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Floods are among the most dangerous and destructive hazards in the world. Stormwater Beneficial Management Practices (BMPs) are a set of strategies that can assist in reducing urban floods and their damages by capturing surface runoff and promoting infiltration. Engagement of citizens in the selection of stormwater BMPs may facilitate the decision-making processes and increase the chance of adopting and maintaining them. Due to existence of catastrophic floods in Brazil, implementing BMPs is essential in the urban areas. The objective of this study is to understand the viewpoints of citizens about a set of stormwater BMPs in Brazil. Moreover, we aim to comprehend whether diverging viewpoints about the BMPs can be associated with existence of different layers in the society. For this purpose, online surveys were used to access wide and diverse groups of citizens from different ages, levels of education and income, as well as geographical location. The questions and descriptions of BMPs were prepared in an accessible language, and then disseminated through various platforms. The responses of more than 1000 participants were analyzed using descriptive and statistical methods. Our results show that the participants found the retention and detention basins, as well as permeable pavement as the most efficient BMPs. Moreover, considering the small-scale practices, although lot related BMPs are considered less efficient, citizens are willing to use green roof, bioretention, and rain barrels in their properties. In addition, most of the respondents support public investments on stormwater BMPs. Our analyses show that participants' age and level of education statistically influenced their choice of BMPs and willingness to pay for their maintenance and construction. These results can help Brazilian policy makers to prepare flood management plans by including stormwater BMPs that would be more accepted by the population. In addition, proposing practices that are aligned with citizens’ perceptions creates a sense of responsibility, and is in accordance with the Brazilian New Framework of Sanitation that includes public participation in policy making.

The magnitudes of dissolved organic carbon (DOC) exports from boreal peatlands to streams through lateral subsurface flow vary during the ice-free season. Peatland water table depth and the alternation of low and high flow in peat-draining streams are thought to drive this DOC export variability. However, calculation of the specific DOC exports from a peatland can be challenging considering the multiple potential DOC sources within the catchment. A calculation approach based on the hydrological connectivity between the peat and the stream could help to solve this issue, which is the approach used in the present research. This study took place from June 2018 to October 2019 in a boreal catchment in northeastern Canada, with 76.7 % of the catchment being covered by ombrotrophic peatland. The objectives were to (1) establish relationships between DOC exports from a headwater stream and the peatland hydrology; (2) quantify, at the catchment scale, the amount of DOC laterally exported to the draining stream; and (3) define the patterns of DOC mobilization during high-river-flow events. At the peatland headwater stream outlet, the DOC concentrations were monitored at a high frequency (hourly) using a fluorescent dissolved organic matter (fDOM) sensor, a proxy for DOC concentration. Hydrological variables, such as stream outlet discharge and peatland water table depth (WTD), were continuously monitored at hourly intervals for 2 years. Our results highlight the direct and delayed control of subsurface flow from peat to the stream and associated DOC exports. Rain events raised the peatland WTD, which increased hydrological connectivity between the peatland and the stream. This led to increased stream discharge (Q) and a delayed DOC concentration increase, typical of lateral subsurface flow. The magnitude of the WTD increase played a crucial role in influencing the quantity of DOC exported. Based on the observations that the peatland is the most important contributor to DOC exports at the catchment scale and that other DOC sources were negligible during high-flow periods, we propose a new approach to estimate the specific DOC exports attributable to the peatland by distinguishing between the surfaces used for calculation during high-flow and low-flow periods. In 2018–2019, 92.6 % of DOC was exported during flood events despite the fact that these flood events accounted for 59.1 % of the period. In 2019–2020, 93.8 % of DOC was exported during flood events, which represented 44.1 % of the period. Our analysis of individual flood events revealed three types of events and DOC mobilization patterns. The first type is characterized by high rainfall, leading to an important WTD increase that favours the connection between the peatland and the stream and leading to high DOC exports. The second is characterized by a large WTD increase succeeding a previous event that had depleted DOC available to be transferred to the stream, leading to low DOC exports. The third type corresponds to low rainfall events with an insufficient WTD increase to reconnect the peatland and the stream, leading to low DOC exports. Our results suggest that DOC exports are sensitive to hydroclimatic conditions; moreover, flood events, changes in rainfall regime, ice-free season duration, and porewater temperature may affect the exported DOC and, consequently, partially offset the net carbon sequestration potential of peatlands.

