrier islands due to aeolian and hydrological forces. However, vegetation on barrier islands is threatened by rising water tables causing hypoxic conditions and storm-surge overwash introducing saline water to the root zone. To better protect barrier island ecosystems, it is critical to identify the relative influence of different hydrological drivers on water table elevation and salinity, and understand how this influence varies spatially and temporally. In this study, three barrier island sites were instrumented with groundwater wells monitoring water level and specific conductance. Using these data, a set of transfer function noise models were calibrated and used to determine the relative influence of hydrologic drivers including precipitation, evapotranspiration, bay and ocean water levels, and wave height on groundwater levels and specific conductance. We found that drivers of water-level change and specific conductance vary strongly among sites, depending primarily on the surface water connectivity and the geology of the island. Sites with close connection to inlets showed more salinization and responded to a larger number of drivers, while sites that were poorly connected to the ocean responded to fewer drivers.

Tidal Calibration of the Gladwin Tensor Strain Monitor (GTSM) Array in Taiwan

AbstractTo ensure the accuracy and reliability of crustal strain measures, sensors require a thorough calibration. In Taiwan, the complicated dynamics of surface and subsurface hydrological processes under semi-tropical climate conditions conjugated with the rough surface topography could have impacted strainmeter deployment, pushing the installation conditions astray from the optimal ones. Here, we analyze the complex response of 11 Gladwin Strain Monitor (GTSM) strainmeter type deployed in north and central Taiwan and we propose a novel calibration methodology which relies on waveform modeling of Earth and ocean tidal strain-related deformations. The approach is completely data-driven, starting from a simple calibration framework and progressively adding complexity in the model depending on the quality of the data. However, we show that a simple quasi-isotropic model (three calibration factors) is generally suitable to resolve the orientation and calibration of 8 instruments out of 11. We also highlight the difficulty of clearly defining the behavior of instruments that are highly affected by hydrological forcing.

C-HAND: near real-time coastal flood mapping

The Texas Gulf Coast region contains significant centers of population, infrastructure, and economy and is threatened by intensifying tropical storms. The flooding from these tropical storms often has multiple compounding drivers. This characteristic presents a complex numerical problem where a simulation must consider multiple hydrologic forcings. While several procedures exist for addressing this problem numerically, they tend to be resource-intensive and cannot be conducted in near real-time. We extend GeoFlood, a reduced physics approach for fluvial flood forecasting, to rapidly predict coastal and compound fluvial-coastal inundation. This method is validated against a numerical ocean circulation model (ADCIRC) simulation of Hurricane Ike, a major coastal flooding event that happened on the Texas Gulf Coast in 2008. We show that the inundation map generated by coastal HAND (c-HAND) has reasonable agreement with the ADCIRC simulation while taking about 1.7% of the time currently needed to run ADCIRC on a supercomputer. While our model correctly predicts 99% of ADCIRC-inundated DEM cells, it also overpredicts inundated area by a factor of approximately 27%. We combine c-HAND with the GeoFlood framework for fluvial flood forecasting to create a compound fluvial-coastal inundation mapping workflow that can be run in near real-time. c-HAND's fast wall-clock time and low CPU requirements can support decision making by first response personnel. The method provides timely and convenient access to crucial information, such as the locations of flooded roads and inundated coastal areas.

Spatio-temporal changes and hydrological forces of wetland landscape pattern in the Yellow River Delta during 1986–2022

