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Changes in trends and variabilities of precipitation, temperature, and evaporation are some of the pronounced effects of climate change. This study assessed the effects of precipitation and temperature trends and variabilities on soil moisture storage. Forty-one years' historical data on precipitation, temperature, and soil moisture were collected for four agrarian local government areas in southern and northern Nigeria. Trend changes and variabilities were analyzed using the Mann Kendall, modified Mann Kendall, Palmer drought severity index, standardized precipitation evaporation index, standardized precipitation index, and temperature indices. Observed precipitation in the northern part experienced very significant decrease and both the southern and northern divides experienced significant temperature increase over the last four decades. However, the temperature indices including hottest days (TXx), summer day (SU35), and diurnal temperature range (DTR) indicated northern Nigeria experienced more extreme temperature increase and thus more drought than southern Nigeria. The northern part had averages TXx, SU35, and DTR of 39.66 °C, 66.49 days, and 17.99 °C, while the southern part had 33.61 °C, 0.075 days, and 9.47 °C, respectively. The continuous increase in temperature and precipitation decrease are attributable to climate change caused by ozone layer depletion. Low precipitation and high temperature resulted to significant reduction of soil moisture storage in northern Nigeria, while soil moisture reductions were limited in southern Nigeria. Effects of temperature and precipitation trends on soil moisture trends therefore emphasize the need for proper water management, continuous soil moisture conservation, and intense irrigation practices, to enhance food production and security. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change is exerting immense pressure on water resources in Iran. This study investigates future precipitation and meteorological droughts across the country considering performances of 41 general circulation models (GCMs). The findings indicate a significant increase in long-term average annual precipitation (LAAP) across Iran with an overall north-to-south increasing gradient, particularly in areas prone to extreme events. However, focusing solely on LAAP is misleading. Projected precipitation reveals substantial inter-annual variability, impacting both the severity and duration of meteorological droughts. For instance, 100-year return period droughts are expected to intensify in severity (The Shared Socio-economic Pathway SSP1-2.6: 4–91%, SSP8-5.5: 46–204%) and duration (SSP1-2.6: 19–76%, SSP8-5.5: 40–127%) across most regions, except the Persian Gulf coastal zone, where droughts may become less severe (SSP1-2.6: 23%, SSP8-5.5: 23%) and shorter in duration (SSP1-2.6: 27%, SSP8-5.5: 10%). Additionally, bivariate frequency analysis suggests that major droughts could become significantly more frequent in the future. [ABSTRACT FROM AUTHOR]

Various indices of climate variability and extremes are extensively employed to characterize potential effects of climate change. Particularly, the semiarid region of Brazil is influenced by adverse effects of these changes, especially in terms of precipitation. In this context, the main objective of the present study was to characterize the regional trends of extreme precipitation indices in the semiarid region of Brazil (SAB), using daily precipitation data from the IMERG V06 product, spanning the period from 1 January 2001 to 31 December 2020. Twelve extreme precipitation indices were considered, which were estimated annually, and their spatial and temporal trends were subsequently analyzed using the nonparametric Mann–Kendall test and Sen's slope. The analysis revealed that the peripheral areas of the SAB, especially in the northwest and extreme south regions, exhibited higher intensity and frequency of extreme precipitation events compared to the central portion of the area. However, a negative trend in event intensity was noted in the north, while positive trends were identified in the south. The frequency of extreme events showed a predominance of negative trends across most of the region, with an increase in consecutive dry days particularly throughout the western SAB. The average total precipitation index was above 1000 mm in the north of the SAB, whereas in the central region, the precipitation averages were predominantly below 600 mm, with rainfall intensity values ranging between 6 and 10 mm/day. Over the span of 20 years, the region underwent an average of 40 consecutive dry days in certain localities. A negative trend was observed in most of the indices, indicating a reduction in precipitation intensity in future decades, with variations in some indices. The dry years observed towards the end of the analyzed period likely contributed to the observed negative trends in the majority of extreme precipitation indices. Such trends directly impact the intensity and frequency of extreme weather events in the SAB. The study is important for highlighting and considering the impacts of changes in precipitation extremes in the semiarid region of Brazil. Based on the obtained results, we advocate the implementation of public policies to address future challenges, such as incorporating adaptations in water resource management, sustainable agricultural practices, and planning for urban and rural areas. [ABSTRACT FROM AUTHOR]

Various indices of climate variability and extremes are extensively employed to characterize potential effects of climate change. Particularly, the semiarid region of Brazil is influenced by adverse effects of these changes, especially in terms of precipitation. In this context, the main objective of the present study was to characterize the regional trends of extreme precipitation indices in the semiarid region of Brazil (SAB), using daily precipitation data from the IMERG V06 product, spanning the period from 1 January 2001 to 31 December 2020. Twelve extreme precipitation indices were considered, which were estimated annually, and their spatial and temporal trends were subsequently analyzed using the nonparametric Mann–Kendall test and Sen’s slope. The analysis revealed that the peripheral areas of the SAB, especially in the northwest and extreme south regions, exhibited higher intensity and frequency of extreme precipitation events compared to the central portion of the area. However, a negative trend in event intensity was noted in the north, while positive trends were identified in the south. The frequency of extreme events showed a predominance of negative trends across most of the region, with an increase in consecutive dry days particularly throughout the western SAB. The average total precipitation index was above 1000 mm in the north of the SAB, whereas in the central region, the precipitation averages were predominantly below 600 mm, with rainfall intensity values ranging between 6 and 10 mm/day. Over the span of 20 years, the region underwent an average of 40 consecutive dry days in certain localities. A negative trend was observed in most of the indices, indicating a reduction in precipitation intensity in future decades, with variations in some indices. The dry years observed towards the end of the analyzed period likely contributed to the observed negative trends in the majority of extreme precipitation indices. Such trends directly impact the intensity and frequency of extreme weather events in the SAB. The study is important for highlighting and considering the impacts of changes in precipitation extremes in the semiarid region of Brazil. Based on the obtained results, we advocate the implementation of public policies to address future challenges, such as incorporating adaptations in water resource management, sustainable agricultural practices, and planning for urban and rural areas.

