Land Subsidence Rate Research Articles

Monitoring and Analysis of Land Subsidence in Jiaozuo City (China) Based on SBAS-InSAR Technology

Jiaozuo, located in the northwest of Henan Province, is one of the six major anthracite production bases in China. It is susceptible to land subsidence due to over a hundred years of mining history, continuous urbanization, frequent human activities, etc., which poses a great threat to urban infrastructure construction and people’s production and lives. However, traditional leveling techniques are not sufficient for monitoring large areas of land subsidence due to the time-consuming, labor-intensive, and expensive nature of the process. Furthermore, the results of conventional methods may not be timely, rendering them ineffective for monitoring purposes. With the continuous advancement of urbanization, land subsidence caused by groundwater extraction, ground load, and other factors in daily life poses a great threat to urban infrastructure construction and people’s production and lives. In order to monitor the land subsidence in the area of Jiaozuo city, this article uses the Sentienl-1A satellite data covering the city from March 2017 to March 2021 to obtain the accumulated land subsidence and the average land subsidence rate based on the Small Baselines Subset InSAR (SBAS-InSAR) technology. The results indicate that the surface of Jiaozuo area is generally stable, and there has been no large-scale settlement. The settlement rate is roughly between −1 mm/a and 2.2 mm/a, and the areas with obvious land subsidence are mainly located in the southeast and east of Jiaozuo city center. After field investigation, it was found that the land subsidence is mainly caused by two reasons: groundwater excessive mining and excessive surface load. In the northeast of Jiaozuo city, there is a certain uplift area. After on-site investigation, it was found that the area is connected to a tailings pond of an aluminum mine, constantly accumulating abandoned rock masses and sediment, causing an annual uplift rate of +6~+ 24 mm/a. The large-scale extraction of groundwater from farmland in the urban–rural integration area for irrigation of wheat has led to the settlement of buildings in the area with a rate of −11–−74 mm/a.

Scrutinization of land subsidence rate using a supportive predictive model: Incorporating radar interferometry and ensemble soft-computing

Land subsidence is a huge challenge that land and water resource managers are still facing. Radar datasets revolutionize the way and give us the ability to provide information about it, thanks to their low cost. But identifying the most important drivers need for the modeling process. Machine learning methods are especially top of mind amid the prediction studies of natural hazards and hit new heights over the last couple of years. Hence, putting an efficient approach like integrated radar-and-ensemble-based method into practice for land subsidence rate simulation is not available yet which is the main aim of this research. In this study, the number of 52 pairs of radar images were used to identify subsidence from 2014 to 2019. Then, using the simulated annealing (SA) algorithm the key variables affecting land subsidence were identified among the topographical parameters, aquifer information, land use, hydroclimatic variables, and geological and soil factors. Afterward, three individual machine learning models (including Support Vector Machine, SVM; Gaussian Process, GP; Bayesian Additive Regression Tree, BART) along with three ensemble learning approaches were considered for land subsidence rate modeling. The results indicated that the subsidence varies between 0 and 59 cm in this period. Comparing the Radar results with the permanent geodynamic station exhibited a very strong correlation between the ground station and the radar images (R2 = 0.99, RMSE = 0.008). Parsing the input data by the SA indicated that key drivers are precipitation, elevation, percentage of fine-grained materials in the saturated zone, groundwater withdrawal, distance to road, groundwater decline, and aquifer thickness. The performance comparison indicated that ensemble models perform better than individual models, and among ensemble models, the nonlinear ensemble approach (i.e., BART model combination) provided better performance (RMSE = 0.061, RSR = 0.42, R2 = 0.83, PBIAS = 2.2). Also, the distribution shape of the probability density function in the non-linear ensemble model is much closer to the observations. Results indicated that the presence of significant fine-grained materials in unconsolidated aquifer systems can clarify the response of the aquifer system to groundwater decline, low recharge, and subsequent land subsidence. Therefore, the interaction between these factors can be very dangerous and intensify subsidence.

