scholarly journals The Challenge of Spatial Resolutions for GRACE-Based Estimates Volume Changes of Larger Man-Made Lake: The Case of China’s Three Gorges Reservoir in the Yangtze River

2019 ◽  
Vol 11 (1) ◽  
pp. 99 ◽  
Author(s):  
Linsong Wang ◽  
Mikhail K. Kaban ◽  
Maik Thomas ◽  
Chao Chen ◽  
Xian Ma
Keyword(s):  
Volume Change ◽  
Land Surface ◽  
Three Gorges Reservoir ◽  
Water Storage ◽  
In Situ Measurements ◽  
Coarse Grained ◽  
Three Gorges ◽  
Volume Changes ◽  
Near Surface

The Three Gorges Reservoir (TGR) in China, with the largest dam in the world, stores a large volume of water and may influence the Earth’s gravity field on sub-seasonal to interannual timescales. Significant changes of the total water storage (TWS) might be detectable by satellite-based data provided by the Gravity Recovery and Climate Experiment (GRACE) mission. To detect these store water changes, effects of other factors are to be removed first from these data due to band-limited representation of near-surface mass changes from GRACE. Here, we evaluated three current popular land surface models (LSMs) basing on in situ measurements and found that the WaterGAP Global Hydrology Model (WGHM) demonstrates higher correlation than other analyzed models with the in-situ rainfall measurement. Then we used the WGHM outputs to remove climate-induced TWS changes, such as surface water storage, soil, canopy, snow, and groundwater storage. The residual results (GRACE minus WGHM) indicated a strong trend (3.85 ± 2 km3/yr) that is significantly higher than the TGR analysis and hindcast experiments (2.29 ± 1 km3/yr) based on in-situ water level measurements. We also estimated the seepage response to the TGR filling, contributions from other anthropogenic dams, and used in-situ gravity and GPS observations to evaluate dominant factors responsible for the GRACE-based overestimate of the TGR volume change. We found that the modeled seepage variability through coarse-grained materials explained most of the difference between the GRACE based estimate of TGR volume changes and in situ measurements, but the agreement with in-situ gravity observations is considerably lower. In contrast, the leakage contribution from 13 adjacent reservoirs explained ~74% of the TGR volume change derived from GRACE and WGHM. Our results demonstrate that GRACE-based overestimate TGR mass change mainly from the contribution of surrounding artificial reservoirs and underestimated TWS variations in WGHM simulations due to the large uncertainty of WGHM in groundwater component. In additional, this study also indicates that reservoir or lake volume changes can be reliably derived from GRACE data when they are used in combination with relevant complementary observations.

scholarly journals Assessment of Soil Moisture Anomaly Sensitivity to Detect Drought Spatio-Temporal Variability in Romania

Sensors ◽  
2021 ◽  
Vol 21 (24) ◽  
pp. 8371
Author(s):  
Irina Ontel ◽  
Anisoara Irimescu ◽  
George Boldeanu ◽  
Denis Mihailescu ◽  
Claudiu-Valeriu Angearu ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Root Zone ◽  
Standardized Precipitation Index ◽  
In Situ Measurements ◽  
Near Surface ◽  
Soil Moisture Anomaly

This paper will assess the sensitivity of soil moisture anomaly (SMA) obtained from the Soil water index (SWI) product Metop ASCAT, to identify drought in Romania. The SWI data were converted from relative values (%) to absolute values (m3 m−3) using the soil porosity method. The conversion results (SM) were validated using soil moisture in situ measurements from ISMN at 5 cm depths (2015–2020). The SMA was computed based on a 10 day SWI product, between 2007 and 2020. The analysis was performed for the depths of 5 cm (near surface), 40 cm (sub surface), and 100 cm (root zone). The standardized precipitation index (SPI), land surface temperature anomaly (LST anomaly), and normalized difference vegetation index anomaly (NDVI anomaly) were computed in order to compare the extent and intensity of drought events. The best correlations between SM and in situ measurements are for the stations located in the Getic Plateau (Bacles (r = 0.797) and Slatina (r = 0.672)), in the Western Plain (Oradea (r = 0.693)), and in the Moldavian Plateau (Iasi (r = 0.608)). The RMSE were between 0.05 and 0.184. Furthermore, the correlations between the SMA and SPI, the LST anomaly, and the NDVI anomaly were significantly registered in the second half of the warm season (July–September). Due to the predominantly agricultural use of the land, the results can be useful for the management of water resources and irrigation in regions frequently affected by drought.

