Cross Wavelet Research Articles

GRACE-based groundwater drought in the Indochina Peninsula during 1979–2020: Changing properties and possible teleconnection mechanisms

Groundwater is very important for human productivity and daily life, hydrological cycle regulation, and ecosystem stability. However, due to the complex mechanisms of groundwater drought, the spatial and temporal variations of groundwater drought and its driving mechanisms are still not fully understood, especially in Indochina Peninsula. In this work, we used a reconstructed long-term terrestrial water storage dataset from the Gravity Recovery and Climate Experiment (GRACE) emission and a GRACE-based groundwater drought index to investigate the spatial and temporal characteristics of groundwater drought during 1979–2020 in the Indochina Peninsula. The possible teleconnection mechanisms between groundwater drought and the Indian Ocean Dipole (IOD), El Niño-Southern Oscillation (ENSO), and El Niño Modoki (ENSO_M) were also investigated using cross wavelet transform method. The results show that groundwater drought worsens significantly during 1979–2020, and becomes much more frequent and intensified after 2000 in the southern Indochina Peninsula. Both univariate and bivariate (logic ‘or’ and ‘and’) return periods for duration, severity, and peak of groundwater drought are short in the southern Indochina Peninsula, and thus the risk of groundwater drought is high. The IOD, ENSO, and ENSO_M can reduce the intensity of groundwater drought to a certain extent during the warm phases, but only ENSO_M tends to significantly exacerbate the intensity of groundwater drought during the cold phases in the southern Indochina Peninsula. The variations in groundwater drought are dominated by ENSO_M, and are also coupled influenced by the IOD and ENSO in the southern Indochina Peninsula. The results provide valuable information for the sustainable ecological environment and socioeconomic development, especially development of groundwater drought early warning and prediction models in the Indochina Peninsula.

Uncovering time and frequency co-movement among green bonds, energy commodities and stock market

PurposeThis paper aims to examine the comovement among green bonds, energy commodities and stock market to determine the advantages of adding green bonds to a diversified portfolio.Design/methodology/approachGeneric 1 Natural Gas and Energy Select SPDR Fund are used as proxies to measure energy commodities, bonds index of S&P Dow Jones and Bloomberg Barclays MSCI are used to represent green bonds and the New York Stock Exchange is considered to measure the stock market. Granger causality test, wavelet analysis and network analysis are applied to daily price for the select markets from August 26, 2014, to March 30, 2021.FindingsResults from the Granger causality test indicate no causality between any pair of variables, while cross wavelet transform and wavelet coherence analysis confirm strong coherence at a high scale during the pandemic, validating comovement among the three asset classes. In addition, network analysis further corroborates this connectedness, implying a strong association of the stock market with the energy commodity market.Originality/valueThis study offers new evidence of the temporal association among the US stock market, energy commodities and green bonds during the COVID-19 crisis. It presents a novel approach that measures and evaluates comovement among the constituent series, simultaneously using both wavelet and network analysis.

The relation between football clubs and economic growth: the case of developed countries

It is believed that societies use sports as a tool for their social and cultural development. In particular, developed countries have embraced sports as part of their lives and have ranked it as a source of economic growth. The increase in sports consumption and industries can contribute to economic growth, and economic growth can have a positive impact on sports industries vice versa. The wavelet methodology is a way to discover the relationship between sports and economic growth by examining frequency and time domain aspects and identifying shorter to longer-term relationships continuously. The industrial production of different countries has experienced short and medium-term cycles around the year 2020. In contrast, the values of football clubs in these countries have shown minimal fluctuations, despite the emergence of some areas with slight significance. The cross wavelet analysis asserted the significance relation revealed after the covid 19 restrictions, both in shorter and longer periods. The wavelet coherence analysis showed that co-movement was observed in short-term cycles in countries across the periods while long-term co-movements are observed only in only in small areas or generally out of cone of influence.

