Mann-Kendall Statistical Test Research Articles

Influence of large scale climate drivers on hydro-climate variability in Dwangwa River Basin, Malawi

Large-scale climate processes such as the Indian Ocean Dipole (IOD) have significant roles in modulating rainfall and hydrological systems. Understanding such processes can inform adaptation measures for climate change and variability, as well as water resource management and planning. This study investigated the impact of the Indian Ocean Dipole (IOD) on rainfall and discharge variability in the Dwangwa River Basin (DRB) in Malawi, a key inflow to Lake Malawi. Specifically, the study analysed annual rainfall variability trends from 1985 to 2015 using the Coefficient of Variation (CV) and the annual Precipitation Concentration Index (PCI). The significance and direction of rainfall and discharge trends were quantified using the Mann–Kendall trend test at α = 0.05 significance level. To evaluate the association between rainfall and IOD, the Pearson product moment used three IOD phases: positive, negative, and neutral. Simple linear regression was utilised to check the response of the river during the concerned IOD phases. The study found CVs between 20 and 30%, typical of climates with moderate monthly rainfall variability. The PCI ranged from 20 to 30%, suggesting a strongly seasonal and highly variable temporal intra-annual rainfall distribution in the DRB. Moreover, the Mann–Kendall test statistics showed insignificant decrease in annual rainfall trends. Further, the findings demonstrated an insignificant negative correlation between rainfall and positive IOD, with rainfall increases associated with negative IOD, whereas positive IOD is associated with decreased river discharge. Consequently, El Niño and a positive IOD could cause DRB to have low water availability. Therefore, the study demonstrates that rainfall is experiencing a decreasing trend, which is driven by large-scale mechanics.

Pengaruh Perubahan Iklim Terhadap Kedalaman dan Intensitas Hujan

Climate change is a natural phenomenon where there is a very extreme change in the value of climatological elements. The increase in floods and droughts is caused by changes in the characteristics of rain that are far below normal. This study aims to determine the trend of climate change resulting in changes in the characteristics of rain, namely the depth and intensity of rain. Assessment of climate change is seen from trend graphs and the Mann-Kendall statistical test to determine significant changes. To determine the effect of climate change on the characteristics, of rainfall, the calculation is divided into 3 periods, namely period 1 (1994-2003), period 2 (2004-2014), and period 3 (2014-2018). Based on the Mann-Kendall statistical test, climate change also occurred as indicated by a significant Z value, namely the Kediri and Sopak climatological stations 4.64 and 6.26, respectively. The pattern of changes in the daily average rain depth and intensity has occurred from 1994 – 2018 where in period 1 in 1994-2003 there was a normal trend, in period 2 in 2004-2013 there was an increasing trend and in period 3 in 2014-2018, there was an extreme increasing trend

Spatiotemporal variability of fall daily maximum flows in southern Quebec (Canada) from 1930 to 2018

AbstractQuebec is experiencing a significant increase in summer and fall temperatures and rainfall. This study compares the spatiotemporal variability of maximum daily flows generated by rainfall during the fall season (September–December) in relation to this climatic change and physiographic and land use factors. Analysis of the spatial variability of these maximum flows measured from 1930 to 2018 in 17 watersheds revealed that the magnitude of flows is approximately twice as low on the north shore as it is on the south shore south of 47° N. This difference is explained by three main factors: wetlands (negative correlation) and agricultural (positive correlation) surface area, and summer–fall total precipitation (positive correlation). As for the temporal variability of flows, the different Mann–Kendall statistical tests showed a significant increase in flows due to increased rainfall. The increase of flows was more widespread on the north shore than on the south because the storage capacity of wetlands and other water bodies does not change over time to store excess rainfall. On the south shore, the increase in flows over time is limited due to the significant reduction in agricultural areas since the modernization of agriculture. This reduction favored infiltration to the detriment of runoff.

Monitoring and evaluation of vegetation restoration in the Ebinur Lake Wetland National Nature Reserve under lockdown protection.

