scholarly journals Efficient screening of groundwater head monitoring data for anthropogenic effects and measurement errors

2019 ◽  
Author(s):  
Christian Lehr ◽  
Gunnar Lischeid
Keyword(s):  
Groundwater Level ◽  
Measurement Errors ◽  
Monitoring Network ◽  
Anthropogenic Influences ◽  
Monitoring Networks ◽  
Normal Behaviour ◽  
Technical Problems ◽  
Groundwater Head ◽  
Efficient Screening ◽  
Selection Of

Abstract. Groundwater level data is monitored by environmental agencies to support sustainable use of groundwater resources. For this purpose a high spatial coverage of the monitoring networks and continuous monitoring in high temporal resolution is desired. This leads to large data sets that have to be quality checked and analysed to distinguish local anthropogenic influences from natural variability of the groundwater level dynamics at each well. Both technical problems with the measurements as well as local anthropogenic influences can lead to local anomalies in the hydrographs. We suggest a fast and efficient screening method for identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks. The only information required is a set of time series of groundwater head all measured at the same instants of time. For each well of the monitoring network a reference hydrograph is calculated, describing expected normal behaviour at the respective well as it is typical for the monitored region. The reference hydrograph is calculated by multiple linear regression of the observed hydrograph with the stable principal components (PCs) of a principal component analysis of all groundwater head series of the network as predictor variables. The stable PCs are those PCs which were found in a random subsampling procedure to be rather insensitive to the specific selection of analysed observation wells, respectively complete series, and to the specific selection of measurement dates. Hence they can be considered to be representative for the monitored region in the respective period. The residuals of the reference hydrograph describe local deviations from the normal behaviour. Peculiarities in the residuals allow to quality check the data for measurement errors and identify wells with possible anthropogenic influence. The approach was tested with 141 groundwater head series of the state authority groundwater monitoring network in northeast Germany covering the period from 1993 to 2013 in approximately weekly resolution.

scholarly journals Efficient screening of groundwater head monitoring data for anthropogenic effects and measurement errors

2020 ◽  
Vol 24 (2) ◽  
pp. 501-513
Author(s):  
Christian Lehr ◽  
Gunnar Lischeid
Keyword(s):  
Time Series ◽  
Measurement Errors ◽  
Monitoring Network ◽  
Anthropogenic Influences ◽  
Normal Behaviour ◽  
Technical Problems ◽  
Groundwater Head ◽  
Northeastern Germany ◽  
Efficient Screening ◽  
Selection Of

Abstract. Groundwater levels are monitored by environmental agencies to support the sustainable use of groundwater resources. For this purpose continuous and spatially comprehensive monitoring in high spatial and temporal resolution is desired. This leads to large datasets that have to be checked for quality and analysed to distinguish local anthropogenic influences from natural variability of the groundwater level dynamics at each well. Both technical problems with the measurements as well as local anthropogenic influences can lead to local anomalies in the hydrographs. We suggest a fast and efficient screening method for the identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks. The only information required is a set of time series of groundwater heads all measured at the same instants of time. For each well of the monitoring network a reference hydrograph is calculated, describing expected “normal” behaviour at the respective well as is typical for the monitored region. The reference hydrograph is calculated by multiple linear regression of the observed hydrograph with the “stable” principal components (PCs) of a principal component analysis of all groundwater head series of the network as predictor variables. The stable PCs are those PCs which were found in a random subsampling procedure to be rather insensitive to the specific selection of the analysed observation wells, i.e. complete series, and to the specific selection of measurement dates. Hence they can be considered to be representative for the monitored region in the respective period. The residuals of the reference hydrograph describe local deviations from the normal behaviour. Peculiarities in the residuals allow the data to be checked for measurement errors and the wells with a possible anthropogenic influence to be identified. The approach was tested with 141 groundwater head time series from the state authority groundwater monitoring network in northeastern Germany covering the period from 1993 to 2013 at an approximately weekly frequency of measurement.