Assessment of Variability and Attribution of Drought Based on GRACE in China from Three Perspectives: Water Storage Component, Climate Change, Water Balance

Understanding how drought is impacted by both natural and human influences is crucial to the sustainable utilization and protection of water resources. We established a drought severity index (DSI) based on the terrestrial water storage anomaly (TWSA) derived from the GRACE satellite to detect drought characteristics and trends over ten major river basins in China from 2002 to 2017. The influence of natural factors (terrestrial water storage components, precipitation, evapotranspiration, runoff, NDVI, and teleconnection factors (ENSO, PDO, NAO, and AO)) and a human factor (LULC) on drought were investigated and quantified from the perspective of water storage components based on the Theil–Sen trend and Mann–Kendall test method, the perspective of climate change based on cross wavelet transforms, and the perspective of water balance based on Random Forest. The results indicated that (1) almost all humid and arid basins experienced major drought periods during 2002–2006 and 2014–2017, respectively. The southern IRB and central YZRB regions exhibited notable declines in DSI trends, while the majority of the HLRB, IRB, LRB, YRB, HRB, and SWRB experienced significant increases in DSI trends; (2) abnormal groundwater decreases were the main cause of drought triggered by insufficient terrestrial water storage in most basins; (3) ENSO was the strongest teleconnection factor in most humid basins, and NAO, PDO, and AO were the strongest teleconnection factors in the arid basins and PRB. Most significant resonance cycles lasted 12–64 months in 2005–2014; and (4) the influence of an anthropogenic driver (LULC) has become as important as, or more important than, natural factors (runoff and teleconnection factors) on hydrological drought.

An Analysis of Spatial–Temporal Evolution and Propagation Features of Vegetation Drought in Different Sub-Zones of China

The vegetation drought phenomenon will reduce the amount of water available to the vegetation system, change the ecological and hydrological cycles of plants, and affect the aquatic and terrestrial ecosystem in various forms. Therefore, research on the dynamic variation and driving mechanism of vegetation drought will help us recognize and predict the response of vegetation under drought stress conditions, implement appropriate policy measures to deal with the drought crisis, and provide technical support for implementing vegetation protection and alleviating the increasing risk of vegetation drought. However, the dynamic variation of vegetation drought and its dynamic propagation mechanism are still undefined across China. In this study, the spatio-temporal evolutions and pixel-scaled trends of vegetation drought were analyzed during the period between 1999 and 2020. Additionally, the propagation features were investigated between vegetation drought and meteorological drought. Finally, the relationships between vegetation drought and atmospheric teleconnection were explicitly clarified using multivariate cross wavelet transform technology. The results highlighted five key findings: (1) the vegetation drought presented an overall decreasing trend across China in 1999–2020; (2) the most serious vegetation drought occurred in the year 2000, with the average vegetation condition index (VCI) values ranging from 0.36 to 0.46; (3) vegetation droughts were alleviating at the pixel scale for each season; (4) the propagation time from meteorological drought to vegetation drought was shorter in summer (1.26 months) and longer in winter (2.26 months); and (5) the three-factors combination of Pacific North American (PNA), El Niño-Southern Oscillation (ENSO), and Trans Polar Index (TPI) can satisfactorily explain the variations of vegetation drought. This study sheds new viewpoints into the identification of vegetation drought variation across China, which can also be applied in other areas.

Terrestrial ecological drought dynamics and its response to atmospheric circulation factors in the North China Plain

Ecological drought is a natural disaster caused by the shortage of water supply, which can lead to many problems such as the reduction of vegetation greenness, shrinkage of water area, and destruction of biological habitats. In view of the increasingly serious problem of ecological drought in the North China Plain (NCP), the remotely sensed vegetation health index (VHI) was adopted to characterize the ecological drought phenomenon. In this study, the modified Mann-Kendall (MMK) and Hurst index methods were used to analyze the spatio-temporal coupling dynamic variation of ecological drought. Subsequently, the Breaks for Additive Seasons and Trend (BFAST) algorithm was employed to explore the segmented trend characteristics and mutation point types. Furthermore, the cross wavelet transform and multiple wavelet coherence were applied to reveal the mutual effect of atmospheric circulation factors on ecological drought. The results indicated that: (1) the ecological drought showed an overall alleviation trend from 1982 to 2020 in the NCP; (2) the trend type of VHI was interrupted increase in Tianjin (TJ) and Hebei (HB) with a mitigated drought trend, and was decrease to increase in Beijing (BJ) and Henan (HN) with a slight increase and then significant decrease drought trend, and all the mutation points occurred before 2000 during the study period; (3) the most severe ecological drought occurred in winter with a minimal VHI of 0.519, and the least severe ecological drought appeared in summer with a maximal VHI of 0.581; and (4) the combination of atmospheric circulation factors, North Atlantic Oscillation (NAO)-Atlantic Multidecadal Oscillation (AMO)-Arctic Oscillation (AO), had the strongest driving effect on ecological drought, which can be adopted as input variables of drought pre-warning mechanism for improving the accuracy of drought prediction.

