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Hydrology
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Water Quality Monitoring in Streams, Rivers, Lakes and Reservoirs: Novel...
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Water Quality Monitoring in Streams, Rivers, Lakes and Reservoirs: Novel Methods and Applications

A special issue of Hydrology (ISSN 2306-5338). This special issue belongs to the section "Water Resources and Risk Management".

Deadline for manuscript submissions: closed (30 September 2018) | Viewed by 34175

Special Issue Editor

Dr. Gustavious Paul Williams

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, Brigham Young University, Provo, UT 84602, USA
Interests: evaluation of new tools and methods for water quality monitoring and management; real-time forecasting and data analysis; advanced hydroinformatic frameworks; novel applications of remote sensing techniques to water quality monitoring; water quality monitoring remote sensing using unmanned aerial systems
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Technology has made new and novel sensors available and practical for water quality monitoring. Previously, only basic information, such as flow, precipitation, temperature, and a few other parameters, were regularly collected in a near-continuous fashion. Few of these data sets were stored or made easily available for historical or trend analysis. Advances in hydroinformatics now provide efficient access to vast data repositories for historical and near-real time analysis. Field sensors are rugged and can be placed in remote locations or mounted on mobile platforms providing data in locations difficult to access. It is now practical to collect a wide scope of water quality parameters in a near-continuous manner. Similar advances have occurred in remote sensing where multi-spectral and hyper-spectral imagers and other non-contact sensors have the size and cost to make them practical for field applications either hand-held, mounted, or on unmanned aerial systems. We have access to a variety of satellite-collected data previously unavailable, some with long historical records. The amount and types of data available for water quality monitoring has exploded. We generate data sets previously almost unknown in terms of size and scope. For example, you can lower a probe in a reservoir and collect 10 of different parameters every few inches or fly a multi-spectral camera to estimate parameters such as temperature, chlorophyll content, or turbidity on a scale of a few inches, over an entire lake, and repeat this collection on a regular basis. Advances in computing power, data analysis methods, machine learning, cloud storage, distributed hydroinformatics frameworks, and integrated forecasting systems open the door for the application of novel, advanced analysis methods and for new management tools that exploit these new data sets.

Water quality monitoring for streams, rivers, lakes and reservoirs is undergoing a revolution in methods and applications. We need to understand what new sensors are available and how to exploit the resulting data. We need tools to address these huge data sets and use them in effective and efficient manners.

This Special Issue is devoted to highlighting new and novel methods and applications in water quality monitoring. The issue focuses on the use or analysis of new data sets or types, rather than new sensor technology. We encourage studies showing how to combine or analyse disparate data sets to better understand water quality issues or address management concerns. We are interested in new methods that exploit these large data sets. We encourage case studies demonstrating tools optimized for distributed or large data. We invite scholars working on the forefront of recent advances water quality analysis and application to consider submitting their work on topics including but not limited to:

  • New statistical analysis tools or methods for large water quality data sets or data streams;
  • Data fusion methods and applications;
  • Application and use of sensor types new to water quality monitoring;
  • Application and use of new data sets or types to water quality monitoring;
  • Integrated monitoring and modelling for management and forecasting;
  • Applications and advances in hydroinformatics for water quality data;
  • Use of imaging and remote sensing technologies for monitoring trends and processes; and
  • Advanced case studies demonstrating advances or advantages in water quality monitoring.

Dr. Gustavious Paul Williams
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Hydrology is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Big data
  • Hydroinformatics
  • Forecasting and time series analysis
  • Machine learning and analysis
  • Automated data analysis
  • Imaging and remote sensing of water quality
  • Water quality management tools

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Published Papers (6 papers)

