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Mapping and predicting subsidence from spatio-temporal regression models of groundwater-drawdown and subsidence observations

Cartographie et prédiction de la subsidence par des modèles de régression spatio-temporelle des observations sur l’abaissement des eaux souterraines et sur la subsidence

Mapeo y predicción de subsidencia a través de modelos de regresión espacio-temporal de observaciones de extracción de aguas subterráneas y de subsidencia

基于地下水位降深和沉降观测的时空回归模型的沉降制图和预测

Mapeamento e previsão de subsidência a partir de modelos de regressão espaço-temporal de observações de retirada de águas subterrâneas e subsidência

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Abstract

Many regions of the earth are experiencing land subsidence owing to aquifer-system compaction, a consequence of groundwater depletion manifesting as excessive groundwater drawdown. The relation between groundwater drawdown and land subsidence caused by aquifer-system compaction is nonstationary in space and time due to the highly heterogeneous aquifer material, hydraulic and mechanical properties, and spatio-temporal variations in aquifer recharge and groundwater extraction. Annual land subsidence maps are developed using geographical time-slice weighted regression (GTSWR) and geographical temporal weighted regression (GTWR). Considering these spatiotemporal regressions, groundwater drawdown is used as the input parameter to estimate spatial and temporal patterns of land subsidence in both Changhua and Yunlin counties, Taiwan, for an 8-year period. Results indicate that the GTSWR or GTWR models yield greater accuracy with a lower root mean square error (RMSE) than linear regression (LR). The correlation between the predicted and observed data for LR, GTSWR and GTWR is 0.31, 0.93 and 0.94, respectively. In the spatiotemporal models, areas with smaller model coefficients represent over-consolidated sediments, whereas the areas with larger coefficients represent where sediments are normally consolidated. Normally consolidated sediments tend to produce the greatest amount of land subsidence. Annual subsidence patterns reveal that greater levels of subsidence are progressing inland. The greatest level of subsidence occurs in central Yunlin (7 cm/year) due to groundwater extraction. The spatio-temporal regression model is used to predict the effects of reduced groundwater extraction for different areas based on two scenarios of 30 and 50% reductions in groundwater drawdown.

Résumé

Beaucoup de régions du monde connaissent une subsidence du sol par suite de la compaction du système aquifère, conséquence d’un épuisement des eaux souterraines, se manifestant par un rabattement excessif. La relation entre le rabattement des eaux souterraines et la subsidence du sol causés par la compaction du système aquifère est non stationnaire dans l’espace et dans le temps en raison de la forte hétérogénéité du matériel aquifère, de ses propriétés hydrauliques et mécaniques et des variations spatio-temporelles de la recharge aquifère et de l’exploitation des eaux souterraines. Des cartes de la subsidence annuelle du sol sont développées en utilisant une régression pondérée géographiquement et par tranche de temps (RPGTT) et une régression pondérée géographiquement et temporellement (RPGT). En prenant en compte ces régressions spatio-temporelles, l’abaissement des eaux souterraines est utilisé comme le paramètre d’entrée pour évaluer les modèles spatio-temporels de subsidence du sol dans les deux comtés de Changhua et Yunlin, à Taïwan, sur une période de 8 années. Les résultats montrent que les modèles RPGTT ou RPGT fournissent une précision meilleure, accompagnée d’une erreur quadratique moyenne moindre, que la régression linéaire (RL). La corrélation entre les données prédites et les données observées, en ce qui concerne RL, RPGTT et RPGT est respectivement de 0.31, 0.93 et 0.94. Dans les modèles spatio-temporels, les régions avec les coefficients de modèle plus bas signalent des sédiments sur-consolidés, tandis que les régions avec des coefficients plus élevés signalent les lieux où les sédiments sont consolidés normalement. Les sédiments normalement consolidés ont tendance à produire l’amplitude de subsidence du sol la plus grande. Les modèles de subsidence annuelle révèlent que les hauteurs de subsidence les plus grandes progressent vers l’intérieur des terres. La hauteur de subsidence la plus grande se rencontre dans le centre du Yunlin (7 cm/an), du fait de l’affaissement des eaux souterraines. Le modèle de régression spatio-temporelle est utilisé pour prédire les effets d’une exploitation réduite des eaux souterraines dans différentes régions, sur la base de deux scénarios de 30 % et 50 % de réduction de l’abaissement des nappes.

