scholarly journals Investigation of Sentinel-1 Derived Land Subsidence Using Wavelet Tools And Triple Exponential Smoothing Algorithm In Lagos, Nigeria

2021 ◽  
Author(s):  
Femi Emmanuel Ikuemonisan ◽  
Vitalis Chidi Ozebo ◽  
Olawale Babatunde Olatinsu
Keyword(s):  
Soil Moisture ◽  
Land Subsidence ◽  
Groundwater Level ◽  
Exponential Smoothing ◽  
Future Trend ◽  
Smoothing Algorithm ◽  
Groundwater Abstraction ◽  
Groundwater Level Fluctuations ◽  
History Of ◽  
Sbas Insar

Abstract Lagos has a history of long-term groundwater abstraction that is often compounded by the rising indiscriminate private borehole and water well proliferation. This has resulted in various forms of environmental degradation, including land subsidence. Prediction of the temporal evolution of land subsidence is central to successful land subsidence management. In this study, a triple exponential smoothing algorithm was applied to predict the future trend of land subsidence in Lagos. Land subsidence time series is computed with SBAS-InSAR technique with Sentinel-1 acquisitions from 2015 to 2019. Besides, Matlab wavelet tool was implemented to investigate the periodicity within land displacement signal components and to understand the relationship between the observed land subsidence, and groundwater level change and that of soil moisture. Results show that land subsidence in the LOS direction varied approximately between –94 and 15 mm/year. According to the wavelet-based analysis result, land subsidence in Lagos is partly influenced by both groundwater level fluctuations and soil moisture variability. Evaluation of the proposed model indicates good accuracy, with the highest residual of approximately 8%. We then used the model to predict land subsidence between the years 2020 and 2023. The result showed that by the end of 2023 the maximum subsidence would reach 958 mm which is approximately 23% increase.

scholarly journals Land Subsidence Response to Different Land Use Types and Water Resource Utilization in Beijing-Tianjin-Hebei, China

2020 ◽  
Vol 12 (3) ◽  
pp. 457 ◽  
Author(s):  
Chaofan Zhou ◽  
Huili Gong ◽  
Beibei Chen ◽  
Mingliang Gao ◽  
Qun Cao ◽  
...  
Keyword(s):  
Land Use ◽  
Water Resource ◽  
Land Subsidence ◽  
Groundwater Level ◽  
Agricultural Land ◽  
Development And Utilization ◽  
Development Levels ◽  
Small Baseline ◽  
Sbas Insar ◽  
Groundwater Level Changes

The long-term overexploitation of groundwater leads to serious land subsidence and threatens the safety of Beijing-Tianjin-Hebei (BTH). In this paper, an interferometric point target analysis (IPTA) with small baseline subset InSAR (SBAS-InSAR) technique was used to derive the land subsidence in a typical BTH area from 2012 to 2018 with 126 Radarsat-2 and 184 Sentinel-1 images. The analysis reveals that the average subsidence rate reached 118 mm/year from 2012 to 2018. Eleven subsidence features were identified: Shangzhuang, Beijing Airport, Jinzhan and Heizhuanghu in Beijing, Guangyang and Shengfang in Langfang, Wangqingtuo in Tianjin, Dongguang in Cangzhou, Jingxian and Zaoqiang in Hengshui and Julu in Xingtai. Comparing the different types of land use in subsidence feature areas, the results show that when the land-use type is relatively more complex and superimposed with residential, industrial and agricultural land, the land subsidence is relatively more significant. Moreover, the land subsidence development patterns are different in the BTH areas because of the different methods adopted for their water resource development and utilization, with an imbalance in their economic development levels. Finally, we found that the subsidence changes are consistent with groundwater level changes and there is a lag period between land subsidence and groundwater level changes of approximately two months in Beijing Airport, Jinzhan, Jingxian and Zaoqiang, of three months in Shangzhuang, Heizhuanghu, Guangyang, Wangqingtuo and Dongguang and of four months in Shengfang.

One dimensional numerical modeling of land subsidence caused by seasonal groundwater level fluctuations in Kawajima, Japan

2020 ◽  
Author(s):  
Kento Akitaya ◽  
Masaatsu Aichi
Keyword(s):  
Physical Properties ◽  
Land Subsidence ◽  
Groundwater Level ◽  
Seasonal Fluctuation ◽  
Soil Deformation ◽  
Seasonal Fluctuations ◽  
One Dimensional ◽  
Groundwater Level Fluctuations ◽  
Modified Cam Clay ◽  
Cam Clay