A New Method for Continuous Track Monitoring in Regions of Differential Land Subsidence Rate Using the Integration of PS-InSAR and SBAS-InSAR

It is difficult for single time-series Interferometric Synthetic Aperture Radar (InSAR) processing to guarantee the accuracy and efficiency of continuous track monitoring in regions of differential subsidence. This paper proposes a new method, integrating the Persistent Scatterer InSAR (PS-InSAR) with high precision and the Small Baseline Subset InSAR (SBAS-InSAR) with high efficiency for continuous track monitoring in regions of differential land subsidence rates. Based on PS-InSAR processing, the Iterative Self-Organizing Data Analysis Techniques (ISODATA) algorithm is adopted to search the boundary of differential subsidence between slow and fast subsidence rates. The SBAS-InSAR processing with high frequency is used to continuously track and monitor the regions with fast subsidence rates incorporating original data and newly added data into small data sets from time to time according to SAR data updating, the monitoring results of which are obtained from the weighted average of the added results of SBAS-InSAR processing and the original results of PS-InSAR processing. The impact of SAR data updating on the slow subsidence rate region is so weak that it is not necessary to simultaneously update the corresponding monitoring results to improve global efficiency. If the slow subsidence rates region must be remeasured in relation to its previous subsidence, or the proportion of new data capacity alters compared with the original data set, PS-InSAR processing is used to analyze the whole monitoring region again using the complete data set. A case study performed on the west region of the Qinhuai River in Nanjing, China, indicates that the density of monitoring points in the fast-subsidence region is greatly improved, increasing from 711 points/km2 to 2760 points/km2—an increase of 288.2%.

Measuring the Vulnerability of the Urban Fabric in the Face of Land Subsidence (Case Study: District 17 of Tehran)

Urban vulnerability is the probability of damage to cities and urban infrastructures against natural and unnatural events. In this regard, in the last half century, land subsidence has involved many countries as a potential risk for urban, industrial and agricultural areas. With the occurrence of land subsidence, the physical and structural characteristics of the structures built in the city are changed and show different behavior. For this reason, it is important to consider the effect of land subsidence on the urban fabric in which there are exposed and buried structures. The involvement of the 17th district of Tehran with the issue of land subsidence was the initial idea of the current research. Currently, the maximum land subsidence rate in this area is 25 cm per year. The present research has been carried out in the form of spatial studies and by implementing the analytical model in four steps. The results of the model implementation showed that the urban fabric of the 17th district is prone to high vulnerability due to features such as high population and building density, the presence of worn-out fabric, buildings with low structural strength and very low construction quality of buildings. In total, about 657 hectares (80 percent) of the built-up lands in the 17th district of Tehran are under the influence of land subsidence.

Large-Scale Land Subsidence Monitoring and Prediction Based on SBAS-InSAR Technology with Time-Series Sentinel-1A Satellite Data

Rapid urban development in China has aggravated land subsidence, which poses a potential threat to sustainable urban development. It is imperative to monitor and predict land subsidence over large areas. To address these issues, we chose Henan Province as the study area and applied small baseline subset-interferometric synthetic aperture radar (SBAS-InSAR) technology to obtain land deformation information for monitoring land subsidence from November 2019 to February 2022 with 364 multitrack Sentinel-1A satellite images. The current traditional time-series deep learning models suffer from the problems of (1) poor results in extracting a sequence of information that is too long and (2) the inability to extract the feature information between the influence factor and the land subsidence well. Therefore, a long short-term memory-temporal convolutional network (LSTM-TCN) deep learning model was proposed in order to predict land subsidence and explore the influence of environmental factors, such as the volumetric soil water layer and monthly precipitation, on land subsidence in this study. We used leveling data to verify the effectiveness of SBAS-InSAR in land subsidence monitoring. The results of SBAS-InSAR showed that the land subsidence in Henan Province was obvious and uneven in spatial distribution. The maximum subsidence velocity was −94.54 mm/a, and the uplift velocity was 41.23 mm/a during the monitoring period. Simultaneously, the land subsidence in the study area presented seasonal changes. The rate of land subsidence in spring and summer was greater than that in autumn and winter. The prediction accuracy of the LSTM-TCN model was significantly better than that of the individual LSTM and TCN models because it fully combined their advantages. In addition, the prediction accuracies, with the addition of environmental factors, were improved compared with those using only time-series subsidence information.