Resumen

Muchas regiones de la Tierra están experimentando subsidencia del terreno debido a la compactación del sistema acuífero, una consecuencia del agotamiento que se manifiesta por una excesiva extracción de aguas subterráneas. La relación entre la extracción de aguas subterráneas y la subsidencia del terreno causada por la compactación del sistema acuífero no es estacionaria en el espacio y en el tiempo debido a la gran heterogeneidad del material del acuífero, las propiedades hidráulicas y mecánicas y las variaciones espacio-temporales de la recarga del acuífero y la extracción de aguas subterráneas. Los mapas anuales de subsidencia del terreno se elaboran utilizando la regresión ponderada por intervalo de tiempo geográfico (GTSWR) y la regresión ponderada temporaria geográfica (GTWR). Teniendo en cuenta estas regresiones espacio-temporales, la extracción de aguas subterráneas se utiliza como parámetro de entrada para estimar los patrones espaciales y temporales de subsidencia del terreno en los condados de Changhua y Yunlin (Taiwán) durante un período de ocho años. Los resultados indican que los modelos GTSWR o GTWR ofrecen una mayor precisión con un error cuadrático medio inferior (RMSE) que la regresión lineal (LR). La correlación entre los datos previstos y los observados para el LR, el GTSWR y el GTWR es de 0.31, 0.93 y 0.94, respectivamente. En los modelos espacio-temporales, las áreas con coeficientes de modelo más pequeños representan sedimentos excesivamente consolidados, mientras que las áreas con coeficientes más grandes representan los lugares donde los sedimentos se consolidan normalmente. Los sedimentos normalmente consolidados tienden a producir la mayor cantidad de subsidencia del terreno. Los patrones de subsidencia anual revelan que los mayores niveles de subsidencia progresan hacia el interior. El mayor nivel de subsidencia se produce en el Yunlin central (7 cm/año) debido al agotamiento de las aguas subterráneas. El modelo de regresión espacio-temporal se utiliza para predecir los efectos de la reducción de la extracción de aguas subterráneas para diferentes áreas, basándose en dos escenarios de reducción del 30% y el 50% de la extracción de aguas subterráneas.

摘要

由于地下水过度开采造成含水系统压实, 地球上许多地区都出现了地面沉降。由于高度非均质的含水层介质与水力和力学性质, 以及含水层补给和地下水开采时空变化性特征, 地下水位降深与含水层系统压实引起的地面沉降之间的关系在空间和时间上是非平稳的。采用地理时间切片加权回归(GTSWR)和地理时间加权回归(GTWR)绘制年度地面沉降图。考虑到这些时空的回归性, 将地下水开采作为输入参数来估算台湾彰化县和云林县8年期间地面沉降的时空态势。结果表明, 与线性回归(LR)相比, GTSWR或GTWR模型具有更高的准确性, 且均方根误差(RMSE)更低。 LR, GTSWR和GTWR的预测数据与观测数据之间的相关系数分别为0.31、0.93和0.94。在时空模型中, 模型相关系数较小的区域表示超固结, 而相关系数较大的区域表示沉积物一般为正常固结。正常固结的沉积物往往会产生最大的地面沉降。年度沉降模式表明, 内陆区正在发生更大程度的沉降。由于地下水的消耗, 最大的沉降发生在云林中部(7 cm/year)。时空回归模型用于根据地下水位降深下降30%和50%两种情况来预测不同地区地下水开采量减少的影响。

