River Discharge Research Articles

Salinity regulates radial growth of Cynometra ramiflora L. in the Sundarbans mangrove ecosystem

The mangrove biome is threatened by global environmental changes (including sea level rise, SLR) and anthropogenic disturbances. In this context, understanding the growth dynamics of mangrove species is essential for designing effective management plans for critical mangrove ecosystems such as the Bangladesh Sundarbans. Therefore, this study aims to (1) identify the growth-ring boundaries and tested their periodicity (annual nature) in one of the ecologically important understory mangrove species, Cynometra ramiflora in the Bangladesh Sundarbans, (2) determine the influences of climate and river discharge on radial growth, and (3) examine the influence of habitat variables (e.g., regulators, resources and forest structure) on radial growth. The growth-ring boundaries of C. ramiflora were distinct on the polished wood discs and marked by a layer of flattened fibers mixed with parenchyma. The agreement between individual growth-ring series and chronology statistics enabled us to develop for the first time a 42-year-long chronology spanning from 1979 to 2021. The results indicate that growth rings are annual and could be used for age and growth rate estimations. Monsoonal and annual precipitation enhanced radial growth. Growth was also positively influenced by the amount of river discharge received prior to the onset of growth, particularly during the pre-monsoon. Generalized additive models (GAMs) revealed that among the habitat variables, salinity had a strong negative influence on radial growth of the studied species in the Sundarbans, while tree density and diameter heterogeneity (coefficient of variation, CoV of diameter at breast height) had positive influences. This study revealed that C. ramiflora growth is strongly dependent on soil salinity and highlighted the potential of this approach for improving the prediction of future tree growth and distribution in the face of projected global changes, in particular, SLR.

Estimation of inflow discharge to Lake Baikal at upstream section using different satellite-based precipitation and runoff datasets from Upper Angara and Kichera River basins in East Siberia, Russia

Accurate basin-level river discharge estimation is of vital importance across various fields, including water resources, climate change, natural hazards, biodiversity, and energy production. Normally, gauging stations are deemed the most reliable data source for measuring river discharge. However, a significant proportion of the world’s rivers remain ungauged due to a combination of technical, economic, and political constraints. Encouragingly, recent advancements in remote sensing and satellite observation have opened new avenues for global river discharge monitoring, even in ungauged basins, and the availability of extensive datasets and advancements in computing technologies have facilitated the development of numerous modern data-driven techniques. The general objective of this study is to estimate inflow discharge to Lake Baikal at upstream section from Upper Angara and Kichera River Basins using different satellite precipitation and runoff datasets. According to the calculation result, a higher discharge was observed for the power dataset. The obtained results were used to mitigate floods, droughts, bridge design, manage urban drainage systems, and manage the lake ecosystem.

Spatio-temporal characteristics of heavy precipitation events observed over the last decade on the eastern French Mediterranean coastal area

Study regioneastern part of the French Mediterranean coastal area. Study focusThis work focuses on the heavy precipitation events that have affected the eastern part of the French Mediterranean coastal area over the last decade and the river discharges associated with these events. The aim is to make a throughout analysis of the spatio-temporal characteristics of heavy precipitation events, and to evaluate whether a high flow event is associated with them when looking at river discharges on affected catchments. New hydrological insights for the regionBased on radar-based precipitation grids, 158 heavy precipitation events (HPEs) affected the study area over the period 2007–2020. In addition, 65 % of HPEs were associated with a high flow event (HFE) on one or more gauged catchments. Results show that HPEs were more frequent during the beginning of summer and autumn. A large portion of autumn and winter HPEs were associated with a HFE. Looking at the spatio-temporal characteristics of the HPEs, their mean duration was 20 hours, with 42 % of the events lasting less than 6 hours. Some common characteristics among the HPEs that caused HFEs were identified: all HPEs lasting more than 30 hours and all HPEs with catchment precipitation accumulation above 150 mm were associated with a HFE.

