Hydrological Dynamics Research Articles

Tsallis q-Statistics Fingerprints in Precipitation Data across Sicily.

Precipitation patterns are critical for understanding the hydrological and climatological dynamics of any region. Sicily, the largest island in the Mediterranean sea, with its diverse topography and climatic conditions, serves as an ideal case study for analyzing precipitation data, to gain insights into regional water resources, agricultural productivity, and climate change impacts. This paper employs advanced statistical physics methods, particularly Tsallis q-statistics, to analyze sub-hourly precipitation data from 2002 to 2023, provided by the Sicilian Agrometeorological Informative System (SIAS). We investigate several critical variables related to rainfall events, including duration, depth, maximum record, and inter-event time. The study spans two decades (2002-2012 and 2013-2023), analyzing the distributions of relevant variables. Additionally, we examine the simple returns of these variables to identify significant temporal changes, fitting these returns with q-Gaussian distributions. Our findings reveal the scale-invariant nature of precipitation events, the presence of long-range interactions, and memory effects, characteristic of complex environmental processes.

Assessing current and future available resources to supply urban water demands using a high-resolution SWAT model coupled with recurrent neural networks and validated through the SIMPA model in karstic Mediterranean environments

Hydrological simulation in karstic areas is a hard task due to the intrinsic intricacy of these environments and the common lack of data related to their geometry. Hydrological dynamics of karstic sites in Mediterranean semiarid regions are difficult to be modelled mathematically owing to the existence of short wet episodes and long dry periods. In this paper, the suitability of an open-source SWAT method was checked to estimate the comportment of a karstic catchment in a Mediterranean semiarid domain (southeast of Spain), which wet and dry periods were evaluated using box-whisker plots and self-developed wavelet test. A novel expression of the Nash–Sutcliffe index for arid areas (ANSE) was considered through the calibration and validation of SWAT. Both steps were completed with 20- and 10-year discharge records of stream (1996–2015 to calibrate the model as this period depicts minimum gaps and 1985–1995 to validate it). Further, SWAT assessments were made with records of groundwater discharge and relating SWAT outputs with the SIMPA method, the Spain’s national hydrological tool. These methods, along with recurrent neural network algorithms, were utilised to examine current and predicted water resources available to supply urban demands considering also groundwater abstractions from aquifers and the related exploitation index. According to the results, SWAT achieved a “very good” statistical performance (with ANSE of 0.96 and 0.78 in calibration and validation). Spatial distributions of the main hydrological processes, as surface runoff, evapotranspiration and aquifer recharge, were studied with SWAT and SIMPA obtaining similar results over the period with registers (1980–2016). During this period, the decreasing trend of rainfalls, characterised by short wet periods and long dry periods, has generated a progressive reduction of groundwater recharge. According to algorithms prediction (until 2050), this declining trend will continue reducing groundwater available to meet urban demands and increasing the exploitation index of aquifers. These results offer valuable information to authorities for assessing water accessibility and to provide water demands in karstic areas.

Optimization of Manning's roughness coefficient using 1-dimensional hydrodynamic modelling in the perennial river system: A case of lower Narmada Basin, India.

This research bears significant implications for river management, flood forecasting, and ecosystem preservation in the Lower Narmada Basin. A more precise estimation of Manning's Roughness Coefficeint (n) will enhance the accuracy of hydraulic models and facilitate informed decision-making regarding flood risk management, water resource allocation, and environmental conservation efforts. Ultimately, this study aspires to contribute to the sustainable management of perennial river systems in India and beyond by offering a robust methodology for optimizing Manning's n tailored to the complex hydrological dynamics of the Lower Narmada Basin. Through a synthesis of empirical evidence and computational modelling, it seeks to empower stakeholders with actionable insights toward preserving and enhancing these invaluable natural resources. Using the new HEC-RAS v 6.0, a one-dimensional hydrodynamic model was developed to predict overbank discharge at different points along the basin. The study analyzes water levels, stream discharges, and river stage, optimizing Manning's n and required flood risk management. The model predicted a strong output agreement with R2, NSE, and RMSE for the 2020 eventas 0.83, 0.81, and 0.36, respectively, with an optimum Manning's n of 0.03. The lower Narmada Basin part near the coastal zone (validation point) appears inundated frequently. The paper aims to provide insights into optimizing Manning's coefficient, which can ultimately lead to better water flow predictions and more efficient water management in the region.

