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The frequency of extreme events is increasing on a global scale due to various factors, accounting for natural disasters such as landslides, floods, and droughts. The Heilongjiang province, which is agriculturally important for China, has suffered from several extreme rainfall events in the past as a high-latitude plain region. Analysis of historical rainfall characteristics of the Heilongjiang region helps to understand and predict the behavior of extreme rainfall events and provides a reference for reducing agricultural economic losses. Precipitation data from 1974 to 2017 is selected to estimate whether rainfall series in Heilongjiang obey the Gumbel-Logistic Model. We compared the Gumbel-Logistic Model based on the data length of 5 years, 10 years, and 20 years with the original 44-year data to determine the appropriate data partition length and explore the reasons for errors. Combining the empirical frequency and KS test, this paper concludes that the proposed model is appropriate for the representation of the joint distribution of monthly extreme rainfall and annual rainfall. The proposed model improves the rainfall assessment of these two positively correlated variables in Heilongjiang. Univariate and bivariate return periods and correlation coefficients are derived as the basis for assessing the risk of extreme rainfall. The proposed model obtained from the 5-year data length has the largest error, while the distribution curve is progressively smoother as the data length increases. [ABSTRACT FROM AUTHOR]

This paper analyzes the correlation between precipitation and deforestation and trends in the Brazilian Amazon Biome from 1985 to 2021 using rain gauge measurements. A total of 187 stations were selected after filtering time series with less than 10% of missing data. Deforestation data was acquired using the MapBiomas dataset. Results show that deforestation rates vary across different locations and may be influenced by various factors. The analysis also found significant negative correlations between precipitation and deforestation, with a range of − 0.49 to − 0.33, as well as significant positive correlations, with a range of 0.34 to 0.57. These findings underscore the importance of continued monitoring of precipitation patterns in the region and the urgent need for measures to mitigate deforestation, which is critical for the hydrological cycle of the region. The highest deforestation rates and values were found between latitude 0 and − 12.5, where the arc of deforestation lies. Additionally, this study found a significant negative correlation between precipitation patterns and deforestation, indicating that deforestation led to a reduction in rainfall in the region. [ABSTRACT FROM AUTHOR]

Under global warming and climate change (CC), the vulnerable piece of land is India's Eastern-Himalayan-Region (EHR), where millions' livelihood activities are at risk. Quantifying the degree of CC-induced vulnerability over the highly climate-sensitive mountain zone is challenging due to data inadequacy. This paper attempted to assess the CC perception of kiwifruit growers under EHR and the impact of CC on its cultivation. Perceived and scientific data were used for accurate decision-support information. The climatic variables of two locations were analyzed for trend using Mann–Kendall's test. Results suggested that the rainfall trend was found non-significant for both locations, but there was a negative trend during winter for Pasighat and during all seasons for Itanagar. Maximum temperature exhibited an increasing trend for annual, and minimum temperature showed an increasing trend across seasons for the Pasighat region. For the Itanagar region, the maximum temperature was found to have a significantly increasing trend in all seasons, while the minimum temperature showed an increasing trend during winter and annual seasons. Morning relative humidity showed an increasing trend for annual data for Pasighat, while annual evening relative humidity was significantly increased for Itanagar. The study shows that the indigenous farming communities were following shifting cultivation and diversifying their traditional agriculture to kiwifruit cultivation as an adaptation strategy. The community had a clear perception of CC trends and an inadequate perception of the impact on agriculture, therefore, engaging indigenous communities and other stakeholders to postulate a comprehensive adaptation and mitigation strategy for CC. [ABSTRACT FROM AUTHOR]

Understanding the future patterns of precipitation behaviour in unique geographical areas, largely determined by their orography and local scale, can help lay the foundations for a new precipitation model for the design of the city's main urban drainage infrastructures (intensity-duration-frequency curves, mathematical functions that relate precipitation intensity to duration and frequency of occurrence, hereafter IDF, for the short-, medium- and long-term future). This will definitely contribute to the improvement of the city's resilience to the effects of climate change. In this paper, the projections of a subset of climate change models from both the sixth phase of the Coupled Model Intercomparison Project (CMIP6; with a total of 5 simulations) and Euro-CORDEX (for a set of 51 simulations) have been adjusted to the municipality of Alicante (in the southeast of Spain), using the Climadjust tool (climadjust.com). These projections contain different climatic variables. The rainfall variable has been used to derive a new framework of boundary conditions to help design more resilient infrastructure for torrential rainfall events and urban flooding. The projections corresponding to three climate change scenarios (CMIP6: SSP1-2.6, SSP2-4.5, SSP5-8.5; and Euro-CORDEX: RCP2.6, RCP4.5, RCP8.5) are considered with daily resolution and, by applying statistical techniques of temporal disaggregation (by means of a cascade model), hourly (and sub-hourly, reaching 30-min resolution) disaggregation. The results at hourly and 30-min resolutions are used to construct IDF curves of future climate, grouped into short-term (years 2015 to 2040), medium-term (years 2041 to 2070) and long-term (years 2071 to 2100) sub-scenarios. The selected future climate IDFs for an adverse climate change scenario (SSP2-4.5 and SSP5-8.5) show increases in rainfall intensities, higher the shorter the rainfall duration, for return periods greater than or equal to 25 years, whereas for return periods under 25 years the current IDFs can be representative of future scenarios. Current calculations and future projection of the torrentiality index for severe climate change scenarios, as well as the climate change factors, show an increase in the frequency and magnitude of the heaviest rainfall. This fact corroborates the hypotheses of greater general torrentiality in future rainfall in this specific area of the Spanish Mediterranean coast. [ABSTRACT FROM AUTHOR]

The El Niño Southern Oscillation (ENSO) is a major influence on interannual variability of rainfall in stations in the tropical southwest Pacific. Predictions of seasonal rainfall, especially a season or two ahead, are of great value to these countries. This paper therefore examines the correlations over ~ 60 years between seasonal rainfall and 8 ENSO indicators at 16 island stations, allowing for lead times. The results show the influence on rainfall of the position and movement of the South Pacific Convergence Zone (SPCZ) during ENSO events, and that the Southern Oscillation Index (SOI), the sea surface temperature anomaly in the central Pacific (Niño 3.4), and the warm water volume in the eastern Pacific (WWV1) have longer lead times compared to most other ENSO indicators. These indicators can therefore be used with confidence in SCOPIC, a widely used statistical tool for prediction of seasonal rainfall. (As global climate models generally have systematic errors in their depiction of the SPCZ, they cannot yet be used directly to reliably predict seasonal rainfall in this region.) For several sites near the SPCZ, we find that a moderately good forecast of rainfall in both spring and summer can be made from indicators measured in June–July (i.e., 3–6 months in advance.) [ABSTRACT FROM AUTHOR]