As Earth's atmospheric temperatures and human populations increase, more people are becoming vulnerable to natural and human-induced disasters. This is particularly true in Central America, where the growing human population is experiencing climate extremes (droughts and floods), and the region is susceptible to geological hazards, such as earthquakes and volcanic eruptions, and environmental deterioration in many forms (soil erosion, lake eutrophication, heavy metal contamination, etc.). Instrumental and historical data from the region are insufficient to understand and document past hazards, a necessary first step for mitigating future risks. Long, continuous, well-resolved geological records can, however, provide a window into past climate and environmental changes that can be used to better predict future conditions in the region. The Lake Izabal Basin (LIB), in eastern Guatemala, contains the longest known continental records of tectonics, climate, and environmental change in the northern Neotropics. The basin is a pull-apart depression that developed along the North American and Caribbean plate boundary ∼ 12 Myr ago and contains > 4 km of sediment. The sedimentological archive in the LIB records the interplay among several Earth System processes. Consequently, exploration of sediments in the basin can provide key information concerning: (1) tectonic deformation and earthquake history along the plate boundary; (2) the timing and causes of volcanism from the Central American Volcanic Arc; and (3) hydroclimatic, ecologic, and geomicrobiological responses to different climate and environmental states. To evaluate the LIB as a potential site for scientific drilling, 65 scientists from 13 countries and 33 institutions met in Antigua, Guatemala, in August 2022 under the auspices of the International Continental Scientific Drilling Program (ICDP) and the US National Science Foundation (NSF). Several working groups developed scientific questions and overarching hypotheses that could be addressed by drilling the LIB and identified optimal coring sites and instrumentation needed to achieve the project goals. The group also discussed logistical challenges and outreach opportunities. The project is not only an outstanding opportunity to improve our scientific understanding of seismotectonic, volcanic, paleoclimatic, paleoecologic, and paleobiologic processes that operate in the tropics of Central America, but it is also an opportunity to improve understanding of multiple geological hazards and communicate that knowledge to help increase the resilience of at-risk Central American communities.

Airborne LiDAR scanning is a promising approach to providing high-resolution products that are appropriate for different applications, such as flood management. However, the vertical accuracy of airborne LiDAR point clouds is not constant and varies in space. Having a better knowledge of their accuracy will assist decision makers in more accurately estimating the damage caused by flood. Data producers often report the total estimation of errors by means of comparison with a ground truth. However, the reliability of such an approach depends on various factors including the sample size, accessibility to ground truth, distribution, and a large enough diversity of ground truth, which comes at a cost and is somewhat unfeasible in the larger scale. Therefore, the main objective of this article is to propose a method that could provide a local estimation of error without any third-party datasets. In this regard, we take advantage of geostatistical ordinary kriging as an alternative accuracy estimator. The challenge of considering constant variation across the space leads us to propose a non-stationary ordinary kriging model that results in the local estimation of elevation accuracy. The proposed method is compared with global ordinary kriging and a ground truth, and the results indicate that our method provides more reliable error values. These errors are lower in urban and semi-urban areas, especially in farmland and residential areas, but larger in forests, due to the lower density of points and the larger terrain variations.