ContextEstuarine wetlands provide valuable ecosystem services, but 20–78% of coastal wetlands are facing the risk of loss by the end of the century. The Yellow River Delta (YRD) wetland, one of the most productive delta areas in the world, has undergone dramatic changes under the influence of a precipitous drop of sediment delivery and runoff, coupled with the invasion of Spartina alterniflora. Monitoring the spatio-temporal patterns, thresholds, and drivers of change in wetland landscapes is critical for sustainable management of delta wetlands.ObjectivesGenerate annual mapping of salt marsh vegetation in the YRD wetland from 1986 to 2022, analyze the trends of wetland patch area and landscape pattern, and explain the hydrological drivers of landscape pattern evolution.MethodsWe combined Landsat 5‒8 and Sentinel-2 images, vegetation phenology, remote sensing indices, and Random Forest supervised classification to map the typical salt marsh vegetation of the YRD. We applied piecewise linear regression to analyze YRD wetland changes and stepwise multiple linear regression to assess the impact of hydrological factors on landscape pattern.ResultsWe identified three stages of landscape pattern evolution with 1997 and 2009 as critical junctures, including the rapid expansion stage, gradual decline stage, and bio-invasion stage. In the rapid expansion stage, the wetland area expanded by 70%, while the typical salt marsh vegetation (Phragmites australis) area was reduced by 25%. In the gradual decline stage, the wetland was reduced by 21% and the Phragmites australis area was reduced by 16%. In the bio-invasion stage, coverage of Spartina alterniflora expanded rapidly, with a 68-fold increase in area relative to 2009, expanding at an average rate of 344 hm2 per year.ConclusionsAreas of total wetland, tidal flat, and Phragmites australis were significantly influenced by cumulative sediment delivery and cumulative runoff, which together explained 61.5%, 75.7% and 63.8% of their variation, respectively. Wetland and tidal flat areas increased with cumulative sediment delivery, while cumulative runoff had a weak negative effect. For Phragmites australis, cumulative runoff had a positive effect, whereas cumulative sediment delivery had a negative effect. Water resources regulation measures should be taken to prevent the degradation of wetland ecosystems, and intervention measures can be implemented during the seedling stage to control the invasion of Spartina alterniflora.

Defects detection of pier and abutments foundations: an overview of a recent experience in Basilicata (Southern Italy)

Piers and abutments are bridge supports temporarily or permanently immersed into the water, that may suffer erosion and scour phenomena. As known, the latter involves different processes at different spatial scales not easy to investigate in a combined approach, such as: contraction scour, local scour, gradation of the soil bed, river channel dynamic, climate change and hydrological forces. To this, it should be added that piers/abutments are highly exposed to environmental actions, such as earthquake, landslides and floods that may seriously compromise their stability and, consequently, lead the bridge or an its portion to an out-of-service, or else to a failure. Therefore, it is important to properly identify and assess defects extent and severity on piers and abutments, in order to evaluate their conservation status, and to formulate a judgment for ranking priorities within a multi-level and multi-criteria framework, such as the recent guidelines issued by the Italian Ministry of Infrastructures and Transport. To this regard, in this work an overview of some significant defects related to piers and abutments, with particular emphasis to the ones due to the interaction with the water flow, are illustrated and commented. They are collected in an on-going campaign of inspections performed in the Basilicata region (South of Italy), concerning bridges falling within State Roads networks. Comments are made from a structural and hydraulic point view, in order to highlight the inspected defects importance within the Classes of Attention, according to Italian Guidelines for bridges.

Hydrology Drives Crustal Deformation and Modulates Seismicity in the Matese Massif (Italy)