The Western Ghats (WG) is one of the important biodiversity hotspots and rainfall intense regions in south Asia. In this paper, the long-term trend analysis is carried out during southwest monsoon rainfall over different latitudinal regions of the WG. Further, the gridded daily rainfall data (0.25 × 0.25 spatial grid resolution) is classified into six intensity bins: dry, low, moderate, heavy, very heavy, and extreme, from 1951 to 2015. A detailed trend and variability analysis of rainfall and its frequency in different intensity bins along with their relative contribution were carried out. The results of different rainfall intensity bins depict that the moderate and low intensity bins contribute a maximum share in seasonal rainfall with 45% and 20%, respectively, from the 30% and 50% rainy days in all the regions. Heavy and very heavy intensity bins together contribute about 30% of the seasonal rainfall from about 9% rainy days. Extreme intensity bin recorded an alarming share of 6% with only 1% rainy day occurrences. In general, the relative contribution shows an ascending pattern of moderate, low, very heavy, heavy, extreme, and dry intensity bins. A decreasing trend in rainfall was observed in heavy and very heavy intensity bins, while other intensity bins illustrated mixed trends in different study regions. Extreme rainfall events marked a major shift upward in the WG region during the study period. Overall, our result portrays a negative trend in rainfall during the southwest monsoon season over all the study regions during the study period. [ABSTRACT FROM AUTHOR]

Estimating precipitation over large spatial areas remains a challenging problem for hydrologists. Satellite-based remote sensing rainfall products have the advantage of large-scale synchronous coverage, but the reliability of their inversion still needs to be improved. Correcting the bias of satellite-based precipitation estimates (SPEs) is a major challenge in applications such as environmental modeling, hydrology, and water resource management. In this paper, a new bias correction method—a Gaussian process regression (GPR) model method based on rain intensity classification—is proposed to improve the accuracy of a satellite precipitation product—the Integrated Multi-satellite Retrievals for GPM (IMERG) Final Run (FR) product—at the daily scale in the Guangdong-Hong Kong-Macao Greater Bay Area. By comparing the effects of the proposed method with those of other existing classical correction methods, namely, quantile mapping (QM), the support vector machine (SVM) approach and direct GPR, it is found that all four methods improve the accuracy of the FR product to varying degrees. GPR based on rain intensity classification has the best effect of FR product improvement, with the CORR increasing from 0.55 to 0.59, the RMSE ranging from 14.37 to 12.79 mm/day, and the BIAS ranging from − 0.14 to 0.03 during the validation period. GPR without rain intensity classification also yields good results, with the QM and SVM methods being the least effective. [ABSTRACT FROM AUTHOR]

Rainfall onset dates (ROD), rainfall cessation dates (RCD) and length of rainy season (LRS) are crucial for crop production and food security in Eastern Africa yet scantily documented. This paper seeks to investigate the spatial patterns of these parameters. Data used are Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS v2.0) and National Oceanic and Atmospheric Administration (NOAA) gridded temperature. Threshold of 1 mm for rainy day, 20 mm over 5 days with at least 3 rain days and dry spell not exceeding 7 days in the next 21 days were used to determine RODs. The Potential Evapotranspiration (PET) and Water Balance (WB) criteria were computed to determine RCDs. The differences between ROD and RCD were used in calculating LRS. The results showed early rainfall cessation over more than 30 counties in Kenya lead to shortened rainy season by 10–20 days during MAM season. Similarly, 20–40 days early onset dates are observed in most counties in upper Nile, Unity, and Jonglei states in South Sudan, while 20–40 days delayed rainfall onset was observed in Khartoum and southern parts of Nile state western Darfur, eastern and Aljazeera states in Sudan, most parts of Ethiopia and Eritrea districts during JJA season. Highlands of western and Nyanza region in Kenya, most parts of Uganda observed rainfall onset by March and no sign of cessation before November. Early cessation over northern Uganda districts was behind shortened LRS, furthermore, the early RODs over western and southwestern Uganda districts increased LRS. Prolonged dry conditions over northern Sudan, southeastern parts of South Sudan, northern Kenya, central Somalia, northern Darfour, Kordofan and northern parts of Sudan exacerbated by significant delayed onset and early cessation of rainfall. These findings are important for rain-fed agricultural planning and food security in the IGAD region of Eastern Africa. [ABSTRACT FROM AUTHOR]

Rainfall in the Moist Tropical Caribbean Region (MTCR) in Costa Rica occurs practically throughout the year, with the quarters June–August (JJA) and December–February (DJF) concentrating over 70% of annual rainfall. Contrarily, in March–April and September–October, it rains below 100 mm per month. This seasonal rainfall behavior makes the region ideal for producing bananas (Musa spp.) and pineapple (Ananas comosus) for export (10% and 8% of total exports in 2021, respectively). A national-scale study determined that agriculture in the MTCR is one of the most vulnerable sectors to climate changes. However, the climate in this region has been poorly studied so far. This research analyzed the spatial and temporal variability of annual, monthly, and seasonal (DJF, JJA, SO) rainfall in the MTCR and how they change in the study period based on quality-checked series of daily rainfall from 28 weather stations in two periods: 1985–2009 and 1997–2019. The results show that rainfall regimes in the region are variable in space and throughout the year, with peaks occurring close to the mountain range and minimum values close to the coast. Trends were statistically significant in the period 1985–2009 with a predominance of significant positive trends in DJF and significant negative trends in SO. No significant trends (positive or negative) were observed in the period 1997–2019. JJA rainfall has uneven regional distribution and presents a positive and significant trend in the mountain region. This paper contributes to filling the knowledge gap in rainfall seasonality, variability, and trends in a region where banana and pineapple commercial plantations are fundamental to the country's economy thus providing information to decision-making in the agri-food sector to reduce the negative impacts of changing rainfall regimes. [ABSTRACT FROM AUTHOR]

In this paper, Japan Meteorological Agency (JRA-55) reanalysis and observational rainfall datasets from the National Climate Center (NCC) of China, as well as satellite datasets from the Tropical Rainfall Measuring Mission (TRMM), Global Precipitation Climatology Project (GPCP), and International Satellite Cloud Climatology Project (ISCCP), are used. The correlation coefficient and fast Fourier transform (FFT) low-pass filter are also used, in order to reveal the interdecadal decrease in autumn rainfall in Southeast China (SEC) after 1990. The close and robust relationship between the interdecadal variation in autumn rainfall in SEC and sea surface temperature (SST) in the tropical Pacific is investigated. The most significant and stable region of correlation is located in 10° S–10° N, 160° E–160° W, in which there also exists interdecadal warming after 1990. Furthermore, the interdecadal warming of SST can induce Gill responses of the atmosphere: a cyclone anomaly is produced on each side of the equator in the lower troposphere, with a westerly anomaly to the west of the dateline, and an anticyclone anomaly is produced in the upper troposphere. In particular, the cyclone anomaly on the northern side of the equator is located in the Northwest Pacific (NWP), and its ambient northerly airflow weakens meridional water vapor transport, as well as the local descending motion and low-troposphere divergence, in favor of the interdecadal decrease in SEC rainfall after 1990. In addition, the sensitive experiments with ECHAM-5.4 model also confirm that the interdecadal warming in the region (10° S–10° N, 160° E–160° W) would motivate the atmospheric Gill response and thereby cause the sinking motion in SEC and support the interdecadal decrease in autumn rainfall in SEC. [ABSTRACT FROM AUTHOR]

Climate change may cause adverse impact on agricultural production that could jeopardize food availability and security. In this paper, we investigate how changes in mean values and variability of weather variables may affect rice yield using panel data for 30 districts of Tamil Nadu State from 1971 to 2018. We estimate a fixed-effects regression model with panel-corrected standard errors. Results show that rainfall and temperature have a statistically significant impact on rice yield. Furthermore, weather variability, measured as standard deviations of temperature and rainfall, has a negative effect on rice yields. We use the Coupled Model Inter-comparison Project-5 outputs and dynamically downscaled the weather outputs using the regional climate model. The results of the climate model showed that the rise in temperature and disruptions in rainfall patterns including both excessive and deficient rainfall events in Tamil Nadu will continue in the future under different climate scenarios. Projected changes in the weather variables are likely to decrease rice yield in Tamil Nadu from 0.7 to 6.3% under the low emission scenario and 4.1 to 20.1% in the high emission scenario during 2022–2050 (relative to 1971–2018). These projections have implications for the planning and targeting of climate adaptation technologies such as drought-tolerant and flood-tolerant varieties to lessen the adverse impact of weather variability due to climate change in the future. [ABSTRACT FROM AUTHOR]

