Analysing the contribution of snow water equivalent to the terrestrial water storage over Canada

2019 ◽  
Vol 34 (2) ◽  
pp. 175-188 ◽  
Author(s):  
Ala Bahrami ◽  
Kalifa Goïta ◽  
Ramata Magagi
Keyword(s):  
Snow Water Equivalent ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
Snow Water

scholarly journals Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment

2021 ◽  
Vol 13 (6) ◽  
pp. 1223
Author(s):  
Manuela Girotto ◽  
Rolf Reichle ◽  
Matthew Rodell ◽  
Viviana Maggioni
Keyword(s):  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Water Storage ◽  
Synthetic Data ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Precipitation Estimates ◽  
Terrestrial Water ◽  
Snow Water ◽  
Gravity Recovery

The Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On (GRACE-FO) mission provide unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. Established GRACE data assimilation techniques directly adjust the simulated water storage components to improve the estimation of groundwater, streamflow, and snow water equivalent. Such techniques artificially add/subtract water to/from prognostic variables, thus upsetting the simulated water balance. To overcome this limitation, we propose and test an alternative assimilation scheme in which precipitation fluxes are adjusted to achieve the desired changes in simulated TWS. Using a synthetic data assimilation experiment, we show that the scheme improves performance skill in precipitation estimates in general, but that it is more robust for snowfall than for rainfall, and it fails in certain regions with strong horizontal gradients in precipitation. The results demonstrate that assimilation of TWS observations can help correct (adjust) the model’s precipitation forcing and, in turn, enhance model estimates of TWS, snow mass, soil moisture, runoff, and evaporation. A key limitation of the approach is the assumption that all errors in TWS originate from errors in precipitation. Nevertheless, the proposed approach produces more consistent improvements in simulated runoff than the established GRACE data assimilation techniques.

scholarly journals Analysing the contribution of snow water equivalent to the terrestrial water storage over Canada

2020 ◽  
Vol 34 (8) ◽  
pp. 2007-2008
Author(s):  
Ala Bahrami ◽  
Kalifa Goïta ◽  
Ramata Magagi
Keyword(s):  
Snow Water Equivalent ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
Snow Water

scholarly journals A time series assessment of terrestrial water storage and its relationship with hydro-meteorological factors in Gilgit-Baltistan region using GRACE observation and GLDAS-Noah model

2021 ◽  
Vol 3 (5) ◽  
Author(s):  
Dostdar Hussain ◽  
Aftab Ahmed Khan ◽  
Syed Najam Ul Hassan ◽  
Syed Ali Asad Naqvi ◽  
Akhtar Jamil
Keyword(s):  
Snow Water Equivalent ◽  
Gravity Data ◽  
Water Storage ◽  
Indus Basin ◽  
Flood Hazards ◽  
Terrestrial Water Storage ◽  
Satellite Gravity ◽  
Terrestrial Water ◽  
Gilgit Baltistan

AbstractMountains regions like Gilgit-Baltistan (GB) province of Pakistan are solely dependent on seasonal snow and glacier melt. In Indus basin which forms in GB, there is a need to manage water in a sustainable way for the livelihood and economic activities of the downstream population. It is important to monitor water resources that include glaciers, snow-covered area, lakes, etc., besides traditional hydrological (point-based measurements by using the gauging station) and remote sensing-based studies (traditional satellite-based observations provide terrestrial water storage (TWS) change within few centimeters from the earth’s surface); the TWS anomalies (TWSA) for the GB region are not investigated. In this study, the TWSA in GB region is considered for the period of 13 years (from January 2003 to December 2016). Gravity Recovery and Climate Experiment (GRACE) level 2 monthly data from three processing centers, namely Centre for Space Research (CSR), German Research Center for Geosciences (GFZ), and Jet Propulsion Laboratory (JPL), System Global Land Data Assimilation System (GLDAS)-driven Noah model, and in situ precipitation data from weather stations, were used for the study investigation. GRACE can help to forecast the possible trends of increasing or decreasing TWS with high accuracy as compared to the past studies, which do not use satellite gravity data. Our results indicate that TWS shows a decreasing trend estimated by GRACE (CSR, GFZ, and JPL) and GLDAS-Noah model, but the trend is not significant statistically. The annual amplitude of GLDAS-Noah is greater than GRACE signal. Mean monthly analysis of TWSA indicates that TWS reaches its maximum in April, while it reaches its minimum in October. Furthermore, Spearman’s rank correlation is determined between GRACE estimated TWS with precipitation, soil moisture (SM) and snow water equivalent (SWE). We also assess the factors, SM and SWE which are the most efficient parameters producing GRACE TWS signal in the study area. In future, our results with the support of more in situ data can be helpful for conservation of natural resources and to manage flood hazards, droughts, and water distribution for the mountain regions.

