2012
DOI: 10.1002/hyp.9381
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Simulating effect of anthropogenic activities and climate variation on Liulin Springs discharge depletion by using the ARIMAX model

Abstract: Based on the groundwater development process, and regional economic and social developing history, we divided the spring hydrological process of the Liulin Springs Basin into two periods: pre‐1973 and post‐1974. In the first period (i.e. 1957–1973), the spring discharge was affected by climate variation alone, and in the second period (i.e. 1974–2009), the spring discharge charge was influenced by both climate variation and human activities. A piecewise analysis strategy was used to differentiate the contribut… Show more

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Cited by 11 publications
(4 citation statements)
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“…These subsurface-focused or integrated (surface and subsurface flow) models can be used to conduct predictive scenarios to determine the effects of climate change, urbanization, or increasing groundwater use on small (and large) springs, which is required to implement mitigation measures to protect ecological habitats. Numerical modeling studies mostly focus on karstic settings in terms of interpretation of spring flow mechanisms using parsimonious lumped-parameter models (Hao et al, 2012;Amoruso et al, 2011;Barrett and Charbeneau, 1997;Bonacci and Bojanic, 1991) distributed and/or lumped parameter equivalent porous media models (Dragoni et al, 2013;Chen et al, 2013;Doummar et al, 2012;Scanlon et al, 2003); and channel flow (Eisenlohr et al, 1997) or purely conduit flow (Halihan and Wicks, 1998) formulations. Equivalent porous media approaches for non-karstic fractured bedrock springs are also reported in the literature (e.g., Farlin et al, 2013;Swanson et al, 2006;Swanson and Bahr, 2004).…”
Section: Introductionmentioning
confidence: 99%
“…These subsurface-focused or integrated (surface and subsurface flow) models can be used to conduct predictive scenarios to determine the effects of climate change, urbanization, or increasing groundwater use on small (and large) springs, which is required to implement mitigation measures to protect ecological habitats. Numerical modeling studies mostly focus on karstic settings in terms of interpretation of spring flow mechanisms using parsimonious lumped-parameter models (Hao et al, 2012;Amoruso et al, 2011;Barrett and Charbeneau, 1997;Bonacci and Bojanic, 1991) distributed and/or lumped parameter equivalent porous media models (Dragoni et al, 2013;Chen et al, 2013;Doummar et al, 2012;Scanlon et al, 2003); and channel flow (Eisenlohr et al, 1997) or purely conduit flow (Halihan and Wicks, 1998) formulations. Equivalent porous media approaches for non-karstic fractured bedrock springs are also reported in the literature (e.g., Farlin et al, 2013;Swanson et al, 2006;Swanson and Bahr, 2004).…”
Section: Introductionmentioning
confidence: 99%
“…Generally, an ARIMA model consists of an autoregressive (AR) model, a difference process that deals with non-stationary data, and a moving average (MA) model, with details presented in Hao et al, (2013).…”
Section: Autoregressive Integrated Moving Average (Arima)mentioning
confidence: 99%
“…e hyperparameters, i.e., the learning rate (LR), the maximum depth (MD), the maximum features (MF), the minimum sample split (MSS), and the minimum sample leaf (MSL), are searched for using Bayesian optimization based on the Gaussian processes. [58] are linear stochastic models that are obtained by combining the AR and the MA models, and they are used to model the dependent stochastic components of a time series. ARMA models can be written as follows:…”
Section: Gbrt Model For Learning Predictionmentioning
confidence: 99%