Comprehensive Hydrological Analysis of the Buha River Watershed with High-Resolution SHUD Modeling

This study utilizes the Simulator of Hydrologic Unstructured Domains (SHUD) model and the China Meteorological Forces Dataset (CMFD) to investigate the hydrological dynamics of the Buha River watershed, a critical tributary of Qinghai Lake, from 1979 to 2018. By integrating high-resolution terrestrial and meteorological data, the SHUD model simulates streamflow variations and other hydrological characteristics, providing valuable insights into the region’s water balance and runoff processes. Key findings reveal a consistent upward trend in precipitation and temperature over the past four decades, despite minor deviations in daily precipitation intensity and relative humidity data. The SHUD model demonstrates high accuracy on a monthly scale, with Nash–Sutcliffe Efficiency (NSE) values of 0.72 for the calibration phase and 0.61 for the validation phase. The corresponding Kling–Gupta Efficiency (KGE) values are 0.73 and 0.49, respectively, underscoring the model’s applicability for hydrological forecasting and water resource management. Notably, the annual runoff ratios for the Buha River fluctuate annually between 0.11 and 0.21, with significant changes around 2007 correlating with a shift in Qinghai Lake’s water levels. The analysis of water balance indicates a net leakage over long-term periods, with spatial alterations in leakage and replenishment along the river. Furthermore, snow accumulation, which increases with altitude, significantly contributes to streamflow during the melting season. Despite the Buha River basin’s importance, research on its hydrology remains limited due to data scarcity and minimal human activity. This study enhances the understanding of the Buha River’s hydrological processes and highlights the necessity for improved dataset accuracy and model parameter optimization in future research.

When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling

Abstract. Recent advances in differentiable modeling, a genre of physics-informed machine learning that trains neural networks (NNs) together with process-based equations, have shown promise in enhancing hydrological models' accuracy, interpretability, and knowledge-discovery potential. Current differentiable models are efficient for NN-based parameter regionalization, but the simple explicit numerical schemes paired with sequential calculations (operator splitting) can incur numerical errors whose impacts on models' representation power and learned parameters are not clear. Implicit schemes, however, cannot rely on automatic differentiation to calculate gradients due to potential issues of gradient vanishing and memory demand. Here we propose a “discretize-then-optimize” adjoint method to enable differentiable implicit numerical schemes for the first time for large-scale hydrological modeling. The adjoint model demonstrates comprehensively improved performance, with Kling–Gupta efficiency coefficients, peak-flow and low-flow metrics, and evapotranspiration that moderately surpass the already-competitive explicit model. Therefore, the previous sequential-calculation approach had a detrimental impact on the model's ability to represent hydrological dynamics. Furthermore, with a structural update that describes capillary rise, the adjoint model can better describe baseflow in arid regions and also produce low flows that outperform even pure machine learning methods such as long short-term memory networks. The adjoint model rectified some parameter distortions but did not alter spatial parameter distributions, demonstrating the robustness of regionalized parameterization. Despite higher computational expenses and modest improvements, the adjoint model's success removes the barrier for complex implicit schemes to enrich differentiable modeling in hydrology.

Soil hydro-physical variables and crop residues determinate runoff, soil loss, and glyphosate and AMPA concentration in the aqueous phase under simulated rainfall events.

Soil structural degradation and water erosion processes were observed even in no-tillage schemes in the Pampas region. Within these conservation systems, agrochemical application per hectare is one of the highest globally. Thus, this entails a serious risk of water contamination. The objectives of this study were to (1) test the hypothesis that the hydrological dynamics and sediment concentration related to surface runoff were conditioned by soil structure regardless of the presence of maize (Zea mays L.) crop residue and (2) assess the incidence of maize crop residue on glyphosate and aminomethylphosphonic acid (AMPA) concentration in runoff. The soil under study corresponded to Arroyo Dulce Series (Typic Argiudoll silty loam soil). Rain simulations were performed in the laboratory on undisturbed soil samples. Total runoff and infiltration rate were similar between treatments with C(+) and without C(-) maize crop residues (C(+) 1381.40mL and 14.27mm h-1, C(-): 1529.70mL and 21.67mm h-1). The C(-) treatments showed a higher sediment concentration than C(+) (1.58 and 0.42g 100mL-1, respectively). Glyphosate and AMPA average values in runoff were 15.9 and 33.9µg L-1. High variability of the hydro-physical properties and occurrence of soil structure, particularly platy ones, were detected. The hydrological variables were conditioned mainly by the occurrence of platy structures regardless of crop residue presence. Glyphosate concentration was increased in the first runoff event by the presence of corn residues, while AMPA concentrations were higher in the second runoff event in both residue treatments. In this study, maize residue on the soil surface protected the soil from sediment detachment but did not change runoff or infiltration. Thus, the implementation of agricultural management practices that promote vegetative residue cover has shown positive results to erosion.