In cold regions, ice jams frequently result in severe flooding due to a rapid rise in water levels upstream of the jam. Sudden floods resulting from ice jams threaten human safety and cause damage to properties and infrastructure. Hence, ice-jam prediction tools can give an early warning to increase response time and minimize the possible damages. However, ice-jam prediction has always been a challenge as there is no analytical method available for this purpose. Nonetheless, ice jams form when some hydro-meteorological conditions happen, a few hours to a few days before the event. Ice-jam prediction can be addressed as a binary multivariate time-series classification. Deep learning techniques have been widely used for time-series classification in many fields such as finance, engineering, weather forecasting, and medicine. In this research, we successfully applied convolutional neural networks (CNN), long short-term memory (LSTM), and combined convolutional–long short-term memory (CNN-LSTM) networks to predict the formation of ice jams in 150 rivers in the province of Quebec (Canada). We also employed machine learning methods including support vector machine (SVM), k-nearest neighbors classifier (KNN), decision tree, and multilayer perceptron (MLP) for this purpose. The hydro-meteorological variables (e.g., temperature, precipitation, and snow depth) along with the corresponding jam or no-jam events are used as model inputs. Ten percent of the data were excluded from the model and set aside for testing, and 100 reshuffling and splitting iterations were applied to 80 % of the remaining data for training and 20 % for validation. The developed deep learning models achieved improvements in performance in comparison to the developed machine learning models. The results show that the CNN-LSTM model yields the best results in the validation and testing with F1 scores of 0.82 and 0.92, respectively. This demonstrates that CNN and LSTM models are complementary, and a combination of both further improves classification.

Abstract This study investigates possible trends and teleconnections in temperature extremes in New South Wales (NSW), Australia. Daily maximum and minimum temperature data covering the period 1971–2021 at 26 stations located in NSW were used. Three indices, which focus on daily maximum temperature, daily minimum temperature, and average daily temperature in terms of Excessive Heat Factor (EHF) were investigated to identify the occurrence of heatwaves (HWs). The study considered HWs of different durations (1-, 5-, and 10-days) in relation to intensity, frequency, duration, and their first occurrence parameters. Finally, the influences of three global climate drivers, namely – the El Niño/Southern Oscillation (ENSO), the Southern Annular Mode (SAM), and the Indian Ocean Dipole (IOD) were investigated with associated heatwave attributes for extended Austral summers. In this study, an increasing trend in both hot days and nights was observed for most of the selected stations within the study area. The increase was more pronounced for the last decade (2011–2021) of the investigated time period. The number, duration and frequency of the heatwaves increased over time considering the EHF criterion, whereas no particular trend was detected in cases of TX90 and TN90. It was also evident that the first occurrence of all the HWs shifted towards the onset of the extended summer while considering the EHF criterion of HWs. The correlations between heatwave attributes and climate drivers depicted that heatwave over NSW was positively influenced by both the IOD and ENSO and negatively correlated with SAM. The findings of this study will be useful in formulating strategies for managing the impacts of extreme temperature events such as bushfires, floods, droughts to the most at-risk regions within NSW.

Hydrological time series often present nonstationarities such as trends, shifts, or oscillations due to anthropogenic effects and hydroclimatological variations, including global climate change. For water managers, it is crucial to recognize and define the nonstationarities in hydrological records. The nonstationarities must be appropriately modeled and stochastically simulated according to the characteristics of observed records to evaluate the adequacy of flood risk mitigation measures and future water resources management strategies. Therefore, in the current study, three approaches were suggested to address stochastically nonstationary behaviors, especially in the long-term variability of hydrological variables: as an overall trend, shifting mean, or as a long-term oscillation. To represent these options for hydrological variables, the autoregressive model with an overall trend, shifting mean level (SML), and empirical mode decomposition with nonstationary oscillation resampling (EMD-NSOR) were employed in the hydrological series of the net basin supply in the Lake Champlain-River Richelieu basin, where the International Joint Committee recently managed and significant flood damage from long consistent high flows occurred. The detailed results indicate that the EMD-NSOR model can be an appropriate option by reproducing long-term dependence statistics and generating manageable scenarios, while the SML model does not properly reproduce the observed long-term dependence, that are critical to simulate sustainable flood events. The trend model produces too many risks for floods in the future but no risk for droughts. The overall results conclude that the nonstationarities in hydrological series should be carefully handled in stochastic simulation models to appropriately manage future water-related risks.