Climate change impacts on hydrology and water resources in East Africa considering CMIP3, CMIP5, and CMIP6

Climate change impacts on the hydrology and water resources in East Africa require proper planning of regional adaptation measures. Past studies on the subject matter tended to focus on catchment instead of regional scale. To support actionable policy regarding regional planning of water resources management, this paper reviewed studies conducted in East Africa based on CMIP3, CMIP5, and CMIP6. Compared to the historical condition, temperature of East Africa over the mid- to late 21st century will increase by varying extents over the range 0.17–7°C with about 2.6°C on average. Future precipitation over East Africa will increase by 8.5% on average. Changes in discharge based on CMIP3, CMIP5, and CMIP6 will vary from −25.3 to +60.5%, −42.5 to 129%, and −23.4 to 69%, respectively. Ensemble means of the changes in discharge across the study area based on CMIP3, CMIP5, and CMIP6 will be by 25.2, 19.2, and 19.1%, respectively. Future peak river discharge across East Africa will increase by 8% on average. These results are important for planning regional climate change adaptation across East Africa. To support local or catchment adaptation, results of climate change impacts of temperature, precipitation and discharge of about 50 catchments across East Africa can be found summarized in this review paper. It is recommended that both regional and local policies or approaches should be considered for climate change adaptation to ensure a balanced, effective and sustainable framework for climate resilience in support of measures for tackling broad and specific needs cohesively.

Advanced streamflow forecasting for Central European Rivers: The Cutting-Edge Kolmogorov-Arnold networks compared to Transformers

Accurate streamflow forecasting is crucial for effective water resource management, flood mitigation, and maintaining ecological balance, especially in Central Europe’s major rivers. This study investigates the performance of two advanced deep learning algorithms—Kolmogorov-Arnold Network (KAN) and Transformers—using long-term hydrological data from four key rivers: the Rhine, Danube, Elbe, and Oder. These rivers, characterized by varying flow regimes and significant human and climatic impacts, serve as vital arteries for both commerce and ecosystems.The dataset comprises historical daily streamflow data, alongside derived input parameters such as moving averages and flow rate changes, used to predict short-term (1 to 7 days) river discharges. The KAN model, leveraging learnable spline-based activation functions, was developed to enhance accuracy and interpretability in capturing complex hydrological patterns. In contrast, the Transformer model uses advanced attention mechanisms, which excel in handling sequential data with long-range dependencies.Results show that the KAN model significantly outperforms the Transformer in short-term forecasts (up to 3 days). For 1-day forecasts, the KAN achieved R2 values of 0.975 for the Rhine, 0.956 for the Danube, 0.992 for the Elbe, and 0.969 for the Oder, with MAPE values ranging from 3.20 % to 8.09 %. At the 3-day horizon, the KAN’s R2 values remained high, demonstrating its robustness. However, as the forecasting horizon extends to 7 days, the performance of both models converges.These findings underscore the methodological novelty of KAN, which provides enhanced short-term predictive capabilities compared to existing methods, offering valuable insights for improving water resource management strategies in complex hydrological settings.

Hydrokinetic energy site selection under high seasonality: The i-IHE index

A novel index is developed to select sites for hydrokinetic energy exploitation in coastal areas with high resource seasonality – the typical case being estuaries whose hydrodynamics are highly influenced by variable river discharges. The intra-annual Integrated Hydrokinetic Energy (i-IHE) index is constructed by means of a new penalty function that modifies the Integrated Hydrokinetic Energy (IHE) index. The i-IHE index thus defined is applied to a case study: the Miño Estuary (NW Iberian Peninsula), with seasonal variations of up to 1000% in the riverine discharge. The hydrodynamics are modelled by the state-of-the-art Delft3D-FLOW code using a high-resolution 2DH grid. The results confirm a strong variability in the resource, with time periods shorter than seasons. The results obtained for the penalty function throughout the Miño Estuary, with values ranging between 0.5 and 0.75, show the relevance of the i-IHE index in order to not overestimate the potential of coastal areas subject to large intra-annual variability. Compared to the conventional IHE index, the novel i-IHE index highlights smaller areas with greater energy potential. The larger exploitable energy in these areas, along with the lower costs and fewer restrictions for hydrokinetic energy operation, will be of interest to developers and stakeholders.

