Large Drainage Area Research Articles

Distribution of flow characteristics and productivity evaluation of herringbone wells in bottom water reservoirs

Abstract With the characteristics of large drainage area and low drilling cost, the herringbone wells have become an important way to increase the well production, delay the coning and ridge of bottom water and improve the development effect. Due to the complexity of the herringbone wells flow characteristics, results that exists certain gap between the current production prediction and the actual production, so it is of great significance to study the flow and productivity of herringbone wells. In this paper, taking bottom-water reservoir as the research object, considering the interference between branch wellbore and perforations, analysis the flow characteristics and the productivity sensitivity factors of herringbone wells. The results show that the transient flow time in the reservoir is short, the closer to the wellbore, the greater the pressure changes. With the increases of the branches number, branches length and branch angle, the per unit length of the wellbore radial inflow decreases, and the decrease slows down after exceeding 3 branches. In the high anisotropy reservoirs, the phase angle has greater impact on the productivity of the herringbone well when the perforation density is low, at 180°, phase angle is best; when the perforation density exceeds 16 shots/m, the increase of productivity degree decreases. This study provides important guiding significance for the actual application in oil fields to optimizing the herringbone wells shape, rational production allocation, specifying reasonable work systems and enhance oil recovery in the bottom water reservoirs.

Quantitative fecal pollution assessment with bacterial, viral, and molecular methods in small stream tributaries

Stream water quality can be impacted by a myriad of fecal pollution sources and waste management practices. Identifying origins of fecal contamination can be challenging, especially in high order streams where water samples are influenced by pollution from large drainage areas. Strategic monitoring of tributaries can be an effective strategy to identify conditions that influence local water quality. Water quality is assessed using fecal indicator bacteria (FIB); however, FIB cannot differentiate sources of fecal contamination nor indicate the presence of disease-causing viruses. Under different land use scenarios, three small stream catchments were investigated under ‘wet’ and ‘dry’ conditions (Scenario 1: heavy residential; Scenario 2: rural residential; and Scenario 3: undeveloped/agricultural). To identify fecal pollution trends, host-associated genetic targets HF183/BacR287 (human), Rum2Bac (ruminant), GFD (avian), and DG3 (canine) were analyzed along with FIB (Escherichia coli and enterococci), viral indicators (somatic and F+ coliphage), six general water quality parameters, and local rainfall. Levels of E. coli exceeded single sample maximum limits (235 CFU/100 mL) in 70.7 % of samples, enterococci (70 CFU/100 mL) in 100 % of samples, and somatic coliphage exceeded advisory thresholds (600 PFU/L) in 34.1 % of samples. The detection frequency for the human-associated genetic marker was highest in Scenario 3 (50 % of samples) followed by Scenario 2 (46 %), while the ruminant-associated marker was most prevalent in Scenario 1 (64 %). Due to the high proportion of qPCR-based measurements below the limit of quantification, a Bayesian data analysis approach was applied to investigate links between host-associated genetic marker occurrence with that of rainfall and fecal indicator levels. Multiple trends associated with small stream monitoring were revealed, emphasizing the role of rainfall, the utility of fecal source information to improve water quality management. And furthermore, water quality monitoring with bacterial or viral methodologies can alter the interpretation of fecal pollution sources in impaired waters.

The Application of Remote Sensing Technology in Post-Disaster Emergency Investigations of Debris Flows: A Case Study of the Shuimo Catchment in the Bailong River, China

The Bailongjiang River Basin is a high-risk area for debris flow in China. On 17 August 2020, a debris flow occurred in the Shuimo catchment, Wen County, which blocked the Baishui River, forming a barrier lake and causing significant casualties and property damage. In this study, remote sensing, InSAR, field surveys, and unmanned aerial vehicle (UAV) techniques were used to analyze the causal characteristics, material source characteristics, dynamic processes, and disaster characteristics after the debris flow. The results showed that the Shuimo catchment belongs to low-frequency debris flows, with a recurrence cycle of more than 100 years and concealed features. High vegetation coverage (72%) and a long main channel (11.49 km) increase the rainfall-triggering conditions for debris flow occurrence, making it more hidden and less noticed. The Shuimo catchment has a large drainage area of 31.26 km2, 15 tributaries, significant elevation differences of 2017 m, and favorable hydraulic conditions for debris flow. The main sources of debris flow material supply are channel erosion and slope erosion, which account for 84.4% of the total material. The collapse of landslides blocking both sides of the main channel resulted in an amplification of the debris flow scale, leading to the blockage of the Baishui River. The scale of the accumulation fan is 28 × 104 m3, and the barrier lake area is 37.4 × 104 m2. The formation mechanism can be summarized as follows: rainfall triggering → shallow landslides → slope debris flow → channel erosion → landslide damming → dam failure and increased discharge → deposition and river blockage. The results of this study provide references for remote sensing emergency investigation and analysis of similar low-frequency and concealed debris flows, as well as a scientific basis for local disaster prevention and reduction.

