Hydrological Variables Research Articles

Roles of climatic and anthropogenic factors in shaping Holocene vegetation and fire regimes in Great Dismal Swamp, eastern USA

The Great Dismal Swamp wetland, spanning >400 km2 along the Virginia and North Carolina border, was shaped by a complex combination of geomorphic, climatic, and anthropogenic forcings during the last 14,000 years. Pollen, macrofossils, charcoal, and physical properties from sediment cores at seven sites provide a detailed record of the spatial heterogeneity of the wetland and the roles played by natural hydrologic variability, wildfire, and human modification of drainage in shaping vegetation and habitats. Cold-temperate forests occupied regional uplands from at least 13.5–10.3 cal ka BP. Marshes dominated by grasses and other herbaceous taxa began developing along low-elevation streams as early as 10.3 cal ka BP, resulting in accumulation of organic silts. Long-hydroperiod, peat accumulating marshes, with abundant floating aquatic plants, developed as early as 9.6 cal ka BP, as rapid rates of sea-level rise elevated the water table and facilitated wetland development and peat accumulation along stream courses. By the mid-Holocene (c. 7–6.5 cal ka BP), when local sea-level rise began slowing and reached about 12–15 m below present, shorter hydroperiod, peat-accumulating marshes dominated the landscape, with increased wildfire activity. Great Dismal Swamp vegetation shifted from marshes to peat-accumulating forested wetlands by c. 3.7 cal ka BP; these were dominated by varying combinations of Nyssa (tupelo), Taxodium (cypress), and Chamaecyparis thyoides (Atlantic white cedar). Wildfires were infrequent during this time, and the forested wetlands persisted, with minor compositional changes related to climate-driven fluctuations in stream flow, until colonial ditching and logging began in the swamp during the late 18th century. These activities decreased cypress and cedar populations, and, by the mid-20th century, expanded ditching resulted in even drier conditions and expansion of maple-gum (dominated by Acer and Liquidambar), and pine-pocosin (dominated by Pinus) forests. The distribution of these forests differs from that of the late Holocene and represents a fundamental shift in hydrology, peat structure, vegetation, and fire regime due to landscape alterations of the last few centuries.

BIOCLIMATIC DROUGHT AND ITS TRENDS IN CALIFORNIA STATE (U.S.)

Drought occurs naturally all over the world. Global warming has led an increase in the areas affected by this phenomenon. The study of drought involves the analysis of indicators and indices used to assess changes in the hydrological cycle of a region. A large number of indices developed for drought monitoring are based on meteorological and hydrological variables. This research has applied the Ombroxeric Index for the first time in California. It is based on the concept of ombroxericity: a condition of the territory characterised from an ombrothermic point of view, which can develop in relatively humid areas/zones, depending on the time scale of the study (i.e. monthly, seasonal or annual basis). Temperatures and precipitation from 180 meteorological stations have been considered for a period of observation from 1980 to 2016. In order to know the evolution of this index, a modified trend analysis based on the Mann-Kendall test and a modified Sen slope analysis were used. Empirical Bayesian Kriging was applied as an interpolation technique. Results are presented in both surface and contour maps. California showed a wide range of ombroxeric levels that went from upper strong dry in northern zones, to lower weak arid. Trend results showed an increase in bioclimatic drought throughout the whole State. In addition, summer showed the highest levels on the Ombroxeric Index. An increase in drought trends was observed at the seasonal level, being more pronounced in summer and spring. In both cases are increases in the central (+5 and +3.6 units year−1 respectively) and the southern (+4 and +4.5 units year−1 respectively) areas of the State. Increasing frequency and severity of droughts could have implications for the management of water resources and the survival of Californian vegetation types, such as conifers.

A Hierarchical Temporal Scale Framework for Data‐Driven Reservoir Release Modeling