Resumo

Muitas regiões da Terra estão passando por subsidência devido à compactação do sistema aquífero, uma consequência do esgotamento das águas subterrâneas que se manifesta como uma captação excessiva de águas subterrâneas. A relação entre captação de água subterrânea e subsidência de terreno causada pela compactação do sistema aquífero não é estacionária no espaço e no tempo devido ao material aquífero altamente heterogêneo, propriedades hidráulicas e mecânicas e variações espaço-temporais na recarga do aquífero e extração de água subterrânea. Mapas anuais de subsidência de terra são desenvolvidos usando regressão geográfica ponderada por fatia de tempo (GTSWR) e regressão geográfica ponderada temporal (GTWR). Considerando essas regressões espaço-temporais, a captação de água subterrânea é usada como parâmetro de entrada para estimar padrões espaciais e temporais de subsidência da terra nos condados de Changhua e Yunlin, Taiwan, por um período de 8 anos. Os resultados indicam que os modelos GTSWR ou GTWR produzem maior precisão com um erro quadrático médio da raiz (RMSE) menor do que a regressão linear (RL). A correlação entre os dados previstos e observados para RL, GTSWR e GTWR é de 0.31, 0.93 e 0.94, respectivamente. Nos modelos espaço-temporais, áreas com coeficientes de modelo menores representam sedimentos superconsolidados, enquanto as áreas com coeficientes maiores representam onde os sedimentos são normalmente consolidados. Sedimentos normalmente consolidados tendem a produzir a maior quantidade de subsidência de terreno. Os padrões anuais de subsidência revelam que maiores níveis de subsidência estão progredindo para o interior. O maior nível de subsidência ocorre no centro de Yunlin (7 cm/ano) devido ao esgotamento das águas subterrâneas. O modelo de regressão espaço-temporal é usado para prever os efeitos da redução da extração de águas subterrâneas em diferentes áreas, com base em dois cenários de redução de 30% e 50% na captação de águas subterrâneas.

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References

  • Abidin HZ, Djaja R, Darmawan D, Hadi S, Akbar A, Rajiyowiryono H, Sudibyo Y, Meilano I, Kasuma M, Kahar J (2001) Land subsidence of Jakarta (Indonesia) and its geodetic monitoring system. Nat Hazards 23:365–387

    Article  Google Scholar 

  • Alley WM, Healy RW, LaBaugh JW, Reilly TEJs (2002) Flow and storage in groundwater systems. Sci 296:1985–1990

  • Brown S, Nicholls R (2015) Subsidence and human influences in mega deltas: the case of the Ganges–Brahmaputra–Meghna. Sci Total Environ 527:362–374

    Article  Google Scholar 

  • Changnon SA, Huff FA, Hsu C-FJJoC (1988) Relations between precipitation and shallow groundwater in Illinois. J Clim 1:1239–1250

  • Chu H-J, Kong S-J, Chang C-H (2018) Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression. Int J Appl Earth Obs 65:1–11

    Article  Google Scholar 

  • Ericson JP, Vörösmarty CJ, Dingman SL, Ward LG, Meybeck MJG, Change P (2006) Effective sea-level rise and deltas: causes of change and human dimension implications. Glob Planet Change 50:63–82

  • Fotheringham AS, Brunsdon C, Charlton M (2003) Geographically weighted regression: the analysis of spatially varying relationships. Wiley, Chichester, UK

  • Fotheringham AS, Crespo R, Yao J (2015) Geographical and temporal weighted regression (GTWR). Geogr Anal 47:431–452

    Article  Google Scholar 

  • Galloway DL, Erkens G, Kuniansky EL, Rowland JC (2016) Preface: Land subsidence processes. Hydrogeol J 24:547–550

    Article  Google Scholar 

  • Gong H, Pan Y, Zheng L, Li X, Zhu L, Zhang C, Huang Z, Li Z, Wang H, Zhou C (2018) Long-term groundwater storage changes and land subsidence development in the North China plain (1971–2015). Hydrogeol J 26:1417–1427

    Article  Google Scholar 

  • Hsieh C-S, Shih T-Y, Hu J-C, Tung H, Huang M-H, Angelier J (2011) Using differential SAR interferometry to map land subsidence: a case study in the Pingtung plain of SW Taiwan. Nat Hazards 58:1311–1332

    Article  Google Scholar 

  • Hsu W-C, Chang H-C, Chang K-T, Lin E-K, Liu J-K, Liou Y-A (2015) Observing land subsidence and revealing the factors that influence it using a multi-sensor approach in Yunlin County, Taiwan. Remote Sens 7:8202–8223

    Article  Google Scholar 

  • Huang B, Wu B, Barry M (2010) Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. Int J Geogr Inf Sci 24:383–401

    Article  Google Scholar 

  • Hung WC, Liu CS (2007). Taiwan land subsidence monitoring and surveying analysis. Report of Industrial Technology Research Institute (ITRI), Hsinchu. 335 pp. (in Chinese)

