Non-Uniform Morphodynamics of the Negro River Estuary Mouth, Argentina (2013-2021)

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Huck, Grethel García Bu Bucogen, Marina P. Cipolletti, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8370254/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Estuarine mouths exhibit complex morphodynamics controlled by the interplay of river discharge, tides, longshore drift, and sediment transport, which can influence navigation, ecology, and coastal management. The objective of this study is to document the spatio-temporal evolution of the Negro River mouth by examining variations in the river’s width at different points along the estuary and analyzing the movement of sediment lobes present in the mouth. To achieve this, 62 Landsat-8 images (2013–2021) were analyzed, combining Mann-Kendall trend tests with multifractal metrics to assess persistence, complexity, and asymmetry along four transect sections (W1–W4). Results reveal distinct morphodynamic domains: W1 shows sustained widening, high complexity, and right-skewed variability associated with river–tide–sediment interactions; W2 exhibits dynamic equilibrium with no net trend, minimal complexity, and left-skewed behavior; W3 resumes widening under strong tidal and longshore influence; and W4 displays pronounced narrowing, low complexity, and a regime dominated by sandbank progradation. High persistence values (H ≥ 0.85) indicate strong system inertia, although longer-term cycles may not be captured. Direct local anthropogenic impacts were negligible, but river regulation and droughts may modulate flow patterns. These findings demonstrate the spatially heterogeneous evolution of the estuary mouth, providing a quantitative framework for attributing morphodynamic processes. The methodology and results are applicable to other delta-influenced estuaries, supporting improved understanding and management of estuarine systems under fluvial, tidal, and sedimentary controls. Geomorphology Hydrology estuarine morphodynamics channel width variation sandbank satellite images trends Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. INTRODUCTION The mouth of an estuary is a critical component of estuarine monitoring because it directly influences the interaction between terrestrial and marine ecosystems. It provides essential information on water levels and tidal inflows (Coast KZN 2024; Liu et al. 2021). Estuarine mouths are dynamic systems, constantly changing in size and shape, particularly those that open intermittently or remain continuously open (Fig. 1 ) (Estuary Watch Victoria 2024). Understanding their behavior is essential for maintaining ecological health and managing estuarine resources sustainably. The state of an estuary mouth is affected by both natural and human factors. Natural factors include seasonal river flows, tidal action, sedimentation, coastal erosion, and long-term climatic changes (Pushpa et al. 2022; Michael & Murphy 2020). High river flows transport sediments seaward, whereas low flows cause sediment accumulation, narrowing channels and reducing water depth (Grasso et al. 2021 ). Human activities such as coastal construction, river channelization, dredging, and urbanization also alter estuarine dynamics (Zhang et al. 2022 ). These changes can influence the mouth’s ability to remain open, particularly during low-flow periods, contributing to partial or complete closure (Perillo et al. 2005 ; Pereyra et al. 2014 ). International studies show that prolonged mouth closures are common in some estuaries, such as those in southern Africa and Australia, causing hypersalinity and changes in biodiversity (Whitfield et al. 2008 ; Scharler et al. 2020 ; Hastie & Smith 2006 ). In America, examples such as Old Woman Creek and Los Peñasquitos Lagoon (USA) demonstrate how human intervention and natural sandbars affect mouth opening and closure (NOAA 2021 ; NCCOS PROJECT 2018). In Argentina, the Quequén Grande River illustrates how dredging and channel widening alter hydrodynamics and salinity structure, influencing mouth dynamics (Perillo et al. 2005 ; Pereyra et al. 2014 ). The Negro River, the largest river in Argentine Patagonia, flows through a single channel whose mouth is one of the most complex in South America, characterised by shifting sandbanks, variable currents, and temporary blockages (UNL-DPA Río Negro 2004a, 2004b). Tidal dynamics govern the water level in the Negro River, with their effects extending as far as 45 km upstream, reaching the vicinity of San Javier (Río Negro 2017). The migration of sandbanks and narrowing of the main channel directly affect mouth opening and closure, particularly during low river flows (Del Río et al. 1991 ). The objective of this study is to document the spatio-temporal evolution of the Negro River mouth by examining variations in the river’s width at different points along the estuary and analyzing the movement of sediment lobes present in the mouth. This research is expected to provide updated information on the geomorphological evolution of the Negro River mouth, contributing to scientific knowledge and supporting sustainable estuary management. Findings will help policymakers and environmental managers develop strategies for sediment management, biodiversity protection, and conservation of one of Patagonia’s most important ecological systems. 2. STUDY AREA AND REGIONAL SETTING The Negro River (NR) is part of the hydrographic system formed by the Limay, Neuquén, and Negro rivers, originating at the confluence of the Limay and Neuquén rivers (AIC 2022) (Fig. 2 a). It is the largest allochthonous river in Argentina (Piccolo and Perillo 1999 ), with a length of approximately 637 km in a NW–SE direction (MADS 2016). Discharge fluctuations are driven by the pluvio-nival regime of its main tributaries, with peak flows in autumn–winter and spring due to heavy rainfall and snowmelt, and minimum flows in March–April (Gianola Otamendi 2019; Soldano 1947 ). Upstream dams regulate the river’s flow (Fig. 2 a) (Longo 2025), resulting in an average discharge of ~ 1,000 m³ s⁻¹, whereas prior to regulation flows could exceed 6,000 m³ s⁻¹ (De Jong and Mare 2014 ). The study focuses on the mouth of the Negro River, located in the lower basin of the system, between − 41.025° S and − 41.05833° S, and − 62.7° W and − 62.8167° W, in the northeastern region of Argentine Patagonia (Fig. 2 b,c) (Soldano 1947 ; SSRH 2010 ). The mouth is situated on a coastal plain with meanders, abandoned channels, islands, and varying beach morphologies (Archuby 2016). Beaches on the Río Negro Province side are wide (100–250 m), whereas those on the Buenos Aires Province side are less developed (del Río et al. 1991 ). Sand dunes, both stabilized and mobile, reach heights between 5 and 11 m (Cortizo and Isla 2021 ). The ebb delta at the river mouth comprises a main channel 6–8 m deep, influenced by sediment banks, particularly the Miguel and La Hoya Banks, which form a partially open frontal shield (Piccolo and Perillo 1999 ; Subsecretaría de Recursos Hídricos de la Nación 2015) (Fig. 2 c). The dynamics of the estuary are strongly influenced by the tidal regime, which governs water levels and extends its influence up to 45 km upstream to San Javier (Río Negro 2017). Tides are semi-diurnal and macrotidal, with a maximum height of 4.4 m at high tide and a minimum of 0.71 m at low tide (UNL-DPA Río Negro 2004). Average wave height is 0.67 m, reaching 1.20 m during high-energy events, with a predominant direction from the southeast, generating net littoral transport of ~ 900,000 m³/year toward the northeast (del Río et al. 1991 ; Lanfredi 1987). Ebb currents are generally stronger than flood currents, reaching up to 2.5 m/s, while slack water lasts 30 minutes upstream and 17 minutes downstream. This area, with an arid to semi-arid climate and annual precipitation below 400 mm, is influenced by regional atmospheric dynamics, including occasional “Sudestadas,” which produce strong southerly and southeasterly winds (Prohaska 1976 ; Garreaud 2009; Bianchi 2016 ; Coronato et al. 2017; Mazzoni 2010 a; SMN 1989). 3. DATA AND METHODS 3.1 Acquisition of Geomorphological Datasets The present study was based on a dataset of 62 Landsat 8 OLI/TIRS Level 2 satellite images of the Negro River estuary, spanning the period 2013–2021. Images were selected from the publicly available database provided by the United States Geological Survey (USGS 2021 ), including only those with less than 10% cloud cover. The images have a spatial resolution of 30 m and underwent atmospheric correction prior to analysis (Appendix 1). An initial visual analysis of the dataset was carried out to identify variations in the width of the main channel, determine open- or closed-mouth states, and assess the evolution of sediment lobes and banks within the ebb delta. This approach allowed for the identification of spatial and temporal patterns in the estuary’s fluvial dynamics. For the delineation of water bodies and watercourses, true-colour images were generated using spectral bands B2, B3, and B4 in the blue, green, and red channels, respectively, employing QGIS (QGIS.org). Images from the same season but under different tidal phases were compared to detect geomorphological variations at the estuary mouth. Details of the image processing workflow are summarized in Fig. 3 . To characterize the geomorphological structure in more detail, four key widths of the estuary were measured (Fig. 2 b): W1 corresponds to the marshland and a small island, which may indicate sediment accumulation; W2 represents the central narrowing of the river mouth; W3 is defined as the inter-deltaic distance of the main channel; and W4 serves as an indicator of delta elongation. These measurements provide critical information for assessing spatial variations and potential sediment migration processes within the estuary. Complementary datasets were also employed to enhance the analysis, including bathymetric data obtained from an echo sounder survey, flow measurements at the Primera Angostura station (SNIH 2024 ), and modelled tidal information from the Naval Hydrographic Service (SHN 2024). This additional information supports a more quantitative assessment of the estuary’s dynamics, complementing the observations derived from satellite imagery. 3.2 Analysis of NR mouth widths The analysis of NR mouth widths was carried out using basic descriptive statistics. The mean, maximum, and minimum values were computed for each time series (W1, W2, W3, and W4). These statistical measures provided a detailed characterization of width variability across the series, contributing to a better understanding of the study area's morphological dynamics. The trend in the time series was assessed using the non-parametric Mann-Kendall test at a 95% confidence level (α = 0.05) (Mann 1945 ; Kendall 1975 ; Berger 1986 ; Yu et al. 2007; Gavrilov et al. 2016; Patle et al. 2016; Silva Alves and Silva Nóbrega 2017 ; Prabhakar et al. 2018; Shivam et al. 2019 ; Machiwal et al. 2021 ). The null hypothesis, as formulated by Mann ( 1945 ) and Kendall ( 1975 ), assumes that the data are independent and identically distributed. The rejection of the null hypothesis indicates that the data do not satisfy these assumptions and, therefore, should not be considered independent and identically distributed random variables. The identified trends were classified following the criteria established by Alves et al. (2015) and Silva Alves and Silva Nóbrega ( 2017 ). Based on this approach, the Mann-Kendall test provides Z-statistic values to determine the significance of temporal trends: Z ≥ 1.96 indicates a significant increasing trend, Z ≤ -1.96 a significant decreasing trend, and values between − 1.96 and 1.96 reflect the absence of a statistically significant trend at the 95% confidence level (Kendall 1975 ). In addition, breakpoints in the detected trends were calculated to identify moments of significant change in the estuary’s width over time, allowing for a more detailed assessment of geomorphological dynamics. Multifractal analysis provides a quantitative metric of system complexity. In this study, an objective measure is employed to assess the morphodynamics of the Río Negro deltaic system. A broad spectrum denotes a multifactorial system, in which the interaction and nonlinearity of forcing mechanisms across multiple scales generate heterogeneous and unpredictable behavior. Conversely, a narrow spectrum indicates a system of low complexity, dominated by a single prevailing process that enforces a more homogeneous and predictable dynamic. The persistence of observed trends and the structural characteristics of the time series were analyzed using Multifractal Detrended Fluctuation Analysis (MFDFA). Unlike traditional methods such as Fast Fourier Transform, MFDFA calculates the multifractal spectrum and its dimensions, offering the advantage of being less sensitive to the length of the time series under study (Kantelhardt et al. 2002 , 2006; Baranowski et al. 2015 , 2019 ). Introduced by Kantelhardt et al. ( 2002 ), MFDFA is widely used for its ability to characterize time series through multifractal dimensions, thereby identifying long-term correlations (Kantelhardt et al. 2002 ; López-Lambraño et al. 2017; Baranowski et al. 2019 ). This methodology is particularly effective for analyzing signals influenced by non-stationary processes (Gómez and Poveda 2008 ; Zhou and Leung 2010 ; Morales Martínez et al. 2021 ). Applying MFDFA, we computed the multifractal spectrum and its dimensions using width time series from the NR mouth, providing valuable insights into the persistence and complexity of the signal. The analyzed multifractal dimensions include the Hurst exponent (α₀), complexity (w), and asymmetry (r). The Hurst exponent quantifies the persistence or significance of trends within the time series (Rodríguez Aguilar 2012 ; Nieto et al. 2016 ). According to Baranowski et al. ( 2015 , 2019 ) and Santos da Silva et al. ( 2020 ), values > 0.5 indicate persistent trends (long-term memory), values < 0.5 indicate anti-persistent behavior (short-term memory), and a value of 0.5 denotes no dependence, making projections highly uncertain. The values \(\:{\alpha\:}_{max}\) and \(\:{\alpha\:}_{min}\) represent the extreme points of the time series. The complexity of the multifractal spectrum (w) is determined as the difference between \(\:{\alpha\:}_{max}\) and \(\:{\alpha\:}_{min}\) . The asymmetry (r) of the multifractal spectrum indicates the dominance of small or large fluctuations relative to the historical mean. A left-skewed spectrum suggests the prevalence of large fluctuations and high variability, whereas a right-skewed spectrum implies smaller fluctuations corresponding to natural variations. A symmetric spectrum indicates a balanced distribution of large and small variations in time series variability. The calculation of this multifractal dimension follows the formula proposed by Santos da Silva et al. ( 2020 ). $$\:r=\frac{({\alpha\:}_{max}-{\alpha\:}_{0})}{({\alpha\:}_{0}-{\alpha\:}_{min})}$$ 4 If r > 1, the spectrum is right-skewed, indicating minor fluctuations that reflect natural variations. If r < 1, the spectrum is left-skewed, suggesting pronounced fluctuations and significant variability within the series. If r = 1, the spectrum is symmetric, with variability driven by natural fluctuations, leading to high variation in the variable’s cycles. 