AbstractAs an important anthropogenic interference in the hydrologic cycle, reservoir operation behavior remains challenging to be properly represented in hydrologic models, thus limiting the capability of predicting streamflow under the interactions between hydrologic variability and operational preferences. Data‐driven models provide a promising approach to capture relationships embedded in historical records. Similar to hydrologic processes that vary across temporal scales, reservoir operations manifest themselves at different timescales, prioritizing different operation targets to mitigate streamflow variability at a given time scale. To capture the interaction of reservoir operation across time scales, we proposed a hierarchical temporal scale framework to investigate the behaviors of over 300 major reservoirs across the Contiguous United States with a wide range of streamflow conditions. Data‐driven models were constructed to simulate reservoir releases at monthly, weekly, and daily scales, where decisions at short‐term scales interact with long‐term decisions. We found that the hierarchical temporal scale configuration could compensate for the absence of key explanatory variables as model inputs, thereby efficiently capturing the release decisions of reservoirs situated in the west. Model‐based sensitivity analysis shows that for more than one‐third of the studied reservoirs, the release schemes, as a function of decision variables, vary at different time scales, suggesting that operators commonly face complicated trade‐offs to serve multiple designed purposes. The proposed hierarchical temporal scale approach is flexible to incorporate various data‐driven models and decision variables to derive reservoir operation rules, providing a robust framework to understand the feedback between natural streamflow variability and human interferences across time scales.

IoT, machine learning and photogrammetry in small hydropower towards energy and digital transition: potential energy and viability analyses

This research aims to evaluate and put into practise the design of a small hydropower plant on a stream at São Vicente, in Madeira Island, supported by internet of things (IoT). The photogrammetry technique is also used with a comprehensive digital transformation, in which new concepts, methods and models, such as machine learning (ML), and big data analytics play an important role due to the huge availability time series that have to be exploited in hydropower design studies. Nowadays, digitalization and massive data availability are imposing new ways to address many of the current challenges associated with the energy and digital transition. This research is based on a simple small hydropower design, to present an integrated methodology using new methods assigned by an internet protocol system, which includes the development of different steps and components supported by GIS, photogrammetry and the use of advanced tools, with the support of a drone survey with internet communication (IoT) that allow the generation of experimentally-based estimates in situ characterization, the volumetric flow, the hydrological data treatment, the hydraulic calculations and economic estimations for a real hydro project. Therefore, hydrological variables, hydraulic analysis and topographical survey are carried out in the IoT application platform supported by new tools and methods to optimise the size of hydraulic structures, estimate the performance and potential of the hydropower plant towards the best solution for energy and digital transition. Firstly, the data-base for the all study and posterior sizing of the case study of hydropower plant are defined and then the corresponding analyses and results are presented. Then, the cost estimation for the construction, maintenance and operation of the selected elements that compose the hydropower topology are determined, as well as the respective economic balance, considering the annual energy production. In addition, both economic and environmental return on investment is discussed. Finally, an analysis to equate the cost estimates and the respective benefits of hydropower generation using this new approach applicability is stablished, taking into account some economic indicators to determine the profitability of the project.

An evaluation framework for downscaling and bias correction in climate change impact studies

Climate change impact studies commonly use impact models (such as hydrological or crop models) forced with corrected climate input data from global climate models. A range of downscaling and bias correction methods have been developed to increase the spatial resolution and remove systematic biases in climate model outputs to be applied before use in impact models. Many studies have focused on evaluating such approaches for the climate variables they aim to correct. However, due to nonlinear error propagation there can be large remaining biases in model outputs, even when ingesting bias corrected climate forcings.Here we propose an impact-centric evaluation framework for downscaling and bias correction methods to be used in climate change risk assessments. This framework evaluates and compares the strengths and limitations of downscaling and bias correction in the impact domain, highlighting approaches that lead to reduced biases in impact variables of interest. We demonstrate the evaluation framework in the context of assessing downscaling and bias correction methods for hydrological projections in Australia. Our results show that although all downscaling and bias correction methods evaluated perform adequately for the input climate variables, their errors vary markedly when the impact is modelled. Our proposed evaluation framework involves selecting a number of key performance metrics, and ranking the four downscaling and bias correction methods to compute an overall ranking, highlighting the best-performing methods for each statistical metric and the overall best-performing approach. We present an application of this approach using performance metrics relevant to hydrological applications, relating to mean biases, variability, heavy precipitation and peak runoff days, and dry conditions.For impact studies related to hydrological applications, we find that multi-variate bias correction that considers cross-correlations, temporal auto-correlations and biases at multiple time scales (daily to annual) performs best in reducing biases in hydrological output variables for Australia. Our proposed evaluation approach can be applied to a wide range of climate change applications where downscaling and bias correction are required, including impacts on agricultural production, wildfires, energy generation, human health, ecosystem functioning, and water resource management.