Abstract We analyze the interplay between hydrology, deformation, and seismicity in the Matese massif, located in the Italian Southern Apennines. We find that this area is characterized by the concurrent action of two hydrologically driven processes: the first is the deformation detected by the Global Navigation Satellite Systems (GNSS) data in the shallowest part (above the elevation of the major springs) of the Earth crust, in phase with the hydrological forcing; the second is the triggering of seismicity at depth with a delay suggesting a downward diffusive process. We study the first process by applying a principal component analysis to the GNSS displacements time series, aiming to identify a common signal describing the largest data variance. We find that the maximum horizontal displacements associated with the first principal component (PC1) are larger than 1 cm in two GNSS sites, and the PC1 temporal evolution is well correlated and in phase with the flow of the largest spring of the region, which we consider as proxy of the water content of the massif. This suggests that the main source of horizontal deformation is the water content fluctuations in the shallow portion of the Matese aquifer, in particular within fractures located in correspondence of the main mapped faults. The deformation rates caused by this process are one order of magnitude larger than the tectonic ones. Finally, we infer the second process by observing the correlation between the background seismicity and the spring discharge with a time lag of 121 days. In our interpretation, downward diffusive processes, driven by aquifer water content variations, propagate pore-pressure waves that affect the fault’s strength favoring the occurrence of microearthquakes. This is supported by the values of hydraulic diffusivity (1.5 m2/s) and rock permeability (3.2–3.8×10−13 m2), which are compatible with what is observed in karstified limestones.

Modelling seasonal landslide motion: Does it only depend on fluctuations in normal effective stress?

AbstractLandslide motion is often simulated with interface‐like laws able to capture changes in frictional strength caused by the growth of the pore water pressure and the consequent reduction of the effective stress normal to the plane of sliding. Here it is argued that, although often neglected, the evolution of all the 3D stress components within the basal shear zone of landslides also contributes to changes in frictional strength and must be accounted for to predict changes in seasonal velocity. For this purpose, an augmented sliding‐consolidation model is proposed which allows for the computation of excess pore pressure development and downslope sliding with any constitutive law with 3D stress evolution. Simulations of idealised infinite slope models subjected to hydrologic forcing are used to study the role of in‐situ stress conditions and stress rate multiaxiality. Specifically, a Drucker‐Prager perfectly plastic model is used to replicate frictional failure and shear deformation at the base of landslides. The model reveals that conditions amenable to the shearing of a frictional interface are met only after numerous rainfall cycles, that is, when multiaxial stress rates are suppressed. In this case, the landslide is predicted to move through a seasonal ratcheting controlled only by the effective stress component normal to the plane of sliding. By contrast, in newly formed landslides, the multiaxial stress evolution is found to produce further regimes of motion, from plastic shakedown to cyclic failure, neither of which can be captured by interface‐like frictional laws. Notably, the model suggests that a transition across these regimes can emerge in response to an aggravation of the magnitude of forcing, implying that (i) fluctuations in climate may alter the seasonal trends of motion observed today; (ii) our ability to quantify landslide‐induced risks is impaired unless proper geomechanical models are used to examine their long‐term dynamics.

Hydrological and Geological Controls for the Depth Distribution of Dissolved Oxygen and Iron in Silicate Catchments

AbstractDissolved Oxygen (DO) plays a key role in reactive processes and microbial dynamics in the critical zone. Recent observations showed that fractures can provide rapid pathways for oxygen penetration in aquifers, triggering unexpected biogeochemical processes. In the shallow subsurface, DO reacts with electron donors, such as Fe2+ coming from mineral dissolution. Yet, little is known about the factors controlling the spatial heterogeneity and distribution of oxygen with depth. Here we present a reduced analytical model describing the coupled evolution of DO and Fe2+ as a function of fluid travel time in silicate catchments. Our model, validated from fully resolved reactive transport simulations, predicts a linear decay of DO with time, followed by a rapid non‐linear increase of Fe2+ concentrations up to a far‐from‐equilibrium steady‐state. The relative effects of geological and hydrological forcings are quantified through a Damköhler number (Da) and a lithological number (Λ). We use this framework to investigate the depth distribution of DO and Fe2+ in two catchments with similar environmental contexts but contrasted hydrochemical properties. We show that hydrochemical differences are explained by small variations in Da but orders of magnitude variations in Λ. Therefore, we demonstrate that the hydrological and geological drivers controlling hydrochemistry in silicate catchments can be discriminated by analyzing jointly the O2 and Fe2+ evolution with depth. These findings provide a new conceptual framework to understand and predict the evolution of DO in modern groundwater, which plays an important role in critical zone processes.

