scholarly journals How to design optimal eDNA sampling strategies for biomonitoring in river networks

2020 ◽  
Author(s):  
Luca Carraro ◽  
Julian B. Stauffer ◽  
Florian Altermatt
Keyword(s):  
Measurement Errors ◽  
Best Practice ◽  
Transport Model ◽  
Sampling Site ◽  
River Network ◽  
Decay Rates ◽  
River Networks ◽  
Sampling Effort ◽  
Biodiversity Crisis ◽  
Model Predictions

AbstractThe current biodiversity crisis calls for appropriate and timely methods to assess state and change of bio-diversity. In this respect, environmental DNA (eDNA) is a highly promising tool, especially for aquatic ecosystems. While initial eDNA studies assessed biodiversity at a few sites, technology now allows analyses of samples from many points at a time. However, the selection of these sites has been mostly motivated on an ad-hoc basis, and it is unclear where to position sampling sites in a river network to most effectively sample biodiversity. To this end, hydrology-based models might offer a unique guidance on where to sample eDNA to reconstruct the spatial patterns of taxon density based on eDNA data collected across a watershed.Here, we performed computer simulations to identify best-practice criteria for the choice of positioning of eDNA sampling sites in river networks. To do so, we combined a hydrology-based eDNA transport model with a virtual river network reproducing the scaling features of real rivers. In particular, we conducted simulations investigating scenarios of different number and location of eDNA sampling sites in a riverine network, different spatial taxon distributions, and different eDNA measurement errors.We identified best practices for sampling site selection for taxa that have a scattered versus an even distribution across the network. We observed that, due to hydrological controls, non-uniform patterns of eDNA concentration arise even if the taxon distribution is uniform and decay is neglected. We also found that uncertainties in eDNA concentration estimates do not necessarily hamper model predictions. Knowledge of eDNA decay rates improves model predictions, highlighting the need for empirical estimates of these rates under relevant environmental conditions. Our simulations help define strategies for the design of eDNA sampling campaigns in river networks, and can guide the sampling effort of field ecologists and environmental authorities.

scholarly journals An Innovative Framework for Real Time Monitoring of Pollutant Point Sources in River Networks

2021 ◽  
Author(s):  
Mehdi Mazaheri ◽  
J. M. V. Samani ◽  
Fulvio Boano
Keyword(s):  
Real Time ◽  
Transport Model ◽  
River Network ◽  
Point Sources ◽  
Release Time ◽  
River Networks ◽  
Observation Data ◽  
Geostatistical Approach ◽  
Pollutant Sources ◽  
Simultaneous Identification

Abstract The simultaneous identification of location and source release history in complex river networks is a very complicated ill-posed problem, particularly in a case of multiple unknown pollutant sources with time-varying release pattern. This study presents an innovative method for simultaneous identification of the number, locations and release histories of multiple pollutant point sources in a river network using minimum observation data. Considering two different type of monitoring stations with an adaptive arrangement as well as real-time data collection at those stations and using a reliable numerical flow and transport model, at first the number and suspected reach of presence of pollutant sources are determined. Then the source location and its intensity function is calculated by solving inverse source problem using a geostatistical approach. A case study with three different scenarios in terms of the number, release time and location of pollutant sources are discussed, concerning a river network with unsteady and non-uniform flow. Results showed the capability of the proposed method in identifying of sought source characteristics even in complicated cases with simultaneous activity of multiple pollutant sources.

Identifying global hotspots of plastic waste accumulation along river networks

2021 ◽  
Author(s):  
Jesus Gomez-Velez ◽  
Stefan Krause
Keyword(s):  
Multiple Scales ◽  
Transport Model ◽  
Freshwater Ecosystems ◽  
River Network ◽  
Plastic Waste ◽  
Trapping Efficiency ◽  
River Networks ◽  
Clear Understanding ◽  
Relative Location ◽  
Plastic Pollution