Tracing the culprits over the destruction of three coastal communities during a rouge rainstorm on August 2021 in north central Türkiye

Extreme floods resulting from one of the heaviest ever documented rain storms over several urbanized coastal sub-districts in north-central Türkiye in August 2021, caused severe destruction, considerable property damage and tens of casualties. Peak discharges following the event were mostly greater than the projected 50-year and 100-year ones. This study aimed to examine the causes and consequences of this catastrophic event that occurred in the sub-districts of Bozkurt and Catalzeytin within Kastamonu, and Ayancik within Sinop provinces. Real-time 24-h precipitation follow-up by two neighboring Doppler Radar stations disclosed the spatial distribution of the phenomenon as it unfolded. Relative damages that occurred within each district following the event were evaluated, taking into account the meteorological, topographical and hydrological variables. Steadily increasing precipitation from 0 mm/h at midnight on August 11th to 35–40 mm/h around 4 p.m. in all sub-districts, kept on falling without slowing down until the following midnight, even further increasing to 45–47 mm/h in Bozkurt. Heavily forested feeder basins, 91.4% in Ayancik, 92.2% in Bozkurt and 88.2% in Catalzeytin were overwhelmed by the amount of precipitation, and quickly saturated surfaces allowed the excessive runoff to reach the sub-districts down the main channel. Tracing the entire phenomenon as it progressed over the sub-districts showed how site selection and unfavorable topography worsened the outcome. Topographic indices calculation showed that the main channels and their immediate surroundings in all sub-districts were the confirmed no-go zones for urban developments. The events highlighted several lessons for city and flood managers regarding flood planning, mitigation, response and recovery.

A Pareto‐Based Sensitivity Analysis and Multiobjective Calibration Approach for Integrating Streamflow and Evaporation Data

AbstractEvaporation is gaining increasing attention as a calibration and evaluation variable in hydrologic studies that seek to improve the physical realism of hydrologic models and go beyond the long‐established streamflow‐only calibration. However, this trend is not yet reflected in sensitivity analyses aimed at determining the relevant parameters to calibrate, where streamflow has traditionally played a leading role. On the basis of a Pareto optimization approach, we propose a framework to integrate the temporal dynamics of streamflow and evaporation into the sensitivity analysis and calibration stages of the hydrologic modeling exercise, here referred to as “Pareto‐based sensitivity analysis” and “multiobjective calibration.” The framework is successfully applied to a case study using the Variable Infiltration Capacity (VIC) model in three catchments located in Spain as representative of the different hydroclimatic conditions within the Iberian Peninsula. Several VIC vegetation parameters were identified as important to the performance estimates for evaporation during sensitivity analysis, and therefore were suitable candidates to improve the model representation of evaporative fluxes. Sensitivities for the streamflow performance, in turn, were mostly driven by the soil and routing parameters, with little contribution from the vegetation parameters. The multiobjective calibration experiments were carried out for the most parsimonious parameterization after a comparative analysis of the performance gains and losses for streamflow and evaporation, and yielded optimal adjustments for both hydrologic variables simultaneously. Results from this study will help to develop a better understanding of the trade‐offs resulting from the joint integration of streamflow and evaporation data into modeling frameworks.

The CoralHydro2k database: a global, actively curated compilation of coral δ18O and Sr ∕ Ca proxy records of tropical ocean hydrology and temperature for the Common Era

Abstract. The response of the hydrological cycle to anthropogenic climate change, especially across the tropical oceans, remains poorly understood due to the scarcity of long instrumental temperature and hydrological records. Massive shallow-water corals are ideally suited to reconstructing past oceanic variability as they are widely distributed across the tropics, rapidly deposit calcium carbonate skeletons that continuously record ambient environmental conditions, and can be sampled at monthly to annual resolution. Climate reconstructions based on corals primarily use the stable oxygen isotope composition (δ18O), which acts as a proxy for sea surface temperature (SST), and the oxygen isotope composition of seawater (δ18Osw), a measure of hydrological variability. Increasingly, coral δ18O time series are paired with time series of strontium-to-calcium ratios (Sr/Ca), a proxy for SST, from the same coral to quantify temperature and δ18Osw variability through time. To increase the utility of such reconstructions, we present the CoralHydro2k database, a compilation of published, peer-reviewed coral Sr/Ca and δ18O records from the Common Era (CE). The database contains 54 paired Sr/Ca–δ18O records and 125 unpaired Sr/Ca or δ18O records, with 88 % of these records providing data coverage from 1800 CE to the present. A quality-controlled set of metadata with standardized vocabulary and units accompanies each record, informing the use of the database. The CoralHydro2k database tracks large-scale temperature and hydrological variability. As such, it is well-suited for investigations of past climate variability, comparisons with climate model simulations including isotope-enabled models, and application in paleodata-assimilation projects. The CoralHydro2k database is available in Linked Paleo Data (LiPD) format with serializations in MATLAB, R, and Python and can be downloaded from the NOAA National Center for Environmental Information's Paleoclimate Data Archive at https://doi.org/10.25921/yp94-v135 (Walter et al., 2022).