For a long time, human activities have been prohibited in ecologically protected areas in the Ebinur Lake Wetland National Nature Reserve (ELWNNR). The implementation of total closure is one of the main methods for ecological protection. For arid zones, there is a lack of in-depth research on whether this measure contributes to ecological restoration in the reserve. The Normalized Difference Vegetation Index (NDVI) is considered to be the best indicator for ecological monitoring and has a key role to play in assessing the ecological impacts of total closure. In this study, we used Sentinel-2, Landsat-8, and Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing data to select optimal data and utilized Sen slope estimation, Mann-Kendall statistical tests, and the geographical detector model to quantitatively analyze the normalized difference vegetation index (NDVI) dynamics and its driving factors. Results were as follows: (1) The vegetation distribution of the Ebinur Lake Wetland National Nature Reserve (ELWNNR) had obvious spatial heterogeneity, showing low distribution in the middle and high distribution in the surroundings. The correlation coefficients of Landsat-8 and MODIS, Sentinel-2 and MODIS, and Sentinel-2 and Landsat-8 were 0.952, 0.842, and 0.861, respectively. The NDVI calculated from MODIS remote sensing data was higher than the value calculated by Landsat-8 and Sentinel-2 remote sensing images, and Landsat-8 remote sensing data were the most suitable data. (2) NDVI indicated more degraded areas on the whole, but the ecological recovery was obvious in the localized areas where anthropogenic closure was implemented. The ecological environment change was the result of the joint action of man and nature. Man-made intervention will change the local ecological environment, but the overall ecological environment change was still dominated by natural environmental factors. (3) Factors affecting the distribution of NDVI in descending order were as follows: precipitation > evapotranspiration > land use type > elevation > vegetation type > soil type > soil erosion > slope > temperature > slope direction. Precipitation was the main driver of vegetation change in ELWNNR. The synergistic effect of the factors showed two-factor enhancement and nonlinear enhancement, and the combined effect of the driving factors would increase the influence on NDVI.

TIME SERIES ANALYSIS OF LAND USE AND OCCUPATION IN PERMANENT PRESERVATION AREAS OF THE PARAÍBA RIVER BASIN

Any alteration caused in riparian areas of hydrographic basins directly causes changes in the dynamics of rivers, lakes, and urban areas with the elements of energy flow, dissolved material and sediments. Better planning, management and protection of natural resources in the areas of a river basin require knowledge of the characteristics of water courses, vegetation and land use and occupation. The present work aimed to analyze a time series of land use and occupation data in Permanent Preservation Areas (PPAs) of the Paraíba River Basin according to the precepts of the Brazilian Forest Code and with the aid of geoprocessing tools and the Pettitt test and Mann-Kendall test statistics. The Pettitt test showed a temporal change in the average natural cover starting in 2007, with an increase of 4.2%. Based on the Mann-Kendall test, there was a downward trend in the cover of riparian forests in 36.72% of the PPAs, prevailing over PPAs with upward trends, in the period from 1985 to 2006, demonstrating greater degradation of riparian forest areas during this period, mainly in the Middle and Lower Paraíba River regions. For the period from 2006 to 2020, there was a significant change in areas with upward trends in land with natural cover (28.31%), with an increase of 12.5% compared to the previous period.

