Search Results (15)

This study presents an integrated spatiotemporal assessment of drought conditions in the Thung Kula Ronghai region of Northeastern Thailand from 2001 to 2023. Multiple satellite-derived drought indices, including SPI, SPEI, RDI, and AI, together with NDVI anomalies, were used to detect seasonal and long-term drought dynamics affecting rainfed Hom Mali rice production. The results show that dry season droughts now affect up to 17 percent of the region’s agricultural land in some years, while severe drought zones persist across more than 2.5 million hectares over the 20-year period. In the most recent 5 years, approximately 50 percent of cultivated areas experienced moderate to severe drought conditions. The RDI showed the strongest correlation with NDVI anomalies (r = 0.22), indicating its relative value for assessing vegetation response to moisture deficits. The combined index approach delineated high-risk sub-regions, particularly in central Thung Kula Ronghai and lower Surin, where drought frequency and severity have intensified. These findings underscore the region’s increasing exposure to dry-season water stress and highlight the need for site-specific irrigation development and adaptive cropping strategies. The methodological framework demonstrated here provides a practical basis for improving drought monitoring and early warning systems to support the resilience of Thailand’s high-value rice production under changing climate conditions. Full article

The quality of hydrological data has a significant impact on hydrological models, where stable and anomaly-free hydrological time series typically yield more valuable patterns. In this paper, we conduct data analysis and propose an online hydrological data quality control method based on an adaptive differential evolution algorithm according to the characteristics of hydrological data. Taking into account the characteristics of continuity, periodicity, and seasonality, we develop a Periodic Temporal Long Short-Term Memory (PT-LSTM) predictive control model. Building upon the real-time nature of the data, we apply the Adaptive Differential Evolution algorithm to optimize PT-LSTM, creating an Online Composite Predictive Control Model (OCPT-LSTM) that provides confidence intervals and recommended values for control and replacement. The experimental results demonstrate that the proposed data quality control method effectively manages data quality; detects data anomalies; provides suggested values; reduces reliance on manual intervention; provides a solid data foundation for hydrological data analysis work; and helps hydrological personnel in water resource scheduling, flood control, and other related tasks. Meanwhile, the proposed method can also be applied to the analysis of time series data in other industries. Full article

We consider the challenge of detecting and clustering point and collective anomalies in streaming data that exhibit significant nonlinearities and seasonal structures. The challenge is motivated by detecting problems in a communications network, where we can measure the throughput of nodes, and wish to rapidly detect anomalous traffic behaviour. Our approach is to train a neural network-based nonlinear autoregressive exogenous model on initial training data, then to use the sequential collective and point anomaly framework to identify anomalies in the residuals generated by comparing one-step-ahead predictions of the fitted model with the observations, and finally, we cluster the detected anomalies with fuzzy c-means clustering using empirical cumulative distribution functions. The autoregressive model is sufficiently general and robust such that it provides the nearly (locally) stationary residuals required by the anomaly detection procedure. The combined methods are successfully implemented to create an adaptive, robust, computational framework that can be used to cluster point and collective anomalies in streaming data. We validate the method on both data from the core of the UK’s national communications network and the multivariate Skoltech anomaly benchmark and find that the proposed method succeeds in dealing with different forms of anomalies within the nonlinear signals and outperforms conventional methods for anomaly detection and clustering. Full article