The present paper aims to compute climatological zones apt for the cultivation of pitaya based on trends in the occurrence of climate change events from the IPCC (Intergovernmental Panels on Climate Change) in Brazil. We used temperature and precipitation data from 4942 cities collected on the NASA/POWER platform (National Aeronautics and Space Administration/Prediction of Worldwide Energy Resources) from 1990 to 2020 to elaborate on the current scenario. The climate change scenarios were obtained using the CHELSA platform (Climatologies at high resolution for the earth's land surface areas) and corresponded to the periods 2041–2060 and 2061–2080 associated with four IPCC climate change scenarios. The spatialization of the data occurred according to the bioclimatic classes designed to meet the thermal and water needs of the crop. In the current scenario, class B5 has a predominance of 37.07% of the country, characterizing the Midwest, Southeast, and Northeast regions, as well as the state of Paraná, as suitable for the cultivation of pitaya. Projections of temperature increase and reduction of accumulated rainfall were found throughout Brazil, but with greater impact in the North and Northeast regions, which had the greatest reduction of areas suitable for the cultivation of pitaya with a greater predominance of classes B8 and B9. In the South and Southeast regions, class B5 makes up a large part of the areas that remained suitable for the production of this fruit until 2080. The results suggest that climate change does not benefit the cultivation of pitaya in some regions of Brazil because the dimensions of the areas suitable for economic production are restricted. [ABSTRACT FROM AUTHOR]

The present paper investigates the time-series properties of average rainfall and temperature readings of countries in Southeast Asia (Brunei, Cambodia, Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Timor-Leste, and Vietnam) using monthly data, spanning from 1901 to 2016. Specifically, we analyze the time trend, persistence, and seasonality properties of the series using I(d) fractional integration framework, an approach that is found to be robust in dealing with climatological series. Antipersistence is observed in the rainfall series of Cambodia, Laos, Myanmar, and Vietnam while long memory is found in the rainfall series of the remaining countries. Thus, those countries with antipersistent rainfall are prone to erratic rainfall distribution which could lead to flooding or drought. Long memory is observed in the distribution of rainfall across all countries with increasing trend found in some cases. Both climatic variables display evidence supporting seasonality with stronger seasonality observed in Cambodia, Laos, Myanmar, Thailand, and Vietnam. Findings in the present paper are of policy relevance to climate monitoring agencies and the government. [ABSTRACT FROM AUTHOR]

The climate change impact appears as a decreasing or increasing monotonic trend in hydro-meteorology time series records due to greenhouse gas (GHG) emissions causing to global warming and climate change impacts. On the other hand, there may be abrupt changes in the form of jumps in these series due to natural and engineering activities. Although trend identification methods are rather common in the literature, jump determination conventional methodologies are rather rare and their applications present some restrictive asumptions like the serial independence and normal (Gaussian) probability distribution function (PDF). The methodology presented in this paper is away from each of such assumptions and it depicts the minimum number of upcrossing along horizontal truncation levels within the time series variation domain. The applications of the methodology are given for annual Danube River discharge records, Romania; New Jersey rainfall and temperature records, USA; monthly rainfall records and Van Lake level fluctuations, Turkey. [ABSTRACT FROM AUTHOR]

The southern coastal belt of West Africa (SCWA) with its high population density and many major cities, combined to the low elevation and poor urban planning, is very vulnerable to floods resulting from extreme rainfall events. The aim of this paper is to analyze the characteristics of extreme rainfall in the SCWA during the 1981–2015 period, in terms of frequency, intensity, seasonality, and trends. Therefore, daily rainfall of 31 stations located in the southern part of Côte d'Ivoire, Ghana, Togo, and Benin and rainfall estimation products combining in situ observations and satellites rainfall estimation data have been used. For each station and pixel, the local 95th percentile (P95) computed on all rain days of at least 1 mm was used to define extreme rainfall events. Rainfall on the coastal belt is heavier than further inland, with P95 values reaching 82 and 52 mm/day for coastal and continental stations, respectively. Extreme rainfall along the coast occurs predominantly between May and July. Interannual variations of different indicators of extreme rainfall show a broad agreement between rain gauge data and rainfall estimates from CHIRPS (Climate Hazards Group InfraRed Precipitation with Station) data. In the southern part of Côte d'Ivoire and Togo/Benin, increase of number of extreme rainfall event (NP95) and stability number of days with rainfall less than P95 (NL95) are recorded, which induces an increase of total rainfall. But, in the southern part of Ghana, there is a stable total rainfall due to an increase in NP95 compensated by a decrease in NL95. [ABSTRACT FROM AUTHOR]

Rainfall hindcasting is one of the most challenging tasks in the hydrometeorological forecasting community. The current ad hoc data-driven approaches appear to be insufficient for forecasting rainfall. The task becomes more difficult, when the forecasts are over a long period of time. To increase the accuracy of seasonal rainfall hindcasting, this paper introduces an ensemble evolutionary model that integrates two genetic programming techniques: gene expression programming (GEP) and multi-stage genetic programming (MSGP). To demonstrate the development and validation procedures of the new model, the rainfall data from the Antalya meteorology station was used. The model performance was evaluated in terms of different statistical measures and compared with that of the state-of-the-art gradient boosted decision tree (GBT) model developed as a reference model in this study. The performance results during testing showed that the proposed ensemble model has increased the seasonal forecasting accuracy of the GEP and MSGP models up to 30%. The GBT was found comparable to the proposed model during training period; however, it drastically underestimated extreme wet seasons during testing. [ABSTRACT FROM AUTHOR]

Trend analyses are the necessary tools for depicting possible general increase or decrease in a given time series. There are many versions of trend identification methodologies such as the Mann-Kendall trend test, Spearman's tau, Sen's slope, regression line, and Şen's innovative trend analysis. The literature has many papers about the use, cons and pros, and comparisons of these methodologies. In this paper, a completely new approach is proposed based on the crossing properties of a time series. It is suggested that the suitable trend from the centroid of the given time series should have the maximum number of crossings (total number of up-crossings or down-crossings). This approach is applicable whether the time series has dependent or independent structure and also without any dependence on the type of the probability distribution function. The validity of this method is presented through extensive Monte Carlo simulation technique and its comparison with other existing trend identification methodologies. The application of the methodology is presented for a set of annual daily extreme rainfall time series from different parts of Turkey and they have physically independent structure. [ABSTRACT FROM AUTHOR]

