River dynamics are currently comprehensively studied at either a bankline or reach-scale level. Monitoring large-scale and long-term river extent dynamics provides fundamental insights relevant to ...the impact of climatic factors and anthropogenic activities on fluvial geomorphology. This study analyzed the two most populous rivers, Ganga and Mekong, to understand the river extent dynamics using 32 years of Landsat satellite data (1990–2022) in a cloud computing platform. This study categorizes river dynamics and transitions using the combination of pixel-wise water frequency and temporal trends. This approach can demarcate the river channel stability, areas affected by erosion and sedimentation, and the seasonal transitions in the river. The results illustrate that the Ganga river channel is found to be relatively unstable and very prone to meandering and migration as almost 40 % of the river channel has been altered in the past 32 years. The seasonal transitions, such as lost seasonal and seasonal to permanent changes are more prominent in the Ganga river, and the dominance of meandering and sedimentation in the lower course is also illustrated. In contrast, the Mekong river has a more stable course with erosion and sedimentation observed at sparse locations in the lower course. However, the lost seasonal and seasonal to permanent changes are also dominant in the Mekong river. Since 1990, Ganga and Mekong rivers have lost approximately 13.3 % and 4.7 % of their seasonal water respectively, as compared to the other transitions and categories. Factors such as climate change, floods, and man-made reservoirs could all be critical in triggering these morphological changes.
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•Model provides reliable spatio-temporal information of river extent changes.•Model investigates large-scale and long-term river extent dynamics.•Model recognize the changes using water frequency and MNDWI temporal trend.•Ganga and Mekong rivers have lost seasonal for an area of 13.3 and 4.7 %.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
Land subsidence involves either elastic (recoverable) or inelastic (unrecoverable) soil compaction within an aquifer. Elastic or inelastic subsidence is tradionallly identified on the basis of the ...relation between deformation and hydraulic head changes. This study aims to determine the statistical hydraulic head rule for inelastic subsidence mitigation in groundwater management. By focusing on Yunlin County in Taiwan as the study area, this research effectively distinguishes between unrecoverable and recoverable subsidence using the head rule with the statistical threshold, which is calibrated by an optimal linear search. Result shows that considering the head rule can obtain similar patterns of subsidence with the traditional model from the stress–strain diagram. Inelastic subsidence accounts for approximately 15% of all instances, notably occurring during the early months of each year. Inelastic subsidence usually happened in the mid-fan and distal fan. This study can rapidly identify when and where unrecoverable subsidence happens. Groundwater management within the head threshold would be implemented for unrecoverable subsidence mitigation.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
•A new composite index is proposed for seasonal drought detection.•The index is derived from precipitation, soil-moisture, and SIF datasets.•Model is developed under nonparametric joint probabilistic ...framework.•Seasonal drought events occurred from Indian during monsoon periods.•Spatial severity, duration or intensity helps to identify drought susceptible area.
Effective drought monitoring is crucial for mitigation efforts and the implementation of early warning systems. Due to the intricate nature of drought phenomena, its impact cannot be accurately characterized solely through the use of univariate indicators. This study introduces a composite version of the modified multivariate standardized drought index (MMSDI) for the Indo-Gangetic Plains (IGP) in India. The MMSDI integrates precipitation, soil moisture, and solar-induced chlorophyll fluorescence datasets from 2001 to 2020, utilizing a nonparametric joint probabilistic framework. It combines three consolidated drought indices: standardized precipitation index (SPI), standardized soil moisture index (SSI), and standardized solar-induced chlorophyll fluorescence Index (SSIFI). The drought patterns among SPI, SSI, SSIFI, and MMSDI indices are compared for short, and mid-term time scales (herein 1, 3 and 6 month) over IGP. The performance of MMSDI is evaluated through quantitative metrics e.g. probability of detection (POD), false alarm ration (FAR) and critical success index (CSI) at various time scales. Results reveal that MMSDI provides a reliable estimation of the severity and spatial coverage of major drought events over IGP at various time scales. The MMSDI demonstrates superior effectiveness in identifying and characterizing the spatial severity of drought, i.e. the visibility of the drought hotspots. The MMSDI has the capability to assess agrometeorological drought, indicating its potential as a valuable tool for identifying regions vulnerable to drought. Overall, this modified drought index derived from multi-sensor datasets produced comprehensive insights for policymakers in implementing effective agricultural drought management practices to understand intricate drought phenomena over IGP region in India.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
Abstract
The data quality of low-cost sensors has received considerable attention and has also led to PM
2.5