This paper presents a comprehensive picture of extreme precipitation across Nepal from 1976 to 2015 based on high‐quality daily precipitation observations from 28 precipitation stations spatially distributed across the country. We computed 11 extreme precipitation indices. The selected indices allow the analysis of extreme events (such as flood/inundation) related to heavy precipitation and evaluate the linkage with indications of agricultural production and drought (such as consecutive dry and wet days). Using a simple daily intensity index (SDII) and the number of days with daily precipitation ≥10 mm (R10), along with the total amount of annual precipitation (PRCPTOT), we summarized the distribution of the wet periods of the year at different locations. We divided the study period into two halves (T1: 1976–1995 and T2: 1996–2015) to examine the temporal mean, trends, and their shift. We emphasized the changes in those indices between these two periods across the country. Our results show that although the amount of precipitation (in terms of PRCPTOT and SDII) did not change significantly between the two periods (i.e., T1 and T2), there exists a clearly increasing trend of extreme precipitation indices in T2 compared to T1. In addition, the consecutive dry days show the rising tendency of elongated dry spells that can adversely affect plant/crop growth in the future. Our study recommends the need for effective management of both precipitation extremes (i.e., drier and wetter), for instance, by introducing irrigation facilities and flood mitigation measures. Plain Language Summary: We computed different precipitation indices using daily data from 28 precipitation stations spatially distributed across Nepal to obtain a clear picture of extreme precipitation from 1976 to 2015. We divided the study period into two halves of 20 years each. This study emphasizes the changes in magnitudes and trends of extreme precipitation between the two periods. Overall, we find that heavy precipitation indices increased significantly in the later period. Notably, dry spells (i.e., no rainfall) for consecutive days showed a remarkably increasing trend. Our results indicate the need for mitigation of this extreme precipitation to prevent both floods and drought. Key Points: We divide the study period (1976–2015) into two halves to analyze precipitation patterns and their shiftWe emphasize changes in the precipitation indices across the country between the two periodsWe find a clearly increasing trend of extreme precipitation indices in the recent period when compared to the earlier one [ABSTRACT FROM AUTHOR]

Abstract This paper presents a comprehensive picture of extreme precipitation across Nepal from 1976 to 2015 based on high‐quality daily precipitation observations from 28 precipitation stations spatially distributed across the country. We computed 11 extreme precipitation indices. The selected indices allow the analysis of extreme events (such as flood/inundation) related to heavy precipitation and evaluate the linkage with indications of agricultural production and drought (such as consecutive dry and wet days). Using a simple daily intensity index (SDII) and the number of days with daily precipitation ≥10 mm (R10), along with the total amount of annual precipitation (PRCPTOT), we summarized the distribution of the wet periods of the year at different locations. We divided the study period into two halves (T1: 1976–1995 and T2: 1996–2015) to examine the temporal mean, trends, and their shift. We emphasized the changes in those indices between these two periods across the country. Our results show that although the amount of precipitation (in terms of PRCPTOT and SDII) did not change significantly between the two periods (i.e., T1 and T2), there exists a clearly increasing trend of extreme precipitation indices in T2 compared to T1. In addition, the consecutive dry days show the rising tendency of elongated dry spells that can adversely affect plant/crop growth in the future. Our study recommends the need for effective management of both precipitation extremes (i.e., drier and wetter), for instance, by introducing irrigation facilities and flood mitigation measures.

Accurate accounting of spatiotemporal variability of precipitation is essential for understanding the changing climate. Among the available precipitation estimates, the Global Precipitation Measurement (GPM) is an international satellite network providing advanced global precipitation estimates. The integrated multi-satellite retrievals for GPM (IMERG) algorithm combines information from the GPM satellite constellation to estimate precipitation and yields a better performance in detecting precipitation events and spatial resolution. Here, we used twenty years (2001–2020) of IMERG Final data over the entire Nepal to analyze the spatial and temporal distribution of precipitation. This study evaluates the dynamic characteristics of the precipitation amounts, intensities, frequencies, and other relevant data across Nepal, using four IMERG datasets: (i) microwave only, (ii) infrared only, (iii) multi satellites gauge uncalibrated, and (iv) multi satellites gauge calibrated. A total of 28 precipitation indices was computed: threshold-based counts, consecutive days, precipitation amounts and extremes, precipitation intensity, percentile-based extremities, proportion-based indices, and additional seasonal indices. Results show that all four IMERG datasets are promising in capturing spatial details. The frequency of wet days corresponds with ground-based precipitation. Still, most indices, including consecutive wet days, annual and monsoon precipitation, and days when precipitation equaled or exceeded 20 and 50 mm, were substantially underestimated. In addition, the microwave-only dataset highly underestimated the precipitation amount. Notably, a substantial proportion of false alarms is a problem for all four IMERG datasets. Moreover, our results demonstrate that the IMERG uncalibrated dataset tends to overestimate precipitation during heavy precipitation events. These advantages and shortcomings of IMERG datasets over the rugged terrain of Nepal can provide useful feedback for sensor and algorithm developers to overcome limitations and improve retrieval algorithms. The study findings are helpful to the broader data users and practitioners for effective water decision applications. [ABSTRACT FROM AUTHOR]

Climate change has a wide range of impacts on Pakistan's water resources system, followed by increased magnitude, frequency and intensity of extreme events. Therefore, investigating the variations in extreme climatic events is vital in planning and developing agriculture and water conservation projects. Hence, the present study investigated the spatiotemporal variability of extreme precipitation indices (EPIs) over the Jhelum River basin (JRB) using the Mann-Kendall (MK) and innovative trend analysis (ITA) methods. In addition, variations in the event-based extreme precipitation (EEPs) and time distribution patterns (TDPs) were analyzed because EPIs considered the daily precipitation values only and the combined effect of the extreme events that last for more than 1 day were often neglected. The results show that the fixed and station-related EPIs, i.e., R10, R20, R25, CWD, Prcpt and R95p, exhibited significant decreasing trends at 06, 05, 04, 04, 07 and 05 stations, whereas an increasing trend was evident for consecutive dry days (CDD) at nine stations with a magnitude of 0.33 days/year, indicating a rise in the drought events compared to floods. Moreover, precipitation events with an amount of 40–290 mm, duration of 4–7 days and concentration ratio of 0.7–0.95 were found to be dominant across JRB. Therefore, an increase in the CDD and a decrease in the consecutive wet days (CWD) may change the magnitude, intensity and frequency of extreme precipitation events and consequently pressure the availability of water resources. Therefore, the findings of this study may help understand the precipitation variability over JRB and play a pivotal role in future development projects. [ABSTRACT FROM AUTHOR]

Brazil's semiarid region (SAB) has a heterogeneous precipitation distribution, with the occurrence of periodic droughts and occasional extreme rainfall events. The precipitation monitoring system in this region is insufficient, but remote sensing products can provide information on rainfall in areas with low data coverage. Thus, the main objective of this study was to evaluate 12 extreme precipitation indices calculated using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data in comparison with indices calculated from data measured by rain gauges for different SAB locations. To evaluate the IMERG product, we used rainfall data measured by 56 rain gauges during the period from 1 January 2000 to 31 December 2020. The satellite product was evaluated through juxtaposition between the IMERG and actual rainfall data, by calculating the statistical indices bias, root-mean-squared error, Spearman correlation, and probability density function. The results showed that most of the extreme precipitation indices were well represented by the satellite data, except for the simple precipitation intensity index (SDII), in which case the correlation coefficient was 0.2. This result can be explained as this index is calculated from the exact value of daily precipitation, while the other indices are estimated by rainfall values above some thresholds. On the other hand, total annual precipitation and precipitation above 1 mm presented Spearman correlation reaching 0.97 in some locations. We conclude that the IMERG database is adequate to represent the maximum precipitation in the Brazilian semiarid region, and the extreme precipitation indices had good performance according to the region where the maximum rain occurred. [ABSTRACT FROM AUTHOR]