Standardized heavy rainfall profiles may contribute to a best knowledge concerning flash floods, ground erosion and runoff. Taking advantage of the rain rate gauges network of Barcelona city and applying a short integration time of 5 min, the heavy rainfall records with amounts above 25 mm and longer than 60 min have been selected along 15 years, being detected 499 records corresponding to 67 episodes. The amount distribution of these records are analysed at time deciles. By grouping these standardized rainfall profiles according to their similarity by means of the average linkage clustering algorithm, 10 clusters are derived, each one of them characterized by different time evolution of rainfall. The representative standardized rainfall profiles for every one of the clusters, together with maximum 5-min rain amounts and rainfall amounts characterizing them, permit to distinguish rainfall patterns. In addition, the extreme heavy rainfalls which may lead to flash floods are identified, as also their respective synoptic situations. In short, this analysis offers a description of heavy rainfall patterns in Barcelona city, complementing previous papers on the normalized intensity curves and 5-min intensity return periods. These heavy rainfall analyses would be very useful when designing drainage and sewerage systems in urban areas as Barcelona, where flash floods may be expected due to episodes of notable rainfall amount and intensity. The imperviousness density of the soil and an appropriate sewerage structure should be adapted to mitigate the effects of these copious and intense episodes. [ABSTRACT FROM AUTHOR]

The present study reports a thorough information theory–based investigation of the time series of rainfall in seasonal scale as well as yearly scale in the Himalayas during the summer monsoon (June–September). In this paper, the inbred uncertainty of rainfall series has been evaluated through Shannon entropy by fitting a probability distribution. The entropy has been measured for seasonal scales as well as yearly scales. A few parameters relating to the summer monsoon rainfall series over parts of North Mountainous India (NMI) from 1844 to 2005 are considered for computing entropy. Maximization of Shannon entropy is done to examine the parallel contributions of these parameters in this meteorological phenomenon. Finally, we have shown how entropy is affected by the fluctuation of mean rainfall in seasonal scale as well as yearly scale. We have also computed the decade-wise intrinsic uncertainty of rainfall series as rainfall is the most important parameter for climate risk. Besides, we also fitted the probability distribution curve generated by rainfall series. [ABSTRACT FROM AUTHOR]

Forecasting of Indian summer monsoon rainfall (ISMR) is a complex problem for the hydrologists and meteorologists. The time series and data-driven methods have been used as complementary tools for forecasting ISMR against the complex physically based dynamical models due to scarcity of data and the simplicity of former approaches. The use of hybrid decomposition data–driven models is the recent improvement among the different approaches for rainfall forecasting, but these approaches differ significantly in the framework adopted. This paper presents an adaptive hybrid modelling framework so called Adaptive Ensemble Empirical Mode Decomposition-Artificial Neural Network (AEEMD-ANN) model for forecasting ISMR, which performs the forecasts adaptively as and when new information is added. The performance of the popular EEMD-ANN hybrid hindcast and forecast experiments in the prediction of All-India SMR and southwest monsoon rainfall of the state of Kerala is compared with the proposed method. The AEEMD-ANN method achieved a predictive skill of 0.78 and 0.91, respectively for rainfall predictions for Kerala and All-India. AEEMD-ANN method performed reasonably well in capturing the hydrologic extremes when compared with EEMD-ANN forecast method, with better accuracy in capturing the drought years. The proposed method is found to be successful in capturing the extreme low SWM rainfall of year 2002 for All-India and the extreme high rainfall of Kerala 2018 with an error percentage of 1.09% and 0.52%, respectively. [ABSTRACT FROM AUTHOR]

This paper addresses two fundamental questions on climate change and variability: to what extent has climate changed and/or varied over years in two districts of different agro-ecological regions or zones and how do any changes differ between the zones or districts? Given the rural-rural migration pattern observed between the districts, understanding climate change risk to rural livelihoods cannot be overemphasised. To assess change and variability, we utilise rainfall data-records over a 36-year period from 1980 to 2016. Results show that there are wide variations and differences within and between the districts. Evidence suggests a general reduction in both annual rainfall and wet days. There is also ground to suggest that the rainy season duration is becoming shorter, given that rainfall onset is increasingly starting late, while cessation is increasingly coming early. Dry spells frequency and duration trends within rainy season show an increase over the period examined. We conclude that local climate in both areas has changed over the period investigated. However, while Livingstone seems to have experienced more droughts and unreliable rainfall, Kabwe experienced a bigger change in both rainfall and rainy season duration. We further conclude that migrants into Kabwe and other inhabitants are not any safer from climate change risk. [ABSTRACT FROM AUTHOR]

This paper includes a study which applied homogeneity and trend tests on time series of monthly mean temperature and monthly total rainfall recorded in the provinces of Bartın, Bolu, Düzce, Kastamonu, Sinop, and Zonguldak of western Black Sea region in Turkey. The Runs test was used to check the homogeneity of both variables. Analyses were made using Mann-Kendall (MK) trend test, Şen trend test, and linear trend test for the time period 1960–2017. According to the results of all tests, although monthly total rainfall of Bartın, Kastomonu, Sinop, and Zonguldak stations has inhomogeneous time series, Bolu and Düzce have homogeneous time series. The monthly mean temperature of all stations has no homogeneity. Although Kastamonu and Sinop have statistically significant increasing trends according to the linear trend test, other provinces have no statistically significant trends for both variables. There are no statistically significant trends for both variables in all stations according to the MK trend test; however, there are statistically significant increasing trends for monthly mean temperature in all stations according to the Şen trend test. Monthly total rainfall of Düzce (17072) has a statistically significant decreasing trend at 95% confidence interval; other stations have statistically significant increasing trends according to the Şen trend test. [ABSTRACT FROM AUTHOR]

In this paper, an investigation of the temporal rainfall variability in the South Island of New Zealand has been carried out using a high-quality monthly dataset of 152 rain gages with more than 50 years of observation. Possible trends in seasonal and annual rainfall values have been detected by means of the Mann–Kendall test and of a new graphical technique, Şen's method, which allows the trend identification of the low, medium, and high values of a series. As a result, different behaviors emerged in the South Island, with a rainfall increase in the southwest regions and a decrease in northeast areas. Moreover, the comparison of the trend methodologies has revealed different trend results (increasing, decreasing, or trendless time series). In particular, this study shows that Şen's method could be successfully used in the evaluation of peak and low values of data for the trend analysis of seasonal and annual rainfall values. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the Weather Research and Forecasting (WRF) model in simulating wet and dry West African monsoon (WAM) rainfall seasons. Three model experiments with varying selected microphysics (MP), cumulus convection (CU), and planetary boundary layer (PBL) schemes based on previous study were performed. Each of the model combinations is used to run four WAM seasons that consist of two wet (2008 and 2010) and two dry years (2001 and 2011). To investigate the behavior of WAM in the context of wet and dry years, the four seasons were used to compute composites of wet and dry WAM seasons in terms of rainfall amount. The analyses majorly focus on the rainfall composites relative to rainfall from Global Precipitation Climatology Project (GPCP) and Tropical Rainfall Measurement Mission (TRMM) as well as temperature, moisture, and atmospheric circulation fields with respect to NCEP reanalyses. This study documents significant sensitivity in simulation of the West African monsoon to the choices of the MP, CU, and PBL schemes. The simulation with the combination of WRF single moment 5 (WSM5) MP, Yonsei University (YSU) PBL, and new Simplified Arakawa-Schubert CU (WSM5-YSU-nSAS) shows good spatial distribution pattern of rainfall and the dynamics associated with the monsoon. Quantitatively, the combination shows less agreement in distinguishing the selected WAM seasons compared with the Goddard MP, Mellor-Yamada-Janjic PBL, and Betts-Miller-Janjić CU (GD-MYJ-BMJ) and the WSM5, Mellor-Yamada-Nakanishi-Niino 2.5 level and new Tiedtke CU (WSM5-MYNN-nTDK). Also, the dynamical structures of the wet and dry WAM circulation composites are reasonably reproduced in GD-MYJ-BMJ and WSM5-YSU-nSAS. The GD-MYJ-BMJ was able to distinguish between wet and dry years and thus underscores its potential to reproduce climate change signals in future work. [ABSTRACT FROM AUTHOR]