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Research

14 pages, 2655 KiB  
Article
Eye in the Sky: Using UAV Imagery of Seasonal Riverine Canopy Growth to Model Water Temperature
by Ann Willis and Eric Holmes
Hydrology 2019, 6(1), 6; https://doi.org/10.3390/hydrology6010006 - 9 Jan 2019
Cited by 5 | Viewed by 3989
Abstract
Until recently, stream temperature processes controlled by aquatic macrophyte shading (i.e., the riverine canopy) was an unrecognized phenomenon. This study aims to address the question of the temporal and spatial scale of monitoring and modeling that is needed to accurately simulate canopy-controlled thermal [...] Read more.
Until recently, stream temperature processes controlled by aquatic macrophyte shading (i.e., the riverine canopy) was an unrecognized phenomenon. This study aims to address the question of the temporal and spatial scale of monitoring and modeling that is needed to accurately simulate canopy-controlled thermal processes. We do this by using unmanned aerial vehicle (UAV) imagery to quantify the temporal and spatial variability of the riverine canopy and subsequently develop a relationship between its growth and time. Then we apply an existing hydrodynamic and water temperature model to test various time steps of canopy growth interpolation and explore the balance between monitoring and computational efficiencies versus model performance and utility for management decisions. The results show that riverine canopies modeled at a monthly timescale are sufficient to represent water temperature processes at a resolution necessary for reach-scale water management decisions, but not local-scale. As growth patterns were more frequently updated, negligible changes were produced by the model. Spatial configurations of the riverine canopy vary interannually; new data may need to be gathered for each growth season. However, the risks of inclement field conditions during the early growth period are a challenge for monitoring via UAVs at sites with access constraints. Full article
(This article belongs to the Special Issue Water Quality Monitoring in Streams, Rivers, Lakes and Reservoirs: Novel Methods and Applications)
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11 pages, 1815 KiB  
Article
Hydrostats: A Python Package for Characterizing Errors between Observed and Predicted Time Series
by Wade Roberts, Gustavious P. Williams, Elise Jackson, E. James Nelson and Daniel P. Ames
Hydrology 2018, 5(4), 66; https://doi.org/10.3390/hydrology5040066 - 2 Dec 2018
Cited by 32 | Viewed by 11976
Abstract
Hydrologists use a number of tools to compare model results to observed flows. These include tools to pre-process the data, data frames to store and access data, visualization and plotting routines, error metrics for single realizations, and ensemble metrics for stochastic realizations to [...] Read more.
Hydrologists use a number of tools to compare model results to observed flows. These include tools to pre-process the data, data frames to store and access data, visualization and plotting routines, error metrics for single realizations, and ensemble metrics for stochastic realizations to calibrate and evaluate hydrologic models. We present an open-source Python package to help characterize predicted and observed hydrologic time series data called hydrostats which has three main capabilities: Data storage and retrieval based on the Python Data Analysis Library (pandas), visualization and plotting routines using Matplotlib, and a metrics library that currently contains routines to compute over 70 different error metrics and routines for ensemble forecast skill scores. Hydrostats data storage and retrieval functions allow hydrologists to easily compare all, or portions of, a time series. For example, it makes it easy to compare observed and modeled data only during April over a 30-year period. The package includes literature references, explanations, examples, and source code. In this note, we introduce the hydrostats package, provide short examples of the various capabilities, and provide some background on programming issues and practices. The hydrostats package provides a range of tools to make characterizing and analyzing model data easy and efficient. The electronic supplement provides working hydrostats examples. Full article
(This article belongs to the Special Issue Water Quality Monitoring in Streams, Rivers, Lakes and Reservoirs: Novel Methods and Applications)
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Graphical abstract