Geochemical evidence for annual variability of fluoride levels in deep groundwater on land subsidence plain in Cangzhou, Hebei Province, China

Ecological water supplementation effectively controls the land subsidence rate and protects the groundwater environment. However, the annual variability and evolutionary mechanisms of F− concentration in deep groundwater during ecological water supplementation are poorly understood. In total, 92 deep groundwater samples from 2012 to 2021 were collected and analyzed in the Cangzhou land subsidence area. The results show that the range of F− concentrations in deep groundwater was 0.10–7.83 mg/L during 2012–2021, with 44.08 % of samples exceeding China's national standards (1.00 mg/L) for drinking water. The F− concentration in deep groundwater increased steadily during 2012–2017 and exhibited a notable decrease during 2018–2021. Halite dissolution was more effective after ecological water supplementation, inducing a decrease in F− concentration by dilution effect in deep groundwater. Meanwhile, stronger cation exchange facilitated F− desorption into the deep groundwater after ecological water supplementation. This research will aid in improving our understanding of the influence of ecological water supplementation on groundwater quality and provide new insights for the sustainable management of deep groundwater resources.

ASSESSMENT OF LAND DEFORMATION AND LAND USE RELATIONSHIP IN GARMSAR ALLUVIAL FAN USING SENTINEL-1 DATA

Abstract. Differential Synthetic Aperture Radar Interferometry (DInSAR) allows displacements to be detected with millimeter accuracy as well as more advanced methods, such as Persistent Scatterer Interferometry (PSI). Sentinel-1 data have been collected systematically under the COPERNICUS program at a high temporal resolution with global coverage, helping us to build a wide user community and develop miscellaneous SAR-based applications. In the Garmsar alluvial fan, the long-term groundwater overexploitation due to agricultural and urban demands, the utilization of urban space, and erosion have led to land deformation. In this study, the analysis of land subsidence in Garmsar fan was assessed by using the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique based on 20 Sentinel-1 SAR images from January 2019 to June 2022. Distinct variations of land subsidence were found in the study regions however, it can be seen in most land use types. The maximum annual land subsidence rate has occurred in urban areas with an average rate of 95.2 mm/year from 2019 to 2022. Analysis showed that serious land subsidence mainly occurred in the following land use types: urban areas, agricultural lands, and bare lands.

Land Subsidence Susceptibility Mapping Using Interferometric Synthetic Aperture Radar (InSAR) and Machine Learning Models in a Semiarid Region of Iran

Most published studies identify groundwater extraction as the leading cause of land subsidence (LS). However, the causes of LS are not only attributable to groundwater extraction. Other land-use practices can also affect the occurrence of LS. In this study, radar interferometric techniques and machine learning (ML) models were used for the prediction, susceptibility zoning, and prioritization of influential variables in the occurrence of LS in the Bakhtegan basin. The LS rate was characterized by applying an interferometric synthetic aperture radar (InSAR). The recursive feature elimination (RFE) method was used to detect and select the dominant combination of indicators to prepare an LS susceptibility map. Three ML models, including random forest (RF), k-nearest neighbors (KNN), and classification and regression trees (CART), were used to develop predictive models. All three models had acceptable performance. Among the ML models, the RF model performed the best (i.e., Nash–Sutcliffe efficiency, Kling–Gupta efficiency, correlation coefficient, and percent bias metrics of 0.76, 0.78, 0.88, and 0.70 for validating phase, respectively). The analysis conducted on all three ML model outputs showed that high and very high LS susceptibility classes were located on or near irrigated agricultural land. The results indicate that the leading cause of land LS in the study region is not due to groundwater withdrawals. Instead, the distance from dams and the proximity to anticlines, faults, and mines are the most important identifiers of LS susceptibility. Additionally, the highest probability of LS susceptibility was found at distances less than 18 km from synclines, 6 to 13 km from anticlines, 23 km from dams, and distances less than 20 to more than 144 km from mines. The validated methods presented in this study are reproducible, transferrable, and recommended for mapping LS susceptibility in semiarid and arid climate zones with similar environmental conditions.

