River Level Research Articles

Study on the late quaternary activity of the Jinyang-Ningnan segment of the Lianfeng fault zone

The Lianfeng fault zone is located at the junction of the Daliangshan subblock and the South China block and is the southern boundary of the Daliangshan subblock. It is a very active tectonic area, which is of great significance to understanding its seismogenic environment. However, there are relatively little existing data on its activity and the timing of its latest activity. In the Jinyang-Ningnan segment of the Lianfeng fault zone along the Jinsha River, remote sensing image interpretation, field geologic surveys, high-precision drone measurements, and sample dating indicate that the river terraces T1 (the lowest, 15–40 m above the local river level) and T2 (60–90 m above the local river level) developed in the mid-Holocene and late Pleistocene. This indicates that the Jinsha River basin has been strongly downcutting since the Late Pleistocene. The fault profiles are mainly exposed in the T1 and T2 terraces of the Jinsha River. Based on this information in combination with the fault characteristics of the sedimentary profile and stratigraphic dating, it is believed that the Lianfeng fault zone was active at least along the Jinyang-Ningnan segment, with the latest activity occurring no earlier than the early to mid-Holocene.

Analysis of Water Level Monitoring using GNSS Interferometric Reflectometry in River Waters

GNSS Interferometric Reflectometry (GNSS-IR) is one of the newest explorations of Global Navigation Satellite System (GNSS) signals which utilizes multipath signals to calculate the vertical distance from the reflecting surface to the geodetic antenna/receiver. However, scientific-grade or geodetic GNSS instruments are expensive, which is a limiting factor for their prompt and more widespread deployment as a dedicated environmental sensor. Hence, in this study, low-cost GNSS receiver devices were used to monitor the surface level of PT Garam River in Pamekasan Regency using the GNSS-IR method. The study location of PT Garam River which is quite close to the sea causes the water level to vary following the tides at the sea. This is because there are needs for advanced methodologies to limit GNSS observations in a much narrower area. The river in this case is a more challenging study location in terms of monitoring the water level. Calculation of river water level height with GNSS-IR observations is based on determining the peak frequency on the periodogram resulting from signal-to-noise ratio (SNR) data extraction. The two-weeks GNSS-IR observation in this study were applied by installing sideway orientation of GNSS antenna towards the river. This will be done to see how effective the strength of the reflected signal received by the low-cost GNSS devices are in narrow footprint area. Conventional tide gauge near the GNSS-IR site was used to evaluate the accuracy of GNSS-IR in river water level monitoring. Tide analysis is carried out to obtain tidal constituents and tide predictions using least square harmonics estimation (LSE). The resulting accuracy of the validation data is at an RMSE of 15.35 cm, with the correlation value of 0.94. The type of river tides in the study location based on the calculation results is mixed tide prevailing semidiurnal. Tide prediction for 20 days gives promising results with an RMSE of 16 cm. These results indicate that low-cost GNSS device has a promising capability for water level monitoring using GNSS-IR method in a narrow reflector area.

Evaluation of Organic Pollution Using Algal Diversity in Rivers of Cotabato City, Bangsamoro Autonomous Region in Muslim Mindanao (BARMM), Mindanao Island, Philippines

This study investigated the water quality and the organic pollution level of rivers in Cotabato City, specifically the Rio Grande de Mindanao, Matampay, Esteros, and Tamontaka rivers. The physicochemical characteristics of water in these rivers were determined in the laboratory, and the level of organic pollution was determined using Palmer’s algal pollution index. Water quality assessment showed that the dissolved oxygen (DO) in Matampay River and the biological oxygen demand (BOD) in Esteros River exceed the minimum standard set by the Department of Environment and Natural Resources (DENR) for water quality in class C rivers. Results also showed that there were thirty (30) algal genera belonging to twelve (12) classes were observed in Cotabato City rivers. Algal genera belonging to Chlorophyceae and Bacillariophyceae were found to be the most abundant in these rivers. Using Palmer’s algal pollution index, the Rio Grande de Mindanao showed a probability of high organic pollution, while the rest of the rivers indicated a lack of organic pollution. For a more thorough assessment of the Cotabato City rivers, it is advisable to consider more comprehensive measures, such as extending the sampling duration and expanding the number of sampling stations.

