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Bangladesh’s 710 km-long coastal region is highly vulnerable, with over 29% of the country’s population residing in coastal districts. While existing literature examines inland precipitation patterns, analysis of precipitation variability along the coastal regions remains limited. This study analyzed annual and seasonal precipitation trends across 17 coastal meteorological stations, covering varying periods from 1948 to 2021, using Sen’s slope estimator and the non-parametric Mann-Kendall test to quantify and identify trends, respectively. The Mann-Kendall test is particularly suitable for this analysis due to its robustness in handling non-normal distributions, fluctuations, and outliers common in precipitation data. It identifies monotonic trends in time series data, which is crucial for determining significant changes in annual or seasonal precipitation patterns. Sen’s slope estimator provides an estimate of the rate of change, which is essential for quantifying precipitation trends in Bangladesh’s coastal regions. The findings showed high interannual and spatial precipitation variability across stations, governed primarily by monsoonal dynamics. Winter precipitation in Satkhira showed a noticeable increase at a rate of 0.47 mm/year (95% C.I: 0.05–0.89), according to a seasonal analysis. Satkhira is influenced by both local and regional climate dynamics, as it is located near the Sundarbans in southwest Bangladesh. In addition to impacting the local climate, the Sundarbans mangrove forest may also modify the microclimate, resulting in higher wintertime humidity and precipitation. Deforestation and other human-caused land use changes in Satkhira have the potential to disrupt local hydrological cycles and boost rainfall in off-peak monsoon seasons like winter. Global warming has caused changes in weather patterns, with certain areas seeing more fluctuation in precipitation, including more rainfall in the winter. Significant positive annual rainfall trends were found at Khulna (5.93 mm/year (95% C.I: 1.45–9.94)), Khepupara (10.88 mm/year (95% C.I: 2.99–18.48)), and Hatiya (12.90 mm/year (95% C.I: 1.29–21.83)), while a significant decreasing trend was observed at Bhola (-9.61 mm/year (95% C.I: 17.90 to -2.87)). Most stations exhibited widespread, non-significant increasing trends over both seasonal and annual timescales. This comprehensive analysis of coastal precipitation patterns could be useful for regional water resource management, agricultural planning, and flood mitigation strategies. The study’s findings could offer key inputs for developing policies and plans to enhance the climate resilience of vulnerable coastal populations in Bangladesh, contributing to more effective disaster risk reduction and climate change adaptation efforts. [ABSTRACT FROM AUTHOR]

The IMD gridded rainfall dataset is used to study the extreme rainfall events in Northeast India. Analysis of data from 1960 to 2019 reveals a significant decrease in extreme rainfall events in the region. However, when verified with the physical indicators of extreme rainfall events such as CAPE and CIN, the trend in CAPE is missing. CAPE and CIN are expected to have high and low values, respectively, during an extreme event. It is indicated that the decrease in trend can partly be explained by trends in low CIN cases (favourable for deep convection). Another observational dataset – Rainfall Estimates on a Gridded Network (REGEN), does not show any trend in the number of extreme rain events. It is suggested that the IMD rainfall dataset shows robustness in depicting extreme rain events consistent with ERA reanalysis. However, REGEN, considered high-quality data for the study of long-term trends, needs to be validated and corrected for erroneous values. [ABSTRACT FROM AUTHOR]

This study presents a comprehensive analysis of rainfall trends in the middle Euphrates region of Iraq, extending from 1980 to 2018, including wide region between the stations of Samawa, Al-Najaf, Karbala, Hilla, and Diwaniya. The research aims to fill a critical gap in the understanding of regional hydrological patterns and provide essential insights for sustainable water resource management. Using advanced statistical methods, such as the MannKendall test and Sen’s slope estimator, along with autocorrelation and cross-correlation analyses, we detected slight trends that were not previously reported. Principally, the Mann-Kendall test indicated no significant overall trends, while Sen’s slope estimator identified slight positive and negative trends at specific stations, highlighting local climatic variations. the findings reveal that all stations, except Samawa, indicated stationarity and homogeneity, with a particularly strong positive mutual correlation between Karbala and Hilla stations (0.7693 at lag 0). This research contributes new insights into rainfall variability in the Middle Euphrates, in Iraq, which presents significant data to improve water resource management strategies and inform future hydrological studies in the region. [ABSTRACT FROM AUTHOR]

Rainfall plays a vital role in the field of agriculture as it affects agricultural production and associated economy. However, the changing trend of rainfall has become a global concern. So the study of changes in the trend of rainfall is necessary. In the present study, an innovative trend analysis method was adopted to assess the changing trend in the state of Nagaland. Data of 40 years was taken for performing the trend analysis using ITA. The entire process of trend change analysis was automated using Python programming. The analysis indicated that out of the 11 stations considered, three stations indicated a rising trend, eight indicated falling trends (annual), four rising and seven falling (monsoon), 0 rising and 11 falling (winter). The extent of trend change varied from –34.5 to 1.1. The spatial distribution of the trend change was also performed. It was observed that the southeast part of Nagaland’s rising trend was more pronounced compared to the southwest. The change was more prominent during the winter season followed by pre-monsoon and monsoon. The trend analysis is important for making appropriate water management decisions, such as water conservation in areas with falling trends and soil conservation in areas affected by rising trends. [ABSTRACT FROM AUTHOR]