A new repository of electrical resistivity tomography and ground-penetrating radar data from summer 2022 near Ny-Ålesund, Svalbard

Abstract. We present the geophysical data set acquired in summer 2022 close to Ny-Ålesund (western Svalbard, Brøggerhalvøya Peninsula, Norway) as part of the project ICEtoFLUX. The aim of the investigation is to characterize the role of groundwater flow through the active layer as well as through and/or below the permafrost. The data set is composed of electrical resistivity tomography (ERT) and ground-penetrating radar (GPR) surveys, which are well-known geophysical techniques for the characterization of glacial and hydrological processes and features. Overall, 18 ERT profiles and 10 GPR lines were acquired, for a total surveyed length of 9.3 km. The data have been organized in a consistent repository that includes both raw and processed (filtered) data. Some representative examples of 2D models of the subsurface are provided, that is, 2D sections of electrical resistivity (from ERT) and 2D radargrams (from GPR). The resistivity models revealed deep resistive structures, probably related to the heterogeneous permafrost, which are often interrupted by electrically conductive regions that may relate to aquifers and/or faults. The interpretation of these data can support the identification of the active layer, the occurrence of spatial variation in soil conditions at depth, and the presence of groundwater flow through the permafrost. To a large extent, the data set can provide new insight into the hydrological dynamics and polar and climate change studies of the Ny-Ålesund area. The data set is of major relevance because there are few geophysical data published about the Ny-Ålesund area. Moreover, these geophysical data can foster multidisciplinary scientific collaborations in the fields of hydrology, glaciology, climate, geology, and geomorphology, etc. The geophysical data are provided in a free repository and can be accessed at https://doi.org/10.5281/zenodo.10260056 (Pace et al., 2023).

Integrating real-time sensor data for improved hydrogeotechnical modelling in landslide early warning in Western Himalaya

This study presents a comprehensive investigation into developing a real-time monitoring framework for a localized landslide early warning system (LEWS), focusing on the hydrological dynamics of an unsaturated slope near the Joshimath Badrinath Highway in Uttarakhand, India. Considering the steepness of the slope and its adjacency to the highway, continuous monitoring and stability assessment are crucial. The framework encompasses primary phases of monitoring, modelling, forecasting, and warning. Monitoring collected hydrological and meteorological data, used for modelling and calibration. Validation using Taylor diagrams ensured accuracy by comparing predicted and monitored data. The calibrated hydrological model guided slope stability modelling to identify instability factors. A machine learning algorithm detected potential instability. Forecasting predicted unstable periods, triggering warnings if the factor of safety (Fs) values drop below 1.5. The study provided important insights into slope stability, highlighting the significance of vegetation parameters in accurately assessing slope stability using the Fs. Calibration of the hydrogeological model, particularly considering rainfall, climate, and vegetation data, improved the alignment between predicted and observed volumetric water content (VWC), especially in shallower depths. However, modelling hydrological dynamics at greater depths remains challenging, emphasizing the need for refined approaches. Machine learning techniques, specifically a Random Forest model, achieve high accuracy in predicting Fs, identifying VWC at 0.3-m and 3-m depths as pivotal variables. Temporal evaluation suggests a 12-month simulation as optimal, showing consistent performance across depths. Overall, the study advances slope stability modelling and offers insights into sustainable slope management practices, highlighting the complex interplay between climate, vegetation, and hydrological dynamics in unsaturated slopes. This approach lays a foundation for effective LEWS in Indian Himalayan states.

