scholarly journals Investigation of correlation of the variations in land subsidence (detected by continuous GPS measurements) and methodological data in the surrounding areas of Lake Urmia

2012 ◽  
Vol 19 (6) ◽  
pp. 675-683 ◽  
Author(s):  
K. Moghtased-Azar ◽  
A. Mirzaei ◽  
H. R. Nankali ◽  
F. Tavakoli
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Salt Lake ◽  
Linear Trend ◽  
Groundwater Resources ◽  
Time Interval ◽  
Gps Measurements ◽  
Lake Urmia ◽  
North West ◽  
Continuous Gps

Abstract. Lake Urmia, a salt lake in the north-west of Iran, plays a valuable role in the environment, wildlife and economy of Iran and the region, but now faces great challenges for survival. The Lake is in immediate and great danger and is rapidly going to become barren desert. As a result, the increasing demands upon groundwater resources due to expanding metropolitan and agricultural areas are a serious challenge in the surrounding regions of Lake Urmia. The continuous GPS measurements around the lake illustrate significant subsidence rate between 2005 and 2009. The objective of this study was to detect and specify the non-linear correlation of land subsidence and temperature activities in the region from 2005 to 2009. For this purpose, the cross wavelet transform (XWT) was carried out between the two types of time series, namely vertical components of GPS measurements and daily temperature time series. The significant common patterns are illustrated in the high period bands from 180–218 days band (~6–7 months) from September 2007 to February 2009. Consequently, the satellite altimetry data confirmed that the maximum rate of linear trend of water variation in the lake from 2005 to 2009, is associated with time interval from September 2007 to February 2009. This event was detected by XWT as a critical interval to be holding the strong correlation between the land subsidence phenomena and surface temperature. Eventually the analysis can be used for modeling and prediction purposes and probably stave off the damage from subsidence phenomena.

scholarly journals Long term dynamics of OH<sup>∗</sup> temperatures over Middle Europe: Trends and solar correlations

2016 ◽  
Author(s):  
Christoph Kalicinsky ◽  
Peter Knieling ◽  
Ralf Koppmann ◽  
Dirk Offermann ◽  
Wolfgang Steinbrecht ◽  
...  
Keyword(s):  
Time Series ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Linear Trend ◽  
Temperature Time Series ◽  
Time Interval ◽  
Radio Flux ◽  
Solar Radio Flux ◽  
Temperature Time

Abstract. We present the analysis of annual average OH∗ temperatures in the mesopause region derived from measurements of the GRound based Infrared P-branch Spectrometer (GRIPS) at Wuppertal (51° N, 7° E) in the time interval 1988 to 2015. The current study uses a 7 year longer temperature time series compared to the latest analysis regarding the long term dynamics of OH* temperatures measured at Wuppertal. This additional time of observation leads to a change in characterisation of the observed long term dynamics. We perform a multiple linear regression using the solar radio flux F10.7cm (11-year cycle of solar activity) and time to describe the temperature evolution. The analysis leads to a linear trend of (−0.089&amp;pm;0.055) K year−1 and a sensitivity to the solar activity of (4.2&amp;pm;0.9) K (100 SFU)−1 (r2 of fit 0.6). However, one linear trend in combination with the 11-year solar cycle is not sufficient to explain all observed long term dynamics. Actually we find a clear trend break in the temperature time series in middle of 2006. Before this break point there is an explicit negative linear trend of (−0.22&amp;pm;0.08) K year−1 and after 2006 the linear trend turns positive with a value of (0.38&amp;pm;0.23) K year−1. This apparent trend break can also be described using a long periodic oscillation. One possibility is to use the 22-year solar cycle that describes the reversal of the solar magnetic field (Hale cycle). A multiple linear regression using the solar radio flux and the solar polar magnetic field as parameters leads to the regression coefficients Csolar = (5.0&amp;pm;0.7) K (100 SFU)−1 and Chale = (1.8&amp;pm;0.5) K (100 µT)−1 (r2 = 0.71). But the best way to describe the OH* temperature time series is to use the solar radio flux and a 24-year oscillation. A multiple linear regression using these parameters leads to a sensitivity to the solar activity of (4.3&amp;pm;0.7) K (100 SFU)−1 and an amplitude of the 24-year oscillation A = (1.95&amp;pm;0.43) K (r2 = 0.77). The most important finding here is that using these parameters for the multiple linear regression an additional linear trend is no longer needed. Moreover, with the knowledge of this 24-year oscillation the linear trends derived in this and in a former study of the Wuppertal data series can be reproduced by just fitting a line to the corresponding part (time interval) of the oscillation. This actually means that depending on the analysed time interval completely different linear trends with respect to magnitude and sign can be observed. This fact is of essential importance for any comparison between different observations and model simulations. After detrending the temperature time series regarding the 11-year solar cycle and the 24-year oscillation multi-annual oscillations (MAOs) remain. A harmonic analysis finds three pronounced oscillations with periods of (2.69&amp;pm;0.06) years, (3.15&amp;pm;0.07) years, and (4.54&amp;pm;0.17) years. The corresponding amplitudes are (1.03&amp;pm;0.33) K, (1.03&amp;pm;0.33) K, and (0.91&amp;pm;0.36) K, respectively.