Understanding Volume Change in Lithium-Ion Cells during Charging and Discharging Using In Situ Measurements

2007 ◽  
Vol 154 (1) ◽  
pp. A14 ◽  
Author(s):  
Xianming Wang ◽  
Yoshitsugu Sone ◽  
Go Segami ◽  
Hitoshi Naito ◽  
Chisa Yamada ◽  
...  
Keyword(s):  
Volume Change ◽  
Lithium Ion ◽  
In Situ Measurements ◽  
Lithium Ion Cells

scholarly journals Numerical simulation of extreme snow melt observed at the SIGMA-A site, northwest Greenland, during summer 2012

2015 ◽  
Vol 9 (1) ◽  
pp. 495-539
Author(s):  
M. Niwano ◽  
T. Aoki ◽  
S. Matoba ◽  
S. Yamaguchi ◽  
T. Tanikawa ◽  
...  
Keyword(s):  
Heat Flux ◽  
Grain Size ◽  
Latent Heat ◽  
Latent Heat Flux ◽  
In Situ Measurements ◽  
The Arctic ◽  
Snow Surface ◽  
Near Surface ◽  
Surface Snow

Abstract. The surface energy balance (SEB) from 30 June to 14 July 2012 at site SIGMA (Snow Impurity and Glacial Microbe effects on abrupt warming in the Arctic)-A, (78°03' N, 67°38' W; 1490 m a.s.l.) on the northwest Greenland Ice Sheet (GrIS) was investigated by using in situ atmospheric and snow measurements, as well as numerical modeling with a one-dimensional, multi-layered, physical snowpack model called SMAP (Snow Metamorphism and Albedo Process). At SIGMA-A, remarkable near-surface snowmelt and continuous heavy rainfall (accumulated precipitation between 10 and 14 July was estimated to be 100 mm) were observed after 10 July 2012. Application of the SMAP model to the GrIS snowpack was evaluated based on the snow temperature profile, snow surface temperature, surface snow grain size, and shortwave albedo, all of which the model simulated reasonably well. However, comparison of the SMAP-calculated surface snow grain size with in situ measurements during the period when surface hoar with small grain size was observed on-site revealed that it was necessary to input air temperature, relative humidity, and wind speed data from two heights to simulate the latent heat flux into the snow surface and subsequent surface hoar formation. The calculated latent heat flux was always directed away from the surface if data from only one height were input to the SMAP model, even if the value for roughness length of momentum was perturbed between the possible maximum and minimum values in numerical sensitivity tests. This result highlights the need to use two-level atmospheric profiles to obtain realistic latent heat flux. Using such profiles, we calculated the SEB at SIGMA-A from 30 June to 14 July 2012. Radiation-related fluxes were obtained from in situ measurements, whereas other fluxes were calculated with the SMAP model. By examining the components of the SEB, we determined that low-level clouds accompanied by a significant temperature increase played an important role in the melt event observed at SIGMA-A. These conditions induced a remarkable surface heating via cloud radiative forcing in the polar region.

Towards an operationally capable satellite-based 0-100 cm soil moisture dataset from C3S

2021 ◽  
Author(s):  
Adam Pasik ◽  
Wolfgang Preimesberger ◽  
Bernhard Bauer-Marschallinger ◽  
Wouter Dorigo
Keyword(s):  
Soil Moisture ◽  
Soil Profile ◽  
Land Surface ◽  
Root Zone ◽  
Soil Layer ◽  
Land Surface Model ◽  
In Situ Measurements ◽  
Surface Model ◽  
T Values