Effects of Periodic Fluctuation of Water Level on Solute Transport in Seasonal Lakes in Poyang Floodplain System

AbstractSeasonal lakes appear in floodplain systems due to the seasonal variation of water level of rivers or main lakes and play an important ecological function. Previous studies have focused on exchange flux between the surface water and groundwater in seasonal lakes, but the process of solute transport at the sediment‐water interface under the effect of seasonal water level fluctuations remains unclear. In this study, laboratory flume experiments and numerical simulations were conducted to investigate non‐reactive solute transport and exchange in a seasonal lake under effects of hydrological connectivity, water level amplitude, sediment permeability and solute source. The results show that the recharge pattern between seasonal lakes and rivers or main lakes is determined by the seasonal water level. The solute transports from the groundwater of seasonal lakes to the main lake during the dry season, while the reverse applies during the wet season. The perennial hydrological isolation increases the concentration in the overlying water and pore water of the seasonal lake by 20.5% and 28.7%, respectively. The solute concentration in the overlying water and pore water of the seasonal lake decreases by 55.2% and 51.5%, respectively, when solute source occurs in the main lake compared with that occurs in the seasonal lake. The amplification of water level amplitudes leads to an increase of 6.46% and 10.55% in the solute concentration drop from the overlying water, respectively. These findings contribute to improving water resource utilization and protecting aquatic ecological environment in floodplain systems.

Implementasi Alat Pemantau Debit dan Ketinggian Air Sungai Berbasis Internet of Things Untuk Penanggulangan Banjir

The increasing frequency and intensity of floods in Cirebon City demands innovative solutions to reduce the impact of damage and risks to society and infrastructure. requires the latest approaches to risk management and prevention. This research focuses on the implementation of an Internet of Things (IoT)-based river discharge and water level monitoring tool designed to improve flood detection and prevention capabilities in Cirebon City. The main problems faced include accurate measurements, real-time monitoring, and rapid response to river water fluctuations. By combining the latest sensors and IoT technology, this tool is able to provide accurate data about water discharge and river levels continuously. The first stage in developing the Internet of Things (IoT) is identification and study of flooding problems in Cirebon City and analysis of the need for a monitoring system for flood prevention. Second, design a monitoring tool concept that meets the needs and specifications and determine the type of sensors, hardware and IoT technology that will be used. Third, choose a sensor to measure river discharge and water level. Fourth, build a monitoring tool prototype based on conceptual design. Fifth, Testing and Validation. The results of this research are based on river tests in the city of Cirebon, there are 4 rivers that frequently flood and the results of the test are that the Kalijaga River has a height of 20cm in the Safe level category, the Kedung Pane River has a height of 15cm in the Safe Level Category, the Kesunean River has a height of 10cm in the Safe Level Category and the Sukalila River has a height of 17cm in the Safe level category this is influenced by dry weather. Then the data collected from monitoring tools can be used to analyze flood patterns, trigger factors and impacts. Then, from this data, a flood classification analysis can be carried out based on the level of river water discharge, so that it can be classified as light, medium or heavy floods based on the amount of water flowing.

A study on compound flood prediction and inundation simulation under future scenarios in a coastal city