Since the industrial revolution, the Earth's ambient temperature has been rising at an accelerating pace, partially due to the release of greenhouse gases (e.g., carbon dioxide) into the atmosphere. Consequently, precipitation may become nonstationary, leading to new patterns of watershed hydrology with more frequent and/or severe floods and droughts. Non-stationarity occurs when one or more of precipitation characteristics exhibit step changes, temporal trends, and/or variance deviations. The objective of this study was to detect historical variations in precipitation characteristics across coastal and terrestrial Virginia, USA. Thirteen indices were selected to represent precipitation characteristics for amount, intensity, spell, maximum, and exceedance. A modified Mann-Kendall technique was applied to detect step changes and/or temporal trends in these indices on an annual basis. The results indicated that most rain gauges showed a statistically significant step change in one or more indices between 1948 and 2019, with more step increases than step decreases. This indicates that precipitation across Virginia has exhibited non-stationarity, which must be considered for water management. This phenomenon is likely true for most coastal regions worldwide. [Display omitted] • Identified indices to surrogate precipitation characteristics. • Detected step changes and spatiotemporal trends of these indices. • Showed more intense storms with a longer duration as climate changes. • Revealed the non-stationarity of precipitation in coastal Virginia. [ABSTRACT FROM AUTHOR]

Understanding the precipitation–yield relationships is crucial for evaluating the impacts of climate variability on crop yield. As a north–south transitional zone and major wheat-producing province in China, the Anhui was chosen for this study. Data on county-level yield, daily precipitation, and monthly large-scale atmospheric circulation indices were collected, and then 13 precipitation indices were calculated during different wheat-growing stages. The K-means clustering algorithm, trend detection analysis, Pearson correlation and stepwise regression analysis were used to reflect the variation characteristics of wheat yield and to identify crucial precipitation indices influencing climate-driven wheat yield (CDWY). The main results were as follows: (1) the wheat yield from 74 counties in Anhui was divided into three sub-regions, i.e., southern, central, and northern Anhui. (2) While wheat yield had all increasing trends in three sub-regions, a notable inter-annual fluctuation was shown for CDWY in northern Anhui, which was dominated by the quasi 7-year oscillation. (3) Total precipitation of extreme rainy days in the filling-maturation period posed significant negative impacts on CDWY for southern and northern Anhui. In contrast, the number of light rain during the same period had a critical impact on the central region. Furthermore, these crucial precipitation indices were mainly influenced by different atmospheric circulations. (4) Optimal yield prediction models were constructed based on three predictors as year, crucial precipitation indices, and corresponding atmospheric circulations for each sub-region, which had an average relative error from 8.98% to 12.58%. These findings will provide a scientific basis for understanding yield fluctuations by combining crucial precipitation indices and atmospheric circulations in Anhui. [ABSTRACT FROM AUTHOR]

Climate extreme events affect people whose livelihoods are reliant on the water resources of the Volta Basin in West Africa. Therefore, decision-makers and policymakers need reliable predictions of these extremes on several time scales to develop an Early Warning System (EWS) to combat the devastating impact of extreme climatic events. In that vein, the study evaluated the performance of 41 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in simulating six (6) extreme precipitation events developed by the Expert Team on Climate Change Detection and Indices (ETCCDI), over the Volta Basin for the period 1985–2014. The spatial biases as well as the temporal variations of the indices were analyzed using the simple bias and the Kling-Gupta Efficiency (KGE) respectively. Subsequently, the best models (models with low biases and KGE close to 1) were used to analyze the trends. The modified Mann-Kendall test showed an increasing trend in the observed heavy and very heavy rainy days (R10 mm and R20 mm), very wet and extremely wet days (R95p and R99p) but decreasing trend in the consecutive wet and dry days (CWD and CDD). Generally, models had difficulty reproducing the temporal patterns, however, the MPI-ESM1-2-LR, reproduced well the observed CDD, R10 mm and R20 mm whereas, the IITM-ESM, TaiESM1 and the CMCC-CM2-SR5 reproduced CWDs, R95p and R99p respectively. The Ensemble mean of the models showed robust performance in reproducing the observed R95p and R99p. The future evolution of these extreme poses threats to agriculture, and flood occurrence over the basin.

Climate services provide information on El Niño-Southern Oscillation (ENSO) evolution and predicted seasonal precipitation, broadly used by decision makers from the agriculture sector. However, soil moisture participates in a more complex soil–atmosphere interaction at subseasonal scale. This work aims to identify the ENSO signal on subseasonal precipitation indices and to assess soil moisture response during austral spring–summer over the southern Argentine Pampas. From daily precipitation, 16 indices were analyzed, and the temperature vegetation dryness index (TVDI) was computed as representative of soil moisture availability. In general, the different precipitation indices presented coherence with wetter (drier) climate conditions under the El Niño (La Niña) phase. A strong signal was found for precipitation frequency in November and accumulation in December, whereas reversal and a weak signal was observed during January, crucial for summer crops. The analysis of soil–moisture interaction suggests that positive precipitation anomalies during El Niño can be reinforced by high soil moisture stored in previous months (e.g., during El Niño 2002–2003). The drying process increases in soils with low water retention capacity, producing a spatially heterogeneous impact (e.g., El Niño 2009–2010). The dry pattern expected for La Niña events was observed in 2007–2008, affecting regions with high water retention and productivity. In addition, long wet spells presented a stronger influence in these regions. Owing to the spatial and temporal heterogeneity observed at subseasonal scale, this study suggests the need for the joint analysis of atmospheric variables and soil moisture content for medium-term agricultural planning in the context of ENSO events in the southern Argentine Pampas. [ABSTRACT FROM AUTHOR]

Drought is a natural phenomenon with various social, environmental, and economic effects. Adverse impacts of drought at a regional and global scale have become more evident with climate change. The Seyhan and Ceyhan basins were chosen as study areas in Turkey. Drought analyses were performed using data from 14 meteorological stations gathered between January 1989 and July 2020 in Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI), based on 3-4-6-12 monthly scales. SPI and SPEI are common indices used for drought tracking. The coefficient of determination (R² ) was found to be 0.954 and used to analyze the regression between the annual SPI and SPEI. According to SPI and SPEI annual analyses results of study area, the driest year was found to be 2014. SPEI determined more arid periods than SPI for the same time scales. Analyses reveal that the dry period in basins has increased in the last decade and the region is facing a risk of drought. Analyses were made to predict and evaluate risks and to help the efficient use of agricultural lands and water resources in the study area during dry periods. [ABSTRACT FROM AUTHOR]

A direct outcome of the global climate change is the modification of seasonal precipitation patterns, apparent on a monthly temporal scale. In Central Europe, it includes more frequent high-intensity rainfalls, occurring mostly during spring and summer. These heavy rainfalls induce pluvial floods in urban areas due to a high percentage of impervious surfaces and limited drainage systems. This hazard affects Central European cities and impacts many receptors, including lives, infrastructures, private properties or the functioning of cities. This study focuses on the monitoring of precipitation events with the aim of revealing precipitation patterns across two different types of land cover: urban and suburban/rural areas of Novi Sad (Serbia). Measurements from seven rain gauge stations were used as input data, covering the 2015-2018 period. The precipitation data were analysed using 12 precipitation indices, nine of which were defined by the Expert Team on Climate Change Detection and Indices (ETCCDI), while three additional were designed specifically for this study. Based on their locations, the stations were classified into the 'urbanized' and 'non-urbanised' group. The concept of the Local Climate Zone (LCZ) was used to classify urbanised areas LCZ1-8, non-urbanised areas LCZ9 and the land cover zones (LCZ A-G). A statistical analysis based on the ANOVA F-test was conducted, defining the significance threshold at 90% for a = 0.01 and at 95% for a = 0.05. The obtained results revealed one relation for the CDD index at 95% and two relations at 90% for CDD and Rp95 indices, showing a relation between heavy rainfall and the type of land cover. Statistical results underline the need for a denser station network and longer monitoring periods in order to proceed to stronger statistical tests and make it possible to establish other relations with the climate indices.

