Mann-Kendall Statistical Test Research Articles

Monitoring and mapping of drought in a semi-arid region: case of the Merguellil watershed, central Tunisia.

Drought is defined as a period of time characterized by below-normal water availability, which may affect crops, animals and the environment. Recently, drought was shown to be more frequent and more intense, implying thereby the need for monitoring and analysis of this natural hazard. The present study aims to examine the spatial extent and temporal variation of droughts in the Merguellil watershed, located in central Tunisia. This contribution was mainly based on the analysis of annual and monthly rainfall time series recorded over the period (1983-2018) in 19 stations spread throughout the study watershed. Rainfall trend was first examined using the Mann-Kendall statistical test. Then, statistical (standard precipitation index (SPI) and Palmer drought severity index (PDSI)), spectral (continuous wavelet transform (CWT)) and mapping (geographical information system (GIS)) techniques were used to identify extreme dry events and to characterize their severity and their spatial and temporal extents. The results obtained revealed the recurrence and frequency of drought conditions in the Merguellil watershed over the study period. Seven drought sequences (1983-1984, 1986-1989, 1992-1995, 1999-2002, 2007-2009, 2013-2015 and 2017-2018), with different levels of severity, were distinguished based on the computed SPI and PDSI values. Spectral analysis of rainfall data also showed the occurrence of significant droughts in recent years. The period starting from 2010 was shown to be marked by recurrent episodes of drought in the Merguellil watershed. Extreme drought events mapping over this period confirmed drought severity at both time and space scales. All of these findings may be helpful for developing programs of water resource management in the study watershed.

Impact of Temperature Changes on Groundwater Levels in Nzoia River Basin, Kenya

Climate change poses uncertainties to the supply and management of water resources under the observed increase in surface temperatures all over Africa. The aim of this study is to assess the impact of temperature changes on groundwater levels in Nzoia River Basin. Temperature and groundwater level variability and trends has been analyzed using the parametric test of Linear regression and the non-parametric Mann-Kendall statistical test. Temperature data was obtained from the Kenya meteorological department (KMD) whereas groundwater level data was collected from Water resources management agency (WRMA). Linear regression of the annual groundwater levels in Nzoia River Basin between 2011 and 2017 revealed a decreasing trend ranging from -0.49 ft/year (Kitale Golf Club) to -0.03 ft/year (Kakamega Tande School). Mann-Kendall statistical test also showed decreasing groundwater levels for all observation wells with the results for Kitale Golf Club and Mois Bridge Quarry observation wells being statistically significant, whereas those for Kapsabet Boys High School, Kakamega Mwikalikha School, Kakamega Tande School and Busia Town Prison were statistically insignificant at 5% significance level. The highest decline in groundwater levels was observed in the upper catchment of the basin.There are significant increases in annual tempratures for Kitale and Kakamega stations in the period 1979 - 2014. Kitale showed annual maximum temprature rising at 0.0006260C/year; annual minimum temperature rising at 0.0011630C/year and the annual mean temprature rising at 0.0008940C/year. Kakamega had annual maximum temprature rising at 0.0007710C/year; annual minimum tempratures rising at 0.0004710C/year and the annual mean tempratures rising at 0.0006230C/year. Eldoret showed falling maximum temprature at - 0.002020C/year; rising minimum temperature at 0.0008130C/year and falling mean temperatures at - 0.001420C/year. The results for Kitale and Eldoret stations showed statistically significant trends whereas those for Kakamega station had a statistically insignificant trend. In Nzoia River Basin, Kitale and Eldoret, annual minimum tempratures are rising faster than the maximum whereas in Kakamega it’s the annual maximum tempratures that are rising faster than the minimum. Kitale and Kakamega stations showed rising annual mean temperatures whereas Eldoret showed falling annual mean tempratures. As one would expect, temperatures in Nzoia River Basin are expected to be rising; however, the case of falling temperatures recorded at Eldoret international airport might occur because this region of Rift valley has highly protected natural resources and a high forest cover is available all the year round. Another possible explanation to this could be the changing cloudness around Eldoret station. Kitale and Kakamega showed annual mean tempratures rising at about 0.10C per century and Eldoret showed mean temperatures falling at about -1.40C per century. The findings for Kitale and Kakamega stations compare well with IPCC Third Assessment Report estimated global warming rate of 0.60C during the twentieth century and other studies from the African continent and East African region.The decreasing trend in groundwater levels in the basin appears to be linked to climate change. Increases in temperature have an impact on the hydrologic cycle because they enhance evaporation of accessible surface water and vegetation transpiration. As a result, these changes have an impact on precipitation volumes, timings, and intensity rates, as well as indirect effects on water flux and storage in surface and subsurface reservoirs. While changes in important long-term climatic factors such as air temperature, precipitation, and evapotranspiration directly affect surface water supplies, the interaction between changing climate variables and groundwater is more intricate and little understood. For efficient and long-term groundwater resource management, understanding long-term temperature variability and trends, as well as the corresponding reaction of groundwater levels, is critical. Despite the fact that groundwater level records are only available for a short period of time, they include essential information that may be utilized to establish strategies for managing the basin's limited groundwater resources.