Studying rainfall patterns is very important because agricultural production and flood conditions depend on proper water management. Therefore, accurately identifying trends in climate scenarios is essential to achieve this goal. This study, therefore, analyses rainfall trends using the Mann–Kendall test (MKT), modified Mann–Kendall test (MMKT), Spearman rank correlation (SRC), Şen slope estimator (SSE), and innovative trend analysis method (ITAM). This investigation analyses annual, monsoon, autumn, summer, and winter rainfall trends using the most extensive hydrometeorological time series from 1901 to 2020. Five such methods of trend analysis use 120 years of gridded meteorological data from the India Meteorological Department for the neighbouring four river basins Kangsabati, Keliaghai, Silabati, and Dwarkeswer in east India. For the winter period, no significant trend is detected using the MKT, MMKT, SRC, and SSE. While the ITAM detects a significant trend at 88% of grid points of the study area. During other seasons, the MKT, MMKT, SRC and SSE notice trends for 76% of grid points with less significance than the ITAM method. Overall results obtained using the ITAM and MMKT methods are proved to be more effective in detecting sensitive trends. This study can serve as scientific support for the identification and strategic mitigation of climatic change impacts on water management to reduce the risk of climate change. [ABSTRACT FROM AUTHOR]

The main purpose of this paper was to investigate the monthly, seasonal, and annual rainfall variability in the Mae Klong River Basin in Thailand using the Mann–Kendall (MK) test, Sen's slope method, Spearman's Rho (SR) test, and the innovative trend analysis (ITA) method. The monthly rainfall data of eight stations for the period 1971–2015 were used for trend analysis. Datasets with significant serial correlation were corrected by the trend-free pre-whitening (TFPW) approach for statistical methods. The MK test showed increasing rainfall trends for five out of eight stations in the dry season while 50% of stations indicated increasing trends in the wet season. On an annual scale, 75% of the stations exhibited increasing rainfall trends. The results of the SR test were in line with the MK test for seasonal and annual rainfall. The ITA method showed comparable findings with those of the statistical methods. For the entire basin, trend analysis found increasing rainfall on both seasonal and annual scales by all the tests. The findings of this study could benefit water supply and management, drought monitoring, agricultural production activities, and socioeconomic development in the Mae Klong River Basin in the future. HIGHLIGHTS Statistical methods and graphical methods for rainfall trends are compared.; Datasets with significant serial correlation were corrected by the trend-free pre-whitening (TFPW) approach for statistical methods.; The results of Spearman's Rho test were in line with the Mann–Kendall test for seasonal and annual rainfall.; The innovative trend analysis method showed comparable findings with those of the statistical methods.;

The objective of this research is to select the best orbital sensor for rainfall estimates (monthly and annual scales) and to analyze the frequency and magnitude of extreme rainfall events and their trends and disruptions based on the use of satellite rainfall product data for the Cananeia–Iguape Coastal System (CICS). Data from four satellite rainfall products were used to identify the correspondence with seven points on the surface of the study area. Statistical metrics were used to identify the best satellite rainfall product. After identifying the sensor with the best performance in estimating orbital precipitation, extreme events were identified by the Standardized Precipitation Index (SPI) on a one-month (SPI-1), three-month (SPI-3), and twelve-month (SPI-12) scale. Trend and rupture detection in the time series were performed using different statistical techniques (Mann–Kendall, Pettitt, Standard Normal Homogeneity Test, or Buishand test). Among the satellite rainfall products, CHIRPS had the best measurements for the analyzed points on the surface. The year 1983 was characterized as very rainy, also marked by the occurrence of El Niño, and was marked by the rupture of the rains at all points (IDs 1, 2, 3, 4, 5, 6, and 7) analyzed in the month of June. The decrease in monthly rainfall was more significant in the months of February (at points IDs 1, 2, 3, 5, and 7) and April (IDs 1, 3, 5, and 7). Decreased rainfall may cause CICS mangrove shrinkage. These results showed the importance of studying rainfall in an area with mangroves in order to understand the dynamics of vegetation in the face of climate change. [ABSTRACT FROM AUTHOR]

Precipitation is the main component of any hydrological system, and it also controls the availability of water resources of any region. Some recent studies have shown that climatic factors which are controlling the hydrology of any system must be studied on local scale than on a global scale. Although climatic changes are occurring on a global scale, the trends and the effects of climatic changes can vary from place to place. In this paper an attempt is made to carry out the analysis on annual rainfall data of 13 rain gauges in the Punjab (Bahawalpur, Bahawalnagar, Faisalabad, Lahore, Jhelum, Rafique, Sargodha, Murree, Multan, Lahore PBO, Mianwali, Khanpur), to determine the variability of rain for different regions of Punjab. Tends of rainfall have been determined for these regions using the Mann-Kendall Test. To detect these changes in the data the data was subjected to intervention analysis (using cumulative summation / CUSUM method). The mean monthly rainfall data for a period of 40 years was collected from PMD (Pakistan Meteorological Department). From the analysis of the data a significant trend was observed in the rainfall of the Punjab. [ABSTRACT FROM AUTHOR]

Climate variability is recognized as one of the greatest challenges of our world today and is predicted to have further adverse impacts on coffee production. Specific to Africa, Ethiopia is the leading coffee producer on the continent and is ranked 10th in global coffee export rankings; its coffee production is extremely sensitive to climate change, threatening the country's economy. This study was designed to assess the impact of climatic change and variability on smallholder farmers' coffee production in the Mattu district in southwest Ethiopia. Relevant secondary data from the Ethiopian national meteorology agency and the district agricultural office. The study shows the mean annual temperature in the district as 19.67°C over the past 32 years (1990-2022), and an increasing upward trend by a factor of 0.014°C per year and 0.14°C per decade. The mean total rainfall in the Mattu district over the same 32-year period was 1,408.6 mm, and the mean annual rainfall was 117.38 mm. The rainfall temporal pattern shows a decreasing trend over this period by a factor of 24.23 mm annually and 242.3 mm per decade. The district coffee production data confirm that temperature variability significantly affects coffee production at P<0.05. Given these trends, all stakeholders should further build and empower the ongoing community traditional and conventional natural resources management, and the government and related NGOs need to initiate policies, projects, strategies (including transformative new adaptation and mitigation strategies), and incentives that can assist smallholder farmers in sustainable coffee production. [ABSTRACT FROM AUTHOR]