Spatial-temporal tendencies of the ice regime of the Dnipro Cascade reservoirs

Formulation of the problem. Knowledge about the formation, destruction and trends of the ice regime of rivers and reservoirs is very important for hydropower, shipping, fisheries, etc. There are almost no studies that evaluated the trends, homogeneity and stationarity of the ice regime of the Dnipro Cascade reservoirs. At the same time, such research is relevant especially in the conditions of a changing climate. The objective of this paper is evaluation of spatio-temporal trends of a observation series for the ice regime of the Dnipro Cascade reservoirs based on a complex approach using statistical and graphical methods. Methods. The research used statistical methods, namely the Pearson method for establishing of the trend equation in the time series and the correlation coefficient between variables, and the Mann-Kendell statistical non-parametric test for assessing the statistical significance of the trend. Among the graphic methods, the mass curve and residual mass curve were used to assess the homogeneity and stationarity of observation series, respectively, as well as combined chronological graphs for spatial analysis, determination of synchronous and in-phase (or on the contrary) long-term cyclic fluctuations of the ice regime characteristics at various water gauges. The research was carried out based on the observation data for dates of ice appearance, ice freeze-up, ice break-up (i.e., melt onset), ice disappearance for 35 water gauges. Results. According to the Mann-Kendall statistical test, contradictory results were obtained regarding the stationarity of the observation series of the ice regime of the Dnipro Cascade reservoirs. At the same time, according to graphic analysis, such series turned out to be quasi-homogeneous and quasi-stationary, since they have unfinished phases of increase and decrease of long-term cyclical fluctuations. In turn, the cyclic fluctuations are characterized by synchronous and in-phase, which indicates the same temporal and spatial tendencies of the ice regime of six reservoirs. The dates of the appearance of main phases of the ice regime of the Dnipro Cascade reservoirs are characterized by significant variability. Scientific novelty and practical significance. For the first time, modern knowledge about the ice regime characteristics of the Dnipro Cascade reservoirs were obtained based on the simultaneous application of statistical and graphical methods. In addition, the research results can be used for further research, namely any statistical processing (determination of probabilistic characteristics, search for prognostic dependencies, generalizations, etc.).

Assessing the impact of meteorological and agricultural drought on maize yields to optimize irrigation in Heilongjiang Province, China

Agriculture is an essential support for social and economic development, most affected by weather and climate. Drought in agriculture significantly threatens food security and social stability. Understanding drought variability in the cultivated land can lay the foundation for crop management. Considering the effects of drought on the actual crop growth process and precise irrigation under limited water resources is a great challenge. In this study, standardized precipitation evapotranspiration index (SPEI) and soil water content were used to study the spatiotemporal characteristics of drought during maize growth in Heilongjiang Province. The distributed AquaCrop model and grey theory were used to study the relationship between irrigation, drought and maize yield at the grid scale, and the current irrigation effect was evaluated by simulating the comparison of crop yield under non-irrigation and irrigated conditions, so as to provide a basis for optimizing the irrigation system in the future. In addition, Future drought trends were also examined using the Mann-Kendall statistical test, Sen's slope, and Hurst index. The results are as follows: (1) Based on remote sensing inversion and distributed AquaCrop model, the relationship between drought and crop yield can be studied at grid scale, which can refine the impact of drought on crop yield in different cells. (2) Irrigation can increase crop yields by at least 20–40% in about 60% of areas in the Heilongjiang Province. (3) In response to the drought characteristics of alternating wet and dry variations or consistent wet and dry variations in different months, inter-regional water transfer can be used to cope with or ensure increased irrigation in drought-prone months (May and June) to achieve water conservation and efficiency. Evaluating the current irrigation effect by simulating crop yield under non-irrigated conditions and crop yield under irrigated conditions can provide a better theoretical basis for optimizing irrigation systems, in order to improve regional crop yields and reduce drought risks.

Spatio-Temporal Dynamics and Physico-Hydrological Trends in Rainfall, Runoff and Land Use in Paraíba Watershed

The detection and monitoring of changes in land use and land cover play a crucial role in understanding land degradation and are fundamental to preserving agroecosystems. Their association with hydrological information allows essential responses to changes in hydrological patterns to be identified, contributing to water security in watersheds. Therefore, this study aimed to assess spatio-temporal dynamics and physico-hydrological trends in rainfall, runoff and land use in the Paraíba watershed. The study was conducted in the Paraíba watershed, using land use data and information from pluviometric and fluviometric stations with temporal series of more than 30 years. The Mann-Kendall statistical test was adopted to verify trends. Results indicate annual reduction trends for both native forest area and water bodies in the Paraíba watershed. On the other hand, the area designated for agriculture showed a significant increase. The correlation analysis between water bodies and forests (R² = 0.63) highlights a strong association between the decrease in forest area and the reduction in water availability, influencing the decrease in annual flow. These results serve as a warning to expand water resource management for the region, aiming to preserve and to enhance sustainable use. Therefore, the implementation of conservation measures, monitoring procedures, and adequate management is required to face the challenges imposed by climate change and land use and occupation, ensuring the water availability for the future.