This paper analyses a recently created continuous 305-year (1711–2016) monthly rainfall series for the island of Ireland. The findings are as follows. The excess skewness in the monthly series may be eradicated by using a Box-Cox transformation with parameter equal to 0.6: a value very similar to that found for the U.K. and its regions. There is no evidence of either an overall stochastic trend or of evolving monthly seasonal patterns, but positive linear trends are found for January, March, and December and a negative linear trend is found for July. Analysis of the seasonal and annual series (which require no transformation) confirms the implication from the monthly data that winters have become progressively wetter and summers progressively drier, with the positive linear trend for winter being twice the size of the negative summer trend. Since there is no trend in either spring or autumn rainfall, annual rainfall shows a positive linear trend. Given that the rainfall series exists for over three centuries, breaks and structural shifts in the model were investigated. Five breaks were identified, three of which occurred in the early portion of the series during the eighteenth century. However, trends were found to be much more stable from the middle of the nineteenth century. For the seasonal series, only a single break, at 1790 for the winter series, was found: it was only after this break that winters became wetter; before then, winter rainfall had a negative trend. In terms of predictability, predictions from the model were found to be more volatile during the second half of the eighteenth century and again from 1976 onwards. [ABSTRACT FROM AUTHOR]

Monitoring the spatio-temporal variability of rainfall regimes in the Amazon basin is difficult because (1) time series of remote sensing–based rainfall estimates are still too short for long-time variability analysis and (2) rain gauge time series are not fully reliable and operational in their current state due to frequent gaps and zero values. The objective of this paper is to introduce a quality control and reconstruction procedure designed to produce a robust database of rain gauge–based daily rainfall in the Amazon basin. Despite the low density and heterogeneous spatial distribution of the rain gauges network, we eliminated unexpected values and produced accurate estimates using spatial and mathematical relationships with neighboring rain gauges. Three reconstruction methods were tested: the nearest neighbor approach (NN), the arithmetic mean with neighboring stations (AM), and the multiple imputation by chained equations used with the predictive mean matching procedure (MICE). The quality of the reconstruction has been assessed through the mean annual rainfall and the mean annual number of rainy days. We concluded that the AM approach performed better at the scale of the whole Amazon basin. This method has then been preferred to reconstruct the whole database of rainfall time series. [ABSTRACT FROM AUTHOR]

Partial trend method (PTM) is an innovative and efficient trend analysis method, especially for sub-trends in different magnitudes. However, it depends on graphs and identifies trends subjectively, which limit the application for a large mass of data and cannot reveal significant variations from natural variability and randomness. To remedy these shortcomings, this paper applies a trend index derived from the PTM plot, and develops a nonparametric bootstrap approach to identify statistically significant trends. The improved PTM is used to detect trends of annual rainfall, monthly rainfall, monsoon rainfall, annual number of rainy days, and average intensity of daily rainfall in Hainan Island from 1950s to 2014. The Mann–Kendall test is also employed for a comparison purpose. The PTM reveals that, except at the Sanya station, annual rainfall and monsoon rainfall generally show an insignificant change in various magnitudes. Rainy days and daily rainfall intensity show a prevailing decreasing and increasing trend, respectively. The opposite trends explain the no trend in annual rainfall. The trends revealed by the PTM and the Mann–Kendall test show high consistency. This demonstrates that the PTM index combined with the nonparametric bootstrap method is a reliable technique. The improved PTM makes it flexible to detect overall and partial trends by graphical analysis, or detect significant trends for a mass of data by an index. Not limited to rainfall variables, this method is hoped to analyze trends in other fields. [ABSTRACT FROM AUTHOR]

In this paper, daily characteristics of the Central Africa rainfall are assessed using the regional model REMO in the framework of contributions to the CORDEX-Africa project. The model is used to dynamically downscale two global climate models (MPI-ESM-LR and EC-EARTH) for the present (1981–2005) and future (2041–2065, 2071–2095) climate under the Representative Concentration Pathway (RCP) 2.6, 4.5, and 8.5 emission scenarios. A substantial spatio-temporal variability of the daily precipitation characteristics is obtained, as well as varying inferences for individual indices. For the present days, both REMO's runs capture reasonably well the mean seasonal rainfall, the frequency of wet days, the threshold of extreme rainfall, and the cumulative frequency of daily rainfall. The model better simulates the frequency of rainy days than their intensity. It is found that origins of model biases differ as a function of regions. Over the continent, boundary conditions tend to influence the spatial distribution of rainfall whereas over oceanic and coastal regions, REMO's physics seems to dominate over the boundary forcing. The projected frequency of wet days shows a decrease along the twenty-first century over most part of the continent. Throughout the century, all scenarios of REMO decrease the rate of rainfall with increasing intensity, and which will be noticeable in the Sahelian region at late twenty-first century. Furthermore, the extreme event thresholds decrease over Sahelian regions and increase along the coastal regions. [ABSTRACT FROM AUTHOR]

The study of climatic variables in large scales with surface meteorological stations is limited due to the low density of these stations in many regions, possible sources of errors related to missing data, and uncertainties about the calibration sensors. Global gridded data (GD) systems can minimize these problems. Thus, studies that validate GDs with "ground truth" are important for several applications such as climate change. The objective of this study was to compare long series of surface data with 10-day estimates of average air temperature (T) and precipitation (P) using data from the European Center for Medium-Range Weather Forecast (ECMWF) and the National Aeronautics and Space Administration (NASA) for important agricultural locations in the states of Minas Gerais and São Paulo in Brazil. Despite the different spatial resolutions between ECMWF and NASA, the purpose of this paper was to evaluate the two data sources as they are readily available. The GD performance was evaluated by linear regression analysis. Analyses were performed for each meteorological variable for entire years and separated by seasons. The estimates of T from both ECMWF and NASA systems were accurate with the minimum Willmott concordance index (d) and RMSEp of 0.86, 0.37 °C, respectively, and precision with R2 0.61. The estimates of P had a minimum R2, d, and RMSEp of 0.48, 0.79, 2.15 °C respectively. The decreasing orders of (R2) were autumn > winter > spring > summer for T and winter > autumn > spring > summer for P, varying from 0.93 to 0.61 for T and from 0.77 to 0.48 for P. [ABSTRACT FROM AUTHOR]

The Mediterranean region is an area potentially vulnerable to climatic changes. In fact, it is characterized by a significant precipitation variability resulting from synoptic dynamics of hazardous events moving and evolving along the Mediterranean basin. In this paper, the results of an investigation on rainfall trend in the Sardinia region are presented. The trend analysis has been performed by means of the Mann–Kendall test and of the Theil–Sen estimator applied on annual, seasonal, and monthly scales. To this aim, a monthly database of precipitation registered at 158 rain gauges, with an average observation of 84 years, was used for the analyses. The results mainly evidenced a rainfall amount reduction in the winter months and an increase during the summer months. The monthly rainfall distribution throughout the year was also investigated by means of the precipitation concentration index (PCI). A negative trend emerged and, more specifically, a tendency towards a more uniform rainfall distribution on the Sardinia region throughout the year. [ABSTRACT FROM AUTHOR]