Modeling rainfall-driven transport of Glyphosate in the vadose zone of two experimental sites in North-East Italy

A vertical one-dimensional analysis of infiltration processes and mobility of a tracer (potassium bromide) and a glyphosate-based herbicide, both subjected to hydrological forcing, was performed. Glyphosate is a widespread herbicide whose potential harmfulness and mobility under hydrological forcing have not been fully understood yet. Here, the spatio-temporal evolution of the two compounds was monitored for one year in two experimental sites (Settolo - Valdobbiadene, Colnù - Conegliano), located within the production area of the Prosecco wine (Treviso, Italy). In each experimental site the activities were carried out on two 25 m2 plots located at distances of 50-100 m from each other. The interpretative analyses considered rainwater infiltration as the driving mechanism of the herbicide transport and allowed us to obtain the calibration of a one-dimensional hydrologic model in each monitored plot. Different scenarios of the tracer evolution were simulated considering the pedologic properties of the shallower soil layers, the status of the plant coverage and of the root apparati, leading to a satisfactory reproduction of the observations in both the experimental sites. Modeling the mobility of the herbicide, considering also the degradation to its metabolite AMPA, proved to be more challenging, due to the tendency of glyphosate to be adsorbed to the soil matrix rather than be dissolved in water and transported toward deeper soil layers. Nevertheless, the analysis of model results for tracer and herbicide, compared with in situ observations, suggests that the transport of the glyphosate can take place even when it is adsorbed to the soil, through the movement, triggered by intense precipitation events, of microscopic soil particles within preferential flow paths.

Dynamics of streamflow permanence in a headwater network: Insights from catchment-scale model simulations

The hillslope and channel dynamics that govern streamflow permanence in headwater systems have important implications for ecosystem functioning and downstream water quality. Recent advancements in process-based, semi-distributed hydrologic models that build upon empirical studies of streamflow permanence in well-monitored headwater catchments show promise for characterizing the dynamics of streamflow permanence in headwater systems. However, few process-based models consider the continuum of hillslope-stream network connectivity as a control on streamflow permanence in headwater systems. The objective of this study was to expand a process-based, catchment-scale hydrologic model to better understand the spatiotemporal dynamics of headwater streamflow permanence and to identify controls of streamflow expansion and contraction in a headwater network. Further, we aimed to develop an approach that enhanced the fidelity of model simulations, yet required little additional data, with the intent that the model might be later transferred to catchments with limited long-term and spatially explicit measurements. This approach facilitated network-scale estimates of the controls of streamflow expansion and contraction, albeit with higher degrees of uncertainty in individual reaches due to data constraints. Our model simulated that streamflow permanence was highly dynamic in first-order reaches with steep slopes and variable contributing areas. The simulated stream network length ranged from nearly 98±2% of the geomorphic channel extent during wet periods to nearly 50±10% during dry periods. The model identified a discharge threshold of approximately 1 mm d−1, above which the rate of streamflow expansion decreases by nearly an order of magnitude, indicating a lack of sensitivity of streamflow expansion to hydrologic forcing during high-flow periods. Overall, we demonstrate that process-based, catchment-scale models offer important insights on the controls of streamflow permanence, despite uncertainties and limitations of the model. We encourage researchers to increase data collection efforts and develop benchmarks to better evaluate such models.

