Regional Time Series Research Articles

Regional Time-Series Coding Network and Multi-View Image Generation Network for Short-Time Gait Recognition.

Gait recognition is one of the important research directions of biometric authentication technology. However, in practical applications, the original gait data is often short, and a long and complete gait video is required for successful recognition. Also, the gait images from different views have a great influence on the recognition effect. To address the above problems, we designed a gait data generation network for expanding the cross-view image data required for gait recognition, which provides sufficient data input for feature extraction branching with gait silhouette as the criterion. In addition, we propose a gait motion feature extraction network based on regional time-series coding. By independently time-series coding the joint motion data within different regions of the body, and then combining the time-series data features of each region with secondary coding, we obtain the unique motion relationships between regions of the body. Finally, bilinear matrix decomposition pooling is used to fuse spatial silhouette features and motion time-series features to obtain complete gait recognition under shorter time-length video input. We use the OUMVLP-Pose and CASIA-B datasets to validate the silhouette image branching and motion time-series branching, respectively, and employ evaluation metrics such as IS entropy value and Rank-1 accuracy to demonstrate the effectiveness of our design network. Finally, we also collect gait-motion data in the real world and test them in a complete two-branch fusion network. The experimental results show that the network we designed can effectively extract the time-series features of human motion and achieve the expansion of multi-view gait data. The real-world tests also prove that our designed method has good results and feasibility in the problem of gait recognition with short-time video as input data.

Are the Regional Precipitation and Temperature Series Correlated? Case Study from Dobrogea, Romania

In the context of climate change, this article tries to answer the question of whether a correlation exists between the precipitation and temperature series at a regional scale in Dobrogea, Romania. Six sets of time series are used for this aim, each of them containing ten series—precipitation and temperatures—recorded at the same period at the same hydro-meteorological stations. The existence of a monotonic trend was first assessed for each individual series. Then, the Regional time series (RTS) (one for a set of series) were built and the Mann–Kendall test was employed to test the existence of a monotonic trend for RTSs. In an affirmative case, Sen’s method was employed to determine the slope of the linear trend. Finally, nonparametric trend tests were utilized to verify if there was a correlation between the six RTSs. This study resulted in the fact that the only RTS presenting an increasing trend was that of minimum temperatures, and there was a weak correlation between the RTS of minimum precipitations and maximum temperatures.

Sentinel-3 SLSTR active fire (AF) detection and FRP daytime product - Algorithm description and global intercomparison to MODIS, VIIRS and landsat AF data

The Sea and Land Surface Temperature Radiometer (SLSTR) senses the Earth from onboard two concurrently operating European Copernicus Sentinel-3 (S3) satellites. As the Terra platform carrying the Moderate Resolution Imaging Spectroradiometer (MODIS) is reaching its end of life, the S3 Active Fire Detection and FRP products generated from data captured by S3 SLSTR are expected to soon become the main global active fire (AF) product for the mid-morning and evening low Earth orbit timeslots. The S3 night-time AF product issued by the European Space Agency (ESA) has been operational since March 2020, and here we report on the significant adjustments made to enable the generation of a complimentary daytime product. Similar to MODIS, SLSTR possesses two middle infrared channels, both a ‘standard’ (normal gain; S7) channel and a ‘fire’ (low-gain; F1) channel - but in contrast to MODIS by day even the ambient background land surface is often saturated in the SLSTR standard gain MIR (S7) channel. This saturation necessitates far greater use of the F1 channel data by day for active fire detection than by night, even though F1 has characteristics which make its data more challenging to combine with that from the other SLSTR thermal infrared channels. Here we report on the approaches used to combine S7 and F1 data for optimized daytime AF detection, and also detail the other algorithm adjustments found necessary to include in the daytime AF product algorithm. We compare the resulting daytime SLSTR AF product data to that generated from near-simultaneous views provided by MODIS onboard Terra. When both sensors detect the same active fire cluster at similar time, there is minimal bias shown between the two FRP retrievals (the ordinary least squares linear best fit between matched SLSTR and MODIS per-fire FRP matchups has a slope of 0.97). At the regional scale, the S3 product detects 70% of the AF pixels that the matching MODIS product reports, but also provides a further (16%) set of unique AF pixel detections. Regional FRP totals derived from SLSTR appear slightly lower than those from MODIS, and the OLS linear best fit between these regional FRP matchup datasets has a slope of 0.91. This is largely due to SLSTR performing less well in detecting the lowest FRP fires by day, whereas by night the S3 product performs a little better than MODIS due to the increased night-time use of S7 in the earlier AF pixel detection stages. Global fire mapping at a 0.25° grid cell resolution shows very similar daytime fire patterns and FRP totals from S3 and Terra MODIS, with SLSTR detecting around twice the number of AF pixels due to the algorithm being more effective at identifying low FRP pixels at the edges of fire clusters. Regional time series case studies also show very similar temporal patterns between S3 and Terra MODIS. Longer-term intercomparisons such as these will provide the knowledge necessary to use MODIS and SLSTR AF products together to analyse long-term AF trends. Comparing near simultaneous observations of fires by SLSTR and from the 30 m spatial resolution Landsat Operational Land Image (OLI) data, we find that once there are around 150 OLI active fire pixels detected within the area of an SLSTR pixel, the chances of that SLSTR pixel being classed as an active fire by the daytime algorithm rises to almost 100%. The daytime SLSTR AF Detection and FRP product based on the algorithm described herein has been fully operational since March 2022 and is available from the Sentinel-3 Science Hub (https://scihub.copernicus.eu/).