Canopy development influences early successional stream ecosystem function but not biotic assemblages

Determining how streams develop naturally, particularly the ecological role of newly developed riparian canopy cover, is essential to understanding the factors that structure new stream communities and provides valuable information for restoring highly disturbed ecosystems. However, attempts to understand primary succession in riverine ecosystems have been hindered by a lack of data owing to the infrequent formation of new rivers on the landscape. In the present study, we used five streams formed following the 1980 eruption of Mount St. Helens (WA, USA) to examine the influence of canopy cover development on algal and benthic macroinvertebrate assemblages, biomass, and organic matter processing. Newly established closed canopy reaches had less available light, but no significant differences in algal biomass or macroinvertebrate assemblages compared to open canopy reaches. Instead, algal and macroinvertebrate communities were structured mainly by hydrologic differences among watersheds. In contrast, organic matter processing rates were sensitive to canopy cover development, and rates were faster under closed canopies, especially in late summer or after terrestrial preconditioning. After 40 years of stream and riparian primary successional development, canopy cover strongly influences ecosystem function, but aquatic organism assembly was more influenced by physio-chemical and hydrologic variation. Our findings provide insight into the development of in-stream assemblages and ecosystem functions, which is also relevant to efforts to address major disturbances to stream channels, such as volcanic eruptions, floods, forest fires, and clear-cut logging.

Exploring the Relationship between Hydroclimate and Lake Area in Source Area of the Yellow River: Implications for the Paleoclimate Studies

The large tectonic lake is one of the most important water bodies in the source area of the Yellow River (SAYR), northeastern Qinghai-Tibet Plateau (QTP). It plays a key role in decelerating climatic change and regulating regional climate patterns. In this study, we used Landsat images (MSS, TM, ETM+ and OLI) of Lake Gyaring and Lake Ngoring (the Two Sisters Lakes), which are the two largest tectonic lakes in the SAYR, to determine annual lake area fluctuations from 1986 to 2020. The results show that lake area increases were generally consistent with a warming trend in the SAYR. The temperature signals were separated from the lake area changes by using a detrending analysis and found that the processed data are closely correlated with variations of precipitation and streamflow in the SAYR, and the previously reported paleoclimate records, which include the δ18O record from stalagmite, A/C (Artemisia/Chenopodiaceae) ratio from lake sediment and scPDSI (self-calibrating Palmer Drought Severity Index) from the tree ring on the northeastern margin of the QTP. The phase of relatively large lake areas typically coincides with a negative excursion in δ18O, a high A/C ratio, and elevated scPDSI values, while the opposite is true for smaller lake areas. It is suggested that the total area of the Two Sisters Lakes is closely associated with hydroclimatic conditions in the SAYR. Furthermore, an association of high TSI anomalies with the water area expansion of the Two Sisters Lakes is also observed, implying that solar activity is the key driving factor for the hydrologic variability in the SAYR on decadal timescales. The findings of our study highlight the validity of previous paleoclimate archives in the northeastern QTP and demonstrate the potential of using remote sensing techniques to investigate paleoclimate.

Geospatial-Based Analytical Hierarchy Process (AHP) and Weighted Product Model (WPM) Techniques for Mapping and Assessing Flood Susceptibility in the Wadi Hanifah Drainage Basin, Riyadh Region, Saudi Arabia