A log-additive neural model for spatio-temporal prediction of groundwater levels

Deep neural networks are powerful models capable of learning useful representations from large complex datasets for the purpose of prediction. Such models offer great potential in applied statistical applications if they can be assembled into architectures that facilitate interpretability and quantify uncertainty. This work presents such an application, where observed groundwater levels at monitoring wells in the Indo-Gangetic Basin, Bangladesh, were modelled using readily available spatial and spatio-temporal predictors. We constructed a model with two deep neural sub-architectures embedded within a log-additive statistical model. The sub-architectures allowed for the partitioning of groundwater dynamics arising from: (i) local hydrological forcings; and (ii) broad-scale spatio-temporal trends. The separability of these two components facilitated interpretation of the dominant groundwater trends in the basin. The log-additive statistical model also allowed for the quantification of uncertainty in predictions of groundwater levels. We observed good coverage of out-of sample data, indicating that uncertainty quantification from the deep neural statistical model was reliable. From an application standpoint, we demonstrate that groundwater dynamics can be reliably and easily predicted at large spatial and temporal scales that may aid governments in managing water resources. From a methodological perspective, we demonstrate a modelling approach that harnesses the powerful attributes of deep neural networks, whilst retaining some of the desirable properties of statistical models.

A coupled streamflow and water temperature (VIC-RBM-CE-QUAL-W2) model for the Nechako Reservoir

Study regionNechako Reservoir, British Columbia, Canada. Study focusHydrological regulation affect both hydrological and thermal conditions in the reservoir and downstream reach, subsequently disrupting fish habitats. This paper aims at developing an integrated model simulating physical processes that govern the quantity and quality of inflow, reservoir, and outflow water of the Nechako Reservoir. Such a model would help stakeholders understand the response of in-reservoir water temperature stratification and downstream water temperature to changes in inflow and reservoir operation under future climate change. New hydrological insights for the regionThe model was calibrated against historical reservoir levels and in-reservoir and outlet water temperature field data. The integrated model simulated accurately the wide variation of reservoir levels as well as the in-reservoir water temperature at Kenney Dam and the outlet temperature. Sensitivity analysis shows that reservoir water temperature particularly the epilimnion is sensitive to changes in both meteorological and hydrological forcing. Forcing the model with different outflow scenarios shows the weak sensitivity of temperature of water released to outflow rates. Given epilimnion water releases at the spillway, the Summer Temperature Management Program could be inefficient to provide cool water in the Nechako River during the critical period of salmon migration in a warming climate. However, colder water remains available at depth at Kenney Dam to potentially mitigate and better control downstream water temperature.

Oxygenation of a karst subterranean estuary during a tropical cyclone: mechanisms and implications for the carbon cycle

AbstractSeasonal precipitation affects carbon turnover and methane accumulation in karst subterranean estuaries, the region of coastal carbonate aquifers where hydrologic and biogeochemical processes regulate material exchange between the land and ocean. However, the impact that tropical cyclones exert on subsurface carbon cycling within karst landscapes is poorly understood. Here, we present 5‐month‐long hydrologic and chemical records from 1 and 2 km inland from the coastline within the Ox Bel Ha Cave System in the northeastern Yucatan Peninsula. The record encompasses wet and dry seasons and includes the impact of rainfall during the development of Tropical Storm Hanna in October 2014. Methane accumulated in highest concentrations at the inland site, especially during the wet season preceding the storm. Intense rainfall led to episodic increases in water level and salinity shifts at both sites, indicating a spatially widespread hydrologic response. The most profound storm effect was a ~ 0.8 mg L−1 pulse of dissolved oxygen that declined to zero within 2 weeks and corresponded with a reduction of methane. A positive shift in methane's stable carbon isotope content from −62.6‰ ± 0.6‰ before the storm to −44.0‰ ± 2.4‰ after the storm indicates microbial methane oxidation was a mechanism for the loss of groundwater methane. Post‐storm methane concentrations did not recover to pre‐storm levels during the observation period, suggesting tropical cyclones have long‐lasting (months) effects on the carbon cycle. Compared to seasonal effects, mixing and oxygen inputs during storm‐induced hydrologic forcing have an outsized biogeochemical influence within stratified coastal aquifers.