Accurate accounting of spatiotemporal variability of precipitation is essential for understanding the changing climate. Among the available precipitation estimates, the Global Precipitation Measurement (GPM) is an international satellite network providing advanced global precipitation estimates. The integrated multi-satellite retrievals for GPM (IMERG) algorithm combines information from the GPM satellite constellation to estimate precipitation and yields a better performance in detecting precipitation events and spatial resolution. Here, we used twenty years (2001–2020) of IMERG Final data over the entire Nepal to analyze the spatial and temporal distribution of precipitation. This study evaluates the dynamic characteristics of the precipitation amounts, intensities, frequencies, and other relevant data across Nepal, using four IMERG datasets: (i) microwave only, (ii) infrared only, (iii) multi satellites gauge uncalibrated, and (iv) multi satellites gauge calibrated. A total of 28 precipitation indices was computed: threshold-based counts, consecutive days, precipitation amounts and extremes, precipitation intensity, percentile-based extremities, proportion-based indices, and additional seasonal indices. Results show that all four IMERG datasets are promising in capturing spatial details. The frequency of wet days corresponds with ground-based precipitation. Still, most indices, including consecutive wet days, annual and monsoon precipitation, and days when precipitation equaled or exceeded 20 and 50 mm, were substantially underestimated. In addition, the microwave-only dataset highly underestimated the precipitation amount. Notably, a substantial proportion of false alarms is a problem for all four IMERG datasets. Moreover, our results demonstrate that the IMERG uncalibrated dataset tends to overestimate precipitation during heavy precipitation events. These advantages and shortcomings of IMERG datasets over the rugged terrain of Nepal can provide useful feedback for sensor and algorithm developers to overcome limitations and improve retrieval algorithms. The study findings are helpful to the broader data users and practitioners for effective water decision applications.

Global warming is already having a negative impact on vital sectors on which human development depends, such as water resource availability. In this study, the changes and abrupt change timing of climatic extreme indices, aridity and drought over the Region of South Aegean are captured using the Mann–Kendall and Pettitt tests, while the latter variables are correlated with the water volume transported by ships to the region as well as the relevant costs. The region's climate is shifting to warmer conditions with less precipitation, since significantly positive trends were noted with regard to the number of tropical nights, warm nights, warm days, the warm spell duration index and the diurnal temperature range; significant negative trends were observed in relation to the number of cool nights, cool days and the cold spell duration index, with the change-point year for the latter variables being 2006. Inaddition, 7/11 precipitation related indices exhibited a downward trend, while significantly negative trends were observed with regard to the number of consecutive dry days, with the timing of the abrupt change being 2001. The Aridity Index (AI) reveals that the region's climate characterization is changing from dry and sub-humid to semi-arid conditions, whilst the Reconnaissance Drought Index standardized (RDIst) and the Standardized Precipitation Index (SPI) indices suggests an amplification of drought phenomena over the Region. The tourism variables illustrated a significant positive trend, with the timing of the abrupt change being registered during 2006–2009, whilst the correlation analysis between tourism variables and water transfers implies that the surge on water transfer by ships to the Region occurred between 1998 and 2008. This can be mainly attributed to the changes in climate patterns. The correlation analysis documents a strong positive correlation between the water transfer dataset and the diurnal temperature range, and a moderately negative association with the precipitation related indices, annual precipitation, drought phenomena and aridity with 7/11. [ABSTRACT FROM AUTHOR]

Brazil’s semiarid region (SAB) has a heterogeneous precipitation distribution, with the occurrence of periodic droughts and occasional extreme rainfall events. The precipitation monitoring system in this region is insufficient, but remote sensing products can provide information on rainfall in areas with low data coverage. Thus, the main objective of this study was to evaluate 12 extreme precipitation indices calculated using Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data in comparison with indices calculated from data measured by rain gauges for different SAB locations. To evaluate the IMERG product, we used rainfall data measured by 56 rain gauges during the period from 1 January 2000 to 31 December 2020. The satellite product was evaluated through juxtaposition between the IMERG and actual rainfall data, by calculating the statistical indices bias, root-mean-squared error, Spearman correlation, and probability density function. The results showed that most of the extreme precipitation indices were well represented by the satellite data, except for the simple precipitation intensity index (SDII), in which case the correlation coefficient was 0.2. This result can be explained as this index is calculated from the exact value of daily precipitation, while the other indices are estimated by rainfall values above some thresholds. On the other hand, total annual precipitation and precipitation above 1 mm presented Spearman correlation reaching 0.97 in some locations. We conclude that the IMERG database is adequate to represent the maximum precipitation in the Brazilian semiarid region, and the extreme precipitation indices had good performance according to the region where the maximum rain occurred.

In this study, an analysis of present day climate simulation (1998–2008) is presented for the Central African (CA) region with the COnsortium for Small‐scale MOdelling in CLimate Mode (CCLM) regional climate model, forced by the ERA‐Interim (ERAINT) reanalysis data. The ability of the CCLM to simulate the observed precipitation with particular focus on the mean spatial pattern, low‐level circulation, seasonal cycles, and daily characteristics is evaluated. Likewise, the added value of the regional model CCLM compared to the driving ERAINT reanalysis is also investigated. It is shown that ERAINT and CCLM exhibit quite different sign of bias, which is an indication of the importance of internal variability and fine scale processes representation for the simulation of surface climate. Despite the CCLM is constantly dry over southern CA, the model succeeds to reproduce reasonably the mean spatial patterns of precipitation and low‐level circulation features, along with the associated seasonal cycles over the whole CA and majority of the five selected analysis sub‐regions. Results also show that daily precipitation indices are well represented, although the better performance greatly depends on the considered seasons. Nevertheless, CCLM substantially outperforms the ERAINT daily precipitation characteristics, thus highlighting the added value of the downscaling exercise over the region. The analysis of daily precipitation indices also reveals that the dry character of the model could probably be connected to the underestimation of the simulated less intense events, which in turn result to an overestimation of the simulated dry spell duration. [ABSTRACT FROM AUTHOR]

