A Granger causality analysis of groundwater patterns over a half-century

Singh, Nitin K.; Borrok, David M.

doi:10.1038/s41598-019-49278-8

Cited by 18 publications

(11 citation statements)

References 47 publications

(53 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For instance, groundwater level may determine the water available for irrigation and plays a critical role in the sustenance of water intensive crops, such as rice. Our findings showed a substantial decline in groundwater level for most study counties ( Fig 1 ), which can be attributed to excessive pumping for irrigation [ 35 – 36 ]. It is likely that the current rate of groundwater decline may not sustain rice production in the future, highlighting a need to develop sustainable adaption strategies to optimize groundwater usage and to maintain rice yield in the region.…”

Section: Discussionmentioning

confidence: 97%

“…A modeling study from the North China Plains demonstrated that limiting groundwater irrigation can lead to 40% reduction in crop production [ 32 ]. Recently, causal linkages between groundwater levels and rice yield have been estimated over 50 years in the agricultural regions of Louisiana [ 35 ], where irrigation is mostly dominated by pumping [ 36 ]. At the same time, energy-related variables have been shown to influence the production of agricultural commodities [ 37 – 38 ].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Bayesian framework to unravel food, groundwater, and climate linkages: A case study from Louisiana

2020

Self Cite

View full text Add to dashboard Cite

Advancing our understanding of the connections among groundwater, food, and climate is critical to meet global food demands while optimizing water resources usage. However, our understanding of the linkages among groundwater, food, and climate is still limited. Here, we offer a Bayesian framework to simulate crop yield at a regional scale and quantify its relationships and associated uncertainty with climate, groundwater, agricultural, and energyrelated variables. We implemented the framework in the rice-producing regions of Louisiana from 1960-2015. To build a parsimonious model, we used a probability-based variable selection approach to detect the key drivers of rice yield. Rice yield increased, groundwater declined, and area planted declined or did not change over 56yrs. The number of irrigation wells, groundwater level, air temperature, and area planted were found to be the key drivers of rice yield. The regression coefficients showed that rice yield was positively related to groundwater level, and negatively related to area planted and the number of irrigation wells. The limited influence of N fertilizer was noted on rice yield for the period when fertilizer data were available. The inverse relationship between rice yield and area planted pointed to the adaption of efficient crop management practices that maintained or increased yield, despite the decline in area planted. The farmers' ability to install irrigation wells during droughts sustained the yields over long-term but not short-term. This decline in rice yield in response to drought over the short-term might explain the negative relation between yield and irrigation wells. Overall, this work highlighted the uncertainty in relationships between rice yield and key drivers and quantified the intimate connection between food and groundwater. This work may have implications for managing two highly competing commodities (i.e., groundwater and food) in agricultural regions.

show abstract

Section: Discussionmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

A Bayesian framework to unravel food, groundwater, and climate linkages: A case study from Louisiana

2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…To acquire the maximum causality, we applied the Akaike information criterion (AIC; Akaike (1974)) to automatically select the optimal lag length for each model. Following Singh and Borrok (2019), the maximum lag was calculated as shown in Equation ()

italicMax italicLag = 12 {(\frac{T}{100})}^{\frac{1}{4}}

where T is the length of the time series. 3.Two‐tailed F tests were used to test the null hypothesis (H 0 : ENSO does not cause runoff signature variation) of Granger causality at the 5% significance level.…”

Section: Methodsmentioning

confidence: 99%

Section: Granger Causalitymentioning

confidence: 99%

Impacts of El Niño–southern oscillation on global runoff: Characteristic signatures and potential mechanisms