scholarly journals CONVERSE TRENDS OF THE TERRESTRIAL AND GROUND WATER STORAGE CHANGES IN CANADA AND THE UNITED STATES

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1793-1796
Author(s):  
H. Wang ◽  
L. Xiang ◽  
H. Steffen ◽  
P. Wu ◽  
L. Jiang ◽  
...  
Keyword(s):  
United States ◽  
Snow Water Equivalent ◽  
Water Storage ◽  
High Water ◽  
The United States ◽  
Water Usage ◽  
Grace Data ◽  
Terrestrial Water ◽  
Snow Water ◽  
Satellite Altimetry Data

<p><strong>Abstract.</strong> We study the terrestrial water storage (TWS) and groundwater storage (GWS) changes in Canada and United States. We employ the separation approach from Wang et al. (2013) together with the improved GRACE data of Release 6 for a longer time span until December, 2016. The TWS signals from lake levels are derived from satellite altimetry data over the lakes while TWS signals due to soil moisture (SM) and snow water equivalent (SWE) changes from hydrology models. There are four significant trend anomalies in North America for both TWS and GWS changes. Two positive anomalies are found in Canada with their centers in the provinces of Saskatchewan and Quebec, respectively, due to increased precipitation and/or increased runoff in their surroundings. Two negative anomalies are shown in the United States with their centers in California and the northwest of Texas, respectively, which are due to decreased precipitation and, especially for California, high water usage for agriculture.</p>

scholarly journals Differences in Response of Terrestrial Water Storage Components to Precipitation over 168 Global River Basins

2019 ◽  
Vol 20 (9) ◽  
pp. 1981-1999 ◽  
Author(s):  
Yafeng Zhang ◽  
Bin He ◽  
Lanlan Guo ◽  
Daochen Liu
Keyword(s):  
Land Surface ◽  
Snow Water Equivalent ◽  
Time Lag ◽  
Water Storage ◽  
Correlation Coefficients ◽  
Global Scale ◽  
Time Lags ◽  
Terrestrial Water Storage ◽  
Global Pattern ◽  
Terrestrial Water

Abstract A time lag exists between precipitation P falling and being converted into terrestrial water. The responses of terrestrial water storage (TWS) and its individual components to P over the global scale, which are vital for understanding the interactions and mechanisms between climatic variables and hydrological components, are not well constrained. In this study, relying on land surface models, we isolate five component storage anomalies from TWS anomalies (TWSA) derived from the Gravity Recovery and Climate Experiment mission (GRACE): canopy water storage anomalies (CWSA), surface water storage anomalies (SWSA), snow water equivalent anomalies (SWEA), soil moisture storage anomalies (SMSA), and groundwater storage anomalies (GWSA). The responses of TWSA and of the individual components of TWSA to P are then evaluated over 168 global basins. The lag between TWSA and P is quantified by calculating the correlation coefficients between GRACE-based TWSA and P for different time lags, then identifying the lag (measured in months) corresponding to the maximum correlation coefficient. A multivariate regression model is used to explore the relationship between climatic and basin characteristics and the lag between TWSA and P. Results show that the spatial distribution of TWSA trend presents a similar global pattern to that of P for the period January 2004–December 2013. TWSA is positively related to P over basins but with lags of variable duration. The lags are shorter in the low- and midlatitude basins (1–2 months) than those in the high-latitude basins (6–9 months). The spatial patterns of the maximum correlations and the corresponding lags between individual components of the TWSA and P are consistent with those of the GRACE-based analysis, except for SWEA (3–8 months) and CWSA (0 months). The lags between GWSA, SMSA, and SWSA to P can be arranged as GWSA &gt; SMSA ≥ SWSA. Regression analysis results show that the lags between TWSA and P are related to the mean temperature, mean precipitation, mean latitude, mean longitude, mean elevation, and mean slope.

scholarly journals Using GRACE Data to Study the Impact of Snow and Rainfall on Terrestrial Water Storage in Northeast China

2020 ◽  
Vol 12 (24) ◽  
pp. 4166
Author(s):  
An Qian ◽  
Shuang Yi ◽  
Le Chang ◽  
Guangtong Sun ◽  
Xiaoyang Liu
Keyword(s):  
Northeast China ◽  
Snow Water Equivalent ◽  
Southern Oscillation ◽  
Water Storage ◽  
Summer Rainfall ◽  
Terrestrial Water Storage ◽  
Maximum Rainfall ◽  
Monthly Total Precipitation ◽  
Terrestrial Water ◽  
The Impact