Spatial and temporal patterns of rainfall are governed by complex interactions between climate and landscape perturbations including deforestation, fire, and drought. Previous research demonstrated that rainfall in portions of the Amazon Basin has intensified, resulting in more extreme droughts and floods. The basin has global impacts on climate and hydrologic cycles; thus, it is critical to understand how precipitation patterns and intensity are changing. Due to insufficient precipitation gauges, we analyzed the variability and seasonality of rainfall over the Amazon Basin from 1982 to 2018 using high-resolution gridded precipitation products. We developed several precipitation indices and analyzed their trends using the Mann–Kendall test (Mann 1945; Kendall, 1975) to identify significant changes in rainfall patterns over time and space. Our results show landscape scale changes in the timing and intensity of rainfall events. Specifically, wet areas of the western Basin have become significantly wetter since 1982, with an increase of 182 mm of rainfall per year. In the eastern and southern regions, where deforestation is widespread, a significant drying trend is evident. Additionally, local alterations to precipitation patterns were also observed. For example, the Tocantins region has had a significant increase in the number of dry days during both wet and dry seasons, increasing by about 1 day per year. Surprisingly, changes in rainfall amount and number of dry days do not consistently align. Broadly, over this 37-year period, wet areas are trending wetter and dry areas are trending drier, while spatial anomalies show structure at the scale of hundreds of kilometers. [ABSTRACT FROM AUTHOR]

In the context of global climate change, it is important to monitor abnormal changes in extreme precipitation events that lead to frequent floods. This research used precipitation indices to describe variations in extreme precipitation and analyzed the characteristics of extreme precipitation in four climatic (arid, semi-arid, semi-humid and humid) regions across China. The equidistant cumulative distribution function (EDCDF) method was used to downscale and bias-correct daily precipitation in eight Coupled Model Intercomparison Project Phase 5 (CMIP5) general circulation models (GCMs). From 1961 to 2005, the humid region had stronger and longer extreme precipitation compared with the other regions. In the future, the projected extreme precipitation is mainly concentrated in summer, and there will be large areas with substantial changes in maximum consecutive 5-day precipitation (Rx5) and precipitation intensity (SDII). The greatest differences between two scenarios (RCP4.5 and RCP8.5) are in semi-arid and semi-humid areas for summer precipitation anomalies. However, the area of the four regions with an increasing trend of extreme precipitation is larger under the RCP8.5 scenario than that under the RCP4.5 scenario. The increasing trend of extreme precipitation in the future is relatively pronounced, especially in humid areas, implying a potential heightened flood risk in these areas.

This study focuses on the long-term (2011 ~ 2100) changes in precipitation indices under different climate change scenarios in Subansiri river basin, a tributary of Brahmaputra River in Northeast India. The fifth phase coupled model intercomparison project (CMIP5) data for different representative concentration pathways (RCPs) scenarios as suggested by Intergovernmental Panel on Climate Change (IPCC) is used for the study. Statistical downscaling model (SDSM) is used to downscale the precipitation. Three global climate model (GCM) datasets from earth system model and geophysical fluid dynamics laboratory (namely ESM2G, ESM2M, and GFDL-CM3) with four RCP scenarios (namely RCP2.6, RCP4.5, RCP6.0, and RCP8.5) were used for generating daily precipitation time series for a time period of 2011 to 2100. Precipitation based indices were computed on a multi-decadal time scale to detect the changes in precipitation pattern and its distribution. Total five precipitation stations were selected for downscaling and precipitation indices analysis. The non-parametric Mann-Kendall test method was used for the trend analysis of the precipitation indices. Indices based analysis of the precipitation shows an increase in the intensity of precipitation whereas a decrease in number of precipitation days on the lower part of the basin. The increase in number of rainy days followed by a decrease in intensity in the upper station was observed. Longterm (2011 ~ 2100) future projection of precipitation for annual precipitation shows statistically significant increase of minimum 1.8% at station 1 (for RCP2.6) to maximum 11% in station 3 (for RCP8.5) as compared to observed period annual precipitation. [ABSTRACT FROM AUTHOR]

Regionalization on the basis of the properties of hydro-meteorological data helps in identifying the regions reflecting the similar characteristics which could be useful in designing hydrological structures as well as planning and management of water resources of the region. In this study, rainfall data of northeast India were utilized for calculation of extreme precipitation indices as suggested by expert team on climate change detection and monitoring. Trend analysis of the indices was carried out using Mann–Kendall nonparametric test, and Sen's slope estimator was used for calculating the magnitude of trend. Further fuzzy c-means method was used for clustering of the selected stations on the basis of six parameters of all these precipitation indices, i.e., latitude, longitude, mean, standard deviation, minimum value and maximum value. Three cluster validity indices, namely fuzzy performance index, modified partition entropy and cluster separation index were used for selecting the optimum cluster numbers. Analysis shows insignificant trend for the indices like consecutive dry days and consecutive wet days, whereas maximum 1-day precipitation (R1 day) and maximum 5-day precipitation (R5 day) are not showing any clear trend. It is observed that the number of rainy days is decreasing followed by increasing 1-day precipitation. Cluster analysis of the precipitation indices shows five major clusters for most of the indices. [ABSTRACT FROM AUTHOR]

This study presented the spatio-temporal characteristics of extreme precipitation events in the Northern Highlands of Pakistan (NHPK). Daily precipitation observations of 30 in situ meteorological stations from 1961 to 2014 were used to estimate the 11 extreme precipitation indices. Additionally, trends in time distribution patterns (TDPs) and return periods were also investigated for event based extreme precipitations (EEP). Results found that the precipitation events with an amount of 160–320 mm and with a concentration ratio of 0.8–1.0 and a duration of 4–7 consecutive days were dominant. The frequency of heavy, very heavy and extremely heavy precipitation days decreased, whereas the frequency of wet, very wet and extremely wet days increased. Most of the indices, generally, showed an increasing trend from the northeast to middle parts. The extreme precipitation events of the 20 and 50-year return period were more common in the western and central areas of NHPK. Moreover, the 20 and 50-year return levels depicted higher values (up to 420 mm) for an event duration with all daily precipitation extremes dispersed in the first half (TDP1) in the Chitral, Panjkora and Jhelum Rivers basins, whilst the maximum values (up to 700 mm) for an event duration with all daily precipitation extremes dispersed in the second half (TDP2) were observed in the eastern part of the NHPK for 20-year and eastern and south-west for 50-year, respectively.

This paper examines changes in rainfall in New South Wales (NSW), Australia using daily rainfall data from 200 rainfall stations covering the period of 1945–2014. The Mann–Kendall test is applied to identify trends in the selected rainfall indices, while the Pettitt change point test is employed to determine the direction and timing of a change point. Van Bell and Hughes homogeneity test is applied to examine homogeneity of the observed trends. It is found that the southern half of NSW is dominated by a negative trend in annual total rainfall, and autumn and spring rainfall. Based on the Pettitt change point test, it is found that annual maximum 24-h rainfall data in NSW is dominated by a negative shift. Based on the Van Belle and Hughes method, it is found that NSW is dominated by non-homogeneous trends in monthly maximum daily rainfall data. Overall, the results do not show any consistent pattern of rainfall trends in NSW. The findings of this study contribute towards the growing body of knowledge on the nature of changes in rainfall based on the latest data in NSW. [ABSTRACT FROM AUTHOR]

