scholarly journals Assessment of Three Common Methods for Estimating Terrestrial Water Storage Change with Three Reanalysis Datasets

2020 ◽  
Vol 33 (2) ◽  
pp. 511-525 ◽  
Author(s):  
Shanshan Deng ◽  
Suxia Liu ◽  
Xingguo Mo
Keyword(s):  
Water Balance ◽  
Hydrological Cycle ◽  
Regional Scale ◽  
Water Storage ◽  
Balance Method ◽  
Terrestrial Water Storage ◽  
Water Balance Method ◽  
Terrestrial Water ◽  
The Impact ◽  
Storage Change

AbstractTerrestrial water storage change (TWSC) plays a crucial role in the hydrological cycle and climate system. To date, methods including 1) the terrestrial water balance method (PER), 2) the combined atmospheric and terrestrial water balance method (AT), and 3) the summation method (SS) have been developed to estimate TWSC, but the accuracy of these methods has not been systematically compared. This paper compares the spatial and temporal differences of the TWSC estimates by the three methods comprehensively with the GRACE data during the 2002–13 period. To avoid the impact of different inputs in the comparison, three advanced reanalysis datasets are used, namely 1) the National Centers for Environmental Prediction (NCEP)–Department of Energy (DOE) Reanalysis II (NCEP R2), 2) the ECMWF interim reanalysis (ERA-Interim), and 3) the Japanese 55-Year Reanalysis (JRA-55). The results show that all estimates with PER and AT considerably overestimate the long-term mean on a regional scale because the data assimilation in the reanalysis opens the water budget. The difficulty of atmospheric observation and simulation in arid and polar tundra regions is the documented reason for the failure of the AT method to represent the TWSC phase over 30% of the region found in this study. Although the SS result exhibited the best overall agreement with GRACE, the amplitude of TWSC based on SS differed substantially from that of GRACE and the similarity coefficient of the global distribution between the SS-derived estimate and GRACE is still not high. More detailed considerations of groundwater and human activities, for example, irrigation and reservoir impoundments, can help SS to achieve a higher accuracy.

scholarly journals Reconstructing Terrestrial Water Storage Variations from 1980 to 2015 in the Beishan Area of China

Geofluids ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-13 ◽  
Author(s):  
Wenjie Yin ◽  
Litang Hu ◽  
Shin-Chan Han ◽  
Menglin Zhang ◽  
Yanguo Teng
Keyword(s):  
Remote Sensing ◽  
Water Balance ◽  
Water Storage ◽  
Balance Method ◽  
Disposal Site ◽  
Terrestrial Water Storage ◽  
Water Balance Method ◽  
Terrestrial Water ◽  
Beishan Area

Terrestrial water storage (TWS) is a key element in the global and continental water cycle. Since 2002, the Gravity Recovery and Climate Experiment (GRACE) has provided a highly valuable dataset, which allows the study of TWS over larger river basins worldwide. However, the lifetime of GRACE is too short to demonstrate long-term variability in TWS. In the Beishan area of northwestern China, which is selected as the most prospective site for high-level radioactive waste (HLRW) disposal, the assessment of long-term TWS changes is crucial to understand disposal safety. Monthly and annual TWS changes during the past 35 years are reconstructed using GRACE data, other remote sensing products, and the water balance method. Hydrological flux outputs from multisource remote sensing products are analyzed and compared to select appropriate data sources. The results show that a decreasing trend is found for GRACE-filtered and Center for Space Research (CSR) mascon solutions from 2003 to 2015, with slopes of −2.30 ± 0.52 and −1.52 ± 0.24 mm/year, respectively. TWS variations independently computed from the water balance method also show a similar decreasing trend with the GRACE observations, with a slope of −0.94 mm/year over the same period. Overall, the TWS anomalies in the Beishan area change seasonally within 10 mm and have been decreasing since 1980, keeping a desirable dry condition as a HLRW disposal site.

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 Impact of variability in the hydrological cycle components on vegetation growth in an alpine basin of the southeastern Tibet Plateau, China

2021 ◽  
Author(s):  
Chunguang Ban ◽  
Zongxue Xu ◽  
Depeng Zuo ◽  
Rui Zhang ◽  
Hao Chen ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Snow Water Equivalent ◽  
Hydrological Cycle ◽  
Water Storage ◽  
Tibet Plateau ◽  
Upstream Region ◽  
Terrestrial Water Storage ◽  
Vegetation Growth ◽  
Terrestrial Water ◽  
The Impact