Anthropogenic gadolinium in the Tone River (Japan): an update showing a 7.7-fold increase from 1996 to 2020

BackgroundAnthropogenic gadolinium (Gd), originating from Gd-based contrast agents (GBCAs) used in magnetic resonance imaging (MRI), is widely identified in the aquatic environment with concerns about toxicity and accumulation. We aimed to present new data on anthropogenic Gd in the Tone River, which has the largest drainage area in Japan, and then to compare the current data with those obtained in 1996.MethodsThe water samples were collected on August 9−10, 2020, at 15 different locations of the Tone River in Japan. The concentrations of the rare earth elements (REEs) were measured by inductively coupled plasma-mass spectrometry and normalized to Post-Archean Australian Shale to construct shale-normalized REE patterns. The degree of Gd-anomaly was defined as the percentage of anthropogenic Gd to the geogenic background and used to compare the water samples from different locations. Pearson’s correlation coefficients were calculated.ResultsAll the samples displayed positive Gd anomalies. The Gd-anomaly ranged from 121 to 6,545% and displayed a repeating decrease-and-increase trend. The Gd-anomaly showed strong positive correlations to the number of hospitals (r = 0.88; p < 0.001) and their MRI units (r = 0.89; p < 0.001).ConclusionsOur study revealed notable anomalies of Gd concentrations in river water in Japan, with strong positive correlations to the number of major hospitals and their MRI units. Compared with the previous report in 2000, the Gd-anomaly in Tone River increased from 851% (sampled in 1996) to 6,545%, i.e., 7.7 times, reflecting the increased use of GBCAs in hospitals.Relevance statementNotable Gd concentration anomalies in river water in Japan were observed. This result underlines the importance of more extensive research on anthropogenic gadolinium, and investigations of risks to human health as well as the development of effective removal technologies may be necessary.Key points• All water samples from Tone River displayed positive Gd anomalies.• The Gd anomalies increased to 7.7 times higher over the past 24 years.• Correlations between Gd values and the number of hospitals and MRI units were observed.Graphical

GIS-based methodology for culvert location evaluation on railways: a case study of the Qena–Safaga Track in Egypt

ABSTRACT Railway infrastructure plays a critical role in transportation networks, and ensuring its integrity and resilience is of utmost importance. Culverts are vital components of railway tracks, providing drainage and water management to prevent structural damage and disruptions. Identifying suitable locations for culverts requires careful evaluation and consideration of various factors. A study using GIS techniques was conducted on an existing commercial railway track in the Eastern Desert of Upper Egypt to assess the effectiveness of existing culverts in preventing flash floods. The culvert suitability index map revealed that areas with high slopes and large drainage areas were more susceptible to water accumulation, indicating the need for culverts. The study also highlighted areas where culverts could be installed without significantly impacting existing infrastructure. It was recommended to install culverts in 27 locations along the track where they intersect with watercourses. Existing culverts covered just 93 watercourses, while 5 specialized culverts needed to be relocated. The findings have significant implications for railway engineering, as using GIS techniques streamlines the process of culvert location evaluation, saving time and resources. The systematic approach ensures culverts are installed in the most appropriate locations, minimizing flooding risks and ensuring the safety and efficiency of railway operations.