Southeastern South America (SESA) experienced some of the largest positive trends in total and extreme precipitation of the Southern Hemisphere during the last decades. The City of Buenos Aires, the second largest mega‐city of SESA, has been particularly hard‐hit by these trends due to its flat topography and poor natural drainage. Recent studies suggest that these precipitation extremes may exacerbate even further under global warming, but the physical mechanisms responsible for such events have not been documented for this region so far. This study quantifies the relative contributions of dynamics (atmospheric fronts) and thermodynamics (vertical stability) on the observed variations and trends of daily, seasonal and annual precipitation over the City of Buenos Aires between 1981 and 2020 by splitting the precipitation events into convective and stratiform. Results show that the relative contributions from dynamics and thermodynamics depend on the season under consideration: the positive trends in summer precipitation have been favoured by a net increase in vertical instability with a negligible contribution from dynamics, while the increased frequency of fronts in autumn and winter, when no changes in vertical instability have been observed, has contributed to the higher frequency of cold‐season convective events. [ABSTRACT FROM AUTHOR]

Climate change impact drivers includes increased rainfall variability, increased extreme events, and changes in the mean behaviour of hydro-meteorological variables such as air temperature, rainfall, and runoff. As such, this study assessed the potential impacts of climate change on surface water resources in a semi-arid Tugwi Mukosi catchment of Zimbabwe. The Mann-Kendall trend analysis, climate change downscaling and streamflow modelling were used as the primary methodology. The analysis of historical hydro-climatic data trends were performed using the Mann-Kendall trend test and Sen's slope method. Equally, observed daily rainfall was extended using bias-corrected CHIRPS satellite rainfall. Thus, a total of 9 Global Climate Models including (CCCma-CanESM2, Miroc Miroc 5, MOHCHadGEM2, MPI-M-MPI-ESM-LR, and NCCNorESM1-M) on the Sveriges Meteorologiska och Hydrologiska Institut (SMHI) Rosby Centre Atmosphere model version 4 (RCA4) RCM under the Representative Concentrated Pathways 4.5 and 8.5 were extracted and analysed for the 2030s (2021–2050) and 2060s (2051–2080) period. The results show significant (p

Southern Australia's rainfall is highly variable and influenced by factors across scales from synoptic weather to large‐scale circulation and remote climate modes of variability. Anthropogenic climate change and natural variability modulate these factors and their interactions. However, studies often focus on changes in selected parts of the climate system with less emphasis on the system as a whole. As such, it is difficult to gain a complete understanding of how southern Australia's rainfall responds to broad‐scale changes in the climate system. We step through the existing literature on long‐term changes in synoptic‐to‐large‐scale atmospheric circulation and drivers of climate variability to form a more complete story of rainfall changes across southern Australia. This process reveals that the most robust change is the observed winter decline in rainfall as it is consistent with several changing climatic factors: decreasing rainfall from weather systems, strengthening subtropical ridge, poleward shifts in the Hadley Cell and the Southern Annular Mode, and increasing frequency of positive Indian Ocean Dipole events. In other seasons, particularly summer, changes in atmospheric circulation and drivers may not agree with observed rainfall changes, highlighting gaps in our knowledge of atmospheric dynamics and climate change processes. Future work should focus on research across temporal‐ and spatial‐scales, better understanding of jet interactions, the influence of stratospheric processes on the troposphere, and instances of contrasting trends in drivers and southern Australian rainfall changes. This article is categorized under:Paleoclimates and Current Trends > Modern Climate ChangePaleoclimates and Current Trends > Detection and AttributionAssessing Impacts of Climate Change > Observed Impacts of Climate Change [ABSTRACT FROM AUTHOR]

Even though an extensive amount of climate change studies have been carried out in different parts of the world, Sri Lanka is one of the least focused countries in this regard. Climate projections are important and encouraged to manage the futuristic adverse impacts. Identifying this research gap, future rainfall projections were carried out in three provinces in Sri Lanka, i.e. Northwestern (Puttalam and Kurunegala), Western (Katunayake and Colombo), and Southern province (Galle and Hambantota). The Canadian Earth System Model (CanESM2) under the Representation Concentration Pathways (RCP8.5) was downscaled using the Statistical DownScaling Model (SDSM). Non-parametric tests, including Mann-Kendall (MK) test and the Sen’s Slope estimator, were used to determine the significance of trends and magnitude of the slope of the historical trends (1990-2019) and future projected trends (2020-2100). The trends were analyzed for four major seasons in Sri Lanka, including First Inter-monsoon (FIM), Southwest monsoon (SWM), Second Inter-monsoon (SIM), and the Northeast monsoon (NEM). The standard error and model bias at rainfall stations were 0.014-0.034 mm and 1-1.1 respectively, which are acceptable when compared to previous studies. Several significant rainfall trends were identified, including positive trends in the mid-future (2041-2070), and negative trends in the far-future (2071-2100). In addition, rainfall indices, including Rx5day, R20mm, Consecutive dry days (CDD), and Consecutive wet days (CWD) were tested in future projected and historical rainfalls. The results of the present study will be useful for policymakers for decision-making processes in water resources management and agriculture.