Ground Surface Deformation Analysis Integrating InSAR and GPS Data in the Karstic Terrain of Cheria Basin, Algeria

Karstic terrains are usually dominated by aquifer systems and/or underground cavities. Overexploitation of groundwater in such areas often induces land subsidence and sometimes causes sinkholes. The Cheria basin in Algeria suffers from severe land subsidence issues, and this phenomenon has been increasing in recent years due to population expansion and uncontrolled groundwater exploitation. This work uses GPS data and persistent scatterer interferometry synthetic aperture radar (PS-InSAR) techniques to monitor the land subsidence rate by employing Sentinel-1 satellite data for the period from 2016 to 2022. Our results demonstrate that the Cheria basin experiences both uplift and subsidence in places, with an overall substantial change in the land surface. The total cumulative subsidence over 6 years reached a maximum of 500 mm. Comparison of land deformation between PSI and GPS showed root mean square error (RMSE) values of about 2.83 mm/year, indicating that our analyzed results are satisfactorily reproducing the actual changes. Nonetheless, these results can be used to extract the susceptible zones for vertical ground displacement and evaluate the surface deformation inventory map of the region for reducing damages (e.g., human losses, economic impact, and environmental degradation) that may occur in the future (e.g., sinkholes) and can be further utilized in perspective for a sinkhole early warning system.

Geodetic evidence of land subsidence in Cirebon, Indonesia

The subsidence phenomena occur in numerous cities in Indonesia (Jakarta, Bandung, and Semarang) and have been observed over the last few decades using geodetic techniques such as interferometric synthetic aperture radar (InSAR) and the Global Navigation Satellite System (GNSS). Three variables are considered the leading causes of substantial subsidence in those cities: urban development, excessive groundwater removal, and geological context. Considering these considerations, we assess surface displacement in the surrounding area of Cirebon, Indonesia, which has similar background circumstances. We used 43 Sentinel-1A images covering from 2014 to 2021 and processed them using the small baseline subset (SBAS) method obtaining 78 interferograms for time series analysis. A weighted correction from two weather models was used to correct tropospheric errors. The results from the InSAR time series were compared with the continuous GNSS time series (2010–2021) processed using GAMIT/GLOBK GNSS processing softwares. Cirebon experienced a slow land subsidence rate with an average magnitude of -2.6 mm/yr. Furthermore, we underline that more significant subsidence rates were found in several locations. Conic-shaped subsidence with a diameter of around 1 km was observed in a highly inhabited area, with a maximum rate of up to -17 mm/yr near the cone’s center. We also found more prominent land subsidence along the coastline with magnitudes up to -25 mm/yr. Surface lowering with a magnitude of up to -32 mm/yr also affects power plant regions close to Cirebon city. Meanwhile, the fastest subsidence rate was observed over the salt evaporation field area of about -50 mm/yr. We attribute the natural and anthropogenic factors to causing the subsidence in this area.