<p>Multiple satellite-based global surface soil moisture (SSM) datasets are presently available, these however, address exclusively the top layer of the soil (0-5cm). Meanwhile, root-zone soil moisture cannot be directly quantified with remote sensing but can be estimated from SSM using a land surface model. Alternatively, soil water index (SWI; calculated from SSM as a function of time needed for infiltration) can be used as a simple approximation of root-zone conditions. SWI is a proxy for deeper layers of the soil profile which control evapotranspiration, and is hence especially important for studying hydrological processes over vegetation-covered areas and meteorological modelling.</p><p>Here we introduce the advances in our work on the first operationally capable SWI-based root-zone soil moisture dataset from C3S Soil Moisture v201912 COMBINED product, spanning the period 2002-2020. The uniqueness of this dataset lies in the fact that T-values (temporal lengths ruling the infiltration) characteristic of SWI were translated into particular soil depths making it much more intuitive, user-friendly and easily applicable. Available are volumetric soil moisture values for the top 1 m of the soil profile at 10 cm intervals, where the optimal T-value (T-best) for each soil layer is selected based on a range of correlation metrics with in situ measurements from the International Soil Moisture Network (ISMN) and the relevant soil and climatic parameters.<br>Additionally we present the results of an extensive global validation against in situ measurements (ISMN) as well as the results of investigations into the relationship between a range of soil and climate characteristics and the optimal T-values for particular soil depths.</p>

Exploration of Terrestrial Water Storage Characterization via Assimilation of Ground-based GPS Observations of Vertical Displacement and GRACE TWS Retrievals

2020 ◽  
Author(s):  
Gaohong Yin ◽  
Barton Forman ◽  
Jing Wang
Keyword(s):  
Great Basin ◽  
Land Surface ◽  
Water Storage ◽  
Vertical Displacement ◽  
Estimation Accuracy ◽  
Terrestrial Water Storage ◽  
Drought Period ◽  
Terrestrial Water ◽  
Gps Observations

<p>Accurate estimation of terrestrial water storage (TWS) is crucial in the characterization of the terrestrial hydrologic cycle. The launch of GRACE and GRACE Follow-On (GRACE-FO) missions provide an unprecedented opportunity to monitor the change in TWS across the globe. However, the spatial and temporal resolutions provided by GRACE/GRACE-FO are often too coarse for many hydrologic applications. Land surface models (LSMs) provide estimates of TWS at a finer spatio-temporal resolution, but most LSMs lack complete, all-encompassing physical representations of the hydrological system such as deep groundwater storage or anthropogenic influences (e.g., groundwater pumping and surface water regulation). In recent years, geodetic measurements from the ground-based Global Positioning System (GPS) network have been increasingly used in hydrologic studies based on the elastic response of the Earth’s surface to mass redistribution. This study explores the potential of improving our knowledge in TWS change via merging the information provided by ground-based GPS, GRACE, and the NASA Catchment Land Surface Model (Catchment), especially for the TWS change during an extended drought period.</p> <p> </p> <p>Ground-based GPS observations of vertical displacement and GRACE TWS retrievals were assimilated into the Catchment LSM, respectively, using an ensemble Kalman filter (EnKF) in order to improve the estimation accuracy of TWS change. The data assimilation (DA) framework effectively downscaled TWS into its constituent components (e.g., snow and soil moisture) as well as improved estimates of hydrologic fluxes (e.g., runoff). Estimated TWS change from the open loop (OL; without assimilation) and GPS DA (i.e., using GPS-based vertical displacement during assimilation) simulations were evaluated against GRACE TWS retrievals. Results show that GPS DA improved estimation accuracy of TWS change relative to the OL, especially during an extended drought period post-2011 in the western United States (e.g., the correlation coefficient R<sub>OL</sub> = 0.46 and R<sub>GPSDA</sub> = 0.82 in the Great Basin). The performance of GPS DA and GRACE DA in estimating TWS constituent components and hydrologic fluxes were evaluated against in situ measurements. Results show that GPS DA improves snow water equivalent (SWE) estimates with improved R values found over 76% of all pixels that are collocated with in situ stations in the Great Basin. The findings in this study indicate the potential use of GPS DA and GRACE DA for TWS characterization. Both GRACE and ground-based GPS provide complementary TWS change information, which helps correct for missing physics in the LSM. Additionally, this study provides motivation for a multi-variate assimilation approach to simultaneously merge both GRACE and ground-based GPS into an LSM to further improve modeled TWS and its constituent components.</p>

scholarly journals Validation of Collection 6 MODIS land surface temperature product using in situ measurements

2019 ◽  
Vol 225 ◽  
pp. 16-29 ◽  
Author(s):  
Si-Bo Duan ◽  
Zhao-Liang Li ◽  
Hua Li ◽  
Frank-M. Göttsche ◽  
Hua Wu ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
In Situ Measurements ◽  
Modis Land Surface Temperature
Sign in / Sign up

Export Citation Format

Share Document