In this paper, we examine the statistical properties of rainfall data and temperature in six sub-Saharan African countries in the western, eastern, and southern regions (Botswana, Ethiopia, Ghana, Nigeria, Uganda, and South Africa) using time series data spanning between 1900 and 2012. By using linear trends, seasonality, and long-range dependence models, in fractional or I(d) frameworks, the results first indicate that time trends are required in most cases to explain the time series properties of the climatic series. Evidence of anti-persistence (d < 0) or I(0) behavior is found for the rainfall data, while long memory (d > 0) is found for the temperature data. Evidence of structural breaks are only found in the cases of Ethiopia, Ghana, and Uganda for the temperature data. With both series displaying significant evidence of seasonality and by working with the seasonally differenced data, the results show evidence of I(0) behavior or anti-persistence (d < 0) for the rainfall data but long memory (d > 0) for the temperature data. Testing the causality between the two variables, the results indicate evidence of causality in the two directions in all cases except for the case of the temperature on the rainfall in South Africa. The implication of the results obtained here is that erratic or constant rainfall is expected in Africa in the future while temperature is likely to continue to increase, and these subsequently lead to future warming experiences. [ABSTRACT FROM AUTHOR]

In this study, region-of-influence (ROI) approach was used for modeling extreme values of 24-h maximum rainfall using seven groups of defined attributes including climatic, geographical, and statistical attributes and their hybrids. According to the degree of importance and the role of each defined attribute, different weighting scenarios were defined for each of the attributes relating to the corresponding group. The goal of this paper is to select the best weighting scenario for attributes in the ROI model for estimating more accurate and reliable quantiles of 24-h maximum rainfall over Urmia Lake Basin. The investigation of 140 pooling groups built with the ROI approach showed that each of the seven groups of defined attributes has a different performance in the estimation of quantiles. Moreover, results showed that, in most cases, performance of the models with weighted attributes was better than that of the non-weighted ones. Among the seven categories, the statistical attributes and their combination with climatic and geographical attributes had the best estimation of quantiles in terms of low relative error. In addition, results showed that skewness plays an effective role in the estimation of quantiles. In general, it can be concluded that by applying appropriate weights for available attributes in regions without data, the acceptable and reliable results of extreme values can be earned at ungauged stations. Furthermore, results showed that the use of median of metric distance as one of the threshold options can obtain better results in the estimation of quantiles. [ABSTRACT FROM AUTHOR]

Climate variability has been a threat to the socio-economic development of Ethiopia. This paper examined the changes in rainfall, minimum, and maximum temperature extremes of Jemma Sub-Basin of the Upper Blue Nile Basin for the period of 1981 to 2014. The nonparametric Mann-Kendall, seasonal Mann-Kendall, and Sen's slope estimator were used to estimate annual trends. Ten rainfall and 12 temperature indices were used to study changes in rainfall and temperature extremes. The results showed an increasing trend of annual and summer rainfall in more than 78% of the stations and a decreasing trend of spring rainfall in most of the stations. An increase in rainfall extreme events was detected in the majority of the stations. Several rainfall extreme indices showed wetting trends in the sub-basin, whereas limited indices indicated dryness in most of the stations. Annual maximum and minimum temperature and extreme temperature indices showed warming trend in the sub-basin. Presence of extreme rainfall and a warming trend of extreme temperature indices may suggest signs of climate change in the Jemma Sub-Basin. This study, therefore, recommended the need for exploring climate induced risks and implementing appropriate climate change adaptation and mitigation strategies. [ABSTRACT FROM AUTHOR]

This paper assesses the spatiotemporal characteristics of agricultural droughts and wet conditions in the Free State Province of South Africa for the period between 1960 and 2013. Since agriculturally, the Free State Province is considered the bread basket of the country, understanding the variability of drought and wet conditions becomes necessary. The Standardised Precipitation Index (SPI) computed from gridded monthly precipitation data was used to assess the rainfall extreme conditions. Hot spot analysis was used to divide the province into five homogenous clusters where the spatiotemporal characteristics for each cluster were analysed. The results show a west to east increase in seasonal average total precipitation. However, the eastern part of the province demonstrates higher occurrences of droughts, with SPI ≤ − 1.282. This is despite the observation that the region shows a recent increase in droughts unlike the western region. It is also noted that significant differences in drought/wet intensities between clusters are more pronounced during the early compared to the late summer period. [ABSTRACT FROM AUTHOR]

Given the general notion that dynamical downscaling leads to added accuracy in both historical simulations as well as climate change projections, this paper investigates its validity over India using historical data (1975-2005) from the CORDEX models and their driving global climate models (GCMs) from Coupled Model Intercomparison Project Phase 5 (CMIP5), and comparing them against observed temperature and rainfall. We find that downscaling invariably leads to an improvement in the spatial pattern of surface air temperature, but compared to the driving GCMs, the errors in magnitude after downscaling are even worse in some cases. In regard to JJAS rainfall simulations, the CMIP5 driving GCMs are found to be superior to their dynamically downscaled counterparts both in terms of spatial patterns as well as magnitude of errors. Both CMIP5 driving GCMs as well as the CORDEX models underestimate rainfall during JJAS; however, negative bias in CORDEX models is worse. Unlike the driving CMIP5 GCMs, their dynamically downscaled counterparts simulate an early onset followed by a slow and late withdrawal of the Indian summer monsoon rainfall. The frequency of occurrence of rainfall intensities is simulated well by both sets of models in the lower intensity regime (0-20 mm/day); however, for higher intensities, the driving CMIP5 GCMs underestimate whereas the CORDEX models overestimate. [ABSTRACT FROM AUTHOR]

In this paper, a statistical forecast model using the time-scale decomposition method is established to do the seasonal prediction of the rainfall during flood period (FPR) over the middle and lower reaches of the Yangtze River Valley (MLYRV). This method decomposites the rainfall over the MLYRV into three time-scale components, namely, the interannual component with the period less than 8 years, the interdecadal component with the period from 8 to 30 years, and the interdecadal component with the period larger than 30 years. Then, the predictors are selected for the three time-scale components of FPR through the correlation analysis. At last, a statistical forecast model is established using the multiple linear regression technique to predict the three time-scale components of the FPR, respectively. The results show that this forecast model can capture the interannual and interdecadal variation of FPR. The hindcast of FPR during 14 years from 2001 to 2014 shows that the FPR can be predicted successfully in 11 out of the 14 years. This forecast model performs better than the model using traditional scheme without time-scale decomposition. Therefore, the statistical forecast model using the time-scale decomposition technique has good skills and application value in the operational prediction of FPR over the MLYRV. [ABSTRACT FROM AUTHOR]

In this paper, a nonhydrostatic Weather Research and Forecasting (WRF) model has been used to simulate the extreme precipitation event of 25 November 2009, over Jeddah, Saudi Arabia. The model is integrated in three nested (27, 9, and 3 km) domains with the initial and boundary forcing derived from the NCEP reanalysis datasets. As a control experiment, the model integrated for 48 h initiated at 0000 UTC on 24 November 2009. The simulated rainfall in the control experiment depicts in well agreement with Tropical Rainfall Measurement Mission rainfall estimates in terms of intensity as well as spatio-temporal distribution. Results indicate that a strong low-level (850 hPa) wind over Jeddah and surrounding regions enhanced the moisture and temperature gradient and created a conditionally unstable atmosphere that favored the development of the mesoscale system. The influences of topography and heat exchange process in the atmosphere were investigated on the development of extreme precipitation event; two sensitivity experiments are carried out: one without topography and another without exchange of surface heating to the atmosphere. The results depict that both surface heating and topography played crucial role in determining the spatial distribution and intensity of the extreme rainfall over Jeddah. The topography favored enhanced uplift motion that further strengthened the low-level jet and hence the rainfall over Jeddah and adjacent areas. On the other hand, the absence of surface heating considerably reduced the simulated rainfall by 30% as compared to the observations. [ABSTRACT FROM AUTHOR]

