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 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 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 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 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.

Spatio-temporal coupling between Terrestrial Water Storage and Vegetation Index in Shule River Basin

2019 ◽  
Vol 39 (14) ◽  
Author(s):  
岳东霞 YUE Dongxia ◽  
苗俊霞 MIAO Junxia ◽  
朱敏翔 ZHU Minxiang ◽  
周妍妍 ZHOU Yanyan ◽  
邹明亮 ZOU Mingliang ◽  
...  
Keyword(s):  
River Basin ◽  
Vegetation Index ◽  
Water Storage ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
Temporal Coupling ◽  
Spatio Temporal

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 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 > 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.

Sign in / Sign up

Export Citation Format

Share Document