Coastal cities are affected by the compound effects of flood hazard systems, including the ocean, river, and coastal land, within which the disaster-induced factors interact complexly. To explore the combined effects of multiple disaster-causing factors under changing environment, this study first constructed univariate distribution by maximum daily precipitation of the year at Zhuhai Station from 1962 to 2020 and maximum daily average river discharge of the year at Makou Station from 1951 to 2005. Based on the integrated model of the validated Delft3D-FLOW and HEC-RAS hydrodynamic models, the river level process of compound floods was simulated for 24 h, and the effects of extreme compound flood events involving storm surge, rainfall and river flood under future scenarios is analyzed. In this study, the Modaomen waterway in Zhuhai City of China is used as the study area. The results show: (1) The Lognormal distribution is selected as the optimal distribution, and the design values of precipitation and discharge in different recurrence periods are calculated based on Lognormal distribution. The precipitation and discharge for the 100-year return period were 450.3 mm and 51000 m3, respectively, which could be used in the compound flood simulation; (2) The application of Delft3D-FLOW model ensures the accurate simulation of the compound flood process. In the different future scenarios of rising sea levels and increasing wind intensity (WI), the effect of multiple disaster-causing factors results to a more serious inundation than that of a single disaster-causing factor, with the increase of maximum storm surge level from 8.09 % to 20.31 %; and (3) Fluvial flooding and storm surge became the major causes of compound flood when the city coastal protection is vulnerable, while in the city with robust coastal protection, precipitation serves as a main disaster-causing factor. Taking the inundation under scenario S1 as an example, when the seawall elevation is 2 m, the composite flooding causes significant inundation in the riparian area (the total inundation area is 15.29 km2), whereas when the seawall elevation is raised to 2.5 m, the inundation area in the riparian area caused by the compound flood decreases significantly (the total inundation area is 0.03 km2). This study is expected to provide scientific references on disaster prevention planning, compound disaster risk management and urban resilience construction along the coastline, as well as have a significant impact on modern economic growth, social stability, and the security in coastal cities.

Integrating molecular microbial methods to improve faecal pollution management in rivers with designated bathing waters

Rivers are at risk from a variety of pollution sources. Faecal pollution is of particular concern since it disperses pathogenic microorganisms in the aquatic environment. Currently, faecal pollution levels in rivers is monitored using faecal indicator bacteria (FIB) that do not offer information about pollution sources and associated risks. This study used a combined molecular approach, along with measurements of water quality, to gain information on pollution sources, and risk levels, in a newly designated recreational bathing site in the River Wharfe (UK). Physico-chemical parameters were monitored in situ, with water quality multiparameter monitoring sondes installed during the 2021 bathing season. The molecular approach was based on quantitative PCR (qPCR)-aided Microbial Source Tracking (MST) and 16S rRNA gene metabarcoding to obtain a fingerprint of bacterial communities and identify potential bioindicators.The analysis from the water quality sondes showed that ammonium was the main parameter determining the distribution of FIB values. Lower faecal pollution levels were detected in the main river when compared to tributaries, except for samples in the river located downstream of a wastewater treatment plant. The faecal pollution type (anthropogenic vs. zoogenic) changed the diversity and the structure of bacterial communities, giving a distinctive fingerprint that can be used to inform source. DNA-based methods showed that the presence of human-derived bacteria was associated with Escherichia coli spikes, coinciding with higher bacterial diversity and the presence of potential pathogenic bacteria mainly of the genus Mycobacterium, Aeromonas and Clostridium. Samples collected after a heavy rainfall event were associated with an increase in Bacteroidales, which are markers of faecal pollution, including Bacteroides graminisolvens, a ruminant marker associated with surface run-off from agricultural sources. The combined use of qPCR and 16S rRNA sequencing was able to identify pollution sources, and novel bacterial indicators, thereby aiding decision-making and management strategies in recreational bathing rivers.

Deep-Learning-Based Water Quality Monitoring and Early Warning Methods: A Case Study of Ammonia Nitrogen Prediction in Rivers

In line with rapid economic development and accelerated urbanization, the increasing discharge of wastewater and agricultural fertilizer usage has led to a gradual rise in ammonia nitrogen levels in rivers. High concentrations of ammonia nitrogen pose a significant challenge, causing eutrophication and adversely affecting the aquatic ecosystems and sustainable utilization of water resources. Traditional ammonia nitrogen detection methods suffer from limitations such as cumbersome sample handling and analysis, low sensitivity, and lack of real-time and dynamic feedback. In contrast, automated monitoring and ammonia nitrogen prediction technologies offer more efficient methods and accurate solutions. However, existing approaches still have some shortcomings, including sample processing complexity, interference issues, and the absence of real-time and dynamic information feedback. Consequently, deep learning techniques have emerged as promising methods to address these challenges. In this paper, we propose the application of a neural network model based on Long Short-Term Memory (LSTM) to analyze and model ammonia nitrogen monitoring data, enabling high-precision prediction of ammonia nitrogen indicators. Moreover, through correlation analysis between water quality parameters and ammonia nitrogen indicators, we identify a set of key feature indicators to enhance prediction efficiency and reduce costs. Experimental validation demonstrates the potential of our proposed approach to improve the accuracy, timeliness, and precision of ammonia nitrogen monitoring and prediction, which could provide support for environmental management and water resource governance.