ABSTRACT: The estimated future changes of precipitation and precipitation‐related climate indices for the Carpathian Region are analysed in this paper. Eleven regional climate model (RCM) simulations from the ENSEMBLES project taking into account the intermediate A1B emission scenario are used, hence, the analysis also includes uncertainty assessments. Validation results showed that in the target area RCM simulations overestimate precipitation throughout the year, except in summer, when underestimation occurs. In order to eliminate these systematic errors before the detailed analysis, percentile‐based bias correction is applied to the raw RCM outputs, for which the high‐quality CARPATCLIM database (1961–2010) serves as a reference. Using the bias‐corrected RCM simulations, precipitation totals and several precipitation‐related indices (describing both wet and dry climatic conditions) are calculated for the 1961–2100 time period on seasonal scale for the entire Carpathian Region, and also for five subregions (Slovakia, Ukraine, Hungary, Romania, and Serbia) within this area. The results from RCM simulations are inconsistent for some indices in some seasons, thus introducing uncertainties to the climatic projections. However, according to our results, drier summers are very likely to occur in the future, mainly in the southern parts of the selected domain. More frequent and more intense extreme precipitation events are estimated for winter and autumn, especially in the northern regions. Our conclusions suggest that the spatial distribution of precipitation is not likely to change remarkably in the future, however, the annual distribution of precipitation is projected to be restructured. [ABSTRACT FROM AUTHOR]

The contiguous US exhibits a wide variety of precipitation regimes, first, because of the wide range of latitudes and altitudes. The physiographic units with a basic meridional configuration contribute to the differentiation between east and west in the country while generating some large interior continental spaces. The frequency distribution of daily precipitation amounts almost anywhere conforms to a negative exponential distribution, reflecting the fact that there are many small daily totals and few large ones. Positive exponential curves, which plot the cumulative percentages of days with precipitation against the cumulative percentage of the rainfall amounts that they contribute, can be evaluated through the Concentration Index. The Concentration Index has been applied to the contiguous United States using a gridded climate dataset of daily precipitation data, at a resolution of 0.25°, provided by CPC/NOAA/OAR/Earth System Research Laboratory, for the period between 1956 and 2006. At the same time, other rainfall indices and variables such as the annual coefficient of variation, seasonal rainfall regimes and the probabilities of a day with precipitation have been presented with a view to explaining spatial CI patterns. The spatial distribution of the CI in the contiguous United States is geographically consistent, reflecting the principal physiographic and climatic units of the country. Likewise, linear correlations have been established between the CI and geographical factors such as latitude, longitude and altitude. In the latter case the Pearson correlation coefficient (r) between this factor and the CI is −0.51 ( p -value < 0.001). For annual probability of days with precipitation and the CI there is also a significant and negative correlation, r = −0.25 ( p -value < 0.001). [ABSTRACT FROM AUTHOR]

Changes in precipitation are critical indicators of climate change. In this study, the daily precipitation records from 10 meteorological stations in the Heihe River Basin, Northwest China from 1961−2016, precipitation indices, climate indices, and the normalized differential vegetation index (NDVI) were investigated using the Pearson, Kendall, and Spearman correlation coefficients; Theil-Sen Median; Mann−Kendall test; and wavelet coherence. The results indicated that the occurrences (fractional contributions) of 1−2-day wet periods were 81.3% (93.9%) and 55.3% (82.1%) in the upper (UHRB) and middle (MHRB) reaches of the Heihe River Basin, respectively. The spatial distribution of the occurrences (fractional contributions) was almost consistent with non-significant increases/decreases at stations. The ATP, ATD, API, and AMRD increased, while precipitation regimes suggest that dry seasons are getting wetter, and wet seasons are getting drier, although these changes were not significant. Wavelet coherence analyses showed that climate indices influenced precipitation, mainly its concentration, on a 4- to 78.6-month timescale. The Pearson, Kendall, and Spearman correlation coefficients showed weak lagged linkages between precipitation and the North Arctic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO). The NDVI of grasslands, meadows and coniferous forests was significantly and positively correlated with precipitation, while the NDVI of alpine vegetation, swamps and shrubs was negatively and significantly correlated with precipitation in the UHRB. The NDVI of grasslands was significantly and positively correlated, but the NDVI of shrubs, coniferous forests and cultivated vegetation was negatively and significantly correlated with precipitation in the MHRB. The correlation between cultivated vegetation and natural precipitation in the MHRB may have been weakened by human activities.

Situated at the transition between the mid-latitudes and the sub-tropics, southern Africa has a climatic dichotomy between winter- and summer-rainfall zones (WRZ and SRZ). The latitudinal extent of the winter-rainfall zone during the late Pleistocene remains contentiously debated within the regional palaeoscience literature. One method posited to reflect the seasonality of rainfall at a given location throughout late Pleistocene records for South Africa is the ratio of fossil pollen Asteraceae to Poaceae. Although adopted for a range of southern African locations, the veracity of this method has not been tested. This study explores the extent to which this ratio can discriminate between the SRZ and WRZ, and the extent of the region subject to fluctuation during the late Quaternary. The ratio is found to successfully discriminate regions which are, and would have remained during the past 20,000 cal yr BP, in the SRZ and WRZ exclusively. On the basis of these statistics, it appears that WRZ conditions can be inferred from ratio scores >0.6 and SRZ conditions from scores <0.2. For locations situated between 28 and 32°S, no clear discrimination can be made. It is argued that this region has been subjected to fluctuations in the latitudinal extent of the Westerlies and consequently in the influence of mid-latitude cyclones over the past 20,000 cal yr BP, with the rugged topography of the Drakensberg Mountains resulting in a complex precipitation climatology controlled by both frontal and orographic uplift. [ABSTRACT FROM AUTHOR]

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The West African monsoon intraseasonal variability has huge socio-economic impacts on local populations but understanding and predicting it still remains a challenge for the weather prediction and climate scientific community. This paper analyses an ensemble of simulations from six regional climate models (RCMs) taking part in the coordinated regional downscaling experiment, the ECMWF ERA-Interim reanalysis (ERAI) and three satellite-based and observationally-constrained daily precipitation datasets, to assess the performance of the RCMs with regard to the intraseasonal variability. A joint analysis of seasonal-mean precipitation and the total column water vapor (also called precipitable water- PW) suggests the existence of important links at different timescales between these two variables over the Sahel and highlights the relevance of using PW to follow the monsoon seasonal cycle. RCMs that fail to represent the seasonal-mean position and amplitude of the meridional gradient of PW show the largest discrepancies with respect to seasonal-mean observed precipitation. For both ERAI and RCMs, spectral decompositions of daily PW as well as rainfall show an overestimation of low-frequency activity (at timescales longer than 10 days) at the expense of the synoptic (timescales shorter than 10 days) activity. Consequently, the effects of the African Easterly Waves and the associated mesoscale convective systems are substantially underestimated, especially over continental regions. Finally, the study investigates the skill of the models with respect to hydro-climatic indices related to the occurrence, intensity and frequency of precipitation events at the intraseasonal scale. Although most of these indices are generally better reproduced with RCMs than reanalysis products, this study indicates that RCMs still need to be improved (especially with respect to their subgrid-scale parameterization schemes) to be able to reproduce the intraseasonal variance spectrum adequately. [ABSTRACT FROM AUTHOR]

