Detection of the spatial patterns of water storage variation over China in recent 70 years

Chen, Zheng; Jiang, Weiguo; Wu, Jianjun; Chen, Kun; Deng, Yue; Jia, Kai; Mo, Xun

doi:10.1038/s41598-017-06558-5

Cited by 20 publications

(19 citation statements)

References 45 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The TWSA time series derived from the GLDAS models have good correlations with those of GRACE observations in the Pearl River basin. Such good correlations have been reported in previous studies in the same basin (Chen et al, ; Luo et al, ). This is because human activities have lower influence to water storage changes and precipitation is the major recharge source of water storage in the Pearl River basin (Chen et al, ).…”

Section: Discussionsupporting

confidence: 89%

“…Such good correlations have been reported in previous studies in the same basin (Chen et al, ; Luo et al, ). This is because human activities have lower influence to water storage changes and precipitation is the major recharge source of water storage in the Pearl River basin (Chen et al, ). Thus, the conclusion should be drawn that the machine learning models can obtain comparable results with the GLDAS models in the Pearl River basin.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Can Terrestrial Water Storage Dynamics be Estimated From Climate Anomalies?

Jing

Zhao

Yao

et al. 2020

Earth and Space Science

View full text Add to dashboard Cite

Freshwater stored on land is an extremely vital resource for all the terrestrial life on Earth. But our ability to record the change of land water storage is weak despite its importance. In this study, we attempt to establish a data‐driven model for simulating terrestrial water storage dynamics by relating climate forcings with terrestrial water storage anomalies (TWSAs) from the Gravity Recovery and Climate Experiment (GRACE) satellites. In the case study in Pearl River basin, China, the relationships were learned by using two ensemble learning algorithms, the Random Forest (RF) and eXtreme Gradient Boost (XGB), respectively. The TWSA in the basin was reconstructed back to past decades and compared with the TWSA derived from global land surface models. As a result, the RF and XGB algorithms both perform well and could nicely reproduce the spatial pattern and value range of GRACE observations, outperforming the land surface models. Temporal behaviors of the reconstructed TWSA time series well capture those of both GRACE and land surface models time series. A multiscale GRACE‐based drought index was proposed, and the index matches the Standardized Precipitation‐Evapotranspiration Index time series at different time scales. The case analysis for years of 1963 and 1998 indicates the ability of the reconstructed TWSA for identifying past drought and flood extremes. The importance of different input variables to the TWSA estimation model was quantified, and the precipitation of the prior 2 months is the most important variable for simulating the TWSA of the current month in the model. Results of this study highlight the great potentials for estimating terrestrial water storage dynamics from climate forcing data by using machine learning to achieve comparable results than complex physical models.

show abstract

Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Can Terrestrial Water Storage Dynamics be Estimated From Climate Anomalies?

Jing

Zhao

Yao

et al. 2020

Earth and Space Science

View full text Add to dashboard Cite

show abstract

“…Between 1979 and the 2010s, water storage decreased significantly [36], and GWS decreased more significantly than TWS. As shown in Figure 3a, the number of TWSAs is less than 0 in most years.…”

Section: Discussionmentioning

confidence: 99%

The Impact of Precipitation Deficit and Urbanization on Variations in Water Storage in the Beijing-Tianjin-Hebei Urban Agglomeration

et al. 2017

Self Cite

View full text Add to dashboard Cite

Depletion of water resources has threatened water security in the Beijing-Tianjin-Hebei urban agglomeration, China. However, the relative importance of precipitation and urbanization to water storage change has not been sufficiently studied. In this study, both terrestrial water storage (TWS) and groundwater storage (GWS) change in Jing-Jin-Ji from 1979 to the 2010s were investigated, based on the global land data assimilation system (GLDAS) and the EartH2Observe (E2O) outputs, and we used a night light index as an index of urbanization. The results showed that TWS anomaly varied in three stages: significant increase from 1981 to 1996, rapid decrease from 1996 to 2002 and increase from 2002 to the 2010s. Simultaneously, GWS has decreased with about 41.5 cm (500% of GWS in 1979). Both urbanization and precipitation change influenced urban water resource variability. Urbanization was a relatively important factor to the depletion of TWS (explains 83%) and GWS (explains 94%) since the 1980s and the precipitation deficit explains 72% and 64% of TWS and GWS variabilities. It indicates that urbanization coupled with precipitation deficit has been a more important factor that impacted depletion of both TWS and GWS than climate change only, in the Jing-Jin-Ji region. Moreover, we suggested that the cumulative effect should be considered when discussing the relationship between influence factors and water storage change.

show abstract

“…In fact, previous studies have reported the contribution and restriction of P to TWS. For instance, Chen et al (2017) revealed the liability of P to TWS (r=0.78) in the PRB. Mo et al (2016) found TWS more strongly explained (60%) by annual P in river basins in south China than in north China.…”

Section: Causal Roles Of Water and Dryness In Vegetation Changesmentioning

confidence: 99%

Assessing the large-scale plant-water relations using remote sensing products in the humid subtropical Pearl River Basin in south China

Wang¹,

Duan²,

Liu³

et al. 2020

Preprint

View full text Add to dashboard Cite

Abstract. Vegetation interact closely with water resources. Conventional studies of plant-water relations at the field scale are fundamental for understanding the mechanisms of how plants alter and adapt to environmental changes, while large-scale studies can be more practical for regional land use and water management towards mitigating climate change impacts. In this study, we investigated the changes in total water storage (TWS), aridity index (AI) and vegetation greenness, productivity and their interactions in the Pearl River Basin since 2002. Results show overall increase of TWS especially in the middle reaches where vegetation greenness and productivity also increased. This region dominated by croplands was identified as the hotspot for changes and interactions between water and vegetation in the basin. Vegetation was more strongly affected by TWS than precipitation (P) at both the annual and monthly scales. Further examination showed that the influence of P on vegetation in wet years was stronger than dry years, while the TWS impact was stronger in dry years than wet years; moreover, greenness responded faster and productivity slower to dryness changes in dry years than wet years. The lag effects resulted in nonlinearity between water and vegetation. This study implies that vegetation in the basin uses rainwater prior to water storage until it gets dry, and the degree of water restriction on vegetation was higher than that of water consumption by vegetation even in this rain-abundant region.

show abstract

Detection of the spatial patterns of water storage variation over China in recent 70 years

Cited by 20 publications

References 45 publications

Can Terrestrial Water Storage Dynamics be Estimated From Climate Anomalies?

Can Terrestrial Water Storage Dynamics be Estimated From Climate Anomalies?

The Impact of Precipitation Deficit and Urbanization on Variations in Water Storage in the Beijing-Tianjin-Hebei Urban Agglomeration

Assessing the large-scale plant-water relations using remote sensing products in the humid subtropical Pearl River Basin in south China

Contact Info

Product

Resources

About