Climate variability has significant impact on agricultural production especially in low-income countries where agriculture largely relies on rainfall, but only a few studies explored this issue at local scale. Therefore, this study was conducted to characterize local climate and assess farmers' perceptions and adaptation strategies to climate variability in the rural areas of Dire Dawa administration. Historical rainfall and temperature data (1987–2017) were obtained from National Meteorological Agency (NMA) of Ethiopia, while data of farmers' perceptions and adaptation strategies were collected from a total of 120 household heads through survey questionnaire, key informant interviews and focus group discussions. The results revealed that the area received an average annual rainfall of 568.3 mm with main rainy season (kiremt) contributing 70.7% to annual rainfall. The earliest and latest onset dates of kiremt season were 15th of April and 2nd of August, respectively. The amount of annual and kiremt rainfall totals showed low and medium variability with a coefficient variability (CV) of 18.3% and 27.7%, respectively, whereas short rainy season (belg) rainfall had high variability with a CV of 43.9%. Climate variability perception analysis showed that an overwhelming majority of the respondents (90%) perceived a decrease in the annual rainfall and 91.7% detected an increase in annual average temperature in the study area. Farmers of the study area were well aware of the changes in rainfall and temperature and thus employed a range of adaptation practices. Soil and water conservation practices (100%), off-farm income diversification (63%), planting drought-tolerant varieties (50%) and changing of planting date (45%) were the main adaptation strategies employed in the study area to avert the negative effects of climate variability. The findings imply that the area has been experiencing palpable changes in climate variables during the study period against which farmers exercised multiple adaptation strategies. However, farmers in the area are still face hardship as a result of climate variability which necessitates improving farmers’ resilience through innovative mechanisms and better extension services.

The North-Western Indian Himalayan Region (NW-IHR) is the source of some of India's major rivers and hosts a few of the highest mountain peaks in the world. The region is also a major source of water for the population living in the plains. We study the rainfall trends, magnitudes, and seasonal and annual variations in the NW-IHR during the last 120 years (1901–2020). The temporal trends in rainfall are estimated using the Mann-Kendall Test (MKT) and Modified Mann-Kendall Test (MMKT) at 10%, 5%, and 1% significance levels. The ACC at lag-1 was calculated and tested at 5% significance level for the MMKT. The magnitude of the seasonal and annual rainfall was calculated using Sen's Slope (SS), Simple Linear Regression (SLR), Innovative Trend Analysis (ITA), and Spearman's Rho (SR). The variability of seasonal and annual rainfalls was studied using the Coefficient of Variation (CV). The results of our analysis revealed both increasing and decreasing trends at various levels of significance during seasonal and annual rainfalls. The majority of the districts in the northern region showed increasing trends, while the districts in the southern region showed decreasing trends. CV was higher for seasonal rainfalls as compared to annual rainfalls. Our analysis of trends and variability will help the local stakeholders for efficient planning and understand the risk and vulnerability of the region at large. [ABSTRACT FROM AUTHOR]

Rainfall is one of the dominating climatic parameters that affect water availability. Trend analysis is of paramount significance to understand the behavior of hydrological and climatic variables over a long timescale. The main aim of the present study was to identify trends and analyze existing linkages between rainfall and streamflow in the Nilwala River Basin (NRB) of Southern Sri Lanka. An investigation of the trends, detection of change points and streamflow alteration, and linkage between rainfall and streamflow were carried out using the Mann–Kendall test, Sen's slope test, Pettitt's test, indicators of hydrological alteration (IHA), and Pearson's correlation test. Selected rainfall-related extreme climatic indices, namely, CDD, CWD, PRCPTOT, R25, and Rx5, were calculated using the RClimdex software. Trend analysis of rainfall data and extreme rainfall indices demonstrated few statistically significant trends at the monthly, seasonal, and annual scales, while streamflow data showed non-significant trends, except for December. Pettitt's test showed that Dampahala had a higher number of statistically significant change points among the six rainfall stations. The Pearson coefficient correlation showed a strong-to–very-strong positive relationship between rainfall and streamflow. Generally, both rainfall and streamflow showed non-significant trend patterns in the NRB, suggesting that rainfall had a higher impact on streamflow patterns in the basin. The historical trends of extreme climatic indices suggested that the NRB did not experience extreme climates. The results of the present study will provide valuable information for water resource planning, flood and disaster mitigation, agricultural operations planning, and hydropower generation in the NRB. [ABSTRACT FROM AUTHOR]

Study region: Peninsular Spain. Study focus: There is strong evidence that climate change has produced a decrease in precipitation combined with an increased frequency, intensity and magnitude of high-intensity storm events in Peninsular Spain. Such events have been particularly recurrent on the Spanish Mediterranean coast in recent years. This study’s first objective is to update the trends in the magnitude and seasonality of precipitation in Spain from 1951 to 2019 at different time scales. Secondly, we analyse how extreme precipitation events recorded in recent years can modify water infrastructure design flows. A QGIS plugin was programmed using Python to calculate the design flows in accordance with the methodologies legally established in Spain. New hydrological insights for the region: The results confirm the decreasing trend in annual precipitation in most of the Spanish territory. This decrease was particularly significant during March and June. Moreover, the average increases of around 30–40 % in design flows, which reached double the values obtained in the reference period of 1951–1990 in some basins, confirm the relevance of incorporating high-intensity storm events recorded in recent years into flow calculations in Peninsular Spain. Design flows obtained without using the most recent rainfall data can be underestimated, with profound risk implications. Therefore, there is a sufficient technical basis to draw attention to this problem and recommend its formal consideration by competent authorities.

This paper describes the long-term grid-point rainfall trends in the context of climate change, recent regionalised rainfall decline and drought events for mainland Portugal, which is teleconnected, in most cases, to the trends of mathematical descriptions of the North Atlantic Oscillation (NAO) during the century from October 1919 to September 2019. Grid-point rainfall dataset (1919–2019, from 126 centroids in a regular mesh over the country) have been constructed from high-quality ground-based data and as such, it provides a reliable source for the analysis of rainfall trends at different timescales: October–December, January–March, December–March, and the hydrological year. The Mann–Kendall (MK) coupled with Sen's slope estimator test are applied to quantify the trends. The Sequential Mann–Kendall (SQMK) analysis is implemented to obtain the fluctuation of the progressive trends along the studied 100-year period. Because of their pivotal role in linking and synchronising climate variability, teleconnections to the North Atlantic Ocean are also explored to explain the rainfall trends over the Portuguese continuum. The results provide a solid basis to explain the climate change effects on the Portuguese rainfall based on significant associations with strong negative correlations between changes in rainfall and in NAO indices. These strong opposing correlations are displayed in most of the winter seasons and in the year. After the late 1960s, a generalised rainfall decrease emerges against a background of significant upward trends of the NAO; such coupled behaviour has persisted for decades. Regionalised droughts at three identified climatic regions, based on factor analysis and Standardised Precipitation Index (SPI), are also discussed, concluding that the frequency of severe droughts may increase again, accompanied by a stronger influence of the recently more positive and unusual winter season and annual NAO indices. [ABSTRACT FROM AUTHOR]