4. RESULTS 4.1 Description of Negro River based on true color images from Landsat The analysis of a dataset comprising 62 satellite images of the Negro River mouth in Argentina indicates that 62% of the images exhibit a decreasing trend in the width of the main channel within the ebb delta. In contrast, the remaining 38% depict an open-mouth state. These findings suggest a notable pattern in the region’s fluvial dynamics, with potential implications for navigation, local ecology, and water resource management, which will be further discussed. The most relevant results derived from the analysis of the LANDSAT 8 C2 L2 image dataset (Figs. 4 and 5 ) provide insights into the state of the NR mouth. The images in Fig. 4 (covering the 2013–2014 period) depict an open-mouth state, where the main channel is delineated by two lobes (1 and 2), remaining unobstructed while exhibiting variations in their shapes. Additionally, sediment bank formations (outlined in yellow) are visible inside the estuary mouth on the right side migrating towards the southern bank. Figure 5 presents the most relevant results regarding geomorphological changes from 2017 to 2021. During this period, a reduction in the width of the main channel (delineated by frontal lobes 1 and 2) was observed. Additionally, Banco Miguel exhibited a displacement toward the northern coast. As a result, the coastal lagoon formed between Banco La Hoya and the northern coast shows a tendency to close. Furthermore, the sediment bank (outlined in red) within the estuary mouth has expanded in size and migrated to the opposite side of the estuary. Table 1 River discharge (Q) Negro River, tidal level, and phase during the satellite image acquisition for representative subset of dates. Day Month Year Tide level (m) Phase Q ( \(\:{m}^{3}{s}^{-1}\) ) 25 9 2013 0.661 High tide 421.57 12 9 2014 1.504 High tide 606.39 17 12 2014 2.186 Low tide 351.14 6 10 2017 3.18 High tide 362.97 25 12 2017 0.689 High tide 375.45 3 2 2021 0.28 High tide 392.36 23 3 2021 2.03 Low tide 416.86 Significant variations in Miguel Bank were observed under different tidal and discharge conditions (Table 1 ), which includes a subset of 7 representative acquisition dates selected to illustrate the range of hydrological scenarios. The complete dataset of 62 satellite images is provided in Appendix 1. These dates correspond to the satellite images displayed in Figs. 4 and 5 , which visually exemplify the contrasting hydromorphological conditions described. No high-tide scenes showed a closed mouth in our 62-image dataset (Methods; Appendix 2), consistent with sustained river–ocean exchange during high tide. Conversely, during low tides and reduced river discharge, sediment accumulation increased, primarily driven by wave action transporting sediments from the ocean toward the coast. This accumulation contributed to channel narrowing, consistent with the findings of del Río et al. ( 1991 ), who highlighted the role of wave-induced sediment transport in the geomorphological dynamics of the river mouth. These observations suggest that tidal forces, in conjunction with river discharge, play a critical role in preventing the complete closure of the river mouth, counteracting sediment deposition influenced by wave activity. During the analyzed periods, specific dates, such as 25/09/2013 and 17/12/2014, showed an open river mouth. In contrast, on 25/12/2017 and 23/03/2021 (Fig. 5 ), the mouth exhibited marked narrowing. These variations occurred independently of tidal phases and river discharge levels, suggesting that additional factors, such as sandbank growth and migration, significantly influence the state of the river mouth. This observation supports the hypothesis that tidal forces alone do not fully determine the mouth's condition, as sediment dynamics also play a crucial role. 4.2 Statistical description of the width time series The statistical analysis of the time series related to the analyzed widths at the NR mouth is presented in Table 2 , revealing notable variability among the four series. W1 exhibited the highest average width (~ 1077 m), followed by W3 (~ 870 m), whereas W2 and W4 displayed lower average widths of ~ 529 m and ~ 550 m, respectively. In terms of extreme values, W1 recorded a maximum width of 1275 m and a minimum of 613 m, while W2 showed a narrower range, with a maximum of 619 m and a minimum of 408 m. Conversely, W3 demonstrated the widest range, varying between 1324 m and 700 m, whereas W4 exhibited a maximum width of 1198 m and a minimum of 329 m. These differences highlight the substantial variability in channel width across the analyzed series, emphasizing the range of observed values and the dynamic nature of the RN mouth. Table 2 Statistical analysis of the width time series. Measurement performed using 30 m spatial resolution images. Width Mean Maximum Minimum STD z-value W1 1077 1275 613 170 4.41 W2 529 619 408 39 -1.78 W3 870 1324 700 119 3.11 W4 550 1198 329 177 -6.84 The standard deviation (STD) results highlighted notable differences in the dispersion of width data across the analyzed series, which are crucial for understanding the variability and nature of each dataset. These differences have significant implications for the analysis and interpretation of results. The highest standard deviation was observed in W1, indicating that width values in this series deviated more from the mean, suggesting greater variability in channel width. In contrast, W2 exhibited the lowest standard deviation, implying that its width values remained closer to the mean, reflecting lower variability. Meanwhile, W3 and W4 presented intermediate standard deviations, signifying a moderate level of dispersion—greater than W2 but lower than W1—suggesting that width values in these series showed moderate variability. The Mann–Kendall Z-statistics for W1–W4 are 4.41, − 1.78, 3.11, and − 6.84, respectively (Table 2 ). Consistent with these values, Mann–Kendall testing indicates increasing trends in W1 and W3, no discernible long-term trend in W2, and a decreasing trend in W4. Breakpoint analysis further identifies significant shifts in trend between 2013 and 2021—specifically at 2018 (W1), 2016 (W2), 2016 (W3), and 2015 (W4) (Fig. 6 ). The width time-series trajectories mirror these inferences: W1 and W3 widen over time, W4 narrows, and W2 remains statistically stationary. The structure and persistence of the time series were analyzed using multifractal analysis, with multifractal dimensions computed through the MFDFA method (Fig. 7 ). The Hurst exponent quantifies the degree of persistence in time series data, where values close to 1 indicate strong persistence, suggesting that observed conditions are likely to remain stable in the future. In this analysis, the Hurst exponent values for W1, W2, W3, and W4 were 0.95, 0.85, 0.99, and 0.86, respectively. These results indicate that W1, W3, and W4 exhibit high persistence, while W2 shows slightly lower persistence. Compared to the other series, the data patterns in W2 appear to be more susceptible to change over time. The complexity of the width time series remained low for W2, W3, and W4, with values not exceeding 0.11, whereas W1 exhibited a higher complexity of 0.45. This measure reflects how data variability evolves within a time series, where higher values indicate greater variability or structural complexity. In this case, the elevated complexity (Fig. 7 ) observed in W1 suggests that its data exhibit more pronounced fluctuations compared to the other series, which may have significant implications for result interpretation and predictive model development. Additionally, based on the calculation of the r-parameter, the asymmetry values for W1, W2, W3, and W4 were 1.02, 1.02, 1.02, and 1.03, respectively. These values indicate that the spectra were slightly right skewed across all series, suggesting minor fluctuations in response to natural variations. For ease of comparison, all coefficients are summarized together in Table 3 . Table 3 Summary of results. Trend MK \(\:{\varvec{\alpha\:}}_{\varvec{m}\varvec{a}\varvec{x}}\:-\:{\varvec{\alpha\:}}_{\varvec{m}\varvec{i}\varvec{n}}\) Hurst r W1 4.41 0.447 0.95 1.021 W2 -1.78 0.004 0.85 0.9820 W3 3.11 0.104 0.99 1.021 W4 -6.84 0.005 0.86 1.026 5. DISCUSSION The most relevant results of this study indicate a clear spatial differentiation along the Negro River estuary, with distinct dynamics in each of the four analysed sections (W1–W4). W1, the upstream section, exhibits strong and sustained widening (Z = 4.41, H = 0.95) and the highest complexity (Δα = 0.447), reflecting a multifactorial, heterogeneous fluvial dynamic. W2 shows high stability over the long term (Z = − 1.78, Δα = 0.004) but is subject to abrupt periodic events, indicating a dynamic equilibrium. W3 experiences significant widening (Z = 3.11, H = 0.99) with moderate complexity, while W4, at the estuary mouth, is characterized by marked narrowing (Z = − 6.84), high persistence, low complexity, and a right-skewed spectrum, suggesting a simple and predictable morphological process dominated by the progradation of littoral sandbanks. Across the estuary, high persistence values (H ≥ 0.85) demonstrate strong system inertia, where both changes and stable states tend to persist over time. When compared with previous studies, these results both confirm and update historical observations. The dynamics of the Miguel and La Hoya sandbanks, as revealed by satellite imagery, continue to shape the estuary mouth, consistent with trends described by Del Río et al. ( 1991 ) for 1936–1986. The narrowing of the main channel to a minimum of 329 m reflects ongoing sandbank progradation and longshore drift, in agreement with previous reports on ebb-delta asymmetry. The high complexity in upstream sections and the simple morphology at the mouth also align with expected tidal and fluvial influences documented in the literature (Langbein et al. 1966; Leopold et al. 1960; Wohl 2020 ; Zhou and Endreny 2020 ). Additionally, the study highlights the potential influence of more recent factors such as flow regulation since the 1970s and recent droughts (Fenoglio 2019; Diario Río Negro 2022), which may have accelerated sedimentation processes and modified estuarine morphology. Anthropogenic impacts are considered minimal in this area, with dam operation being the main indirect influence (Longo 2025). A strength of this study lies in the integration of multifractal metrics with the Mann-Kendall trend test, allowing the detection of both persistent and abrupt changes in estuarine dynamics. The analysis of four strategically selected cross-sections provides a spatially resolved assessment of geomorphological evolution, complemented by bathymetric and hydrodynamic data. However, limitations should be acknowledged: the temporal window of the satellite dataset (2013–2021) captures short- to medium-term trends but may not reflect longer-term cycles or abrupt regime shifts. The resolution of satellite imagery also constrains the detection of very fine-scale morphological changes. Despite these limitations, the study demonstrates the importance of systematic monitoring of estuarine mouths. Understanding the spatial and temporal evolution of channel widths and sediment dynamics is critical for navigation, ecological conservation, and water resource management. Such analyses provide essential information for anticipating morphological changes, supporting sustainable management strategies, and informing policymakers about the vulnerability and resilience of estuarine systems. 