Capturing the Dynamics of Dissolved Organic Carbon (DOC) in Tidal Saltmarsh Estuaries Using Remote‐Sensing‐Informed Models

AbstractThe fluxes of dissolved organic carbon (DOC) through tidal marsh‐influenced estuaries remain poorly quantified and have been identified as a missing component in carbon‐cycle models. The extreme variability inherent to these ecosystems of the land‐ocean interface challenge our ability to capture DOC‐concentration dynamics and to calculate accurate DOC fluxes. In situ discrete and continuous measurements provide high‐quality estimates of DOC concentration, but these strategies are constrained spatially and temporally and can be costly to operate. Here, field measurements and high‐spatial‐resolution remote sensing were used to train and validate a predictive model of DOC‐concentration distributions in the Plum Island Estuary (PIE), a mesotidal saltmarsh‐influenced estuary in Massachusetts. A large set of field measurements collected between 2017 and 2023 was used to develop and validate an empirical algorithm to retrieve DOC concentration with a ±15% uncertainty from Sentinel‐2 imagery. Implementation on 141 useable images produced a 6‐year time series (2017–2023) of DOC distributions along the thalweg. Analysis of the time series helped identify river discharge, tidal water level (WL), and a marsh enhanced vegetation index 2 as predictors of DOC distribution in the estuary, and facilitated the training and validation of a simple model estimating the distribution. This simple model was able to predict DOC along the PIE thalweg within ±16% of the in situ measurements. Implementation for three years (2020–2022) illustrated how this type of remote‐sensing‐informed models can be coupled with the outputs hydrodynamic models to calculate DOC fluxes in tidal marsh‐influenced estuaries and estimate DOC export to the coastal ocean.

Towards a new generation of fluvial facies models for the interpretation of ancient deposits, based on inter‐annual peak discharge variance of modern rivers

ABSTRACTFluvial facies models based on river planform are flawed and not fully fit for purpose. An alternative approach to classifying fluvial deposits is based on river discharge characteristics. A recent paper showed that the co‐efficient of variance of annual peak discharge (CVQp) correlates well with the characteristics of preserved alluvium for a suite of modern rivers. Here, the database of rivers is expanded and augmented, and the utility of this statistic confirmed. For modern rivers, plotting the running‐value of CVQp for a set interval (for example, 30 years) over a longer discharge record shows that the statistic varies both spatially and temporally. The CVQp can rise abruptly at a given location in response to a single flow event if that event is significantly larger than is common in that river. Such abrupt changes in CVQp are superimposed on longer‐term patterns of more gradual change stimulated by environmental factors or anthropogenic effects such as dam construction. If the period of calculation for CVQp is relatively short, ‘anomalous’ events appear more frequently. Sequential studies of modern rivers indicate that such anomalous events have a high probability of being preserved in the alluvial record of the river, and suggest that, over the timeframes of channel filling and abandonment, they may constitute the dominant (characteristic) portion of the record in high CVQp systems. Since flow events with strongly peaked hydrographs (flashy discharges) are more common in high CVQp systems and leave a record dominated by upper flow regime stratification styles, it follows that the alluvial record of high CVQp rivers will be dominated by upper flow regime stratification. In contrast, in lower CVQp systems the occurrence of an event that changes the running CVQp value significantly may form a distinct event deposit, and the temporarily changed CVQp is unrepresentative for the duration of the channel's life. The dominance of upper flow regime stratification, together with decreased preservation of identifiable macroform structure, increased lateral lithological heterogeneity, and in many cases increased preservation of in situ tree fossils in channel deposits of higher CVQp rivers, forms the basis for a series of new facies models for low, moderate, high and very high/ultra‐high CVQp alluvial systems and deposits. The new models can be applied to ancient successions either in combination with traditional, planform‐based models, or not. These models provide information about ancient palaeoenvironmental conditions, and changes in such conditions over time at various scales via stratigraphic investigations.

Quantitative Analysis of Planform Changes in the Lower Mahaweli River, Sri Lanka

ABSTRACT The development of hydraulic structures has impacted the river discharge and sediment transportation, thus highlighting the river planform changes. Among 103 river catchments in Sri Lanka, the Mahaweli River is the longest river with the largest basin. Many development projects over the years diversely impacted the changes in river masks. However, no study has been conducted to quantify the planform changes in the lower Mahaweli River. Therefore, a comprehensive study was conducted to analyse the river planform changes over 30 years (1991-2021) from Damanewewa to Trincomalee. Freely available remotely sensed satellite data with 30 m resolution were used in the analysis. These images were processed using the QGIS mapping tool and RivMAP toolbox in MATLAB. Major changes were identified at the downstream part of the river and an oxbow lake formation was also observed. The average width for the entire reach (Wra) was identified as 14.83 m and channel width average (Wavg) was noted as 18.09 m. In addition, erosion and accretion rates were calculated, and the cumulative sequence of these rates was increased over the years affecting the change in channel width. Furthermore, the migration rates were also computed with generated river centerline. Highest migration rate reached about 400 m/yr, in the downstream which finally leads to severe meandering. Results revealed that this methodology can be applied to similar river planform analysis. Further, these results showcase the potential importance of analyzing channel stability as well as for water resource management.