Abstract As an in‐depth profile control agent, water‐soluble phenolic resin crosslinking polyacrylamide weak gel has been widely used in the middle and high water cut stage of water flooding reservoir. In this study, the phenolic resin was synthesized by two‐step alkali catalysis. Factors influencing the synthesis of phenolic resin, including the molar ratio of phenol and formaldehyde, catalyst types, reaction time, were investigated with hydroxylmethyl and aldehyde content as the criterion. When the molar ratio of phenolic resin was 1:2 and NaOH was catalyst, at 80°C for 4 h, the phenolic resin had the highest hydroxymethyl content (49.37%) and the lowest free aldehyde content (2.95%). Weak gel was formed by the reaction of LT002‐polyacrylamide with phenolic resin. Taking the gelation time and strength as criteria, the factors influencing the crosslinking property, including hydroxymethyl content, crosslinker addition, and polyacrylamide concentration were investigated respectively. Under optimal formulation, the property investigation shows that the hydroxymethyl group in the phenolic resin can be crosslinked with the amide group in polyacrylamide, the gelation time is long (50–60 h), and the gelation strength is larger than 5 × 10 4  mPa s, which is conductive to the plugging of deep oil layers. When the permeability was 5061 × 10 −3  μm 2 , the plugging rate was 72.73%.

Research in hydrological sciences is constantly evolving to provide adequate answers to address various water-related issues. Methodological approaches inspired by mathematical and physical sciences have shaped hydrological sciences from its inceptions to the present day. Nowadays, as a better understanding of the social consequences of extreme meteorological events and of the population’s ability to adapt to these becomes increasingly necessary, hydrological sciences have begun to integrate knowledge from social sciences. Such knowledge allows for the study of complex social-ecological realities surrounding hydrological phenomena, such as citizens’ perception of water resources, as well as individual and collective behaviors related to water management. Using a mixed methods approach to combine quantitative and qualitative approaches has thus become necessary to understand the complexity of hydrological phenomena and propose adequate solutions for their management. In this paper, we detail how mixed methods can be used to research flood hydrology and low-flow conditions, as well as in the management of these hydrological extremes, through the analysis of case studies. We frame our analysis within the three paradigms (positivism, post-positivism, and constructivism) and four research designs (triangulation, complementary, explanatory, and exploratory) that guide research in hydrology. We show that mixed methods can notably contribute to the densification of data on extreme flood events to help reduce forecasting uncertainties, to the production of knowledge on low-flow hydrological states that are insufficiently documented, and to improving participatory decision making in water management and in handling extreme hydrological events.

Abstract An intensity–duration–frequency (IDF) curve describes the relationship between rainfall intensity and duration for a given return period and location. Such curves are obtained through frequency analysis of rainfall data and commonly used in infrastructure design, flood protection, water management, and urban drainage systems. However, they are typically available only in sparse locations. Data for other sites must be interpolated as the need arises. This paper describes how extreme precipitation of several durations can be interpolated to compute IDF curves on a large, sparse domain. In the absence of local data, a reconstruction of the historical meteorology is used as a covariate for interpolating extreme precipitation characteristics. This covariate is included in a hierarchical Bayesian spatial model for extreme precipitations. This model is especially well suited for a covariate gridded structure, thereby enabling fast and precise computations. As an illustration, the methodology is used to construct IDF curves over Eastern Canada. An extensive cross-validation study shows that at locations where data are available, the proposed method generally improves on the current practice of Environment and Climate Change Canada which relies on a moment-based fit of the Gumbel extreme-value distribution.