<p>Global plastic pollution is affecting ecosystems and human health globally. Proposing solutions and coping strategies for this threat requires a clear understanding of the processes controlling the fate and transport of mismanaged plastics at multiple scales, going from watersheds to regions and even continents. River corridors are the primary conveyor and trap for mismanaged plastic produced within the landscape and eventually released to the ocean. New approaches that apply technological sensing innovations for monitoring plastic waste in aquatic environments can improve observations and plastic waste datasets globally. However, our understanding of when, where, and how to target monitoring is limited, reducing the benefit gained. There is therefore a critical demand for predictions of hotspots (as well as hot moments) of plastic accumulation along river networks globally, in order to optimize observational capacity.     </p><p>Here, we present a new global flow and transport model for plastic waste in riverine environments. Our model predicts that only a small fraction (roughly 2.5%) of the global mismanaged plastic that entered rivers since the 1950s has been delivered to the ocean by 2020, with an overwhelming majority sequestered in freshwater ecosystems. Furthermore, we predict the patterns of mismanaged plastic accumulation and its residence time depend on (i) the topology and geometry of the river network, (ii) the relative location of plastic sources, and (ii) the relative location and trapping efficiency of flow regulation structures, primarily large dams. Our results highlight the role of rivers as major sinks for plastic waste and the need for targeted remedial strategies that consider the structure of the river network and anthropogenic regulation when proposing intervention measures and sampling efforts.</p>

scholarly journals Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models

2021 ◽  
Vol 10 (3) ◽  
pp. 186
Author(s):  
HuiHui Zhang ◽  
Hugo A. Loáiciga ◽  
LuWei Feng ◽  
Jing He ◽  
QingYun Du
Keyword(s):  
Spatial Scales ◽  
Digital Elevation Models ◽  
Regional Scale ◽  
Drainage Density ◽  
River Network ◽  
Landscape Patterns ◽  
Estimation Methods ◽  
River Networks ◽  
Flow Accumulation ◽  
Digital Elevation

Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studies have considered the geomorphologic complexity in the FAT estimation and river network extraction. Recent studies estimated influencing factors’ impacts on the river length or drainage density without considering anthropogenic impacts and landscape patterns. This study contributes two FAT estimation methods. The first method explores the statistical association between FAT and 47 tentative explanatory factors. Specifically, multi-source data, including meteorologic, vegetation, anthropogenic, landscape, lithology, and topologic characteristics are incorporated into a drainage density-FAT model in basins with complex topographic and environmental characteristics. Non-negative matrix factorization (NMF) was employed to evaluate the factors’ predictive performance. The second method exploits fractal geometry theory to estimate the FAT at the regional scale, that is, in basins whose large areal extent precludes the use of basin-wide representative regression predictors. This paper’s methodology is applied to data acquired for Hubei and Qinghai Provinces, China, from 2001 through 2018 and systematically tested with visual and statistical criteria. Our results reveal key local features useful for river network extraction within the context of complex geomorphologic characteristics at relatively small spatial scales and establish the importance of properly choosing explanatory geomorphologic characteristics in river network extraction. The multifractal method exhibits more accurate extracting results than the box-counting method at the regional scale.

scholarly journals A new vector-based global river network dataset accounting for variable drainage density

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Peirong Lin ◽  
Ming Pan ◽  
Eric F. Wood ◽  
Dai Yamazaki ◽  
George H. Allen
Keyword(s):  
Spatial Variability ◽  
Flow Direction ◽  
River System ◽  
Drainage Density ◽  
River Network ◽  
River Networks ◽  
Digital Elevation ◽  
Machine Learning Approach ◽  
Elevation Model ◽  
National Hydrography Dataset

AbstractSpatial variability of river network drainage density (Dd) is a key feature of river systems, yet few existing global hydrography datasets have properly accounted for it. Here, we present a new vector-based global hydrography that reasonably estimates the spatial variability of Dd worldwide. It is built by delineating channels from the latest 90-m Multi-Error-Removed Improved Terrain (MERIT) digital elevation model and flow direction/accumulation. A machine learning approach is developed to estimate Dd based on the global watershed-level climatic, topographic, hydrologic, and geologic conditions, where relationships between hydroclimate factors and Dd are trained using the high-quality National Hydrography Dataset Plus (NHDPlusV2) data. By benchmarking our dataset against HydroSHEDS and several regional hydrography datasets, we show the new river flowlines are in much better agreement with Landsat-derived centerlines, and improved Dd patterns of river networks (totaling ~75 million kilometers in length) are obtained. Basins and estimates of intermittent stream fraction are also delineated to support water resources management. This new dataset (MERIT Hydro–Vector) should enable full global modeling of river system processes at fine spatial resolutions.

scholarly journals Evaluation of chemical transport model predictions of primary organic aerosol for air masses classified by particle-component-based factor analysis