Application of GRACE-based satellite estimates in the assessment of flood potential: A case study of Gangetic-Brahmaputra basin, India.

The high frequency of flood occurrences and the uneven distribution of hydrological stations make it difficult to monitor large-scale floods. Emergence of the Gravity Recovery and Climate Experiment (GRACE) satellite system sets up a new era of large-scale flood monitoring without much reliance on in situ hydrological observations. The GRACE-derived flood potential index (FPI) exhibits its ability to monitor major events of 2003, 2004, 2007, and 2008 over the Indo-Gangetic-Brahmaputra Basin (IGBB). Precipitation and soil moisture are the major influencing factors of flood. However, the response of potential flooding to such parameters is little known. Pearson's lag correlation analysis is used to examine the response of the GRACE-based FPI to precipitation and soil moisture over the study region comparing seasonal time series of the variables. Results exhibited a 2-month lagged response of FPI to precipitation in the Upper Gangetic Yamuna Chambal Basin (UGYCB) and the Lower Gangetic Basin (LGB) and 1-month lagged response in the Lower Brahmaputra Basin (LBB). With context to soil moisture, a 1-month lag is observed in the Gangetic basins, and no lag is observed in the LBB. Event wise analysis of the lags portrays slightly varying lags for different events; however, it provides a picture on the interaction between these variables. This study also assesses the agreement between FPI and satellite-based river discharge, i.e. Dartmouth Flood Observatory (DFO) discharge. A good correlation (> 0.60) between the two is observed. Threshold values of FPI are determined for the LBB due to its annual flood frequency. The nearly similar accuracy of threshold FPI, determined using DFO discharge, in monitoring floods and the predictive skill measure of FPI for LBB to the previous studies demonstrates the utility of satellite-based discharge in the quantification of threshold FPI values for different percentile floods.

The influence of the Biobio Canyon on the circulation and coastal upwelling/downwelling off central Chile

The influence of Biobio Canyon on the circulation and coastal upwelling and downwelling was evaluated by using high-resolution hydrodynamic simulations of the coastal ocean off central Chile, a highly productive zone of the Southern Humboldt Current System that supports important coastal ecosystem services. The model bathymetry was GEBCO 2020 with a spatial resolution of 500 m, which allows a relatively fine representation of the complex topography of the region. To understand the role of the Biobio Canyon, contrasting experiments were run. A Reference Simulation (RS) considering the canyon bathymetry and the Biobio River discharge, a Simulation Experiment 1 (SE1) considering only the canyon bathymetry without the Biobio River discharge, and a Simulation Experiment 2 (SE2) only with the Biobio River discharge (without the canyon). During upwelling conditions, the characteristic asymmetry in the along-canyon circulation was observed, with onshore flow over the northern side of the canyon and most of the upper water column, and offshore flow over the upstream canyon wall. In contrast, the cross-shore flow was limited to a narrow band over the northern wall of the canyon in RS and SE1 during downwelling conditions. Not many differences were detected between the RS and SE1 during both upwelling and downwelling conditions, which suggest that the canyon imposes a major effect on the circulation in comparison to the river discharge. Our results exhibit a net onshore transport onto the continental shelf during coastal upwelling and, net offshore transport restricted to the area south of the canyon during downwelling conditions. Finally, the advection of high-density waters on the northern shelf dominates during upwelling conditions in RS and SE1. Similarly, presence of the canyon promotes higher bottom density over the shelf even during downwelling conditions, as compared to the no-canyon experiment.

Bathymetry and discharge estimation in large and data-scarce rivers using an entropy-based approach

ABSTRACT This study implements an entropy theory-based approach to infer bathymetry for 29 selected cross-sections along a 1740 km reach of the Congo River. A genetic algorithm optimization approach is used based on an analysis of near-surface velocity measurements to generate a random sample of 1000 bathymetry profiles from which the analysis is carried out. The resulting simulated bathymetry shows good agreement compared to the measurements obtained via Accoustic Doppler Current Profiler (ADCP), with a correlation that varies from 0.49 to 0.88. The bathymetry results are subsequently used to estimate the two-dimensional cross-sectional flow velocity distribution and, consequently, to calculate the river discharge. The mean errors observed for flow area, discharge, and mean velocity are found to be 2.7%, 1.3%, and 1%, respectively. This study demonstrates, for the first time, the successful application of an entropy-based approach to estimate bathymetry and discharge in large rivers and has significant implications for remote sensing applications.