Effect of Check Dam on Sediment Load Under Vegetation Restoration in the Hekou-Longmen Region of the Yellow River

With years of vegetation restoration and check dam construction on the Loess Plateau, the sediment load of the middle reaches of the Yellow River have decreased sharply; however, the effects of check dam on this decrease of sediment load with such extensive vegetation restoration remains unclear. In order to further clarify the effects of check dam on sediment load reduction under vegetation restoration, we calculated vegetation coverage and check dam index based on multi-source remote sensing data, and calculated sediment reduction rate caused by human activities by Mann-Kendall statistical test and double cumulative curve, then established regression equations incorporating the check dam index and the sediment reduction rate using data from different geomorphic regions with different vegetation coverages. The results showed that sediment load in the Hekou-Longmen region and its 17 tributaries decreased significantly every year, and the change in sediment load could be divided into 3 typical periods: the base period (P1), the period mainly impacted by check dam construction (P2) and the period with comprehensive impact of check dam construction and vegetation restoration (P3). Compared with sediment load of the tributaries during P1, the sediment load decreased by 60.96% during P2 and by 91.76% during P3. Compared with the contribution of human activities to the reduction in sediment load in P2, the contribution of human activities in P3 increased significantly, while that of precipitation decreased slightly. The sediment reduction effect of check dams is greater in basins with low vegetation coverage than in basins with high vegetation coverage. There are differences in sediment reduction effect of vegetation restorations in different geomorphic regions, and the effect of vegetation restoration alone have certain upper limits. Such as, the upper limit of sediment reduction rate of vegetation restoration for rivers flowing through the sandstorm region is 47.86%. Hence, only combined the construction of check dam with vegetation restoration can it achieve more significant sediment reduction benefit and control soil erosion more effectively.

Effect of Rainfall Variability on Groundwater Levels in Nzoia River Basin, Kenya

Groundwater is a critical supply of freshwater in Africa, providing safe drinking water near to where it is needed for homes, agricultural, and industrial uses. Due to the high vulnerability of surface water resources to anthropogenic activities and climate change, groundwater consumption has increased dramatically over the last 50 years and is likely to continue rising in future. This study examines the effect of rainfall variability on groundwater levels using rainfall and groundwater level fluctuation data from Nzoia River Basin, Kenya. A cross-sectional survey design was used. Three counties were randomly selected from the basin for study with Busia representing the lower catchment, Kakamega middle catchment and Trans Nzoia upper catchment. Groundwater is the main drinking water resource in the basin supplying 78.8% of the population. Knowledge of long-term rainfall variability and the associated response of the basin’s groundwater resources are essential for efficient and sustainable groundwater management. Despite the relatively short period for which water level records are available, these records contain valuable information that can be used beneficially in the development of strategies to assist in the responsible management of our limited groundwater resources. Rainfall and groundwater levels were analysed using the parametric test of linear regression and the non-parametric Mann–Kendall statistical test. Rainfal data for this study was obtained from the Kenya Meteorological Department (KMD) and the Groundwater levels from Kenya Water Resources Management Agency (WRMA). Pearson moment correlation was used to check the relationship between monthly rainfall and monthly groundwater levels. Correlations between groundwater levels and rainfall will help assess aquifer vulnerability to climate change in the basin. Our results indicate that rainfall variability induced by climate change has greatly impacted groundwater levels in the basin. Annual groundwater levels have been steadly falling at the rate between 0.03 ft/year (Kakamega Tande School) and 0.49 ft/year (Kitale Golf Club) in the basin. Annual rainfall over the basin has shown stations recording both decreasing and increasing trends. The falling groundwater level trends indicate a cause for concern and provide a scientific basis for the national and county governments in the basin to strategize on ground water development and management for sustainable water use.