GNSS Horizontal Motion Field in the Beijing Plain in View of The Variation Characteristics of The 3D Deformation Field

In view of the fact that there is no unified understanding of the GNSS horizontal velocity field in the Beijing Plain and the serious land subsidence in this area, we collected GNSS data from 2011 to 2021 and Sentinel 1A data from 2017 to 2021 and conducted high-precision GNSS data processing and PS-InSAR verification in order to determine the reason for the differences in the GNSS horizontal velocity field in the Beijing Plain. The results show that, under the stable Eurasian framework, the horizontal velocity of GNSS stations in the Beijing Plain is significantly inconsistent. The velocity of all GNSS stations ranged from −1.32 to 10.41 mm/yr in the E component and from −8.83 to 3.00 mm/yr in the N component. From 2011 to 2021, there was significant uneven land subsidence in the Beijing Plain, and the maximum land subsidence rate from 2017 to 2021 reached 107 mm/yr. In analyzing the observation data of the GNSS and InSAR, we conclude that the land subsidence in the Beijing Plain will indeed affect the GNSS horizontal velocity field in the subsidence area. Under the EURA_I08 reference framework, the horizontal deformation field in the Beijing Plain is mainly caused by the tectonic activity-derived overall SEE-direction movement, accompanied by the velocity field anomaly caused by local land subsidence.

Monitoring Land Subsidence and its Effect on Surabaya Coastal Inundation by Utilizing GPS Campaigns Data

Land subsidence is a process of land movement based on a certain datum (geodetic reference frame) in which there are various variables causing it. This phenomenon occurs in many big cities in Indonesia, which is the city of Surabaya. The Surabaya city has a type of soil in the form of alluvial soil which is prone to movement. The accumulation of land subsidence that occurs will certainly cause its own losses. As a result, it can damage urban infrastructure so that in the future it can cause disruption to the environment, economic activities, and social life in the region. In addition, the Surabaya area is also a coastal area. Coastal areas have characteristics where this area will be strongly influenced by natural events. One of the natural events that affect the oceans and coastal areas is global temperature changes. The phenomenon of global temperature changes can affect sea level rise. If sea level rise occurs accompanied by an increase in the rate of land subsidence, this can result in flooding or inundation in coastal areas. Considering that the city of Surabaya is a metropolitan city that has a high population density and a continuous level of development, mitigation efforts are important in following up on land subsidence in this area. One of the efforts that can be applied is by conducting GPS observations. The outcomes of the GPS observations were then processed using scientific software in the form of GAMIT/GLOBK. According to the study’s findings indicated that Surabaya is experiencing land subsidence in the northern and eastern coastal areas with a range of 0.2 – 83.3 mm/year. The point that experienced a significant decline was in the border area of Tandes and Asemrowo, namely station BM35. Furthermore, land subsidence conditions influence the extent of tidal inundation, where in 2032 tidal inundation will inundate 105.411 km2.

A Preliminary Result of Land subsidence Monitoring by Using Low-Cost GNSS in Bandung Basin

Land Subsidence is one of the issues faced by major cities in Indonesia. The need for a land subsidence monitoring station that operates continuously is one of the most important aspects today. The current use of low-cost GNSS (Global Navigation Satellite System) is expected to help in dealing with this problem. This study aims to evaluate Low-Cost GNSS for monitoring land subsidence in Bandung Basin. The Measurement of Low-Cost GNSS using Chip-GNSS U-Blox (single frequency) and the microstrip antenna was assembled itself. The monitoring of land subsidence was carried out at one point(TK01) with an observation period from March 2021 to December 2021. The result of data processing shows the value of the coordinate standard deviation of the TK01 point during the measurement period ranges from ± 1.5 cm. The results of vertical component processing show the standard deviation in the vertical direction is ± 3.5 cm. The rate of land subsidence resulting from linear regression at the point of TK01 from March 2021 to December 2021 in this study was -13.9 cm/year with a standard deviation value of ±0.32 cm/year.