Investigating sediment dynamics on a continental shelf mud patch under the influence of a macrotidal estuary: A numerical modeling analysis

Shelf mud patches represent major sinks for fine-grained particles on continental shelves, as well as for carbon and contaminants of continental origin. The West Gironde Mud Patch (WGMP) is an interesting example of such offshore marine systems as it is an active mud deposition area located offshore the Gironde estuarine mouth (France) at depths between 30 and 70 m. It is known to be the trap of fine particles coming from the estuary, but the contribution of this material to the total mass of the depocenter is poorly quantified. In addition, despite the economic and ecological issues at stake, the response of such subtidal sedimentary structure to the combination of tidal currents, waves, and river supply remain poorly understood. Thus, using a realistic 3-D hydrodynamic and mixed (mud/sand) sediment transport model, this study aims at investigating the sediment dynamics of the WGMP under different hydrometeorological conditions. The analysis of the residual fluxes at the estuarine mouth exhibited large discrepancies between the different sediment classes as well as for contrasted hydro-and meteorological conditions induced by different dominant transport mechanisms. During winter, the reinforced density gradients drive strong up-estuary baroclinic circulation at the bottom that dominates the sediment dynamics over the barotropic export of mud particles. The model also reproduced the signature of a subtidal mud accumulation area over the continental shelf around 30–40 m water depth, on the proximal side of the observed WGMP. On average over two years, 26% of the mud mass accumulating on the simulated subtidal mudflat comes from the estuary. The trapping efficiency of this mud patch is negatively correlated with the significant wave height. Moreover, due to the estuarine turbid plume being more concentrated and developed at the surface during high river discharge, the trapping efficiency of the mud body is enhanced compared to lower discharge. This study highlights the sensitivity of mud and sand fluxes to vertical and horizontal residual circulation, and points out the uncertainties associated with the simulation of short-term (i.e., years) fine particle deposits compared to long-term (i.e., centuries) sediment accumulation trends. In addition, these results show the primordial effects of both wave action and riverine sediment supply on the dynamics of such subtidal muddy structures, which raises concern about their fate facing climate change and human activities in the future.

Influence of data source and copula statistics on estimates of compound flood extremes in a river mouth environment

Abstract. Coastal and riverine floods are major concerns worldwide as they can impact highly populated areas and result in significant economic losses. In a river mouth environment, interacting hydrological and oceanographical processes can enhance the severity of floods. The compound flood hazards from high sea levels and high river discharge are often estimated using copulas, among other methods. Here, we systematically investigate the influence of different data sources coming from observations and models as well as the choice of copula on extreme water level estimates. While we focus on the river mouth at the city of Halmstad (Sweden), the approach presented is easily transferable to other sites. Our results show that the choice of data sources can considerably impact the results up to 10 % and 15 % for the river time series and 3 % to 4.6 % for the sea level time series under the 5- and 30-year return periods, respectively. The choice of copula can also strongly influence the outcome of such analyses up to 13 % and 9.5 % for the 5-year and 30-year return periods. Each percentage refers to the normalized difference in return level results we can expect when choosing a certain copula or input dataset. The copulas found to statistically best fit our datasets are the Clayton, BB1, and Gaussian (once) ones. We also show that the compound occurrence of high sea levels and river runoff may lead to heightened flood risks as opposed to considering them independent processes and that, in the current study, this is dominated by the hydrological driver. Our findings contribute to framing existing studies, which typically only consider selected copulas and datasets, by demonstrating the importance of considering uncertainties.

