scholarly journals Supplementary material to "A reexamination of the dry gets drier and wet gets wetter paradigm over global land: insight from terrestrial water storage changes"

2022 ◽  
Author(s):  
Jinghua Xiong ◽  
Shenglian Guo ◽  
Jie Chen ◽  
Jiabo Yin
Keyword(s):  
Water Storage ◽  
Terrestrial Water Storage ◽  
Global Land ◽  
Terrestrial Water ◽  
Supplementary Material

scholarly journals Bridging Terrestrial Water Storage Anomaly During GRACE/GRACE-FO Gap Using SSA Method: A Case Study in China

Sensors ◽  
2019 ◽  
Vol 19 (19) ◽  
pp. 4144 ◽  
Author(s):  
Li ◽  
Wang ◽  
Zhang ◽  
Wen ◽  
Zhong ◽  
...  
Keyword(s):  
Water Storage ◽  
Terrestrial Water Storage ◽  
Global Land ◽  
Terrestrial Water ◽  
Land Data Assimilation ◽  
Land Data Assimilation System ◽  
Data Assimilation System ◽  
Assimilation System ◽  
Better Than

The terrestrial water storage anomaly (TWSA) gap between the Gravity Recovery and Climate Experiment (GRACE) and its follow-on mission (GRACE-FO) is now a significant issue for scientific research in high-resolution time-variable gravity fields. This paper proposes the use of singular spectrum analysis (SSA) to predict the TWSA derived from GRACE. We designed a case study in six regions in China (North China Plain (NCP), Southwest China (SWC), Three-River Headwaters Region (TRHR), Tianshan Mountains Region (TSMR), Heihe River Basin (HRB), and Lishui and Wenzhou area (LSWZ)) using GRACE RL06 data from January 2003 to August 2016 for inversion, which were compared with Center for Space Research (CSR), Helmholtz-Centre Potsdam-German Research Centre for Geosciences (GFZ), Jet Propulsion Laboratory (JPL)’s Mascon (Mass Concentration) RL05, and JPL’s Mascon RL06. We evaluated the accuracy of SSA prediction on different temporal scales based on the correlation coefficient (R), Nash–Sutcliffe efficiency (NSE), and root mean square error (RMSE), which were compared with that of an auto-regressive and moving average (ARMA) model. The TWSA from September 2016 to May 2019 were predicted using SSA, which was verified using Mascon RL06, the Global Land Data Assimilation System model, and GRACE-FO results. The results show that: (1) TWSA derived from GRACE agreed well with Mascon in most regions, with the highest consistency with Mascon RL06 and (2) prediction accuracy of GRACE in TRHR and SWC was higher. SSA reconstruction improved R, NSE, and RMSE compared with those of ARMA. The R values for predicting TWS in the six regions using the SSA method were 0.34–0.98, which was better than those for ARMA (0.26–0.97), and the RMSE values were 0.03–5.55 cm, which were better than the 2.29–5.11 cm RMSE for ARMA as a whole. (3) The SSA method produced better predictions for obvious periodic and trending characteristics in the TWSA in most regions, whereas the detailed signal could not be effectively predicted. (4) The predicted TWSA from September 2016 to May 2019 were basically consistent with Global Land Data Assimilation System (GLDAS) results, and the predicted TWSA during June 2018 to May 2019 agreed well with GRACE-FO results. The research method in this paper provides a reference for bridging the gap in the TWSA between GRACE and GRACE-FO.

scholarly journals Global terrestrial water storage connectivity revealed using complex climate network analyses

2015 ◽  
Vol 22 (4) ◽  
pp. 433-446 ◽  
Author(s):  
A. Y. Sun ◽  
J. Chen ◽  
J. Donges
Keyword(s):  
Spatial Patterns ◽  
Land Surface ◽  
Water Storage ◽  
Data Sets ◽  
Terrestrial Water Storage ◽  
Land Surface Models ◽  
Data Set ◽  
Surface Models ◽  
Global Land ◽  
Terrestrial Water