The evolution of Australian island geographies and the emergence and persistence of Indigenous maritime cultures

Australia was first peopled by maritime voyagers who intentionally crossed from Indonesia using watercraft 65,000 years ago. Despite this, the Holocene archaeological record suggests that many Australian islands were abandoned for several thousand years after separation from the mainland, and only visited again in the last few thousand years. The implication is that coastal peoples reintroduced watercraft into their maritime repertoire only in the recent past. Since first-peopling, sea levels have fluctuated over 120 m, transforming the coastlines and offshore island geographies. In this study, we assess Australia's offshore islands over the range of past sea levels, to discover that most of the Australian coastline has far more islands now than at any time in the human past. Australia's island-rich coastlines emerged with the high sea levels of the late Holocene. The existing island geographies of the Pilbara, the Great Barrier Reef, the Bass Strait and the Rottnest Shelf have no Pleistocene equivalents, meaning coastal peoples encountered new maritime opportunities as modern sea level was established (c. 7 ka). We present regional time series, and detailed paleogeographical maps of these changes, produced by a fully reproducible GIS method. We contextualise this in a review of Australian island archaeology, and demonstrate that key exceptions to this geographical trend are the Kimberley and the Torres Strait – which have evidence for earlier and longer continuous island sequences. These exceptions provided the opportunity for regional persistence of Pleistocene seafaring.

Application of the bomb radiocarbon chronometer with eye lens core Δ14C for age validation in deepwater reef fishes

Eye lens core Δ14C of Gulf of Mexico outer shelf and upper slope reef fishes matched a regional Δ14C reference time series, but otolith cores were depleted in 14C relative to the reference series. This is due to C in eye lens protein being derived from metabolic sources, with the ultimate source being epipelagic phytoplankton. In contrast, otolith C is derived mainly (70%–80%) from dissolved inorganic C, which is depleted in 14C beyond the epipelagic. Analyzing Δ14C in eye lens cores provides a novel approach for validating age estimates of deepwater (>200 m) fishes, which were unresolved in earlier applications of the bomb 14C chronometer.

EPT: A data-driven transformer model for earthquake prediction

The leading causes of earthquakes are crustal movements, plate movements, and collisions. In recent years, many researchers in earthquake prediction have been predicting earthquakes from historical seismic data in local areas. This approach ignores the underlying internal patterns of crustal motion, plate movement, and collisions. This paper proposes a purely data-driven deep learning model called EPT. The model uses gated feature extraction blocks (GFEB) to mine potential crustal motion and plate movement patterns from global historical seismic catalog data. It uses them to aid mainshock prediction in each local, provincial region. Experiments show that this approach improves model prediction accuracy by up to 50 percent. We also use multi-headed self-attention for the first time to capture long-term dependencies within regional time series, highlighting links between focal features and compensating for the difficulty of focusing on longer-term information in long-term time series with long short-term memory networks (LSTM). In addition, we also use the gradient harmonization mechanism classification (GHMC) loss function for the first time in earthquake prediction, effectively addressing the problem of uneven data distribution across different earthquake magnitude ranges. Finally, we validated the effectiveness of the EPT model in five provincial datasets in mainland China, and the experimental results all achieved an accuracy of over 90 percent.