6. CONCLUSION The present study demonstrates that the Negro River estuary mouth exhibits a non-uniform morphodynamic behavior, characterized by distinct patterns along the W1–W4 transect. Upstream and mid-estuary sections (W1 and W3) show sustained widening, the mouth section (W4) exhibits narrowing, and W2 functions as a dynamic equilibrium reach. High persistence values (H ≥ 0.85) indicate strong system inertia, where both states of change and stability tend to persist beyond short-term fluctuations. The main takeaway is that estuarine evolution is spatially heterogeneous and strongly conditioned by the interplay of fluvial, tidal, and sedimentary processes. This study provides a robust methodological contribution by integrating multifractal metrics with Mann-Kendall trend analysis, allowing for a quantitative assessment of morphodynamic complexity, nonlinearity, and heterogeneity. The approach objectively identifies variability regimes, sediment dynamics, and transient transitions, and can be applied to other estuarine systems with similar deltaic mouths. These findings are relevant for navigation, estuary–ocean connectivity, and the management of fluvial and coastal resources, providing a framework for interpreting the combined effects of natural forcing and anthropogenic influences such as flow regulation. Looking forward, the study highlights several avenues for future research. First, the development of automated methodologies for shoreline segmentation and high-resolution cross-section extraction would improve the efficiency and consistency of geomorphological monitoring. Second, morphodynamic models incorporating hydrodynamic thresholds and regulated flow scenarios could enhance predictive capabilities, supporting management decisions under changing climatic and hydrological conditions. Expanding the temporal coverage of satellite imagery and incorporating high-resolution bathymetric surveys would further strengthen the understanding of long-term estuarine evolution. References Adams JB, Van Niekerk L (2020) Ten principles to determine environmental flow requirements for temporarily closed estuaries. Water, 12(7), 1944. https://doi.org/10.3390/w12071944 Archuby F, Salgado L, Brezina SS, Parras AM (2016) Dos orillas, dos mundos: Paleontología del Alto Valle del río Negro. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET). https://ri.conicet.gov.ar/handle/11336/44469 Autoridad Interjurisdiccional de las Cuencas de los Ríos Limay, Neuquén y Negro (AIC) (2020a) El control de las crecidas. Sistema de Emergencias Hídricas y Mitigación del Riesgo (4ª ed.) Autoridad Interjurisdiccional de las Cuencas de los Ríos Limay, Neuquén y Negro (AIC) (2020c) El control de las crecidas. Sistema de Emergencias Hídricas y Mitigación del Riesgo. http://www.aic.gov.ar/sitio/archivos/202007/el%20control%20de%20las%20crecidas%202020.pdf Autoridad Interjurisdiccional de las Cuencas de los Ríos Limay, Neuquén y Negro (AIC) (2022b) La cuenca. http://www.aic.gov.ar/sitio/lacuenca Baranowski P, Gos M, Krzyszczak J, Siwek K, Kieliszek A, Tkaczyk P (2019) Multifractality of meteorological time series for Poland on the basis of MERRA-2 data. Chaos Solitons Fractals 127:318–333. https://doi.org/10.1016/j.chaos.2019.07.008 Baranowski P, Krzyszczak J, Sławinski C, Hoffmann H, Kozyra J, Nieróbca A, Siwek K, Gluza A (2015) Multifractal analysis of meteorological time series to assess climate impacts. Climate Res 65:39–52. https://doi.org/10.3354/cr01321 Berger A (1986) Annual and seasonal climatic variations in the Northern Hemisphere and Europe during the last century. Ann Geophys 4(B4):385–400 Bianchi E (2016) Dinámica espacio–temporal de la relación entre el clima y el funcionamiento de los ecosistemas de Patagonia Norte. Universidad Nacional del Comahue Bird ECF (2008) Coastal geomorphology: An introduction. Wiley-Blackwell Brieva C (2018) Caracterización y análisis multidisciplinario de la información hidrológica en cuencas. Programa Nacional Agua (PNAGUA). https://inta.gob.ar/sites/default/files/caracterizacion_de_cuencas_0.pdf Chilton D, Hamilton DP, Nagelkerken I, Cook P, Hipsey MR, Reid R, Sheaves M, Waltham NJ, Brookes J (2021) Environmental flow requirements of estuaries: Providing resilience to current and future climate and anthropogenic changes. Front Environ Sci 9:764218. https://doi.org/10.3389/fenvs.2021.764218 Cooper JAG (1993) Sedimentation in a river-dominated estuary. Sedimentology 40(5):979–1017. https://doi.org/10.1111/j.1365-3091.1993.tb01369.x Cortizo LC, Isla FI (2021) Dinámica de la barrera medanosa e islas de barrera de Patagones (Buenos Aires, Argentina). Latin Am J Sedimentology Basin Anal 19(1):47–63 De Jong MG, Mare MD (2014) Regulación de caudales, ecosistemas y asentamientos humanos en el Alto Valle del Río Negro. Boletín Geográfico 29:40–58 del Río JL, Colado URES, Gaido ES (1991) Estabilidad y dinámica del delta de reflujo de la boca del Río Negro, vol 46. Revista de la Asociación Geológica Argentina, pp 325–332 Dissanayake P, Brown J, Yates M (2022) Morphodynamic Evolution and Sustainable Development of Coastal Systems. J Mar Sci Eng 10(5):647. https://doi.org/10.3390/jmse10050647 Farr TG, Rosen PA, Caro E, Crippen R, Duren R, Hensley S, Kobrick M, Paller M, Rodríguez E, Roth L, Seal D, Shaffer S, Shimada J, Umland J, Werner M, Oskin M, Burbank D, Alsdorf D (2007) The Shuttle Radar Topography Mission. Rev Geophys 45:1–33. 10.1029/2005RG000183 Finotello A, D’Alpaos A, Bogoni M, Ghinassi M, Lanzoni S (2020) Remotely sensed planform morphologies reveal fluvial and tidal nature of meandering channels. Sci Rep 10:54. https://doi.org/10.1038/s41598-019-56992-w Gómez JD, Poveda G (2008) Estimación del espectro multifractal para series de precipitación horaria en los Andes tropicales de Colombia. Revista de la Academia Colombiana de Ciencias Exactas. Físicas y Naturales 32(125):2317. https://doi.org/10.18257/raccefyn.32(125).2008.2317 Grasso F, Bismuth E, Verney R (2021) Unraveling the impacts of meteorological and anthropogenic changes on sediment fluxes along an estuary–sea continuum. Sci Rep 11:20230. https://doi.org/10.1038/s41598-021-99502-7 Hale SS, Cicchetti G, Deacutis CF (2016) Eutrophication and hypoxia diminish ecosystem functions of benthic communities in a New England estuary. Front Mar Sci 3:249. https://doi.org/10.3389/fmars.2016.00249 Hastie BF, Smith SDA (2006) Benthic macrofaunal communities in intermittent estuaries during a drought. J Exp Mar Biol Ecol 330(2):356–367. https://doi.org/10.1016/j.jembe.2005.12.039 Kantelhardt JW, Zschiegner SA, Koscielny-Bunde E, Havlin S, Bunde A, Stanley HE (2002) Multifractal detrended fluctuation analysis of nonstationary time series. Phys A 316(1–4):87–114 Kendall MG (1975) Rank correlation methods, 4th edn. Charles Griffin Largier JL (2023) Recognizing low-inflow estuaries as a common estuary paradigm. Estuaries Coasts 46(8):1949–1970. https://doi.org/10.1007/s12237-023-01271-1 Levin LA, Mendoza GF, Neira C, Giddings SN, Crooks JA (2023) Consequences of mouth closure and hypoxia-induced state changes in low-inflow estuaries: Benthic community and trait-based response. Estuaries Coasts 46(8):2128–2147. https://doi.org/10.1007/s12237-022-01132-3 López Lambraño AA (2012) Análisis multifractal y modelación de la precipitación. Tesis de doctorado, Facultad de Ingeniería, Universidad Autónoma de Querétaro, 125–177. Repositorio Institucional de la UAQ. http://ri-ng.uaq.mx/handle/123456789/531 Machiwal D, Moharana PC, Kumar S, Srivastava V, Bhandari SL (2021) Exploring Temporal Dynamics of Spatially - Distributed Groundwater Levels by Integrating Time Series Modeling with Geographic Information System. Geocarto Int 36(12):1325–1345. https://doi.org/10.1080/10106049.2019.1648561 Mann HB (1945) Non-parametric tests against trend. Econometria 13:246 Marani M, Lanzoni S, Zandolin D, Seminara G, Rinaldo A (2002) Tidal meanders. Water Resour Res 38(11):1225. https://doi.org/10.1029/2001WR000404 McSweeney SL, Kennedy DM, Rutherfurd ID (2017) The daily-scale entrance dynamics of intermittently open/closed estuaries. Earth Surf Proc Land 43(1):187–206. https://doi.org/10.1002/esp.4280 McSweeney S, Stout JC, Kennedy DM (2020) Variability in infragravity wave processes during estuary artificial entrance openings. Earth Surf Proc Land 45(13):3244–3259. https://doi.org/10.1002/esp.4974 Mazzoni E (2010) Desertificación en la Patagonia. Developments Earth Surf Processes 13:2–7. 10.1016/S0928-2025(08)10017-7 Ministerio de Ambiente y Desarrollo Sustentable (MADS) (2016) Resolución 206/2016: Unidad de Evaluación de Riesgos Ambientales - Desígnese. Publicada en el Boletín Nacional del 24 de junio de 2016. Gobierno de la República Argentina. Retrieved from https://www.argentina.gob.ar/normativa/nacional/resoluci%C3%B3n-206-2016-262263 O’Shea M, Murphy J (2020) Developing a Process Driven Morphological Model for Long Term Evolution of a Dynamic Coastal Embayment. Open J Mar Sci 10(3):93–109. 10.4236/OJMS.2020.103007 Montagna PA, Palmer TA, Pollack JB (2023) Effect of temporarily opening and closing the marine connection of a river estuary. Estuaries Coasts 46(8):2208–2219. https://doi.org/10.1007/s12237-022-01159-6 Morales Martínez JL, Segovia-Domínguez I, Quiros Rodríguez I, Horta-Rangel FA, Sosa-Gómez G (2021) A modified Multifractal Detrended Fluctuation Analysis (MFDFA) approach for multifractal analysis of precipitation. Physica A 565:125611. https://doi.org/10.1016/j.physa.2020.125611 Nagumo N, Egashira S (2022) Effect of water level variation on the channel width in the river mouth, 10th International Conference on Geomorphology, Coimbra, Portugal, 12–16 ICG2022-362. https://doi.org/10.5194/icg2022-362 NOAA (2021) Estuarine circulation. Retrieved from https://oceanservice.noaa.gov/education/tutorial_estuaries/est05_circulation.html National Centers for Coastal Ocean Science (NCCOS) (2018) Causes and consequences of hypoxic events in low-inflow estuaries in Southern California. Retrieved from https://coastalscience.noaa.gov/project/causes-consequences-of-hypoxic-events-in-low-inflow-estuaries-southern-california/ Nieto HD, Álvarez JE, Rodríguez EL (2016) Análisis de persistencia en acciones financieras en el mercado colombiano a través de la metodología de Rango Reescalado (R/S). Cuadernos Latinoamericanos de Administración, XII (22), 23–32. ISSN: 1900–5016 Owens PN (2007) Background and summary of this issue on sediment linkages. J Soils Sediments 7(4):273–276. https://doi.org/10.1065/jss2007.10.255 Pereyra FX, Piccolo MC, Perillo GME (2014) Hydrodynamics and sediment dynamics in the Quequén Grande estuary. Retrieved from https://ri.conicet.gov.ar/bitstream/handle/11336/4608/CONICET_Digital_Nro.6100_A.pdf Perillo GME, Pérez DE, Piccolo MC, Palma E, Cuadrado DG (2005) Geomorphologic and physical characteristics of a human impacted estuary: Quequén Grande River Estuary, Argentina. Estuarine, Coastal and Shelf Science 62, 301–312 Perillo GME, Piccolo MC (1991) Tidal response in the Bahía Blanca Estuary. J Coastal Res 7(2):437–449. https://www.jstor.org/stable/4297812 Perillo GME, Piccolo MC (1990) Physical characteristics of the Bahía Blanca Estuary. Estuarine. Coastal Shelf Sci 31(3):303–317. https://doi.org/10.1016/0272-7714(90)90003-2 Piccolo MC, Perillo GME (1999) Physical characteristics of the Río de la Plata estuary. In: Perillo GME (ed) Estuaries of South America: Their geomorphology and dynamics. Springer-, Berlin, pp 15–34. https://doi.org/10.1007/978-3-642-60131-6 Prabhakar AK, Singh KK, Lohani AK, Chandniha SK (2017) Long-term rainfall variability assessment using modified Mann-Kendall test over Champua watershed. Odisha J Agrometeorology 19(3):288–289. https://journal.agrimetassociation.org/index.php/jam/article/view/676/571 Prohaska F (1976) The Climate of Argentina, Paraguay, and Uruguay. World Survey of Climatology, 2, 532 Rodríguez Aguilar R (2012) El coeficiente de Hurst y el parámetro α -estable para el análisis de series financieras. Aplicación al mercado cambiario mexicano. Contaduría y Administración 59(1):149–173 Santos da Silva H, Santos Silva JR, Stosic T (2020) Multifractal analysis of air temperature in Brazil. Physica A. 549 https://doi.org/10.1016/j.physa.2020.124333 Scharler UM, Lechman K, Radebe T, Jerling HL (2020) Effects of prolonged mouth closure in a temporarily open/closed estuary: A summary of the responses of invertebrate communities in the uMdloti Estuary, South Africa. Afr J Aquat Sci 45(1–2):1–10. https://doi.org/10.2989/16085914.2019.1689911 Shivam G, Goyal MK, Sarma AK (2019) Index-based study of future precipitation changes over Subansiri river catchment under changing climate. J Environ Inform 34(1):1–14 Silva Alves KMA, Silva Nóbrega R (2017) Tendencia pluviométrica y concentración estacional de precipitación en la cuenca hidrográfica del río Moxotó – Pernamcuco – Brasil. Revista Geográfica de América Cent 58:295–313 ISSN 1011 – 484 SMN (1989a) Inundaciones, sudestadas y crecientes repentinas. Boletín Informativo nº 2. 3ª edición. Buenos Aires SHN (2021) Servicio Hidrológico Naval. Tablas de Marea Astronómica. Fecha de consulta: 18 de octubre de 2021. http://www.hidro.gov.ar/oceanografia/tmareas/RE_Mareas.asp SNIH (2021) Sistema Nacional de Información Hídrica. Consultado: 1ro de septiembre de 2021. https://www.argentina.gob.ar/obras-publicas/hidricas/base-de-datos-hidrologica-integrada SNIH (2024) Sistema Nacional de Información Hídrica. Consultado: 15 de septiembre de 2024 SSRH (2010) Atlas de Cuencas y Regiones Hídricas Superficiales de la República Argentina. Subsecretaría de Recursos Hídricos. https://www.argentina.gob.ar/obras-publicas/hidricas/cartografia-hidrica-provincial SSRH (2015) Atlas de Cuencas y Regiones Hídricas Superficiales de la República Argentina. Subsecretaría de Recursos Hídricos. Cuenca río Negro – Cuenca N° 64. https://www.argentina.gob.ar/sites/default/files/64.pdf Soldano FA (1947) Régimen de aprovechamiento de la red fluvial argentina. Módulo III: Región Patagónica. Editorial Cimera. 