Abstract. Terrestrial water storage (TWS) exerts a key control in global water, energy, and biogeochemical cycles. Although certain causal relationship exists between precipitation and TWS, the latter quantity also reflects impacts of anthropogenic activities. Thus, quantification of the spatial patterns of TWS will not only help to understand feedbacks between climate dynamics and the hydrologic cycle, but also provide new insights and model calibration constraints for improving the current land surface models. This work is the first attempt to quantify the spatial connectivity of TWS using the complex network theory, which has received broad attention in the climate modeling community in recent years. Complex networks of TWS anomalies are built using two global TWS data sets, a remote sensing product that is obtained from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, and a model-generated data set from the global land data assimilation system's NOAH model (GLDAS-NOAH). Both data sets have 1° × 1° grid resolutions and cover most global land areas except for permafrost regions. TWS networks are built by first quantifying pairwise correlation among all valid TWS anomaly time series, and then applying a cutoff threshold derived from the edge-density function to retain only the most important features in the network. Basinwise network connectivity maps are used to illuminate connectivity of individual river basins with other regions. The constructed network degree centrality maps show the TWS anomaly hotspots around the globe and the patterns are consistent with recent GRACE studies. Parallel analyses of networks constructed using the two data sets reveal that the GLDAS-NOAH model captures many of the spatial patterns shown by GRACE, although significant discrepancies exist in some regions. Thus, our results provide further measures for constraining the current land surface models, especially in data sparse regions.

scholarly journals A reexamination of the dry gets drier and wet gets wetter paradigm over global land: insight from terrestrial water storage changes

2022 ◽  
Author(s):  
Jinghua Xiong ◽  
Shenglian Guo ◽  
Jie Chen ◽  
Jiabo Yin
Keyword(s):  
Climate Change ◽  
Land Surface ◽  
Climate Models ◽  
Water Storage ◽  
Global Climate Models ◽  
Historical Period ◽  
Terrestrial Water Storage ◽  
Wetting And Drying ◽  
Global Land ◽  
Terrestrial Water

Abstract. The “dry gets drier and wet gets wetter” (DDWW) paradigm has been widely used to summarize the expected trends of the global hydrologic cycle under climate change. However, the paradigm is challenged over land due to different measures and datasets, and is still unexplored from the perspective of terrestrial water storage anomaly (TWSA). Considering the essential role of TWSA in wetting and drying of the land surface, here we built upon a large ensemble of TWSA datasets including satellite-based products, global hydrological models, land surface models, and global climate models to evaluate the DDWW hypothesis during the historical (1985–2014) and future (2071–2100) periods under various scenarios. We find that 27.1 % of global land confirms the DDWW paradigm, while 22.4 % of the area shows the opposite pattern during the historical period. In the future, the DDWW paradigm is still challenged with the percentage supporting the pattern lower than 20 %, and both the DDWW-validated and DDWW-opposed proportion increase along with the intensification of emission scenarios. Our findings will provide insights and implications for global wetting and drying trends from the perspective of TWSA under climate change.

Links between global terrestrial water storage and large-scale modes of climatic variability

2020 ◽  
Author(s):  
Tiewei Li
Keyword(s):  
Land Surface ◽  
Large Scale ◽  
Water Cycle ◽  
Southern Oscillation ◽  
Global Climate ◽  
Climatic Variability ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Global Land ◽  
Terrestrial Water

<p>Large-scale modes of climatic variability, or teleconnections, influence global patterns of climate variability and provide a framework for understanding complex responses of the global water cycle to global climate. Here, we examine how Terrestrial Water Storage (TWS) responds to 14 major teleconnections (TCs) during the 2003–2016 period based on data obtained from the Gravity Recovery and Climate Experiment (GRACE). By examining correlations between the teleconnections and TWS anomalies (TWSA) data, we find these teleconnections significantly influence TWSA over more than 80.8% of the global land surface. The El Niño-Southern Oscillation (ENSO), the Pacific Decadal Oscillation (PDO), and the Atlantic Multidecadal Oscillation (AMO) are significantly correlated with TWSA variations in 55.8%，56.2% and 60% the global land surface, while other teleconnections affect TWSA at regional scales. We also explore the TCs’ effect on three key hydrological components, including precipitation (P), evapotranspiration (ET) and runoff (R), and their contribution to TWSA variations in 225 river basins. It’s found the TCs generally exert the comprehensive but not equally impact on all three components (P, ET and R). Our findings demonstrate a significant and varying effect of multiple TCs in terrestrial hydrological balance.</p>

scholarly journals Global terrestrial water storage connectivity revealed using complex climate network analyses

