scholarly journals Evaluation of GLDAS-1 and GLDAS-2 Forcing Data and Noah Model Simulations over China at the Monthly Scale

2016 ◽  
Vol 17 (11) ◽  
pp. 2815-2833 ◽  
Author(s):  
Wen Wang ◽  
Wei Cui ◽  
Xiaoju Wang ◽  
Xi Chen
Keyword(s):  
Water Cycle ◽  
Water Storage ◽  
Temperature Data ◽  
Large Temperature ◽  
Runoff Water ◽  
Monthly Scale ◽  
Terrestrial Water ◽  
Land Data Assimilation System ◽  
Temperature Errors ◽  
Gravity Recovery

Abstract The Global Land Data Assimilation System (GLDAS) is an important data source for global water cycle research. Using ground-based measurements over continental China, the monthly scale forcing data (precipitation and air temperature) during 1979–2010 and model outputs (runoff, water storage, and evapotranspiration) during 2002–10 of GLDAS models [focusing on GLDAS, version 1 (GLDAS-1)/Noah and GLDAS, version 2 (GLDAS-2)/Noah] are evaluated. Results show that GLDAS-1 has serious discontinuity issues in its forcing data, with large precipitation errors in 1996 and large temperature errors during 2000–05. While the bias correction of the GLDAS-2 precipitation data greatly improves temporal continuity and reduces the biases, it makes GLDAS-2 precipitation less correlated with observed precipitation and makes it have larger mean absolute errors than GLDAS-1 precipitation for most months over the year. GLDAS-2 temperature data are superior to GLDAS-1 temperature data temporally and spatially. The results also show that the change rates of terrestrial water storage (TWS) data by GLDAS and the Gravity Recovery and Climate Experiment (GRACE) do not match well in most areas of China, and both GLDAS-1 and GLDAS-2 are not very capable of capturing the seasonal variation in monthly TWS change observed by GRACE. Runoff is underestimated in the exorheic basins over China, and runoff simulations of GLDAS-2 are much more accurate than those of GLDAS-1 for two of the three major river basins of China investigated in this study. Evapotranspiration is overestimated in the exorheic basins in China by both GLDAS-1 and GLDAS-2, whereas the overestimation of evapotranspiration by GLDAS-2 is less than that by GLDAS-1.

scholarly journals Terrestrial Water Storage Changes of Permafrost in the Three-River Source Region of the Tibetan Plateau, China

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Min Xu ◽  
Shichang Kang ◽  
Qiudong Zhao ◽  
Jiazhen Li
Keyword(s):  
Water Balance ◽  
Water Cycle ◽  
Source Region ◽  
Water Storage ◽  
Permafrost Zone ◽  
The Tibetan Plateau ◽  
Continuous Permafrost ◽  
Terrestrial Water ◽  
Gravity Recovery ◽  
Storage Change

Changes in permafrost influence water balance exchanges in watersheds of cryosphere. Water storage change (WSC) is an important factor in water cycle. We used Gravity Recovery and Climate Experiment (GRACE) satellite data to retrieve WSC in the Three-River Source Region and subregions. WSC in four types of permafrost (continuous, seasonal, island, and patchy permafrost) was analyzed during 2003–2010. The result showed that WSC had significant change; it increased by9.06±0.01 mm/a (21.89±0.02×109 m3) over the Three-River Source Region during the study period. The most significant changes of WSC were in continuous permafrost zone, with a total amount of about13.94±0.48×109 m3. The spatial distribution of WSC was in state of gain in the continuous permafrost zone, whereas it was in a state of loss in the other permafrost zones. Little changes of precipitation and runoff occurred in study area, but the WSC increased significantly, according to water balance equation, the changes of runoff and water storage were subtracted from changes of precipitation, and the result showed that changes of evaporation is minus which means the evaporation decreased in the Three-River Source Region during 2003–2010.