18 pages, 4857 KiB  
Article
An Empirical Mode-Spatial Model for Environmental Data Imputation
by Benjamin Nelsen, D. Alexandra Williams, Gustavious P. Williams and Candace Berrett
Hydrology 2018, 5(4), 63; https://doi.org/10.3390/hydrology5040063 - 17 Nov 2018
Cited by 6 | Viewed by 3712
Abstract
Complete and accurate data are necessary for analyzing and understanding trends in time-series datasets; however, many of the available time-series datasets have gaps that affect the analysis, especially in the earth sciences. As most available data have missing values, researchers use various interpolation [...] Read more.
Complete and accurate data are necessary for analyzing and understanding trends in time-series datasets; however, many of the available time-series datasets have gaps that affect the analysis, especially in the earth sciences. As most available data have missing values, researchers use various interpolation methods or ad hoc approaches to data imputation. Since the analysis based on inaccurate data can lead to inaccurate conclusions, more accurate data imputation methods can provide accurate analysis. We present a spatial-temporal data imputation method using Empirical Mode Decomposition (EMD) based on spatial correlations. We call this method EMD-spatial data imputation or EMD-SDI. Though this method is applicable to other time-series data sets, here we demonstrate the method using temperature data. The EMD algorithm decomposes data into periodic components called intrinsic mode functions (IMF) and exactly reconstructs the original signal by summing these IMFs. EMD-SDI initially decomposes the data from the target station and other stations in the region into IMFs. EMD-SDI evaluates each IMF from the target station in turn and selects the IMF from other stations in the region with periodic behavior most correlated to target IMF. EMD-SDI then replaces a section of missing data in the target station IMF with the section from the most closely correlated IMF from the regional stations. We found that EMD-SDI selects the IMFs used for reconstruction from different stations throughout the region, not necessarily the station closest in the geographic sense. EMD-SDI accurately filled data gaps from 3 months to 5 years in length in our tests and favorably compares to a simple temporal method. EMD-SDI leverages regional correlation and the fact that different stations can be subject to different periodic behaviors. In addition to data imputation, the EMD-SDI method provides IMFs that can be used to better understand regional correlations and processes. Full article
(This article belongs to the Special Issue Water Quality Monitoring in Streams, Rivers, Lakes and Reservoirs: Novel Methods and Applications)
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17 pages, 2029 KiB  
Article
Evaluating Remote Sensing Model Specification Methods for Estimating Water Quality in Optically Diverse Lakes throughout the Growing Season
by Carly Hyatt Hansen and Gustavious Paul Williams
Hydrology 2018, 5(4), 62; https://doi.org/10.3390/hydrology5040062 - 14 Nov 2018
Cited by 19 | Viewed by 4412
Abstract
Spectral images from remote sensing platforms are extensively used to estimate chlorophyll-a (chl-a) concentrations for water quality studies. Empirical models used for estimation are often based on physical principles related to light absorption and emission properties of chl-a and [...] Read more.
Spectral images from remote sensing platforms are extensively used to estimate chlorophyll-a (chl-a) concentrations for water quality studies. Empirical models used for estimation are often based on physical principles related to light absorption and emission properties of chl-a and generally relying on spectral bands in the green, blue, and near-infrared bands. Because the physical characteristics, constituents, and algae populations vary widely from lake to lake, it can be difficult to estimate coefficients for these models. Many studies select a model form that is a function of these bands, determine model coefficients by correlating remotely-measured surface reflectance data and coincidentally measured in-situ chl-a concentrations, and then apply the model to estimate chl-a concentrations for the entire water body. Recent work has demonstrated an alternative approach using simple statistical learning methods (Multiple Linear Stepwise Regression (MLSR)) which uses historical, non-coincident field data to develop sub-seasonal remote sensing chl-a models. We extend this previous work by comparing this method against models from literature, and explore model performance for a region of lakes in Central Utah with varying optical complexity, including two relatively clear intermountain reservoirs (Deer Creek and Jordanelle) and a highly turbid, shallow lake (Utah Lake). This study evaluates the suitability of these different methods for model parameterization for this area and whether a sub-seasonal approach improves performance of standard model forms from literature. We found that while some of the common spectral bands used in literature are selected by the data-driven MLSR method for the lakes in the study region, there are also other spectral bands and band interactions that are often more significant for these lakes. Comparison of model fit shows an improvement in model fit using the data-driven parameterization method over the more traditional physics-based modeling approaches from literature. This suggests that the sub-seasonal approach and exploitation of information contained in other bands helps account for lake-specific optical characteristics, such as suspended solids and other constituents contributing to water color, as well as unique (and season-specific) algae populations, which contribute to the spectral signature of the lake surface, rather than only relying on a generalized optical signature of chl-a. Consideration of these other bands is important for development of models for long-term and entire growing season applications in optically diverse water bodies. Full article