warnings. However, the calibration of low-cost sensor measurements in an environment with ...high relative humidity is critical. This study proposes an efficient calibration and mapping approach based on real-time spatial model. The study carried out spatial calibration, which automatically collected measurements of low-cost sensors and the regulatory stations, and investigated the spatial varying pattern of the calibrated low-cost sensor data. The low-cost PM
2.5
sensors are spatially calibrated based on reference-grade measurements at regulatory stations. Results showed that the proposed spatial regression approach can explain the variability of the biases from the low-cost sensors with an R-square value of 0.94. The spatial calibration and mapping algorithm can improve the bias and decrease to 39% of the RMSE when compared to the nonspatial calibration model. This spatial calibration and real-time mapping approach provide a useful way for local communities and governmental agencies to adjust the consistency of the sensor network for improved air quality monitoring and assessment.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
In the near future, multi-sensor fusion will be the core component to navigate the autonomous driving platforms. This paper proposes new strategies to cope with the integration of an inertial ...navigation system (INS), a global navigation satellite system (GNSS), and light detection and ranging (LIDAR) to achieve simultaneous localization and mapping (INS/GNSS/LiDAR SLAM) especially in GNSS challenging environments where GNSS signals are blocked or contaminated with reflected signals. The proposed strategies implement a high level of integration with various information received from multiple sensors to collectively compensate for the specific drawbacks of those sensors included in the integrated system. The first strategy is to solve the divergence and drift problems of SLAM using the initial pose information from INS and the proposed refreshing process using an INS/GNSS integrated system. In addition, an updated mechanization is designed to qualify those received measurements based on cross validation of separate types of data. This mechanization is to ensure all measurements are reliable for the Extended Kalman Filter (EKF) update process. Moreover, the SLAM-derived information plays a major role to recognize the vehicle movement which assists the system to accurately apply those appropriate vehicle motion constraint models. The preliminary results presented in this study illustrate that proposed algorithm performs superior than the traditional INS/GNSS integration scheme and provides absolute navigation accuracy of 2 meters and 0.6% of distance traveled in GNSS-denied as well as 1.2 meters in GNSS-hostile environments, respectively.
•Spatial regression-based model provides reliable information for head mapping.•Model determines relationships between head, rainfall and pumping volume.•Spatial inconsistency between head and ...pumping is identified.•Spatial pattern of the hydraulic head is identified.•The model considers without extensive physical model calibration.
The equilibrium of natural groundwater systems can be disrupted by excessive withdrawal. Accurate estimation of groundwater levels is needed to assess water-level fluctuations caused by groundwater withdrawal and seasonal distributions of precipitation. This study aims to estimate the next-month’s groundwater levels using monthly real-world data that includes rainfall, electricity-estimated pumping volumes, and current groundwater levels invoking time-dependent spatial regression. The new approach involves state-estimation and change-estimation methods, which will be evaluated to determine the optimal model based on its root mean square error values. The response of estimated future (next-month’s) groundwater levels within the alluvial fan in Changhua and Yunlin, Taiwan is based on monthly precipitation and pumping. This study yields a data-driven explanation of how water levels temporally and spatially respond to groundwater pumping and rainfall infiltration in different regions within the alluvial fan. Results indicate that the proximal fan yields the smallest response to decreased groundwater levels and subsequent increases in pumping. The effect of reducing groundwater levels is greater in the southern areas of the study site than in the northern areas. Water levels in the mid-fan and distal-fan in the southern area show a greater drawdown due to larger pumping volumes compared to the northern area.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPUK, ZAGLJ, ZRSKP
This paper explores the spatio-temporal patterns of particulate matter (PM) in Taiwan based on a series of methods. Using fuzzy c-means clustering first, the spatial heterogeneity (six clusters) in ...the PM data collected between 2005 and 2009 in Taiwan are identified and the industrial and urban areas of Taiwan (southwestern, west central, northwestern, and northern Taiwan) are found to have high PM concentrations. The PM10-PM2.5 relationship is then modeled with global ordinary least squares regression, geographically weighted regression (GWR), and geographically and temporally weighted regression (GTWR). The GTWR and GWR produce consistent results; however, GTWR provides more detailed information of spatio-temporal variations of the PM10-PM2.5 relationship. The results also show that GTWR provides a relatively high goodness of fit and sufficient space-time explanatory power. In particular, the PM2.5 or PM10 varies with time and space, depending on weather conditions and the spatial distribution of land use and emission patterns in local areas. Such information can be used to determine patterns of spatio-temporal heterogeneity in PM that will allow the control of pollutants and the reduction of public exposure.