Floods, intensified by climate change, pose major challenges for flood zone management in Quebec. This report addresses these issues through two complementary aspects: a historical analysis of the evolution of flood zone management in Quebec and the projected impact of the cartographic and regulatory overhaul, as well as an exploration of the imaginary surrounding the flood-prone territory of the city of Lachute, which has faced recurrent floods for decades and yet continues to be inhabited. The historical analysis reveals that the major floods of 1974, 1976, 2017, and 2019 marked significant turning points in Quebec’s risk management, particularly by highlighting gaps in the regulatory framework and flood zone mapping. The adoption of the Act Respecting Land Use Planning and Development (LAU) in 1979 and the Policy for the Protection of Shorelines, Littorals, and Floodplains (PPRLPI) in 1987 represented a shift toward a preventive approach. However, inconsistencies, insufficient updates to maps, and uneven enforcement of standards have hindered their effectiveness. The catastrophic floods of 2017 and 2019 triggered a regulatory overhaul, a modernization of mapping, and measures to strengthen community resilience. In 2022, a transitional regime came into effect to tighten the regulation of activities in flood zones, pending the adoption of a risk-based management framework. However, to this day, the regulatory perimeters proposed in the modernization project fail to account for the adaptive capacities deployed by communities to live with water, thus providing a biased interpretation of flood risk. The second part explores the social and cultural representations associated with Lachute’s flood-prone territory. It highlights the complex relationships that have developed between residents and the Rivière du Nord through successive flooding episodes and the adaptation strategies implemented to cope, particularly by those who have repeatedly experienced flooding. These residents have come to live with overflow events and to (co)exist with water, challenging the persistent notion that flood-prone areas are inherently dangerous. While local strategies are sometimes innovative, they remain constrained by a regulatory framework that disregards the human experience of the territory and the specific ways in which people inhabit exposed areas to learn to manage flood risks. In summary, this report underscores the urgency of a territorialized, risk-based approach to modernizing flood zone management. It also highlights the need to look beyond cartographic boundaries and better integrate human and cultural dimensions into planning policies, as illustrated in the case of Lachute, to more accurately reflect the true level of risk. These reflections aim to promote more coherent, sustainable, and acceptable management, planning, and development of exposed territories in response to the growing challenges posed by climate change.

Extreme precipitation events play a crucial role in shaping the vulnerability of regions like Algeria to the impacts of climate change. To delve deeper into this critical aspect, this study investigates the changing patterns of extreme precipitation across five sub-regions of Algeria using data from 33 model simulations provided by the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP-CMIP6). Our analysis reveals a projected decline in annual precipitation for four of these regions, contrasting with an expected increase in desert areas where annual precipitation levels remain low, typically not exceeding 120 mm. Furthermore, key precipitation indices such as maximum 1-day precipitation (Rx1day) and extremely wet-day precipitation (R99p) consistently show upward trends across all zones, under both SSP245 and SSP585 scenarios. However, the number of heavy precipitation days (R20mm) demonstrates varied trends among zones, exhibiting stable fluctuations. These findings provide valuable foresight into future precipitation patterns, offering essential insights for policymakers and stakeholders. By anticipating these changes, adaptive strategies can be devised to mitigate potential climate change impacts on crucial sectors such as agriculture, flooding, water resources, and drought.

Heavy rainfall events in the warm season (May–September) over the Tibetan Plateau (TP) region and its downstream areas are often closely related to eastward-propagating Tibetan Plateau Vortices (TPVs). Hence, improving the prediction of TPVs and their associated convective activity is of paramount importance, given the significant potential impacts they can have on densely populated downstream regions, including but not limited to flooding and damages. In this study, a typical long-lived TPV that occurred in July 2008 was used for the first time to explore the benefit of assimilating satellite all-sky infrared radiances on the cloud and precipitation prediction of the TPV-induced eastward-propagating mesoscale convective system (MCS). The all-sky infrared radiances from the water vapor (WV) channel of the geostationary Meteosat-7 and other conventional observations were assimilated into a 4-km grid spacing regional model using the ensemble Kalman filter. The results revealed that the all-sky infrared data assimilation improved the cloud, precipitation, dynamical, and thermodynamical analyses as well as 0–12-hr deterministic and ensemble forecasts. Compared with the experiment in which the all-sky infrared radiances were not assimilated (non-radiance experiment), the experiment with assimilated all-sky infrared radiances yielded clearly improved initial wind and cloud fields, 1–12-hr cloud forecasts, and 1–6-hr precipitation forecasts. This study indicates that assimilation of all-sky satellite radiances has the potential for improving the operational cloud and precipitation forecasts over the TP and its downstream areas.