Effects of Conversion of Rubber to Oil Palm Plantations on Soil Properties and Hydrological Dynamics in the Low Country Wet Zone of Sri Lanka

A study was conducted to investigate the impacts of converting rubber plantations into oil palm plantations on soil properties and soil hydrology. Soil organic carbon (SOC), bulk density (BD), aggregate stability (AS), saturated hydraulic conductivity (Ks), soil water retention, texture, thermal properties, and pH were determined using soil samples collected from different depths of a twelve-year-old oil palm and rubber cultivated fields located in low country wet zone of Sri Lanka. In each field, volumetric water content (VWC) of soil was continuously measured at four soil depths (0-25, 25-50, 50-75, and 75-100 cm) over a seven-month period. While the study revealed a 40% lower SOC in 0-25 cm soil layer of the oil palm field compared to the rubber field, no significant changes were observed in BD, porosity, pore size distribution, AS, and Ks for the two fields. However, the volumetric heat capacity of rubber grown soil was significantly higher than that of the oil palm grown soil. Oil palm utilized the most water from 25-75 cm soil layer; whereas, rubber extracted more water from deeper soil layers (75-100 cm). Soil water depletion in oil palm field was faster during dry periods than in rubber fields highlighting the need to examine the soil water extraction patterns of oil palm during extended dry spells in future studies. Overall, the conversion of rubber into oil palm plantations showed no significant impact on most of the soil properties and soil hydrology after twelve years of conversion.

Distribution, sources, and burial flux of black carbon in sediment of Daye lake, central China

Black carbon (BC), primarily originating from fossil fuel and biomass combustion, holds significance for global carbon cycling, climate change, and human health. Despite a lake's role as a carbon sink, detailed information about BC sedimentary burial flux and sink in its sediment remains insufficiently explored. The current study investigates the distribution, sources, and burial flux of BC and its subtypes (char and soot) in the surface sediment of Daye Lake, the largest lake in Huangshi City, central China. BC concentrations in the sediment ranged from 0.10 to 3.60 mg/g, corresponding to 0.40%–17.02% of organic carbon (OC). Higher values of BC and BC/OC observed in the western region suggest direct terrestrial input via river discharge and surface runoff, influenced by anthropogenic activities. In contrast, variations in char/soot ratios reflect diverse combustion sources and hydrological dynamics in different regions. The indications from BC/OC and char/soot ratios imply that fossil fuel combustion is the predominant sources. The weak correlations between BC and OC suggest that they may come from different sources or undergo different processes that affect their distribution in the lake sediment. However, a stronger correlation was observed between BC and soot, as well as between char and soot, indicating potential similarities in their input pathways. The BC burial flux displays notable variations across the lake, ranging from 0.69 to 24.07 g/m2/yr, with elevated values observed in the western region. The BC sink in the sediments of Daye Lake was estimated to be 0.635 Gg/yr. Though locally small, it significantly contributes to the broader picture of BC burial in Chinese lakes and the global distribution of BC in lake ecosystems.

Fish and macroinvertebrates respond differently to seasonal drying in tropical non‐perennial streams

AbstractSurface water drying challenges the persistence of lotic biological assemblages in non‐perennial streams and rivers. However, the effects of natural hydrological disturbances on an assemblage depend on individual species characteristics. In this study, we investigated the structure and association of fish and macroinvertebrate assemblages during two phases in non‐perennial streams (flowing vs. disconnected pools). We sampled fish and macroinvertebrate assemblages in five non‐perennial stream reaches located in the Caatinga (Brazilian semi‐arid region). Fish species were resistant to hydrological dynamics in the streams. On the other hand, some macroinvertebrate taxa from the flowing phase disappeared, and new taxa colonized the disconnected pools. The absence of lotic insects following flow cessation and the colonization of disconnected pools by lentic taxa facilitated macroinvertebrate assemblages persistence in these dynamic streams. Our study showed that fish and macroinvertebrate assemblages respond differently to the same natural flow cessation, which leads us to predict that flow changes will yield different assemblages, depending on the taxa. Thus, it is crucial to consider multi‐assemblage responses to effectively manage and conserve non‐perennial stream ecosystems in a tropical semi‐arid region.