Spatio-Temporal Variations of Discharge and Sediment in Rivers Flowing into the Anzali Lagoon

In the last few years, trend identification has become an important issue in hydrological time-series analyses; it is also a difficult task, due to the variety of models and the impact of climate change on the river flow regime. Due to the vital importance of the Anzali Lagoon to the environment of the region, and the threat to its health caused by the volume or amount of inlet sediments, we decided to study the changes in flow and sediment in the rivers flowing into the Anzali Lagoon. For the present study, the long-term monthly, seasonal, and annual sediment and discharge data of seven stations were obtained during the period 1985–2019. According to the available information, the trend of sediment load variation was investigated at different time scales. In this study, the Mann–Kendall statistical test, the double-mass curve, and performance fitting were used to assess the seasonal and annual trends in sediment and river flow. The results showed that at Aghamahale station, the low relationship between discharge and sediment compared with that at other stations was due to the low slope and constant water of the Behmbar River, which caused the sediments to settle and decreased their carrying rate. Moreover, Nokhaleh station had the largest share of sediment transfer to the lagoon during 2002–2012. Sediment details also show that the highest amount of sediment in all stations occurred in non-crop seasons—i.e., from October to January—and was directly dependent on the amount of rainfall in these areas. The results of the sediment analysis also indicate that the discharge and the subsequent sediment loads from upstream to downstream were high over the summer. Furthermore, the rivers downstream demonstrated springtime peaks in the sediment loads and discharge, probably owing to snow melting.

Trend Analysis and Temporal and Spatial Distribution of Wet Bulb Globe Temperature as a Heat Stress Index in Iran during the Summer Season over a 30-year Period

Introduction: Global warming is one of the most important environmental problems that have raised researchers’ attention. The present study aimed to analyze heat stress trends using the Wet Bulb Globe Temperature (WBGT) index in the country of Iran during the summer over a 30-year period. Materials and Methods: Daily summertime statistical data regarding mean temperature and mean relative humidity, taken from 40 synoptic meteorological stations across Iran during a 30-year period were obtained from the Iranian National Meteorological Department. The De Martonne climate classification system was used to categorize various climate regions of Iran. The WBGT index was calculated using the formula given by the Australian Bureau of Meteorology. The Mann-Kendall statistical test and the Sen's slope estimator were used to analyze the trends of the WBGT index. Results: The WBGT index had an upward trend during the three months of June, July, and August in 71.42%, 57.14%, and 66.66% of all stations and this trend was statistically significant in 53.32%, 50%, and 42.85% of those stations, respectively. Moreover, throughout the summer, 45% of the WBGT index measurements were in the medium range (18-23°C), 37.5% were in the high range (23-28°C), and 17.5% were in the very high range (> 28°C). Conclusion: The WBGT index followed an upward trend during the summer, especially in semi-arid regions of Iran. Considering the phenomenon of global warming, it is essential to monitor, plan ahead, and take necessary precaution measures for sensitive populations who are at high risk areas of the country.