159–219 Sun H, Gu X, Zhu J, Yu Z, Zhang Y (2019) Fractal nature of groundwater level fluctuations affected by riparian zone vegetation water use and river stage variations. Sci Rep 9(1):15383. https://doi.org/10.1038/s41598-019-51657-0 Tweedley JR, Dittmann SR, Whitfield AK, Withers K, Hoeksema SD, Potter IC (2019) Hypersalinity: Global distribution, causes, and present and future effects on the biota of estuaries and lagoons. In E. Wolanski, J. W. Day, M. Elliott, R. Ramachandran (Eds.), Coasts and estuaries (pp. 523–546). Elsevier. https://doi.org/10.1016/B978-0-12-814003-1.00030-7 USGS (2021) U.S. Geological Survey. https://www.usgs.gov Whitfield AK, Adams JB, Bate GC, Wooldridge TH et al (2008) A multidisciplinary study of a small temporarily open/closed South African estuary. Afr J Mar Sci 30(3):453–473. https://doi.org/10.2989/AJMS.2008.30.3.2.636 Wohl E (2020) Rivers in the Landscape; John Wiley and Sons. Hoboken, NJ, USA. [Google Scholar] Yu JY, Kao HY (2007) Decadal changes of El Niño persistence barrier in SST and ocean heat content indices: 1958–2001. Geophys Res Lett 112(D13). 10.1029/2006JD007654 Zhang X, Lin P, Chen X (2022) Coastal protection by planted mangrove forest during Typhoon Mangkhut. J Mar Sci Eng 10(9):1288. https://doi.org/10.3390/jmse10091288 Zhou Y, Leung Y (2010) Multifractal temporally weighted detrended fluctuation analysis and its application in the analysis of scaling behavior in temperature series. Journal of Statistical Mechanics: Theory and Experiment, 2010(06), P06021. https://doi.org/10.1088/1742-5468/2010/06/P06021 Zhou T, Endreny T (2020) The straightening of a river meander leads to extensive losses in flow complexity and ecosystem services. Water 12(6):1680. https://doi.org/10.3390/w12061680 Additional Declarations The authors declare no competing interests. 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07:24:35","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":209912,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8370254/v1/7c733a6cd52e0538e66c1fd8.html"},{"id":98379132,"identity":"bc9c7577-c054-42e2-95cb-43563d475680","added_by":"auto","created_at":"2025-12-17 07:24:40","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":25944,"visible":true,"origin":"","legend":"\u003cp\u003eClassification of estuary mouth states considering river-ocean dynamics. Adapted from Coast KZN (2024).\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8370254/v1/68605b38d453263554c7fe2f.jpg"},{"id":98379147,"identity":"7433842a-9bb5-47f7-b40e-472094906805","added_by":"auto","created_at":"2025-12-17 07:24:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":65783,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Area: a) Limay, Neuquén, and Negro hydrological system (AIC, 2024), b) Lower Rio Negro Hydrographic Basin (Soldano 1947; Farr et al. 2007; SSRH 2010; INDEC 2010; IGN 2018, 2022), where W1, W2, W3, and W4 are the cross-sections where the width was measured, c) 3D map of depth of Negro River mouth, d) Relative location of the study area.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8370254/v1/97d1bfa08a79707dc44f0d36.jpg"},{"id":98379101,"identity":"cd5877bc-9ece-41cb-9445-a5ee46767b4c","added_by":"auto","created_at":"2025-12-17 07:24:32","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":40706,"visible":true,"origin":"","legend":"\u003cp\u003eMethodology used to obtain the mouth width measure.\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8370254/v1/4dfe588e9b5242e7ce29baf6.jpg"},{"id":98379124,"identity":"ea083856-6868-4a5f-869a-1887c59fd8a5","added_by":"auto","created_at":"2025-12-17 07:24:36","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3564079,"visible":true,"origin":"","legend":"\u003cp\u003eUSGS Landsat 8 True Color Images of Negro River open mouth situation: a) 25/09/2013, b) 12/09/2014, c) 17/12/2014, where 1) and 2) represent Banco Miguel and Banco La Hoya, respectively.\u003c/p\u003e","description":"","filename":"Fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8370254/v1/f1a6fa746340e5e2ffca1e08.jpg"},{"id":98379115,"identity":"41102d1a-9ffe-471c-ae24-9ebbba2a4476","added_by":"auto","created_at":"2025-12-17 07:24:34","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":3992027,"visible":true,"origin":"","legend":"\u003cp\u003eUSGS Landsat 8 True Color Images of Negro River trend to close its mouth situation: a) 25/12/2017, b) 12/12/2018, c) 03/02/2021, d) 23/03/2021, where 1) and 2) represent Banco Miguel and Banco La Hoya, respectively.\u003c/p\u003e","description":"","filename":"Fig5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8370254/v1/6d1bf5f5a240492e9bce95e1.jpg"},{"id":98379092,"identity":"4fc9f352-4352-473b-b62c-3eebc31cbc47","added_by":"auto","created_at":"2025-12-17 07:24:31","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":64847,"visible":true,"origin":"","legend":"\u003cp\u003eBreaking point trends for the width time series analyzed.\u003c/p\u003e","description":"","filename":"Fig6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8370254/v1/5d4c932f1c37de5752713bba.jpg"},{"id":98379078,"identity":"93c3273c-98c2-473a-a067-71f89ccbca36","added_by":"auto","created_at":"2025-12-17 07:24:29","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":43048,"visible":true,"origin":"","legend":"\u003cp\u003eMultifractal analysis of time series: a) W1. b) W2. c) W3. d) W4.\u003c/p\u003e","description":"","filename":"Fig7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8370254/v1/6db867f15fe60f445a516bd2.jpg"},{"id":98445778,"identity":"b13cd507-27ad-4c05-a1fb-6d6bde0116ae","added_by":"auto","created_at":"2025-12-17 17:21:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":8401524,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8370254/v1/6900cf45-9664-4af5-9081-17261e934cf8.pdf"},{"id":98379126,"identity":"b6a46ca6-82de-43a2-9dc5-562595e8141c","added_by":"auto","created_at":"2025-12-17 07:24:37","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":22796,"visible":true,"origin":"","legend":"","description":"","filename":"APPENDIX.docx","url":"https://assets-eu.researchsquare.com/files/rs-8370254/v1/831b27ca3eb36143fba10280.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eNon-Uniform Morphodynamics of the Negro River Estuary Mouth, Argentina (2013-2021)\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. INTRODUCTION","content":"\u003cp\u003eThe mouth of an estuary is a critical component of estuarine monitoring because it directly influences the interaction between terrestrial and marine ecosystems. It provides essential information on water levels and tidal inflows (Coast KZN 2024; Liu et al. 2021). Estuarine mouths are dynamic systems, constantly changing in size and shape, particularly those that open intermittently or remain continuously open (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) (Estuary Watch Victoria 2024). Understanding their behavior is essential for maintaining ecological health and managing estuarine resources sustainably.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe state of an estuary mouth is affected by both natural and human factors. Natural factors include seasonal river flows, tidal action, sedimentation, coastal erosion, and long-term climatic changes (Pushpa et al. 2022; Michael \u0026amp; Murphy 2020). High river flows transport sediments seaward, whereas low flows cause sediment accumulation, narrowing channels and reducing water depth (Grasso et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Human activities such as coastal construction, river channelization, dredging, and urbanization also alter estuarine dynamics (Zhang et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). These changes can influence the mouth\u0026rsquo;s ability to remain open, particularly during low-flow periods, contributing to partial or complete closure (Perillo et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Pereyra et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInternational studies show that prolonged mouth closures are common in some estuaries, such as those in southern Africa and Australia, causing hypersalinity and changes in biodiversity (Whitfield et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Scharler et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Hastie \u0026amp; Smith \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). In America, examples such as Old Woman Creek and Los Pe\u0026ntilde;asquitos Lagoon (USA) demonstrate how human intervention and natural sandbars affect mouth opening and closure (NOAA \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; NCCOS PROJECT 2018). In Argentina, the Quequ\u0026eacute;n Grande River illustrates how dredging and channel widening alter hydrodynamics and salinity structure, influencing mouth dynamics (Perillo et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Pereyra et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Negro River, the largest river in Argentine Patagonia, flows through a single channel whose mouth is one of the most complex in South America, characterised by shifting sandbanks, variable currents, and temporary blockages (UNL-DPA R\u0026iacute;o Negro 2004a, 2004b). Tidal dynamics govern the water level in the Negro River, with their effects extending as far as 45 km upstream, reaching the vicinity of San Javier (R\u0026iacute;o Negro 2017). The migration of sandbanks and narrowing of the main channel directly affect mouth opening and closure, particularly during low river flows (Del R\u0026iacute;o et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1991\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe objective of this study is to document the spatio-temporal evolution of the Negro River mouth by examining variations in the river\u0026rsquo;s width at different points along the estuary and analyzing the movement of sediment lobes present in the mouth. This research is expected to provide updated information on the geomorphological evolution of the Negro River mouth, contributing to scientific knowledge and supporting sustainable estuary management. Findings will help policymakers and environmental managers develop strategies for sediment management, biodiversity protection, and conservation of one of Patagonia\u0026rsquo;s most important ecological systems.\u003c/p\u003e"},{"header":"2. STUDY AREA AND REGIONAL SETTING","content":"\u003cp\u003eThe Negro River (NR) is part of the hydrographic system formed by the Limay, Neuqu\u0026eacute;n, and Negro rivers, originating at the confluence of the Limay and Neuqu\u0026eacute;n rivers (AIC 2022) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). It is the largest allochthonous river in Argentina (Piccolo and Perillo \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1999\u003c/span\u003e), with a length of approximately 637 km in a NW\u0026ndash;SE direction (MADS 2016). Discharge fluctuations are driven by the pluvio-nival regime of its main tributaries, with peak flows in autumn\u0026ndash;winter and spring due to heavy rainfall and snowmelt, and minimum flows in March\u0026ndash;April (Gianola Otamendi 2019; Soldano \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1947\u003c/span\u003e). Upstream dams regulate the river\u0026rsquo;s flow (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea) (Longo 2025), resulting in an average discharge of ~\u0026thinsp;1,000 m\u0026sup3; s⁻\u0026sup1;, whereas prior to regulation flows could exceed 6,000 m\u0026sup3; s⁻\u0026sup1; (De Jong and Mare \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe study focuses on the mouth of the Negro River, located in the lower basin of the system, between \u0026minus;\u0026thinsp;41.025\u0026deg; S and \u0026minus;\u0026thinsp;41.05833\u0026deg; S, and \u0026minus;\u0026thinsp;62.7\u0026deg; W and \u0026minus;\u0026thinsp;62.8167\u0026deg; W, in the northeastern region of Argentine Patagonia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb,c) (Soldano \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1947\u003c/span\u003e; SSRH \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The mouth is situated on a coastal plain with meanders, abandoned channels, islands, and varying beach morphologies (Archuby 2016). Beaches on the R\u0026iacute;o Negro Province side are wide (100\u0026ndash;250 m), whereas those on the Buenos Aires Province side are less developed (del R\u0026iacute;o et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1991\u003c/span\u003e). Sand dunes, both stabilized and mobile, reach heights between 5 and 11 m (Cortizo and Isla \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe ebb delta at the river mouth comprises a main channel 6\u0026ndash;8 m deep, influenced by sediment banks, particularly the Miguel and La Hoya Banks, which form a partially open frontal shield (Piccolo and Perillo \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e1999\u003c/span\u003e; Subsecretar\u0026iacute;a de Recursos H\u0026iacute;dricos de la Naci\u0026oacute;n 2015) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). The dynamics of the estuary are strongly influenced by the tidal regime, which governs water levels and extends its influence up to 45 km upstream to San Javier (R\u0026iacute;o Negro 2017). Tides are semi-diurnal and macrotidal, with a maximum height of 4.4 m at high tide and a minimum of 0.71 m at low tide (UNL-DPA R\u0026iacute;o Negro 2004). Average wave height is 0.67 m, reaching 1.20 m during high-energy events, with a predominant direction from the southeast, generating net littoral transport of ~\u0026thinsp;900,000 m\u0026sup3;/year toward the northeast (del R\u0026iacute;o et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Lanfredi 1987). Ebb currents are generally stronger than flood currents, reaching up to 2.5 m/s, while slack water lasts 30 minutes upstream and 17 minutes downstream. This area, with an arid to semi-arid climate and annual precipitation below 400 mm, is influenced by regional atmospheric dynamics, including occasional \u0026ldquo;Sudestadas,\u0026rdquo; which produce strong southerly and southeasterly winds (Prohaska \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e1976\u003c/span\u003e; Garreaud 2009; Bianchi \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Coronato et al. 2017; Mazzoni \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2010\u003c/span\u003ea; SMN 1989).