Hydrologic interpretation of machine learning models for 10-daily streamflow simulation in climate sensitive upper Indus catchments

Machine learning for hydrologic modeling has seen significant recent development and has been suggested as a valuable augmentation to physical hydrological modeling, especially in data-scarce catchments. In Pakistan, surface water flows predominantly originate from the transboundary Upper Indus sub-catchments of Chenab, Jhelum, Indus, and Kabul rivers. These catchments have large drainage areas, climate-driven streamflows, high variations in elevation, and limited streamflow gauge coverage. Hence, using machine learning models for data-driven river flow modeling may be well-suited for these catchments. However, hydrologic interpretability of machine learning models is important for the practical use of such models for these catchments. Thus, the current study besides evaluating the potential of three machine learning models (XGBOOST, Classification and Regression Trees(CART), and RandomForest) for streamflow simulation also focused on the hydrologic interpretation of machine learning models using SHapley Additive exPlananations (SHAP). All of these models performed well and the range of R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\ extrm{R}^2$$\\end{document} and Nasche-Efficiency for all three models lies between 0.61 to 0.90. Moreover, SHAP correctly identified minimum temperature as the most critical feature in glacier-fed Indus and Chenab catchments. It also provides logical insights into interactions between minimum temperature and precipitation for the indus and Chenab catchment. The findings of this study strongly illustrate the usefulness of SHAP analysis in interpreting the behavior of data-scarce high-elevation climate-sensitive catchments using tree-based machine learning models.

Improving the probabilistic drought prediction with soil moisture information under the ensemble streamflow prediction framework

Reliable drought prediction should be preceded to prevent damage from potential droughts. In this context, this study developed a hydrological drought prediction method, namely ensemble drought prediction (EDP) to reflect drought-related information under the ensemble streamflow prediction framework. After generating an ensemble of standardized runoff index by converting the ensemble of generated streamflow, the results were adopted as the prior distribution. Then, precipitation forecast and soil moisture were used to update the prior EDP. The EDP + A model included the precipitation forecast with the PDF-ratio method, and the observed soil moisture index was reflected in the former EDP and EDP + A via Bayes’ theorem, resulting in the EDP + S and EDP + AS models. Eight basins in Korea with more than 30 years of observation data were applied with the proposed methodology. As a result, the overall performance of the four EDP models yielded improved results than the climatological prediction. Moreover, reflecting soil moisture yielded improved evaluation metrics during short-term drought predictions, and in basins with larger drainage areas. Finally, the methodology presented in this study was more effective during periods with less intertemporal variabilities.

Comparison of antibiotic-resistant Escherichia coli and extra-intestinal pathogenic E. coli from main river basins under different levels of the sewer system development

Antimicrobial-resistant Escherichia coli in the aquatic environments is considered a strong indicator of sewage or animal waste contamination and antibiotic pollution. Sewer construction and wastewater treatment plant (WWTP) infrastructure may serve as concentrated point sources of contamination of antibiotic-resistant bacteria and antibiotic resistance genes. In this study, we focused on the distribution of antimicrobial-resistant E. coli in two rivers with large drainage areas and different urbanisation levels. E. coli from Kaoping River with drainage mainly from livestock farming had higher resistance to antibiotics (e.g. penicillins, tetracyclines, phenicols, aminoglycosides, and sulpha drugs) and presented more positive detection of antibiotic-resistance genes (e.g. ampC, blaTEM, tetA, and cmlA1) than that from Tamsui River. In Kaoping River with a lower percentage of sewer construction nearby (0–30%) in contrast to a higher percentage of sewer construction (55–92%) in Tamsui River, antimicrobial-resistant E. coli distribution was related to livestock farming waste. In Tamsui River, antimicrobial resistant E. coli isolates were found more frequently in the downstream drainage area of WWTPs with secondary water treatment than that of WWTPs with tertiary water treatment. The Enterobacterial Repetitive Intergenic Consensus (ERIC) PCR showed that the fingerprinting group was significantly related to the sampling site (p < 0.01) and sampling date (p < 0.05). By utilising ERIC-PCR in conjunction with antibiotic susceptibility and antibiotic-resistance gene detection, the relationship among different strains of E. coli could be elucidated. Furthermore, we identified the presence of six extra-intestinal pathogenic E. coli isolates and antibiotic-resistant E. coli isolates near drinking water sources, posing a potential risk to public health through community transmission. In conclusion, this study identified environmental factors related to antibiotic-resistant bacteria and antibiotic-resistance gene contamination in rivers during urban development. The results facilitate the understanding of specific management of different waste streams across different urban areas. Periodic surveillance of the effects of WWTPs and livestock waste containing antibiotic-resistant bacteria and antibiotic-resistance genes on river contamination is necessary.