Land subsidence prediction model based on its influencing factors and machine learning methods

Land subsidence has caused huge economic losses in the Beijing plains (BP) since 1980s. Building land subsidence prediction models that can predict the development of land subsidence is of great significance for improving the safety of cities and reducing economic losses in Eastern Beijing plains. The pattern of evolution of land subsidence is affected by many factors including groundwater level in different aquifers, thicknesses of compressible layers, and static and dynamic loads caused by urban construction. First, we used the small baseline subset Interferometric Synthetic Aperture Radar (SBAS‐InSAR) technology on 47 ENVISAT ASAR images and 48 RADARSAT‐2 images and used Persistent Scatterers Interferometric Aperture Radar (PS-InSAR) technology on 27 Sentinel-1 images to obtain the land subsidence monitoring results from June 2003 to September 2018. Second, the accuracy of the InSAR monitoring results was validated by using leveling benchmark land subsidence monitoring results. Finally, we built land subsidence rate prediction models and land subsidence gradient prediction models by combining land subsidence influencing factors and four machine learning methods including support vector machine (SVM), Gradient Boosting Decision Tree (GBDT), Random forest (RF) and Extremely Randomized Trees (ERT). The findings show: (1) The InSAR monitoring results revealed that the Beijing Plain has experienced serious land subsidence during the 2003–2010, 2011–2015 and 2016–2018 periods. (2): The InSAR monitoring results agreed well with the leveling benchmark monitoring results with the Pearson correlation coefficients of two monitoring results were all greater than 0.95 during the 2003–2010, 2011–2015 and 2016–2018 periods, respectively. (3): We found that the land subsidence prediction based on ERT method is the optimal model among four land subsidence prediction models and that the prediction performance of land subsidence prediction model based on ERT method will be greatly improved when apply this prediction model in sub study areas where the land subsidence mechanism is similar owning to the similar hydrogeological parameters.

Land Subsidence Characteristics and Numerical Analysis of the Impact on Major Infrastructure in Ningbo, China

For the construction and safe operation of major infrastructure in coastal cities, the impact of regional land subsidence that has occurred or is slowly proceeding deserves attention. Previous studies have mainly focused on the surrounding land subsidence caused during construction or operation, as well as the superposition effect of land subsidence caused by groundwater extraction. However, research on the different impacts of damage due to land subsidence in the construction and operation of urban infrastructure needs to be carried out according to the actual geological environmental conditions, reflected in parameters such as the soil properties and common loads. Numerical simulation cannot fully reflect the details of reality; however, it can avoid the influence of other conditions to focus on different factors influencing land subsidence and thus highlight the contribution of a single factor influencing land subsidence. Therefore, in this paper, we adopt field measurement data and carry out a numerical simulation analysis of different influencing factors. First, taking the Ningbo Jiangdong subsidence center (now located in Yinzhou District) as an example, area growth, cumulative subsidence and the occurrence and development of the subsidence rate of a typical urban subsidence funnel area are analyzed. Then, taking the Ningbo Chunxiao–Meishan area as an example, based on the physical and mechanical characteristics of the main soil layers in the coastal reclamation area, a numerical analysis of the self-weight/backfill and surcharge consolidation settlement of the soil layer (considering the water permeability/impermeability of the bottom surface) and a numerical analysis of the nonuniform settlement caused by pile foundation engineering are carried out. Finally, the Ximenkou–Gulou area is taken as the analysis object. Numerical simulation of metro tunnel pipeline deformation is carried out considering uniform/nonuniform settlement. The results show that the comprehensive prohibition of groundwater exploitation is beneficial to slow the land subsidence rate, while the sedimentation of silty clay in Layer 4 (muddy clay) is the largest among all the soil layers. Compared with uniform settlement, nonuniform settlement is more likely to cause connection failure between tunnel segments. The above research results can provide references for the prevention and control of land subsidence and thus the safe operation of major infrastructure.

Stacking- and voting-based ensemble deep learning models (SEDL and VEDL) and active learning (AL) for mapping land subsidence.