\u003c/p\u003e"},{"header":"3. DATA AND METHODS","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Acquisition of Geomorphological Datasets\u003c/h2\u003e \u003cp\u003eThe present study was based on a dataset of 62 Landsat 8 OLI/TIRS Level 2 satellite images of the Negro River estuary, spanning the period 2013\u0026ndash;2021. Images were selected from the publicly available database provided by the United States Geological Survey (USGS \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), including only those with less than 10% cloud cover. The images have a spatial resolution of 30 m and underwent atmospheric correction prior to analysis (Appendix 1).\u003c/p\u003e \u003cp\u003eAn initial visual analysis of the dataset was carried out to identify variations in the width of the main channel, determine open- or closed-mouth states, and assess the evolution of sediment lobes and banks within the ebb delta. This approach allowed for the identification of spatial and temporal patterns in the estuary\u0026rsquo;s fluvial dynamics. For the delineation of water bodies and watercourses, true-colour images were generated using spectral bands B2, B3, and B4 in the blue, green, and red channels, respectively, employing QGIS (QGIS.org). Images from the same season but under different tidal phases were compared to detect geomorphological variations at the estuary mouth. Details of the image processing workflow are summarized in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo characterize the geomorphological structure in more detail, four key widths of the estuary were measured (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb): W1 corresponds to the marshland and a small island, which may indicate sediment accumulation; W2 represents the central narrowing of the river mouth; W3 is defined as the inter-deltaic distance of the main channel; and W4 serves as an indicator of delta elongation. These measurements provide critical information for assessing spatial variations and potential sediment migration processes within the estuary.\u003c/p\u003e \u003cp\u003eComplementary datasets were also employed to enhance the analysis, including bathymetric data obtained from an echo sounder survey, flow measurements at the Primera Angostura station (SNIH \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and modelled tidal information from the Naval Hydrographic Service (SHN 2024). This additional information supports a more quantitative assessment of the estuary\u0026rsquo;s dynamics, complementing the observations derived from satellite imagery.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Analysis of NR mouth widths\u003c/h2\u003e \u003cp\u003eThe analysis of NR mouth widths was carried out using basic descriptive statistics. The mean, maximum, and minimum values were computed for each time series (W1, W2, W3, and W4). These statistical measures provided a detailed characterization of width variability across the series, contributing to a better understanding of the study area's morphological dynamics.\u003c/p\u003e \u003cp\u003eThe trend in the time series was assessed using the non-parametric Mann-Kendall test at a 95% confidence level (α\u0026thinsp;=\u0026thinsp;0.05) (Mann \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1945\u003c/span\u003e; Kendall \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1975\u003c/span\u003e; Berger \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Yu et al. 2007; Gavrilov et al. 2016; Patle et al. 2016; Silva Alves and Silva N\u0026oacute;brega \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Prabhakar et al. 2018; Shivam et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Machiwal et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The null hypothesis, as formulated by Mann (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1945\u003c/span\u003e) and Kendall (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1975\u003c/span\u003e), assumes that the data are independent and identically distributed. The rejection of the null hypothesis indicates that the data do not satisfy these assumptions and, therefore, should not be considered independent and identically distributed random variables.\u003c/p\u003e \u003cp\u003eThe identified trends were classified following the criteria established by Alves et al. (2015) and Silva Alves and Silva N\u0026oacute;brega (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Based on this approach, the Mann-Kendall test provides Z-statistic values to determine the significance of temporal trends: Z\u0026thinsp;\u0026ge;\u0026thinsp;1.96 indicates a significant increasing trend, Z \u0026le; -1.96 a significant decreasing trend, and values between \u0026minus;\u0026thinsp;1.96 and 1.96 reflect the absence of a statistically significant trend at the 95% confidence level (Kendall \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e1975\u003c/span\u003e). In addition, breakpoints in the detected trends were calculated to identify moments of significant change in the estuary\u0026rsquo;s width over time, allowing for a more detailed assessment of geomorphological dynamics.\u003c/p\u003e \u003cp\u003eMultifractal analysis provides a quantitative metric of system complexity. In this study, an objective measure is employed to assess the morphodynamics of the R\u0026iacute;o Negro deltaic system. A broad spectrum denotes a multifactorial system, in which the interaction and nonlinearity of forcing mechanisms across multiple scales generate heterogeneous and unpredictable behavior. Conversely, a narrow spectrum indicates a system of low complexity, dominated by a single prevailing process that enforces a more homogeneous and predictable dynamic.\u003c/p\u003e \u003cp\u003eThe persistence of observed trends and the structural characteristics of the time series were analyzed using Multifractal Detrended Fluctuation Analysis (MFDFA). Unlike traditional methods such as Fast Fourier Transform, MFDFA calculates the multifractal spectrum and its dimensions, offering the advantage of being less sensitive to the length of the time series under study (Kantelhardt et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2002\u003c/span\u003e, 2006; Baranowski et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Introduced by Kantelhardt et al. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), MFDFA is widely used for its ability to characterize time series through multifractal dimensions, thereby identifying long-term correlations (Kantelhardt et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; L\u0026oacute;pez-Lambra\u0026ntilde;o et al. 2017; Baranowski et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This methodology is particularly effective for analyzing signals influenced by non-stationary processes (G\u0026oacute;mez and Poveda \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Zhou and Leung \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Morales Mart\u0026iacute;nez et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eApplying MFDFA, we computed the multifractal spectrum and its dimensions using width time series from the NR mouth, providing valuable insights into the persistence and complexity of the signal. The analyzed multifractal dimensions include the Hurst exponent (α₀), complexity (w), and asymmetry (r). The Hurst exponent quantifies the persistence or significance of trends within the time series (Rodr\u0026iacute;guez Aguilar \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Nieto et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). According to Baranowski et al. (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) and Santos da Silva et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), values\u0026thinsp;\u0026gt;\u0026thinsp;0.5 indicate persistent trends (long-term memory), values\u0026thinsp;\u0026lt;\u0026thinsp;0.5 indicate anti-persistent behavior (short-term memory), and a value of 0.5 denotes no dependence, making projections highly uncertain.\u003c/p\u003e \u003cp\u003eThe values \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{max}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{min}\\)\u003c/span\u003e\u003c/span\u003e represent the extreme points of the time series. The complexity of the multifractal spectrum (w) is determined as the difference between \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{max}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\alpha\\:}_{min}\\)\u003c/span\u003e\u003c/span\u003e. The asymmetry (r) of the multifractal spectrum indicates the dominance of small or large fluctuations relative to the historical mean. A left-skewed spectrum suggests the prevalence of large fluctuations and high variability, whereas a right-skewed spectrum implies smaller fluctuations corresponding to natural variations. A symmetric spectrum indicates a balanced distribution of large and small variations in time series variability. The calculation of this multifractal dimension follows the formula proposed by Santos da Silva et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:r=\\frac{({\\alpha\\:}_{max}-{\\alpha\\:}_{0})}{({\\alpha\\:}_{0}-{\\alpha\\:}_{min})}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIf r\u0026thinsp;\u0026gt;\u0026thinsp;1, the spectrum is right-skewed, indicating minor fluctuations that reflect natural variations.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIf r\u0026thinsp;\u0026lt;\u0026thinsp;1, the spectrum is left-skewed, suggesting pronounced fluctuations and significant variability within the series.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eIf r\u0026thinsp;=\u0026thinsp;1, the spectrum is symmetric, with variability driven by natural fluctuations, leading to high variation in the variable\u0026rsquo;s cycles.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. RESULTS","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Description of Negro River based on true color images from Landsat\u003c/h2\u003e \u003cp\u003eThe analysis of a dataset comprising 62 satellite images of the Negro River mouth in Argentina indicates that 62% of the images exhibit a decreasing trend in the width of the main channel within the ebb delta. In contrast, the remaining 38% depict an open-mouth state. These findings suggest a notable pattern in the region\u0026rsquo;s fluvial dynamics, with potential implications for navigation, local ecology, and water resource management, which will be further discussed.\u003c/p\u003e \u003cp\u003eThe most relevant results derived from the analysis of the LANDSAT 8 C2 L2 image dataset (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) provide insights into the state of the NR mouth. The images in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e (covering the 2013\u0026ndash;2014 period) depict an open-mouth state, where the main channel is delineated by two lobes (1 and 2), remaining unobstructed while exhibiting variations in their shapes. Additionally, sediment bank formations (outlined in yellow) are visible inside the estuary mouth on the right side migrating towards the southern bank.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents the most relevant results regarding geomorphological changes from 2017 to 2021. During this period, a reduction in the width of the main channel (delineated by frontal lobes 1 and 2) was observed. Additionally, Banco Miguel exhibited a displacement toward the northern coast. As a result, the coastal lagoon formed between Banco La Hoya and the northern coast shows a tendency to close. Furthermore, the sediment bank (outlined in red) within the estuary mouth has expanded in size and migrated to the opposite side of the estuary.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRiver discharge (Q) Negro River, tidal level, and phase during the satellite image acquisition for representative subset of dates.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDay\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMonth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTide level (m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePhase\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{m}^{3}{s}^{-1}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.661\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh tide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e421.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.504\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh tide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e606.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow tide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e351.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh tide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e362.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.689\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh tide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e375.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh tide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e392.