scholarly journals Random Forest-Based Reconstruction and Application of the GRACE Terrestrial Water Storage Estimates for the Lancang-Mekong River Basin

2021 ◽  
Vol 13 (23) ◽  
pp. 4831
Author(s):  
Senlin Tang ◽  
Hong Wang ◽  
Yao Feng ◽  
Qinghua Liu ◽  
Tingting Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Random Forest ◽  
Correlation Coefficient ◽  
River Basin ◽  
Water Storage ◽  
Mekong River ◽  
Climatic Research Unit ◽  
Terrestrial Water Storage ◽  
Mekong River Basin ◽  
Terrestrial Water

Terrestrial water storage (TWS) is a critical variable in the global hydrological cycle. The TWS estimates derived from the Gravity Recovery and Climate Experiment (GRACE) allow us to better understand water exchanges between the atmosphere, land surface, sea, and glaciers. However, missing historical (pre-2002) GRACE data limit their further application. In this study, we developed a random forest (RF) model to reconstruct the monthly terrestrial water storage anomaly (TWSA) time series using Global Land Data Assimilation System (GLDAS) and Climatic Research Unit (CRU) data for the Lancang-Mekong River basin. The results show that the RF-built TWSA time series agrees well with the GRACE TWSA time series for 2003–2014, showing that correlation coefficients (R) of 0.97 and 0.90 at the basin and grid scales, respectively, which demonstrates the reliability of the RF model. Furthermore, this method is used to reconstruct the historical TWSA time series for 1980–2002. Moreover, the discharge can be obtained by subtracting the evapotranspiration (ET) and RF-built terrestrial water storage change (TWSC) from the precipitation. The comparison between the discharge calculated from the water balance method and the observed discharge showed significant consistency, with a correlation coefficient of 0.89 for 2003–2014 but a slightly lower correlation coefficient (0.86) for 1980–2002. The methods and findings in this study can provide an effective means of reconstructing the TWSA and discharge time series in basins with sparse hydrological data.

scholarly journals Interactive Contribution of Indian Summer Monsoon and Western North Pacific Monsoon to Water Level and Terrestrial Water Storage in the Mekong Basin

2021 ◽  
Vol 13 (17) ◽  
pp. 3399
Author(s):  
Taoran Shi ◽  
Hok Sum Fok ◽  
Zhongtian Ma
Keyword(s):  
Linear Regression ◽  
Water Level ◽  
River Basin ◽  
Water Storage ◽  
Multivariate Linear Regression ◽  
Mekong River ◽  
Terrestrial Water Storage ◽  
Mekong River Basin ◽  
Terrestrial Water ◽  
The Impact

Water level (WL) and terrestrial water storage (TWS) are two important indicators for early alerts of hydrological extremes. Their variation is governed by precipitation under monsoon variability, in particular in the Mekong river basin, where it is affected by the interaction between the Indian summer monsoon (ISM) and western North Pacific monsoon (WNPM). This study aimed to quantify the contributions of two monsoons to the water levels of four hydrological stations (i.e., My Thuan, Can Tho, Chau Doc and Tan Chau) on the Mekong Delta and the terrestrial water storage of the entire Mekong River basin through relative importance analysis. Three methods—multivariate linear regression; Lindeman, Merenda and Gold (LMG); and the proportional marginal variance decomposition (PMVD) methods—were selected to quantitatively obtain the relative influence of two monsoons on water level and TWS. The results showed that, from 2010 to 2014, the proportions of the ISM impacts on the water level obtained with the three methods ranged from 55.48 to 81.35%, 50.69 to 57.55% and 55.41 to 93.64% via multivariate linear regression, LMG and PMVD, respectively. Further analysis showed that different choices of time spans could lead to different results, indicated that the corresponding proportion would be influenced by other factors, such as El Niño–Southern Oscillation (ENSO). The removal of ENSO further enlarged the relative importance of the ISM, and the mean values of the four stations were increased by 8.78%, 2.04% and 14.92%, respectively, via multivariate linear regression, LMG and PMVD. Meanwhile, based on the analysis of terrestrial water storage, it was found that the impact of the ISM on the whole Mekong River basin was dominant: the proportions of the impact of the ISM on terrestrial water storage increased to 68.79%, 54.60% and 79.43%, which rose by 11.24%, 2.96% and 19.77%, respectively, via linear regression, LMG and PMVD. The increases almost equaled the quantified proportion for the ENSO component. Overall, the novel technique of quantifying the contributions of monsoons to WL and TWS can be applied to the influence of other atmospheric factors or events on hydrological variables in different 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.