Extreme precipitation events can lead to disastrous floods, which are the most significant natural hazards in the Mediterranean regions. Therefore, a proper characterization of these events is crucial. Extreme events defined as annual maxima can be modeled with the generalized extreme value (GEV) distribution. Owing to spatial heterogeneity, the distribution of extremes is non-stationary in space. To take non-stationarity into account, the parameters of the GEV distribution can be viewed as functions of covariates that convey spatial information. Such functions may be implemented as a generalized linear model (GLM) or with a more flexible non-parametric non-linear model such as an artificial neural network (ANN). In this work, we evaluate several statistical models that combine the GEV distribution with a GLM or with an ANN for a spatial interpolation of the GEV parameters. Key issues are the proper selection of the complexity level of the ANN (i.e., the number of hidden units) and the proper selection of spatial covariates. Three sites are included in our study: a region in the French Mediterranean, the Cap Bon area in northeast Tunisia, and the Merguellil catchment in central Tunisia. The comparative analysis aim at assessing the genericity of state-of-the-art approaches to interpolate the distribution of extreme precipitation events.

Abstract Collecting data on the dynamic breakup of a river's ice cover is a notoriously difficult task. However, such data are necessary to reconstruct the events leading to the formation of ice jams and calibrate numerical ice jam models. Photogrammetry using images from remotely piloted aircraft (RPA) is a cost-effective and rapid technique to produce large-scale orthomosaics and digital elevation maps (DEMs) of an ice jam. Herein, we apply RPA photogrammetry to document an ice jam that formed on a river in southern Quebec in the winter of 2022. Composite orthomosaics of the 2-km ice jam provided evidence of overbanking flow, hinge cracks near the banks and lengthy longitudinal stress cracks in the ice jam caused by sagging as the flow abated. DEMs helped identify zones where the ice rubble was grounded to the bed, thus allowing ice jam thickness estimates to be made in these locations. The datasets were then used to calibrate a one-dimensional numerical model of the ice jam. The model will be used in subsequent work to assess the risk of ice interacting with the superstructure of a low-level bridge in the reach and assess the likelihood of ice jam flooding of nearby residences.

In agricultural fields, tile drains represent potential pathways for the migration of solutes, such as nitrates, in groundwater and surface water bodies. Tile drain flow is controlled by the temporal and spatial dynamics of the shallow groundwater table, which results from complex interactions between climate, topography and soil heterogeneity. Studies on the effect of topsoil heterogeneity on shallow water and drainage dynamics by fully 3D surface water and groundwater flow modeling are limited. The objective of our study is to demonstrate the use of depth specific electrical conductivity (EC) estimates to improve hydrological simulations in a tile-drained field. The model was applied to a field site in Denmark where times series of drainage discharge and water table elevations are available. Clay-rich soil zones were identified in a tile-drained field using depth specific electrical conductivity estimates generated by the inversion of apparent electrical conductivity data measured using an electromagnetic induction instrument. One model that included the low-permeability clayey zones in the soil layers down to a depth of 1.2 m was compared to a simpler model that assumed homogeneous soil layers. Both models simulate drainage discharge that compares well to the observations. However, including the clayey zones improves the simulation of hydraulic heads, and water table fluctuations, and generates flooded areas that are more representative of those observed during the wet seasons. Our results suggest that the simulation of water table fluctuations can be improved when the soil heterogeneity determined from depth specific EC estimates is included in integrated hydrological models. A better representation of the subsurface flow dynamics will also improve subsequent simulations of the transport and fate of agrochemical substances leaching from fields such as nitrate, which may deteriorate the quality of groundwater and surface water bodies.