Dynamics of Terrestrial Water Storage Change from Satellite and Surface Observations and Modeling

2010 ◽  
Vol 11 (1) ◽  
pp. 156-170 ◽  
Author(s):  
Qiuhong Tang ◽  
Huilin Gao ◽  
Pat Yeh ◽  
Taikan Oki ◽  
Fengge Su ◽  
...  
Keyword(s):  
Water Cycle ◽  
Regional Scale ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Surface Observation ◽  
Grace Data ◽  
Terrestrial Water ◽  
Gravity Recovery ◽  
Storage Change ◽  
Surface Observations

Abstract Terrestrial water storage (TWS) is a fundamental component of the water cycle. On a regional scale, measurements of terrestrial water storage change (TWSC) are extremely scarce at any time scale. This study investigates the feasibility of estimating monthly-to-seasonal variations of regional TWSC from modeling and a combination of satellite and in situ surface observations based on water balance computations that use ground-based precipitation observations in both cases. The study area is the Klamath and Sacramento River drainage basins in the western United States (total area of about 110 000 km2). The TWSC from the satellite/surface observation–based estimates is compared with model results and land water storage from the Gravity Recovery and Climate Experiment (GRACE) data. The results show that long-term evapotranspiration estimates and runoff measurements generally balance with observed precipitation, suggesting that the evapotranspiration estimates have relatively small bias for long averaging times. Observations show that storage change in water management reservoirs is about 12% of the seasonal amplitude of the TWSC cycle, but it can be up to 30% at the subbasin scale. Comparing with predevelopment conditions, the satellite/surface observation–based estimates show larger evapotranspiration and smaller runoff than do modeling estimates, suggesting extensive anthropogenic alteration of TWSC in the study area. Comparison of satellite/surface observation–based and GRACE TWSC shows that the seasonal cycle of terrestrial water storage is substantially underestimated by GRACE.

scholarly journals RELATIONS BETWEEN GRACE-DERIVED WATER STORAGE CHANGE WITH PRECIPITATION AND TEMPERATURE OVER KAIDU RIVER BASIN, CHINA

2016 ◽  
Vol XLI-B8 ◽  
pp. 405-407
Author(s):  
J. Huang ◽  
Q. Zhou
Keyword(s):  
River Basin ◽  
Water Cycle ◽  
Water Storage ◽  
Well Being ◽  
Satellite Mission ◽  
Temperature And Precipitation ◽  
Terrestrial Water ◽  
Kaidu River ◽  
Gravity Recovery ◽  
Precipitation And Temperature

Water is essential for human survival and well-being, and important to virtually all sectors of the economy. In the aridzone of China’s west, water resource is the controlling factor on the distribution of human settlements. Water cycle variation is sensitive to temperature and precipitation, which are influenced by human activity and climate change. Satellite observations of Earth’s time-variable gravity field from the Gravity Recovery and Climate Experiment (GRACE) mission, which enable direct measurement of changes of total terrestrial water storage, could be useful to aid this modelling. In this pilot study, TWS change from 2002 to 2013 obtained from GRACE satellite mission over the Kaidu River Basin in Xinjiang, China is presented. Precipitation and temperature data from in-situ station and National Satellite Meteorological Centre of China (NSMC) are analysed to examine whether there is a statistically significant correlation between them.

scholarly journals RELATIONS BETWEEN GRACE-DERIVED WATER STORAGE CHANGE WITH PRECIPITATION AND TEMPERATURE OVER KAIDU RIVER BASIN, CHINA

2016 ◽  
Vol XLI-B8 ◽  
pp. 405-407 ◽  
Author(s):  
J. Huang ◽  
Q. Zhou
Keyword(s):  
River Basin ◽  
Water Cycle ◽  
Water Storage ◽  
Well Being ◽  
Satellite Mission ◽  
Temperature And Precipitation ◽  
Terrestrial Water ◽  
Kaidu River ◽  
Gravity Recovery ◽  
Precipitation And Temperature