A stochastic model of geomorphic risk due to episodic river aggradation and degradation

In some steep valleys, flood-induced changes in river bed elevation pose significantly greater risks to infrastructure than floodwaters alone. Over the short term, the river may aggrade or degrade by several meters during a single flood. Whereas floodwaters recede after each event, moreover, riverbed changes add up over successive floods. To quantify the resulting geomorphic risk and its evolution over time, we propose in this paper a new stochastic model of river bed elevation change. The bed is assumed to rise and drop according to a random walk, driven by the composition of two gamma processes that respectively pace the hydrologic forcing and the geomorphic response. The model can therefore incorporate various sources of uncertainty, associated with precipitation and debris flow activity within the contributing watershed. To test the model, we apply it to a highly active montane river, the Laonong River in southwestern Taiwan. Model calibration is achieved from a combination of long and short term data, including radiocarbon-dated deposits and modern river records. The modelled distributions fit the data well, including the likelihood of extreme changes. The model also produces a reasonable hindcast of the geomorphic damage suffered over the last ten years by Highway 20, a vulnerable road link sited along the river, and can be used to forecast future geomorphic risk.

The Unabated Atmospheric Carbon Losses in a Drowning Wetland Forest of North Carolina: A Point of No Return?

Coastal wetlands provide the unique biogeochemical functions of storing a large fraction of the terrestrial carbon (C) pool and being among the most productive ecosystems in the world. However, coastal wetlands face numerous natural and anthropogenic disturbances that threaten their ecological integrity and C storage potential. To monitor the C balance of a coastal forested wetland, we established an eddy covariance flux tower in a natural undrained bottomland hardwood forest in eastern North Carolina, USA. We examined the long-term trends (2009–2019) in gross primary productivity (GPP), ecosystem respiration (RE), and the net ecosystem C exchange (NEE) seasonally and inter-annually. We analyzed the response of C fluxes and balance to climatic and hydrologic forcings and examined the possible effects of rising sea levels on the inland groundwater dynamics. Our results show that in 2009, a higher annual GPP (1922 g C m−2 yr−1) was observed than annual RE (1554 g C m−2 yr−1), resulting in a net C sink (NEE = −368 g C m−2 yr−1). However, the annual C balance switched to a net C source in 2010 and onwards, varying from 87 g C m−2 yr−1 to 759 g C m−2 yr−1. The multiple effects of air temperature (Tair), net radiation (Rn), groundwater table (GWT) depth, and precipitation (p) explained 66%, 71%, and 29% of the variation in GPP, RE, and NEE, respectively (p < 0.0001). The lowering of GWT (−0.01 cm to −14.26 cm) enhanced GPP and RE by 35% and 28%, respectively. We also observed a significant positive correlation between mean sea level and GWT (R2 = 0.11), but not between GWT and p (R2 = 0.02). Cumulative fluxes from 2009 to 2019 showed continuing C losses owing to a higher rate of increase of RE than GPP. This study contributes to carbon balance accounting to improve ecosystem models, relating C dynamics to temporal trends in under-represented coastal forested wetlands.

Recent advances in the investigation of a slow‐moving landslide in the Three Gorges Reservoir area, China

AbstractMany slow‐moving landslides in the Three Gorges Reservoir (TGR) area exhibit episodic movement patterns in response to variations in the reservoir water level and seasonal rainfall. These landslides are typically characterized by pre‐existing shear zones composed of thick, clay‐rich layers that are sensitive to hydrological forcings. The residual shear strength of these shear zones is believed to control the dynamic movements of these landslides. In this article, we review the recent advances in the investigation of a large slow‐moving landslide, the Huangtupo landslide, in the TGR area. This landslide has been chosen as a benchmark for studying similar landslides in this area. This review focuses mainly on the research carried out by the authors and their collaborators over the last 5 years on topics including the landslide mechanisms and forcings, rheological behaviors and constitutive models of the landslide materials, the numerical simulation of clastic shear‐zone soils, and the effects landslide‐induced tsunamis would have on the ships in the reservoir. In addition, new perspectives and challenges for future research on slow‐moving landslides are provided.