scholarly journals Recognizing factors affecting decline in groundwater level using wavelet-entropy measure (case study: Silakhor plain aquifer)

2019 ◽  
Vol 21 (3) ◽  
pp. 510-522 ◽  
Author(s):  
Mehdi Komasi ◽  
Soroush Sharghi

Abstract The most important approach to identify the behavior of hydrological processes is time series analysis of this process. Wavelet-entropy measure has been considered as a criterion for the degree of time series fluctuations and consequently uncertainty. Wavelet-entropy measure reduction indicates the reduction in natural time series fluctuations and thus, the occurrence of an unfavorable trend in time series. In this way, to identify the main cause of declining aquifer water level in the Silakhor plain, monthly time series of rainfall, temperature and output discharge were divided into three different time periods. Then, these time series were decomposed to multiple frequent time series by wavelet transform and then, the wavelet energies were computed for these decomposed time series. Finally, wavelet-entropy measure was computed for each different time period. Given the entropy reduction of about 71, 13 and 10.5% for discharge, rainfall and temperature time series respectively, it can be concluded that fluctuation decrease of discharge time series has relatively more effect on groundwater level oscillation patterns with respect to the rainfall and temperature time series. In this regard, it could be concluded that the climate change factors are not facing significant changes; thus, human activities can be regarded as the main reason for the declining groundwater level in this plain.

2015 ◽  
Vol 51 (1) ◽  
pp. 198-212 ◽  
Author(s):  
Dylan J. Irvine ◽  
Roger H. Cranswick ◽  
Craig T. Simmons ◽  
Margaret A. Shanafield ◽  
Laura K. Lautz

2021 ◽  
Vol 1 (1) ◽  
Author(s):  
Malvina Silvestri ◽  
Federico Rabuffi ◽  
Massimo Musacchio ◽  
Sergio Teggi ◽  
Maria Fabrizia Buongiorno

In this work, the land surface temperature time series derived using Thermal InfraRed (TIR) satellite data offers the possibility to detect thermal anomalies by using the PCA method. This approach produces very detailed maps of thermal anomalies, both in geothermal areas and in urban areas. Tests were conducted on the following three Italian sites: Solfatara-Campi Flegrei (Naples), Parco delle Biancane (Grosseto) and Modena city.


2021 ◽  
Author(s):  
Christopher Kadow ◽  
David Hall ◽  
Uwe Ulbrich

<p>Historical temperature measurements are the basis of global climate datasets like HadCRUT4. This dataset contains many missing values, particularly for periods before the mid-twentieth century, although recent years are also incomplete. Here we demonstrate that artificial intelligence can skilfully fill these observational gaps when combined with numerical climate model data. We show that recently developed image inpainting techniques perform accurate monthly reconstructions via transfer learning using either 20CR (Twentieth-Century Reanalysis) or the CMIP5 (Coupled Model Intercomparison Project Phase 5) experiments. The resulting global annual mean temperature time series exhibit high Pearson correlation coefficients (≥0.9941) and low root mean squared errors (≤0.0547 °C) as compared with the original data. These techniques also provide advantages relative to state-of-the-art kriging interpolation and principal component analysis-based infilling. When applied to HadCRUT4, our method restores a missing spatial pattern of the documented El Niño from July 1877. With respect to the global mean temperature time series, a HadCRUT4 reconstruction by our method points to a cooler nineteenth century, a less apparent hiatus in the twenty-first century, an even warmer 2016 being the warmest year on record and a stronger global trend between 1850 and 2018 relative to previous estimates. We propose image inpainting as an approach to reconstruct missing climate information and thereby reduce uncertainties and biases in climate records.</p><p>From:</p><p>Kadow, C., Hall, D.M. & Ulbrich, U. Artificial intelligence reconstructs missing climate information. <em>Nature Geoscience</em> <strong>13, </strong>408–413 (2020). https://doi.org/10.1038/s41561-020-0582-5</p><p>The presentation will tell from the journey of changing an image AI to a climate research application.</p>


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