Correlations between COVID-19 cases and temperature, air humidity, and social isolating rate with cross wavelet transform and wavelet coherence: Case study of New York and São Paulo cities

The COVID-19 pandemic originated in 2019 and has become an endemic disease that we must learn to live with, similar to other strains of influenza. The Organization (WHO) declared on May 5, 2023, in Geneva, Switzerland, the end of the Public Health Emergency of International Concern regarding COVID-19. As vaccines become more widely available and the pandemic appears to be improved, our focus shifts to the challenges we still face. Understanding how external factors like temperature, air humidity, and social isolation impact the spread of the SARS-CoV-2 virus remains a crucial challenge beyond our control. In this study, potential links between the number of COVID-19 cases in São Paulo City (SPC) and New York City (NWC) were explored. Our analysis was carried out utilizing the continuous wavelet transform, alongside other tools such as cross-wavelet transform and wavelet coherence. Based on our findings, there appears to be a correlation between the variables related to low frequencies, which aligns with previous research on the topic. Particularly, our research has revealed a connection between COVID-19 cases and factors such as temperature, air humidity, and social isolation rates. Regarding the latter, our findings indicate that implementing social distancing measures was a wise public policy decision, although the correlation with daily COVID-19 cases requires careful analysis. For this study, we analyzed data from February of 2020, when the first cases were reported in the cities under investigation, SPC and NWC, up until December 31, 2022, by which time the vaccination campaign was well under way.

Displacements of Fushun west opencast coal mine revealed by multi-temporal InSAR technology

ABSTRACT In this paper, the Multi-Temporal Interferometric Synthetic Aperture Radar (MT-InSAR) technology is adopted to monitor the Line of Sight (LOS) displacement of Fushun West Opencast Coal Mine (FWOCM) and its surrounding areas in northeast China using Sentinel-1 Synthetic Aperture Radar (SAR) images acquired from 2018 to 2022. The spatial-temporal evolution of urban subsidence and the south-slope landslide are both analyzed in detail. Comparison with ground measurements and cross-correlation analysis via cross wavelet transform with monthly precipitation data are also conducted, to analyze the influence factors of displacements in FWOCM. The monitoring results show that a subsidence basin appeared in the urban area near the eastern part of the north slope in 2018, with settlement center located at the intersection of E3000 and fault F1. The Qian Tai Shan (QTS) landslide on the south slope, which experienced rapid sliding during 2014 to 2016, presents seasonal deceleration and acceleration with precipitation, with the maximum displacement in vicinity of the Liushan paleochannel. The results of this paper have fully taken in account for the complications of large topographic relief, geological conditions, spatial distribution and temporal evolution characteristics of surface displacements in opencast mining area. The wide range and long time series dynamic monitoring of opencast mine are of great significance to ensure mine safety production and geological disaster prevention in the investigated mining area.

Long time-series variation of crop yield under drought stress and drought vulnerability curves in Songnen Plain, Northeast China

It is of practical significance to reveal the long-term characteristics of drought and its impacts on crop yield in Songnen Plain, an important commodity grain base in Northeast China. This study firstly explored the long time-series variation of Modified Soil Water Deficit Index (MSWDI) and crop yield at multiple sites in Songnen Plain during 1960–2014 based on numerical simulation with the World Food Studies (WOFOST) model. This study then revealed the mutation of the drought and crop yield with three mutation point test methods, and thereafter explored their relationship between the drought and crop yield with the cross wavelet transform (XWT) and drought vulnerability curve. Results suggested the precipitation was at the lowest level in the 1970 s, which significantly increased in the 1980 s and thereafter exhibited a slow declining trend. Mutation points of drought conditions occurred at multiple stations in 1983, and the crop yields also showed obvious mutation in 1983 or 1984 at two stations. Besides, the drought and crop yield had significant 2–4 year resonance cycles during 1978–1980 at four stations. In particular, periods with high correlation between these two variables were generally years prone to abrupt changes. In addition, determination coefficients of the drought vulnerability curves based on the linear function, cubic curve function and hyperbolic tangent function reached 0.55, 0.65, and 0.64, respectively, suggesting the cubic curve function performed best. The drought vulnerability curve based on the cubic curve function suggested the crop yield loss showed a firstly decreasing and then increasing trend, which may be related to the compensation mechanism of crops. Overall, combination of the mechanistic drought index and crop models provides an effective way to reveal impacts of drought stress on crop yield and offer support information for coping with drought.

