Estimating fluctuation quantities from time series of water-table depths using models with a stochastic component

Knotters, M.; Walsum, P.E.V. van

doi:10.1016/s0022-1694(96)03278-7

Cited by 70 publications

(53 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…From the monitoring of the water table levels time series were calculated the mean highest (MHWL) and mean lowest water level (MLWL) for the period as described by Knotters and Van Walsum (1997). The difference between MLWL and MHWL was our response variable, which is the amplitude of water table fluctuation during the monitored period that started in April 2004 and finished in March 2013.…”

Section: Multivariate Statistical Modellingmentioning

confidence: 99%

Analyses of Water Table Depths Variations in an Outcrop Area of the Guarani Aquifer System in Brotas/Sp-Brazil

Manzione

Soldera

Paes³

et al. 2015

R_I

View full text Add to dashboard Cite

Several factors may influence variations in ground water levels. Some of these factors present static behavior while others present dynamic changes over time. The differences in management operations in agricultural sites, plant development and agricultural practices have direct impact on the unsaturated zone, as the levels of recharge and water tables can respond differently due to local conditions. Different information sources may be used and integrated in a statistical model to reveal the responses of ground water levels under certain conditions. Understanding these processes involves meeting a way of representing correlated variables together to form a new and smaller set of derived variables with minimum loss of information, removing redundancy or duplication. The objective of this study was to analyze the variations in water table depths using information from a groundwater monitoring network in an outcrop area of the Guarani Aquifer System (GAS) in Brotas, São Paulo, Brazil, associated with soil, vegetation and terrain variables, which possibly influence the groundwater dynamics. A factorial analysis was applied to identify the underlying factors which would explain this pattern of correlation within these sets of study variables. The main factors influencing the variations in water table over the monitoring period were as follows: sand particle size, terrain attributes, soil texture, soil and crop management and vegetation. This methodology can be useful for groundwater management, policy making and regulation of soil use in watersheds, and regional studies, for example, maximizing information in data analysis.Keywords: factorial analysis, statistical modeling, recharge, Brazil MANZIONE, R.L.; SOLDERA, B.C.; PAES, C.O.; WENDLAND, E. ANÁLISES DAS VARIAÇÕES DOS NÍVEIS FREÁTICOS EM UMA ÁREA DE AFLORAMENTO DO SISTEMA AQUÍFERO GUARANI EM BROTAS/SP-BRASIL RESUMODiversos fatores influenciam as variações de níveis freáticos. Alguns desses fatores apresentam comportamento estático enquanto outros apresentam mudanças dinâmicas ao longo do tempo. As diferenças nas operações de manejo do solo em áreas agrícolas, desenvolvimento de plantas e práticas agrícolas tem impactos diretos na zona não saturada uma vez que os níveis de recarga

show abstract

Section: Multivariate Statistical Modellingmentioning

confidence: 99%

Analyses of Water Table Depths Variations in an Outcrop Area of the Guarani Aquifer System in Brotas/Sp-Brazil

Manzione

Soldera

Paes³

et al. 2015

R_I

View full text Add to dashboard Cite

show abstract

“…한 모형에 의해 변수들 간의 관계를 설명하거나 예측할 수 있어 (Kim and Kang, 2010), 홍수 예측, 강의 수위, 지하수위를 예측하는데 이용되고 있다 (Knotters and Walsum, 1997;Kim and An, 2011;Choi et al, 2012).…”

Section: 변수들의 관측된 값을 이용하여 모형을 추정한 다음 추정unclassified

Development of the Autoregressive and Cross-Regressive Model for Groundwater Level Prediction at Muan Coastal Aquifer in Korea

Kim¹,

Yeo

2014

Journal of Soil and Groundwater Environment

View full text Add to dashboard Cite

Coastal aquifer in Muan, Jeonnam, has experienced heavy seawater intrusion caused by the extraction of a substantial amount of groundwater for the agricultural purpose throughout the year. It was observed that groundwater level dropped below sea level due to heavy pumping during a dry season, which could accelerate seawater intrusion. Therefore, water level needs to be monitored and managed to prevent further seawater intrusion. The purpose of this study is to develop the autoregressive-cross-regressive (ARCR) models that can predict the present or future groundwater level using its own previous values and pumping events. The ARCR model with pumping and water level data of the proceeding five hours (i.e., the model order of five) predicted groundwater level better than that of the model orders of ten and twenty. This was contrary to expectation that higher orders do increase the coefficient of determination (R 2 ) as a measure of the model's goodness. It was found that the ARCR model with order five was found to make a good prediction of next 48 hour groundwater levels after the start of pumping with R 2 higher than 0.9.

show abstract

“…To avoid groundwater level fluctuation data biased by overly wet or dry years, three years is the minimum measuring period that should be considered (Mew et al 1997;Wamelink et al 2002). According to Knotters & van Walsum (1997), a period of at least t = 30 yr is needed to calculate a reliable mean groundwater level, representative of climatic conditions. Therefore, we also computed the MSL from simulated groundwater levels over the period 1970-2000.…”

Section: Calculation Of Msl T and F Mmentioning

confidence: 99%

The need of data harmonization to derive robust empirical relationships between soil conditions and vegetation

Bartholomeus

Witte

Bodegom

et al. 2008

J Vegetation Science

View full text Add to dashboard Cite

Estimating fluctuation quantities from time series of water-table depths using models with a stochastic component

Cited by 70 publications

References 10 publications

Analyses of Water Table Depths Variations in an Outcrop Area of the Guarani Aquifer System in Brotas/Sp-Brazil

Analyses of Water Table Depths Variations in an Outcrop Area of the Guarani Aquifer System in Brotas/Sp-Brazil

Development of the Autoregressive and Cross-Regressive Model for Groundwater Level Prediction at Muan Coastal Aquifer in Korea

The need of data harmonization to derive robust empirical relationships between soil conditions and vegetation

Contact Info

Product

Resources

About