2012 ◽  
Vol 12 (2) ◽  
pp. 5939-6018
Author(s):  
C. A. Stroud ◽  
M. D. Moran ◽  
P. A. Makar ◽  
S. Gong ◽  
W. Gong ◽  
...  
Keyword(s):  
Factor Analysis ◽  
Transport Model ◽  
Chemical Transport ◽  
Organic Aerosol ◽  
Point Sources ◽  
Urban Site ◽  
Stable Model ◽  
Chemical Transport Model ◽  
The Us ◽  
Model Predictions

Abstract. Observations from the 2007 Border Air Quality and Meteorology Study (BAQS-Met 2007) in southern Ontario (ON), Canada, were used to evaluate Environment Canada's regional chemical transport model predictions of primary organic aerosol (POA). Environment Canada's operational numerical weather prediction model and the 2006 Canadian and 2005 US national emissions inventories were used as input to the chemical transport model (named AURAMS). Particle-component-based factor analysis was applied to aerosol mass spectrometer measurements made at one urban site (Windsor, ON) and two rural sites (Harrow and Bear Creek, ON) to derive hydrocarbon-like organic aerosol (HOA) factors. Co-located carbon monoxide (CO), PM2.5 black carbon (BC), and PM1 SO4 measurements were also used for evaluation and interpretation, permitting a detailed diagnostic model evaluation. At the urban site, good agreement was observed for the comparison of daytime campaign PM1 POA and HOA mean values: 1.1 μg m−3 vs. 1.2 μg m−3, respectively. However, a POA overprediction was evident on calm nights due to an overly-stable model surface layer. Biases in model POA predictions trended from positive to negative with increasing HOA values. This trend has several possible explanations, including (1) underweighting of urban locations in particulate matter (PM) spatial surrogate fields, (2) overly-coarse model grid spacing for resolving urban-scale sources, and (3) lack of a model particle POA evaporation process during dilution of vehicular POA tail-pipe emissions to urban scales. Furthermore, a trend in POA bias was observed at the urban site as a function of the BC/HOA ratio, suggesting a possible association of POA underprediction for diesel combustion sources. For several time periods, POA overprediction was also observed for sulphate-rich plumes, suggesting that our model POA fractions for the PM2.5 chemical speciation profiles may be too high for these point sources. At the rural Harrow site, significant underpredictions in PM1 POA concentration were found compared to observed HOA concentration and were associated, based on back-trajectory analysis, with (1) transport from the Detroit/Windsor urban complex, (2) longer-range transport from the US Midwest, and (3) biomass burning. Daytime CO concentrations were significantly overpredicted at Windsor but were unbiased at Harrow. Collectively, these biases provide support for a hypothesis that combines a current underweighting of PM spatial surrogate fields for urban locations with insufficient model vertical mixing for sources close to the urban measurement sites. The magnitude of the area POA emissions sources in the US and Canadian inventories (e.g., food cooking, road and soil dust, waste disposal burning) suggests that more effort should be placed at reducing uncertainties in these sectors, especially spatial and temporal surrogates.

scholarly journals On the propagation of diel signals in river networks using analytic solutions of flow equations

2015 ◽  
Vol 12 (8) ◽  
pp. 8175-8220 ◽  
Author(s):  
M. Fonley ◽  
R. Mantilla ◽  
S. J. Small ◽  
R. Curtu
Keyword(s):  
Analytic Solutions ◽  
River Network ◽  
Low Flow ◽  
River Networks ◽  
Signal Delay ◽  
Flow Conditions ◽  
Flow Equations ◽  
Linear Transport Equation ◽  
Daily Evapotranspiration ◽  
Self Similar

Abstract. Two hypotheses have been put forth to explain the magnitude and timing of diel streamflow oscillations during low flow conditions. The first suggests that delays between the peaks and troughs of streamflow and daily evapotranspiration are due to processes occurring in the soil as water moves toward the channels in the river network. The second posits that they are due to the propagation of the signal through the channels as water makes its way to the outlet of the basin. In this paper, we design and implement a theoretical experiment to test these hypotheses. We impose a baseflow signal entering the river network and use a linear transport equation to represent flow along the network. We develop analytic streamflow solutions for two cases: uniform and nonuniform velocities in space over all river links. We then use our analytic solutions to simulate streamflows along a self-similar river network for different flow velocities. Our results show that the amplitude and time delay of the streamflow solution are heavily influenced by transport in the river network. Moreover, our equations show that the geomorphology and topology of the river network play important roles in determining how amplitude and signal delay are reflected in streamflow signals. Finally, our results are consistent with empirical observations that delays are more significant as low flow decreases.