scholarly journals A decision support tool for optimising groundwater-level monitoring networks using an adaptive genetic algorithm

2021 ◽  
Author(s):  
Antonios Parasyris ◽  
Katerina Spanoudaki ◽  
Emmanouil A. Varouchakis ◽  
Nikolaos A. Kampanis
Keyword(s):  
Genetic Algorithm ◽  
Groundwater Level ◽  
Monitoring Network ◽  
Decision Support Tool ◽  
Adaptive Method ◽  
Adaptive Genetic Algorithm ◽  
Monitoring Networks ◽  
Support Tool ◽  
Groundwater Levels ◽  
Geostatistical Methods

Abstract Mapping of the spatial variability of sparse groundwater-level measurements is usually achieved by means of geostatistical methods. This work tackles the problem of deficient sampling of an aquifer, by employing an innovative integer adaptive Genetic Algorithm (iaGA) coupled with geostatistical modelling by means of ordinary kriging, to optimise the monitoring network. Fitness functions based on three different errors are used for removing a constant number of boreholes from the monitoring network. The developed methodology has been applied to the Mires basin in Crete, Greece. The methodological improvement proposed concerns the adaptive method for the GA, which affects the crossover–mutation fractions depending on the stall parameter, aiming at higher accuracy and faster convergence of the GA. The initial dataset consists of 70 monitoring boreholes and the applied methodology shows that as many as 40 boreholes can be removed, while still retaining an accurate mapping of groundwater levels. The proposed scenario for optimising the monitoring network consists of removing 30 boreholes, in which case the estimated uncertainty is considerably smaller. A sensitivity analysis is conducted to compare the performance of the standard GA with the proposed iaGA. The integrated methodology presented is easily replicable for other areas for efficient monitoring networks design.

scholarly journals Optimal design of groundwater-level monitoring networks

2017 ◽  
Vol 19 (6) ◽  
pp. 920-929 ◽  
Author(s):  
Fahimeh Mirzaie-Nodoushan ◽  
Omid Bozorg-Haddad ◽  
Hugo A. Loáiciga
Keyword(s):  
Groundwater Level ◽  
Groundwater Monitoring ◽  
Design Method ◽  
Monitoring Network ◽  
Optimization Method ◽  
Monitoring Networks ◽  
Nsga Ii ◽  
Design Data ◽  
Groundwater Levels ◽  
Level Monitoring

Abstract Groundwater monitoring plays a significant role in groundwater management. This study presents an optimization method for designing groundwater-level monitoring networks. The proposed design method was used in the Eshtehard aquifer, in central Iran. Three scenarios were considered to optimize the locations of the observation wells: (1) designing new monitoring networks, (2) redesigning existing monitoring networks, and (3) expanding existing monitoring networks. The kriging method was utilized to determine groundwater levels at non-monitoring locations for preparing the design data base. The optimization of the groundwater monitoring network had the objectives of (1) minimizing the root mean square error and (2) minimizing the number of wells. The non-dominated sorting genetic algorithm (NSGA-II) was applied to optimize the network. Inverse distance weighting interpolation was used in NSGA-II to estimate the groundwater levels while optimizing network design. Results of the study indicate that the proposed method successfully optimizes the design of groundwater monitoring networks that achieve accuracy and cost-effectiveness.

DEVELOPMENT OF INTEGRATED MONITORING NETWORK FOR MEASURING SALINITY OF SEA WATER

2018 ◽  
pp. 18-26
Author(s):  
Sima Ajdar qizi Askerova
Keyword(s):  
Surface Waters ◽  
Sea Water ◽  
Water Environment ◽  
Monitoring Network ◽  
Monitoring Networks ◽  
The Caspian Sea ◽  
Integrated Monitoring ◽  
Ecological Control ◽  
Different Depths ◽  
Optimal Construction