Precipitation extremes play a key role in flooding risks over the Huaihe River Basin, which is important to understand their hydrological impacts. Based on observed daily precipitation and streamflow data from 1958 to 2009, eight precipitation indices and three streamflow indices were calculated for the study of hydrological impacts of precipitation extremes. The results indicate that the wet condition intensified in the summer wet season and the drought condition was getting worse in the autumn dry season in the later years of the past 50 years. The river basin had experienced higher heavy rainfall-related flooding risks in summer and more severe drought in autumn in the later of the period. The extreme precipitation events or consecutive heavy rain day events led to the substantial increases in streamflow extremes, which are the main causes of frequent floods in the Huaihe River Basin. The large inter-annual variation of precipitation anomalies in the upper and central Huaihe River Basin are the major contributor for the regional frequent floods and droughts. [ABSTRACT FROM AUTHOR]

Daily precipitation statistics as simulated by the ERA-Interim-driven EURO-CORDEX regional climate model (RCM) ensemble are evaluated over two distinct regions of the European continent, namely the European Alps and Spain. The potential added value of the high-resolution 12 km experiments with respect to their 50 km resolution counterparts is investigated. The statistics considered consist of wet-day intensity and precipitation frequency as a measure of mean precipitation, and three precipitation-derived indicators (90th percentile on wet days-90pWET, contribution of the very wet days to total precipitation-R95pTOT and number of consecutive dry days-CDD). As reference for model evaluation high resolution gridded observational data over continental Spain (Spain011/044) and the Alpine region (EURO4M-APGD) are used. The assessment and comparison of the two resolutions is accomplished not only on their original horizontal grids (approximately 12 and 50 km), but the high-resolution RCMs are additionally regridded onto the coarse 50 km grid by grid cell aggregation for the direct comparison with the low resolution simulations. The direct application of RCMs e.g. in many impact modelling studies is hampered by model biases. Therefore bias correction (BC) techniques are needed at both resolutions to ensure a better agreement between models and observations. In this work, the added value of the high resolution (before and after the bias correction) is assessed and the suitability of these BC methods is also discussed. Three basic BC methods are applied to isolate the effect of biases in mean precipitation, wet-day intensity and wet-day frequency on the derived indicators. Daily precipitation percentiles are strongly affected by biases in the wet-day intensity, whereas the dry spells are better represented when the simulated precipitation frequency is adjusted to the observed one. This confirms that there is no single optimal way to correct for RCM biases, since correcting some distributional features typically leads to an improvement of some aspects but to a deterioration of others. Regarding mean seasonal biases before the BC, we find only limited evidence for an added value of the higher resolution in the precipitation intensity and frequency or in the derived indicators. Thereby, evaluation results considerably depend on the RCM, season and indicator considered. High resolution simulations better reproduce the indicators' spatial patterns, especially in terms of spatial correlation. However, this improvement is not statistically significant after applying specific BC methods. [ABSTRACT FROM AUTHOR]

Both statistical and dynamical downscaling methods are well established techniques to bridge the gap between the coarse information produced by global circulation models and the regional-to-local scales required by the climate change Impacts, Adaptation, and Vulnerability (IAV) communities. A number of studies have analyzed the relative merits of each technique by inter-comparing their performance in reproducing the observed climate, as given by a number of climatic indices (e.g. mean values, percentiles, spells). However, in this paper we stress that fair comparisons should be based on indices that are not affected by the calibration towards the observed climate used for some of the methods. We focus on precipitation (over continental Spain) and consider the output of eight Regional Climate Models (RCMs) from the EURO-CORDEX initiative at 0.44 resolution and five Statistical Downscaling Methods (SDMs) -analog resampling, weather typing and generalized linear models- trained using the Spain044 observational gridded dataset on exactly the same RCM grid. The performance of these models is inter-compared in terms of several standard indices -mean precipitation, 90th percentile on wet days, maximum precipitation amount and maximum number of consecutive dry days- taking into account the parameters involved in the SDM training phase. It is shown, that not only the directly affected indices should be carefully analyzed, but also those indirectly influenced (e.g. percentile-based indices for precipitation) which are more difficult to identify. We also analyze how simple transformations (e.g. linear scaling) could be applied to the outputs of the uncalibrated methods in order to put SDMs and RCMs on equal footing, and thus perform a fairer comparison. [ABSTRACT FROM AUTHOR]

In the Mediterranean climate regions, drought events are expected to affect the growth of forests ecosystems by changing trees growth rates and eventually inducing shifts in their growth patterns. Cork oak ( Quercus suber L.) is a strictly western Mediterranean tree species periodically harvested for its bark, the cork. So far, cork oak has received limited attention for dendroclimatological studies due to its typical faint and erratic tree wood rings. Moreover, its distinct cork rings chronologies have been completely neglected. In this study we introduce an approach using cork ring chronologies dated back 9–10 years for climate response. Despite enhancing interannual variability and increasing statistical response to short-term climatic variability, still poorly understood, this study will possibly allow infer long-term climate response. We analyzed the cork ring chronologies of 55 cork samples collected in mature (under exploitation) trees in three distinct locations in southwestern Portugal. Cork growth recorded a high climate signal, with highly significant and coherent responses to the yearly climate-related sources of variation. We successfully assessed trends of cork growth via correlation analysis including selected climate variables among mean monthly temperature, monthly precipitation and, on an annual basis, eight precipitation indices. The high mean sensitivities and inter-series correlations found for cork ring chronologies combined with the significant variance explained by climate variables suggest that climate is likely one dominant signal that affects cork growth, but local environmental stresses can decisively affect this (climate) signal. Assuming cork growth as a proxy for cork oak growth, it seems conceivable that despite the trees being highly resistant to drought stress, cork oak woodlands in southwestern Portugal would have to face lesser growth in a global warming scenario. [ABSTRACT FROM AUTHOR]

Climate change in complex mountain regions has an impact on the change of water resources, especially in arid areas. Here, we use long-term meteorological and hydrological station observation data to analyze the time series of climate indices and runoff to study the variability of climate in the Kaidu River Basin. The analysis results are as follows: 1) the variability rate of low temperature indices are of greater magnitude than high temperature indices; 2) overall, for the river basin, frost days and ice days all exhibited decreasing trends, and growing season lengths increased considerably; 3) during the past 50 years, overall precipitation has increased in the river basin, but there are some differences in some seasons, and precipitation from June to August accounts for approximately 66% of the annual precipitation; and 4) temperature lapse rate and precipitation of the mountain region are major factors influencing the change of runoff for the Kaidu River Basin, temperature lapse rates are the main factor influencing the run off change in the spring and fall, and precipitation in the mountain region is the major factor influencing the runoff change in the summer. Generally, climate change in complex mountain regions will be expected to seriously affect water resources in arid regions. [ABSTRACT FROM AUTHOR]

