A data-based predictive model for spatiotemporal variability in stream water quality

Guo, Danlu; Lintern, Anna; Webb, J. Angus; Ryu, Dongryeol; Bende‐Michl, Ulrike; Liu, Shuci; Western, Andrew W.

doi:10.5194/hess-24-827-2020

Cited by 34 publications

(25 citation statements)

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“…In addition, limited model performance in the untransformed space may also relate to the small number of outliers with high concentrations evident in Figure 3. More generally, such performance deterioration can also occur where there is higher skewness in the raw data, as previously illustrated when modelling the spatiotemporal variability of water quality for same study region (e.g., Guo et al, 2020). The impacts of data skewness on performance is less clearly seen in this study when the focus is modelling quantiles, as the constituents having lower model performance (i.e., TP, FRP and NO x ) do not have systematically higher skewness compared to other constituents ( Figure S4).…”

Section: Predictive Power For Water Quality Quantiles and Practicalmentioning

confidence: 78%

“…The causes of these trends remain an open question. A previous study for the same region illustrated that TSS concentrations may have experienced systematic shifts due to the large‐scale prolong drought between 1997 and 2009 in south‐east Australia (the “Millennium drought”) (Guo et al, 2020). Further, changes in land use and management occurring at the finer spatial scales have been found to relate to trends in stream nutrient levels (Smith et al, 2013).…”

Section: Discussionmentioning

confidence: 99%

“…Figure S13 shows the timeseries of three sites that have the greatest magnitudes of significant trends for each constituent. However, in relative to the large number of sites showing significant trends, the deterioration of model performance on sites with significant trends is only marginal ( Figure S14 (Guo et al, 2020). Further, changes in land use and management occurring at the finer spatial scales have been found to relate to trends in stream nutrient levels (Smith et al, 2013).…”

Section: Limitationsmentioning

confidence: 99%

“…In contrast, statistical models are more suited for data‐based inference. Most statistical models tend to predict either spatial patterns of average conditions or time series at specific locations, although some spatiotemporal statistical approaches exist (e.g., Guo et al, 2020) that make it possible to predict timeseries and then calculate the quantiles at individual locations. Previous modelling studies have shown that differences in average ambient water quality conditions across locations can be explained by a set of catchment landscape characteristics (Chang, 2008; A. Lintern, Webb, Ryu, Liu, Bende‐Michl, et al, 2018; A. Lintern, Webb, Ryu, Liu, Waters, et al, 2018; Onderka et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

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Predicting quantiles of water quality from catchment characteristics

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Water quality is often highly variable both in space and time, which poses challenges for modelling the more extreme concentrations. This study developed an alternative approach to predicting water quality quantiles at individual locations. We focused on river water quality data that were collected over 25 years, at 102 catchments across the State of Victoria, Australia. We analysed and modelled spatial patterns of the 10th, 25th, 50th, 75th and 90th percentiles of the concentrations of sediments, nutrients and salt, with six common constituents: total suspended solids (TSS), total phosphorus (TP), filterable reactive phosphorus (FRP), total Kjeldahl nitrogen (TKN), nitrate‐nitrite (NOx), and electrical conductivity (EC). To predict the spatial variation of each quantile for each constituent, we developed statistical regression models and exhaustively searched through 50 catchment characteristics to identify the best set of predictors for that quantile. The models predict the spatial variation in individual quantiles of TSS, TKN and EC well (66%–96% spatial variation explained), while those for TP, FRP and NOx have lower performance (37%–73% spatial variation explained). The most common factors that influence the spatial variations of the different constituents and quantiles are: annual temperature, percentage of cropping land area in catchment and channel slope. The statistical models developed can be used to predict how low‐ and high‐concentration quantiles change with landscape characteristics, and thus provide a useful tool for catchment managers to inform planning and policy making with changing climate and land use conditions.

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Section: Predictive Power For Water Quality Quantiles and Practicalmentioning

confidence: 78%

Section: Discussionmentioning

confidence: 99%

Section: Limitationsmentioning

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Section: Introductionmentioning

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“…The development, validation, and calibration of water quality and ecology models rely on high resolution temporal and spatial data obtained by field monitoring to improve the simulation and prediction results of models. The lack of detailed, reliable and recent water quality data is usually the main obstacle for the proper application of models [35,36]. This leads to inaccurate results and predictions, and it is thus difficult to properly judge environmental indicators and provide a realistic overview of aquatic environments, which may result in poor water management decisions.…”

Section: Discussionmentioning

confidence: 99%

Innovative Water Quality and Ecology Monitoring Using Underwater Unmanned Vehicles: Field Applications, Challenges and Feedback from Water Managers

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With climate change and urban development, water systems are changing faster than ever. Currently, the ecological status of water systems is still judged based on single point measurements, without taking into account the spatial and temporal variability of water quality and ecology. There is a need for better and more dynamic monitoring methods and technologies. Aquatic drones are becoming accessible and intuitive tools that may have an important role in water management. This paper describes the outcomes, field experiences and feedback gathered from the use of underwater drones equipped with sensors and video cameras in various pilot applications in The Netherlands, in collaboration with local water managers. It was observed that, in many situations, the use of underwater drones allows one to obtain information that would be costly and even impossible to obtain with other methods and provides a unique combination of three-dimensional data and underwater footage/images. From data collected with drones, it was possible to map different areas with contrasting vegetation, to establish connections between fauna/flora species and local water quality conditions, or to observe variations of water quality parameters with water depth. This study identifies opportunities for the application of this technology, discusses their limitations and obstacles, and proposes recommendation guidelines for new technical designs.Water 2020, 12, 1196 2 of 20 regulations (e.g., water framework directive) require extensive monitoring and the classification of water bodies based on environmental indicators, and set high standards to comply with [5,6]. Data accessibility and readiness for use is crucial to enable real-time water management [7]. There is a growing demand for innovative and efficient methods and approaches that can take advantage of the high potential of new technologies that are increasingly accessible to professionals from different areas [8].Currently, the monitoring of water quality is primarily conducted by collecting samples to be analyzed in laboratories, which are sometimes complemented with static continuous sensors for certain parameters. These methods are labor intensive, expensive, and only provide results after several days or weeks, and are therefore incapable of mapping rapid changes in the environment [9,10]. Static water quality sensors can provide valuable time series of the seasonal variation of parameters, but require frequent maintenance and have high costs and short lifetimes. For these reasons, only a few units are installed in specific locations of water bodies (e.g., near water supply inlets), resulting in high costs and inefficiency in monitoring large areas. Ecological research and the inspection of underwater objects and infrastructure are often performed by divers or from visual observations and manual collection of samples.There are several examples of successful environmental applications using different mobile platform setups [11].In maritime environments, underwater drones/ROVs (underwater re...

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Dispersed Urban-Stormwater Control Improved Stream Water Quality in a Catchment-Scale Experiment

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A data-based predictive model for spatiotemporal variability in stream water quality

Abstract: Selection of key model predictorsKey predictors for the model were selected in a processinformed and data-driven manner based on our two preced-

Cited by 34 publications

References 85 publications

Predicting quantiles of water quality from catchment characteristics

Predicting quantiles of water quality from catchment characteristics

Innovative Water Quality and Ecology Monitoring Using Underwater Unmanned Vehicles: Field Applications, Challenges and Feedback from Water Managers

Dispersed Urban-Stormwater Control Improved Stream Water Quality in a Catchment-Scale Experiment

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