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLow tide\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e416.86\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSignificant variations in Miguel Bank were observed under different tidal and discharge conditions (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which includes a subset of 7 representative acquisition dates selected to illustrate the range of hydrological scenarios. The complete dataset of 62 satellite images is provided in Appendix 1. These dates correspond to the satellite images displayed in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, which visually exemplify the contrasting hydromorphological conditions described. No high-tide scenes showed a closed mouth in our 62-image dataset (Methods; Appendix 2), consistent with sustained river\u0026ndash;ocean exchange during high tide. Conversely, during low tides and reduced river discharge, sediment accumulation increased, primarily driven by wave action transporting sediments from the ocean toward the coast. This accumulation contributed to channel narrowing, consistent with the findings of del R\u0026iacute;o et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1991\u003c/span\u003e), who highlighted the role of wave-induced sediment transport in the geomorphological dynamics of the river mouth. These observations suggest that tidal forces, in conjunction with river discharge, play a critical role in preventing the complete closure of the river mouth, counteracting sediment deposition influenced by wave activity.\u003c/p\u003e \u003cp\u003eDuring the analyzed periods, specific dates, such as 25/09/2013 and 17/12/2014, showed an open river mouth. In contrast, on 25/12/2017 and 23/03/2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), the mouth exhibited marked narrowing. These variations occurred independently of tidal phases and river discharge levels, suggesting that additional factors, such as sandbank growth and migration, significantly influence the state of the river mouth. This observation supports the hypothesis that tidal forces alone do not fully determine the mouth's condition, as sediment dynamics also play a crucial role.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Statistical description of the width time series\u003c/h2\u003e \u003cp\u003eThe statistical analysis of the time series related to the analyzed widths at the NR mouth is presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, revealing notable variability among the four series. W1 exhibited the highest average width (~\u0026thinsp;1077 m), followed by W3 (~\u0026thinsp;870 m), whereas W2 and W4 displayed lower average widths of ~\u0026thinsp;529 m and ~\u0026thinsp;550 m, respectively. In terms of extreme values, W1 recorded a maximum width of 1275 m and a minimum of 613 m, while W2 showed a narrower range, with a maximum of 619 m and a minimum of 408 m. Conversely, W3 demonstrated the widest range, varying between 1324 m and 700 m, whereas W4 exhibited a maximum width of 1198 m and a minimum of 329 m. These differences highlight the substantial variability in channel width across the analyzed series, emphasizing the range of observed values and the dynamic nature of the RN mouth.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical analysis of the width time series. Measurement performed using 30 m spatial resolution images.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidth\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaximum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMinimum\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSTD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ez-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eW1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e613\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e170\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eW2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e529\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e408\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eW3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e870\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eW4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-6.84\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe standard deviation (STD) results highlighted notable differences in the dispersion of width data across the analyzed series, which are crucial for understanding the variability and nature of each dataset. These differences have significant implications for the analysis and interpretation of results. The highest standard deviation was observed in W1, indicating that width values in this series deviated more from the mean, suggesting greater variability in channel width. In contrast, W2 exhibited the lowest standard deviation, implying that its width values remained closer to the mean, reflecting lower variability. Meanwhile, W3 and W4 presented intermediate standard deviations, signifying a moderate level of dispersion\u0026mdash;greater than W2 but lower than W1\u0026mdash;suggesting that width values in these series showed moderate variability.\u003c/p\u003e \u003cp\u003eThe Mann\u0026ndash;Kendall Z-statistics for W1\u0026ndash;W4 are 4.41, \u0026minus;\u0026thinsp;1.78, 3.11, and \u0026minus;\u0026thinsp;6.84, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Consistent with these values, Mann\u0026ndash;Kendall testing indicates increasing trends in W1 and W3, no discernible long-term trend in W2, and a decreasing trend in W4. Breakpoint analysis further identifies significant shifts in trend between 2013 and 2021\u0026mdash;specifically at 2018 (W1), 2016 (W2), 2016 (W3), and 2015 (W4) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The width time-series trajectories mirror these inferences: W1 and W3 widen over time, W4 narrows, and W2 remains statistically stationary.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe structure and persistence of the time series were analyzed using multifractal analysis, with multifractal dimensions computed through the MFDFA method (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The Hurst exponent quantifies the degree of persistence in time series data, where values close to 1 indicate strong persistence, suggesting that observed conditions are likely to remain stable in the future. In this analysis, the Hurst exponent values for W1, W2, W3, and W4 were 0.95, 0.85, 0.99, and 0.86, respectively. These results indicate that W1, W3, and W4 exhibit high persistence, while W2 shows slightly lower persistence. Compared to the other series, the data patterns in W2 appear to be more susceptible to change over time.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe complexity of the width time series remained low for W2, W3, and W4, with values not exceeding 0.11, whereas W1 exhibited a higher complexity of 0.45. This measure reflects how data variability evolves within a time series, where higher values indicate greater variability or structural complexity. In this case, the elevated complexity (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e) observed in W1 suggests that its data exhibit more pronounced fluctuations compared to the other series, which may have significant implications for result interpretation and predictive model development. Additionally, based on the calculation of the r-parameter, the asymmetry values for W1, W2, W3, and W4 were 1.02, 1.02, 1.02, and 1.03, respectively. These values indicate that the spectra were slightly right skewed across all series, suggesting minor fluctuations in response to natural variations. For ease of comparison, all coefficients are summarized together in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of results.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrend MK\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\varvec{\\alpha\\:}}_{\\varvec{m}\\varvec{a}\\varvec{x}}\\:-\\:{\\varvec{\\alpha\\:}}_{\\varvec{m}\\varvec{i}\\varvec{n}}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHurst\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003er\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eW1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eW2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-1.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.9820\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eW3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eW4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-6.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. DISCUSSION","content":"\u003cp\u003eThe most relevant results of this study indicate a clear spatial differentiation along the Negro River estuary, with distinct dynamics in each of the four analysed sections (W1\u0026ndash;W4). W1, the upstream section, exhibits strong and sustained widening (Z\u0026thinsp;=\u0026thinsp;4.41, H\u0026thinsp;=\u0026thinsp;0.95) and the highest complexity (Δα\u0026thinsp;=\u0026thinsp;0.447), reflecting a multifactorial, heterogeneous fluvial dynamic. W2 shows high stability over the long term (Z = \u0026minus;\u0026thinsp;1.78, Δα\u0026thinsp;=\u0026thinsp;0.004) but is subject to abrupt periodic events, indicating a dynamic equilibrium. W3 experiences significant widening (Z\u0026thinsp;=\u0026thinsp;3.11, H\u0026thinsp;=\u0026thinsp;0.99) with moderate complexity, while W4, at the estuary mouth, is characterized by marked narrowing (Z = \u0026minus;\u0026thinsp;6.84), high persistence, low complexity, and a right-skewed spectrum, suggesting a simple and predictable morphological process dominated by the progradation of littoral sandbanks. Across the estuary, high persistence values (H\u0026thinsp;\u0026ge;\u0026thinsp;0.85) demonstrate strong system inertia, where both changes and stable states tend to persist over time.\u003c/p\u003e \u003cp\u003eWhen compared with previous studies, these results both confirm and update historical observations. The dynamics of the Miguel and La Hoya sandbanks, as revealed by satellite imagery, continue to shape the estuary mouth, consistent with trends described by Del R\u0026iacute;o et al. (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1991\u003c/span\u003e) for 1936\u0026ndash;1986. The narrowing of the main channel to a minimum of 329 m reflects ongoing sandbank progradation and longshore drift, in agreement with previous reports on ebb-delta asymmetry. The high complexity in upstream sections and the simple morphology at the mouth also align with expected tidal and fluvial influences documented in the literature (Langbein et al. 1966; Leopold et al. 1960; Wohl \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Zhou and Endreny \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Additionally, the study highlights the potential influence of more recent factors such as flow regulation since the 1970s and recent droughts (Fenoglio 2019; Diario R\u0026iacute;o Negro 2022), which may have accelerated sedimentation processes and modified estuarine morphology. Anthropogenic impacts are considered minimal in this area, with dam operation being the main indirect influence (Longo 2025).\u003c/p\u003e \u003cp\u003eA strength of this study lies in the integration of multifractal metrics with the Mann-Kendall trend test, allowing the detection of both persistent and abrupt changes in estuarine dynamics. The analysis of four strategically selected cross-sections provides a spatially resolved assessment of geomorphological evolution, complemented by bathymetric and hydrodynamic data. However, limitations should be acknowledged: the temporal window of the satellite dataset (2013\u0026ndash;2021) captures short- to medium-term trends but may not reflect longer-term cycles or abrupt regime shifts. The resolution of satellite imagery also constrains the detection of very fine-scale morphological changes. Despite these limitations, the study demonstrates the importance of systematic monitoring of estuarine mouths. Understanding the spatial and temporal evolution of channel widths and sediment dynamics is critical for navigation, ecological conservation, and water resource management. Such analyses provide essential information for anticipating morphological changes, supporting sustainable management strategies, and informing policymakers about the vulnerability and resilience of estuarine systems.\u003c/p\u003e"},{"header":"6. CONCLUSION","content":"\u003cp\u003eThe present study demonstrates that the Negro River estuary mouth exhibits a non-uniform morphodynamic behavior, characterized by distinct patterns along the W1\u0026ndash;W4 transect. Upstream and mid-estuary sections (W1 and W3) show sustained widening, the mouth section (W4) exhibits narrowing, and W2 functions as a dynamic equilibrium reach. High persistence values (H\u0026thinsp;\u0026ge;\u0026thinsp;0.85) indicate strong system inertia, where both states of change and stability tend to persist beyond short-term fluctuations. The main takeaway is that estuarine evolution is spatially heterogeneous and strongly conditioned by the interplay of fluvial, tidal, and sedimentary processes.\u003c/p\u003e \u003cp\u003eThis study provides a robust methodological contribution by integrating multifractal metrics with Mann-Kendall trend analysis, allowing for a quantitative assessment of morphodynamic complexity, nonlinearity, and heterogeneity. The approach objectively identifies variability regimes, sediment dynamics, and transient transitions, and can be applied to other estuarine systems with similar deltaic mouths. These findings are relevant for navigation, estuary\u0026ndash;ocean connectivity, and the management of fluvial and coastal resources, providing a framework for interpreting the combined effects of natural forcing and anthropogenic influences such as flow regulation.\u003c/p\u003e \u003cp\u003eLooking forward, the study highlights several avenues for future research. First, the development of automated methodologies for shoreline segmentation and high-resolution cross-section extraction would improve the efficiency and consistency of geomorphological monitoring. Second, morphodynamic models incorporating hydrodynamic thresholds and regulated flow scenarios could enhance predictive capabilities, supporting management decisions under changing climatic and hydrological conditions. Expanding the temporal coverage of satellite imagery and incorporating high-resolution bathymetric surveys would further strengthen the understanding of long-term estuarine evolution.