Using Chemical Transport Model Predictions To Improve Exposure Assessment of PM2.5 Constituents

2019 ◽  
Vol 6 (8) ◽  
pp. 456-461 ◽  
Author(s):  
Jianlin Hu ◽  
Bart Ostro ◽  
Hongliang Zhang ◽  
Qi Ying ◽  
Michael J. Kleeman
Keyword(s):  
Exposure Assessment ◽  
Transport Model ◽  
Chemical Transport ◽  
Chemical Transport Model ◽  
Model Predictions

A Simple Mathematical Model of Intradialytic Sodium Kinetics: “in vivo” Validation During Hemodialysis with Constant or Variable Sodium

1996 ◽  
Vol 19 (7) ◽  
pp. 393-403 ◽  
Author(s):  
M. Ursino ◽  
L. Colì ◽  
G. La Manna ◽  
M. Grilli Cicilioni ◽  
V. Dalmastri ◽  
...  
Keyword(s):  
Mathematical Model ◽  
Measurement Errors ◽  
Sodium Concentration ◽  
Mean Deviation ◽  
Algebraic Characterization ◽  
Dialysis Session ◽  
Simple Mathematical Model ◽  
Model Predictions ◽  
Dialysate Sodium

A simple mathematical model of the intradialytic relationship between natraemia and dialysate sodium concentration is presented. The model includes a bicompartmental description of sodium, urea and fluid kinetics and an algebraic characterization of diffusive/convective mass-transfer across the dialysis membrane. Its ability to provide realistic responses has been validated comparing model predictions by a priori parameter tuning against quantities measured during in vivo sessions with both constant and variable dialysate sodium concentration. A quantitative analysis of model predictions indicates that the mean deviation between data calculated by the model and those measured in vivo is 1.32 mEq/l for sodium and 0.76 mmol/l for urea, values which do not greatly exceed the measurement errors of current instruments. The model's predictive capacity thus proves reliable. The ability of the model to calculate the amount of sodium removed and the time course of intra-extracellular volumes during the dialysis session makes it possible to forecast the patient's clinical tolerance to a given sodium dialysate concentration.

scholarly journals Quantification of drainable water storage volumes on landmasses and in river networks based on GRACE and river runoff using a cascaded storage approach – first application on the Amazon

2020 ◽  
Vol 24 (3) ◽  
pp. 1447-1465 ◽  
Author(s):  
Johannes Riegger
Keyword(s):  
Phase Shift ◽  
River Runoff ◽  
River Discharge ◽  
Time Lag ◽  
Water Storage ◽  
River Network ◽  
River Networks ◽  
Time Constants ◽  
Tropical Conditions

Abstract. The knowledge of water storage volumes in catchments and in river networks leading to river discharge is essential for the description of river ecology, the prediction of floods and specifically for a sustainable management of water resources in the context of climate change. Measurements of mass variations by the GRACE gravity satellite or by ground-based observations of river or groundwater level variations do not permit the determination of the respective storage volumes, which could be considerably bigger than the mass variations themselves. For fully humid tropical conditions like the Amazon the relationship between GRACE and river discharge is linear with a phase shift. This permits the hydraulic time constant to be determined and thus the total drainable storage directly from observed runoff can be quantified, if the phase shift can be interpreted as the river time lag. As a time lag can be described by a storage cascade, a lumped conceptual model with cascaded storages for the catchment and river network is set up here with individual hydraulic time constants and mathematically solved by piecewise analytical solutions. Tests of the scheme with synthetic recharge time series show that a parameter optimization either versus mass anomalies or runoff reproduces the time constants for both the catchment and the river network τC and τR in a unique way, and this then permits an individual quantification of the respective storage volumes. The application to the full Amazon basin leads to a very good fitting performance for total mass, river runoff and their phasing (Nash–Sutcliffe for signals 0.96, for monthly residuals 0.72). The calculated river network mass highly correlates (0.96 for signals, 0.76 for monthly residuals) with the observed flood area from GIEMS and corresponds to observed flood volumes. The fitting performance versus GRACE permits river runoff and drainable storage volumes to be determined from recharge and GRACE exclusively, i.e. even for ungauged catchments. An adjustment of the hydraulic time constants (τC, τR) on a training period facilitates a simple determination of drainable storage volumes for other times directly from measured river discharge and/or GRACE and thus a closure of data gaps without the necessity of further model runs.

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