Trend analysis of rainfall from 1981-2022 over Zambia

There is insufficient paucity of information on trends in long-term monthly and decadal rainfall in Zambia. This study assessed the monthly and decadal trends in rainfall over the agro-ecological regions (AERs) and Zambia from 1981 to 2022. The Mann-Kendall test statistic was used at 5 % significant level to compute trends in rainfall at monthly and decadal time step on CHIRPS v2 at 0.05° resolution. R/RStudio Sen's slope estimator was used to give the magnitude of the observed trends. The monthly rainfall time series trend over Zambia ranges from −0.04 to 0.03. The decadal trend analysis of rainfall at annual and monthly time step exhibits a decreasing/increasing trend with Sen's slope between −49.27 and 71.26 mm. Decadal trend at annual time step in AERIII, AERIIa, AERIIa and AERI exhibits a Sen's slope of −44.11 to 62.48 mm, −15.29 to 41.58 mm, −6.08 and 71.26 mm, and 2.20–64.86 mm, respectively. The decadal trend at monthly time step in AERIII, AERIIa, AERIIa and AERI exhibits a Sen's slope of −132.08 to −3.15 mm, −123.39 to −8.57 mm, −73.08 to −15.17 mm, and −80.02 to −5.21 mm, respectively. Decrease in rainfall is expected to affect agriculture, energy, water resources, sanitation and socio-economic aspects. Rainfall pattern shows spatio-temporal variability over Zambia. The results provide valuable input into the National Adaptation Plan and also useful for strategic planning purposes in water resources management under a changing climate. It is evident that spatio-temporal time steps utilized in this study provides new insights of rainfall trends at seasonal, monthly, and annual and decadal time steps.

Mechanistic interaction between climate variables rainfall and temperature on surface water quality and water treatment costs at the Barekese Headworks, Ghana: A time series analysis and water quality index modelling approach

Extreme rainfall and temperatures are climate variables that threaten global water supplies and surface water quality (SWQ). To understand how rainfall and temperature interact with surface water quality and water treatment costs, this study, unlike previous ones, uses time series analysis (TSA) and water quality index (WQI) modelling to fill significant research gaps. The study uses data from the Barekese Water Treatment Headworks, Ghana. Water quality data from 2000 to 2019 for the Barekese Headworks were obtained from the Ghana Water Company Limited. Rainfall data for the catchment was obtained from the Ghana Meteorological Agency. The Mann-Kendall statistical test for trend and the Canadian Council of Ministers of the Environment (CCME) water quality index was applied to data sets. The Mann-Kendall trend test showed no significant change in annual temperatures. An increasing trend for annual rainfall was observed, but this was not statistically significant (Z = 0.21). Sen's slope estimator (Q) showed that rainfall increases at 3.03 mm annually. pH correlated negatively with rainfall (r = - 0.15). Correlations were observed between rainfall and temperature and dissolved oxygen (DO), turbidity, Total Dissolved Solids (TDS), Nitrate (NO3−), Phosphate (PO43−), and Manganese (Mn). Rainfall was observed to increase the cost of liming, coagulation, and disinfection. A 20.26% deterioration in SWQ was observed from 2009 to 2019. The SWQ over the period under study and according to the CCME water quality index was 80% marginal, 10% fair and 10% poor. The findings reveal that the concurrent use of TSA and WQI modelling can help elucidate how rainfall and temperature interact with SWQ and water treatment costs. It further contributes knowledge to attaining the Africa Union Agenda 2063 on climate resilience and the Sustainable Development Goals (SDG) 6 and 6.3 on universal water access and quality improvement.