  • Hung W-C, Hwang C, Chang C-P, Yen J-Y, Liu C-H, Yang W-HJEES (2010) Monitoring severe aquifer-system compaction and land subsidence in Taiwan using multiple sensors: Yunlin, the southern Choushui River Alluvial Fan. Environ Earth Sci 59:1535–1548

  • Jang CS, Chen SK, Ching-Chieh L (2008) Using multiple-variable indicator kriging to assess groundwater quality for irrigation in the aquifers of the Choushui River alluvial fan. Hydrol Process 22:4477–4489

    Article  Google Scholar 

  • Lee M, Yu C-Y, Chiang P-C, Hou C-H (2018) Water–energy Nexus for multi-criteria decision making in water resource management: a case study of Choshui River basin in Taiwan. Water 10:1740

    Article  Google Scholar 

  • Li Y, Gong H, Zhu L, Li X (2017) Measuring spatiotemporal features of land subsidence, groundwater drawdown, and compressible layer thickness in Beijing plain, China. Water 9:64

    Article  Google Scholar 

  • Liu C-H, Pan Y-W, Liao J-J, Huang C-T, Ouyang S (2004) Characterization of land subsidence in the Choshui River alluvial fan, Taiwan. Environ Geol 45:1154–1166

    Article  Google Scholar 

  • Livingston ML, Garrido A (2004) Entering the policy debate: an economic evaluation of groundwater policy in flux. Water Resour Res 40. https://doi.org/10.1029/2003WR002737

  • Othman A, Sultan M, Becker R, Alsefry S, Alharbi T, Gebremichael E, Alharbi H, Abdelmohsen KJSiG (2018) Use of geophysical and remote sensing data for assessment of aquifer depletion and related land deformation. Surv Geophys 39:543–566

  • Rodell M, Velicogna I, Famiglietti JS (2009) Satellite-based estimates of groundwater depletion in India. Nature 460:999–1002. https://doi.org/10.1038/nature08238

    Article  Google Scholar 

  • Sundell J, Haaf E, Norberg T, Alén C, Karlsson M, Rosén L (2019) Risk mapping of groundwater-drawdown-induced land subsidence in heterogeneous soils on large areas. Risk Anal 39:105–124

    Article  Google Scholar 

  • Syvitski JPM (2008) Deltas at risk. Sustain Sci 3:23–32

  • Syvitski JP, Vörösmarty CJ, Kettner AJ, Green PJS (2005) Impact of humans on the flux of terrestrial sediment to the global coastal ocean. Science 308:376–380

  • Tangdamrongsub N, Han S-C, Jasinski MF, Šprlák MJRSoE (2019) Quantifying water storage change and land subsidence induced by reservoir impoundment using GRACE, Landsat, and GPS data. Rem Sensing Environ 233, Art. no. 111385

  • Tsai M-S, Hsu K-C (2018) Identifying poromechanism and spatially varying parameters of aquifer compaction in Choushui River alluvial fan, Taiwan. Eng Geol 245:20–32

    Article  Google Scholar 

  • Tung H, Hu J-CJT (2012) Assessments of serious anthropogenic land subsidence in Yunlin County of central Taiwan from 1996 to 1999 by Persistent Scatterers InSAR. Technophysics 578:126–135

  • UN (2014) Water scarcity. United Nations. http://www.un.org/waterforlifedecade/scarcity.shtml. Accessed 25 November 2019

  • Wang C-T, Chen K-S, Hu J-C, Chang W-Y, Boerner WM (2011) Mapping land uplift and subsidence in the industrial parks in northern Taiwan by radar interferometry. Int J Rem Sensing 32:6527–6538

  • Yu H-L, Chu H-J (2010) Understanding space–time patterns of groundwater system by empirical orthogonal functions: a case study in the Choshui River alluvial fan, Taiwan. J Hydrol 381:239–247

    Article  Google Scholar 

Download references

Acknowledgements

The authors would like to thank the editors and anonymous reviewers for providing suggestions for paper improvement.

Funding

This study was supported by Water Resources Agency and Ministry of Science and Technology (MOST), Taiwan.

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Correspondence to Hone-Jay Chu.

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Ali, M.Z., Chu, HJ. & Burbey, T.J. Mapping and predicting subsidence from spatio-temporal regression models of groundwater-drawdown and subsidence observations. Hydrogeol J 28, 2865–2876 (2020). https://doi.org/10.1007/s10040-020-02211-0

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