scholarly journals Performance Evaluation of a Potential Component of an Early Flood Warning System—A Case Study of the 2012 Flood, Lower Niger River Basin, Nigeria

2019 ◽  
Vol 11 (17) ◽  
pp. 1970 ◽  
Author(s):  
Idowu ◽  
Zhou
Keyword(s):  
Correlation Coefficient ◽  
River Basin ◽  
Water Budget ◽  
Water Storage ◽  
Warning System ◽  
Terrestrial Water Storage ◽  
Flood Warning ◽  
Terrestrial Water ◽  
Niger River ◽  
Flood Warning System

Floods frequently occur in Nigeria. The catastrophic 2012 flood in Nigeria claimed 363 lives and affected about seven million people. A total loss of about 2.29 trillion Naira (7.2 billion US Dollars) was estimated. The effect of flooding in the country has been devastating because of sparse to no flood monitoring, and a lack of an effective early flood warning system in the country. Here, we evaluated the efficacy of using the Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage anomaly (TWSA) to evaluate the hydrological conditions of the Lower Niger River Basin (LNRB) in Nigeria in terms of precipitation and antecedent terrestrial water storage prior to the 2012 flood event. Furthermore, we accessed the potential of the GRACE-based flood potential index (FPI) at correctly predicting previous floods, especially the devastating 2012 flood event. For validation, we compared the GRACE terrestrial water storage capacity (TWSC) quantitatively and qualitatively to the water budget of TWSC and Dartmouth Flood Observatory (DFO) respectively. Furthermore, we derived a water budget-based FPI using Reager’s methodology and compared it to the GRACE-derived FPI quantitatively. Generally, the GRACE TWSC estimates showed seasonal consistency with the water budget TWSC estimates with a correlation coefficient of 0.8. The comparison between the GRACE-derived FPI and water budget-derived FPI gave a correlation coefficient of 0.9 and also agreed well with the flood reported by the DFO. Also, the FPI showed a marked increase with precipitation which implies that rainfall is the main cause of flooding in the study area. Additionally, the computed GRACE-based storage deficit revealed that there was a decrease in water storage prior to the flooding month while the FPI increased. Hence, the GRACE-based FPI and storage deficit when supplemented with water budget-based FPI could suggest a potential for flood prediction and water storage monitoring respectively.

scholarly journals Integrating GRACE products and land surface models to estimate changes in key components of terrestrial water storage in the Nile River Basin

2020 ◽  
Author(s):  
Zemede M. Nigatu ◽  
Dongming Fan ◽  
Wei You
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
River Basin ◽  
Land Surface ◽  
Signal To Noise Ratio ◽  
Water Storage ◽  
Nile River ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
Nile River Basin

Abstract The Nile River Basin (NRB) is facing extreme pressure on its water resources due to an alarmingly increasing population that is extremely vulnerable in aspects of irrigation and hydropower. The NRB ascends itself to remotely sensed approaches with high resolution of spatial and temporal coverage as disparate to ground-based in-situ observations due to its size and limited access from basin countries. The Gravity Recovery and Climate Experiment (GRACE) allow a unique opportunity to investigate the changes in key components of Terrestrial Water Storage (TWS). Differences in tuning parameters and processing strategies result in GRACE TWS solutions with regionally specific variations and error patterns. We explored the spatiotemporal changes of the TWS time series, trend, uncertainties, and signal-to-noise ratio (SNR) among different GRACE TWS. We had also investigated the key terrestrial water storage components (surface water, soil moisture, and groundwater storage changes). The results show that the uncertainty of GRACE spherical harmonic (SH) solutions are higher than the mass concentration (mascon) over the NRB, and the Center for Space Research-mascons (CSR-M) noted the first best performance. Substantially, significant long-term (2003–2017) negative groundwater and soil moisture trend demonstrates a potential depletion over NRB. Despite an increase in precipitation and TWS time series, the rate of decline noted to increase rapidly from 2008, thus indicating the possibility of human-induced change ( e.g., for irrigation purposes). Thus, the result of this study provides a guiding principle for future studies in TWS change-related hydro-climatic change over NRB and similar basins.