Investigating river water quality assessment through non‐parametric analysis: A case study of the Godavari River in India

AbstractThis study examines temporal variations in water quality parameters of Godavari River. The present study examines the concentrations of seven water quality parameters, namely total alkalinity (Alk), calcium (Ca), chlorine (Cl), total hardness (Hard), magnesium (Mg), sodium (Na), and sulfate (SO4), using data from 1981 to 2005 at seven gauging stations, that is, Mancherial, Pathagudem, Nowrangpur, Jagdalpur, Perur, Konta, and Polavaram. A seasonal analysis (pre‐monsoon, monsoon, and post‐monsoon) is conducted to detect trends in water quality parameters. The study also uses normality tests Kolmogorov–Smirnov (K–S) and Shapiro–Wilk (S–W) which reveals nonnormal distribution in the majority of datasets, necessitating non‐parametric analysis. The concentration of Alk, Ca, and Hard is found to be significantly increasing trend at some stations during all three seasons, along with significantly decreasing trend found in the concentrations of Cl, Na, and SO4 using non‐parametric Mann–Kendall's (M–K) and Sen's Slope test. The concentration of Mg depicts mixed trends at different stations which may be due the variation in agricultural practices or industrial activities surrounding the stations. It is evident that none of the parameter exhibits statistically significant trends across all seasons at any station. The results of Sen's Slope test are found to be in good agreement with Mann–Kendall's test statistics. A second order auto‐regressive model (AR (2)) is successfully employed to predict and forecast the water quality parameters in present and future time steps. The authenticity of AR (2) model is assessed using Nash‐Sutcliffe (NSE) efficiency and coefficient of determination (R2). The values of NSE and R2 are found to be more than 0.85 for all parameters during all the three seasons, along with, the values of mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). The results of coefficient of variation (CV) and persistence‐criterion (PC) also depicts that AR (2) can successfully be utilized in forecasting the water quality parameters. The research contributes insights for effective water resource management in Godavari River basin.

Deployment of a Deep Learning Automated Cardiac Sub-Structure Contouring Algorithm to Measure Coronary Dose Exposure Trends in Lung Cancer Radiation Therapy

Radiation dose exposure to the left anterior descending coronary artery (LAD) is associated with major adverse cardiac events (MACE) and all-cause mortality (ACM) after radiotherapy (RT) for locally-advanced non-small cell lung cancer (LA-NSCLC). Segmentation of cardiac sub-structures has not been routinely performed historically, and the purpose of the study was to deploy a deep learning (DL)-based automated cardiac sub-structure contouring algorithm to calculate LAD radiation dose exposure and understand temporal trends in a large population. We previously trained a DL algorithm to segment cardiac substructures, using research contours from 560 LA-NSCLC patients (pts), and validated the LAD auto-segmentation algorithm geometrically, dosimetrically and with outcomes. In this study, we deployed the DL algorithm to auto-segment the LAD and heart on planning CT scans in an independent cohort of 1708 consecutive LA-NSCLC pts treated at an academic center and 4 affiliated community centers from 2014-21. We calculated the LAD volume receiving 15 Gy (V15), and mean heart dose (MHD) using the DL contours and the treated plan. We analyzed the distribution of key dosimetric values for heart (LAD V15, MHD) and lung (V20, mean lung dose [MLD], V5) over time and used the Mann-Kendall statistical test for trend. The median prescribed dose was 50 Gy. 63% of patients were treated with intensity-modulated radiation therapy (IMRT) and 37% 3D-conformal (Table), with increasing IMRT use over time. The median lung V20, V5, MLD, and MHD did not significantly change over time (p>0.05). The median LAD V15 decreased significantly (p<0.05 for trend) in 2020-1, with significantly more cases achieving a V15<10% compared to prior years (p<0.05). The LAD distribution shift coincides with the 2020 publication of a study showing an association of V15<10% with decreased risk of MACE/ACM. DL-based auto-segmentation of cardiac substructures can be deployed at large-scale to study trends in dose exposure to newly identified critical organs-at-risk such as the LAD at the population level. Lowering LAD dose did not impact other cardiopulmonary constraints, but further work is needed to study impact on outcomes.