Consequences of global climate change are predicted to increase risks to crop production in the future. However, the possible broader impact of climate change on social-ecological systems still needs to be evaluated. Therefore, the present study focuses on one such globally important agricultural social-ecological system referred to as the Village Tank Cascade System (VTCS) in the dry zone of Sri Lanka. The VTCS has considerable potential to withstand seasonal climate variability mainly through continuous supply of water by the village tank storage throughout the year. The current study aimed to investigate trends of climate variability and possible impacts on paddy production in the North and North-central VTCS zone. Observed and projected rainfall and temperature data were analysed to evaluate the past variability trends (1970 to 2020) and model future (up to 2100) scenarios of climate variability and trends. Long-term observed rainfall and temperature data (1946 to 2020) were analysed to identify possible anomalies. The Maximum Entropy (MaxEnt) model has been used to predict the situation of future paddy farming (2050 and 2070) under two climate scenarios (RCP4.5 and RCP8.5) of the Intergovernmental Panel on Climate Change (IPCC). Six variables that would affect paddy growth and yield quality were used alongside the average monthly rainfall and temperature of two Global Climate Models (MIROC5 and MPI-ESM-LR). Climate suitability for two paddy cultivation seasons (Yala and Maha) were predicted for current and future climate scenarios. The findings revealed that observed and projected climate changes show considerable deviation of expected rainfall and temperature trends across the VTCS zone. Temperature exhibits warming of approximately 1.0 °C during the declared Global Warming Period (1970 to 2020) in the study area. In addition, there is a trend of significant warming by 0.02 °C/year, RCP4.5 and 0.03 °C/year, RCP8.5 from 1950 to 2100. Rainfall (1970–2020) shows high interannual variability but trends were not significant and less discernible. However, long-term projected rainfall data (1950–2100) analysis detected a significant (p = 0) upward trend (2.0 mm/year, RCP4.5 and 2.9 mm/year, RCP8.5), which is expected to continue up to the end of this century. Further, the study revealed some shifts in temperature towards higher values and positive anomalies in rainfall affecting seasonality and the likelihood of more extreme occurrences in the future, especially during the Maha cultivation season. The MaxEnt model predicts the following under future climate scenarios: (i) spatio-temporal shifts (conversions) in climate suitability for paddy farming in the VTCS zone; (ii) substantial low and moderate suitability areas that are currently suitable will remain unchanged; (iii) up to 96% of highly suitable and 38% of moderately suitable paddy growing areas in the VTCS zone will be at risk due to a decline in future climate suitability; and (iv) expansion of lower suitability areas by approximately 22% to 37%, due to conversion from moderate suitability areas. The study provides evidence that the continuous warming trend with increasing variability in rainfall and shifting seasonality could increase the vulnerability of future paddy farming in the VTCS. Thus, findings of this study will help planners to make more targeted solutions to improve adaptive capacity and regain the resilience to adjust the paddy farming pattern to deal with predicted climate variability and change. Full article

Anomaly detection across critical infrastructures is not only a key step towards detecting threats but also gives early warnings of the likelihood of potential cyber-attacks, faults, or infrastructure failures. Owing to the heterogeneity and complexity of the cybersecurity field, several anomaly detection algorithms have been suggested in the recent past based on the literature; however, there still exists little or no research that points or focuses on Non-Pattern Anomaly Detection (NP-AD) in Time-Series at the time of writing this paper. Most of the existing anomaly detection approaches refer to the initial profiling, i.e., defining which behavior represented by time series is “normal”, whereas everything that does not meet the criteria of “normality” is set as “abnormal” or anomalous. Such a definition does not reflect the complexity and sophistication of anomaly nature. Under different conditions, the same behavior may or may not be anomalous. Therefore, the authors of this paper posit the need for NP-AD in Time-Series as a step toward showing the relevance of deviating or not conforming to expected behaviors. Non-Pattern (NP), in the context of this paper, illustrates non-conforming patterns or a technique of deviating with respect to some characteristics while dynamically adapting to changes. Based on the experiments that have been conducted in this paper, it has been observed that the likelihood of NP-AD in Time-Series is a significant approach based on the margins of data streams that have been used from the perspective of non-seasonal time series with outliers, the Numenta Anomaly Benchmark (NAB) dataset and the SIEM SPLUNK machine learning toolkit. It is the authors’ opinion that this approach provides a significant step toward predicting futuristic anomalies across diverse cyber, critical infrastructures, and other complex settings. Full article

Anomaly detection is the foundation of intelligent operation and maintenance (O&M), and detection objects are evaluated by key performance indicators (KPIs). For almost all computer O&M systems, KPIs are usually the machine-level operating data. Moreover, these high-frequency KPIs show a non-Gaussian distribution and are hard to model, i.e., they are intricate KPI profiles. However, existing anomaly detection techniques are incapable of adapting to intricate KPI profiles. In order to enhance the performance under intricate KPI profiles, this study presents a seasonal adaptive KPI anomaly detection algorithm ASAD (Adaptive Seasonality Anomaly Detection). We also propose a new eBeats clustering algorithm and calendar-based correlation method to further reduce the detection time and error. Through experimental tests, our ASAD algorithm has the best overall performance compared to other KPI anomaly detection methods. Full article