Temperature Variability and Trends in Nzoia River Basin, Kenya

Nzoia River Basin is one of the regions that is highly vulnerable to climate change in Kenya. Many attempts have been made to identify and quantify the impact of climate change on socio-economic sectors and ecosystems using global studies resulting into recommendations on policy changes aimed at generating sustainable mitigation and adaptation strategies. This top-down approach by using global studies lacks the required local and regional climate change specificities to address the regional and local climate change challenges. Temperature is one of the most important components of the climatic parameters widely measured as a starting point towards the apprehension of climate change courses. This study aims at filling the top-down approach knowledge gaps in Nzoia River Basin by assessing temperature variability and trends at three stations for the period 1979 to 2014, using Linear regression analysis and Mann-Kendall statistical test. Monthly maximum and minimum temperature data for Kitale, Kakamega and Eldoret stations was obtained from Kenya Meteorological Department, Nairobi, Kenya. The main findings reveal that Kakamega has highest temperatures, followed by Kitale and the lowest temperatures are found at Eldoret. This trend seems to go with altitude as the lowest temperatures are found at highest altitudes and highest temperatures at lowest altitudes. There are significant increases in annual temperatures for Kitale and Kakamega stations, with Kitale showing annual maximum temprature rising at 0.0006260 C/year; annual minimum temperature rising at 0.0011630 C/year and the annual mean temprature rising at 0.0008940 C/year. Kakamega shows annual maximum temperature rising at 0.0007710 C/year; annual minimum temperatures rising at 0.0004710 C/year and the annual mean temperatures rising at 0.0006230 C/year. Eldoret shows falling maximum temperature at - 0.002020 C/year; rising minimum temperature at 0.0008130 C/year and falling mean temperatures at - 0.001420 C/year. The results for Kitale and Eldoret stations show statistically significant trends whereas those for Kakamega station were statistically insignificant. Eldoret annual minimum temperatures are rising faster than the maximum whereas in Kakamega it’s the annual maximum temperatures that are rising faster than the minimum. Kitale and Kakamega show annual mean temperatures rising at about 0.10C per century which compares well with IPCC Third Assessment Report estimated global warming rate of 0.6 0C during the twentieth century and other studies from the African continent and East African region.

Evaluating Trends of Land Productivity Change and Their Causes in the Han River Basin, China: In Support of SDG Indicator 15.3.1

The Han River Basin is a main agricultural production area and a water source for the middle route of the South-to-North Water Diversion Project in China. Over the past 20 years, human exploitation and ecological construction have disturbed the sustainability of land productivity in the Han River Basin. Theil–Sen trend analysis, Mann–Kendall statistical test, and Hurst index methods were applied to examine spatial–temporal trends and sustainability characteristics of land net primary productivity (NPP) change in the Han River Basin from 2001 to 2019 using MOD17A3 NPP product, natural, and socio-economic data obtained from Google Earth Engine (GEE). The findings demonstrated that the interannual variation of land NPP exhibited a fluctuating upward trend, with a more pronounced growth rate from 2001 to 2010 than from 2011 to 2019. The spatial heterogeneity of land NPP was evident, with high values in the west and low values in the east. Of the basin area, 57.82% presented a significant increase in land NPP, while only 0.96% showed a significant decrease. In the future, land NPP in the Han River Basin will present sustained growth. The results were also compared with Trends.Earth’s calculations for the SDG 15.3.1 sub-indicator of land productivity. In addition, the spatial heterogeneity of factors influencing land NPP change was explored using a multiscale geographically weighted regression (MGWR) model. Precipitation and population count were the dominant factors in most regions. Besides, precipitation, population count, and human modification all exhibited inhibitory effects on the increase in land NPP except for elevation. The research can provide a scientific basis for tracking land degradation neutrality (LDN) progress and achieving sustainable socio-ecological development of the Han River Basin.

Analysis of Rainfall Variability and Trends Over Nzoia River Basin, Kenya

Nzoia river is mainly rain fed and the basin is one of the regions that is highly vulnerable to climate change in Kenya. Understanding rainfall variability and trends is important for better water resources management and economic development in the basin. The aim of this study is to assess variability and trends in rainfall at 13 sites within Nzoia River Basin over the period, 1970 to 2001, using the parametric test of Linear regression analysis and the non-parametric Mann–Kendall statistical test. Data for this study was obtained from the Kenya Meteorological Department (KMD). The basin experiences four rainfall seasons in a year as a result of the Inter-Tropical Convergence Zone (ITCZ). There are two rainy seasons and two dry seasons. Annual rainfall through Linear regression analysis shows 6 stations, Kaimosi Tea Estate Ltd, Kakamega Meteorological Station, Bungoma Water Supply, Nzoia Forest Station, Malava Forest Station and Webuye Agricultural Office with declining rainfalls. The remaining 7 stations, Leissa Farm Kitale, Turbo Forest Nursery, Chorlim ADC Farm, Kaptagat Forest Station, Kimilili Agricultural Department, Bunyala Irrigation Scheme and Kadenge Yala Swamp showed increasing rainfalls. The majority of stations with increasing annual rainfall are in the upper catchment whereas those with decreasing rainfall are in the middle and lower catchment. Only 3 out of the 13 stations showed statistically significant trends in rainfall with two in the upper catchment and one in the middle; the remaining 10 stations had statistically insignificant trends. These observed changes in rainfall, although most time series are not convincing as they show predominantly no significance, along with the reported climatic warming in most parts of the basin may have future implications on human health, water resources management, various plant and animal species bio-diversity and the overall economic development of the basin.