Monitoring of sea water condition is one of major requirements for carrying out the reliable ecological control of water environment. Monitoring networks contain such elements as sea buoys, beacons, etc. and are designated for measuringvarious hydrophysical parameters, including salinity of sea water. Development of specialized network and a separate buoy system for measuring thesea water salinity at different depths makes it possible to determine major regularities of processes of pollution and self-recovery of the sea waters. The article describes the scientific and methodological basics for development of this specialized network and questions of its optimal construction. It is well-known that at a depth of 30-45 m of the Caspian Sea salinity decreases and then at a depth of 45-60 m salinity is fully recovered. The mentioned changes of salinity at the relatively upper layer of sea waters is of special interest for studying the effect of ocean-going processes on the climate forming in the Caspian area. In terms of informativeness of measurements of surface waters salinity, the most informative is a layer ata 30-60 m depth, where inversion and recovery of salinity take place. It is shown that in most informative subrange of measurements, i. e. at a depth of 30-60 m optimization of regime of measurements complex should be carried out in order to increase the effectiveness of held researches. It is shown that at a depth of 35-50 m choice of the optimum regime of measurements makes it possible to obtain the maximum amount of information.

National Park Service geologic type section inventory: Mojave Desert Inventory & Monitoring Network

2021 ◽  
Author(s):  
Tim Henderson ◽  
Vincent Santucci ◽  
Tim Connors ◽  
Justin Tweet
Keyword(s):  
Mojave Desert ◽  
National Park ◽  
National Park Service ◽  
Monitoring Network ◽  
Geographic Area ◽  
Support Network ◽  
Monitoring Networks ◽  
The Public ◽  
Type Section ◽  
Park Service

A fundamental responsibility of the National Park Service (NPS) is to ensure that park resources are preserved, protected, and managed in consideration of the resources themselves and for the benefit and enjoyment by the public. Through the inventory, monitoring, and study of park resources, we gain a greater understanding of the scope, significance, distribution, and management issues associated with these resources and their use. This baseline of natural resource information is available to inform park managers, scientists, stakeholders, and the public about the conditions of these resources and the factors or activities that may threaten or influence their stability and preservation. There are several different categories of geologic or stratigraphic units (supergroup, group, formation, member, bed) that represent a hierarchical system of classification. The mapping of stratigraphic units involves the evaluation of lithologies, bedding properties, thickness, geographic distribution, and other factors. Mappable geologic units may be described and named through a rigorously defined process that is standardized and codified by the professional geologic community (North American Commission on Stratigraphic Nomenclature 2005). In most instances when a new geologic unit such as a formation is described and named in the scientific literature, a specific and well-exposed section or exposure area of the unit is designated as the type section or other category of stratotype (see “Definitions” below). The type section is an important reference exposure for a named geologic unit which presents a relatively complete and representative example for this unit. Geologic stratotypes are important both historically and scientifically, and should be available for other researchers to evaluate in the future.. The inventory of all geologic stratotypes throughout the 423 units of the NPS is an important effort in documenting these locations in order that NPS staff recognize and protect these areas for future studies. The focus adopted for completing the baseline inventories throughout the NPS was centered on the 32 inventory and monitoring networks (I&M) established during the late 1990s. The I&M networks are clusters of parks within a defined geographic area based on the ecoregions of North America (Fenneman 1946; Bailey 1976; Omernik 1987). These networks share similar physical resources (e.g., geology, hydrology, climate), biological resources (e.g., flora, fauna), and ecological characteristics. Specialists familiar with the resources and ecological parameters of the network, and associated parks, work with park staff to support network-level activities such as inventory, monitoring, research, and data management. Adopting a network-based approach to inventories worked well when the NPS undertook paleontological resource inventories for the 32 I&M networks. The planning team from the NPS Geologic Resources Division who proposed and designed this inventory selected the Greater Yellowstone Inventory & Monitoring Network (GRYN) as the pilot network for initiating this project. Through the research undertaken to identify the geologic stratotypes within the parks of the GRYN methodologies for data mining and reporting on these resources were established. Methodologies and reporting adopted for the GRYN have been used in the development of this report for the Mojave Desert Inventory & Monitoring Network (MOJN). The goal of this project is to consolidate information pertaining to geologic type sections that occur within NPS-administered areas, in order that this information is available throughout the NPS to inform park managers and to promote the preservation and protection of these important geologic landmarks and geologic heritage resources. The review of stratotype occurrences for the MOJN shows there are currently no designated stratotypes for Joshua Tree National Park (JOTR) or Manzanar National Historic Site (MANZ); Death Valley...