Climate change is a global phenomenon but disproportionately affects smallholder farmers, prompting them to use various coping and adaptation strategies to counter the problem. This study aimed to examine the trends of climate parameters, assess farmers’ perception of climate change, and identify the strategies of adaptation measures in central Ethiopia. Climate data were obtained from the National Meteorological Agency. Survey data were collected from 120 randomly selected households in 2017 and complemented with focus group discussions. The Mann–Kendall approach was used to detect climate trends, while a rainfall anomaly was calculated using the rainfall anomaly index. Multinomial logit model was used to examine determinants of farmers’ adaptation to the perceived change. In most of the cases, farmers’ perceptions were in accordance with climate trend analyses. Farmers used crop diversification, adjustments of planting dates, destocking of livestock, seasonal migration, crop rotation, and climate information services to adapt to climate-related shocks. Empirical results showed that the age and education of the household heads, family size, access to extension services, and farm and nonfarm incomes had a significant association with the adaptation practices farmers took. The existence of strong correlations between the demographic, socio-institutional variables, and the choice of adaptation strategies suggests the need to strengthen local institutions to enhance the adaptation of smallholder farmers to climate change. Full article

The global water cycle is becoming more intense in a warming climate, leading to extreme rainstorms and floods. In addition, the delicate balance of precipitation, evapotranspiration, and runoff affects the variations in soil moisture, which is of vital importance to agriculture. A systematic examination of climate change impacts on these variables may help provide scientific foundations for the design of relevant adaptation and mitigation measures. In this study, long-term variations in the water cycle over China are explored using the Regional Climate Model system (RegCM) developed by the International Centre for Theoretical Physics. Model performance is validated through comparing the simulation results with remote sensing data and gridded observations. The results show that RegCM can reasonably capture the spatial and seasonal variations in three dominant variables for the water cycle (i.e., precipitation, evapotranspiration, and runoff). Long-term projections of these three variables are developed by driving RegCM with boundary conditions of the Geophysical Fluid Dynamics Laboratory Earth System Model under the Representative Concentration Pathways (RCPs). The results show that increased annual average precipitation and evapotranspiration can be found in most parts of the domain, while a smaller part of the domain is projected with increased runoff. Statistically significant increasing trends (at a significant level of 0.05) can be detected for annual precipitation and evapotranspiration, which are 0.02 and 0.01 mm/day per decade, respectively, under RCP4.5 and are both 0.03 mm/day per decade under RCP8.5. There is no significant trend in future annual runoff anomalies. The variations in the three variables mainly occur in the wet season, in which precipitation and evapotranspiration increase and runoff decreases. The projected changes in precipitation minus evapotranspiration are larger than those in runoff, implying a possible decrease in soil moisture. Full article

This study analyzed the variability of rainfall and temperature in southwest coastal Bangladesh and assessed the impact of such variability on local livelihood in the last two decades. The variability analysis involved the use of coefficient of variation (CV), standardized precipitation anomaly (Z), and precipitation concentration index (PCI). Linear regression analysis was conducted to assess the trends, and a Mann–Kendall test was performed to detect the significance of the trends. The impact of climate variability was assessed by using a livelihood vulnerability index (LVI), which consisted of six livelihood components with several sub-components under each component. Primary data to construct the LVIs were collected through a semi-structed questionnaire survey of 132 households in a coastal polder. The survey data were triangulated and supplemented with qualitative data from focused group discussions and key informant interviews. The results showed significant rises in temperature in southwest coastal Bangladesh. Though there were no discernable trends in annual and seasonal rainfalls, the anomalies increased in the dry season. The annual PCI and Z were found to capture the climate variability better than the currently used mean monthly standard deviation. The comparison of the LVIs of the present decade with the past indicated that the livelihood vulnerability, particularly in the water component, had increased in the coastal polder due to the increases in natural hazards and climate variability. The index-based vulnerability analysis conducted in this study can be adapted for livelihood vulnerability assessment in deltaic coastal areas of Asia and Africa. Full article