Trend of Changes in Phenological Components of Iran’s Vegetation Using Satellite Observations

Investigating vegetation changes, especially plant phenology, can yield valuable information about global warming and climate change. Time series satellite observations and remote sensing methods offer a great source of information on distinctions and changing aspects of vegetation. The current study aimed to determine the trend and rate of changes in some phenological components of Iran’s vegetation. In this regard, the current study employed the daily NDVI (Normalized Difference Vegetation Index) product of the AVHRR sensor with a spatial resolution of 0.05° × 0.05°, named AVH13C1. Then, using the HANTS algorithm, images of amplitude zero, annual amplitude, and annual phase were prepared annually from 1982 to 2019. Using TIMESAT software, the starting, end, and length of time of growing season were calculated for each pixel time series to prepare annual maps. The Mann–Kendall statistical test was used to investigate the significance of changes during the study period. On average in the entire area of Iran, the annual phase was declining with a trend of −0.6° per year, and the time for the start and end of the season was declining by −0.3 and −0.65 days per year, respectively. Major changes were noticed in the northeast, west, and northwest regions of Iran, where the annual phase declined with a trend of −0.9° per year. Since the annual growth cycle of the plant (equivalent to 356 days) was in the form of a sinusoidal signal, and the angular changes in the sine wave were between zero and 360°, each degree of change was equivalent to 1.01 days per year. Therefore, the reduction in the annual phase by −0.9 degrees almost means a change in the time (due to the earlier negative start phase) of the start of the annual growth signal by −0.9 days per year. The time of the start and end of the growing season declined by −0.6 and −1.33 days per year, respectively. The reduction in annual phase and differences in time of the starting season from 1982 to 2019 indicate the acceleration and earlier initiation of various phenological processes in the area.

Quantification of Mountainous Hydrological Processes in the Aktash River Watershed of Uzbekistan, Central Asia, over the Past Two Decades

Estimation of hydrological processes is critical to water resource management, water supply planning, ecological protection, and climate change impact assessment. Mountains in Central Asia are the major source of water for rivers and agricultural practices. The disturbance of mountain forests in the region has altered the hydrological processes and accelerated soil erosion, mudflow, landslides, and flooding. We used the SWAT (Soil and Water Assessment Tool) model calibrated and validated with remote sensing data to quantify the mountainous hydrological processes in the Aktash River watershed (ARW) of Uzbekistan, Central Asia. Simulations showed that the daily surface runoff and streamflow closely responded to daily precipitation. Groundwater discharge reached its maximum in winter because of snowmelt. The wet months were from July to December, and the dry months were from January to June. The magnitudes of the seasonal hydrological processes were in the following order: fall &gt; summer &gt; winter &gt; spring for precipitation and surface runoff; summer &gt; spring &gt; fall &gt; winter for evapotranspiration (ET); winter &gt; spring &gt; fall &gt; summer for snowmelt; fall &gt; winter &gt; summer &gt; spring for water yield and streamflow; and winter &gt; fall &gt; spring &gt; summer for groundwater discharge. The Mann–Kendall statistical test revealed a significant increasing trend for the annual precipitation (τ = 0.45, p &lt; 0.01) and surface runoff (τ = 0.41, p &lt; 0.02) over the past 17 years from 2003 to 2019. Compared to rangeland, forested land decreased monthly and annual average surface runoff by 20%, and increased monthly and annual average groundwater recharge by about 5%. Agricultural land had much higher unit-area values (mm/km2/y) of ET, groundwater recharge, and water yield than those of urban, forest, and range lands. Our research findings provide useful information to farmers, foresters, and decision makers for better water resource management in the ARW, Central Asia, and other mountain watersheds with similar conditions.

Temporal and Spatial Variations of Potential and Actual Evapotranspiration and the Driving Mechanism over Equatorial Africa Using Satellite and Reanalysis-Based Observation

