Spatial and temporal patterns of soil water content in an agroecological production system

Neves, Hugo Hermsdorff das; Mata, Maria Gabriela Ferreira da; Guerra, José Guilherme Marinho; Carvalho, Daniel Fonseca de; Wendroth, Ole; Ceddia, Marcos Bacis

doi:10.1590/1678-992x-2016-0213

Cited by 11 publications

(2 citation statements)

References 26 publications

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“…Many studies have confirmed the usefulness of the time stability approach. The approach has been widely used to identify the most stable sampling sites for representing the mean soil moisture conditions in a specific area (Cosh et al, 2006;Dorigo et al, 2011;Martínez-Fernández and Ceballos, 2003;Neves et al, 2017;Sur et al, 2013;Zhang and Shao, 2013;Zhou et al, 2007). Many studies have used TSA to validate and calibrate the remote sensing of soil moisture (Colliander et al, 2017;Cosh et al, 2004;Cosh et al, 2008;Crow et al, 2012;Jackson et al, 2012;Peng et al, 2017;Wagner et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Temporal Stability Analysis for the Evaluation of Spatial and Temporal Patterns of Surface Water Quality

Zhang

Zhu

et al. 2022

Water Resour Manage

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Better characterizing the spatio-temporal pattern of water quality would increase the ability to effectively manage water resources. This study applied the concept of temporal stability analysis (TSA) to explore the temporal characteristics of spatial variability in surface water quality. Monitoring data from 41 monitoring stations in Qiantang River, China for 2017-2019 were used to assess four indicators: dissolved oxygen (DO), permanganate index (CODMn), 2 total phosphorus (TP), and ammonia nitrogen (NH3-N). A Spearman's rank correlation for each pair of monitoring times demonstrated that the spatial pattern of water quality was maintained for a specific period of time.A temporal analysis of relative differences was applied to examine the temporal stability of the sampling sites. The mean concentrations, with acceptable errors, were estimated from the representative sites identified for the four water quality indicators. Different metrics were assessed to identify the temporally stable sites. The standard deviation of the relative difference (SDRD) and index of temporal stability (ITS) were found to be better for identifying the stable sites compared to the mean absolute bias error (MABE) and root mean square error (RMSE) in this study. A correlation analysis between the temporal stability indices and potential influencing factors showed that land use proportions (forest, built-up land, and agricultural land), and socio-economic indicators (gross domestic product [GDP] and population density) were closely associated with the temporal stability of water quality. The results showed evidence that the TSA method was feasible and effective in identifying the temporal stability of surface water quality and optimizing the water quality monitoring program. This study's method and findings can help improve surface water quality monitoring strategies and water resource management.

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Section: Introductionmentioning

confidence: 99%

Temporal Stability Analysis for the Evaluation of Spatial and Temporal Patterns of Surface Water Quality

Zhang

Zhu

et al. 2022

Water Resour Manage

View full text Add to dashboard Cite

show abstract

Section: Introductionmentioning

confidence: 99%

Temporal Stability Analysis for the Evaluation of Spatial and Temporal Patterns of Surface Water Quality

Zhang

Zhu

et al. 2021

Preprint

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Better characterizing the spatio-temporal pattern of water quality would increase the ability to effectively manage water resources. This study applied the concept of temporal stability analysis (TSA) to explore the temporal characteristics of spatial variability in surface water quality. Monitoring data from 41 monitoring stations in Qiantang River, China for 2017–2019 were used to assess four indicators: dissolved oxygen (DO), permanganate index (CODMn), total phosphorus (TP), and ammonia nitrogen (NH3–N). A Spearman’s rank correlation for each pair of monitoring times demonstrated that the spatial pattern of water quality was maintained for a specific period of time. The TP concentration was most temporally stable compared with the other three indicators across the study area. A temporal analysis of relative differences was applied to examine the temporal stability of the sampling sites. The mean concentrations, with acceptable errors, were estimated from the representative sites identified for the four water quality indicators. Different metrics were assessed to identify the temporally stable sites. The standard deviation of the relative difference (SDRD) and index of temporal stability (ITS) were found to be better for identifying the stable sites compared to the mean absolute bias error (MABE) and root mean square error (RMSE) in this study. A correlation analysis between the temporal stability indices and potential influencing factors showed that land use proportions (forest, built-up land, and agricultural land), and socio-economic indicators (gross domestic product [GDP] and population density) were closely associated with the temporal stability of water quality. The results showed evidence that the TSA method was feasible and effective in identifying the temporal stability of surface water quality and optimizing the water quality monitoring program. This study’s method and findings can help improve surface water quality monitoring strategies and water resource management.

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Spatial and Temporal Variability of Soil Moisture, Its Measurement and Methods for Analysis: A Review

Sharma,

Swami

2023

River, Sediment and Hydrological Extremes: Causes, Impacts and Management

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Spatial and temporal patterns of soil water content in an agroecological production system

Cited by 11 publications

References 26 publications

Temporal Stability Analysis for the Evaluation of Spatial and Temporal Patterns of Surface Water Quality

Temporal Stability Analysis for the Evaluation of Spatial and Temporal Patterns of Surface Water Quality

Temporal Stability Analysis for the Evaluation of Spatial and Temporal Patterns of Surface Water Quality

Spatial and Temporal Variability of Soil Moisture, Its Measurement and Methods for Analysis: A Review

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