Abstract Vegetation is affected by hydrological cycle components that have altered under the influence of climate change. Therefore, it is necessary to investigate the impact of hydrological cycle components on regional vegetation growth, especially in alpine regions. In this study, we employed multiple satellite observations to comprehensively investigate the spatial heterogeneity of hydrological cycle components in the Yarlung Zangbo River (YZR) basin for the period 1982–2014 and to determine the underlying mechanisms driving regional vegetation growth. Results showed that the normalized difference vegetation index (NDVI) values during May–October were high, and the NDVI values increased from the upper reaches of the YZR to its lower reaches, reflecting the enhancement of vegetation growth. Annual precipitation, precipitation-actual evapotranspiration (AET), and snow water equivalent (SWE) all affect terrestrial water storage in the YZR basin through changes in soil moisture (SM), i.e., SM is the intermediate variable. Seasonal variability of vegetation is controlled mainly by precipitation, temperature, AET, SM anomaly, and SWE. Groundwater storage anomalies (GWA) and terrestrial water storage anomalies (TWSA) were not reliable indicators of vegetation growth in the YZR basin and the midstream and downstream regions. The effects of GWA and TWSA on vegetation occurred in the upstream region.

scholarly journals Data assimilation of GRACE terrestrial water storage estimates into a regional hydrological model of the Rhine River basin

2015 ◽  
Vol 19 (4) ◽  
pp. 2079-2100 ◽  
Author(s):  
N. Tangdamrongsub ◽  
S. C. Steele-Dunne ◽  
B. C. Gunter ◽  
P. G. Ditmar ◽  
A. H. Weerts
Keyword(s):  
Correlation Coefficient ◽  
River Basin ◽  
Hydrological Cycle ◽  
Water Storage ◽  
Model Parameters ◽  
Hydrological Models ◽  
Rhine River ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Terrestrial Water

Abstract. The ability to estimate terrestrial water storage (TWS) realistically is essential for understanding past hydrological events and predicting future changes in the hydrological cycle. Inadequacies in model physics, uncertainty in model land parameters, and uncertainties in meteorological data commonly limit the accuracy of hydrological models in simulating TWS. In an effort to improve model performance, this study investigated the benefits of assimilating TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) data into the OpenStreams wflow_hbv model using an ensemble Kalman filter (EnKF) approach. The study area chosen was the Rhine River basin, which has both well-calibrated model parameters and high-quality forcing data that were used for experimentation and comparison. Four different case studies were examined which were designed to evaluate different levels of forcing data quality and resolution including those typical of other less well-monitored river basins. The results were validated using in situ groundwater (GW) and stream gauge data. The analysis showed a noticeable improvement in GW estimates when GRACE data were assimilated, with a best-case improvement of correlation coefficient from 0.31 to 0.53 and root mean square error (RMSE) from 8.4 to 5.4 cm compared to the reference (ensemble open-loop) case. For the data-sparse case, the best-case GW estimates increased the correlation coefficient from 0.46 to 0.61 and decreased the RMSE by 35%. For the average improvement of GW estimates (for all four cases), the correlation coefficient increases from 0.6 to 0.7 and the RMSE was reduced by 15%. Only a slight overall improvement was observed in streamflow estimates when GRACE data were assimilated. Further analysis suggested that this is likely due to sporadic short-term, but sizeable, errors in the forcing data and the lack of sufficient constraints on the soil moisture component. Overall, the results highlight the benefit of assimilating GRACE data into hydrological models, particularly in data-sparse regions, while also providing insight on future refinements of the methodology.

The Impact of Noah-MP Physical Parameterizations on Modeling Water Availability during Droughts in the Texas-Gulf Region

2021 ◽  
Author(s):  
Wen-Ying Wu ◽  
Zong-Liang Yang ◽  
Michael Barlage
Keyword(s):  
Leaf Area Index ◽  
Leaf Area ◽  
Water Availability ◽  
Water Storage ◽  
Rooting Depth ◽  
Surface Model ◽  
Terrestrial Water Storage ◽  
Area Index ◽  
Terrestrial Water ◽  
The Impact

AbstractTexas is subject to severe droughts, including the record-breaking one in 2011. To investigate the critical hydrometeorological processes during drought, we use a land surface model, Noah-MP, to simulate water availability and investigate the causes of the record drought. We conduct a series of experiments with runoff schemes, vegetation phenology, and plant rooting depth. Observation-based terrestrial water storage, evapotranspiration, runoff, and leaf area index are used to compare with results from the model. Overall, the results suggest that using different parameterizations can influence the modeled water availability, especially during drought. The drought-induced vegetation responses not only interact with water availability but also affect the ground temperature. Our evaluation shows that Noah-MP with a groundwater scheme produces a better temporal relationship in terrestrial water storage compared with observations. Leaf area index from dynamic vegetation is better simulated in wet years than dry years. Reduction of positive biases in runoff and reduction of negative biases in evapotranspiration are found in simulations with groundwater, dynamic vegetation, and deeper rooting zone depth. Multi-parameterization experiments show the uncertainties of drought monitoring and provide a mechanistic understanding of disparities in dry anomalies.

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