Colored noise in river level oscillations as triggering factor for unstable dynamics in a landslide model with displacement delay

In the present paper we examine the effect of the noise in river level oscillation on the landslide dynamics. The analysis is conducted in several phases. In the first phase, we analyze the multi-annual level oscillation of the Kolubara and the Ibar river (Serbia). Based on the observed dataset, we suggest a deterministic model for the river level oscillation with the additional contribution of the noise part, which we confirm to have the properties of colored noise. In the second phase of the research, we introduce the influence of the river-level oscillation, with the included effect of colored noise in the spring-block delay model of landslide dynamics. Results of the research indicate conditions under which the effect of river noise has both stabilizing and destabilizing effects on the landslide dynamics. The effect of noise intensity D and correlation time ε is systematically analyzed in interaction with delayed interaction, spring stiffness and friction parameters. It is determined that the landslide dynamics is sensitive to the change of noise intensity and that the increase of noise intensity leads to onset of unstable landslide dynamics. On the other hand, results obtained indicate that the examined model of landslide dynamics is rather robust towards the change of correlation time ε. Interaction of this parameter and some of the friction parameters leads to stabilization of landslide dynamics, which confirms the importance of the influence of the noise color in river level oscillations on the landslide dynamics.

The use of temporary, permeable structures in the lower burdekin river: A novel approach to river-level management

A review of in-channel structures finds a wide range of purposes, including enhancement of the storage capacity of rivers to improve water supply reliability and/or flood protection, and various other hydro-ecological, water quality, geomechanical and sediment transport functions. The installation of in-channel structures is typically intended to be permanent, or at least semi-permanent. The purpose of this note is to document the novel use of temporary in-channel structures in the lower reaches of the Burdekin River, within the Australian dry tropics. Their application reduces freshwater discharge to the sea during low-flow periods thereby enhancing the sustainability of irrigated agriculture within the Lower Burdekin Delta. Temporary structures are constructed in the Burdekin River to enhance the reliability of pumping stations by raising the river level locally and preventing sea water from reaching extraction points. The structures are designed to restrict river flow only partially during low-flow periods and to wash away during high-flow events, which provides several benefits, including the passage of riverine fauna and reduced river siltation. The long-term utilisation (1950s to present) of temporary structures in the Burdekin River is testament to their successful implementation, which may offer benefits over permanent structures for other dry-tropical regions.

Water level prediction using long short-term memory neural network model for a lowland river: a case study on the Tisza River, Central Europe

BackgroundPrecisely predicting the water levels of rivers is critical for planning and supporting flood hazard and risk assessments and maintaining navigation, irrigation, and water withdrawal for urban areas and industry. In Hungary, the water level of rivers has been recorded since the early nineteenth century, and various water level prediction methods were developed. The Discrete Linear Cascade Model (DLCM) has been used since 1980s. However, its performance is not always reliable under the current climate-driven hydrological changes. Therefore, we aimed to test machine learning algorithms to make 7-day ahead forecasts, choose the best-performing model, and compare it with the actual DLCM.ResultsAccording to the results, the Long Short-Term Memory (LSTM) model provided the best results in all time horizons, giving more precise predictions than the Baseline model, the Linear or Multilayer Perceptron Model. Despite underestimating water levels, the validation of the LSTM model revealed that 68.5‒76.1% of predictions fall within the required precision intervals. Predictions were relatively accurate for low (≤ 239 cm) and flood stages (≥ 650 cm), but became less reliable for medium stages (240–649 cm).ConclusionsThe LSTM model provided better results in all hydrological situations than the DLCM. Though, LSTM is not a novel concept, its encoder–decoder architecture is the best option for solving multi-horizon forecasting problems (or “Many-to-Many” problems), and it can be trained effectively on vast volumes of data. Thus, we recommend testing the LSTM model in similar hydrological conditions (e.g., lowland, medium-sized river with low slope and mobile channel) to get reliable water level forecasts under the rapidly changing climate and various human impacts.Graphical