Water resources are important for agricultural, industrial, and urban development. In this paper, we analyzed the influence of rainfall and snowfall on variations in terrestrial water storage (TWS) in Northeast China from Gravity Recovery and Climate Experiment (GRACE) gravity satellite data, GlobSnow snow water equivalent product, and ERA5-land monthly total precipitation, snowfall, and snow depth data. This study revealed the main composition and variation characteristics of TWS in Northeast China. We found that GRACE provided an effective method for monitoring large areas of stable seasonal snow cover and variations in TWS in Northeast China at both seasonal and interannual scales. On the seasonal scale, although summer rainfall was 10 times greater than winter snowfall, the terrestrial water storage in Northeast China peaked in winter, and summer rainfall brought about only a sub-peak, 1 month later than the maximum rainfall. On the interannual scale, TWS in Northeast China was controlled by rainfall. The correlation analysis results revealed that the annual fluctuations of TWS and rainfall in Northeast China appear to be influenced by ENSO (EI Niño–Southern Oscillation) events with a lag of 2–3 years. In addition, this study proposed a reconstruction model for the interannual variation in TWS in Northeast China from 2003 to 2016 on the basis of the contemporary terrestrial water storage and rainfall data.

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 Multiple Remotely Sensed Lines of Evidence for a Depleting Seasonal Snowpack in the Near East

2019 ◽  
Vol 11 (5) ◽  
pp. 483 ◽  
Author(s):  
Yeliz Yılmaz ◽  
Kristoffer Aalstad ◽  
Omer Sen
Keyword(s):  
Snow Cover ◽  
Snow Water Equivalent ◽  
Near East ◽  
Water Storage ◽  
Reanalysis Data ◽  
Terrestrial Water Storage ◽  
Data Set ◽  
Terrestrial Water ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Snow Cover Duration

The snow-fed river basins of the Near East region are facing an urgent threat in the form of declining water resources. In this study, we analyzed several remote sensing products (optical, passive microwave, and gravimetric) and outputs of a meteorological reanalysis data set to understand the relationship between the terrestrial water storage anomalies and the mountain snowpack. The results from different satellite retrievals show a clear signal of a depletion of both water storage and the seasonal snowpack in four basins in the region. We find a strong reduction in terrestrial water storage over the Gravity Recovery and Climate Experiment (GRACE) observational period, particularly over the higher elevations. Snow-cover duration estimates from Moderate Resolution Imaging Spectroradiometer (MODIS) products point towards negative and significant trends up to one month per decade in the current era. These numbers are a clear indicator of the partial disappearance of the seasonal snow-cover in the region which has been projected to occur by the end of the century. The spatial patterns of changes in the snow-cover duration are positively correlated with both GRACE terrestrial water storage decline and peak snow water equivalent (SWE) depletion from the ERA5 reanalysis. Possible drivers of the snowpack depletion are a significant reduction in the snowfall ratio and an earlier snowmelt. A continued depletion of the montane snowpack in the Near East paints a bleak picture for future water availability in this water-stressed region.

scholarly journals Reconstruction of terrestrial water storage anomalies in Northwest China during 1948–2002 using GRACE and GLDAS products

2018 ◽  
Vol 49 (5) ◽  
pp. 1594-1607 ◽  
Author(s):  
Peng Yang ◽  
Jun Xia ◽  
Chesheng Zhan ◽  
Tiejun Wang
Keyword(s):  
Time Series ◽  
Snow Water Equivalent ◽  
Southern Oscillation ◽  
Water Storage ◽  
Negative Relationship ◽  
Northwest China ◽  
Support Vector ◽  
Terrestrial Water Storage ◽  
Optimal Method ◽  
Terrestrial Water

Abstract Commencement of the Gravity Recovery and Climate Experiment (GRACE) provides an alternative way to monitor changes in terrestrial water storage (TWS) at large scales. However, GRACE dataset spans from 2002 to present, which greatly limits the application of GRACE data for long-term hydrological studies. Thus, the general linear model (GLM), random forest (RF), support vector machines (SVM), and artificial neural networks (ANN) methods were used to reconstruct the time series of terrestrial water storage anomalies (TWSA, i.e., remove the average value from the time series) in Northwest China (NWC) during 1948–2002 based on the GRACE TWSA during 2003–2015 and hydrological data from the Global Land Data Assimilation System (GLDAS) during 1948–2010. The results showed that soil moisture (SM) anomalies, or the combination of SM, canopy water (CW), and snow water equivalent (SWE) anomalies were better than the other anomalies of GLDAS in NWC. RF method can be regarded as the optimal method to reconstruct TWSA in NWC in the four models. A negative relationship was found between the reconstructed TWSAs and El Niño-Southern Oscillation (ENSO). The method could also offer an approach to reconstruct TWSA and drought events in large river basins during the past several decades.

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