ENSO Forcing of Rainfall Erosivity in the Wuding River Basin

Rainfall erosivity (RE) is an important indicator of the ability of rainfall to cause soil erosion and is linked to ENSO through the transport of rainfall. Accurate assessment of RE and improved understanding of RE are essential for soil erosion prediction, optimization of soil and water conservation measures, and ecological management and restoration. Therefore, the Wuding River Basin, a typical ecologically fragile area, is selected as the research area. The erosivity model based on daily rainfall was first used to calculate RE, and the temporal and periodic characteristics of RE are studied. Then the effects of the Southern Oscillation Index (SOI), the Surface Temperature of the Central East Equatorial Pacific Ocean (SST), and the Multivariate ENSO Index (MEI) on RE are explored by using cross wavelet technique. Results indicated that: (1) the concentration of rainfall leads to the largest RE values in summer compared with other seasons, accumulating about 69% of the annual RE; (2) The overall trend of increasing RE in the Wuding River basin is not significant, but shows an abrupt change in 2015; (3) the influence of the SOI and SST indices of ENSO events on RE is significant, which is shown to be a statistically significant correlation (95% confidence level), indicating that ENSO has a strong influence on the changing pattern of RE. These findings are helpful in predicting soil erosion and are significant for developing further erosion control measures.

Reconstructing Continuous Ice Sheet Elevation Changes in the Amundsen Sea Sector During 2003–2021 by Merging Envisat, ICESat, CryoSat‐2, and ICESat‐2 Multi‐Altimeter Observations

AbstractThe Amundsen Sea (AS) sector in West Antarctica accounts for a significant proportion of Earth's ice losses and is the largest contributor of Antarctica's mass loss. To evaluate its contribution to global sea‐level rise, we reconstruct the long‐term continuous surface elevation changes (CSEC) record of the AS sector by an improved least‐squares plane fitting method (ILSPFM), which merged the relative surface elevation change (SEC) series instead of height from Envisat, ICESat, CryoSat‐2, and ICESat‐2 missions during 2003–2021. The accuracy of CSEC is improved by 25.9% using ILSPFM. The average rate of CSEC in the AS sector was −24.25 ± 0.48 cm yr−1 during 2003–2021. The largest signals of SEC are found over Pine Island, Thwaites, and Pope Glaciers, with the largest decline of SEC over Pope Glacier with a total SEC of −82.44 ± 7.21 m and an annual change rate of −4.34 ± 0.38 m yr−1. The ridge between Pine Island and Thwaites Glaciers is found in the AS sector, indicating that the change of ice sheet is dynamic thinning and closely related to the topography and the distance from the grounding line. Compared with meteorological data sets, we find that the codirectional fluctuation in CSEC is delayed by 3 months with surface temperature, and the precipitation leading SEC series as the phase arrow points straight down from the cross wavelet transform. Our new record shows that the AS sector thinned rapidly from 2003 to 2021 but decelerated from 2019 to 2021, and it was clearly correlated to the surface temperature, precipitation, and local terrain.

Connection of Compound Extremes of Air Temperature and Precipitation with Atmospheric Circulation Patterns in Eastern Europe

Recent studies show an increase in the frequency of compound extremes in air temperature and precipitation in many parts of the world, especially under dry and hot conditions. Compound extremes have a significant impact on all spheres of human activity, such as health, agriculture, and energy. Features of atmospheric circulation are closely related to the occurrence of anomalies in air temperature and precipitation. The article analyzes the relationship of atmospheric circulation modes with compound extremes that have had the greatest impact on the Atlantic–European region over the territory of Eastern Europe over the past 60 years on extreme air temperature and precipitation. Combinations of extreme temperature and humidity conditions (indices)—cold-dry (CD), cold-wet (CW), warm-dry (WD) and warm-wet (WW)—were used as compound extremes. Indices of compound extremes were calculated according to the E-OBS reanalysis data. Estimates of the relationship between two time series were carried out using standard correlation and composite analyses, as well as cross wavelet analysis. Phase relationships and time intervals for different climatic indices were different. The period of most fluctuations in the indices of compound extremes was from 4 to 12 years and was observed during 1970–2000. The coherent fluctuations in the time series of the WD and WW indices and the North Atlantic oscillation (NAO) index occurred rather in phase, those in the time series of the CD and WD indices and the Arctic oscillation (AO) index occurred in antiphase, and those in the time series of the WD and WW indices and the Scandinavia pattern (SCAND) index occurred in antiphase. Statistically significant increase in the number of warm compound extremes was found for the northern parts of the study region in the winter season with positive NAO and AO phases.