Evaluation of MODIS, Landsat 8 and Sentinel-2 Data for Accurate Crop Yield Predictions: A Case Study Using STARFM NDVI in Bavaria, Germany

The increasing availability and variety of global satellite products and the rapid development of new algorithms has provided great potential to generate a new level of data with different spatial, temporal, and spectral resolutions. However, the ability of these synthetic spatiotemporal datasets to accurately map and monitor our planet on a field or regional scale remains underexplored. This study aimed to support future research efforts in estimating crop yields by identifying the optimal spatial (10 m, 30 m, or 250 m) and temporal (8 or 16 days) resolutions on a regional scale. The current study explored and discussed the suitability of four different synthetic (Landsat (L)-MOD13Q1 (30 m, 8 and 16 days) and Sentinel-2 (S)-MOD13Q1 (10 m, 8 and 16 days)) and two real (MOD13Q1 (250 m, 8 and 16 days)) NDVI products combined separately to two widely used crop growth models (CGMs) (World Food Studies (WOFOST), and the semi-empiric Light Use Efficiency approach (LUE)) for winter wheat (WW) and oil seed rape (OSR) yield forecasts in Bavaria (70,550 km2) for the year 2019. For WW and OSR, the synthetic products’ high spatial and temporal resolution resulted in higher yield accuracies using LUE and WOFOST. The observations of high temporal resolution (8-day) products of both S-MOD13Q1 and L-MOD13Q1 played a significant role in accurately measuring the yield of WW and OSR. For example, L- and S-MOD13Q1 resulted in an R2 = 0.82 and 0.85, RMSE = 5.46 and 5.01 dt/ha for WW, R2 = 0.89 and 0.82, and RMSE = 2.23 and 2.11 dt/ha for OSR using the LUE model, respectively. Similarly, for the 8- and 16-day products, the simple LUE model (R2 = 0.77 and relative RMSE (RRMSE) = 8.17%) required fewer input parameters to simulate crop yield and was highly accurate, reliable, and more precise than the complex WOFOST model (R2 = 0.66 and RRMSE = 11.35%) with higher input parameters. Conclusively, both S-MOD13Q1 and L-MOD13Q1, in combination with LUE, were more prominent for predicting crop yields on a regional scale than the 16-day products; however, L-MOD13Q1 was advantageous for generating and exploring the long-term yield time series due to the availability of Landsat data since 1982, with a maximum resolution of 30 m. In addition, this study recommended the further use of its findings for implementing and validating the long-term crop yield time series in different regions of the world.

Dynamic changes in the central autonomic network of patients with anorexia nervosa.

Autonomic cardiac dysfunction is a common complication in patients with anorexia nervosa (AN). Despite its high prevalence, physicians often overlook this clinical condition, and little research has been dedicated so far. To probe the functional role of the neurocircuitry underpinning the poorly understood autonomic cardiac dysfunction, we examined dynamic functional differences in the central autonomic network (CAN) between 21 acute AN individuals and 24 age, sex and heart rate-matched healthy controls (HC). We assessed functional connectivity (FC) changes in CAN using seeds in the ventromedial prefrontal cortex, left and right anterior insular cortex, left and right amygdala and dorsal anterior cingulate cortex. The overall FC between the six investigated seeds is reduced in AN individuals compared to HC, although no changes were observed for single connections. Moreover, AN exhibited higher complexity in the FC time series of such CAN regions. Contrary to HC, we found that the degree of complexity between FC and heart rate (HR) series did not correlate in AN, suggesting a shift from central to peripheral control of the heart in AN individuals. Using dynamic FC analysis, we showed that the CAN transits across five functional states with no preference for any. Strikingly, at the state of weakest connectivity, the entropy significantly diverges between healthy and AN individuals, reaching its minimum and maximum values, respectively. Overall, our findings provide evidence that core regions of the CAN engaged in cardiac regulation are functionally affected in acute AN.