Interpretation of Bridge Health Monitoring Data from Satellite InSAR Technology

This paper presents a study on applying satellite Interferometric Synthetic Aperture Radar (InSAR) technology for the remote monitoring of road bridges and interpreting the results from a structural standpoint. The motivation behind this study arises from the widespread deterioration observed in many road bridges worldwide, leading to the need for large-scale, economic, and effective structural health monitoring (SHM) techniques. While traditional contact-type sensors have cost sustainability limitations, remote sensing techniques, including satellite-based InSAR, offer interesting alternative solutions. The objective of this study is three-fold: (i) to process InSAR data specifically for road bridges in operational conditions through the Multi-Temporal InSAR technique and extract displacement time series of reflective targets on their decks; (ii) to interpret the observed millimetric bridge displacements to verify the consistency with expected response to environmental loads and the possibility to detect unexpected behaviours; and (iii) to investigate the correlation between bridge displacements and environmental loads as temperature and river water flow variations. The study focuses on the multi-span prestressed concrete A22 Po River Bridge in Italy, utilising a dataset of X-Band HIMAGE mode Stripmap images acquired over eight years by the satellite constellation COSMO-SkyMed. The study demonstrates the effectiveness of InSAR-based SHM in detecting temperature-induced displacements and identifying different bridge spans simply by studying the sign of the correlation between displacements and temperature variation. It also reveals an unexpected behaviour in a portion of the bridge retrofitted to prevent scour issues a few years before the dataset start date. Furthermore, the correlation between pier displacements and river level variations underscores the importance of considering environmental factors and the geotechnical characteristics of the foundation soils in bridge monitoring. The results obtained from this study are significant with a view to using this satellite InSAR-based monitoring for early detection of anomalous bridge behaviour on a large scale.

Watershed Delineation from DEM by Model Builder in ArcGIS

The water level fluctuates continuously at local, continental, and global levels. Consequently, remote sensing offers extensive data for assessing the location, extent, and variability of change, including the causes and changes that occur and the reactions and repercussions of change. Considering the significance of the water level, this research focuses on describing the methodology and process to determine the water level in the Al-Zab River. This study offers a straightforward and cost-effective approach and procedure for precisely delineating flood danger regions in the Al-Zab River basin utilizing a geographic information system and the existing database. The proposed strategy for regionally dispersing flood risks combines the size of prior flood events and the geographical distribution of their causes. This approach allows for the calculation of a weighted score for each participant's causative component. The regional distribution of the flood hazard intensity level is obtained by categorizing the spatial patterns of the flood hazard index. The 30-m digital elevation model with a high resolution was utilized to evaluate the water level in this river. Furthermore, this work is considered the first step towards enhancing information management in the studied area. This study mainly aims to explain how to outline and define watersheds using ArcGIS for flood mapping. Doi: 10.28991/CEJ-2023-09-11-011 Full Text: PDF

Roadway floods and their associated weather-related conditions: New insights using CARS 511 data for state and federal highways in Nebraska, USA