Water is essential for human survival and well-being, and important to virtually all sectors of the economy. In the aridzone of China’s west, water resource is the controlling factor on the distribution of human settlements. Water cycle variation is sensitive to temperature and precipitation, which are influenced by human activity and climate change. Satellite observations of Earth’s time-variable gravity field from the Gravity Recovery and Climate Experiment (GRACE) mission, which enable direct measurement of changes of total terrestrial water storage, could be useful to aid this modelling. In this pilot study, TWS change from 2002 to 2013 obtained from GRACE satellite mission over the Kaidu River Basin in Xinjiang, China is presented. Precipitation and temperature data from in-situ station and National Satellite Meteorological Centre of China (NSMC) are analysed to examine whether there is a statistically significant correlation between them.

scholarly journals Inverted Algorithm of Terrestrial Water-Storage Anomalies Based on Machine Learning Combined with Load Model and Its Application in Southwest China

2021 ◽  
Vol 13 (17) ◽  
pp. 3358
Author(s):  
Yifan Shen ◽  
Wei Zheng ◽  
Wenjie Yin ◽  
Aigong Xu ◽  
Huizhong Zhu ◽  
...  
Keyword(s):  
Machine Learning ◽  
Southwest China ◽  
Pearson Correlation ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Study Region ◽  
Southwest Yunnan ◽  
Terrestrial Water ◽  
Land Data Assimilation System ◽  
Gravity Recovery

Dense Global Position System (GPS) arrays can be used to invert the terrestrial water-storage anomaly (TWSA) with higher accuracy. However, the uneven distribution of GPS stations greatly limits the application of GPS to derive the TWSA. Aiming to solve this problem, we grid the GPS array using regression to raise the reliability of TWSA inversion. First, the study uses the random forest (RF) model to simulate crustal deformation in unobserved grids. Meanwhile, the new Machine-Learning Loading-Inverted Method (MLLIM) is constructed based on the traditional GPS derived method to raise the truthfulness of TWSA inversion. Second, this research selects southwest China as the study region, the MLLIM and traditional GPS inversion methods are used to derive the TWSA, and the inverted results are contrasted with datasets of the Gravity Recovery and Climate Experiment (GRACE) Mascon and the Global Land Data Assimilation System (GLDAS) model. The comparison shows that values of Pearson Correlation Coefficient (PCC) between the MLLIM and GRACE and GRACE Follow-On (GRACE-FO) are equal to 0.91 and 0.88, respectively; and the values of R-squared (R2) are equal to 0.76 and 0.65, respectively; the values of PCC and R2 between MLLIM and GLDAS solutions are equal to 0.79 and 0.65. Compared with the traditional GPS inversion, the MLLIM improves PCC and R2 by 8.85% and 7.99% on average, which indicates that the MLLIM can improve the accuracy of TWSA inversion more than the traditional GPS method. Third, this study applies the MLLIM to invert the TWSA in each province of southwest China and combines the precipitation to analyze the change of TWSA in each province. The results are as follows: (1) The spatial distribution of TWSA and precipitation is coincident, which is highlighted in southwest Yunnan and southeast Guangxi; (2) this study compares TWSA of MLLIM with GRACE and GLDAS solutions in each province, which indicates that the maximum value of PCC is as high as 0.86 and 0.94, respectively, which indicates the MLLIM can be used to invert the TWSA in the regions with sparse GPS stations. The TWSA based on the MLLIM can be used to fill the vacancy between GRACE and GRACE-FO.

scholarly journals A Multi-Sourced Data Retrodiction of Remotely Sensed Terrestrial Water Storage Changes for West Africa

Water ◽  
2019 ◽  
Vol 11 (2) ◽  
pp. 401 ◽  
Author(s):  
Vagner Ferreira ◽  
Samuel Andam-Akorful ◽  
Ramia Dannouf ◽  
Emmanuel Adu-Afari
Keyword(s):  
West Africa ◽  
Water Storage ◽  
Remotely Sensed ◽  
Climate Indices ◽  
Terrestrial Water Storage ◽  
Drought Assessment ◽  
Grace Data ◽  
Short Period ◽  
Terrestrial Water ◽  
Land Data Assimilation System