The Genetic Composition of Wild Steelhead Based on Spatial Proximity to a Hatchery

AbstractManagement of hatchery and wild stocks of steelhead Oncorhynchus mykiss within the same watershed requires careful consideration when management objectives include the use of the hatchery stock to meet fishery objectives while maintaining the genetic integrity of coexisting wild stocks. This scenario can be further complicated when management objectives for the hatchery stock include supplementation of natural production. The lower Clearwater River in Idaho provided an ideal location to evaluate hatchery steelhead genetic influence within a drainage with separate wild steelhead and supplementation management emphasis areas. We collected 813 genetic samples from wild juvenile steelhead from two sample years in 10 tributaries to the lower Clearwater River to explore genetic structure and assess hatchery influence. Overall, levels of genetic diversity of wild groups were comparable with those observed elsewhere in the Snake River basin. Estimates of effective population size of sample tributaries ranged from 22 to 198 spawners, with a median of 84. All but two tributaries had &gt;50 spawners, and evidence of natural gene flow from other stocks was observed. Results of the study display three zones of hatchery influence in the lower Clearwater River: (1) no influence in lower tributaries with wild steelhead emphasis, (2) moderate influence in wild steelhead tributaries in close proximity to the hatchery facility, and (3) significant influence in hatchery‐supplementation tributaries. Interestingly, a number of wild steelhead reporting groups from elsewhere in the Snake River basin were observed in the assignments of study fish, suggesting movement of wild steelhead among larger drainage areas. Techniques outlined in this study can be used to monitor future hatchery influence in the Clearwater River drainage, track historic versus contemporary genetic influence in the drainage, and apply to other watersheds with hatchery and wild steelhead management objectives.

Evaluation of Runoff Simulation Using the Global BROOK90-R Model for Three Sub-Basins in Türkiye

The use of physically based hydrological models in the observation of hydrological processes has some disadvantages as well as advantages. One of these disadvantages is the large supply of data pertaining to the study area that is required for the model to be run. However, the ability to run the Global BROOK90 R (GB90-R) model for any location and period has made a significant contribution to the science of hydrology. In this study, the GB90-R model was established in three different basins (Çarşamba, Karasu, and Körkün) in Türkiye with different drainage areas and climates, and the flow forecasting performance was comprehensively evaluated. In addition, the evaporation, ground moisture, and snowmelt outputs obtained were examined comparatively. According to the results, Karasu Basin, with the smallest drainage area, was the basin with the highest model success in flow estimation with NSE = 0.670, while Körkün Basin, with the largest drainage area, was the basin with the lowest model success with NSE = 0.337. It is thought that the increase in the drainage area is one of the important factors reducing the success of the model.

The evaluation of N/P fate using the SPARROW model: a case study in an arid and semi-arid region, northern China.

The assessment of nutrients' fate from source to sink is critical to water quality control. As an important ecological reserve in the arid and semi-arid regions of China, the Luanhe River Basin (LRB) has suffered from the deterioration of water quality, thus leading to the urgent management and control. However, few studies have devoted to exploring the fate of N/P contaminations for the entire watershed, due possibly to the large drainage area and heterogeneous watershed composition. Here, we attempt to illustrate N/P contaminations delivery and retention processes using the SPAtially Referenced Regression On Watershed attributes (SPARROW) model. The model reveals 97% of the spatial variability in the TN load and 81% in the TP load, verifying its availability and credibility. The results indicate that anthropogenic sources are dominating the N/P load, which account for 68.5% of N and 74.6% of P inputs. The results highlight the significant retention effects of streams and reservoirs, with 16.4% of N and 13.4% of P removals by streams and 24.3% of N and 10.7% of P removals by reservoirs, respectively. Ultimately, only 49,045.2 t yr-1 (or 16.9%) of N and 1668.7 t yr-1 (or 17.1%) of P being transported to the Bohai Sea. In addition, the analysis of influencing factors showed that regional characteristics (e.g., topography, rainfall), stream size, and delivery distance are potential factors affecting the riverine transport, whereas flow rate and surface area are primarily affecting the reservoirs attenuation. In the future, the watershed water quality management should pay more attention to source management and pollution legacy risks to achieve sustainable and healthy watershed development.