Geostatistical screening of flood events in the groundwater levels of the diverted inner delta of the Danube River: implications for river bed clogging

2018 ◽  
Vol 10 (1) ◽  
pp. 64-78 ◽  
Author(s):  
Balázs Trásy ◽  
Tamás Garamhegyi ◽  
Péter Laczkó-Dobos ◽  
József Kovács ◽  
István Gábor Hatvani
Keyword(s):  
Spatial Autocorrelation ◽  
Groundwater Monitoring ◽  
Shallow Groundwater ◽  
Monitoring Network ◽  
Danube River ◽  
Monitoring Networks ◽  
Flood Events ◽  
Raw Data ◽  
Monitoring Wells ◽  
Autocorrelation Structure

Abstract The efficient operation of shallow groundwater (SGW) monitoring networks is crucial to water supply, in-land water protection, agriculture and nature conservation. In the present study, the spatial representativity of such a monitoring network in an area that has been thoroughly impacted by anthropogenic activity (river diversion/damming) is assessed, namely the Szigetköz adjacent to the River Danube. The main aims were to assess the spatial representativity of the SGW monitoring network in different discharge scenarios, and investigate the directional characteristics of this representativity, i.e. establish whether geostatistical anisotropy is present, and investigate how this changes with flooding. After the subtraction of a spatial trend from the time series of 85 shallow groundwater monitoring wells tracking flood events from 2006, 2009 and 2013, variography was conducted on the residuals, and the degree of anisotropy was assessed to explore the spatial autocorrelation structure of the network. Since the raw data proved to be insufficient, an interpolated grid was derived, and the final results were scaled to be representative of the original raw data. It was found that during floods the main direction of the spatial variance of the shallow groundwater monitoring wells alters, from perpendicular to the river to parallel with it for over a period of about two week. However, witht the passing of the flood, this returns to its original orientation in ~2 months. It is likely that this process is related first to the fast removal of clogged riverbed strata by the flood, then to their slower replacement. In addition, the study highlights the importance of assessing the direction of the spatial autocorrelation structure of shallow groundwater monitoring networks, especially if the aim is to derive interpolated maps for the further investigation or modeling of flow.

scholarly journals Geological monitoring networks for risk management close to large rock cliffs: the case history of Gallivaggio and Cataeggio in the italian Alps

2021 ◽  
Vol 76 (1) ◽  
pp. 85-101
Author(s):  
Luca Dei Cas ◽  
Maria Luisa Pastore ◽  
Andrea Pavan ◽  
Nicola Petrella
Keyword(s):  
Early Warning ◽  
Rock Slope ◽  
Monitoring Network ◽  
Monitoring Networks ◽  
Italian Alps ◽  
Synthetic Aperture Radar Interferometry ◽  
Reliable Monitoring ◽  
Critical Issues ◽  
History Of ◽  
Short Time

Abstract. In areas located near large rock cliffs, risk reduction by early warning monitoring systems highligts potentiality but also critical issues and limits. The paper examines two rock slope failures that occurred in a short time from each other near inhabited areas in the Italian Alps. The viscous behavior of the rock mass was reconstructed through data processing from ground-based Synthetic Aperture Radar Interferometry (InSAR), and elaboration of acceleration and speed curves. Landslides types and underlying complexity associated with rock detachment mechanisms suggest the identification of precautionary alarm thresholds for collapse forecasting. The analysis of financial outlay, both for mitigation works and for monitoring activities, highlight the adequacy and the opportunity to combine passive systems, like embankments or rockfall drapery meshes, with a reliable monitoring network for early warning.

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