A dataset of surface water area with 12-day resolution in Lijiang River Basin from 2015 to 2022

Widely distributed in Guangxi, Karst landforms are characterized by heavy rainfall during the flood season, rapid hydrological dynamics, and notable spatiotemporal variations. A water is an important ecological foundation and natural resource, the timely and accurate acquisition of spatiotemporal changes in surface water bodies holds practical significance for the protection and management of water resources, rapid assessment of drought and flood disasters, and the realization of sustainable development across various economic industries. This dataset takes the Lijiang River Basin in Guilin, Guangxi as the research area. Addressing prevalent challenges such as infrequenct surface water monitoring, limited water body extraction methods due to sparse sample information, and low extraction accuracy in complex terrain areas, we combined the data from Sentinel-1 radar and Sentinel-2 in the study. Considering the different imaging principles of radar and optical sensors, we developed a vector classification method based on empirical thresholds for optical and other multi-source remote sensing data. The method can effectively eliminate interference of most ground object shadows, allowing for the construction of dataset of surface water area with 12-day resolution in Lijiang River Basin from 2015 to 2022. The dataset comprises a total of 196 periods of surface water body vector data. According to the results after verifying the spatial distribution and quantitative accuracy of the dataset, the dataset demonstrates its capability to accurately extract inlets and outlets of rivers, lakes and reservoirs, as well as small water bodies in complex terrain areas. The overall accuracy reaches 92.73%,with a Kappa coefficient of 0.85. This dataset can be used to support water resources protection and sustainable management of the ecological environment in the Lijiang River Basin. Additionally, it can provide reliable data for decision-making in areas such as emergency management, disaster prevention, water conservancy development, and economic development.

Geophysical Investigation of Bulding Distress in Gashua, Nigeria

Gashua is the Headquarter of Bade local government in Yobe State, it has a population of about 125, 000 according to the 2006 population census. It is a densely populated town located in a flood plain within the Chad Basin. In an attempt to proffer solutions to the prevailing building distress in Gashua and its environs, this study was carried out to accurately characterize the subsurface geology of the area, thereby reducing the incidence of building failure in the area to minimum. In this study, electrical resistivity method was used to determine the causes of building distress and failure in Gashua and its environs. Vertical Electrical Sounding (VES) using Schlumberger array and Electrical Resistivity Tomography (ERT) were adopted for the study. The results of the VES survey showed that the area is composed of five geologic layers which are; the topsoil, clay, sand, sandy-clay, and sand. The ERT results showed that most of the building foundations in the study area were laid within the second layer which is a clay formation. The findings of this study showed that the building distress and failure which are common in the study area were caused by the volumetric changes of the clay formation due to hydrological dynamics. The clay formation absorbs water during the raining season and swells up, and during the dry season it shrinks, this changes in the volume of the clay formation underlying the building foundation in the study area induces a significant mechanical stress on the buildings leading to the formation of cracks, voids, fissures and fracturing of the buildings, which eventually culminate into collapse. In this study, electrical resistivity method of geophysical investigation has demonstrated to be a veritable tool for geotechnical soil evaluation. Based on the findings of this study, geophysical investigation of a proposed site...

Hydrologic connectivity and dynamics of solute transport in a mountain stream: Insights from a long-term tracer test and multiscale transport modeling informed by machine learning

The movement of solutes in a watershed is a complex process with multiple interactions and feedbacks across spatial and temporal scales. Modeling the dynamics of solute transport along diverse hydrologic pathways within watersheds – from hillslopes to stream channels and in and out of the hyporheic zones – is challenging but critically important, as these processes integrate and contribute to the biogeochemical functioning of the river corridor up to the river network scale. Here we use results from a long-term network-scale tracer test at the H.J. Andrews experimental forest in western Cascade Mountains, Oregon, USA to inform a multiscale framework for transport in stream corridors. The framework uses a Lagrangian-based subgrid model to represent the effects of hyporheic exchange flow and advective transport at stream network scales. The spatially and temporally resolved stream discharge needed for the transport model is imputed across the river system by an entity-aware long short-term memory network. Modeled concentrations show good agreements with the observations and exhibit power scaling laws indicative of a very wide range of timescales over which hyporheic exchange flow occurs. Our results demonstrate a data-informed modeling framework that links dynamical processes occurring at small scales to a network context to help understand how changes at reach scale cascade into network-scale effects, providing a useful tool for sustainable river basin management.