Assessment of high-resolution regional ocean reanalysis K-ORA22 for the Northwest Pacific

The Korea Institute of Ocean Science and Technology developed the Korea Operational Oceanographic System-Ocean Predictability Experiment for Marine Environment (KOOS-OPEM), a high-resolution (1/24°, 51 vertical levels) ocean prediction model for the Northwest Pacific Ocean that incorporates ensemble optimal interpolation. In this study, we present KOOS-OPEM ReAnalysis version 2022 (K-ORA22), which covers the period from 2011 to 2022. We conducted a comparative analysis between K-ORA22 and other high-resolution (1/10°–1/12°) global reanalyses, including the Hybrid Coordinate Ocean Model, Global Ocean Reanalysis and Simulation (GLORYS), and Bluelink ReAnalysis (BRAN), to demonstrate the reproducibility and reliability of regional characteristics. Statistical comparisons revealed that while K-ORA22 exhibited some warm biases, its sea surface temperature (SST) anomaly correlation after removing the seasonal cycle was approximately 0.87, comparable to other reanalyses. Additionally, K-ORA22 effectively reproduced coastal upwelling, which is characterized by a sharp decrease in SST, as observed by marine meteorological buoys in the Southwest of the East/Japan Sea. K-ORA22 exhibits a warm bias of approximately 0.50 °C around 200 m, slightly higher than those of GLORYS and BRAN, while maintaining a low salinity bias in the subsurface. Notably, K-ORA22 outperformed the other reanalyses in accurately reproducing the unique characteristics of North Pacific and East Sea intermediate waters, characterized by a salinity minimum layer. In addition, K-ORA22 stands out in its ability to accurately reproduce the Yellow Sea Cold Water Mass with a low-temperature root-mean-square error (RMSE) of 0.76 °C in the Yellow Sea (YS) region. However, it exhibited the highest RMSE for salinity in the YS region and Korea/Tsushima Strait, indicating a potential overestimation of river discharge from Korea and China. While the sea surface height (SSH) anomaly correlation of K-ORA22 did not surpass 0.80 in the entire region because of limitations in the background error covariance used, its ability to reproduce the Kuroshio path was comparable to those of other reanalysis datasets. In conclusion, K-ORA22 excels in reproducing the unique characteristics of Korean marginal seas. Still, it exhibits weaknesses, such as the overestimation of river discharge and a somewhat limited ability to simulate SSH variability, compared with other global reanalyses. We plan to enhance K-ORA22 by updating background error covariance, addressing biases related to river discharge and assimilating the best available in situ observations and satellite data.

Silicon balance in an integrated multi-tropical aquaculture ecosystem, Sanggou Bay, China

Farmed aquaculture species play an important role in regulating nutrient cycles in farming systems. Compared with nitrogen and phosphorus, the role of farmed species in the silicon (Si) cycle remains poorly understood. To help reduce this uncertainty, we clarified the sources and sinks of silicate and quantified the Si pools in an aquaculture system in Sanggou Bay (SGB). The results showed that dissolved inorganic nutrient levels were significantly lower during the dry season than during the wet. Dissolved silicate (DSi) is a potential limiting factor for phytoplankton growth during spring, and phosphorus limitation occurs during summer. The budget results indicated that large amounts of nitrogen, phosphate (DIP), and DSi were buried in the sediment or transformed into other forms during both the wet and dry seasons. The nitrogen and DIP cycles were strongly influenced by bivalve excretion and farmed species harvesting; however, these processes had little impact on the Si cycle. Si availability depends on both external inputs and internal recycling. DSi was primarily supplied from the Yellow Sea, with a minor contribution from the river due to river discharge during spring. However, during summer, riverine inflow (accounting for 83% of the total influx) was the major DSi source followed by benthic flux (12%). Biogenic silica (BSi) burial efficiency in the sediment was estimated to be 78% during spring and 23% during summer. The BSi preservation efficiency in bivalves during spring was high (53%), leading to a higher Si retention than in river discharge. Bivalves biodeposition plays an important role in the Si burial process. We suggest that this high retention is essentially controlled by the biodeposition mechanism, which is directly controlled by the exotic suspension feeders. Bivalves have the potential to alter Si retention in the bay by producing large amounts of biodeposits and accelerating the silica cycle, which may lead to more carbon dioxide being absorbed by diatoms.