Why water bodies of Iran have been dried up?

2020 ◽  
Author(s):  
Davood Moshir Panahi ◽  
Saeid Aminjafari ◽  
Bagher Zahabiyon
Keyword(s):  
Time Series ◽  
Crop Yield ◽  
Climatic Factors ◽  
Water Bodies ◽  
Time Interval ◽  
Lake Urmia ◽  
Surface Level ◽  
Urban Rural ◽  
Upstream Catchment ◽  
Natural Water Bodies

&lt;p&gt;The status of natural water bodies in terms of water quality and quantity can be considered a criterion for the environmental status of their upstream catchment. The presence of natural water bodies with good condition can be a sign proper of water resource management activities in the upstream catchment for sustainable development. Iran has been undergoing a rapid development process in recent decades. Nowadays, in most water bodies in Iran, the water level has been decreased and even disappeared in some cases. Lake Urmia is a well-known example of drying lakes in Iran. This study aims at identifying the main effective drivers in drying up of the main lakes in Iran.&lt;/p&gt;&lt;p&gt;Iran is a country with an approximate area of 1,648,000 km2 that has an arid and semi-arid climate with an average precipitation of 311 mm/year. The most important water bodies in Iran are Lake Urmia and Maharloo, Hoor-al-Azim and Gavkhuni Wetlands, and Gorgan Bay. This study focuses on the mentioned waterbodies and upstream catchment information.&lt;/p&gt;&lt;p&gt;At first, climate conditions and changes such as drought and changes in their properties are studied to find the answer to this question. Then, non-climatic factors and their changes such as urban/rural population changes, industrial growth, agricultural changes such as land area, crop yield, and the type of irrigation were studied. To achieve this purpose, the time series of the surface level of these five waterbodies was measured using satellite images. Then the time of significant changes in the time series of the surface level of each waterbody was determined using the Pettit test. As a result, the time interval for each waterbody was divided into a two-time span, before and after the change point. This created a time interval for climatic and non-climatic comparisons to identify effective factors.&lt;/p&gt;&lt;p&gt;The climatic data from the synoptic stations located in and around each waterbody catchment have been used to study the climatic conditions, and the sum of precipitation and mean temperature have been evaluated as the main climate parameters along with the SPIE drought index and characteristic changes. In order to evaluate effective non-climatic factors, changes in urban/rural population factors, agricultural land level, the number of agricultural products, and industrial units were used based on official statistics.&lt;/p&gt;&lt;p&gt;The results of this study indicate that the year of significant changes in the time series of lakes was between 1996 and 2001. Crop yield change growth was the main factor in the upstream catchment of all lakes as a result of changes in the irrigation patterns.&lt;/p&gt;

scholarly journals Multi-Component and Multi-Source Approach for Studying Land Subsidence in Deltas

2020 ◽  
Vol 12 (9) ◽  
pp. 1465 ◽  
Author(s):  
Eleonora Vitagliano ◽  
Umberto Riccardi ◽  
Ester Piegari ◽  
Jean-Paul Boy ◽  
Rosa Di Maio
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Linear Trend ◽  
Accurate Estimation ◽  
Physical Mechanisms ◽  
Original Dataset ◽  
Prediction And Prevention ◽  
Physically Based ◽  
Physically Based Models ◽  
Periodic Components

The coupled effects of climate change and land sinking make deltas and coastal areas prone to inundation and flooding, meaning that reliable estimation of land subsidence is becoming crucial. Commonly, land subsidence is monitored by accurate continuous and discrete measurements collected by terrestrial and space geodetic techniques, such as Global Navigation Satellite System (GNSS), Interferometry Synthetic Aperture Radar (InSAR), and high precision leveling. In particular, GNSS, which includes the Global Positioning System (GPS), provides geospatial positioning with global coverage, then used for deriving local displacements through time. These site-positioning time series usually exhibit a linear trend plus seasonal oscillations of annual and semi-annual periods. Although the periodic components observed in the geodetic signal affect the velocity estimate, studies dealing with the prediction and prevention of risks associated with subsidence focus mainly on the permanent component. Periodic components are simply removed from the original dataset by statistical analyses not based on the underlying physical mechanisms. Here, we propose a systematic approach for detecting the physical mechanisms that better explain the permanent and periodic components of subsidence observed in the geodetic time series. It consists of three steps involving a component recognition phase, based on statistical and spectral analyses of geodetic time series, a source selection phase, based on their comparison with data of different nature (e.g., geological, hydro-meteorological, hydrogeological records), and a source validation step, where the selected sources are validated through physically-based models. The application of the proposed procedure to the Codigoro area (Po River Delta, Northern Italy), historically affected by land subsidence, allowed for an accurate estimation of the subsidence rate over the period 2009–2017. Significant differences turn out in the retrieved subsidence velocities by using or not periodic trends obtained by physically based models.