Quantification of Natural and Anthropogenic Driving Forces of Vegetation Changes in the Three-River Headwater Region during 1982–2015 Based on Geographical Detector Model

The three-river headwater region (TRHR) supplies the Yangtze, Yellow, and Lantsang rivers, and its ecological environment is fragile, hence it is important to study the surface vegetation cover status of the TRHR to facilitate its ecological conservation. The normalized difference vegetation index (NDVI) can reflect the cover status of surface vegetation. The aims of this study are to quantify the spatial heterogeneity of the NDVI, identify the main driving factors influencing the NDVI, and explore the interaction between these factors. To this end, we used the global inventory modeling and mapping studies (GIMMS)-NDVI data from the TRHR from 1982 to 2015 and included eight natural factors (namely slope, aspect, elevation, soil type, vegetation type, landform type, annual mean temperature, and annual precipitation) and three anthropogenic factors (gross domestic product (GDP), population density, and land use type), which we subjected to linear regression analysis, the Mann-Kendall statistical test, and moving t-test to analyze the spatial and temporal variability of the NDVI in the TRHR over 34 years, using a geographical detector model. Our results showed that the NDVI distribution of the TRHR was high in the southeast and low in the northwest. The change pattern exhibited an increasing trend in the west and north and a decreasing trend in the center and south; overall, the mean NDVI value from 1982 to 2015 has increased. Annual precipitation was the most important factor influencing the NDVI changes in the TRHR, and factors, such as annual mean temperature, vegetation type, and elevation, also explained the vegetation coverage status well. The influence of natural factors was generally stronger than that of anthropogenic factors. The NDVI factors had a synergistic effect, exhibiting mutual enhancement and nonlinear enhancement relationships. The results of this study provide insights into the ecological conservation of the TRHR and the ecological security and development of the middle and lower reaches.

Assessing the impact of climate on annual and seasonal discharges at the Sremska Mitrovica station on the Sava River, Serbia

Abstract Flood frequency analysis was performed on annual maxima series for 90 years (1928–2017) of discharge data recorded at the Sremska Mitrovica gauging station on the Sava River. The three-parameter distributions (PearsonIII, Log-PearsonIII) are more suitable for modelling annual maxima than distribution functions with only two parameters (Normal, Log-normal, Gumbel). The Mann–Kendall test statistic indicated that there is no statistically significant trend identified in annual maximum discharges or average annual discharges. A positive increasing trend was observed in annual temperature, while annual precipitation shows a decreasing trend which is non-significant. The seasonality analysis found a statistically non-significant weak negative trend in discharge in spring, summer and autumn and a statistically non-significant weak positive trend in winter. During winter, spring, and summer a non-significant negative trend in precipitation was observed, while autumn has experienced a statistically significant increasing trend. Temperatures show a positive trend in all seasons, but only temperatures during the warm period show a statistically significant increase. The results demonstrate that decreasing discharges of the Sava River at the Sremska Mitrovica gauging station are mainly the consequence of decreasing precipitation and increasing temperature (increasing evaporation), which is consistent with the results of other studies of the region.

Dinâmica do uso do solo e alterações na vazão na bacia do Rio São Francisco no início do Séc. XXI

The aim of this study is to evaluate the changes occurred in the land use of Sao Francisco river basin (BHRSF), and the their influence in the observed streamflows, for the period from 2001 to 2013. In order to identify changes in land use and land cover, images from MODIS satellite - Moderate Resolution Imaging Spectroradiometer plataform were used through a code developed in the DINAMICA EGO software. The generated product presents informations about the quantification of changes in terms of area and the identification of the type and the location of the occured transitions. To analyze the trend in streamflow behavior, 18 fluviometric stations belonging to the national hydrometeorological network, operated by ANA, were used. The Mann-Kendall statistical test was applied and the results demonstrate some significant land cover transitions such as: Forest to Savanna, Woody to Savanna and Cropland to Savanna. It was also observed in 11 of the 18 fluviometric stations studied, a trend of decreasing streamflow over the years. This result may be explained by the extend irrigation activity performed in the basin. The transitions “Savanna to Cropland” and “Woody to Cropland” are assossiated to high levels of water use, which are responsible for the decrease of water supply in the Sao Francisco river basin, and therefore in the streamflows.