Impact of river discharge and suspended sediments on the nearshore benthic environment along the Indian southwest coast

River discharge into the sea and its implications on the environmental setting and fauna in the nearshore represent the intricate interactions among the atmosphere, hydrosphere, lithosphere, and biosphere. This study, based on in-situ and satellite data, presents how spatially varying river discharge laden with suspended sediments structure the hydrography and the nearshore benthic environment over a 590 km southwest (Kerala) coast of India. The 41 rivers that discharge along the Kerala coast are monsoon-driven; they are small but swift and cumulatively supply huge amounts of freshwater and suspended sediments into the Southeastern Arabian Sea (SEAS) during the Southwest Monsoon (SWM) when around 70 % (1925 mm) of the yearly rainfall occurs. These rivers are distributed unevenly along the coastline, with twenty-four of them discharging in the northern region (10.9–12.5°N), nine in the central (9.2–10.9°N), and eight in the south (8–9.2°N). During the SWM, plumes of low salinity (<32) with high clay content were widespread in the northern and central regions of the study area due to heavy river discharge and the presence of crystalline rocks and laterite deposits. In contrast, the low salinity plumes and suspended sediments were markedly low in the southern region due to limited river discharge and a predominance of sedimentary rocks and coastal alluvium that supported sandy sediments. This resulted in a spatial change in benthic sediment texture as well, with the central and northern regions more silty or clayey, while the south was sandy. Interestingly, the sandy south section had a noticeably higher faunal abundance than the rest of the region with an increased abundance of sensitive fauna of molluscs and echinoderms. This is a clear case of how spatially varying river discharge and sediment flux impact the nearshore environment and fauna.

Monitoring river discharge from space: An optimization approach with uncertainty quantification for small ungauged rivers

The number of in-situ stations measuring river discharge, one of the Essential Climate Variables (ECV), is declining steadily, and numerous basins have never been gauged. With the aim of improving data availability worldwide, we propose an easily applicable and transferable approach to estimate reach-scale discharge solely using remote sensing data that is suitable for filling gaps in the in-situ network. We combine 20 years of satellite altimetry observations with high-resolution satellite imagery via a hypsometric function to observe large portions of the reach-scale bathymetry. The high-resolution satellite images, which are classified using deep learning image segmentation, allow for detecting small rivers (narrower than 100m) and can capture small width variations. The unobserved part of the bathymetry is estimated using an empirical width-to-depth function. Combined with precise satellite-derived slope measurements, river discharge is calculated at multiple consecutive cross-sections within the reach. The unknown roughness coefficient is optimized by minimizing the discharge differences between the cross-sections. The approach requires minimal input and approximate boundary conditions based on expert knowledge but is not dependent on calibration. We provide realistic uncertainties, which are crucial for data assimilation, by accounting for errors and uncertainties in the different input quantities. The approach is applied globally to 27 river sections with a median normalized root mean square error of 12% and a Nash–Sutcliffe model efficiency of 0.560. On average, the 90% uncertainty range includes 91% of the in-situ measurements.

Ecological index analysis in the coastal and estuarine waters of the tropical Amazon Region

The Brazilian Amazon Coastal Zone holds the largest estuarine area in the world, located in the Amazon Continental Shelf (ACS). Additionally, it encompasses numerous estuaries and channels, such as Furo do Muriá (FM) and Furo da Laura (FL), which make up the local landscape and favor the primary productivity of water. The objective of this study was using the trophic index (TRIX) and O'Boyle index to determine the trophic state in the ACS area, FL, and FM, compare the results, and relate them to seasonality and river discharge in the sites. In the three estuaries, the applied methodology was the same: temperature, salinity, pH, dissolved oxygen, and saturation rate of dissolved oxygen data were obtained in situ, and the samples were filtered for the analysis of nitrite, nitrate, ammonia, phosphate, and chlorophyll a. The highest TRIX values were found in FL (3.89–3.12) and ACS (4.43–4.44), while for the O'Boyle index, the highest values were found in FL (16.73–100) and FM (43.3–100). Comparing the indices, it was observed that their classification was similar only in the ACS during the period of maximum discharge of the Amazon River (eutrophic), varying slightly in the minimum discharge period (potentially eutrophic for O'Boyle and eutrophicated for TRIX). This difference can be explained by the additional variables considered by the TRIX, such as nutrients and chlorophyll a. The scenario was completely different for FM and FL, where the TRIX showed greater variation owing to the presence of dissolved inorganic nutrients. Both indices can be used to simply and directly reflect the eutrophication conditions of estuarine areas: the joint and multiparametric approach of the indices provides complementary information regarding the dynamics of studied environments through their trophic status.

Prediction of discharge in a tidal river using the LSTM-based sequence-to-sequence models

Prediction of discharge in a tidal river using the LSTM-based sequence-to-sequence models