The post-IR IRSL dating of an ancient dammed lake upstream of the Ganglai gorge in the upper-middle reaches of Yarlung Tsangpo

The middle reaches of Yarlung Tsangpo, situated on the southern Tibetan Plateau, have experienced extensive river damming events due to glacier advances, resulting in the formation of several ancient dammed lakes. These palaeolakes offer invaluable insights into past hydrological and geomorphological dynamics on the Tibetan Plateau. In this study, we reported a newly identified ancient dammed lake, named the Xiaru palaeolake, located upstream of the Ganglai Gorge in the uppermost segment of the middle reaches of Yarlung Tsangpo, and conducted a pioneer chronological investigation of the lake using K-feldspar post-IR IRSL (pIRIR) dating. Quartz minerals from most lacustrine and fluvial sediments can not provide reliable ages characterized with low intensity and large inter-aliquot scatter in equivalent doses (De). In contrast, the K-feldspar exhibited much greater luminescence intensity and narrower De distribution using the pIRIR225 dating protocol. Residual, dose recovery, and fading tests demonstrated that the pIRIR225 signals are well bleached with a relatively low fading rate, making it a more suitable dosimeter for dating applications in the study area. The pIRIR225 ages, derived through single aliquots, standard growth curve (SGC), and single grain measurements, reveal a major phase of the Xiaru Lake between 24.1 ± 1.0 ka and 15.8 ± 1.1 ka, that commenced aligning with the global Last Glacial Maximum. Furthermore, one lacustrine sample dated to 29.9 ± 2.2 ka suggests the possibility of multiple damming events in Ganglai Gorge during the last glacial period, although further evidence is required for a comprehensive interpretation.

Fluvial Dynamics and Hydrological Variability in the Chiriquí Viejo River Basin, Panama: An Assessment of Hydro-Social Sustainability through Advanced Hydrometric Indexes

The objective of this study was to conduct a detailed analysis of the available flow series in the Chiriquí Viejo River basin in Panama. This paper examines the patterns of variation within these series and calculates various hydrological indexes indicative of the region’s hydrology. Utilizing advanced hydrological indexes within the Chiriquí Viejo River basin in Panama, which spans an area of 1376 km2 and supports an estimated population of 100,000 inhabitants, analytical methods were employed to compute indexes such as the Daily Flow Variation Index (QVAR), the Slope of the Flow Duration Curve (R2FDC), the Hydrological Regulation Index (IRH), and the average duration of low (DLQ75) and high (DHQ25) flow pulses. The results indicate moderate flow variability (QVAR of 0.72) and a Hydrological Regulation Index (IRH) of 2.32, signifying a moderate capacity for flow regulation. Notably, low flow events (DLQ75) lasted approximately 3.73 days, while high flow events (DHQ25) lasted around 4.08 days. The study highlights a significant capacity to respond to extreme events, with maximum annual flows reaching 80.25 m3/s and minimum flows dropping to 3.01 m3/s. Despite the significant contribution of the basin to hydroelectric power generation and other economic activities, there is an observed need for sustainable management that accommodates hydrological fluctuations and promotes resource conservation. The conclusions indicate that these findings are critical for future planning and conservation strategies in the region, emphasizing the importance of integrating multidisciplinary approaches for Hydro-Social Sustainability. This novel and holistic approach underscores the interdependence between hydrological dynamics, socio-economic activities, and environmental sustainability, aiming to ensure the long-term resilience of the Chiriquí Viejo basin and its communities.

Precise mapping of coastal wetlands using time-series remote sensing images and deep learning model

Mapping coastal wetlands' spatial distribution and spatiotemporal dynamics is crucial for ecological conservation and restoration efforts. However, the high hydrological dynamics and steep environmental gradients pose challenges for precise mapping. This study developed a new method for mapping coastal wetlands using time-series remote sensing images and a deep learning model. Precise mapping and change analysis were conducted in the Liaohe Estuary Reserve in 2017 and 2022. The results demonstrated the superiority of Temporal Optimize Features (TOFs) in feature importance and classification accuracy. Incorporating TOFs into the ResNet model effectively combined temporal and spatial information, enhancing coastal wetland mapping accuracy. Comparative analysis revealed ecological restoration trends, emphasizing artificial restoration's predominant role in salt marsh vegetation rehabilitation. These findings provide essential technical support for coastal wetland ecosystem monitoring and contribute to the study of sustainability under global climate change.