It is important to understand variations in hydro-meteorological variables to provide crucial information for water resource management and agricultural operation. This study aims to provide comprehensive investigations of hydroclimatic variability in the Bilate watershed for the period 1986 to 2015. Coefficient of variation (CV) and the standardized anomaly index (SAI) were used to assess the variability of rainfall, temperature, and streamflow. Changing point detection, the Mann–Kendell test, and the Sen’s slope estimator were employed to detect shifting points and trends, respectively. Rainfall and streamflow exhibited higher variability in the Bega (dry) and Belg (minor rainy) seasons than in the Kiremt (main rainy) season. Temperature showed an upward shift of 0.91 °C in the early 1990s. Reduction in rainfall (−11%) and streamflow (−42%) were found after changing points around late 1990s and 2000s, respectively. The changing points detected were likely related to the ENSO episodes. The trend test indicated a significant rise in temperature with a faster increase in the minimum temperature (0.06 °C/year) than the maximum temperature (0.02 °C/year). Both annual mean rainfall and streamflow showed significant decreasing trends of 8.32 mm/year and 3.64 mm/year, respectively. With significant increase in temperature and reduction in rainfall, the watershed has been experiencing a decline in streamflow and a shortage of available water. Adaptation measures should be developed by taking the increasing temperature and the declining and erratic nature of rainfall into consideration for water management and agricultural activities. Full article

This study investigated the trends and variability of seasonal and annual rainfall and temperature data over southern Ethiopia using time series analysis for the period 1983–2016. Standard Anomaly Index (SAI), Coefficient of Variation (CV), Precipitations Concentration Index (PCI), and Standard Precipitation Index (SPI) were used to examine rainfall variability and develop drought indices over southern Ethiopia. Temporal changes of rainfall trends over the study period were detected using Mann Kendall (MK) trend test and Sen’s slope estimator. The results showed that the region experienced considerable rainfall variability and change that resulted in extended periods of drought and flood events within the study period. Results from SAI and SPI indicated an inter-annual rainfall variability with the proportions of years with below and above normal rainfall being estimated at 56% and 44% respectively. Results from the Mann Kendall trend test indicated an increasing trend of annual rainfall, Kiremt (summer) and Bega (dry) seasons whereas the Belg (spring) season rainfall showed a significant decreasing trend (p < 0.05). The annual rate of change for mean, maximum and minimum temperatures was found to be 0.042 °C, 0.027 °C, and 0.056 °C respectively. The findings from this study can be used by decision-makers in taking appropriate measures and interventions to avert the risks posed by changes in rainfall and temperature variability including extremes in order to enhance community adaptation and mitigation strategies in southern Ethiopia. Full article

The increasing availability of dense time series of earth observation data has incited a growing interest in time series analysis for vegetation monitoring and change detection. Vegetation monitoring algorithms need to deal with several time series characteristics such as seasonality, irregular sampling intervals, and signal artefacts. While common algorithms based on deterministic harmonic regression models account for intra-annual seasonality, inter-annual variations of the seasonal pattern related to shifts in vegetation phenology due to different temperature and rainfall are usually not accounted for. We propose a transition to stochastic modelling and present a near real-time change detection method that combines a structural time series model with the Kalman filter. The model continuously adapts to new observations and allows to better separate phenology-related deviations from vegetation anomalies or land cover changes. The method is tested in a forest change detection application aiming at the assessment of damages caused by storm events and insect calamities. Forest changes are detected based on the cumulative sum control chart (CUSUM) which is used to decide if new observations deviate from model-based forecasts. The performance is evaluated in two test sites, one in Malawi (dry tropical forest) and one in Austria (temperate deciduous, coniferous and mixed forests) based on Sentinel-2 time series. Both forest areas are characterized by a distinct, but temporally varying leaf-off season. The presented change detection method shows overall accuracies above 99%, users’ accuracies of 76.8% to 88.6%, and producers’ accuracies of 68.2% to 80.4% for the forest change stratum (minimum mapping unit: 0.1 ha). Results are based on visually interpreted points derived by stratified random sampling. A further analysis revealed that increasing the time series density by merging data from two Sentinel-2 orbits yields better forest change detection accuracies in comparison to using data from one orbit only. The resulting increase in users’ accuracy amounts to 7.6%. The presented method is capable of near real-time processing and could be used for a variety of automated forest monitoring applications. Full article