Purpose: Food and agriculture are frequently affected from on-going climate change. A significant percentage of annual harvest is lost due to extreme climatic conditions in different parts of the world. Sri Lanka is considered as a country which is vulnerable to climate change. Therefore, this research presents a detailed analysis to find out the non-linear relationships between the rainfall and paddy harvest in two major provinces of Sri Lanka. Research Method: North-central and North-western provinces as two major agricultural areas were selected for the study. Rainfall trends were identified using non-parametric Mann-Kendall and Sen's slope estimator tests. The artificial neural network (ANN) approach was used to establish non-linear relationships between rainfall and paddy yield. Findings: There was no significant (p > 0.05) linear correlation between rainfall amount and the rainfed paddy yield in tested locations. However, no clear relationship between the rainfall and rain fed yield were found in the 14 predefined functions (polynomial, logarithmic, exponential and trigonometric) derived using ANN where the calculated coefficients of determination were less than 0.3. Research Limitations: Due to lack of other climate variables such as temperatures, a significant relationship was not observed in this study. Originality/value: We have shown that non-linear artificial neural network approach can be used to study the impact of climate on agricultural production in Sri Lanka. [ABSTRACT FROM AUTHOR]

Rainfall is one of the dominating climatic parameters that affect water availability. Trend analysis is of paramount significance to understand the behavior of hydrological and climatic variables over a long timescale. The main aim of the present study was to identify trends and analyze existing linkages between rainfall and streamflow in the Nilwala River Basin (NRB) of Southern Sri Lanka. An investigation of the trends, detection of change points and streamflow alteration, and linkage between rainfall and streamflow were carried out using the Mann–Kendall test, Sen’s slope test, Pettitt’s test, indicators of hydrological alteration (IHA), and Pearson’s correlation test. Selected rainfall-related extreme climatic indices, namely, CDD, CWD, PRCPTOT, R25, and Rx5, were calculated using the RClimdex software. Trend analysis of rainfall data and extreme rainfall indices demonstrated few statistically significant trends at the monthly, seasonal, and annual scales, while streamflow data showed non-significant trends, except for December. Pettitt’s test showed that Dampahala had a higher number of statistically significant change points among the six rainfall stations. The Pearson coefficient correlation showed a strong-to–very-strong positive relationship between rainfall and streamflow. Generally, both rainfall and streamflow showed non-significant trend patterns in the NRB, suggesting that rainfall had a higher impact on streamflow patterns in the basin. The historical trends of extreme climatic indices suggested that the NRB did not experience extreme climates. The results of the present study will provide valuable information for water resource planning, flood and disaster mitigation, agricultural operations planning, and hydropower generation in the NRB.

Abstract Building risk models for present‐day climate requires an understanding of recent climate trends. To estimate the climate change driven component of recent rainfall trends in Europe, we introduce a novel methodology for combining trend estimates from observed data, a climate model ensemble and a default trend of zero. The methodology weights the different trend estimates based on their uncertainty and consistency with observations. We find that the methodology puts low weights on the observational estimates of recent rainfall trends because they are so uncertain and puts higher weights on the trends estimated using the climate model ensemble mean and the default trend of zero. This demonstrates the value of ensemble simulations of past climate for this application. The methodology we describe establishes a probabilistic framework for estimating uncertain climate change trends based on combining estimates from observed data and climate models and could be applied in many other situations.

The Indian Summer Monsoon (ISM) characterizes the hydrometeorological variability across the north Indian region and contributes more than 75% of the annual rainfall during the monsoon (June–September) season. In the present study, we analysed the long-term monsoon rainfall for the Ganga River basin to investigate its spatio-temporal variability. A statistically increasing (10 to 17 mm/year; p < 0.05) trend has been observed in ISM rainfall for the mountainous region since 1980, accompanied by increased temperature. We further note that high, very high and extreme rainfall events are also increasing, enhancing the flash flood risk in the mountainous region. In contrast, the ISM rainfall in the alluvial region is observed to be statistically decreasing (−5 to −20 mm/year; p < 0.05) with the combined influence of reduced vegetation. These findings provide valuable insights into the variations in regional hydrology of the Ganga River basin caused by natural and anthropogenic factors. [ABSTRACT FROM AUTHOR]

Building risk models for present‐day climate requires an understanding of recent climate trends. To estimate the climate change driven component of recent rainfall trends in Europe, we introduce a novel methodology for combining trend estimates from observed data, a climate model ensemble and a default trend of zero. The methodology weights the different trend estimates based on their uncertainty and consistency with observations. We find that the methodology puts low weights on the observational estimates of recent rainfall trends because they are so uncertain and puts higher weights on the trends estimated using the climate model ensemble mean and the default trend of zero. This demonstrates the value of ensemble simulations of past climate for this application. The methodology we describe establishes a probabilistic framework for estimating uncertain climate change trends based on combining estimates from observed data and climate models and could be applied in many other situations. [ABSTRACT FROM AUTHOR]