Field assessment of metal and base cation accumulation in green stormwater infrastructure soils

Green stormwater infrastructure (GSI) is adopted to reduce the impact of stormwater on urban flooding and water quality issues. This study assessed the performance of GSI, like bioretention basins, in accumulating metals. Twenty one GSI basins were considered for this study, which were located in New York and Pennsylvania, USA. Shallow (0–5 cm) soil samples were collected from each site at inlet, pool, and adjacent reference locations. The study analyzed 3 base cations (Ca, Mg, Na) and 6 metals (Cd, Cr, Cu, Ni, Pb, and Zn), some of which are toxic to ecosystem and human health. The accumulation of cations/metals at the inlet and pool differed between the selected basins. However, accumulation was consistently higher at the inlet or the pool of the basin as compared to the reference location. Contrary to prior research, this study did not find significant accumulation with age, suggesting that other factors such as site characteristics (e.g., loading rate) might be confounding. GSI basins that receive water only from parking lots or parking lots and building roofs combined showed higher metals and Na accumulation as compared to the basins that received stormwater only from building roofs. Cu, Mg and Zn accumulation showed a positive relationship with the organic matter content in soil, indicating likely sorption of metals on organic matter. Ca and Cu accumulation was greater in GSI basins with larger drainage areas. A negative relationship between Cu and Na implies that Na loading from de-icers may reduce Cu retention. Overall, the study found that the GSI basins are successfully accumulating metals and some base cations, with highest accumulation at the inlet. Additionally, this study provided evidence of GSI effectiveness in accumulating metals using a more cost efficient and time averaged approach compared to traditional means of stormwater inflow and outflow monitoring.

Calculation of Dry Weather Flows in Pumping Stations to Identify Inflow and Infiltration in Urban Drainage Systems

The performance of most urban drainage systems is adversely affected by unintended connections of groundwater and surface water, often denoted as inflow and infiltration (I&amp;I). Various methods exist to locate and characterise these effects. Yet, it remains difficult to quantify them accurately, especially in terms of spatial distribution over a larger drainage area. One of the reasons for this is the lack of sufficient high-quality sewer flow measurements at a high temporal resolution, which would enable the calibration of detailed spatio-temporal relationships between rainfall and I&amp;I flows. In this paper, a methodology is presented for deriving sewer flow time series from operational measurements at pumping stations, and the results from four pilot locations are discussed. It shows the potential of the methodology to be implemented at a large scale and to contribute to a better understanding and remediation of I&amp;I in urban drainage management planning.

Expanding and Enhancing Streamflow Prediction Capability of the National Water Model Using Real-Time Low-Cost Stage Measurements

Abstract This study demonstrates an approach to expand and improve the current prediction capability of the National Water Model (NWM). The primary objective is to examine the potential benefit of real-time local stage measurements in streamflow prediction, particularly for local communities that do not benefit from the improved streamflow forecasts due to the current data assimilation (DA) scheme. The proposed approach incorporates real-time local stage measurements into the NWM streamflow DA procedure by using synthetic rating curves (SRC) developed based on an established open-channel flow model. For streamflow DA and its evaluation, we used 6-yr (2016–21) data collected from 140 U.S. Geological Survey (USGS) stations, where quality-assured rating curves are consistently maintained (verification stations), and 310 stage-only stations operated by the Iowa Flood Center and the USGS in Iowa. The evaluation result from NWM’s current DA configuration based on the USGS verification stations indicated that DA improves streamflow prediction skills significantly downstream from the station locations. This improvement tends to increase as the drainage scale becomes larger. The result from the new DA configuration including all stage-only sensors showed an expanded domain of improved predictions, compared to those from the open-loop simulation. This reveals that the real-time low-cost stage sensors are beneficial for streamflow prediction, particularly at small basins, while their utility appears to be limited at large drainage areas because of the inherent limitations of lidar-based channel geometry used for the SRC development. The framework presented in this study can be readily applied to include numerous stage-only stream gauges nationwide in the NWM modeling and forecasting procedures.

Paired denitrifying bioreactors with wide orientation for increased drainage flow capacity