This paper assessed the current and mid-century trends in rainfall and temperature over the Mono River watershed. It considered observation data for the period 1981–2010 and projection data from the regional climate model (RCM), REMO, for the period 2018–2050 under emission scenarios RCP4.5 and RCP8.5. Rainfall data were interpolated using ordinary kriging. Mann-Kendall, Pettitt and Standardized Normal Homogeneity (SNH) tests were used for trends and break-points detection. Rainfall interannual variability analysis was based on standardized precipitation index (SPI), whereas anomalies indices were considered for temperature. Results revealed that on an annual scale and all over the watershed, temperature and rainfall showed an increasing trend during the observation period. By 2050, both scenarios projected an increase in temperature compared to the baseline period 1981–2010, whereas annual rainfall will be characterized by high variabilities. Rainfall seasonal cycle is expected to change in the watershed: In the south, the second rainfall peak, which usually occurs in September, will be extended to October with a higher value. In the central and northern parts, rainfall regime is projected to be characterized by late onsets, a peak in September and lower precipitation until June and higher thereafter. The highest increase and decrease in monthly precipitation are expected in the northern part of the watershed. Therefore, identifying relevant adaptation strategies is recommended. Full article

The European Common Agricultural Policy (CAP) post-2020 timeframe reform will reshape the agriculture land use control procedures from a selected risk fields-based approach into an all-inclusive one. The reform fosters the use of Sentinel data with the objective of enabling greater transparency and comparability of CAP results in different Member States. In this paper, we investigate the analysis of a time series approach using Sentinel-2 images and the suitability of the BFAST (Breaks for Additive Season and Trend) Monitor method to detect changes that correspond to land use anomaly observations in the assessment of agricultural parcel management activities. We focus on identifying certain signs of ineligible (inconsistent) use in permanent meadows and crop fields in one growing season, and in particular those that can be associated with time-defined greenness (vegetation vigor). Depending on the requirements of the BFAST Monitor method and currently time-limited Sentinel-2 dataset for the reliable anomaly study, we introduce customized procedures to support and verify the BFAST Monitor anomaly detection results using the analysis of NDVI (Normalized Difference Vegetation Index) object-based temporal profiles and time-series standard deviation output, where geographical objects of interest are parcels of particular land use. The validation of land use candidate anomalies in view of land use ineligibilities was performed with the information on declared land annual use and field controls, as obtained in the framework of subsidy granting in Slovenia. The results confirm that the proposed combined approach proves efficient to deal with short time series and yields high accuracy rates in monitoring agricultural parcel greenness. As such it can already be introduced to help the process of agricultural land use control within certain CAP activities in the preparation and adaptation phase. Full article

The self-adaptive gradient-based thresholding (SAGBT) method is a simple non-interactive coal fire detection approach involving segmentation and a threshold identification algorithm that adapts to the spatial distribution of thermal features over a landscape. SAGBT detects coal fire using multispectral thermal images acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. The method was detailed by our previous work “Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Based on ASTER Data—Part 1, Methodology”. The current study evaluates the performance of SAGBT and validates its results by using ASTER thermal infrared (TIR) images and ground temperature data collected at the Wuda coalfield (China) during satellite overpass. We further analyzed algorithm performance by using nighttime TIR images and images from different seasons. SAGBT-derived fires matched fire spots measured in the field with an average offset of 32.44 m and a matching rate of 70%–85%. Coal fire areas from TIR images generally agreed with coal-related anomalies from visible-near infrared (VNIR) images. Further, high-temperature pixels in the ASTER image matched observed coal fire areas, including the major extreme high-temperature regions derived from field samples. Finally, coal fires detected by daytime and by nighttime images were found to have similar spatial distributions, although fires differ in shape and size. Results included the stratification of our study site into two temperature groups (high and low temperature), using a fire boundary. We conclude that SAGBT can be successfully used for coal fire detection and analysis at our study site. Full article