This paper aimed to map areas prone to flooding in the Wadi Hanifah drainage basin located in the Riyadh region, and identify the most important factors that contribute to flooding through examining the influence of ten topographical, hydrological, and environmental variables affecting flood occurrence. Remote sensing data from Landsat-8, Shuttle Radar Topography Mission (SRTM), and other ancillary datasets were used to map relevant variables. Two weighted overlay techniques were used, including: analytical hierarchy process (AHP) and weighted product model (WPM). A correlation matrix and optimum index factor (OIF) were employed to identify the relative importance of each factor. The two derived flood susceptibility maps were assessed through validation by comparing the locations of historical flood events to susceptibility zones. The results confirmed the validity of the WPM map. The results also showed that nearly 50% of the study area was dominated by the “moderate” flood susceptibility zone, while about 33% of the total land area was classified as a “high” flood susceptibility zone. The “slope” factor was found to be the most effective variable for flood occurrence, followed by the “geology” variable, while the “distance to the drainage network” was the least important variable. The results of the OIF indicated that the best combination of factors dictating the variability of all flood susceptibility areas were “geology”, “land use/cover (LULC)”, and “soil type”. The study findings are expected to be useful in understanding the effects of each factor on the spatial variation in flood occurrence and in improving flood control, and can be reapplied to other regions with similar climatic and environmental conditions worldwide.

Setting a reference for wetland carbon: the importance of accounting for hydrology, topography, and natural variability

Wetland soils are a key global sink for organic carbon (C) and a focal point for C management and accounting efforts. The ongoing push for wetland restoration presents an opportunity for climate mitigation, but C storage expectations are poorly defined due to a lack of reference information and an incomplete understanding of what drives natural variability among wetlands. We sought to address these shortcomings by (1) quantifying the range of variability in wetland soil organic C (SOC) stocks on a depressional landscape (Delmarva Peninsula, USA) and (2) investigating the role of hydrology and relative topography in explaining variability among wetlands. We found a high degree of variability within individual wetlands and among wetlands with similar vegetation and hydrogeomorphic characteristics. This suggests that uncertainty should be presented explicitly when inferring ecosystem processes from wetland types or land cover classes. Differences in hydrologic regimes, particularly the rate of water level recession, explained some of the variability among wetlands, but relationships between SOC stocks and some hydrologic metrics were eclipsed by factors associated with separate study sites. Relative topography accounted for a similar portion of SOC stock variability as hydrology, indicating that it could be an effective substitute in large-scale analyses. As wetlands worldwide are restored and focus increases on quantifying C benefits, the importance of appropriately defining and assessing reference systems is paramount. Our results highlight the current uncertainty in this process, but suggest that incorporating landscape heterogeneity and drivers of natural variability into reference information may improve how wetland restoration is implemented and evaluated.

Enhancing Streamflow Modeling by Integrating GRACE Data and Shared Socio-Economic Pathways (SSPs) with SWAT in Hongshui River Basin, China

Climatic variability and the quantification of climate change impacts on hydrological parameters are persistently uncertain. Remote sensing aids valuable information to streamflow estimations and hydrological parameter projections. However, few studies have been implemented using remote sensing and CMIP6 data embedded with hydrological modeling. This research studied how changing climate influences the hydro-climatic parameters based on the earth system models that participated in the sixth phase of the Coupled Model Intercomparison Project (CMIP6). GRACE evapotranspiration data were forced into the Soil and Water Assessment Tool (SWAT) to project hydrologic responses to future climatic conditions in the Hongshui River basin (HRB) model. A novel approach based on climate elasticity was utilized to determine the extent to which climate variability affects stream flow. CMIP6 SSPs (shared socio-economic pathways) for the second half of the 20th century (1960–2020) and 21st century (2021–2100) projected precipitation (5–16%) for the whole Hongshui River basin (HRB). The ensemble of GCMs projected an increase of 2 °C in mean temperature. The stream flow is projected to increase by 4.2% under SSP-1.26, 6.2% under SSP-2.45, 8.45% under SSP-3.70, and 9.5% under SSP-5.85, based on the average changes throughout the various long-term future scenarios. We used the climate elasticity method and found that climate change contributes 11% to streamflow variability in the Hongshui River basin (HRB). Despite the uncertainty in projected hydrological variables, most members of the modeling ensemble present encouraging findings for future methods of water resource management.

Trend Analysis of Different Climate Parameters and Watering Requirements for Hazelnut in Central Italy Related to Climate Change

In this study, the effects of climate change on the irrigation water requirement of hazelnut trees were investigated in Central Italy. The meteorological variables considered were precipitation, temperature, chilling units, and the Standardized Precipitation Index (SPI) in Central Italy. The hydrological variables were the reference evapotranspiration (ET0) and the water requirement based on soil water balance. Climate data were collected from eight meteorological stations for the period 1974–2021, and ET0 was estimated by the Hargreaves and Samani equation. The SPI index was calculated for a four-month time scale corresponding to the hazelnut growing season (April–August). A statistical analysis of the trends of the variables considered was conducted. The results showed an increasing trend for temperature, ET0, and water requirements, while a decreasing trend was shown for the chilling units. No significant trends were detected for precipitation and SPI.