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAdams JB, Van Niekerk L (2020) Ten principles to determine environmental flow requirements for temporarily closed estuaries. Water, 12(7), 1944. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w12071944\u003c/span\u003e\u003cspan address=\"10.3390/w12071944\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArchuby F, Salgado L, Brezina SS, Parras AM (2016) Dos orillas, dos mundos: Paleontolog\u0026iacute;a del Alto Valle del r\u0026iacute;o Negro. Consejo Nacional de Investigaciones Cient\u0026iacute;ficas y T\u0026eacute;cnicas (CONICET). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ri.conicet.gov.ar/handle/11336/44469\u003c/span\u003e\u003cspan address=\"https://ri.conicet.gov.ar/handle/11336/44469\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAutoridad Interjurisdiccional de las Cuencas de los R\u0026iacute;os Limay, Neuqu\u0026eacute;n y Negro (AIC) (2020a) El control de las crecidas. Sistema de Emergencias H\u0026iacute;dricas y Mitigaci\u0026oacute;n del Riesgo (4\u0026ordf; ed.)\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAutoridad Interjurisdiccional de las Cuencas de los R\u0026iacute;os Limay, Neuqu\u0026eacute;n y Negro (AIC) (2020c) El control de las crecidas. Sistema de Emergencias H\u0026iacute;dricas y Mitigaci\u0026oacute;n del Riesgo. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.aic.gov.ar/sitio/archivos/202007/el%20control%20de%20las%20crecidas%202020.pdf\u003c/span\u003e\u003cspan address=\"http://www.aic.gov.ar/sitio/archivos/202007/el%20control%20de%20las%20crecidas%202020.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAutoridad Interjurisdiccional de las Cuencas de los R\u0026iacute;os Limay, Neuqu\u0026eacute;n y Negro (AIC) (2022b) La cuenca. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.aic.gov.ar/sitio/lacuenca\u003c/span\u003e\u003cspan address=\"http://www.aic.gov.ar/sitio/lacuenca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaranowski P, Gos M, Krzyszczak J, Siwek K, Kieliszek A, Tkaczyk P (2019) Multifractality of meteorological time series for Poland on the basis of MERRA-2 data. Chaos Solitons Fractals 127:318\u0026ndash;333. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.chaos.2019.07.008\u003c/span\u003e\u003cspan address=\"10.1016/j.chaos.2019.07.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaranowski P, Krzyszczak J, Sławinski C, Hoffmann H, Kozyra J, Nier\u0026oacute;bca A, Siwek K, Gluza A (2015) Multifractal analysis of meteorological time series to assess climate impacts. Climate Res 65:39\u0026ndash;52. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3354/cr01321\u003c/span\u003e\u003cspan address=\"10.3354/cr01321\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerger A (1986) Annual and seasonal climatic variations in the Northern Hemisphere and Europe during the last century. Ann Geophys 4(B4):385\u0026ndash;400\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBianchi E (2016) Din\u0026aacute;mica espacio\u0026ndash;temporal de la relaci\u0026oacute;n entre el clima y el funcionamiento de los ecosistemas de Patagonia Norte. Universidad Nacional del Comahue\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBird ECF (2008) Coastal geomorphology: An introduction. Wiley-Blackwell\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrieva C (2018) Caracterizaci\u0026oacute;n y an\u0026aacute;lisis multidisciplinario de la informaci\u0026oacute;n hidrol\u0026oacute;gica en cuencas. Programa Nacional Agua (PNAGUA). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://inta.gob.ar/sites/default/files/caracterizacion_de_cuencas_0.pdf\u003c/span\u003e\u003cspan address=\"https://inta.gob.ar/sites/default/files/caracterizacion_de_cuencas_0.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChilton D, Hamilton DP, Nagelkerken I, Cook P, Hipsey MR, Reid R, Sheaves M, Waltham NJ, Brookes J (2021) Environmental flow requirements of estuaries: Providing resilience to current and future climate and anthropogenic changes. Front Environ Sci 9:764218. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fenvs.2021.764218\u003c/span\u003e\u003cspan address=\"10.3389/fenvs.2021.764218\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCooper JAG (1993) Sedimentation in a river-dominated estuary. Sedimentology 40(5):979\u0026ndash;1017. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1111/j.1365-3091.1993.tb01369.x\u003c/span\u003e\u003cspan address=\"10.1111/j.1365-3091.1993.tb01369.x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCortizo LC, Isla FI (2021) Din\u0026aacute;mica de la barrera medanosa e islas de barrera de Patagones (Buenos Aires, Argentina). Latin Am J Sedimentology Basin Anal 19(1):47\u0026ndash;63\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Jong MG, Mare MD (2014) Regulaci\u0026oacute;n de caudales, ecosistemas y asentamientos humanos en el Alto Valle del R\u0026iacute;o Negro. Bolet\u0026iacute;n Geogr\u0026aacute;fico 29:40\u0026ndash;58\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003edel R\u0026iacute;o JL, Colado URES, Gaido ES (1991) Estabilidad y din\u0026aacute;mica del delta de reflujo de la boca del R\u0026iacute;o Negro, vol 46. Revista de la Asociaci\u0026oacute;n Geol\u0026oacute;gica Argentina, pp 325\u0026ndash;332\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDissanayake P, Brown J, Yates M (2022) Morphodynamic Evolution and Sustainable Development of Coastal Systems. J Mar Sci Eng 10(5):647. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jmse10050647\u003c/span\u003e\u003cspan address=\"10.3390/jmse10050647\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarr TG, Rosen PA, Caro E, Crippen R, Duren R, Hensley S, Kobrick M, Paller M, Rodr\u0026iacute;guez E, Roth L, Seal D, Shaffer S, Shimada J, Umland J, Werner M, Oskin M, Burbank D, Alsdorf D (2007) The Shuttle Radar Topography Mission. Rev Geophys 45:1\u0026ndash;33. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1029/2005RG000183\u003c/span\u003e\u003cspan address=\"10.1029/2005RG000183\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFinotello A, D\u0026rsquo;Alpaos A, Bogoni M, Ghinassi M, Lanzoni S (2020) Remotely sensed planform morphologies reveal fluvial and tidal nature of meandering channels. Sci Rep 10:54. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-019-56992-w\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-56992-w\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eG\u0026oacute;mez JD, Poveda G (2008) Estimaci\u0026oacute;n del espectro multifractal para series de precipitaci\u0026oacute;n horaria en los Andes tropicales de Colombia. Revista de la Academia Colombiana de Ciencias Exactas. F\u0026iacute;sicas y Naturales 32(125):2317. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18257/raccefyn.32(125).2008.2317\u003c/span\u003e\u003cspan address=\"10.18257/raccefyn.32(125).2008.2317\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrasso F, Bismuth E, Verney R (2021) Unraveling the impacts of meteorological and anthropogenic changes on sediment fluxes along an estuary\u0026ndash;sea continuum. Sci Rep 11:20230. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-99502-7\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-99502-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHale SS, Cicchetti G, Deacutis CF (2016) Eutrophication and hypoxia diminish ecosystem functions of benthic communities in a New England estuary. Front Mar Sci 3:249. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fmars.2016.00249\u003c/span\u003e\u003cspan address=\"10.3389/fmars.2016.00249\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHastie BF, Smith SDA (2006) Benthic macrofaunal communities in intermittent estuaries during a drought. J Exp Mar Biol Ecol 330(2):356\u0026ndash;367. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jembe.2005.12.039\u003c/span\u003e\u003cspan address=\"10.1016/j.jembe.2005.12.039\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKantelhardt JW, Zschiegner SA, Koscielny-Bunde E, Havlin S, Bunde A, Stanley HE (2002) Multifractal detrended fluctuation analysis of nonstationary time series. Phys A 316(1\u0026ndash;4):87\u0026ndash;114\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKendall MG (1975) Rank correlation methods, 4th edn. Charles Griffin\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLargier JL (2023) Recognizing low-inflow estuaries as a common estuary paradigm. Estuaries Coasts 46(8):1949\u0026ndash;1970. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12237-023-01271-1\u003c/span\u003e\u003cspan address=\"10.1007/s12237-023-01271-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLevin LA, Mendoza GF, Neira C, Giddings SN, Crooks JA (2023) Consequences of mouth closure and hypoxia-induced state changes in low-inflow estuaries: Benthic community and trait-based response. Estuaries Coasts 46(8):2128\u0026ndash;2147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12237-022-01132-3\u003c/span\u003e\u003cspan address=\"10.1007/s12237-022-01132-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026oacute;pez Lambra\u0026ntilde;o AA (2012) An\u0026aacute;lisis multifractal y modelaci\u0026oacute;n de la precipitaci\u0026oacute;n. Tesis de doctorado, Facultad de Ingenier\u0026iacute;a, Universidad Aut\u0026oacute;noma de Quer\u0026eacute;taro, 125\u0026ndash;177. Repositorio Institucional de la UAQ. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ri-ng.uaq.mx/handle/123456789/531\u003c/span\u003e\u003cspan address=\"http://ri-ng.uaq.mx/handle/123456789/531\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMachiwal D, Moharana PC, Kumar S, Srivastava V, Bhandari SL (2021) Exploring Temporal Dynamics of Spatially - Distributed Groundwater Levels by Integrating Time Series Modeling with Geographic Information System. Geocarto Int 36(12):1325\u0026ndash;1345. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10106049.2019.1648561\u003c/span\u003e\u003cspan address=\"10.1080/10106049.2019.1648561\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMann HB (1945) Non-parametric tests against trend. Econometria 13:246\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarani M, Lanzoni S, Zandolin D, Seminara G, Rinaldo A (2002) Tidal meanders. Water Resour Res 38(11):1225. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1029/2001WR000404\u003c/span\u003e\u003cspan address=\"10.1029/2001WR000404\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcSweeney SL, Kennedy DM, Rutherfurd ID (2017) The daily-scale entrance dynamics of intermittently open/closed estuaries. Earth Surf Proc Land 43(1):187\u0026ndash;206. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/esp.4280\u003c/span\u003e\u003cspan address=\"10.1002/esp.4280\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcSweeney S, Stout JC, Kennedy DM (2020) Variability in infragravity wave processes during estuary artificial entrance openings. Earth Surf Proc Land 45(13):3244\u0026ndash;3259. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/esp.4974\u003c/span\u003e\u003cspan address=\"10.1002/esp.4974\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMazzoni E (2010) Desertificaci\u0026oacute;n en la Patagonia. Developments Earth Surf Processes 13:2\u0026ndash;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/S0928-2025(08)10017-7\u003c/span\u003e\u003cspan address=\"10.1016/S0928-2025(08)10017-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMinisterio de Ambiente y Desarrollo Sustentable (MADS) (2016) Resoluci\u0026oacute;n 206/2016: Unidad de Evaluaci\u0026oacute;n de Riesgos Ambientales - Des\u0026iacute;gnese. Publicada en el Bolet\u0026iacute;n Nacional del 24 de junio de 2016. Gobierno de la Rep\u0026uacute;blica Argentina. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.argentina.gob.ar/normativa/nacional/resoluci%C3%B3n-206-2016-262263\u003c/span\u003e\u003cspan address=\"https://www.argentina.gob.ar/normativa/nacional/resoluci%C3%B3n-206-2016-262263\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eO\u0026rsquo;Shea M, Murphy J (2020) Developing a Process Driven Morphological Model for Long Term Evolution of a Dynamic Coastal Embayment. Open J Mar Sci 10(3):93\u0026ndash;109. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.4236/OJMS.2020.103007\u003c/span\u003e\u003cspan address=\"10.4236/OJMS.2020.103007\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMontagna PA, Palmer TA, Pollack JB (2023) Effect of temporarily opening and closing the marine connection of a river estuary. Estuaries Coasts 46(8):2208\u0026ndash;2219. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12237-022-01159-6\u003c/span\u003e\u003cspan address=\"10.1007/s12237-022-01159-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorales Mart\u0026iacute;nez JL, Segovia-Dom\u0026iacute;nguez I, Quiros Rodr\u0026iacute;guez I, Horta-Rangel FA, Sosa-G\u0026oacute;mez G (2021) A modified Multifractal Detrended Fluctuation Analysis (MFDFA) approach for multifractal analysis of precipitation. Physica A 565:125611. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.physa.2020.125611\u003c/span\u003e\u003cspan address=\"10.1016/j.physa.2020.125611\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNagumo N, Egashira S (2022) Effect of water level variation on the channel width in the river mouth, 10th International Conference on Geomorphology, Coimbra, Portugal, 12\u0026ndash;16 ICG2022-362. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5194/icg2022-362\u003c/span\u003e\u003cspan address=\"10.5194/icg2022-362\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNOAA (2021) Estuarine circulation. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://oceanservice.noaa.gov/education/tutorial_estuaries/est05_circulation.html\u003c/span\u003e\u003cspan address=\"https://oceanservice.noaa.gov/education/tutorial_estuaries/est05_circulation.