Economic Analysis of Temporal Changes in Land Use Pattern of Western Maharashtra

Land is a finite natural resource, the efficient management of which is vital for economic growth and development of the country. It is required for both agriculture and non-agricultural purposes. An analysis of structural changes in the land use pattern over a period of time provides the scope for planned and judicious management of land. In this connection, the present study on ‘An Economic analysis of temporal changes in land use pattern in Western Maharashtra’ was undertaken. For analysis the secondary sources and the necessary regional level time series data on area under land use categories were compiled from various issues of statistical abstract of Maharashtra and other publications of Government. The entire period of 1960-61 to 2019-20 was decomposed into three periods, viz; pre-liberalization, post-liberalization and overall period. A temporal change in land use pattern was estimated with the per cent change and compound growth rates. The temporal changes in land use dynamics of Western Maharashtra region revealed that, land put to non-agricultural uses, miscellaneous tree crops and groves, current fallow and fallows other than current fallow showed an increased trend while, area under forest, barren and uncultivable land, cultivable waste and net area sown registered a decline during Period-II. It is suggested that, during Period-II, the average proportion of forest land is 14.72 per cent, which is far less than the necessary percentage i.e., 33 per cent. Therefore, necessary steps may be taken by the state government in order to restore and maintain ecological balance. Land put to non-agricultural uses inclined throughout all the periods under study indicating an alarming situation. Majority of this conversion to land put to non-agricultural uses has taken place of agricultural land. Hence, the state government may prepare suitable policies controlling conversion of agricultural land, particularly.

Altered functional dynamics gradient in schizophrenia with cigarette smoking.

Schizophrenia is associated with a high prevalence of cigarette smoking. Neural dynamics are spatially structured and shaped by both microscale molecular and macroscale functional architectures, which are disturbed in the diseased brain. The neural mechanism underlying the schizophrenia-nicotine dependence comorbidity remains unknown. In this study, we aimed to test whether there is an interaction between schizophrenia and smoking in brain neural dynamics, and how the main effect of the 2 factors related to the molecular architecture. Functional magnetic resonance imaging data were obtained from 4 groups: schizophrenia and healthy controls with/without smoking. We identified 2 dynamics gradients combined with over 5,000 statistical features of the brain region's time series. The interaction effect was found in the high-order functional network, and the main effect of schizophrenia was in the bilateral orbitofrontal cortices. Moreover, the disease- and smoking-related alteration in brain pattern was associated with spatial distribution of serotonin, cannabinoid, and glutamate. Collectively, these findings supported the self-medication hypothesis in schizophrenia-nicotine dependence with a neural intrinsic dynamics perspective.

Disrupted resting-state functional connectivity of the thalamus in patients with coronary heart disease

BackgroundAlthough homeostasis of the cardiovascular system is regulated by the cerebral cortex via the autonomic nervous system, the role of abnormal brain functional connectivity (FC) networks in patients with cardiac dysfunction remains unclear. Here, we report thalamus-based FC alterations and their relationship with clinical characteristics in patients with coronary heart disease (CHD). MethodsWe employed resting-state functional magnetic resonance imaging (rs-fMRI) to acquire imaging data in twenty-six patients with CHD alongside sixteen healthy controls (HCs). Next, we performed a thalamus-based FC analysis to profile abnormal FC patterns in the whole brain. Subsequently, the mean time series of the brain regions that survived in the FC analysis were used to determine correlations with clinical parameters in patients with CHD. ResultsWe found no statistically significant differences in demographic and clinical data between patients with CHD and HCs. Patients with CHD showed decreased FC patterns between bilateral thalami and left hemisphere, encompassing supplementary motor area, superior frontal gyrus, superior parietal gyrus, inferior parietal gyrus, middle cingulate cortex, lingual gyrus and calcarine sulcus. ConclusionsThese findings not only have implications in clarifying the relationship between cerebral functional imbalance and cardiovascular system, but also provide valuable insights to guide future evaluation and management of cardiac autonomic regulation via the brain-heart axis.