Adequate water resources management includes understanding patterns and spatiotemporal variability of precipitation, as this variable is determinant for ecosystems' stability, food security, and most human activities. Based on satellite estimations validated through ground measurements from 59 meteorological stations, the objective of this study is to evaluate the long-term spatiotemporal variability and trends of the average monthly precipitation in the Magdalena Department, Colombia, for the 1981-2018 period. This heterogeneous region comprises many different ecoregions in its 23,188 km² area. The analysis of spatial variability allowed for the determination of four different subregions based on the differences in the average values of precipitation and the degree of rainfall variability. The trend analysis indicates that the current rainfall patterns contradict previous estimates of a progressive decrease in annual averages due to climate change in the study region, as most of the department does not exhibit statistically significant trends, except for the Sierra Nevada de Santa Marta area, where this study found reductions between 10 mm yr-1 and 30 mm yr-1. The findings of this study also suggest the existence of some links between precipitation patterns with regional phenomena of climate variability and solar activity. [ABSTRACT FROM AUTHOR]

Understanding changes in rainfall at a continental scale can inform adaptation and resilience in all sectors that are sensitive to rainfall. This study examined spatiotemporal changes in annual and seasonal rainfall, extreme and non‐extreme rainfall, and their probability and intensity over the period from 1960 to 2019 across Australia for six rainfall zones (RZs) using high‐quality daily rainfall from 7,593 climate stations. The results revealed statistically significant changes in long‐term rainfall, with an increasing trend in Northern and Central Australia, and a decreasing trend across Southern Australia where the main agricultural areas are located. The cross‐wavelet power spectrum analysis denoted strong coherent resonance cycles in spring–summer or autumn–winter seasons with varying phase differences across six RZs. More specifically, summer rainfall occurred more regularly in the summer‐dominant and arid RZs, whereas winter rainfall became more unreliable in the winter‐ and winter‐dominant RZs. The changes in regional rainfall were characterized by changes in probability and/or intensity of extreme and non‐extreme rainfall revealed through the Spearman Rank correlation. The upward trend in summer‐dominant and arid RZs appears to be a result of increased extreme rainfall intensity and probability, and non‐extreme rainfall probability, especially in summer. The decreased rainfall in the rest of the RZs can be attributed to decreased extreme rainfall probability and non‐extreme rainfall intensity. These results can help decision makers in the agricultural and water resources management sectors identify risks and opportunities, and to devise strategic plans to mitigate droughts through land use planning and construction of infrastructure. [ABSTRACT FROM AUTHOR]

Featured Application: The presented analysis allows one to evaluate the use of rainwater for the purposes of watering plants for indoor cultivation halls potentially located in Wrocław (Poland, Lower Silesia). The aim of this study was to determine the suitability of a rainwater harvesting system to cover the water demand for indoor hydroponic lettuce cultivation located in Wrocław (Poland). The analysis was performed on the basis of the recorded rainfall in Wrocław in 2000–2019. The analyzed cultivation is located in a hall with an area of 300 m2, where the lettuce is grown vertically by the hydroponic method. The calculations of the rainwater harvesting (RWH) system were carried out considering the selection of the tank capacity for the collected water. The operation of the water storage is simulated using a yield after spillage (YAS) algorithm. It was evident that the proposed system might be an auxiliary system that relieves the water supply network or supports other water recovery systems (e.g., the water vapor condensation in a cross-flow heat exchanger operating as an element of the air conditioning system, proposed in Part 1 of this study). The harvesting system for the selected vertical farming indoor hall covers an average of 35.9% of water needs and allows a saving of 146,510 L of water annually for the cultivation. An average water demand coverage increases up to 90.4%, which allows a saving of 340,300 L per year when the RWH system is combined with water recovery from exhaust air from the hall. [ABSTRACT FROM AUTHOR]

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Climate change is supposed to alter not only the mean and variability but also the distribution of rainfall. Changes in rainfall distribution during the monsoon months (June to September) of Bangladesh are evaluated in this study using quantile regression. Monthly rainfall data for the period 1961–2014 recorded at 18 locations distributed over the country were used for this purpose. Distributional changes of monthly rainfall showed downward convergent lines are dominant in peak monsoon months of June, July and August at 28%, 50% and 28% stations, respectively, followed by horizontally divergent lines at 17% of stations during those months. The dominating category of last monsoon month (September) rainfall was found upward divergent lines at 50% stations. The results revealed a decrease in many rainfall quantiles from June to August and increase in September in most of the stations. The increasing trend lines of September rainfall quantiles were found to become more diverse with time, which indicates an increase in rainfall extremes and the possibility of more floods which are already very common in the last month of monsoon in Bangladesh. The decrease in lower quantiles of rainfall in most of the monsoon months may cause an increase in the probability of droughts in the country. The study provided more insight on monsoon rainfall changes and improved understanding of climate change impacts on monsoon rainfall regime which can help in planning climate change adaptations in Bangladesh. [ABSTRACT FROM AUTHOR]

For improving sustainable water resources management and planning at local and regional scales, specifically in small islands, long-term and recent rainfall trends due to temporal shifts in major climatic drivers should be investigated based on dense and long-running ground data series — as done in this research work for the North Atlantic Portuguese island of Madeira (741 km2). Monthly, wet season and annual rainfall trends have been obtained via the non-parametric Mann-Kendall (MK) and the Sen's slope estimator tests for 41 rain gauges spanning from 1940/41 to 2016/17 (77 hydrological years, each starting on October 1). By means of the Sequential Mann-Kendall (SQMK) test, abrupt temporal shifts in the wet season and annual rainfall, and in the North Atlantic Oscillation index (NAOI) series have been identified on the late 1960's — sandwiched by two subperiods with clearly opposite trends. In general, the results suggest considerably and statistically significant decreases in rainfall, exacerbated in recent years at the central region of the island — one of the most important locations in terms of fresh water security. Additionally, this work provides a solid basis to explain the climate change effects on the Madeira rainfall, suggesting that abrupt changes of the island's rainfall variability can be directly linked to those of the North Atlantic Oscillation (NAO) climatic driver based on the strong established teleconnection. These findings are expected to contribute to the improvement of actions towards sustainable water management in the island, and of some other small islands with climatic characteristics influenced as well by large-scale circulation patterns. [ABSTRACT FROM AUTHOR]