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNational Centers for Coastal Ocean Science (NCCOS) (2018) Causes and consequences of hypoxic events in low-inflow estuaries in Southern California. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://coastalscience.noaa.gov/project/causes-consequences-of-hypoxic-events-in-low-inflow-estuaries-southern-california/\u003c/span\u003e\u003cspan address=\"https://coastalscience.noaa.gov/project/causes-consequences-of-hypoxic-events-in-low-inflow-estuaries-southern-california/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNieto HD, \u0026Aacute;lvarez JE, Rodr\u0026iacute;guez EL (2016) An\u0026aacute;lisis de persistencia en acciones financieras en el mercado colombiano a trav\u0026eacute;s de la metodolog\u0026iacute;a de Rango Reescalado (R/S). Cuadernos Latinoamericanos de Administraci\u0026oacute;n, XII (22), 23\u0026ndash;32. ISSN: 1900\u0026ndash;5016\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOwens PN (2007) Background and summary of this issue on sediment linkages. J Soils Sediments 7(4):273\u0026ndash;276. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1065/jss2007.10.255\u003c/span\u003e\u003cspan address=\"10.1065/jss2007.10.255\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePereyra FX, Piccolo MC, Perillo GME (2014) Hydrodynamics and sediment dynamics in the Quequ\u0026eacute;n Grande estuary. Retrieved from \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://ri.conicet.gov.ar/bitstream/handle/11336/4608/CONICET_Digital_Nro.6100_A.pdf\u003c/span\u003e\u003cspan address=\"https://ri.conicet.gov.ar/bitstream/handle/11336/4608/CONICET_Digital_Nro.6100_A.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerillo GME, P\u0026eacute;rez DE, Piccolo MC, Palma E, Cuadrado DG (2005) Geomorphologic and physical characteristics of a human impacted estuary: Quequ\u0026eacute;n Grande River Estuary, Argentina. Estuarine, Coastal and Shelf Science 62, 301\u0026ndash;312\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerillo GME, Piccolo MC (1991) Tidal response in the Bah\u0026iacute;a Blanca Estuary. J Coastal Res 7(2):437\u0026ndash;449. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.jstor.org/stable/4297812\u003c/span\u003e\u003cspan address=\"https://www.jstor.org/stable/4297812\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerillo GME, Piccolo MC (1990) Physical characteristics of the Bah\u0026iacute;a Blanca Estuary. Estuarine. Coastal Shelf Sci 31(3):303\u0026ndash;317. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/0272-7714(90)90003-2\u003c/span\u003e\u003cspan address=\"10.1016/0272-7714(90)90003-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePiccolo MC, Perillo GME (1999) Physical characteristics of the R\u0026iacute;o de la Plata estuary. In: Perillo GME (ed) Estuaries of South America: Their geomorphology and dynamics. Springer-, Berlin, pp 15\u0026ndash;34. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/978-3-642-60131-6\u003c/span\u003e\u003cspan address=\"10.1007/978-3-642-60131-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePrabhakar AK, Singh KK, Lohani AK, Chandniha SK (2017) Long-term rainfall variability assessment using modified Mann-Kendall test over Champua watershed. Odisha J Agrometeorology 19(3):288\u0026ndash;289. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://journal.agrimetassociation.org/index.php/jam/article/view/676/571\u003c/span\u003e\u003cspan address=\"https://journal.agrimetassociation.org/index.php/jam/article/view/676/571\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eProhaska F (1976) The Climate of Argentina, Paraguay, and Uruguay. World Survey of Climatology, 2, 532\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodr\u0026iacute;guez Aguilar R (2012) El coeficiente de Hurst y el par\u0026aacute;metro α -estable para el an\u0026aacute;lisis de series financieras. Aplicaci\u0026oacute;n al mercado cambiario mexicano. Contadur\u0026iacute;a y Administraci\u0026oacute;n 59(1):149\u0026ndash;173\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantos da Silva H, Santos Silva JR, Stosic T (2020) Multifractal analysis of air temperature in Brazil. Physica A. 549\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.physa.2020.124333\u003c/span\u003e\u003cspan address=\"10.1016/j.physa.2020.124333\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eScharler UM, Lechman K, Radebe T, Jerling HL (2020) Effects of prolonged mouth closure in a temporarily open/closed estuary: A summary of the responses of invertebrate communities in the uMdloti Estuary, South Africa. Afr J Aquat Sci 45(1\u0026ndash;2):1\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2989/16085914.2019.1689911\u003c/span\u003e\u003cspan address=\"10.2989/16085914.2019.1689911\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShivam G, Goyal MK, Sarma AK (2019) Index-based study of future precipitation changes over Subansiri river catchment under changing climate. J Environ Inform 34(1):1\u0026ndash;14\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilva Alves KMA, Silva N\u0026oacute;brega R (2017) Tendencia pluviom\u0026eacute;trica y concentraci\u0026oacute;n estacional de precipitaci\u0026oacute;n en la cuenca hidrogr\u0026aacute;fica del r\u0026iacute;o Moxot\u0026oacute; \u0026ndash; Pernamcuco \u0026ndash; Brasil. Revista Geogr\u0026aacute;fica de Am\u0026eacute;rica Cent 58:295\u0026ndash;313 ISSN 1011\u0026thinsp;\u0026ndash;\u0026thinsp;484\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSMN (1989a) Inundaciones, sudestadas y crecientes repentinas. Bolet\u0026iacute;n Informativo n\u0026ordm; 2. 3\u0026ordf; edici\u0026oacute;n. Buenos Aires\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSHN (2021) Servicio Hidrol\u0026oacute;gico Naval. Tablas de Marea Astron\u0026oacute;mica. Fecha de consulta: 18 de octubre de 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.hidro.gov.ar/oceanografia/tmareas/RE_Mareas.asp\u003c/span\u003e\u003cspan address=\"http://www.hidro.gov.ar/oceanografia/tmareas/RE_Mareas.asp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSNIH (2021) Sistema Nacional de Informaci\u0026oacute;n H\u0026iacute;drica. Consultado: 1ro de septiembre de 2021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.argentina.gob.ar/obras-publicas/hidricas/base-de-datos-hidrologica-integrada\u003c/span\u003e\u003cspan address=\"https://www.argentina.gob.ar/obras-publicas/hidricas/base-de-datos-hidrologica-integrada\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSNIH (2024) Sistema Nacional de Informaci\u0026oacute;n H\u0026iacute;drica. Consultado: 15 de septiembre de 2024\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSSRH (2010) Atlas de Cuencas y Regiones H\u0026iacute;dricas Superficiales de la Rep\u0026uacute;blica Argentina. Subsecretar\u0026iacute;a de Recursos H\u0026iacute;dricos. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.argentina.gob.ar/obras-publicas/hidricas/cartografia-hidrica-provincial\u003c/span\u003e\u003cspan address=\"https://www.argentina.gob.ar/obras-publicas/hidricas/cartografia-hidrica-provincial\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSSRH (2015) Atlas de Cuencas y Regiones H\u0026iacute;dricas Superficiales de la Rep\u0026uacute;blica Argentina. Subsecretar\u0026iacute;a de Recursos H\u0026iacute;dricos. Cuenca r\u0026iacute;o Negro \u0026ndash; Cuenca N\u0026deg; 64. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.argentina.gob.ar/sites/default/files/64.pdf\u003c/span\u003e\u003cspan address=\"https://www.argentina.gob.ar/sites/default/files/64.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoldano FA (1947) R\u0026eacute;gimen de aprovechamiento de la red fluvial argentina. M\u0026oacute;dulo III: Regi\u0026oacute;n Patag\u0026oacute;nica. Editorial Cimera. 159\u0026ndash;219\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun H, Gu X, Zhu J, Yu Z, Zhang Y (2019) Fractal nature of groundwater level fluctuations affected by riparian zone vegetation water use and river stage variations. Sci Rep 9(1):15383. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-019-51657-0\u003c/span\u003e\u003cspan address=\"10.1038/s41598-019-51657-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTweedley JR, Dittmann SR, Whitfield AK, Withers K, Hoeksema SD, Potter IC (2019) Hypersalinity: Global distribution, causes, and present and future effects on the biota of estuaries and lagoons. In E. Wolanski, J. W. Day, M. Elliott, R. Ramachandran (Eds.), Coasts and estuaries (pp. 523\u0026ndash;546). Elsevier. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/B978-0-12-814003-1.00030-7\u003c/span\u003e\u003cspan address=\"10.1016/B978-0-12-814003-1.00030-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUSGS (2021) U.S. Geological Survey. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.usgs.gov\u003c/span\u003e\u003cspan address=\"https://www.usgs.gov\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWhitfield AK, Adams JB, Bate GC, Wooldridge TH et al (2008) A multidisciplinary study of a small temporarily open/closed South African estuary. Afr J Mar Sci 30(3):453\u0026ndash;473. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2989/AJMS.2008.30.3.2.636\u003c/span\u003e\u003cspan address=\"10.2989/AJMS.2008.30.3.2.636\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWohl E (2020) Rivers in the Landscape; John Wiley and Sons. Hoboken, NJ, USA. [Google Scholar]\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu JY, Kao HY (2007) Decadal changes of El Ni\u0026ntilde;o persistence barrier in SST and ocean heat content indices: 1958\u0026ndash;2001. Geophys Res Lett 112(D13). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1029/2006JD007654\u003c/span\u003e\u003cspan address=\"10.1029/2006JD007654\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang X, Lin P, Chen X (2022) Coastal protection by planted mangrove forest during Typhoon Mangkhut. J Mar Sci Eng 10(9):1288. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/jmse10091288\u003c/span\u003e\u003cspan address=\"10.3390/jmse10091288\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou Y, Leung Y (2010) Multifractal temporally weighted detrended fluctuation analysis and its application in the analysis of scaling behavior in temperature series. Journal of Statistical Mechanics: Theory and Experiment, 2010(06), P06021. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1088/1742-5468/2010/06/P06021\u003c/span\u003e\u003cspan address=\"10.1088/1742-5468/2010/06/P06021\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou T, Endreny T (2020) The straightening of a river meander leads to extensive losses in flow complexity and ecosystem services. Water 12(6):1680. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/w12061680\u003c/span\u003e\u003cspan address=\"10.3390/w12061680\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Consejo Nacional de Ciencia y Tecnología","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"estuarine morphodynamics, channel width variation, sandbank, satellite images, trends","lastPublishedDoi":"10.21203/rs.3.rs-8370254/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8370254/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEstuarine mouths exhibit complex morphodynamics controlled by the interplay of river discharge, tides, longshore drift, and sediment transport, which can influence navigation, ecology, and coastal management. The objective of this study is to document the spatio-temporal evolution of the Negro River mouth by examining variations in the river\u0026rsquo;s width at different points along the estuary and analyzing the movement of sediment lobes present in the mouth. To achieve this, 62 Landsat-8 images (2013\u0026ndash;2021) were analyzed, combining Mann-Kendall trend tests with multifractal metrics to assess persistence, complexity, and asymmetry along four transect sections (W1\u0026ndash;W4). Results reveal distinct morphodynamic domains: W1 shows sustained widening, high complexity, and right-skewed variability associated with river\u0026ndash;tide\u0026ndash;sediment interactions; W2 exhibits dynamic equilibrium with no net trend, minimal complexity, and left-skewed behavior; W3 resumes widening under strong tidal and longshore influence; and W4 displays pronounced narrowing, low complexity, and a regime dominated by sandbank progradation. High persistence values (H\u0026thinsp;\u0026ge;\u0026thinsp;0.85) indicate strong system inertia, although longer-term cycles may not be captured. Direct local anthropogenic impacts were negligible, but river regulation and droughts may modulate flow patterns. These findings demonstrate the spatially heterogeneous evolution of the estuary mouth, providing a quantitative framework for attributing morphodynamic processes. The methodology and results are applicable to other delta-influenced estuaries, supporting improved understanding and management of estuarine systems under fluvial, tidal, and sedimentary controls.\u003c/p\u003e","manuscriptTitle":"Non-Uniform Morphodynamics of the Negro River Estuary Mouth, Argentina (2013-2021)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-17 07:23:47","doi":"10.21203/rs.3.rs-8370254/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"de90b9b0-5e3c-4362-8814-56520af2c942","owner":[],"postedDate":"December 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59710512,"name":"Geomorphology"},{"id":59710513,"name":"Hydrology"}],"tags":[],"updatedAt":"2025-12-17T07:23:47+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-17 07:23:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8370254","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8370254","identity":"rs-8370254","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}