A single image super resolution method based on cross residual network and wavelet transform

In this paper, we propose a method to realize single image super resolution using a network composed of residual blocks with a cross architecture and wavelet transform. For the high quality of the reconstructed high resolution image, the brightness change must be sharp at the edge and gentle at the flat region. In single image super resolution, it is important to accurately predict the high-frequency components of the image. Hence, we propose a method to realize single image super resolution by estimating the high-frequency component in the wavelet domain using neural network composed of residual blocks with two crossing skip connections.

Analysis of Drought Characteristic of Sichuan Province, Southwestern China

Drought is a widespread and destructive natural hazard and is projected to occur more frequently and intensely, with more severe impacts in a changing environment. In this study, we used the standardized precipitation index (SPI) at various time scales (i.e., 3, 6, and 12 months) to provide an overall view of drought conditions across Sichuan Province, southwestern China, from 1961 to 2016. Then, the relationship between the SPI and the soil moisture anomalies was analyzed. Furthermore, the causes of SPI drought from the perspective of large-scale atmospheric circulation were assessed in the study area. The results showed that most stations with decreasing trends were located in the eastern part of Sichuan Province, while most stations with increasing trends were located in the northwestern part, indicating that the eastern region presented a drying trend, while the northwestern part exhibited a wetting trend. The specific analysis focused on extreme drought indicated an increasing occurrence the probability of extreme drought events, which could induce a high potential drought risk in the study area. The SPI values had a strong relationship with the soil moisture anomalies, and the linear correlation coefficients decreased as the time scale increased. This result indicated that SPI3 (3-month SPI) could be regarded as a good predictor of soil moisture drought. The cross wavelet analysis revealed that the Southern Oscillation Index (SOI) had statistically significant correlations with the SPIs in Sichuan Province. The results of this study are useful for assessing the change in local drought events, which will help reduce the losses caused by drought disasters in Sichuan Province.

Spatial-Temporal Variations of Extreme Precipitation Indices in the Xinjiang Cold Area over the Past 60 Years

The damage of extreme precipitation to the social economy and ecological environment has attracted extensive attention. Daily precipitation data of 59 meteorological stations located in Xinjiang Uygur Autonomous Region were selected, where the temporal and spatial distribution and variation characteristics of extreme precipitation from 1961 to 2017 were comprehensively analyzed by percentile threshold method, precipitation tendency rate method, the trend-free pre-whitening Mann Kendall trend test, Mann Kendall mutation test, and other methods. Further, three methods, the continuous wavelet transform, cross wavelet transform, and wavelet coherence analysis, were selected to analyze the correlation between extreme precipitation index and six atmospheric circulation indexes. Results show that from 1961 to 2017, the precipitation in Xinjiang increased, and the extreme precipitation (R99P), extreme precipitation days (R99D), extreme precipitation contribution rate (R99C), and extreme precipitation intensity (R99I) showed a significantly increasing trend. Due to the influence of topography, the spatial distribution of extreme precipitation is bounded by the Tianshan Mountains, decreasing from northwest to southeast, meeting the characteristics of “Northern Xinjiang is greater than Southern Xinjiang,” indicating obvious spatial heterogeneity. Among the selected atmospheric circulation indexes, Western Pacific Subtropical High Intensity Index (WPSHII) and Western Pacific Subtropical High Intensity Index (SCSSHII) contribute significantly to extreme precipitation events in Xinjiang. There is an obvious positive correlation between SCSSHII and extreme precipitation index. In other words, the greater the SCSSHII, the greater the frequency and frequency of extreme precipitation events in the study area, and the more prone it is to natural disasters such as rainstorms and floods.