(This article belongs to the Special Issue Water Quality Monitoring in Streams, Rivers, Lakes and Reservoirs: Novel Methods and Applications)
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11 pages, 6006 KiB  
Article
Phosphorus Distribution in Delta Sediments: A Unique Data Set from Deer Creek Reservoir
by Warren Casbeer, Gustavious P. Williams and M. Brett Borup
Hydrology 2018, 5(4), 58; https://doi.org/10.3390/hydrology5040058 - 11 Oct 2018
Cited by 6 | Viewed by 3283
Abstract
Recently, Deer Creek Reservoir (DCR) underwent a large drawdown to support dam reconstruction. This event exposed sediments inundated by the reservoir, since dam completion in the early 1940s. This event allowed us to take sediment data samples and evaluate them for phosphorous (P) [...] Read more.
Recently, Deer Creek Reservoir (DCR) underwent a large drawdown to support dam reconstruction. This event exposed sediments inundated by the reservoir, since dam completion in the early 1940s. This event allowed us to take sediment data samples and evaluate them for phosphorous (P) content. It is difficult for normal reservoir sediment studies to have sediment samples at high spatial resolution because of access. During the drawdown, we collected 91 samples on a grid 100 m in one direction and 200 m in the other. This grid defined an area of approximately 750,000 m2 (185 acre). We took both surface samples, and at some sites, vertical samples. We determined water soluble P for all the samples, and P in four other reservoirs or fractions for 19 samples. Results showed water soluble P in the range of 2.28 × 10−3 to 9.81 × 10−3, KCl-P from 2.53 × 10−3 to 1.10 × 10−2, NaOH-P from 5.30 × 10−2 to 4.60 × 10−1, HCl-P from 1.28 × 10−1 to 1.34, and residual (mostly organic) P from 8.23 × 10−1 to 3.23 mg/g. We provide this data set to the community to support and encourage research in this area. We hope this data set will be used and analyzed to support other research efforts. Full article
(This article belongs to the Special Issue Water Quality Monitoring in Streams, Rivers, Lakes and Reservoirs: Novel Methods and Applications)
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20 pages, 5103 KiB  
Article
Measuring and Calculating Current Atmospheric Phosphorous and Nitrogen Loadings to Utah Lake Using Field Samples and Geostatistical Analysis
by Jacob M. Olsen, Gustavious P. Williams, A. Woodruff Miller and LaVere Merritt
Hydrology 2018, 5(3), 45; https://doi.org/10.3390/hydrology5030045 - 15 Aug 2018
Cited by 14 | Viewed by 6029
Abstract
Atmospheric nutrient loading through wet and dry deposition is one of the least understood, yet can be one of the most important, pathways of nutrient transport into lakes and reservoirs. Nutrients, specifically phosphorus and nitrogen, are essential for aquatic life but in excess [...] Read more.
Atmospheric nutrient loading through wet and dry deposition is one of the least understood, yet can be one of the most important, pathways of nutrient transport into lakes and reservoirs. Nutrients, specifically phosphorus and nitrogen, are essential for aquatic life but in excess can cause accelerated algae growth and eutrophication and can be a major factor that causes harmful algal blooms (HABs) that occur in lakes and reservoirs. Utah Lake is subject to eutrophication and HABs. It is susceptible to atmospheric deposition due to its large surface area to volume ratio, high phosphorous levels in local soils, and proximity to Great Basin dust sources. In this study we collected and analyzed eight months of atmospheric deposition data from five locations near Utah Lake. Our data showed that atmospheric deposition to Utah Lake over the 8-month period was between 8 to 350 Mg (metric tonne) of total phosphorus and 46 to 460 Mg of dissolved inorganic nitrogen. This large range is based on which samples were used in the estimate with the larger numbers including results from “contaminated samples”. These nutrient loading values are significant for Utah Lake in that it has been estimated that only about 17 Mg year−1 of phosphorus and about 200 Mg year−1 of nitrogen are needed to support a eutrophic level of algal growth. We found that atmospheric deposition is a major contributor to the eutrophic nutrient load of Utah Lake. Full article
(This article belongs to the Special Issue Water Quality Monitoring in Streams, Rivers, Lakes and Reservoirs: Novel Methods and Applications)
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