Denitrifying bioreactors are a conservation drainage practice for reducing nitrate loads in subsurface agricultural drainage. Bioreactor hydraulic capacity is limited by cross-sectional area perpendicular to flow through the woodchip bed, with excess bypass flow untreated. Paired bioreactors with wide orientations were built in 2017 in Illinois, USA, to treat drainage from a relatively large 29 ha field. The paired design consisted of: a larger, Main bioreactor (LWD: 6.1 × 18.3 × 0.9 m) for treating base flow, and 2) a smaller, Booster bioreactor (7.8 × 13.1 × 0.9 m) receiving bypass flow from the Main bioreactor during periods of high flow. Over three years of monitoring, the paired bioreactor captured 84–92% of the annual drainage discharge which demonstrated an expanded cross-sectional area could improve bioreactor flow capture, even for a large drainage area. However, the paired bioreactors removed 6–28% of the annual N load leaving the field (1.8–5.6 kg N ha−1 removed; 52–161 kg N), which was not a notable improvement compared to bioreactors treating smaller drainage areas. The design operated as intended at low annual flow-weighted hydraulic retention times (HRTs) of usually ≤2 h, but these short HRTs ultimately limited bioreactor nitrate removal efficiency. Daily HRTs of <2 h often resulted in nitrate flushing. The Main bioreactor had higher hydraulic loading as intended and was responsible for the majority of flow captured in each year although not always the most nitrate mass removal. The Booster bioreactor provided better nitrate removal than the Main at HRTs of 3.0–11.9 h, possibly due to its drying cycles which may have liberated more available carbon. This new design approach tested at the field-scale illustrated tradeoffs between greater flow capacity (via increased bioreactor width) and longer HRT (via increased length), given a consistent bioreactor surface footprint.

Fish Species Composition, Distribution and Community Structure in Relation to Environmental Variation in a Semi-Arid Mountainous River Basin, Iran.

We analyzed spatial variation in fish species richness and community composition in the Karun River basin, Iran. Knowledge about fish diversity in the basin is incomplete and varies widely along spatial and temporal scales: The Karun is the longest river in Iran (950 km) with the largest drainage area (about 67,000 km2). Fish samples were collected from 54 sites from July through August 2019 using a backpack electro-fisher. Physico-chemical and habitat parameter data collected at each site included pH, conductivity (μS/cm), dissolved oxygen (mg/L), water temperature (°C), turbidity (NTU), stream width (m), stream depth (m), water velocity (m/s) and elevation (m). In total, 37 species were collected (5241 individuals weighing 110.67 kg). The species collected represented 12 families and 27 genera. A total of 13 endemic species (35.14%), 16 native species (43.24%), and eight non-native species (21.62%) were recorded. Diversity indices were calculated and used to measure the spatial variation in community composition. Relationships between native and endemic species assemblage structure and environmental descriptors were assessed using canonical correspondence analysis (CCA). The first two axes of the canonical correspondence analysis explained 62.57% of the variation in the data. Of the nine environmental descriptors analyzed, eight significantly affected species distribution; however, electrical conductivity and elevation were most influential. Our study provides up-to-date status information on the distribution of freshwater fishes in the Karun River basin. This information is essential for developing conservation and management strategies to support the long-term sustainability of fish populations in the Karun River basin.

Machine Learning Applied to a Modern‐Pleistocene Petrographic Data Set: The Global Prediction of Sand Modal Composition (GloPrSM) Model

AbstractPetrography has long been used as a tool to decipher the sedimentary provenance of sand and sandstone based on relative proportions of detrital grain types. However, there is not yet a predictive framework that constrains the relationship between sand grain composition and the factors that influence it, including bedrock lithology, elevation, slope, basin area, precipitation, and temperature. We used a globally extensive modal point‐count data set of 3,522 Pleistocene to modern sand samples to train an inline series of random forest meta‐estimators to predict relative abundances of quartz (Q), feldspar (F), and lithic (L) grain types and eight subcompositions. The trained random forest models were fit to a global data set of fluvial watersheds to produce the global prediction of sand modal composition (GloPrSM) model which predicts Q‐F‐L of fluvially transported sediment. GloPrSM predicts quartz enrichment in low latitudes (35°N to 35°S), feldspar enrichment near plutonic and metamorphic crystalline terranes in middle to high latitudes, and lithic enrichment near modern and ancient collisional plate boundaries and flood basalts. Our analyses reveal that slope, temperature, and the relative abundance of exposed metamorphic and felsic to intermediate plutonic rocks are the most important predictors of Q‐F‐L composition. In addition, temperatures higher than 15°C and large drainage areas appear to promote quartz enrichment, while steeply sloping environments favor lithic enrichment. The GloPrSM model represents the first global‐scale estimate of fluvial sand compositional modes and illustrates that the spatial distribution of Q‐F‐L at Earth's surface can be predicted from the first‐order factors that generate sediment.