Remotely sensed terrestrial water storage changes (TWSC) from the past Gravity Recovery and Climate Experiment (GRACE) mission cover a relatively short period (≈15 years). This short span presents challenges for long-term studies (e.g., drought assessment) in data-poor regions like West Africa (WA). Thus, we developed a Nonlinear Autoregressive model with eXogenous input (NARX) neural network to backcast GRACE-derived TWSC series to 1979 over WA. We trained the network to simulate TWSC based on its relationship with rainfall, evaporation, surface temperature, net-precipitation, soil moisture, and climate indices. The reconstructed TWSC series, upon validation, indicate high skill performance with a root-mean-square error (RMSE) of 11.83 mm/month and coefficient correlation of 0.89. The validation was performed considering only 15% of the available TWSC data not used to train the network. More so, we used the total water content changes (TWCC) synthesized from Noah driven global land data assimilation system in a simulation under the same condition as the GRACE data. The results based on this simulation show the feasibility of the NARX networks in hindcasting TWCC with RMSE of 8.06 mm/month and correlation coefficient of 0.88. The NARX network proved robust to adequately reconstruct GRACE-derived TWSC estimates back to 1979.

scholarly journals Past terrestrial water storage (1980–2008) in the Amazon Basin reconstructed from GRACE and in situ river gauging data

2011 ◽  
Vol 15 (2) ◽  
pp. 533-546 ◽  
Author(s):  
M. Becker ◽  
B. Meyssignac ◽  
L. Xavier ◽  
A. Cazenave ◽  
R. Alkama ◽  
...  
Keyword(s):  
Large Scale ◽  
Amazon Basin ◽  
Hydrological Modeling ◽  
Water Cycle ◽  
Southern Oscillation ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Direct Estimate ◽  
Terrestrial Water

Abstract. Terrestrial water storage (TWS) composed of surface waters, soil moisture, groundwater and snow where appropriate, is a key element of global and continental water cycle. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) space gravimetry mission provides a new tool to measure large-scale TWS variations. However, for the past few decades, direct estimate of TWS variability is accessible from hydrological modeling only. Here we propose a novel approach that combines GRACE-based TWS spatial patterns with multi-decadal-long in situ river level records, to reconstruct past 2-D TWS over a river basin. Results are presented for the Amazon Basin for the period 1980–2008, focusing on the interannual time scale. Results are compared with past TWS estimated by the global hydrological model ISBA-TRIP. Correlations between reconstructed past interannual TWS variability and known climate forcing modes over the region (e.g., El Niño-Southern Oscillation and Pacific Decadal Oscillation) are also estimated. This method offers new perspective for improving our knowledge of past interannual TWS in world river basins where natural climate variability (as opposed to direct anthropogenic forcing) drives TWS variations.

The uncertainty of storage-based drought indices from GRACE

2020 ◽  
Author(s):  
Peyman Saemian ◽  
Mohammad Javad Tourian ◽  
Nico Sneeuw
Keyword(s):  
Land Surface ◽  
Water Storage ◽  
Extended Period ◽  
Drought Indices ◽  
Terrestrial Water Storage ◽  
Earth’S Gravity Field ◽  
Grace Data ◽  
Temporal And Spatial Variability ◽  
Terrestrial Water ◽  
Gravity Recovery