When persistent rainfall occurs frequently over South China, meso-scale and micro-scale synoptic systems persist and expand in space and time and eventually form meso-scale and long-scale weather processes. The accumulation of multiple torrential rain processes is defined as a 'cumulative effect' of torrential rain (CETR) event. In this paper, daily reanalysis datasets collected by the National Centers for Environmental Prediction-Department of Energy (NCEP-DOE) during 1979-2014 are used to study the anomalous features and causes of heavy CETR events over South China. The results show that there is a significant difference in the spatial distribution of the heavy CETR events. Based on the center position of the CETR, the middle region displayed middle-region-heavy CETR events while the western region displayed west-region-heavy CETR events. El Niño events in the previous period (December, January, February, March (DJFM)) are major extra-forcing factors of middle-region-heavy CETR events, which is beneficial for the continuous, anomalous Philippine Sea anticyclone and strengthens the West Pacific Subtropical High (WPSH), extending it more westward than normal. The primary water vapor source for precipitation in middle-region-heavy CETR events is the Tropical Western Pacific Ocean. The major extra-forcing factor of a west-region-heavy CETR is the negative anomaly in the southern Tropical Indian Ocean (TIO) during the previous period (DJFM). This factor is beneficial for strengthening the cross-equatorial flow and westerly winds from the Bay of Bengal to the South China Sea (SCS) and early SCS summer monsoon onset. The primary water vapor source of precipitation in the west-region-heavy CETR is the southern TIO. [ABSTRACT FROM AUTHOR]

This paper attempts to predict food shortages in advance from the analysis of rainfall during the monsoon months along with other inputs used for crop production, such as land used for cereal production, percentage of area covered under irrigation and fertiliser use. We used six binary classification data mining models viz., logistic regression, Multilayer Perceptron, kernel lab-Support Vector Machines, linear discriminant analysis, quadratic discriminant analysis and k-Nearest Neighbors Network, and found that linear discriminant analysis and kernel lab-Support Vector Machines are equally suitable for predicting per capita food shortage with 89.69 % accuracy in overall prediction and 92.06 % accuracy in predicting food shortage ( true negative rate). Advance information of food shortage can help policy makers to take remedial measures in order to prevent devastating consequences arising out of food non-availability. [ABSTRACT FROM AUTHOR]

The aim of this paper is to identify relative roles of different land-atmospheric conditions, apart from sea surface temperature (SST), in determining early vs. late summer monsoon intensity over India in a high resolution general circulation model (GCM). We find that in its early phase (June-July; JJ), pre-monsoon land-atmospheric processes play major role to modulate the precipitation over Indian region. These effects of pre-monsoon conditions decrease substantially during its later phase (August-September; AS) for which the interannual variation is mainly governed by the low frequency northward propagating intraseasonal oscillations. This intraseasonal variability which is related to mean vertical wind shear has a significant role during the early phase of monsoon as well. Further, using multiple linear regression, we show that interannual variation of early and late monsoon rainfall over India is best explained when all these land-atmospheric parameters are taken together. Our study delineates the relative role of different processes affecting early versus later summer monsoon rainfall over India that can be used for determining its subseasonal predictability. [ABSTRACT FROM AUTHOR]

Lake Urmia, in northwest Iran, is the second most saline lake in the world. During the past two decades, the level of water has markedly decreased. In this paper, climate of the lake region is investigated by using data from four meteorological stations near the lake. The data include climatic parameters such as temperature, precipitation, humidity, wind speed, sunshine hours, number of rain days, and evaporation. Climate around the lake is examined by way of climate classification in the periods before and after the reduction in water level. Rainfall in the lake catchment is also evaluated using both gauge and satellite data. The results show a significant decreasing trend in mean annual precipitation and wind speed and an increasing trend in annual average temperature and sunshine hours at the four stations. Precipitation and wind speed have decreased by 37 mm and 2.7 m/s, respectively, and the mean annual temperature and sunshine hours have increased by 1.4 °C and 41.6 days, respectively, over these six decades. Only the climate of the Tabriz region is seen to have significantly changed, going from semiarid to arid. Gauge records and satellite data show a large-scale decreasing trend in rainfall since 1995. The correlation between rainfall and year-to-year changes in lake level is 0.69 over the period 1965 to 2010. The relationship is particularly strong from the early 1990s to 2005. This suggests that precipitation has played an important role in the documented decline of the lake. [ABSTRACT FROM AUTHOR]

The relationship and linkage of Siberian High (SH) with winter climate over central Asia, East Asia, and Eurasia have been focused in the available literature. The effect of Siberian High over the South Asian region has received little attention. Thus, this article examines the possible teleconnection of SH pressure on winter precipitation variability over the region of South Asia. Moreover, the recent research studies focused on large-scale circulation phenomena: El Nino Southern Oscillation (ENSO) and North Atlantic Oscillation (NAO), and regarded them as responsible for variability of winter rainfall. This paper gives the evidence that the winter rainfall variability is strongly influenced by Siberian High pressure. The effect of Siberian High pressure on winter precipitation over the South Asian region (65-76° E, 33-40° N) has been investigated using singular value decomposition analysis (SVDA) that is performed on gridded datasets of NCEP-NCAR Reanalysis and Climate Research Unit (CRU) of University of East Anglia, UK. SVDA isolates the leading pairs of the coupled signals that mainly explain the relationship between the coupled fields. By employing SVDA, it is found that the Siberian High has dominant impact on precipitation variability during winter season over Northern areas of South Asia. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid model to better predict Indian summer monsoon rainfall. The algorithm considers suitable techniques for processing dense datasets. The proposed three-step algorithm comprises closed itemset generation-based association rule mining for feature selection, cluster membership for dimensionality reduction, and simple logistic function for prediction. The application of predicting rainfall into flood, excess, normal, deficit, and drought based on 36 predictors consisting of land and ocean variables is presented. Results show good accuracy in the considered study period of 37years (1969-2005). [ABSTRACT FROM AUTHOR]

The present paper aims of computing climatology and trend analysis of occurrence and intensity of extreme events of precipitation in subregions of Northeast Brazil (NEB). We used daily rainfall data of 148 rain gauges collected from the hydrometeorological network managed by the National Water Agency during 1972 to 2002 and used quantiles technique in order to select rainfall events. Defining heavy rainfall events as those when at least one rain gauge recorded rainfall above the 95th percentile, normal rainfall was between the 45th and 55th percentiles, and weak rainfall events were under the 5th percentile. The Mann-Kendall nonparametric test was used to calculate the linear trend of the quantity and intensity of rainfall events. The NEB was divided in five subregions using the cluster analysis based on Euclidean distance and Ward's method: Northern coast, Northern semiarid, Northwest, Southern semiarid, and Southern coast. The results suggest that the subregions are less influenced by El Niño and La Niña, and dry areas have higher variability, with the greatest number of intense events. [ABSTRACT FROM AUTHOR]