Assessing the environmental risk and mobility of cobalt in sediment near nonferrous metal mines with risk assessment indexes and the diffusive gradients in thin films (DGT) technique

The Jialing River is the tributary of the Yangtze River with the largest drainage area. In recent years, the Jialing River has suffered a series of environmental problems, such as discharge of industrial effluent and sand mining activities, which have severely threatened the aquatic ecosystem of the river. In the present study, we employed risk assessment indexes, sequential extraction and the diffusive gradients in thin films (DGT) technique to assess environmental risks and study the remobilization of cobalt (Co) in sediments. The potential ecological risk index and risk assessment code results demonstrated that Co may pose a low environmental and ecological risk to the local aquatic environment. However, BCR sequential extraction showed that the sum of the F1, F2 and F3 fractions of Co still accounted for over 50% of the Co in the study areas, indicating that sediments may be a source of Co release. The DGT results showed an increasing trend for DGT-labile Co in deep sediments (−8 cm to −12 cm), and the calculated flux values ranged from 0.08 to 15.54 ng cm2·day−1, indicating that Co tends to transfer across the sediment-water interface at all sampling sites. Correlation analysis showed that F1–Co, F2–Co and F3–Co are the fractions readily captured by DGT and can be used for predicting Co remobilization in sediment. Sand mining activities contribute substantially to the release of Co from the F1 and F3 fractions as a result of strong stirring of sediments and introduction of oxygen into the sediments. The reductive dissolution of iron (Fe) and manganese (Mn) hydroxides or oxides causes the release of Co and Fe/Mn in the sediment, which leads to Co release from the reducible fraction. The above work suggests that sand mining in the Jialing River should be reasonably regulated to prohibit illegal sand mining activities.

Estimation of the time of concentration from morphometric and hydrological monitoring parameters in São Paulo state watersheds

Morphometric analyzes support an understanding of the physical characteristics of hydrographic basins and are commonly used to estimate hydrological variables. This work aims to compare the geomorphological characteristics and the time of concentration (Tc) in two watersheds: upper part of the Jundiaí river (139.3 km2) and Piraí river (209 km2) belonging to the Jundiaí river basin, state of São Paulo. To calculate the Tc, empirical equations and monitored hydrological data were applied. Applying the US Army Corps of Engineers, Ven te Chow, and Kirpich Tc formulas, the Piraí watershed had the highest Tc, with a mean value of 6.29 h with a maximum difference between the three formulas of approximately 27%. The mean Tc for the Jundiaí watershed was 5.18 h, with a 25% variation between the minimum and maximum calculated values. Applying the hydrological method to the data collected between 2018 and 2021 from the São Paulo State Flood Alert System (SAISP) fluviometric and pluviometric stations, the minimum and maximum calculated values were 6.83 h and 23.33 h, respectively, with mean of 14.4 h. We observed higher values for the Piraí watershed, which has a larger drainage area, with Tc varying from 17 h to 49 h and mean of 36.7 h. Note that the average Tc for the Piraí watershed was almost 2.5 times higher than that of the Jundiaí watershed, despite the almost equal values of slope and length of the thalweg, calculated from the empirical equations. These equations based on morphometric parameters underestimated the Tc compared with the values calculated from the hydrological method, presenting 82% and 64% of error, respectively, for the Piraí watershed and the upper part of the Jundiaí watershed.

Evaluating the reliability of synthetic rating curves for continental scale flood mapping

Empirical approaches such as the Height Above Nearest Drainage method in conjunction with Synthetic Rating Curves (HAND-SRC) have emerged as particularly appealing alternatives to the traditional flood mapping techniques due to their lower complexity and fewer data requirements. However, SRCs use Digital Elevation Model (DEM) derived reach averaged hydraulic properties and assume one dimensional steady state flow condition with normal depth. These implicit model assumptions may introduce errors in flood stage and extent estimates using the HAND-SRC approach. This study investigates the reliability of SRC across continental United States (CONUS) by comparing them to the United States Geological Survey’s (USGS) gauge rating curves. Results from this comparison show that the implicit model assumptions used in the SRC-HAND approach add significant error in the SRC derivation. The accuracy of the SRC is found to be related to the stream characteristics, including the bathymetry area, slope of the main channel two-year flow and drainage area. Results also show that SRCs in coastal areas, characterized by low slopes and large drainage areas, have higher error and tend to overpredict the stage height in comparison to the USGS rating curves; whereas they tend to underpredict stage height in the mountainous regions. The SRCs are most reliable for the midwestern plains of Ohio, Mid Atlantic, Tennessee and Upper Mississippi regions, and least reliable (higher error) for the Rocky Mountains. Further, the study finds that Deep Neural Network models can be effectively used to judge the performance of SRC for ungauged river reaches.