2015 ◽  
Vol 2 (2) ◽  
pp. 781-809 ◽  
Author(s):  
A. Y. Sun ◽  
J. Chen ◽  
J. Donges
Keyword(s):  
Spatial Patterns ◽  
Land Surface ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Land Surface Models ◽  
Pairwise Correlation ◽  
Network Analyses ◽  
Surface Models ◽  
Global Land ◽  
Terrestrial Water

Abstract. Terrestrial water storage (TWS) exerts a key control in global water, energy, and biogeochemical cycles. Although certain causal relationships exist between precipitation and TWS, the latter also reflects impacts of anthropogenic activities. Thus, quantification of the spatial patterns of TWS will not only help to understand feedbacks between climate dynamics and hydrologic cycle, but also provide new model calibration constraints for improving the current land surface models. In this work, the connectivity of TWS is quantified using the climate network theory, which has received broad attention in the climate modeling community in recent years. Complex networks of TWS anomalies are built using two global TWS datasets, a remote-sensing product that is obtained from the Gravity Recovery and Climate Experiment (GRACE) satellite mission, and a model-generated dataset from the global land data assimilation system's NOAH model (GLDAS-NOAH). Both datasets have 1 ° × 1 ° resolutions and cover most global land areas except for permafrost regions. TWS networks are built by first quantifying pairwise correlation among all valid TWS anomaly time series, and then applying a statistical cutoff threshold to retain only the most important features in the network. Basinwise network connectivity maps are used to illuminate connectivity of individual river basins with other regions. The constructed network degree centrality maps show TWS hotspots around the globe and the patterns are consistent with recent GRACE studies. Parallel analyses of networks constructed using the two datasets indicate that the GLDAS-NOAH model captures many of the spatial patterns shown by GRACE, although significant discrepancies exist in some regions. Thus, our results provide important insights for constraining land surface models, especially in data sparse regions.

scholarly journals Supplementary material to "Understanding terrestrial water storage variations in northern latitudes across scales"

2017 ◽  
Author(s):  
Tina Trautmann ◽  
Sujan Koirala ◽  
Nuno Carvalhais ◽  
Annette Eicker ◽  
Manfred Fink ◽  
...  
Keyword(s):  
Water Storage ◽  
Terrestrial Water Storage ◽  
Northern Latitudes ◽  
Terrestrial Water ◽  
Supplementary Material

scholarly journals The change of hydrological variables and its effects on vegetation in Central Asia

2021 ◽  
Author(s):  
Qing Peng ◽  
Ranghui Wang ◽  
Yelin Jiang ◽  
Cheng Li ◽  
Wenhui Guo
Keyword(s):  
Soil Moisture ◽  
Central Asia ◽  
Vegetation Dynamics ◽  
Hydrological Cycle ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Spatiotemporal Changes ◽  
Global Land ◽  
Terrestrial Water ◽  
Hydrological Variables

AbstractWater is an important factor that affects local ecological environments, especially in drylands. The hydrological cycle and vegetation dynamics in Central Asia (CA) have been severely affected by climate change. In this study, we employed data from Gravity Recovery and Climate Experiment (GRACE), Global Land Data Assimilation System (GLDAS), Global Land Evaporation Amsterdam Model, and Climate Research Unit to analyze the spatiotemporal changes in hydrological factors (terrestrial water storage (TWS), evapotranspiration, precipitation, and groundwater) in CA from 2003 to 2015. Additionally, the spatiotemporal changes in vegetation dynamics and the influence of hydrological variables on vegetation were analyzed. The results showed that the declining rates of precipitation, evapotranspiration, GRACE-TWS change, GLDAS-TWS change and GW change were 0.40 mm/year, 0.11 mm/year, 50.46 mm/year (p < 0.05), 8.38 mm/year, and 41.18 mm/year (p < 0.05), respectively. Human activity (e.g., groundwater pumping) was the dominant in determining the GW decline in CA. Precipitation dominated the changes in evapotranspiration, GRACE-TWS and GLDAS-TWS (p < 0.05). The 2- to 3-month lagging signal has to do with the transportation from the ground surface to groundwater. The change in the normalized difference vegetation index (NDVI) from 2003 to 2015 indicated the slight vegetation degradation in CA. The results highlighted that precipitation, terrestrial water storage, and soil moisture make important contributions to the vegetation dynamics changes in CA. The effect of precipitation on vegetation growth in spring was significant (p < 0.05), while the soil moisture effect on vegetation in summer and autumn was higher than that of precipitation.

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