Springtime Southwest Cloud Bands in the Central Caribbean

Abstract Southwest cloud bands during spring (April and May) bring rains to central Caribbean islands at the end of the dry season. A cluster analysis of daily 500-hPa geopotential height fields for 1980–2021 identifies a low-west–high-east dipole pattern related to Pacific–North America response to El Niño–Southern Oscillation (ENSO) and springtime wet spells over the Dominican Republic and Puerto Rico. The regional dipole and local rain time series are ranked to identify the top 10 cases for analysis of meteorological conditions. Hovmöller plots of midlevel meridional wind and specific humidity during April 1983 and May 1986 wet spells reveal a standing Rossby wave train pattern that converges moisture onto the leading edge of a subtropical trough. Composite vertical sections during Caribbean Sea wet spells reveal lower easterly–upper westerly wind anomalies over South America associated with the equatorial Madden–Julian oscillation. Thunderstorm clusters within the southwesterly airflow induce multiday wet spells and flash floods. A second statistical method demonstrated how ENSO underpins Caribbean spring climate anomalies via tropical ocean–atmosphere Rossby wave coupling. Historical trends and long-range projections indicate that springtime tropical–midlatitude interactions may diminish due to an accelerating Hadley cell and retreating jet stream, leading to a delayed onset of the wet season across the Antilles Islands.

A Study of the Method for Retrieving the Vegetation Index from FY-3D MERSI-II Data

NDVI data have been widely used to detect and monitor vegetation status at regional, continental, and global scales. FY-3D MERSI-II NDVI (FNDVI) is a critical operational product used in many studies monitoring ecosystems and agriculture and assessing climate change and its risks, including drought and fire. MERSI-II and MODIS have very similar spectral response functions in the red and near-infrared channels, making MERSI/NDVI an effective replacement for MODIS/NDVI (MNDVI). Therefore, it is critical to conduct a thorough evaluation of the product’s quality. In this study, the consistency characteristics of two normalized difference vegetation index (NDVI) products, FY-3D MERSI-II NDVI and MODIS NDVI, were compared and validated at national and regional scales in China from 2020 to 2021. To assess the consistency of these two NDVI datasets, the correlation coefficient, root-mean-square error, and mean bias error were used. The findings revealed that the spatial distribution patterns of FNDVI and MNDVI were highly consistent across the country at the monthly time scale. The correlation coefficients were greater than 0.9475 for the two years 2020–2021, while the average deviation was between 0.02 and 0.05, and the root-mean-square error was 0.11. Based on the difference in the time consistency between FNDVI and MNDVI, the changes in the monthly NDVI values of the two types of satellites are generally consistent across the country. Among the three typical experimental areas, the relative deviation of the regional time series for products was the highest in Xinjiang. The relative average deviation of FNDVI in other regions was low, and its change trend was consistent with that of MODIS.

Time Series Optimal Capacity Determination Model of Regional Comprehensive Energy Considering Multi Energy Complementarity and Load Flexibility

In order to improve the voltage stability margin and convergence speed of the power grid, a regional comprehensive energy time series optimization capacity model is established under the condition of fully considering the multi energy complementarity and load flexibility. The multi energy complementary optimal operation model of regional comprehensive energy system is established, the load flexibility and multi energy complementary characteristics are analyzed, and the robust optimal capacity target of regional comprehensive energy system is set on the basis of considering the load flexibility. From the perspective of time series, an optimized capacity model is constructed, and genetic algorithm is improved to solve the multi-objective optimization model of regional comprehensive energy time series capacity. The experimental results show that this method has good optimization effect, improves the stability margin and convergence speed of power grid voltage, and can find the optimal solution for the established objective function, which effectively improves the effect of capacity determination.