scholarly journals Analysis of long-term terrestrial water storage variations in the Yangtze River basin

2013 ◽  
Vol 17 (5) ◽  
pp. 1985-2000 ◽  
Author(s):  
Y. Huang ◽  
M. S. Salama ◽  
M. S. Krol ◽  
R. van der Velde ◽  
A. Y. Hoekstra ◽  
...  
Keyword(s):  
River Basin ◽  
Yangtze River ◽  
Yangtze River Basin ◽  
Water Storage ◽  
The Yangtze River ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
The Yangtze River Basin ◽  
Lower Yangtze ◽  
Gauging Station

Abstract. In this study, we analyze 32 yr of terrestrial water storage (TWS) data obtained from the Interim Reanalysis Data (ERA-Interim) and Noah model from the Global Land Data Assimilation System (GLDAS-Noah) for the period 1979 to 2010. The accuracy of these datasets is validated using 26 yr (1979–2004) of runoff data from the Yichang gauging station and comparing them with 32 yr of independent precipitation data obtained from the Global Precipitation Climatology Centre Full Data Reanalysis Version 6 (GPCC) and NOAA's PRECipitation REConstruction over Land (PREC/L). Spatial and temporal analysis of the TWS data shows that TWS in the Yangtze River basin has decreased significantly since the year 1998. The driest period in the basin occurred between 2005 and 2010, and particularly in the middle and lower Yangtze reaches. The TWS figures changed abruptly to persistently high negative anomalies in the middle and lower Yangtze reaches in 2004. The year 2006 is identified as major inflection point, at which the system starts exhibiting a persistent decrease in TWS. Comparing these TWS trends with independent precipitation datasets shows that the recent decrease in TWS can be attributed mainly to a decrease in the amount of precipitation. Our findings are based on observations and modeling datasets and confirm previous results based on gauging station datasets.

scholarly journals Trend Dynamics of GRACE Terrestrial Water Storage in the Nile River Basin

2019 ◽  
Author(s):  
Emad Hasan ◽  
Aondover Tarhule
Keyword(s):  
Soil Moisture ◽  
Population Density ◽  
River Basin ◽  
Goodness Of Fit ◽  
Water Storage ◽  
Least Square ◽  
Nile River ◽  
Terrestrial Water Storage ◽  
Terrestrial Water ◽  
Nile River Basin

GRACE-derived Terrestrial Water Storage Anomalies (TWSA) continue to be used in an expanding array of studies to analyze numerous processes and phenomena related to terrestrial water storage dynamics, including groundwater depletions, lake storage variations, snow, and glacial mass changes, as well as floods, droughts, among others. So far, however, few studies have investigated how the factors that affect total water storage (e.g., precipitation, runoff, soil moisture, evapotranspiration) interact and combine over space and time to produce the mass variations that GRACE detects. This paper is an attempt to fill that gap and stimulate needed research in this area. Using the Nile River Basin as case study, it explicitly analyzes nine hydroclimatic and anthropogenic processes, as well as their relationship to TWS in different climatic zones in the Nile River Basin. The analytic method employed the trends in both the dependent and independent variables applying two geographically multiple regression (GMR) approaches: (i) an unweighted or ordinary least square regression (OLS) model in which the contributions of all variables to TWS variability are deemed equal at all locations; and (ii) a geographically weighted regression (GWR) which assigns a weight to each variable at different locations based on the occurrence of trend clusters, determined by Moran’s cluster index. In both cases, model efficacy was investigated using standard goodness of fit diagnostics. The OLS showed that trends in five variables (i.e., precipitation, runoff, surface water soil moisture, and population density) significantly (p<0.0001) explain the trends in TWSA for the basin at large. However, the models R2 value is only 0.14. In contrast, the GWR produced R2 values ranging between 0.40 and 0.89, with an average of 0.86 and normally distributed standard residuals. The models retained in the GWR differ by climatic zone. The results showed that all nine variables contribute significantly to the trend in TWS in the Tropical region; population density is an important contributor to TWSA variability in all zones; ET and Population density are the only significant variables in the semiarid zone. This type of information is critical for developing robust statistical models for reconstructing time series of proxy GRACE anomalies that predate the launch of the GRACE mission and for gap-filling between GRACE and GRACE-FO.

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