Revealing High‐Temporal‐Resolution Flood Evolution With Low Latency Using GRACE Follow‐On Ranging Data

AbstractAn emerging approach is utilizing the line‐of‐sight gravity difference (LGD) between the twin Gravity Recovery and Climate Experiment Follow‐On (GFO) satellites to refine the temporal resolution of water storage estimates from 1 month to days, thus making the data applicable to transient extreme climate events like floods. However, applying the approach to medium‐scale climate events (with mass changes of several tens of gigatons) is challenging due to surrounding signal contamination and low signal‐to‐noise ratios. To address this problem, this study develops an improved algorithm accounting for peripheral signal sources and temporal correlations in mass variation. Two floods in July 2021 in Western Europe and Central China (CC) are chosen as case studies to demonstrate our approach's applicability to moderate floods in complex hydrological settings. The results present the temporal progression of the floods up to a maximum of ∼40 Gt with a scale of 3–5 days. However, the GFO‐derived water gain in CC is much lower than expected values from land surface models, indicating a mass deficit during the flood. We find that the potent manipulation of water resources by human activities might impact the predictive capabilities of these models, thereby misrepresenting the hydrological evolution during the flood event. This study refines the viability of applying GFO data to restore transient dynamics characterizing extreme climate events of ∼20 Gt magnitude. We also provide insights on the use of LGD data for high‐temporal‐resolution estimation of water storage changes and underscore the non‐negligible influence of human interventions on short‐term hydrological dynamics.

Hydrological dynamics and manganese mineralization in the wake of the Sturtian glaciation

The Cryogenian Sturtian and Marinoan glaciations stand as the most extreme climate events in Earth history. Intriguingly, large-scale Mn carbonates characterize the nonglacial interlude of this period in South China. The formation mechanism of Mn carbonates and the potential correlation underlying this temporal association remain elusive. Here, we present an integrated petrographic and geochemical study of drill core materials intercepting the Mn-bearing Datangpo Formation. Rare earth element patterns of these Mn carbonates exhibit positive Ce anomalies and a lack of Y anomalies, which differ from those of modern seawater but resemble marine Mn-Fe oxides. These features, combined with negative carbonate carbon isotope compositions (δ13Ccarb as light as −9.9 ‰ VPDB), indicate the presence of oxide precursors and the incorporation of light carbon during Mn-carbonate precipitation. Phosphate oxygen isotope ratios (δ18Op) of HCl-extractable apatite reveal a ∼4 ‰ difference in the average δ18Op between Mn-rich and Mn-poor rocks. We propose that the more positive δ18Op of Mn-rich samples may result from the dominance of phosphate released by reductive dissolution of metal oxides or a heavier oxygen isotope signal of surrounding waters with which isotopic equilibrium has been reached. We further illustrate how marine invasions into coastal anoxic basins could have triggered the formation of Mn oxides and highlight the role of glaciation–deglaciation in the development of giant Mn deposits.

Groundwater chemical evolution characteristics and human health risk assessment in Shicheng County, Jiangxi Province.

Groundwater plays a pivotal role in the water resources of Shicheng County; however, the issue of excessive fluoride content in groundwater and its associated health risks often goes unnoticed. Groundwater assumes a crucial role in the hydrological dynamics of Shicheng County; nevertheless, the matter concerning elevated levels of fluoride within groundwater and its accompanying health hazards frequently evades attention. The hydrogeochemical analysis, obscure comprehensive water quality assessment based on cloud model, and probabilistic human health risk assessment using Monte Carlo simulation were conducted on 34 collected water samples. The findings indicate that the predominant groundwater hydrochemical types are SO4·Cl-Na and HCO3-Na. The processes of rock weathering and cation exchange play crucial roles in influencing water chemistry. Groundwater samples generally exhibit elevated concentrations of F-, surpassing the drinking water standard, primarily attributed to mineral dissolution. The concentrations of F- in more than 52.94% and 23.53% of the groundwater samples exceeded the acceptable non-carcinogenic risk limits for children and adults, respectively. Considering the inherent uncertainty in model parameters, it is anticipated that both children and adults will have a probability exceeding 49.36% and 30.50%, respectively, of being exposed to elevated levels of F ions in groundwater. The utilization of stochastic simulations, in contrast to deterministic methods, enables a more precise depiction of health risks. The outcomes derived from this investigation possess the potential to assist policymakers in formulating strategies aimed at ensuring the provision of secure domestic water supplies.