This study investigated the actual evapotranspiration (AET) and potential evapotranspiration (PET) seasonality, trends, abrupt changes, and driving mechanisms with global sea surface temperature (SST) and atmospheric circulation patterns over Equatorial Africa (EQA) during 1980–2020. The spatiotemporal characteristics of mean ET were computed based on a 40-year average at annual and seasonal scales. The Mann-Kendall statistical test, the Sen slope test, and the Bayesian test were used to analyze trends and detect abrupt changes. The results showed that the mean annual PET (AET) for 1980–2020 was 110 (70) mm. Seasonal mean PET (AET) values were 112 (72) in summer, 110 (85) in autumn, 109 (84) in winter, and 110 (58) in spring. The MK test showed an increasing (decreasing) rate, and the Sen slope identified upward (downward) at a rate of 0.35 (0.05) mm yr−10. The PET and AET abrupt change points were observed to happen in 1995 and 2000. Both dry and wet regions showed observed weak (strong) correlation coefficient values of 0.3 (0.8) between PET/AET and climate factors, but significant spatiotemporal differences existed. Generally, air temperature, soil moisture, and relative humidity best explain ET dynamics rather than precipitation and wind speed. The regional atmospheric circulation patterns are directly linked to ET but vary significantly in space and time. From a policy perspective, these findings may have implications for future water resource management.

Rainfall data classification using Mann-Kendall test statistics associated with Neuro Fuzzy technique: A case study of Chennai district

Climate change, rainfall, weather forecasting is of great concern during the past two decades as scientists and researchers are cautious in building standard numerical models to simulate and forecast the weather parameters in efficient and reliable way. In India, the monsoon is largely responsible for rainfall. India experiences three distinct seasons throughout the year as a result of the monsoon, which originates from the reversal of the predominant wind direction from Southwest to Northeast. Between June and October, the Southwest monsoon, sometimes known as the “wet” season, brings significant rainfall across the majority of the nation. The focus of this research work is to analyse the data of rainfall existed in the past 100 years (1901–2000) and implementing artificial intelligent methods to frame certain classification of algorithm which can forecast the level of rainfall in the future. Data from 1901–2000 of Chennai district has been taken into account for this research. Statistical evaluations are done based on the database and the tabulated results shows the significance of rainfall. Wavelet analysis of multi resolution criteria is obtained to extract the information of heavy rainfall. Mann Kendall (MK) test statistics is utilized for classifying the rainfall data in four levels viz., very-low, low, moderate, high and very high. Trend analysis for the 17 years is tested using Neuro Fuzzy optimisation algorithm. The efficient training of Neuro fuzzy algorithm forecasts the possible trend using the classification analysis of MK test.

Analysis of spatial and temporal trend of hydro-climatic parameters in the Kilombero River Catchment, Tanzania

Inadequate knowledge on actual water availability, have raised social-economic conflicts that necessitate proper water management. This requires a better understanding of spatial–temporal trends of hydro-climatic variables as the main contributor to available water for use by sectors of economy. The study has analysed the trend of hydro-climatic variables viz. precipitation, temperature, evapotranspiration and river discharge. One downstream river gauge station was used for discharge data whereas a total of 9 daily observed and 29 grided satellite stations were used for climate data. Climate Hazards Group InfraRed Precipitation was used for precipitation data and Observational-Reanalysis Hybrid was used for Temperature data. Mann–Kendall Statistical test, Sen’s slope estimator and ArcMap Inverse Distance Weighted Interpolation functionality were employed for temporal, magnitude and spatial trend analysis respectively. Results confirmed that, spatially, there are three main climatic zones in the study area viz. Udzungwa escarpment, Kilombero valley and Mahenge escarpment. On temporal analysis, with exception of the declining potential evapotranspiration trend, all other variables are on increase. This is with catchment rates of 2.08 mm/year, 0.05 °C/year, 0.02 °C/year, 498.6 m3/s/year and − 2.27 mm/year for precipitation, Tmax, Tmin, river discharge and PET respectively. Furthermore, rainfalls start late by a month (November) while temperatures picks earlier by September and October for Tmax and Tmin respectively. Water availability matches farming season. However, it is recommended to improve water resources management practices to limit flow impairment as expansions in sectors of economy are expected. Furthermore, landuse change analysis is recommended to ascertain actual trend and hence future water uptake.