You

Liu

Zhang

et al. 2021

Hydrological Processes

View full text Add to dashboard Cite

Runoff signatures, including low flow, high flow, mean flow and flow variability, have important implications on the environment and society, predominantly through drought, flooding and water resources. Yet, the response of runoff signatures has not been previously investigated at the global scale, and the influencing mechanisms are largely unclear. Hence, this study makes a global assessment of runoff signature responses to the El Niño and La Niña phases using daily streamflow observations from 8217 gauging stations during 1960-2015. Based on the Granger causality test, we found that $15% of the hydrological stations of multiple runoff signatures show a significant causal relationship with El Niño-southern oscillation (ENSO). The quantiles of all runoff signatures were larger during the El Niño phase than during the La Niña phase, implying that the entire flow distribution tends to shift upward during El Niño and downward during La Niña. In addition, El Niño has different effects on low and high flows: it tends to increase the low and mean flow signatures but reduces the high flow and flow variability signatures. In contrast, La Niña generally reduces all runoff signatures. We highlight that the impacts of ENSO on streamflow signatures are manifested by its effects on precipitation (P), potential evaporation (PET) and leaf area index (LAI) through ENSO-induced atmospheric circulation changes. Overall, our study provides a comprehensive picture of runoff signature responses to ENSO, with valuable insights for water resources management and flood and drought disaster mitigation.

show abstract

“…Granger-causality is a useful method for capturing statistical relationships between VAR model variables and estimates the degree to which one time series predicts future values of another time series [46, 43, 44, 47]. The statistical test for Granger-causality compares the predictive ability of the time series model of interest with and without the putative causal variable and is therefore interpretable as evidence towards a causal relationship, but not as proof of a causal relationship.…”

Section: Methodsmentioning

confidence: 99%

Social contacts and transmission of COVID-19 in British Columbia, Canada

Ringa

Otterstatter

Iyaniwura

et al. 2021

Preprint

View full text Add to dashboard Cite

Close-contact rates are thought to be a significant driving force behind the dynamics of transmission for many infectious respiratory diseases. Efforts to control such infections typically focus on the practice of strict contact-avoidance measures. Yet, contact rates and their relation to transmission, and the impact of control measures, are seldom quantified. Here, we quantify the response of contact rates, transmission and new cases of COVID-19 to public health contact-restriction orders, and the associations among these three variables, in the Canadian province of British Columbia (BC) and within its two most densely populated regional health authorities: Fraser Health Authority (FHA) and Vancouver Coastal Health Authority (VCHA). We obtained time series for self-reported close-contact rates from the BC Mix COVID-19 Survey, new reported cases of COVID-19 from the BC Center for Disease Control, and transmission rates based on dynamic model fits to reported cases. Our study period was from September 13, 2020 to February 19, 2021, during which three public health contact-restriction orders were introduced (October 26, November 7 and November 19, 2020). We used segmented linear regression to quantify impacts of public health orders, Pearson correlation to assess the instantaneous relation between contact rates and transmission, and vector autoregressive modeling to study the lagged relations among the three variables. Overall, declines in contact rates and transmission occurred concurrently with the announcement of public health orders, whereas declines in new cases showed a reporting delay of roughly two weeks. The impact of the first public health order (October 26, 2020) on contact rates and transmission was more pronounced than that of the other two health orders. Contact rates and transmission on the same day were strongly correlated (correlation coefficients = 0.64, 0.53 and 0.34 for BC, FHA, and VCHA, respectively). Moreover, contact rates were a significant time-series driver of COVID-19 and explained roughly 30% and 18% of the variation in new cases and transmission, respectively. Interestingly, increases in transmission and new cases were followed by reduced rates of contact: overall, average daily cases explained about 10% of the variation in provincial contact rates. We show that close-contact rates were a significant driver of transmission of COVID-19 in British Columbia, Canada and that they varied in response to public health orders. Our results also suggest a possible feedback, by which contact rates respond to recent changes in reported cases. Our findings help to explain and validate the commonly assumed, but rarely measured, response of close contact rates to public health guidelines and their impact on the dynamics of infectious diseases.

show abstract

A Granger causality analysis of groundwater patterns over a half-century

Cited by 18 publications

References 47 publications

A Bayesian framework to unravel food, groundwater, and climate linkages: A case study from Louisiana

A Bayesian framework to unravel food, groundwater, and climate linkages: A case study from Louisiana

Impacts of El Niño–southern oscillation on global runoff: Characteristic signatures and potential mechanisms

Social contacts and transmission of COVID-19 in British Columbia, Canada

Contact Info

Product

Resources

About