Considerations for using turbidity as a surrogate for suspended sediment in small, ungaged streams: Time‐series selection, streamflow estimation, and regional transferability

Steffy, Luanne Y.; Shank, Matthew K.

doi:10.1002/rra.3373

Cited by 10 publications

(5 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The use of turbidity (Turb) as single surrogate to predict other water quality parameters, mainly suspended solids (SS) and total P (TP), through data mining or machine learning methods has been addressed in recent years by numerous research works (Jones et al, 2011;Steffy and Shank, 2018;etc.). However, these works are mainly focused on linear relationships.…”

Section: Introductionmentioning

confidence: 99%

Estimation of high frequency nutrient concentrations from water quality surrogates using machine learning methods

2020

View full text Add to dashboard Cite

Continuous high frequency water quality monitoring is becoming a critical task to support water management. Despite the advancements in sensor technologies, certain variables cannot be easily and/or economically monitored in-situ and in real time. In these cases, surrogate measures can be used to make estimations by means of datadriven models. In this work, variables that are commonly measured in-situ are used as surrogates to estimate the concentrations of nutrients in a rural catchment and in an urban one, making use of machine learning models, specifically Random Forests. The results are compared with those of linear modelling using the same number of surrogates, obtaining a reduction in the Root Mean Squared Error (RMSE) of up to 60.1%. The profit from including up to seven surrogate sensors was computed, concluding that adding more than 4 and 5 sensors in each of the catchments respectively was not worthy in terms of error improvement.

show abstract

Section: Introductionmentioning

confidence: 99%

Estimation of high frequency nutrient concentrations from water quality surrogates using machine learning methods

2020

View full text Add to dashboard Cite

show abstract

“…For example, Artificial Neural Networks have been used to predict nitrate from multiple measures including other nutrient concentrations [13], and standard major axis regression to quantify the relationship between turbidity and TSS [14]. One of the most common approaches is to use linear regression to predict TSS from turbidity [4,7,8,11,15,16,17], nitrogen species from turbidity and conductivity [4,10] and phosphorus species from turbidity [4,7,8,11,16,18]. However, these regression models typically fail to account for the temporal autocorrelation (i.e.…”

Section: Introductionmentioning

confidence: 99%

“…We aimed to assess whether relationships between TSS or NOx and the water-quality surrogates differed (i) among sites and (ii) between estuarine and fresh waters. Surrogate approaches are often site-specific and as such suffer from lack of transferability [17]. Thus, we further aimed to assess (iii) whether a single mixed-effects model fit to the water quality surrogates could be used to predict TSS or NOx over multiple locations, and when using data collected by in situ sensors.…”

Section: Introductionmentioning

confidence: 99%

Predicting sediment and nutrient concentrations from high-frequency water-quality data

et al. 2019

View full text Add to dashboard Cite

Water-quality monitoring in rivers often focuses on the concentrations of sediments and nutrients, constituents that can smother biota and cause eutrophication. However, the physical and economic constraints of manual sampling prohibit data collection at the frequency required to adequately capture the variation in concentrations through time. Here, we developed models to predict total suspended solids (TSS) and oxidized nitrogen (NOx) concentrations based on high-frequency time series of turbidity, conductivity and river level data from in situ sensors in rivers flowing into the Great Barrier Reef lagoon. We fit generalized-linear mixed-effects models with continuous first-order autoregressive correlation structures to water-quality data collected by manual sampling at two freshwater sites and one estuarine site and used the fitted models to predict TSS and NOx from the in situ sensor data. These models described the temporal autocorrelation in the data and handled observations collected at irregular frequencies, characteristics typical of water-quality monitoring data. Turbidity proved a useful and generalizable surrogate of TSS, with high predictive ability in the estuarine and fresh water sites. Turbidity, conductivity and river level served as combined surrogates of NOx. However, the relationship between NOx and the covariates was more complex than that between TSS and turbidity, and consequently the ability to predict NOx was lower and less generalizable across sites than for TSS. Furthermore, prediction intervals tended to increase during events, for both TSS and NOx models, highlighting the need to include measures of uncertainty routinely in water-quality reporting. Our study also highlights that surrogate-based models used to predict sediments and nutrients need to better incorporate temporal components if variance estimates are to be unbiased and model inference meaningful. The transferability of models across sites, and potentially regions, will become increasingly important as organizations move to automated sensing for water-quality monitoring throughout catchments.

show abstract

“…Many studies have considered streamflow discharge from the viewpoint of jump‐driven stochastic processes at both the local (Wang & Guo, 2019; Yoshioka et al, 2023) and regional scales (Bertassello, Durighetto, et al, 2022; Bertassello, Jawitz, et al, 2022; Durighetto et al, 2022). Accelerated erosion due to changes in land use requires estimation of highly stochastic turbidity dynamics in a streamflow environment as a principal water quality index (Steffy & Shank, 2018). Probabilistic analysis can be used to derive a flow–rating curve from hydrological time series data (Hrafnkelsson et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Statistical evaluation of a long‐memory process using the generalized entropic value‐at‐risk

Yoshioka,

Yoshioka

2023

Environmetrics

View full text Add to dashboard Cite

The modeling and identification of time series data with a long memory are important in various fields. The streamflow discharge is one such example that can be reasonably described as an aggregated stochastic process of randomized affine processes where the probability measure, we call it reversion measure, for the randomization is not directly observable. Accurate identification of the reversion measure is critical because of its omnipresence in the aggregated stochastic process. However, the modeling accuracy is commonly limited by the available real‐world data. We resolve this issue by proposing the novel upper and lower bounds of a statistic of interest subject to ambiguity of the reversion measure. Here, we use the Tsallis value‐at‐risk (TsVaR) as a convex risk functional to generalize the widely used entropic value‐at‐risk (EVaR) as a sharp statistical indicator. We demonstrate that the EVaR cannot be used for evaluating key statistics, such as mean and variance, of the streamflow discharge due to the blowup of some exponential integrand. We theoretically show that the TsVaR can avoid this issue because it requires only the existence of some polynomial moment, not exponential moment. As a demonstration, we apply the semi‐implicit gradient descent method to calculate the TsVaR and corresponding Radon–Nikodym derivative for time series data of actual streamflow discharges in mountainous river environments.

show abstract

Considerations for using turbidity as a surrogate for suspended sediment in small, ungaged streams: Time‐series selection, streamflow estimation, and regional transferability

Cited by 10 publications

References 35 publications

Estimation of high frequency nutrient concentrations from water quality surrogates using machine learning methods

Estimation of high frequency nutrient concentrations from water quality surrogates using machine learning methods

Predicting sediment and nutrient concentrations from high-frequency water-quality data

Statistical evaluation of a long‐memory process using the generalized entropic value‐at‐risk

Contact Info

Product

Resources

About