scholarly journals Long-term dynamics of OH * temperatures over central Europe: trends and solar correlations

2016 ◽  
Vol 16 (23) ◽  
pp. 15033-15047 ◽  
Author(s):  
Christoph Kalicinsky ◽  
Peter Knieling ◽  
Ralf Koppmann ◽  
Dirk Offermann ◽  
Wolfgang Steinbrecht ◽  
...  
Keyword(s):  
Time Series ◽  
Solar Activity ◽  
Solar Radio ◽  
Linear Trend ◽  
Temperature Time Series ◽  
Time Interval ◽  
Radio Flux ◽  
Solar Radio Flux ◽  
Temperature Time

Abstract. We present the analysis of annual average OH* temperatures in the mesopause region derived from measurements of the Ground-based Infrared P-branch Spectrometer (GRIPS) at Wuppertal (51° N, 7° E) in the time interval 1988 to 2015. The new study uses a temperature time series which is 7 years longer than that used for the latest analysis regarding the long-term dynamics. This additional observation time leads to a change in characterisation of the observed long-term dynamics. We perform a multiple linear regression using the solar radio flux F10.7 cm (11-year cycle of solar activity) and time to describe the temperature evolution. The analysis leads to a linear trend of (−0.089 ± 0.055) K year−1 and a sensitivity to the solar activity of (4.2 ± 0.9) K (100 SFU)−1 (r2 of fit 0.6). However, one linear trend in combination with the 11-year solar cycle is not sufficient to explain all observed long-term dynamics. In fact, we find a clear trend break in the temperature time series in the middle of 2008. Before this break point there is an explicit negative linear trend of (−0.24 ± 0.07) K year−1, and after 2008 the linear trend turns positive with a value of (0.64 ± 0.33) K year−1. This apparent trend break can also be described using a long periodic oscillation. One possibility is to use the 22-year solar cycle that describes the reversal of the solar magnetic field (Hale cycle). A multiple linear regression using the solar radio flux and the solar polar magnetic field as parameters leads to the regression coefficients Csolar = (5.0 ± 0.7) K (100 SFU)−1 and Chale = (1.8 ±  0.5) K (100 µT)−1 (r2 = 0.71). The second way of describing the OH* temperature time series is to use the solar radio flux and an oscillation. A least-square fit leads to a sensitivity to the solar activity of (4.1 ± 0.8) K (100 SFU)−1, a period P  =  (24.8 ± 3.3) years, and an amplitude Csin  =  (1.95 ± 0.44) K of the oscillation (r2 = 0.78). The most important finding here is that using this description an additional linear trend is no longer needed. Moreover, with the knowledge of this 25-year oscillation the linear trends derived in this and in a former study of the Wuppertal data series can be reproduced by just fitting a line to the corresponding part (time interval) of the oscillation. This actually means that, depending on the analysed time interval, completely different linear trends with respect to magnitude and sign can be observed. This fact is of essential importance for any comparison between different observations and model simulations.

Flood Seasonality in the Congo River Basin 

2021 ◽  
Author(s):  
Bola Bosongo Gode ◽  
Neal Jeff ◽  
Tshimanga Raphael ◽  
Hawker Lauwrence ◽  
Trigg Mark A ◽  
...  
Keyword(s):  
Time Series ◽  
Cluster Analysis ◽  
Climate Models ◽  
Flood Frequency ◽  
Congo Basin ◽  
Time Interval ◽  
Flood Events ◽  
Frequency Method ◽  
North West ◽  
Frequency Changes