APPLICATION OF PRINCIPAL COMPONENT ANALYSIS TO DROUGHT INDICATORS OF THREE REPRESENTATIVE CROATIAN REGIONS

Drought has become a very frequent hydrological event globally, including in Croatia. It can generally be explained by air temperature and precipitation changes on an annual and seasonal basis, owing to climate change. To contribute to the knowledge on drought phenomena in Croatia, the changes in air temperature and precipitation over a relatively long period between 1951 and 2018 were analyzed. The meteorological stations included in the research were Osijek, Zagreb, and Split, which represented the climate of the entire country. Drought was estimated using the standardized precipitation evapotranspiration index, which is one of the most comprehensive drought indices. Furthermore, the drought severity and duration were calculated using run theory. These parameters were tested for homogeneity using the standard normal homogeneity test. Only the air temperature exhibited inhomogeneity, with a break year in 1991 (Zagreb and Split) and 1998 (Osijek). The existence of significant temporal trends was tested using the non-parametric Mann–Kendall trend test. The probability of drought occurrence with a certain duration and severity was calculated using the copula function. Finally, principal component analysis was applied to the computed standardized Mann–Kendall test statistic (ZMK ) to define the relevance of each parameter change and their combination in drought occurrence on a seasonal basis. Drought occurrence was less recognizable from 1951 to 1991 (1998). In the second sub-period, the impact of an increasing air temperature was the most significant variable, particularly in Zagreb.

The multifractal evaluation of PM2.5-O3 coordinated control capability in China

Under combined air pollution conditions, the coordinated control of PM2.5 and O3 has significant implications for environmental management. To better understand the coordinated control capability of different metropolises, a prime consideration is how to develop a scientific and objective index for quantifying it. In this paper, based on the multifractal detrended cross-correlation analysis (MF-DCCA) method, a novel dynamical index η is developed to quantitatively determine the capability of metropolis to control PM2.5 and O3 pollution cooperatively. Furthermore, combing with the sliding window method, the high temporal scale resolution of η values of four metropolises (Beijing, Shanghai, Guangzhou, and Chengdu) in China is conducted in order to determine the temporal evolution of η. Then, the monthly variation of η values in the four metropolises is investigated. The U-shaped curves in the monthly mean η values indicate that PM2.5-O3 coordinated control capability of the four metropolises is best in summer. The further discussions about the relations between index η and meteorological conditions have been made. Next, long-term trends of η series of four metropolises are studied by Mann–Kendall statistical test. Result shows that the trends of the η series of four metropolises are similar from 2015 to 2017. After 2017, the η series of Beijing, Shanghai and Chengdu show an increasing trend, while that of Guangzhou show a decreasing trend. This suggests that PM2.5-O3 coordinated control capability of Guangzhou is gradually strengthened after 2017. This may be caused by the combined effect of targeted strategies and special climatic conditions in Guangzhou. The η index could directly quantify the two-pollutant coordinated control capability of different metropolises based on field observation, rather than rely on any subjective factor. It can provide guidance for policy-making of pollution emission reduction, so as to control the combined air pollution.

Variability and time series trend analysis of rainfall and temperature in Dramaga Sub-District, Bogor, Indonesia

Human-induced climate change is considered a threat to communities and the natural system, in particular, agriculture, forestry, economy, health, and well-being. Explicitly, extreme weather events namely flood and long dry season, are great concerns for Indonesia and the region. The purpose of this study is to detect rainfall and temperature trends in the Sub-district of Dramaga for the past thirty years. Dramaga has a huge number of populations relying on rain-fed agriculture, to feed themselves and support food security. Monthly rainfall and temperature data were obtained from the Dramaga Climatology Station of Meteorology, Climatology, and Geophysics Agency from the period 1990-2019. A Mann-Kendall statistical test has been run to detect the statistical significance of time series in comparison with the linear model. The findings indicated no significant trends in the seasonal and annual rainfall in the past thirty years, whereas, annual mean and maximum temperature are rising significantly. Further, the annual minimum temperature does not show a statistically significant increase. Climate events owing to the rainfall variation and Temperature rise threaten smallholder farmers. Results of the study might help decision-makers for climate-smart agricultural projects development at the community level. As to be able, to access environmentally sustainable self-sufficient agriculture.