ABSTRACT The Markov chain statistical technique was employed to fill few missing values of daily precipitation observed at nine long-term (≥50 years of data) climate stations of Western Canada. Statistical properties of the gap filled precipitation data are compared with those of the original data to ensure that this approach preserves the statistical characteristics of the historical data. After gap filling missing data, 10 precipitation indices that represent magnitude and frequency of precipitation properties were computed from those 9 stations and the other 20 stations without missing records. Next, the Wilcoxon rank-sum test was used to detect possible change points of these precipitation indices. Based on the analysed results, there had been an increasing trend in the maximum number of consecutive wet days and the annual maximum 1-day precipitation and a decrease in the maximum number of consecutive dry days over most of British Columbia (BC). The annual total precipitation index of many stations had increased in southern BC and decreased in central parts of Canadian Prairies. Similar change was detected in the number of days when precipitation is equal and greater than 10 or 20 mm, and indices for the annual monthly maximum 1-day precipitation - precipitation that exceeds the 95 and 99 percentiles - and the simple precipitation intensity index. On the basis of the above results, the diurnal temperature range (DTR) of Western Canada has decreased, and predominant negative correlations between precipitation indices and DTR, it seems that Western Canada has generally become wetter since the middle of the 20th century partly because increase in the minimum daily temperature has been higher than the increase in the maximum daily temperature, leading to a decrease in DTR, which is likely related to climatic warming. [ABSTRACT FROM AUTHOR]

A gridded data set of daily precipitation records together with percentile-based precipitation indices was utilized to analyze changes in spatiotemporal pattern of precipitation across China in the last 33 years (1980-2012). Identifiable changes in precipitation structure and distinctive spatial variations in precipitation trends were detected at both annual and seasonal timescales. Generally, a slight but discernable increasing trend was observed in annual precipitation, and precipitation increased during summer and winter but decreased in spring and autumn. In addition, great differences were distinguished in the aspects of precipitation day, precipitation amount and precipitation duration among light, moderate and heavy precipitation. Additional analyses of extreme events including wet day, very wet day, daily maximum precipitation, short dry period and long dry period, were also performed, which indicated an upward trend in days with more precipitation. Study of contributions to the precipitation day and amount showed that light and moderate precipitation contributed greatly to the precipitation day while moderate and heavy precipitation contributed greatly to the precipitation amount. Finally, it could be concluded that it was getting drier in southeast China but wetter in southwest China, with severe droughts in northwest China being relieved during the last 33 years. [ABSTRACT FROM AUTHOR]

Monthly precipitation extremes defined by monthly maximum one-day precipitation amount (Rx1day) and maximum consecutive five-day precipitation amount (Rx5day) were analyzed based on daily precipitation data covering a period of 1957 to 2010 across the Poyang Lake basin, the largest freshwater lake basin in the lower Yangtze River basin, China. Based on the max-stable, impacts of El Niño–Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian Ocean Dipole (IOD), Pacific Decadal Oscillation (PDO) on the annual and seasonal Rx1day and Rx5day regimes in the Poyang Lake basin were evaluated. Results indicated that annual Rx1day and Rx5day were influenced mainly by the ENSO events a year earlier and the relations between annual Rx1day and Rx5day and ENSO were statistically positive. However, influences of climate indices on the seasonal Rx1day and Rx5day are complicated when compared with the influences on annual Rx1day and Rx5day regimes. ENSO and PDO can enhance the spring Rx1day regime of the subsequent year and the same year, respectively, but IOD weakens spring Rx1day in the same year. In summer, ENSO can enhance Rx1day of the subsequent year. However, Rx1day in summer had a decreasing tendency. IOD and NAO influenced the autumn Rx1day in another way, i.e., IOD decreases autumn Rx1day of the subsequent year; while NAO enhances autumn Rx1day of the same year. Meanwhile, ENSO can amplify the summer Rx1day in terms of variability, while IOD and NAO can, respectively, influence autumn and winter Rx1day in terms of mean. Influences of climate indices on annual and seasonal Rx5day are similar to those on Rx1day. Results of this study are of great theoretical as well as practical merit in terms of evaluation of droughts and floods under the influences of climate indices. [ABSTRACT FROM AUTHOR]

ABSTRACT The state of Ceará is a semiarid region in north-eastern Brazil, having a high spatial and temporal variability of precipitation, which poses challenges for water resources management. The objective of this study is to describe and analyse the long-term variation of monthly precipitation indices and their relation to sea surface temperature ( SST) anomalies. Data from 55 weather stations in the state of Ceará from 1974 to 2009 was analysed. In general, a decreasing tendency in monthly precipitation was observed over almost all the state of Ceará. The results point to a tendency for dry months to become dryer and to a decrease in precipitation intensity. SST anomalies from October to March correlate with precipitation indices from January to April, showing decreasing lag times towards the end of the wet season. The most influential SST anomalies locations are Niño 1+2, Niño 3, Niño 3.4 and Global Tropics. [ABSTRACT FROM AUTHOR]

Abstract: Soil particle deposition and/or chemical precipitation can reduce the permeability of drain envelopes and filters. The first step in recognizing clogging phenomena is the identification of the nature of the precipitating materials. Calcium carbonate is a substance of low solubility that precipitates rapidly, forming a hard pan layer in the soil and/or clogging drain envelopes. The main objective of the present study is to investigate the precipitation risk of calcium carbonate in agricultural drain envelopes in the Khuzestan province of Iran. In this study, three indicators namely Ryznar, Langelier and Stiff-Davis indices were employed to assess the precipitation risk of calcium carbonate in agricultural drainage water. Results showed that all agricultural drainage systems in the study area give evidence of a potential risk of calcium carbonate precipitation, but the severity of the problem is different. The results also showed that the Ryznar and Stiff-Davis indices provide a better estimation of the potential precipitation risk of calcium carbonate than the Langelier index. Analyses of soil samples and drain envelopes from a drainage system, installed in the Abadan palm grove, showed that the main chemical component was calcium carbonate. [Copyright &y& Elsevier]

This study investigated long-term variability and trends in Latvia's annual, seasonal, monthly and daily precipitation using data from 10 meteorological stations for the period 1925-2006 and from station Riga University for the period 1850-2006. The obtained results indicate that during the 20th century a significant increase in precipitation has occurred in the cold season while the warm period showed a decreasing tendency. The annual precipitation totals showed a slight decrease, at half of the studied stations, due to opposite tendencies in cold season and warm season. The long-term trend in the annual precipitation in Riga (from 1850) was positive with large interannual and interdecadal variability. The extreme precipitation events were evaluated using a set of nine climate change indices, Of these, number of wet days, 1-day and 5-days maximum precipitation, moderate wet days and very wet days showed a well pronounced positive tendency in the cold period of the year particularly in winter. No overall long-term trend was detected in extreme precipitation in summer. As in the case of 150-year precipitation pattern, extreme precipitation exhibited cyclic fluctuations that were more pronounced than linear changes. The close correlation between North Atlantic oscillation (NAO) and extreme precipitation was found for winter season. [ABSTRACT FROM AUTHOR]

The objective of this study is to provide insight on precipitation indices trends, based on daily precipitation records, and the SPI (Standardized Precipitation Index), based on monthly precipitation records, evaluate correlations and establish relationships between them in the 1911-1999 period. A negative trend was observed in the month of March for several indices, as well as a tendency for the dry semester to start sooner. [ABSTRACT FROM AUTHOR]