&lt;p&gt;Information on flood seasonality is required in many practical applications of hydrology and water resources management. However, an understanding of flood seasonality and how flood frequencies may have changed over time has not been established for the Congo Basin. The main objective of this study is therefore to identify flood seasonality and change in frequency the Congo Basin (CB). The analysis based on six major drainage areas of the CB, where we used a Peaks Over Threshold (POT) flood time-series with three peaks per year. The relative frequency method is applied to identify flood seasons, and then a cluster analysis is performed to classify flood into type based on monthly frequency. The directional statistics method is used to determine the mean day of flood and the flood variability measure. To identify flood frequency changes, the analysis of variance was applied to test the difference between two flood frequency time series blocks before and after the change point year. Results show that four gauging stations exhibit a unimodal flood seasonality distribution while two gauging stations have bimodal flood seasonality distribution, and two significant flood rich months are observed in all studied gauging stations. &amp;#160;The cluster analysis results in four spatially flood types with distinct seasonality characteristics. Mean flood dates show that the time interval between adjacent flood events in the south and south-east is shorter compared to time interval between flood events in the north and north-west. It is observed that, in almost all gauging stations, there is strong flood seasonality, and the geographical location of a watershed is indicative of its flood pattern. Most of significant decreasing frequencies are found in the southern part of the Congo Basin. There are no significant changes in flood frequency identified in the northern and eastern part of the Basin. However, flood frequencies have been increasing in the centre and western part of the Basin. This study suggest that, exploring flood generating factors and the drivers of change can provide insights for understanding the influence of these factors on floods as climate models projected changes in extreme precipitation and aridity in the future. &amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

scholarly journals Sensitivity Analysis of Sentinel-1 Backscatter to Oil Palm Plantations at Pluriannual Scale: A Case Study in Gabon, Africa

2021 ◽  
Vol 13 (11) ◽  
pp. 2075
Author(s):  
J. David Ballester-Berman ◽  
Maria Rastoll-Gimenez
Keyword(s):  
Time Series ◽  
Sensitivity Analysis ◽  
Oil Palm ◽  
Time Interval ◽  
Potential Candidate ◽  
Initial Growth ◽  
Natural Forests ◽  
Wet Season ◽  
Radar Signatures ◽  
The Times

The present paper focuses on a sensitivity analysis of Sentinel-1 backscattering signatures from oil palm canopies cultivated in Gabon, Africa. We employed one Sentinel-1 image per year during the 2015–2021 period creating two separated time series for both the wet and dry seasons. The first images were almost simultaneously acquired to the initial growth stage of oil palm plants. The VH and VV backscattering signatures were analysed in terms of their corresponding statistics for each date and compared to the ones corresponding to tropical forests. The times series for the wet season showed that, in a time interval of 2–3 years after oil palm plantation, the VV/VH ratio in oil palm parcels increases above the one for forests. Backscattering and VV/VH ratio time series for the dry season exhibit similar patterns as for the wet season but with a more stable behaviour. The separability of oil palm and forest classes was also quantitatively addressed by means of the Jeffries–Matusita distance, which seems to point to the C-band VV/VH ratio as a potential candidate for discrimination between oil palms and natural forests, although further analysis must still be carried out. In addition, issues related to the effect of the number of samples in this particular scenario were also analysed. Overall, the outcomes presented here can contribute to the understanding of the radar signatures from this scenario and to potentially improve the accuracy of mapping techniques for this type of ecosystems by using remote sensing. Nevertheless, further research is still to be done as no classification method was performed due to the lack of the required geocoded reference map. In particular, a statistical assessment of the radar signatures should be carried out to statistically characterise the observed trends.

scholarly journals Amount and location of tectonic uplift in the Urmia region of northwest Iran from the Permian to the Neogene

2021 ◽  
Vol 3 (3) ◽  
Author(s):  
Akram Alizadeh
Keyword(s):  
Hypersaline Lake ◽  
Time Interval ◽  
Lake Basin ◽  
Lake Urmia ◽  
Qom Formation ◽  
Northwest Of Iran ◽  
The Subject ◽  
Orogenic Uplift ◽  
East Azerbaijan ◽  
Urmia Lake Basin

AbstractThe Urmia Lake Basin is located between the West and East Azerbaijan provinces in the northwest of Iran. Lake Urmia is the twentieth largest lake and second largest hypersaline lake in the world. Stratigraphic columns have been constructed, using published information, to compare the sedimentary units deposited from the Permian to the Neogene on the east and west sides of the lake, and to use these to quantity subsidence and uplift. East of the lake, the sedimentary section is more complete and has been the subject of detailed stratigraphic studies, including the compilation of measured sections for some units. West of the lake, the section is incomplete and less work has been done; three columns illustrate variations in the preserved stratigraphy for the time interval. In all cases, the columns are capped by the Oligocene–Miocene Qom Formation, which was deposited during a post-orogenic marine transgression and unconformably overlies units ranging from Precambrian to Cretaceous. Permian to Cretaceous stratigraphy is used to measure subsidence in the Lake Urmia basin up to the end of the Cretaceous, and then, the subsequent orogenic uplift, which was followed by further subsidence recorded by the deposition of the Qom Formation in the Oligocene–Miocene.

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