This study examined roadway flooding event data provided by the Nebraska Department of Transportation via the Condition Acquisition Reporting System (CARS) 511 archive (2016–2021). With these data, a novel analysis was completed to further the understanding of where, when, and why (meteorologically) roadway flooding occurs on Nebraska state and federal highways. In the study period, 298 roadway floods occurred, with an annual median of 16 per year. There was a greater risk for roadway floods to transpire more frequently in eastern Nebraska, which was attributed to the spatial climatology of heavy rainfall and the higher density of roadways and rivers. Consequently, this increases the exposure and vulnerability of the highway system across the eastern portion of the state. Further, precursor soil moisture and river conditions proved to be critical initiators of roadway floods, as precipitation totals prior to each flood only averaged at 40 mm, while precursor Palmer Hydrological Drought Index values for 75% of events were above 4.0 and river levels were 200 cm above its daily medians. Overall, the complexities of these roadway flooding events are challenging for proactively forecasting as opposed to reacting to the situation. With the top roadway flooding locations identified along with the weather-related causes, further investigation of roadway flooding hotspots across the state needs to take place for mitigation and resiliency in a changing climate. The methods of this research can be used across other states and by other agencies to analyze and increase the resilience of their respective roadway networks.

Analyzing flood characteristics and establishing a monitoring network for the red river area of Hanoi

Flooding is a phenomenon in which river water rises during the rainy season (in the North of Viet Nam from June to September every year), causing flooding in some alluvial grounds outside the Red River dyke in the Hanoi area. This phenomenon occurs frequently with different scales in area and depth of flooding, which depends on the hydro-meteorological conditions of the entire region of Red river and the ability of flow regulation structures and flood drainage on river. The impacts of flooding due to rising river levels not only cause instability in daily life in residential areas but also change river dynamics and cause increased water pressures and seepage pressures on the dikes. Currently, flooding can be largely controlled by regulating the flow of rivers through hydroelectric dams in upstream areas. However, establishing a flood monitoring network and creating a database to assess the impact of flooding is necessary to minimize damage and improve the efficiency of exploitation and use of the Red River. This article focuses on research on flood risk assessment and the basis for establishing a flood monitoring network on the Red River in the Hanoi area to serve sustainable exploitation of the territory.

A Case Study on Simulation of Urban Inundation by Inland Flooding and River Flooding

Urban flooding can occur due to a combination of factors such as insufficient drainage capacity in stormwater systems, inland flooding from elevated river levels, and river flooding. This study proposes a method for analyzing urban inundation resulting from these complex phenomena using XP-SWMM. This is essentially a model capable of calculating the flow of drainage systems in urban watersheds and analyzing surface inundation. Moreover, it can accommodate irregular channel cross-sections and bridges, enabling river flooding modeling. However, it has limitations in terms of replicating the actual flooding patterns of rivers due to its representation of rivers through one-dimensional links and nodes. This study implemented virtual control structures to reflect the flooding effects caused by river flooding. This approach was applied to the Dongcheon watershed in Busan, where two instances of river and inland flooding coincided in July 2020. The results indicated the appropriateness of the approach.

Modulatory effect of magnesium hydroxide-nanoparticles on lead-induced toxicity in river Clarias gariepinus near mining site

Effluents emerging from unprotected local mining activities pollute the nearby rivers containing aquatic lives. Advent of nanotechnology however, supposedly helps in removal of water contaminants and as well make waste water remediation possible. To this effect, Clarias gariepinus from rivers near mining site at Ijero-Ekiti, Ekiti-State, Nigeria were collected and exposed to 62.5, 125 and 250mg/L suspension of 10nm magnesium hydroxide nanoparticles (MgOHNPs) for 14-days under semi-static conditions. The suspected lead concentration in the river water was estimated using atomic absorption spectrophotometer while the quality of the river water was also examined. Clarias gariepinus collected were sacrificed and alterations in their biochemical and hematological indices were evaluated. The results showed that water sample from river I exhibit highest (0.033ppm) lead concentration while water sample from river III has the lowest (0.006ppm) lead concentration relative to their proximity to the mining site. Besides, the collected fishes demonstrated significant (P<0.05) concentration dependent increase in some biochemical indices and no observable significant alteration in hematologic state of the fishes at tested doses. Hence, results from this study suggest that toxic lead effluent from local mining activities may have elevated lead concentration levels in nearby rivers while exposure to MgOHNPs could evoke alterations in physiological system of the inhabiting aquatic lives.