Hydropower accounts for 16.4% of world's electricity demand. The key element in hydropower generation is the runoff and this runoff totally depends on the precipitation. However, the future climate is predicted to be debatable and can severely affect the water resources around the world. Therefore, a critical question to answer by the research community is, what would be the impact of climate change/variability on hydropower development? Hence, this paper aims to study the impingement of climate change on hydropower generation for Denawaka Ganga mini-hydropower located in Ratnapura district, Sri Lanka. Multi-year rainfall trend analysis for 30 years along with power generation trend study for 6 years have been carried out to evaluate the performance of the hydropower station under possible shifting precipitation pattern. Mann–Kendall test and Sen's slope estimator tests were used to culminate the trend analysis. Seasonal and monthly trend analysis did not render negative trends (except one rain gauge) in rainfall. However, positive rainfall trends were found in several rain gauging stations for several months. Power generation trend study showcased a decreasing trend in electricity generation for January and November. Nevertheless, the results elucidate that the catchment area is not under an intense threat due to the climate variability. ROR: Run-of-river; SW: Southwestern; NE: Northeastern; SNHT: Standard Normal Homogeneity Test [ABSTRACT FROM AUTHOR]

The rainfall series from the South African Astronomical Observatory in Cape Town, South Africa, is one of the longest known single site instrumental records in the southern hemisphere, spanning over 176 years. Rainfall data are analysed to determine trends and periodicity in the series for annual, seasonal and monthly time scales. Using the Mann Kendall test and Sen's slope, significant negative rainfall trends are recorded for the months of March and October, and for the spring season (from September to November). Using the Mann Kendall and its modified versions to account for serial correlation, as well as a multi‐temporal trend analysis, we demonstrate a positive rainfall trend during the first 60 years (i.e., 1841–1900), which thereafter changes to a long‐term (1900–2016) negative trend, but incorporating a shorter 40 years significant positive trend between 1930 and 1970. We identify cyclic patterns with recorded periods of 9–12 years, 16–30 years and 30–42 years for rainfall, the Southern annular mode (SAM) and Southern oscillation index (SOI). In addition to the notable 9–12 years rainfall cycle that is evidently associated with sunspot cycles, 20–30 years and longer 32–40 years rainfall, solar, SAM and SOI cycles are also identified. [ABSTRACT FROM AUTHOR]

Adequate water resources management includes understanding patterns and spatiotemporal variability of precipitation, as this variable is determinant for ecosystems’ stability, food security, and most human activities. Based on satellite estimations validated through ground measurements from 59 meteorological stations, the objective of this study is to evaluate the long-term spatiotemporal variability and trends of the average monthly precipitation in the Magdalena Department, Colombia, for the 1981–2018 period. This heterogeneous region comprises many different ecoregions in its 23,188 km2 area. The analysis of spatial variability allowed for the determination of four different subregions based on the differences in the average values of precipitation and the degree of rainfall variability. The trend analysis indicates that the current rainfall patterns contradict previous estimates of a progressive decrease in annual averages due to climate change in the study region, as most of the department does not exhibit statistically significant trends, except for the Sierra Nevada de Santa Marta area, where this study found reductions between 10 mm yr−1 and 30 mm yr−1. The findings of this study also suggest the existence of some links between precipitation patterns with regional phenomena of climate variability and solar activity.

Understanding the characteristics of extreme rainfall events is necessary for proper planning and management of urban flooding impacts. In this paper, daily rainfall data from four key weather stations for the period 1967-2017 were used to investigate temporal variability in annual, seasonal, and extreme rainfall in the urban catchments of Dar es Salaam, Tanzania. The Mann- Kendall trend analysis and Sen's slope estimator were used to quantify the magnitudes and significance of long-term trends in rainfall. The frequencies of extreme rainfall events were modelled using the Generalized Pareto model. Results of trend analysis provided evidence of a decrease in total annual rainfall, with the highest decrement being 6.59mm per year. The statistical significance of the decrease in total annual rainfall was inconclusive. Observations of increase in both annual and seasonal maximum rainfall were also made; with the highest increments being 1.01mm and 0.79mm per event, for annual and seasonal maximum rainfall, respectively. The statistical significance of the increase in annual maximum rainfall was certain at 3 out of 4 stations. Frequencies of extreme rainfall events investigated using the R6 threshold provided reasonable results based on actual experience in the study area. Results indicated that most of the pluvial and fluvial flooding are from rainfall events with a 2 to 10-year return period. This is indicative of issues with the drainage systems in the area; either in their designed capacity or the reduction of their water carrying capacity due to anthropogenic factors. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the suitability of a rainwater harvesting system to cover the water demand for indoor hydroponic lettuce cultivation located in Wrocław (Poland). The analysis was performed on the basis of the recorded rainfall in Wrocław in 2000–2019. The analyzed cultivation is located in a hall with an area of 300 m2, where the lettuce is grown vertically by the hydroponic method. The calculations of the rainwater harvesting (RWH) system were carried out considering the selection of the tank capacity for the collected water. The operation of the water storage is simulated using a yield after spillage (YAS) algorithm. It was evident that the proposed system might be an auxiliary system that relieves the water supply network or supports other water recovery systems (e.g., the water vapor condensation in a cross-flow heat exchanger operating as an element of the air conditioning system, proposed in Part 1 of this study). The harvesting system for the selected vertical farming indoor hall covers an average of 35.9% of water needs and allows a saving of 146,510 L of water annually for the cultivation. An average water demand coverage increases up to 90.4%, which allows a saving of 340,300 L per year when the RWH system is combined with water recovery from exhaust air from the hall.