<p>Land subsidence caused by seasonal fluctuation of groundwater level caused by agricultural groundwater use was numerically simulated in this study. In the study area, Kawajima, Saitama prefecture, Japan, the hydraulic head has been gradually increasing over time with seasonal fluctuations and the subsurface formations have repeated expansion and compaction. However, the land subsidence progressed because the compaction included the plastic deformation. In this study, vertically one-dimensional model to numerically simulate coupled groundwater flow and soil deformation in Kawajima was developed with modified cam-clay model. Because of the lack of subsurface information, it was difficult to set the physical properties such that the simulated subsidence and the observed subsidence are satisfactorily close to each other. This study applied a genetic algorithm in order to search the set of underground physical properties. The improved set of underground physical properties succeeded to reproduce the observed land subsidence in Kawajima.</p>

scholarly journals Study of moisture transfer through unsaturated zone and groundwater recharge at the emergency site of the Solikamsk-2 Mine (Verkhnekamskoye salt deposit, Russia)

2020 ◽  
Vol 163 ◽  
pp. 06013
Author(s):  
Konstantin Vladimirov ◽  
Anton Nikulenkov ◽  
Aleksey Shvartc
Keyword(s):  
Soil Moisture ◽  
Groundwater Recharge ◽  
Groundwater Level ◽  
Unsaturated Zone ◽  
Moisture Transfer ◽  
Water Balance Components ◽  
Recharge Rate ◽  
Groundwater Level Fluctuations ◽  
Accident Conditions ◽  
Observation Wells

The article provides the results of assessing groundwater recharge at the emergency site of the Solikamsk-2 Mine (Solikamsk, Russia). The assessment was performed using two separate approaches, including the study of the results of groundwater level observations and numerical modeling of soil moisture transfer through the unsaturated zone. The analysis of the groundwater level fluctuations in observation wells made it possible to estimate the recharge rate in natural and anthropogenic (after the accident) conditions. To study the soil moisture transfer in the upper part of the unsaturated zone two moisture sensors were installed at a depth of 0.4 and 0.65 m. To interpret sensors data the numerical model was developed using the HYDRUS 1D software. The modeling results allowed estimating the recharge rate at a depth of 0.65 m and the main water balance components (evaporation, transpiration, surface runoff). A comparison of the two methods showed similar results, allowing them to use for estimating and predicting groundwater recharge in the annual cycle.

Visualizing uncertainty of one-dimensional land subsidence prediction by preparing various local optimal solutions with a genetic algorithm

2021 ◽  
Author(s):  
Kento Akitaya ◽  
Masaatsu Aichi
Keyword(s):  
Genetic Algorithm ◽  
Land Subsidence ◽  
Groundwater Level ◽  
Physical Property ◽  
Optimal Solutions ◽  
Groundwater Level Fluctuations ◽  
Subsidence Prediction ◽  
Subsidence Monitoring ◽  
Inversion Analysis

<p>This study tried to visualize the predictive uncertainty while predicting future land subsidence caused by the groundwater pumping. Because land subsidence modeling is highly uncertain, it is impossible to determine the distribution of subsurface physical property values uniquely. Therefore, we prepared various local optimal solutions through the inversion analysis with a genetic algorithm in order to visualize land subsidence prediction uncertainty. The inversion analysis was conducted using the long-term land subsidence monitoring data at Kawajima in the Kanto Plain, Japan. In this study site, the seasonal groundwater level fluctuations have caused plastic compaction in summer and elastic expansion in winter every year. Obtained multiple sets of subsurface properties were within the range of typical values in the existing literature and satisfactorily reproduced the observed subsidence, showing that the inversion analysis worked well. In addition, the groundwater level scenario analysis was conducted using obtained property sets. This revealed that the subsidences predicted for a sudden groundwater level drop and rapid recovery scenario are more volatile than the subsidences predicted for the stable scenario. This means that it is important to have multiple sets of subsurface properties to predict future land subsidence caused by unprecedented groundwater level fluctuations.</p>

scholarly journals TIME SERIES ANALYSIS OF INSAR DATA TO STUDY LAND SUBSIDENCE INDUCED BY GROUNDWATER LEVEL DECLINE IN SIRJAN PLAIN

2017 ◽  
Vol XLII-4/W4 ◽  
pp. 51-57
Author(s):  
A. Choopani ◽  
M. Dehghani ◽  
M. R. Nikoo ◽  
S. Zeinali
Keyword(s):  
Land Subsidence ◽  
Groundwater Level ◽  
Annual Rainfall ◽  
Interferometric Synthetic Aperture Radar ◽  
Groundwater Level Fluctuations ◽  
Envisat Asar ◽  
Subsidence Rate ◽  
Maximum Subsidence ◽  
Kerman City ◽  
Over Exploitation