This contribution presents a novel methodology based on the feature selection, ensemble deep learning (EDL) models, and active learning (AL) approach for prediction of land subsidence (LS) hazard and rate, and its uncertainty in an area involving two important plains - the Minab and Shamil-Nian plains - in the Hormozgan province, southern Iran. The important features controlling LS hazard were identified by ridge regression. Then, two EDL models were constructed by stacking (SEDL) and voting (VEDL) five dense deep learning (DL) models (model 1 to model 5) for mapping LS hazard. Thereafter, the predictive model performance was assessed by a precision-recall curve and Kolmogorov-Smirnov (KS) plot. A partial dependence plot (PDP), individual conditional expectation plots (ICEP), game theory, and a sensitivity analysis were used for the interpretability of the predictive DL model. According to SEDL - a model with higher accuracy - 34% (1624 km2), 14.7% (698 km2), and 19.2% (912 km2) of the total area were classified as being of very low, low, and moderate hazards, whereas 17.7% (845 km2) and 14.4% (683 km2) of area were classified as being of high and very high hazards, respectively. Based on all interpretability techniques, aquifer loss or groundwater drawdown is the most important feature controlling LS hazard, and it having the greatest impact on the SEDL model output. Based on a Taylor diagram and R2 as model performance assessment indicators, SEDL-AL (with R2 > 95% for training and test datasets) performed better than SEDL for quantify LS rate, the rate of LS ranging between 0 and 48.1cm. The highest rate of LS occurred in the Minab plain - an area located downstream of the Minab Esteghlal dam. SEDL-AL was used to quantify the uncertainty associated with the LS rate. The observed values fell within predictions provided by SEDL-AL, which indicates a high accuracy of our predictive model. Overall, our newly developed modeling techniques are helpful tools for the spatial mapping of LS susceptibility and rate, and its uncertainty.

Spatial information on the rate of subsidence in North Coastal Area of Java and the estimation of inundation in 2031

North Coastal Java Java is a fast-growing area because many big cities such as Jakarta, Cirebon, Pekalongan, Semarang, and Surabaya exist. This area is experiencing several environmental problems, the biggest of which is the problem of the rate of land subsidence. This problem is due to the geological structure. The Java Coast is located on soft soil, and some groundwater utilization activities are less controlled. This study aimed at (1) Obtaining information on the rate of land subsidence in 5 major cities on the northern coast of Java (Jakarta, Cirebon, Pekalongan, Semarang, and Surabaya) using remote sensing satellite data; (2) Analysing the accuracy of land subsidence using field survey data; (3) Estimating the potential of the region to be below 0 masl in 2031; and (4) Analysing the causes of land subsidence. The method used was Difference Interferometry Synthetic Aperture Radar (DInSAR) to determine the rate of land subsidence and simulate the rate of land subsidence in the future. The results showed that the rate of land subsidence in Jakarta (8/year), Cirebon (2 cm/year), Pekalongan (11.2 cm/year), Semarang (6 cm/year), and Surabaya (4.3 cm/year). The results of the research show that the error rate of the surface subsidence rate with validation results in the City of Jakarta is ± 0.01 m. with the value of Nash Sutcliffe Efficiency (NSE) =. 0.787 with a good category. Simulation results in 2031 from 3 big cities show that the area under 0 masl will increase by 2710 ha (Jakarta), 3565 ha (Pekalongan), and 846 ha (Semarang) from 2021. It seems that Pekalongan needs special attention to deal with the rate of land subsidence compared to other cities.