ABSTRACT: Understanding the spatio‐temporal variability of rainfall over mountainous regions such as the Andes is crucial for the maintenance of water resources and ecosystems. This study provided a comprehensive analysis of the signal, statistical significance and spatial pattern of rainfall trends in central and southern Peruvian Andes (CSPA) from 1965 to 2010. Rainfall from 47 quality‐controlled rain gauge stations was examined using the hydrologic calendar year. Total and extreme rainfall indices proposed by the Expert Team on Climate Change Detection and Indices (ETCCDI) were calculated and trends were examined with Mann–Kendall test and Sen's slope estimator. Significant regional patterns of changes in rainfall extremes were investigated and compared with previous studies. Four major regions of stations were identified based on principal component analysis and clustering techniques: Amazon, central Pacific, southern Pacific and Titicaca basins. Statistically significant trend patterns showed that the annual total rainfall has decreased in the Amazon basin, despite the increase in the number of rainy days and some extreme rainfall indices. Decrease in 1‐day and 5‐day yearly maximum rainfall was observed in central Pacific, along with an increase in the number of wet days. Positive trends in indices related to the intensity of very strong daily rainfall were detected in southern Pacific. Titicaca basin showed an increase in the intensity of rainfall extremes. Rainfall variability and trends were evaluated during contrasting El Niño‐Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO). Most stations in the CSPA exhibited positive (negative) anomaly of total and extreme rainfall indices (consecutive dry days) during La Niña (El Niño) years. Positive (negative) anomalies of annual total rainfall and consecutive dry days during positive (negative) PDO occurred in the Amazon, southern Pacific and Titicaca basins, respectively. The ENSO–PDO conditions may have influenced the complex and mostly non‐statistically significant long‐term trends in CSPA. [ABSTRACT FROM AUTHOR]

ABSTRACT: Understanding the effects of climate change and global warming on the South Asian summer monsoon rainfall trend is critically important for millions of inhabitants of this region. This study investigates seasonal (June–September) rainfall trend over the Indo‐Pakistan subcontinent by using 36 climate model outputs from the World Climate Research Programme's Coupled Model Inter‐comparison Project Phase 5. The historical (1951–2005) and future (2006–2100) simulations under two representative concentration pathways (RCPs), RCP4.5 and RCP8.5, are analysed for this purpose. Model reproducibility is evaluated based on spatial correlation of seasonal rainfall and vertically integrated meridional moisture transport between simulated and observed fields. It is found that the majority of models shows reasonable skill in capturing the observed pattern of rainfall climatology and trend over the subcontinent. Our results showed that the models are more skilful in simulating seasonal mean moisture transport than trend over the Arabian Sea and Bay of Bengal. Of the 36 models analysed, only two models HadGEM2‐AO and CNRM‐CM5 closely approximate both the climatology and trend based on statistical performance metrics. Our results suggest that the strengthening of northwards moisture transport over the Arabian Sea and Bay of Bengal is a likely reason for the increasing rainfall trend over Indo‐Pakistan subcontinent in a warmer climate. The RCP8.5 indicates marked increase in both rainfall and moisture transport trends compared to RCP4.5 forcing scenario. [ABSTRACT FROM AUTHOR]

The study of regional rainfall trends over South Asia is critically important for food security and economy, as both these factors largely depend on the availability of water. In this study, South Asian summer monsoon rainfall trends on seasonal and monthly (June-September) time scales have been investigated using three observational data sets. Our analysis identify a dipole-type structure in rainfall trends over the region north of the Indo-Pak subcontinent, with significant increasing trends over the core monsoon region of Pakistan and significant decreasing trends over the central-north India and adjacent areas. The dipole is also evident in monthly rainfall trend analyses, which is more prominent in July and August. We show, in particular, that the strengthening of northward moisture transport over the Arabian Sea is a likely reason for the significant positive trend of rainfall in the core monsoon region of Pakistan. In contrast, over the central-north India region, the rainfall trends are significantly decreasing due to the weakening of northward moisture transport over the Bay of Bengal. The leading empirical orthogonal functions clearly show the strengthening (weakening) patterns of vertically integrated moisture transport over the Arabian Sea (Bay of Bengal) in seasonal and monthly interannual time scales. The regression analysis between the principal components and rainfall confirm the dipole pattern over the region. Our results also suggest that the extra-tropical phenomena could influence the mean monsoon rainfall trends over Pakistan by enhancing the cross-equatorial flow of moisture into the Arabian Sea. [ABSTRACT FROM AUTHOR]

The aim of the present study is to examine the trends in magnitude and intensity of precipitation and peak floods of different magnitudes for seven major river basins in India. Data pertaining to daily flows for about 30-odd years and precipitation for 61 years (from 1951 to 2012) were analysed. Linear trends were calculated for the number of rainy days, rainfall intensity and occurrence of flood peaks for all basins. Using the Sen’s slope estimator, it was found that annual peak rainfall increases for most of the basins in India. From the Mann–Kendall test and Sen’s slope, it was found that the Cauvery and Brahamani and Baitarani basins show a rising trend in the number of rainy days, but the trend was falling for five other basins. When the basins were classified as mountains and plains, it was found that the number of daily rainfall events of different magnitudes was more in the mountains compared to the plains. The rivers which flow from west to east direction have more rainy days compared to those which flow towards the west. It was observed that in general the number of rainy days was falling while the number of intense events was increasing. The number of flood peaks of smaller magnitude in different decades showed slight falling trend. It was also found that there was falling or no trend for severe floods. Anthropogenic activities (construction of storage reservoirs, diversions, urbanization, land-use change, and soil and water conservation measures, etc.) have probably affected the generation of peak floods in the rivers of India. River regulation through storage reservoirs in the past 50 years has resulted in the reduction of peak flows. Hence with the same rainfall, the flood peaks would have increased under virgin conditions. [ABSTRACT FROM AUTHOR]