Influence of Meteorological Factors on the Potential Evapotranspiration in Yanhe River Basin, China

Potential evapotranspiration (ET0) is an essential component of the hydrological cycle, and quantitative estimation of the influence of meteorological factors on ET0 can provide a scientific basis for studying the impact mechanisms of climate change. In the present research, the Penman–Monteith method was used to calculate ET0. The Mann–Kendall statistical test with the inverse distance weighting were used to analyze the spatiotemporal characteristics of the sensitivity coefficients and contribution rates of meteorological factors to ET0 to identify the mechanisms underlying changing ET0 rates. The results showed that the average ET0 for the Yanhe River Basin, China from 1978–2017 was 935.92 mm. Save for a single location (Ganquan), ET0 increased over the study period. Generally, the sensitivity coefficients of air temperature (0.08), wind speed at 2 m (0.19), and solar radiation (0.42) were positive, while that of relative humidity was negative (−0.41), although significant spatiotemporal differences were observed. Increasing air temperature and solar radiation contributed 1.09% and 0.55% of the observed rising ET0 rates, respectively; whereas decreasing wind speed contributed −0.63%, and relative humidity accounted for −0.85%. Therefore, it was concluded that the decrease of relative humidity did not cause the observed ET0 increase in the basin. The predominant factor driving increasing ET0 was rising air temperatures, but this too varied significantly by location and time (intra- and interannually). Decreasing wind speed at Ganquan Station decreased ET0 by −9.16%, and was the primary factor underlying the observed, local “evaporation paradox”. Generally, increase in ET0 was driven by air temperature, wind speed and solar radiation, whereas decrease was derived from relative humidity.

Climate change impacts and transformative adaptation strategies among farming households in the City of Koronadal, Philippines

Farmers in the Roxas mountain range, City of Koronadal used to have bountiful harvests during the time when the city was still free from climate-related hazards. However, this situation has recently changed due to the increasing climate-related risk events. Moreover, localized baseline scientific climate information is limited to foster the development of appropriate adaptations and policies toward climate-resilient communities. This study assessed the climate trends and the changes, impacts, and adaptation strategies of farm households in five barangays in the Roxas mountain range, Koronadal City, South Cotabato. The study conducted household surveys with 265 respondents, focus group discussions, and key informant interviews. In using Mann-Kendall test statistics, time series analysis and one-way analysis of variance, the findings from 1981 to 2012 show increasing trends with significant changes (p <0.01) in mean minimum temperature, increasing by 0.74 °C for three decades. In contrast, mean maximum temperature showed a decreasing trend with an average decrease of 0.65 °C, p <0.01). In three decadal periods, an average increase of 0.04 °C in monthly mean temperature was observed. Rainfall patterns during the same period also show significant changes in the months of June (p <0.01), August, and December (p <0.05); these findings suggest that climate change occurred. Floods, landslides, and droughts were experienced by the communities, which had devastating socioeconomic and environmental impacts. The existing adaptation strategies are just stop-gap solutions that address the effects of climate change but do not consider the root causes. To consider future changes in climate patterns, the socioeconomic and political structure and processes of the communities need to change; this can be achieved if multifaceted drivers of climate change hazards and their impacts are appropriately and immediately addressed. Some grassroot-level transformative adaptation strategies identified in the study consist of socioeconomic facets, specifically, investment in children’s education, financial management, family planning, and development of alternative on-farm and nonfarm livelihood options. The environmental aspect, which includes promoting agroforestry system, water impoundment technologies, and advanced early warning system, were also considered.

Climate Change in Rwanda: The Observed Changes in Daily Maximum and Minimum Surface Air Temperatures during 1961–2014