Determination of tectonic and nontectonic vertical motion rates of the North China Craton using dense GPS and GRACE data

• The elastic (un-)loading response to mass changes is calculated using GRACE data. • The average estimated tectonic uplift rate in the mountain areas is 1.37 ± 0.6 mm/yr. • The maximum land subsidence rate in the North China Plain is 72.0 ± 1.2 mm/yr. • A high-resolution water mass variation model is needed. Previous geodetic vertical observations show that both uplift and subsidence occur in the North China Craton. While significant land subsidence occurs in the North China Plain, uplift is observed in the surrounding mountain areas. However, previous studies neither separate the tectonic and nontectonic vertical motions nor correct for elastic response to near surface mass changes. Here, we reply on the more GPS data from 1999 to 2020 and GRACE data from 2002 to 2020 to estimate the vertical motion rates. After removing the elastic (un-)loading deformation due to near surface mass variations, we obtain the tectonic uplift rates at GPS stations built on bedrock in the mountain areas like the Shanxi Plateau and land subsidence rates mainly in the North China Plain. The tectonic rock uplift mainly occurs in the Shanxi Plateau, Yanshan Montains, Shandong Peninsula and Dabie orogen, with an average uplift rate of 1.37 ± 0.6 mm/yr, which is larger than the long-term geological uplift rate. The land subsidence mainly concentrates in the North China Plain, Shanxi Graben, and Jiangsu province with the maximum subsidence rate of 72 ± 1.2 mm/yr. The mismatch between the geodetic and geological uplift rates suggests that a high-resolution water mass loss in the North China Plain is needed.

Potensi Terjadinya Banjir Rob Akibat Penurunan Muka Tanah dan Kenaikan Muka Air Laut di Kabupaten Badung, Bali


 
 
 
 Badung Regency is one of the tourist areas crowded with visitors, so that development continues to be developed as a support for tourist areas, one of which is a hotel. Hotel construction can cause building loads in a certain period; it can cause land subsidence. Land subsidence that co-occurs with sea-level rise can potentially be a tidal flood in the future. This study aimed to determine the rate of land subsidence in Badung Regency and the potential for tidal flooding due to land subsidence and sea-level rise in Badung Regency. The method used to calculate the rate of land subsidence is Small Baseline Subset Differential Interferometric Synthetic Aperture Radar (SBAS-DInSAR), which utilizes radar satellite imagery data Sentinel-1a IW level 1.1 images and Sentinel-1b IW level 1.1 images. The image data used in the SBAS-DInSAR method consists of 166 image data and Digital Elevation Model (DEM) data with a spatial resolution of 30 m which is used for the topographic phase removal phase. Meanwhile, land subsidence data is used to determine the potential for tidal flooding, and it is predicted using regression; then, an overlay is carried out with the highest tide data along with the value of sea-level rise every year. To facilitate the observation of land subsidence, the daily image data obtained are then averaged into monthly data in each village. The fastest land subsidence rate from 2014 to 2020 occurred in Tuban Village. The prediction of the most significant tidal flood in 2030 to 2040 will occur in Tuban Village due to the influence of rapid land subsidence in the village.
 
 
 

Pemodelan Sistem Dinamik Eco-Drainage di Wilayah Tanah Basah (Studi Kasus Kawasan Kelapa Gading DKI Jakarta)

An Ecological drainage becomes an application in this development project in the Kelapa Gading area. The drainage concept that is applied is a drainage system in which rainwater that falls on road surface runoff will flow directly into the water infiltration system and directly fill the ground surface water. G eologically, it can be seen that North Jakarta is an area with a fairly high ground water level (MAT), and has an average MAT elevation of 2 - 2.5 m. This study intends to determine the ability of this ecodrainage to reduce runoff/floods that often occur in areas with a fairly high MAL. What direct benefits can be felt in the use of ecodrainage in these flood-prone areas, thus making the basic reason for applying this ecodrainage. This study uses a dynamic systems approach. In the preparation of the dynamic system model, Powersim Studio Version 10 software will be assisted. The simulation results show that an increase in rainfall every year will provide an increasing flood potential. By using eco-drainage, it is felt that it is not optimal enough to reduce runoff in areas that have high MAT levels. However, this ecodrainage still provides broad sustainability benefits. The quantity of groundwater will slowly be filled up to the aquier layer through this ecodrainage. This step is considered to be able to help prevent the aquifer layer from being exposed and have an impact on the rate of land subsidence so that the potential for flooding will decrease.