Establishment of Monterrey Pine (Pinus radiata) Plantations and Their Effects on Seasonal Sediment Yield in Central Chile

Sediment production and transport in a basin are generally a function of the degree of soil protection, normally represented by plant cover. In this study, two basins located at similar latitudes but with different hydrological regimens and plant covers were studied, one with a pluvial regimen and forest plantations (Purapel) and another one with the pluvio-nival regimen and native forest (Ñuble). For this purpose, sediment yield was analyzed in both drainage areas using the Mann-Kendall statistical test. Both basins showed larger amounts of sediment production during winter months. In addition, sediment yield trends did not show significant variation in the case of the Ñuble, most likely due to non-relevant changes in plant cover over time. However, there is a sustained decrease in annual sediment release at Purapel, coinciding with the afforestation in the basin, so it is logical to attribute the referred reduction to this process. For the first time, the behavior of two watersheds is contrasted, one covered with native forest and the other one with forest plantations, appreciating that the basin covered with plantations presents a reduction in sediment production over time, which means that forest plantations are efficient in sediment retention, even in contrast to native forest. However, both basins have different types of soil, topography, etc., meaning that more studies are needed to support this theory.

Spatiotemporal patterns of planted forests on the Loess Plateau between 1986 and 2021 based on Landsat NDVI time-series analysis

ABSTRACT Long-term and large scale spatiotemporal patterns of planted forests are essential for evaluating local plantation effectiveness and to promote sustainable afforestation. Satellite remote sensing data provide broad spatial coverage and increasingly long-term and large scale records for spatiotemporal analysis of planted forests. In this study, we developed a dynamic and conversion extraction (DCE) framework by combining the similarity of Landsat normalized difference vegetation index (NDVI) time-series with a change detection algorithm. This framework allows the analysis of spatiotemporal dynamics of planted forests. The proposed framework can be used to extract the planting year and conversion patterns of planted forests by performing the Mann-Kendall (MK) statistic test on the inter-annual Landsat NDVI time-series and measuring the similarity of the intra-annual time-series, respectively. Therefore, it can be applied to addressing the key questions on where, when, and how planted forests conversions have occurred. Took the Loess Plateau in China as an example, we applied the DCE framework to illustrate how planted forests have changed over time and space and have been converted from other land cover types from 1986 to 2021. The producer and user accuracies for the mapping of planted forests were 88.22% and 87.11% in 2021, respectively, and the accuracy of identifying planted forests conversions was 80.25%. The results showed that the newly planted forests experienced four phases: a slow rise from 1986 to 2000, a sharp increase during 2001–2004, a fluctuating pattern from 2005 to 2013, and a reduction until 2020, resulting in a total planted forest area of 7.13 Mha in 2021. In addition, 83.51% of the planted forest pixels underwent conversion from grasslands (58.19% or 4.15 Mha) or croplands (25.32% or 1.81 Mha) during 1986–2021. These results indicate the capability of the DCE framework to capture essential information (planting year and conversion patterns) to support the spatiotemporal analysis of planted forests at large scales.

Simulation of Spatial and Temporal Distribution of Forest Carbon Stocks in Long Time Series—Based on Remote Sensing and Deep Learning

Due to the complexity and difficulty of forest resource ground surveys, remote-sensing-based methods to assess forest resources and effectively plan management measures are particularly important, as they provide effective means to explore changes in forest resources over long time periods. The objective of this study was to monitor the spatiotemporal trends of the wood carbon stocks of the standing forests in the southeastern Xiaoxinganling Mountains by using Landsat remote sensing data collected between 1989 and 2021. Various remote sensing indicators for predicting carbon stocks were constructed based on the Google Earth Engine (GEE) platform. We initially used a multiple linear regression model, a deep neural network model and a convolutional neural network model for exploring the spatiotemporal trends in carbon stocks. Finally, we chose the convolutional neural network model because it provided more robust predictions on the carbon stock on a pixel-by-pixel basis and hence mapping the spatial distribution of this variable. Savitzky–Golay filter smoothing was applied to the predicted annual average carbon stock to observe the overall trend, and a spatial autocorrelation analysis was conducted. Sen’s slope and the Mann–Kendall statistical test were used to monitor the spatial trends of the carbon stocks. It was found that 59.5% of the area showed an increasing trend, while 40.5% of the area showed a decreasing trend over the past 33 years, and the future trend of carbon stock development was plotted by combining the results with the Hurst exponent.