Monitoring of extreme land hydrology events in central Poland using GRACE, land surface models and absolute gravity data

Kuczynska-Siehien, Joanna; Piretzidis, Dimitrios; Sideris, Michael G.; Olszak, T. A.; Szabó, Viktor

doi:10.1515/jag-2019-0003

Cited by 6 publications

(5 citation statements)

References 60 publications

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“…The two-way coupling model of land surface and hydrology has become a new exploration. Through the effective integration with RS, GIS and computer technology, the simulation of hydrological cycle and energy exchange process under changing environment, as well as the evaluation and prediction of the impact on water resources, are realized [52][53][54][55][56]. This model completely solves the land hydrological cycle, and can be effectively fed back to the land surface model, though it is still in the stage of continuous exploration and improvement.…”

Section: Introductionmentioning

confidence: 99%

Attribution and Sensitivity Analysis of Runoff Variation in the Yellow River Basin under Climate Change

et al. 2022

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The Yellow River Basin is a typical arid and semi-arid area, which is very sensitive to climate change. In recent years, it has become the area with the greatest shortage of water resources in China. In this study, a new two-way coupling model of land surface and hydrology has been explored to analyze the impacts of climate change and human activities on the runoff. It is of great theoretical and practical significance for making better management countermeasures and strategies to cope with climate change in the Yellow River Basin. The results showed that: (1) the annual average precipitation in the basin was 470.1 mm, which was higher in the lower reaches than in the middle and upper reaches. The annual average temperature is 5.8 °C. The entire basin showed a remarkable warming speed. The annual average pan evaporation is 1067.3 mm showing a downward trend throughout the basin; (2) from 1987 to 2009, the contribution rate of climate change to runoff change has not fluctuated by more than 5%. Since 2010, the precipitation caused by climate factors has increased runoff by 12~15%. The impact of land use change on runoff has been increasing annually. The influence of projects on runoff change was the leading factor of runoff reduction in the Yellow River Basin, with the contribution rate around 50%; and (3) for every 10% decrease in precipitation, the runoff decreases by 13~15.7%. When the temperature rises by 1.0 °C, the runoff decreases by 2.1~4.2%. The runoff in the upper reaches of the Yellow River was most sensitive to precipitation and temperature changes. This showed that the runoff in the plateau and mountainous areas were highly sensitive to climate change.

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Section: Introductionmentioning

confidence: 99%

Attribution and Sensitivity Analysis of Runoff Variation in the Yellow River Basin under Climate Change

et al. 2022

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“…Therefore, the determined anomalies are characterized by high noise for these ranges. Similar to the analysis performed in Kuczynska-Siehien et al (2019), the GRACE and AMSR-E sensors pick up an anomaly related to the 2010 hydrological flood. However, contrary to the cited article, the SM determined from AMSR-E indicates the occurrence of anomalies in the years 2007-2009, which is not recorded in the GLDAS models.…”

Section: Europementioning

confidence: 64%

Similarities and Differences in the Earth’s Water Variations Signal Provided by Grace and AMSR-E Observations Using Maximum Covariance Analysis at Various Land Cover Data Backgrounds

Szabó

Osińska-Skotak

2023

Artificial Satellites

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The study presents a compatibility analysis of gravimetric observations with passive microwave observations. Monitoring the variability of soil water content is one of the essential issues in climate-related research. Total water storage changes (ΔTWS) observed by Gravity Recovery and Climate Experiment (GRACE), enables the creation of many applications in hydrological monitoring. Soil moisture (SM) is a critical variable in hydrological studies. Advanced Microwave Scanning Radiometer (AMSR-E) satellite products provided unique observations on this variable in near-daily time resolutions. The study used maximum covariance analysis (MCA) to extract principal components for ΔTWS and SM signals. The analysis was carried out for the global area, dividing the discussion into individual continents. The amplitudes of gravimetric and microwave signals were computed via the complex empirical orthogonal function (EOF) and the complex conjugate EOF* to determine the regions for detailed comparison. Similarities and differences in signal convergence results were compared with land cover data describing soil conditions, vegetation cover, urbanization status, and cultivated land. Convergence was determined using Pearson correlation coefficients and cross-correlation. In order to compare ΔTWS and SM in individual seasons, ΔTWS observations were normalized. Results show that naturally forested areas and large open spaces used for agriculture support the compatibility between GRACE and AMSRE observations and are characterized by a good Pearson correlation coefficient >0.8. Subpolar regions with permafrost present constraints for AMSR-E observations and have little convergence with GRACE observations.

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“…By assessing the influence of nearby subsurface water bodies, a comparison between ground-based and satellite data was feasible. The methodology outlined in the work of Kuczynska-Siehien et al (2019) and Szabó and Marjańska (2020) was employed to process absolute gravity data. The ∆TWS prediction results and the given gravity disturbance are presented in figure 3.…”

Section: Local Results and Discussionmentioning

confidence: 99%

Using machine learning techniques to reconstruct the signal observed by the GRACE mission based on AMSR-E microwave data

Szabó,

Osińska-Skotak,

Olszak

2024

Miscellanea Geographica

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This study delves into the synergy between remote sensing and satellite gravimetry, focusing on the utilization of Advanced Microwave Scanning Radiometer (AMSR-E) data for modeling delta Total Water Storage (ΔTWS) values derived from the GRACE mission. Various machine learning algorithms were employed to investigate the concordance between Gravity Recovery and Climate Experiment (GRACE) and AMSR-E observations. Despite the limited correlation in circumpolar permafrost areas, ΔTWS was successfully modeled with an accuracy of a Root Mean Square Error (RMSE) of 3.5 cm. The Amazon region exhibited a notable model error, attributed to significant ΔTWS amplitude; the overall model quality was affirmed by Normalized Root Mean Square Error (NRMSE) and Nash-Sutcliffe Efficiency (NSE) metrics. Importantly, the effectiveness of AMSR-E Soil Moisture (SM) data, encompassing C (frequency of 4–8 GHz) and X (frequency of 8–12 GHz) ranges (~0.04 m and ~0.03 m wavelength, respectively) in modeling ΔTWS, even in heavily forested equatorial regions, was demonstrated.

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Monitoring of extreme land hydrology events in central Poland using GRACE, land surface models and absolute gravity data

Cited by 6 publications

References 60 publications

Attribution and Sensitivity Analysis of Runoff Variation in the Yellow River Basin under Climate Change

Attribution and Sensitivity Analysis of Runoff Variation in the Yellow River Basin under Climate Change

Similarities and Differences in the Earth’s Water Variations Signal Provided by Grace and AMSR-E Observations Using Maximum Covariance Analysis at Various Land Cover Data Backgrounds

Using machine learning techniques to reconstruct the signal observed by the GRACE mission based on AMSR-E microwave data

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