Rwanda has experienced high temperature rising phenomena over the last decades and hence, highly vulnerable to climate change. This paper examined the spatial and temporal variations of daily maximum and minimum surface air temperature (Tmin and Tmax) and diurnal temperature range (DTR). It studied variables at monthly, seasonal and annual time-scales from 1961 to 2014. The study applied various statistical methods such as ordinary least-square fitting, Mann-Kendall, Sen’ slope and Sequential Mann-Kendall statistical test to the new reconstructed ENACTS dataset that cover the period from 1983 to 2014 while pre-1983s recorded data from 24 meteorological stations have been added to complete the lengthiness of ENACTS data. The January to February season did not show a significant trend at seasonal time-scales. The authors decided only to consider March-to-May, June-to-August and October-to-December seasons for further analyses. Topography impacts on temperature classified stations into three regions: region one (R1) (1,000–1,500 m), region two (R2) (1,500–2,000 m) and region three (R3) (≥2,000 m). With high confidence, the results indicate a significant positive trend in both Tmin and Tmax in all three regions during the whole study period. However, the magnitude rate of temperatures change is different in three regions and it varies in seasonal and annual scale. The spatial distributions of Tmax and Tmin represent a siginificant warming trend over the whole country notably since the early 1980s. Surprisingly, Tmin increased at a faster rate than Tmax in R3 (0.27 vs. 0.07°C/decade in March-to-May) and (0.29 vs. 0.04°C/decade in October-to-December), resulting in a significant decrease in the DTR. This is another confirmation of warming in Rwanda. The mutation test application exhibited most of the abrupt changes in the seasonal and annual Tmax and Tmin trends between 1984 and 1990. The present work mainly focus on the spatial and temporal variability of Tmin, Tmax and DTR in Rwanda and their relationship with elevation change, leaving a gap in other potential cause factors explored in the future.

Modeling of relative humidity trends in Iran

Atmospheric humidity has an important role in the energy balance of the planet Earth, and it is effective in explaining climate change and consequently global warming, which has been a major topic of discussion among scientists in recent decades. Therefore, in this study, the maximum and minimum annual and seasonal trends of the relative humidity of 41 synoptic stations in the country during the statistical period of 1960–2019 were evaluated. Three statistical tests were used to analyze the data trend: the Mann–Kendall statistical test, Sen's slope estimator, and linear regression. In the study area, the maximum and minimum annual humidity trends were positive for 83.26% and negative for 17.73% of the stations, respectively. The highest increase in maximum and minimum humidity values, respectively, includes Chabahar in Southeast and Dezful in Southwest stations with a rate of + 2.55 and + 2.10 °C per decade. At the seasonal scale, the highest decrease in maximum and minimum humidity was identified in the summer at Tabas station in the central part of Iran. The highest number of stations with a decreasing or negative trend in the seasonal time series of maximum and minimum humidity occurred in winter with 37 stations. In contrast, most stations had a significant increasing or positive trend in winter and fall. Overall, the results showed that although the rate of decreasing trend of humidity for the study period was higher than the rate of an increasing trend of humidity, there was a similar decreasing trend in the studied variables.

A long-term spatiotemporal analysis of biocrusts across a diverse arid environment: The case of the Israeli-Egyptian sandfield

Spatiotemporal data can be analyzed using spatial, time-series, and machine learning algorithms to extract regional biocrust trends. Analyzing the spatial trends of biocrusts through time, using satellite imagery, may improve the quantification and understanding of their change drivers. The current work strives to develop a unique framework for analyzing spatiotemporal trends of the spectral Crust Index (CI), thus identifying the drivers of the biocrusts' spatial and temporal patterns. To fulfill this goal, CI maps, derived from 31 annual Landsat images, were analyzed by applying advanced statistical and machine learning algorithms. A comprehensive overview of biocrusts' spatiotemporal patterns was achieved using an integrative approach, including a long-term analysis, using the Mann-Kendall (MK) statistical test, and a short-term analysis, using a rolling MK with a window size of five years. Additionally, temporal clustering, using the partition around medoids (PAM) algorithm, was applied to model the spatial multi-annual dynamics of the CI. A Granger Causality test was then applied to quantify the relations between CI dynamics and precipitation. The findings show that 88.7% of pixels experienced a significant negative change, and only 0.5% experienced a significant positive change. A strong association was found in temporal trends among all clusters (0.67 ≤ r ≤ 0.8), signifying a regional effect due to precipitation levels (p < 0.05 for most clusters). The biocrust dynamics were also locally affected by anthropogenic factors (0.58 > CI > 0.64 and 0.64 > CI > 0.71 for strongly and weakly affected regions, respectively). A spatiotemporal analysis of a series of spaceborne images may improve conservation management by evaluating biocrust development in drylands. The suggested framework may also by applied to various disciplines related to quantifying spatial and temporal trends.