Análise temporal de indicadores da Estratégia Saúde da Família sob o olhar da Política Nacional da Atenção Básica

Throughout the three editions of the Brazilian National Primary Health Care Policy (PNAB), changes were made in relation to the structure of the Family Health Strategy (FHS), with emphasis on modifications concerning the priority nature of the FHS as an organization and care strategy in primary health care. The objective was to analyze temporal trends in indicators related to the FHS from the perspective of the three PNAB editions: 2006, 2011, and 2017. This is a descriptive study of the temporal trend of indicators selected from a logical model constructed by components related to the FHS in the three editions of the PNAB. The logical model was developed based on the components Territory/Enrollment, Teams, Work Process, Territory Planning and Management, and Care for Priority Groups by Family Health Teams, each one being represented by selected indicators. The construction of the national and regional time series between 2007 and 2020 was carried out using the Joinpoint software. Most of the indicators showed an upward trend in the first time segments identified by the models, followed by segments of stability or decrease, especially after the year 2017. The indicator Number of community health workers stands out, which decreased after 2017 in most geographical regions and in Brazil. The 2017 PNAB may have discouraged the continuation and expansion of the FHS as the priority model of primary health care, by allowing and financing new teamwork arrangements and processes.

Temporal analysis of Family Health Strategy indicators from the perspective of the Brazilian National Primary Health Care Policy.

Throughout the three editions of the Brazilian National Primary Health Care Policy (PNAB), changes were made in relation to the structure of the Family Health Strategy (FHS), with emphasis on modifications concerning the priority nature of the FHS as an organization and care strategy in primary health care. The objective was to analyze temporal trends in indicators related to the FHS from the perspective of the three PNAB editions: 2006, 2011, and 2017. This is a descriptive study of the temporal trend of indicators selected from a logical model constructed by components related to the FHS in the three editions of the PNAB. The logical model was developed based on the components Territory/Enrollment, Teams, Work Process, Territory Planning and Management, and Care for Priority Groups by Family Health Teams, each one being represented by selected indicators. The construction of the national and regional time series between 2007 and 2020 was carried out using the Joinpoint software. Most of the indicators showed an upward trend in the first time segments identified by the models, followed by segments of stability or decrease, especially after the year 2017. The indicator Number of community health workers stands out, which decreased after 2017 in most geographical regions and in Brazil. The 2017 PNAB may have discouraged the continuation and expansion of the FHS as the priority model of primary health care, by allowing and financing new teamwork arrangements and processes.

How can research on past urban adaptations be made useful for sustainability science?

Non-technical summaryCities in the distant past – as documented by archaeologists and historians – provide an extensive record of urban successes and failures, yet this information has had little impact on the field of sustainability science. I explore two reasons for this situation. First, these scholars have often failed to synthesize their data scientifically, and, second, they have not approached the transfer of past knowledge to present research in a rigorous manner. I organize discussion of these issues around three arguments for the present value of past cities: the urban trajectory argument, the sample size argument, and the laboratory argument.Technical summaryI explore the different ways historical and archaeological data can be deployed to contribute to research on urban sustainability science, emphasizing issues of argumentation and epistemology. I organize the discussion around three types of argument. The urban trajectory argument exploits the long time series of early cities and urban regions to examine change at a long time scale. The sample size argument views the role of early cities as adding to the known sample of settlements to increase understanding of urban similarities and differences. The laboratory argument uses data from past cities to explicitly test models derived from contemporary cities. Each argument is examined for three contrasting epistemological approaches: heuristic analogs, case studies, and quantitative studies. These approaches form a continuum leading from lesser to greater scientific rigor and from qualitative to quantitative frameworks. Much past-to-present argumentation requires inductive logic, also called reasoning by analogy. Sustainability scientists have confused this general form of argument with its weakest version, known as heuristic analogs. I stress ways to improve methods of argumentation, particularly by moving research along the continuum from weaker to stronger arguments.Social media summaryBetter methods of argument allow the past record of urban success and failure to contribute to urban sustainability science.

Time Series Construction of Oregon and Washington Snowfall since 1890 and an Update of California Snowfall through 2020