In response to extreme flood events and an increasing awareness that traditional flood control measures alone are inadequate to deal with growing flood risks, spatial flood risk management strategies have been introduced. These strategies do not only aim to reduce the probability and consequences of floods, they also aim to improve local and regional spatial qualities. To date, however, research has been largely ignorant as to how spatial quality, as part of spatial flood risk management strategies, can be successfully achieved in practice. Therefore, this research aims to illuminate how spatial quality is achieved in planning practice. This is done by evaluating the configurations of policy instruments that have been applied in the Dutch Room for the River policy program to successfully achieve spatial quality. This policy program is well known for its dual objective of accommodating higher flood levels as well as improving the spatial quality of the riverine areas. Based on a qualitative comparative analysis, we identified three successful configurations of policy instruments. These constitute three distinct management strategies: the “program‐as‐guardian”, the “project‐as‐driver,” and “going all‐in” strategies. These strategies provide important leads in furthering the development and implementation of spatial flood risk management, both in the Netherlands and abroad.

L’adaptation au changement climatique est un nouvel enjeu pour la gestion des territoires. Au niveau local, elle apparaît souvent comme une injonction, alors même que, pour l’instant, elle est un concept flou. Elle est présentée comme l’application de bonnes pratiques, mais les questions « qui s’adapte à quoi ? » et « pourquoi ? » demeurent implicites. En explicitant ces éléments, nous proposons de montrer que l’adaptation est une question plurielle et politique. À partir de l’analyse des documents de planification et des plans d’action faisant référence aux changements globaux sur un territoire littoral, nous montrons l’existence de quatre logiques d’adaptation distinctes, plus ou moins transformatrices du système socioécologique, que l’on peut appréhender à partir de la typologie suivante : « contrôler et maintenir », « faire faire », « réguler » et « reconfigurer », qui portent en germe différentes reconfigurations socioéconomiques et politiques. , Since the 2000s, “adaptation” is a new dictate for the management of local territories in France, but its implementation is fairly limited. Adaptation is mainly a semantically unclear and loosely defined concept. Decision-makers could “operationalize” adaptation by simply applying a specific methodology. However, adaptation is not a mere mechanism; it is also a process that implies economic, social and ecological trade-offs for the socio-ecological system. These political dimensions are often unformulated. In order to provide a vehicle to clarify this concept and its political dimensions, we propose a typology of adaptation measures. What does adaptation mean? Adjustment of what (territories, populations, communities, local economies, etc.), to what (climate change, global change) and with what effects? We reviewed local actions and strategic plans related to climate but also to urban planning, flooding and water management on the eastern coastal area of Languedoc Roussillon in Mediterranean France. We conducted and analyzed semi-structured interviews with institutional actors. We analyzed and classified public policy instruments, associated the underlying “logic” (raise limiting factors, create a new awareness, etc.), and their potential effects. Throughout our effort to develop a typology, we have highlighted the political dimensions of adaptation actions and shed a light on trade-offs linked to adaptation choices.

The avulsion time scale of channels on the Yellow River delta (YRD) is about a decade due to the large sediment load, and rapid channel aggradation and progradation. Nevertheless, the Qingshuigou channel has been maintained for about four decades since 1976. This channel provides an ideal opportunity to study channel evolution following avulsion and to examine different avulsion criteria. In this study, we analyzed the geomorphic adjustment of the lower Qingshuigou channel during 1976–2015, and calculated normalized gradient advantage and superelevation at the channel to estimate how close the channel was to avulsion. Results showed that channel evolution processes may be divided into four phases: I (1976–1980) rapid aggradation, II (1980–1985) channel widening and enlargement, III (1985–1996) main channel aggradation and shrinkage, and IV (1996–2015) main channel incision and deepening. Evolution phases I, II and III are similar to the avulsion cycle observed in natural and experimental fluvial systems. The calculated values of normalized gradient advantage and superelevation in early 1990s exceeded the critical values suggested by previous studies, implying that the channel was prone to avulsion. Nevertheless, avulsion was prevented mainly due to limited overbank flows, constriction from artificial dikes, and slowed channel extension as a result of reduced sediment load. The evolution of the Qingshuigou channel confirms previous arguments that superelevation and gradient advantage are not sufficient for avulsion, and multiple factors should be considered, including flood frequency, lateral mobility, sediment diameter, and human interruptions.