•This study explores the spatio-temporal patterns of particulate matter (PM).•Spatial heterogeneity of the PM data is identified using fuzzy clustering.•PM10-PM2.5 relationship is modeled by GWR and GTWR.•GTWR provides spatio-temporal variations of the PM10-PM2.5 relationship.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK
Groundwater drought index characterizes hydrological drought, aquifer characteristics and human disturbance in the hydrological system. For drought management, the values of standardized groundwater ...index (SGI) at local and regional scales are usually determined in a specific site and regional area. The SGI in the studied area is influenced mainly by precipitation, hydrogeology, and human disturbance occurring in the high-usage pumping area. The underlying signals of SGI at local and regional scales can therefore be identified using data clustering and decomposition analysis e.g. empirical orthogonal functions (EOFs). Using cluster analysis, the three primary SGI clusters of the investigated aquifer are identified to be situated at the proximal fan, mid-fan, and distal fan areas. With EOF, the meteorological drought pattern and the trend of long-term pumping in the aquifer are also identified. Specifically, the meteorological drought pattern is mainly from the proximal fan, while the over-pumping signal is from the coastal area of the distal fan. The regional SGI integrated with EOF is a useful and direct way for detecting and quantifying groundwater drought. The proposed method for identifying drought signals and sustainable zone for water supply is a substantial step toward an effective regional groundwater resource planning.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Land subsidence caused by groundwater overexploitation is a serious global problem. The acquisition of spatiotemporal pumping rates and volumes is a first step for water managers to develop a ...strategic plan for mitigating land subsidence. This study investigates an empirical formulation to estimate the monthly maximum pumped volume over a 10‐year period based on electric power consumption data. A spatiotemporal variability analysis of monthly pumped volume is developed to provide an improved understanding of seasonal pumping patterns and the role of irrigation type. The analysis of regional pumped volume provides an approximation of the spatiotemporal patterns of the variations in pumped volume. Results show the effects of climate, seasonal changes in pumping from irrigation, and the local differences in pumping caused to crop types. A seasonal pumped volume peak occurs annually, with the highest and least pumped volumes occurring in March (highest peak) and September (lowest peak), respectively. However, the majority of the historical maximum pumped volumes have occurred during the last few years. Extracted volumes continue to increase in some locations. The analysis reveals increasing trends in pumping, thereby possibly providing the locations where increased effective stresses may lead to land subsidence.
Article impact statement: The study provides the spatiotemporal patterns in pumped volume estimated from electricity consumption data, especially maximum pumped volumes.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Satellites are launched frequently to monitor the Earth’s dynamic surface processes. For example, the Landsat legacy has thrived for the past 50 years, spanning almost the entire application spectrum ...of Earth Sciences. On the other hand, fewer satellites are launched with a single specific mission to address pressing scientific questions, e.g., the study of polar icecaps and their response to climate change using Ice Cloud and the Land Elevation Satellite (ICESat) program with ICESat-1 (decommissioned in 2009) and ICESat-2. ICESat-2 has been operational since 2018 and has provided unprecedented success in space-borne LiDAR technology. ICESat-2 provides exceptional details of topographies covering inland ice, snow, glaciers, land, inland waterbodies, and vegetation in three-dimensional (3D) space and time, offering the unique opportunity to quantify the Earth’s surface processes. Nevertheless, ICESat-2 is not well known to some other disciplines, e.g., Geology and Geomorphology. This study, for the first time, introduces the use of ICESat-2 in aeolian sand dune studies, purely from an ICESat-2 remote sensing data perspective. Two objectives are investigated. first, a simplified approach to understanding ICESat-2 data products along with their application domains. Additionally, data processing methods and software applications are briefly explained to unify the information in a single article. Secondly, the exemplified use of ICESat-2 data in aeolian sand dune environments is analyzed compared to global Digital Elevation Models (DEMs), e.g., Shuttle Radar Topography Mission (SRTM). Our investigation shows that ICESat-2 provides high-resolution topographic details in desert environments with significant improvements to the existing methods, thereby facilitating geological education and field mapping. Aeolian sand dune environments can be better understood, at present, using ICESat-2 data compared to traditional DEM-based methods.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
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