Abstract Time series of snowfall observations from over 500 stations in Oregon (OR) and Washington (WA) were generated for subregions of these states. Data problems encountered were as follows: 1) monthly totals in printed reports prior to 1940 that were not in the digital archive, 2) archived data listed as “missing” that were available, 3) digitized reports after 2010 eliminated good data, and 4) “zero” totals incorrectly entered in the official archive rather than “missing,” especially after 1980. Though addressing these was done, there is reduced confidence that some regional time series are representative of true long-term trends, especially for regions with few systematically reporting stations. For most regions characterized by consistent monitoring and with the most robust statistical reproducibility, we find no statistically significant trends in their periods of record (up to 131 years) for November–April seasonal totals through April 2020. This result includes the main snowfall regions of the Cascade Range. However, snowfall in some lower-elevation areas of OR and WA appear to have experienced declining trends, consistent with an increase in northeastern Pacific Ocean temperatures. Finally, previously constructed time series through April 2011 for regions in California are updated through April 2020 to include the recent, exceptionally low seasonal totals on the western slopes of the Sierra Nevada. This update indicates 2014/15 was the record lowest, 2013/14 was the 5th lowest, and 2012/13 was the 14th lowest of 142 years. Even so, the 1879–2020 linear trend in this key watershed region, though −2.6% decade−1, was not significantly different from zero due to high interannual variability and reconstruction uncertainty.

Dry and Wet Changes and Vegetation Time-Delay Responses in Western China

Due to global warming and other climate changes, it is increasingly important to study the response of regional environmental changes and dynamic changes in vegetation to climate change. Based on meteorological data from the last 60 years, this paper calculates the humidity index of western China under a wide range of long time series in different regions and explores the cross-correlation effect between series by offering a comparison with NDVI data, to analyze the cross-correlation between wet and dry changes and changes in vegetation in western China on a spatial scale. The results show that the spatial distribution of the interdecadal humidity index is different between different regions in western China. For example, the semi-arid and the semi-humid zones of the Weihe River region exhibit significant changes, while the Xinjiang and Qinghai–Tibet regions show a trend of constant wetness, on the whole, and the Sichuan and Yunnan–Guizhou regions are relatively humid and the distribution of wetness and dryness is relatively stable. The distribution of high and low values of the humidity index is very obvious and consistent with that of the distribution of desert bare land and precipitation in western China. In common with the distribution in the humidity index, the maximum correlation number between the NDVI and the humidity index in the whole western region is also significantly different in spatial distribution. There is a positive correlation between the NDVI and the humidity index in 99% of the study area. However, the delay in response time of the NDVI to changes in the humidity index in each region is inconsistent. For example, changes in the NDVI lag changes in the humidity index in the Menggan region by generally either 2 months or 5 months, while in the Sichuan region the delay in response time is generally 3 months. The variation and trend in dry and wet areas are closely related to the geographical location, climate zone, and topographic terrain, which may be the reason for the differences in the distribution of vegetation types and the response time to dry and wet changes. There is significant interaction between the humidity index and the vegetation type or precipitation distribution in western China. The positive correlation between the NDVI and the humidity index means that the positive effect is more sensitive, and the response of grassland is the most sensitive in the ecosystem.

Detecting and explaining long‐term changes in river water quality in south‐eastern Australia

AbstractUnderstanding the temporal changes in river water quality is important for catchment water quality management. This study aims to detect and attribute long‐term trends and abrupt changes in river water quality. We used 26 years of water quality data (1994–2020) collected from 102 river monitoring sites across Victoria, south‐eastern Australia. We analysed six water quality constituents that are of key concerns for Australian catchment management, namely: electrical conductivity (EC), total suspended solids, nitrate‐nitrite, total Kjeldahl nitrogen, total phosphorous and filtered reactive phosphorus. To detect trends and abrupt changes in water quality at each site, a Bayesian ensemble modelling approach was applied, namely, the Bayesian estimator of abrupt change, seasonal change, and trend (BEAST). To explain water quality trends, we then built multivariate regressions to link water quality with streamflow and seasonality, and then compared alternative model structures with and without a change in the regression relationships informed by the changes detected by BEAST. Among the six constituents studied, EC shows the most distinct systematic trends, with 21 sites having a significant increase followed by a non‐significant trend; within the 21 sites, 14 had a significant change point in EC around Year 2010. The regression analyses between water quality and streamflow suggested that the observed systematic change in EC could be largely related to reduced streamflow during the Millennium drought, which greatly impacted the climate and hydrology of south‐eastern Australia over the first decade of 2000. The results of this study can help inform the design of effective mitigation strategies and avoid further degradation of water quality across Victoria. Besides, our trend analysis and attribution approaches are applicable to water quality time series in other regions for robust trend analysis and change point detection.