Streamflow prediction using a hybrid methodology based on variational mode decomposition (VMD) and machine learning approaches

The optimal management of water resources depends on accurate and reliable streamflow prediction. Therefore, researchers have become interested in the development of hybrid approaches in recent years to enhance the performance of modeling techniques for predicting hydrological variables. In this study, hybrid models based on variational mode decomposition (VMD) and machine learning models such as random forest (RF) and K-star algorithm (KS) were developed to improve the accuracy of streamflow forecasting. The monthly data obtained between 1956 and 2017 at the Iranian Bibijan Abad station on the Zohreh River were used for this purpose. The streamflow data were initially decomposed into intrinsic modes functions (IMFs) using the VMD approach up to level eight to develop the hybrid models. The following step models the IMFs obtained by the VMD approach using the RF and KS methods. The ensemble forecasting result is then accomplished by adding the IMFs’ forecasting outputs. Other hybrid models, such as EDM-RF, EMD-KS, CEEMD-RF, and CEEMD-KS, were also developed in this research in order to assess the performance of VMD-RF and VMD-KS hybrid models. The findings demonstrated that data preprocessing enhanced standalone models’ performance, and those hybrid models developed based on VMD performed best in terms of increasing the accuracy of monthly streamflow predictions. The VMD-RF model is proposed as a superior method based on root mean square error (RMSE = 13.79), mean absolute error (MAE = 8.35), and Kling–Gupta (KGE = 0.89) indices.

Long-term daily stream temperature record for Scotland reveals spatio-temporal patterns in warming of rivers in the past and further warming in the future

Stream temperature is directly and indirectly affected by climate change. To be able to project future changes in stream temperature, historic trends and factors influencing these trends need to be understood. There is a demand for daily data to analyse historical trends and future changes in stream temperature. However, long-term daily stream temperature data are rare and observations of coarse temporal resolution (e.g. once-a-month) do not allow for robust trend analyses. Here, we present a methodology to reconstruct a national long-term daily stream temperature record (1960–2080) from 40 years of once-a-month observations (for 45 Scottish catchments). This involved implementing climatic and hydrological variables in generalized additive models. These models were then used in combination with regional climate projections (UKCP18 Strand 3 - RCP8.5) to predict future spatio-temporal temperature patterns.The results indicate that for the Scottish dataset (i) in addition to air temperature, the dominant environmental controls on stream temperature are unique combinations for each catchment; (ii) a general increase of up to 0.06 °C/year in historic stream temperature over all catchments resulted mainly from increases in spring and summer stream temperatures; (iii) future spatial patterns in stream temperature are more homogenous and differ therefore from the past where temperatures in N Scotland were relatively lower; (iv) future changes of up to +4.0 °C in annual stream temperature are strongest in those catchments which show lower stream temperature in the past (NW and W Scotland). These results are important in the context of water quality and stream temperature management. The methodology can be applied to smaller scale sites or to other national/global datasets enabling the analysis of historic trends and future changes at a high temporal resolution.

Sedimentary hydrodynamics of a sandy bay under macrotidal conditions (Saire Bay, France)

ABSTRACT Identified as one of the most vulnerable environment to the impacts of sea-level rise, the morphological and hydrological diversity of coastal areas complicates their study. To understand their evolution, it is essential to characterize their current morpho-sedimentary distributions and processes. Morphologic interpretation, from LiDAR data, coupled with grain-size and geochemical analyses of 200 samples, subsequently interpreted with statistical tools (PCA), allow us to determine the pattern of sedimentary transport of Saire Bay (Normandy, France).Even if the area is mainly dominated by tidal currents, the original morphology of Saire Bay highlights an uncommon complex circulation on a sandy tidal flat. Sedimentary land-sea exchanges, induced by tidal current, do not occur through the river Saire network in the north, but in south, along a less apparent secondary channel. Results provide an essential base to better understand the evolution of the coastline.