<p>Climate change and the growing demand for freshwater have raised the frequency and intensity of extreme events like drought. Satellite observations have improved our understanding of the temporal and spatial variability of droughts. Since March 2002, the Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-On (GRACE-FO) have been observing variations in Earth's gravity field yielding valuable information about changes in terrestrial water storage anomaly (TWSA). The terrestrial water storage vertically integrates all forms of water on and beneath land surface including snow, surface water, soil moisture, and groundwater storage.</p><p>Drought indices help to monitor drought by characterizing it in terms of their severity, location, duration and timing. Several drought indices have been developed based on GRACE water storage anomaly from a GRACE-based climatology, most of which suffer from the short record of GRACE, about 15 years, for their climatology. The limited duration of the GRACE observations necessitates the use of external datasets of TWSA with a more extended period for climatology. Drought characterization comes with its own uncertainties due to the inherent uncertainty in the GRACE data, the various post-processing approaches of GRACE data, and different options for external datasets on the other hand.</p><p>This study offers a method to quantify uncertainties for the storage-based drought index. Moreover, we assess the sensitivity of major global river basins to the duration of the observations. The outcome of the study is invaluable in the sense that it allows for a more informative storage based drought, including uncertainty, thus enabling a more realistic risk assessment.</p>

Ice mass change in the Amur Drainage Basin estimated from multi-source observations

2020 ◽  
Author(s):  
Zhiming Xu ◽  
Zhengtao Wang
Keyword(s):  
High Latitude ◽  
Drainage Basin ◽  
Gravity Data ◽  
Water Storage ◽  
Ice Age ◽  
Mass Change ◽  
Ice Melt ◽  
The Amur River ◽  
Terrestrial Water ◽  
Land Data Assimilation System

<p>The Amur River, with a total length of 4440 kilometers, is one of the major Asian rivers as well as the tenth largest river in the world flowing through China, Mongolia and Russia. As one of the high latitude rivers, the characteristics of terrestrial water storage(TWS) in Amur Drainage Basin are different from those in the middle and low latitude rivers. Its runoff is influenced by precipitation as well as the ice melt water, in this case, the research on this region has a unique scientific significance. In this study, Gravity Recovery and Climate Experiment(GRACE) time-varying gravity data is used to inverse the change of TWS in order to study the seasonal and interannual change of water storage in Amur Drainage Basin. By introducing Global Land Data Assimilation System(GLDAS) hydrological model and Global Precipitation Measurement(GPM) precipitation data, we can get the mass change of ice and snow of this area with water balance method. The result shows that the mass change of ice and snow detected by GRACE fits well with the trend of temperature. Which means GRACE combined with multi-source data has the ability to detect the change of ice and snow in high latitude rivers during the ice age.</p>

The AmazonWater Cycle: Perspectives fromWater Budget Closure and Ocean Salinity

2020 ◽  
pp. 1-44
Author(s):  
J. E. Jack Reeves Eyre ◽  
Xubin Zeng
Keyword(s):  
Water Vapor ◽  
Land Surface ◽  
Water Cycle ◽  
Water Storage ◽  
River Mouth ◽  
Small Subset ◽  
Terrestrial Water Storage ◽  
Crucial Component ◽  
Ocean Salinity ◽  
Terrestrial Water

AbstractGlobal and regional water cycle includes precipitation, water vapor divergence, and change of column water vapor in the atmosphere, and land surface evapotranspiration, terrestrial water storage change, and river discharge which is linked to ocean salinity near the river mouth. The water cycle is a crucial component of the Earth system, and numerous studies have addressed its individual components (e.g., precipitation). Here we assess, for the first time, if remote sensing and reanalysis datasets can accurately and self consistently portray the Amazon water cycle. This is further assisted with satellite ocean salinity measurements near the mouth of the Amazon River. The widely-used practice of taking the mean of an ensemble of datasets to represent water cycle components (e.g., precipitation) can produce large biases in water cycle closure. Closure is achieved with only a small subset of data combinations (e.g., ERA5 reanalysis precipitation and evapotranspiration plus GRACE satellite terrestrial water storage), which rules out the lower precipitation and higher evapotranspiration estimates, providing valuable constraints on assessments of precipitation, evapotranspiration and their ratio. The common approach of using the Óbidos stream gauge (located hundreds of kilometres from the river mouth) multiplied by a constant (1.25) to represent the entire Amazon discharge is found to misrepresent the seasonal cycle, and this can affect the apparent influence of Amazon discharge on tropical Atlantic salinity.

Sign in / Sign up

Export Citation Format

Share Document