Sirjan located in Southwest of Kerman City, Iran, is a city with average annual rainfall of 132mm. Over-exploitation of groundwater used for irrigation of pistachios gardens has caused serious land subsidence in Sirjan Plain. In this research we have used the Interferometric Synthetic Aperture Radar technique to estimate the subsidence rate in Sirjan plain. 18 Interferograms extracted from 12 ENVISAT ASAR images spanning between April 2004 and September 2010, have been studied. The SBAS algorithm was then applied in order to estimate the mean subsidence velocity map. Maximum subsidence rate is estimated as 28cm/yr. Furthermore, groundwater level fluctuations in the study area has been investigated in the piezometer wells located in the study area. Comparing between the results obtained from the interferometry and groundwater level fluctuation maps, shows a strong correlation between head decline in groundwater and land subsidence.

scholarly journals Variation in Soil Properties Regulate Greenhouse Gas Fluxes and Global Warming Potential in Three Land Use Types on Tropical Peat

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 465 ◽  
Author(s):  
Kiwamu Ishikura ◽  
Untung Darung ◽  
Takashi Inoue ◽  
Ryusuke Hatano
Keyword(s):  
Global Warming ◽  
Soil Moisture ◽  
Groundwater Level ◽  
Global Warming Potential ◽  
C:N Ratio ◽  
Co2 Flux ◽  
Nitrate Content ◽  
N2o Flux ◽  
Ch4 Flux ◽  
In The Beginning

This study investigated spatial factors controlling CO2, CH4, and N2O fluxes and compared global warming potential (GWP) among undrained forest (UDF), drained forest (DF), and drained burned land (DBL) on tropical peatland in Central Kalimantan, Indonesia. Sampling was performed once within two weeks in the beginning of dry season. CO2 flux was significantly promoted by lowering soil moisture and pH. The result suggests that oxidative peat decomposition was enhanced in drier position, and the decomposition acidify the peat soils. CH4 flux was significantly promoted by a rise in groundwater level, suggesting that methanogenesis was enhanced under anaerobic condition. N2O flux was promoted by increasing soil nitrate content in DF, suggesting that denitrification was promoted by substrate availability. On the other hand, N2O flux was promoted by lower soil C:N ratio and higher soil pH in DBL and UDF. CO2 flux was the highest in DF (241 mg C m−2 h−1) and was the lowest in DBL (94 mg C m−2 h−1), whereas CH4 flux was the highest in DBL (0.91 mg C m−2 h−1) and was the lowest in DF (0.01 mg C m−2 h−1), respectively. N2O flux was not significantly different among land uses. CO2 flux relatively contributed to 91–100% of GWP. In conclusion, it is necessary to decrease CO2 flux to mitigate GWP through a rise in groundwater level and soil moisture in the region.

scholarly journals Mapping the Groundwater Level and Soil Moisture of a Montane Peat Bog Using UAV Monitoring and Machine Learning

2021 ◽  
Vol 13 (5) ◽  
pp. 907
Author(s):  
Theodora Lendzioch ◽  
Jakub Langhammer ◽  
Lukáš Vlček ◽  
Robert Minařík
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Data ◽  
Groundwater Level ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Sampling Strategy ◽  
Peat Bog ◽  
Strong Impact ◽  
Ground Truth Data

One of the best preconditions for the sufficient monitoring of peat bog ecosystems is the collection, processing, and analysis of unique spatial data to understand peat bog dynamics. Over two seasons, we sampled groundwater level (GWL) and soil moisture (SM) ground truth data at two diverse locations at the Rokytka Peat bog within the Sumava Mountains, Czechia. These data served as reference data and were modeled with a suite of potential variables derived from digital surface models (DSMs) and RGB, multispectral, and thermal orthoimages reflecting topomorphometry, vegetation, and surface temperature information generated from drone mapping. We used 34 predictors to feed the random forest (RF) algorithm. The predictor selection, hyperparameter tuning, and performance assessment were performed with the target-oriented leave-location-out (LLO) spatial cross-validation (CV) strategy combined with forward feature selection (FFS) to avoid overfitting and to predict on unknown locations. The spatial CV performance statistics showed low (R2 = 0.12) to high (R2 = 0.78) model predictions. The predictor importance was used for model interpretation, where temperature had strong impact on GWL and SM, and we found significant contributions of other predictors, such as Normalized Difference Vegetation Index (NDVI), Normalized Difference Index (NDI), Enhanced Red-Green-Blue Vegetation Index (ERGBVE), Shape Index (SHP), Green Leaf Index (GLI), Brightness Index (BI), Coloration Index (CI), Redness Index (RI), Primary Colours Hue Index (HI), Overall Hue Index (HUE), SAGA Wetness Index (TWI), Plan Curvature (PlnCurv), Topographic Position Index (TPI), and Vector Ruggedness Measure (VRM). Additionally, we estimated the area of applicability (AOA) by presenting maps where the prediction model yielded high-quality results and where predictions were highly uncertain because machine learning (ML) models make predictions far beyond sampling locations without sampling data with no knowledge about these environments. The AOA method is well suited and unique for planning and decision-making about the best sampling strategy, most notably with limited data.

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