This paper appraises the association of lightning flash count (FC) with rainfall using the satellite-borne Lightning Imaging Sensor's (LIS) data along with gridded rainfall data (0.5 × 0.5) for Indian summer monsoon seasons over 10 years (2001-2010). During strong El Nino years, 2002 and 2009, FCs were greater in magnitude by about 26.5 % and 37 %, than the long-term average, respectively, while during weak El Nino year (2004), it was more by 8 %. During the same years, the rainfall was deficient by about 10 % than the long-term average. Similarly, a rise in aerosol optical depth (AOD) over its average value (by about 15 % and 20 %) reduces the ratio of rainfall to FC (RLR) by 41 % and 44 % for strong El Nino years 2002 and 2009, respectively, and for weak El Nino year (2004), a 6.5 % rise in AOD lowers the RLR by 20 %. Bowen ratio more by 11 % and 17 % of its average value reduces the RLR by 41 % and 44 % for strong El Nino years 2002 and 2009, respectively, and, also, Bowen ratio higher by 8 % for 2004 declines RLR by 20 %. On the other hand, Bowen ratio less by 9 % and 6 % raises the RLR by 19 % and 56 % for moderate La Nina year (2007) and strong La Nina year (2010), respectively. Results for the daily rainfall, AOD and Bowen ratio over Indian regions, are discussed for strong El Nino and La Nina years. Correlations of FC with AOD and Bowen ratio of 0.66 and 0.71, respectively, while, that of FC with ONI of 0.56 indicates numerous (fewer) break days during El Nino (La Nina) years. [ABSTRACT FROM AUTHOR]

Consecutive dry days (CDDs) can serve as an effective measure of extreme precipitation and seasonal droughts. Using daily rain gauge data from 718 stations in China for the years 1961-2012, this paper documents the spatial and temporal characteristics of CDDs in the cold season (November to March) and the warm season (April to October). The results show that the long-term linear trends in CDDs have significant regional and seasonal differences. In the cold season, 57 % of the stations record decreases in CDDs, whereas in the warm season, 67 % of the stations record increasing trends. Applying the Ensemble Empirical Mode Decomposition (EEMD) to CDDs in selected regions, we found that 3-year oscillations account for most of the variance in CDDs, and this interannual variability is more pronounced in the cold season than in the warm season. Particularly in South China, CDDs in the cold season are most affected by oscillation of about a 3.3-year period. The decadal oscillations in CDDs over North China and the middle-lower reaches of the Yangtze River show larger variance than do other regions. Over North China, decadal variations in CDDs in both seasons are similar, with high phases during the 1990s and greater amplitude during the last 20 years, whereas over middle-lower reaches of Yangtze River, the amplitudes of decadal oscillations in CDDs decreased since the 1990s in both seasons. In terms of secular trends, significant increases in CDDs over Southwest China in the cold season and South China in the warm season are noteworthy as they contribute largely to CDD variance and experience sharper slopes in recent years. [ABSTRACT FROM AUTHOR]

Ground soil heat flux, G , is a difficult-to-measure but important component of the surface energy budget. Over the past years, many methods were proposed to estimate G ; however, the application of these methods was seldom validated and assessed under different weather conditions. In this study, three popular models (force-restore, conduction-convection, and harmonic) and one widely used method (plate calorimetric), which had well performance in publications, were investigated using field data to estimate daily G on clear, cloudy, and rainy days, while the gradient calorimetric method was regarded as the reference for assessing the accuracy. The results showed that harmonic model was well reproducing the G curve for clear days, but it yielded large errors on cloudy and rainy days. The force-restore model worked well only under rainfall condition, but it was poor to estimate G under rain-free conditions. On the contrary, the conduction-convection model was acceptable to determine G under rain-free conditions, but it generated large errors on rainfall days. More importantly, the plate calorimetric method was the best to estimate G under different weather conditions compared with the three models, but the performance of this method is affected by the placement depth of the heat flux plate. As a result, the heat flux plate was recommended to be buried as close as possible to the surface under clear condition. But under cloudy and rainy conditions, the plate placed at depth of around 0.075 m yielded G well. Overall, the findings of this paper provide guidelines to acquire more accurate estimation of G under different weather conditions, which could improve the surface energy balance in field. [ABSTRACT FROM AUTHOR]

This paper aimed at the analysis of rainfall seasonality and variability for the northern part of South-Asian country, Bangladesh. The coefficient of variability was used to determine the variability of rainfall. While rainfall seasonality index ( SI ) and mean individual seasonality index ( $$ \overline{SI_i} $$ ) were used to identify seasonal contrast. We also applied Mann-Kendall trend test and sequential Mann-Kendall test to determine the trend in seasonality. The lowest variability was found for monsoon among the four seasons whereas winter has the highest variability. Observed variability has a decreasing tendency from the northwest region towards the northeast region. The mean individual seasonality index (0.815378 to 0.977228) indicates that rainfall in Bangladesh is 'markedly seasonal with a long dry season.' It was found that the length of the dry period is lower at the northeastern part of northern Bangladesh. Trend analysis results show no significant change in the seasonality of rainfall in this region. Regression analysis of $$ \overline{SI_i} $$ and SI, and longitude and mean individual seasonality index show a significant linear correlation for this area. [ABSTRACT FROM AUTHOR]

In this paper, validation of rain climatic zone classifications for Nigeria is presented based on global radio-climatic models by the International Telecommunication Union-Radiocommunication (ITU-R) and Crane. Rain rate estimates deduced from several ground-based measurements and those earlier estimated from the precipitation index on the Tropical Rain Measurement Mission (TRMM) were employed for the validation exercise. Although earlier classifications indicated that Nigeria falls into zones P, Q, N, and K for the ITU-R designations, and zones E and H for Crane's climatic zone designations, the results however confirmed that the rain climatic zones across Nigeria can only be classified into four, namely P, Q, M, and N for the ITU-R designations, while the designations by Crane exhibited only three zones, namely E, G, and H. The ITU-R classification was found to be more suitable for planning microwave and millimeter wave links across Nigeria. The research outcomes are vital in boosting the confidence level of system designers in using the ITU-R designations as presented in the map developed for the rain zone designations for estimating the attenuation induced by rain along satellite and terrestrial microwave links over Nigeria. [ABSTRACT FROM AUTHOR]

The availability of spatial, high-resolution rainfall data is one of the most essential needs in the study of water resources. These data are extremely valuable in providing flood awareness for dense urban and industrial areas. The first part of this paper applies an optimization-based method to the calibration of radar data based on ground rainfall gauges. Then, the climatological Z-R relationship for the Sahand radar, located in the East Azarbaijan province of Iran, with the help of three adjacent rainfall stations, is obtained. The new climatological Z-R relationship with a power-law form shows acceptable statistical performance, making it suitable for radar-rainfall estimation by the Sahand radar outputs. The second part of the study develops a new heterogeneous random-cascade model for spatially disaggregating the rainfall data resulting from the power-law model. This model is applied to the radar-rainfall image data to disaggregate rainfall data with coverage area of 512 × 512 km to a resolution of 32 × 32 km. Results show that the proposed model has a good ability to disaggregate rainfall data, which may lead to improvement in precipitation forecasting, and ultimately better water-resources management in this arid region, including Urmia Lake. [ABSTRACT FROM AUTHOR]