Monitoring and evaluation of the water quality of the Lower Neches River, Texas, USA

Increasing bacteria levels in the Lower Neches River caused by Hurricane Harvey has been of a serious concern. This study is to analyze the historical water sampling measurements and real-time water quality data collected with wireless sensors to monitor and evaluate water quality under different hydrological and hydraulic conditions. The statistical and Pearson correlation analysis on historical water samples determines that alkalinity, chloride, hardness, conductivity, and pH are highly correlated, and they decrease with increasing flow rate due to dilution. The flow rate has positive correlations with Escherichia coli, total suspended solids, and turbidity, which demonstrates that runoff is one of the causes of the elevated bacteria and sediment loadings in the river. The correlation between E. coli and turbidity indicates that turbidity greater than 45 nephelometric turbidity units in the Neches River can serve as a proxy for E. coli to indicate the bacterial outbreak. A series of statistical tools and an innovative two-layer data smoothing filter are developed to detect outliers, fill missing values, and filter spikes of the sensor measurements. The correlation analysis on the sensor data illustrates that the elevated sediment/bacteria/algae in the river is either caused by the first flush rain and heavy rain events in December to March or practices of land use and land cover. Therefore, utilizing sensor measurements along with rainfall and discharge data is recommended to monitor and evaluate water quality, then in turn to provide early alerts on water resources management decisions.

Assessment of Anthropogenic Impacts on the Genetic Diversity of Phragmites australis in Small-River Habitats

Common reed is often used as a model plant to study the anthropogenic impacts on ecosystems at local and global scales. As a community-forming species, it is directly exposed to the impacts of human activities on the ecosystem. The aim of our study was to evaluate the patterns of genetic diversity in common reed stands located in habitats that are differently affected by anthropogenic factors. We studied whether riverbed modifications, land cover in the neighborhood of the stand and the chemical and physical parameters of the river water affect the genetic diversity of P. australis at the studied sites. Using DNA fingerprinting, we genotyped 747 plants from 42 sites located in 16 small Lithuanian rivers. Bayesian clustering and principal coordinate analysis revealed two main gene pools at the population (river) level. At the site level (i.e., considering all sites independently of their rivers), polymorphism was high even between sites in the same river. Our study revealed a negative relationship between the concentration of nitrogen compounds and the genotypic richness of P. australis populations. We did not find any correlations between the other chemical parameters of the water and the parameters of the genetic diversity. Additionally, there were no genetic differences between sites in modified and unmodified river sections or between sites that differed in land cover type in the neighborhood of the stand.

Drought and Flood Extremes on the Amazon River and in Northeast Brazil, 1790–1900

Abstract Recent severe droughts, extreme floods, and increasing differences between seasonal high and low flows on the Amazon River may represent a twenty-first-century increase in the amplitude of the hydrologic cycle over the Amazon Basin. These precipitation and streamflow changes may have arisen from natural ocean–atmospheric variability, deforestation within the drainage basin of the Amazon River, or anthropogenic climate change. Tree-ring reconstructions of wet-season precipitation extremes, substantiated with historical accounts of climate and river levels on the Amazon River and in northeast Brazil found in the Brazilian Digital Library, indicate that the recent river-level extremes on the Amazon may have been equaled or possibly exceeded during the preinstrumental nineteenth century. The “Forgotten Drought” of 1865 was the lowest wet-season rainfall total reconstructed with tree-rings in the eastern Amazon from 1790 to 2016 and appears to have been one of the lowest stream levels observed on the Amazon River during the historical era according to first-hand descriptions by Louis Agassiz, his Brazilian colleague João Martins da Silva Coutinho, and others. Heavy rains and flooding are described during most of the tree-ring-reconstructed wet extremes, including the complete inundation of “First Street” in Santarem, Brazil, in 1859 and the overtopping of the Bittencourt Bridge in Manaus, Brazil, in 1892. These extremes in the tree-ring estimates and historical observations indicate that recent high and low flow anomalies on the Amazon River may not have exceeded the natural variability of precipitation and streamflow during the nineteenth century. Significance Statement Proxy tree-ring and historical evidence for precipitation extremes during the preinstrumental nineteenth century indicate that recent floods and droughts on the Amazon River may have not yet exceeded the range of natural hydroclimatic variability.