Influence of the rainfall and temperature oscillation on air quality in Kathmandu valley: The wavelet analysis

In this study, wavelet analysis and traditional cross-correlation analysis (TCA) have been employed to explore the temporal characteristics of the Air Quality Index (AQI) (PM2.5) and its relationships with meteorological factors, air temperature, and rainfall in the capital city of Nepal, Kathmandu, during 2017 - 2020. The temporal variation of monthly averaged (AQI) (PM<sub>2.5</sub>) revealed that the air quality was unhealthy in winter, improved in summer, and subsequently became unhealthy again in autumn and spring. We also noticed that the lowest AQI (PM<sub>2.5</sub>) was recorded in 2020, which is due to the COVID-19 pandemic lockdown. By analyzing the time-frequency relationship of (AQI) (PM<sub>2.5</sub>) with air temperature and rainfall, using Cross Wavelet Transform (XWT), we observed the interannual, seasonal, and monthly common periodicity between them. The coherency of XWT is then tested by using Wavelet Transform Coherence (WTC), which showed a negative correlation of AQI (PM<sub>2.5</sub>) with rainfall and air temperature, while an anomaly was seen among AQI (PM<sub>2.5</sub>) and temperature as they were positively associated for a few months in 2017, 2018, and 2020. Moreover, the results of cross-correlation and multiresolution analysis (MRA) are consistent with wavelet analysis, indicating the driving effect of air temperature and rainfall on (AQI) (PM<sub>2.5</sub>).

Using Wearable Sensors to Measure Interpersonal Synchrony in Actors and Audience Members During a Live Theatre Performance

Studying social interaction in real-world settings is of increasing importance to social cognitive researchers. Theatre provides an ideal opportunity to study rich face-to-face interactions in a controlled, yet natural setting. Here we collaborated with Flute Theatre to investigate interpersonal synchrony between actors-actors, actors-audience and audience-audience within a live theatrical setting. Our 28 participants consisted of 6 actors and 22 audience members, with 5 of these audience members being audience participants in the show. The performance was a compilation of acting, popular science talks and demonstrations, and an audience participation period. Interpersonal synchrony was measured using inertial measurement unit (IMU) wearable accelerometers worn on the heads of participants, whilst audio-visual data recorded everything that occurred on the stage. Participants also completed post-show self-report questionnaires on their engagement with the overall scientists and actors performance. Cross Wavelet Transform (XWT) and Wavelet Coherence Transform (WCT) analysis were conducted to extract synchrony at different frequencies, pairing with audio-visual data. Findings revealed that XWT and WCT analysis are useful methods in extracting the multiple types of synchronous activity that occurs when people perform or watch a live performance together. We also found that audience members with higher ratings on questionnaire items such as the strength of their emotional response to the performance, or how empowered they felt by the performance, showed a high degree of interpersonal synchrony with actors during the acting segments of performance. We further found that audience members rated the scientists performance higher than the actors performance on questions related to their emotional response to the performance as well as, how uplifted, empowered, and connected to social issues they felt. This shows the types of potent connections audience members can have with live performances. Additionally, our findings highlight the importance of the performance context for audience engagement, in our case a theatre performance as part of public engagement with science rather than a stand-alone theatre performance. In sum we conclude that interdisciplinary real-world paradigms are an important and understudied route to understanding in-person social interactions.

Assessing the changeability of precipitation patterns using multiple remote sensing data and an efficient uncertainty method over different climate regions of Iran