Seasonal patterns in the structure of the fish larvae assemblages in the lower Amazon River

Abstract This study assessed the phenology and functional structure of parental stocks of fish larvae, and evaluated how the species modulate their reproductive activities as a function of natural hydrological regimes in the lower Amazon River, Brazil. Larvae were collected using conical plankton nets monthly over two hydrological cycles between January and December 2013, and between May 2017 and April 2018. The collections were conducted around an alluvial island located near the municipality of Santarém, Pará, Brazil. After identification, individuals were classified into four functional groups (e.g., life‐history strategy, feeding habits, type of migration, and swimming‐performance/microhabitat‐use in the water column) based on species traits. The relationships between seasonal hydrology, environmental variables and larval assemblage structure were evaluated using multivariate analyses. A total of 31,347 fish larvae were collected, from nine taxonomic orders, 26 families, 40 genera and 53 species. The larval assemblages showed highly variable taxonomic and functional composition between different phases of the hydrological cycle (rising water, high water, receding water and low water) and were strongly influenced by environmental variables. Fish reproductive phenology in the lower Amazon is complex and distributed among the hydrological phases. The different ecological characteristics allow the species to withstand specific environmental conditions during each phase. Changes in the structure of larval assemblages indicate that hydrological variability is a key ecological factor influencing the reproductive dynamics of fish and reflects adaptive strategies used to reduce interspecific competition for space and food, maximising recruitment. The variation of fish reproduction across the hydrological cycle that we observed is a result of different ecological characteristics enabling species and functional groups to adapt to environmental conditions of each phase.

Multivariate Calibration of the SWAT Model Using Remotely Sensed Datasets

Remotely sensed hydrologic variables, in conjunction with streamflow data, have been increasingly used to conduct multivariable calibration of hydrologic model parameters. Here, we calibrated the Soil and Water Assessment Tool (SWAT) model using different combinations of streamflow and remotely sensed hydrologic variables, including Atmosphere–Land Exchange Inverse (ALEXI) Evapotranspiration (ET), Moderate Resolution Imaging Spectroradiometer (MODIS) ET, and Soil MERGE (SMERGE) soil moisture. The results show that adding remotely sensed ET and soil moisture to the traditionally used streamflow for model calibration can impact the number and values of parameters sensitive to hydrologic modeling, but it does not necessarily improve the model performance. However, using remotely sensed ET or soil moisture data alone led to deterioration in model performance as compared with using streamflow only. In addition, we observed large discrepancies between ALEXI or MODIS ET data and the choice between these two datasets for model calibration can have significant implications for the performance of the SWAT model. The use of different combinations of streamflow, ET, and soil moisture data also resulted in noticeable differences in simulated hydrologic processes, such as runoff, percolation, and groundwater discharge. Finally, we compared the performance of SWAT and the SWAT-Carbon (SWAT-C) model under different multivariate calibration setups, and these two models exhibited pronounced differences in their performance in the validation period. Based on these results, we recommend (1) the assessment of various remotely sensed data (when multiple options available) for model calibration before choosing them for complementing the traditionally used streamflow data and (2) that different model structures be considered in the model calibration process to support robust hydrologic modeling.

Effects of high and low flows on abundances of fish species in Central European headwater streams: The role of ecological species traits

Hydrological variability is considered a major structuring factor for biotic and abiotic processes in freshwater ecosystems and is of particular importance to fish communities. We used hydrological indices to investigate the short intermediate-and long-term effects of high- and low-flow patterns on the population abundances of 17 fish species in headwater streams in Germany. Generalized linear models, on average, explained 54 % of the variability in fish abundance, whereas long-term hydrological indices performed better than indices based on shorter periods. Three clusters of species were differentiated in their response patterns to low-flow conditions. Cold stenotherm and demersal species were susceptible to high frequency and long duration but tolerant to the magnitude of low-flow events. In contrast, species with a more benthopelagic habitat preference and tolerance to warmer water were susceptible to magnitude but tolerated larger frequencies of low-flow events. The euryoecious chub (Squalius cephalus), tolerating both long durations and large magnitudes of low-flow events, formed its own cluster. Species responses to high flows were more complex and five clusters of species were differentiated. Species with an equilibrium life history strategy were positively affected by longer durations of high-flow conditions, which may allow them to take advantage of the extended floodplain, whereas opportunistic and periodic species significantly differed by thriving in events characterized by high magnitude and frequency. These response patterns of fish species to high and low flows help understand species-specific risks when hydrological conditions are altered by climate change or direct human intervention.