While the rain gauge measurements are unevenly distributed in many world regions, it is necessary to use remote sensing-based precipitation data at high spatial and temporal resolutions. On the other hand, quantifying the uncertainty of precipitation is a vital issue for hydrometeorological applications globally. For this purpose, applying a new fusion framework using high-resolution remote sensing datasets can provide accurate precipitation evaluation against local observations with low uncertainty. This study examines three weighted fusion-based models containing the Ordered-Weighted-Averaged (OWA) family approach based on the ORLIKE method (OWA-ORLIKE) and ORNESS method (OWA-ORNESS) as well as the Entropy-weight (EW) method to combine different remote sensing precipitation products over different climate zones of Iran. In this case, multiple monthly remotely sensed datasets, including ERA5, ERA5-Land, TerraClimate, GPM, PERSIANN-CDR, TRMM, and CHIRPS, are utilized to assess precipitation patterns versus local measurements, which were gathered by Google Earth Engine (GEE) platform. Furthermore, the K-means algorithm is employed to cluster ground-based precipitation stations based on the climate zones category across Iran. Additionally, the Genetic Optimization Algorithm (GOA) is applied to specify optimal values of weights in weighting-based models. The performance of single and combination datasets are evaluated using statistical error metrics, including Pearson correlation coefficient (PCC), root mean square error (RMSE), Kling Gupta efficiency (KGE), and bias. The Thiessen polygon method has been applied to calculate each cluster's mean precipitation to obtain the optimal weights of stations. Finally, as an efficient uncertainty approach, Cross Wavelet Transform (XWT) method has been used for uncertainty assessment of monthly and seasonal precipitation series. Results indicated that the OWA family as the best fusion model had almost the lowest uncertainty values and best statistical error indices compared to the EW model and all single products in different clusters of Iran. As a motivation part of this study, unlike the single sources of remotely sensed data, multiple datasets can apply together in different climate conditions with various features using combination models, which mentioned uncertainty method has clearly revealed this claim.

Mutation Characteristics of Precipitation Concentration Spatiotemporal Variation and Its Potential Correlation with Low-Frequency Climate Factors in the LRB Area from 1960 to 2020

The precipitation conce ntration degree (PCD) and precipitation concentration period (PCP) in the Liaohe River basin (LRB) from 1960 to 2020 were calculated depending on the daily precipitation data derived from meteorological stations. The mutations of the PCD and PCP were identified by sliding t-test, and spatiotemporal evolution characteristics before and after the mutation point were further analyzed. Cross wavelet transform (CWT) was used to reveal the influence of four low-frequency climate factors (Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), El Niño -Southern Oscillation (ENSO), and Sunspots (SS)) on precipitation concentration. The results were presented as follows: Mutations occurred in the PCD sequence in 1980 and the PCP sequence in 2005 in the LRB. Spatial distribution of the PCD generally increased from the southeast to the northwest and tended to flatten. Over the past 60 years, the annual PCD tended to decrease, with a variation range of 0.53 to 0.80. The PCP was relatively concentrated in early July to early August, decreasing before and increasing after the mutation. Important climatic factors driving the mutation of PCD included PDO, SS, and AO. However, the resonance between climate factors and the PCD was characterized by complexity and diversity. The PCP was mainly affected by AO and SS before the mutation. ENSO had an important influence on both PCD and PCP, but had no significant correlation with mutation occurrence.

Revealing the time lag between slope stability and reservoir water fluctuation from InSAR observations and wavelet tools— a case study in Maoergai Reservoir (China)

ABSTRACT Reservoir water fluctuation in supply and storage cycle have strong triggering effects on landslides on both sides of reservoir banks. Early identification of reservoir landslides and revealing the relationship between slope stability and the triggering factors including reservoir level and rainfall, are of great significance in further protecting nearby residents’ lives and properties. In this paper, based on the small baseline subset time series method (SBAS-InSAR), the potential landslides with active displacements in the river bank of Maoergai hydropower station in Heishui County from 2018 to 2020 were monitored with Sentinel-1 data. As a result, a total of 20 unstable slopes were detected. Subsequently, it was found through a gray correlation analysis that the fluctuation of the reservoir water level is the main triggering factor for the displacement on unstable slopes. This paper applied wavelet tools to quantify the time lag between slope stability and reservoir water fluctuation, revealing that the displacement exhibits a seasonal trend, whose high-frequency signal displacement has an interannual period (1 year). Based on the Cross Wavelet Transform (XWT) analysis, under the interannual scale of one year, the reservoir water fluctuation and nonlinear displacement show a clear common power in wavelet. Additionally, a time lag of 65–120 days between slope stability and reservoir water fluctuations has been found, indicating that the non-linear displacements were behind the water level changes. Among the factors affecting the time lag, the elevation of the points and their distance to the bank shore show Pearson’s correlation coefficients of 0.69 and 0.70, respectively. The observed time lag and correlations could be related to the gradual saturation/drainage processes of the slope and the drainage path. This paper demonstrates the technical support to quantitatively reveal the time lag between slope stability and reservoir water fluctuation by InSAR and wavelet tools, providing strong support for the analysis of the mechanisms of landslides in Maoergai reservoir area.