Seasonal Rainfall Variability Research Articles

Machine Learning Assessment of the Impact of Global Warming on the Climate Drivers of Water Supply to Australia’s Northern Murray-Darling Basin

Droughts and long dry spells, interspersed with intense rainfall events, have been characteristic of the northern Murray-Darling Basin (NMDB), a major Australian agricultural region. The NMDB precipitation results from weather systems ranging from thunderstorms to larger scale events. The larger scale events exhibit high seasonal and annual rainfall variability. To detect attributes shaping the NMDB precipitation patterns, and hence net water inflows to the vast Darling River catchment area, numerous (45) possible attributes were assessed for their influence on rainfall trends. Four periods were assessed: annual, April–May (early cool-season), June–September (remaining cool-season), and October–March (warm-season). Linear and non-linear regression machine learning (ML) methods were used to identify the dominant attributes. We show the impact of climate drivers on the increasingly dry April–May months on annual precipitation and warmer temperatures since the early 1990s. The NMDB water supply was further reduced during 1992–2018 by the lack of compensating rainfall trends for the April–May decline. The identified attributes include ENSO, the Southern Annular Mode, the Indian Ocean Dipole, and both local and global sea surface temperatures. A key finding is the prominence of global warming as an attribute, both individually and in combination with other climate drivers.

High‐resolution climate characteristics of the Arabian Gulf based on a validated regional reanalysis

AbstractThe regional climate of the Arabian Gulf (AG) and its variability are examined based on a 40‐year (1980–2019), 5‐km regional reanalysis of the Arabian Peninsula (AP reanalysis). The AP reanalysis fields were first validated against the available observations over the AG, suggesting that this high‐resolution reanalysis well reproduces the spatio‐temporal features of the AG atmospheric circulations. The validated AP reanalysis fields were then analysed to examine the climatic characteristics over the AG including the monthly mean, maximum and minimum temperatures, and the seasonal variations in winds, relative humidity and rainfall over the AG. The AG climate is mostly dry between May and October, and experiences moderate rainfall between December and January. The higher (lower) pressure difference between the northwest and southeast AG during summer (winter) generates the northwesterly Shamal winds over the north (central) AG. The mean Shamal winds are relatively stronger (weaker) and prolonged (shorter) during summer (winter); however, the short lived Shamal jet events in winter can be occasionally stronger than summer. In terms of interannual variability, the Shamal winds are stronger and more persistent in summer during El Niño years and in winter during La Niña years. These differences are mainly associated with changes in temperature gradients between the eastern AG and northwestern AP.

Spatiotemporal water dynamic modelling of Ramsar-listed lakes on the Victorian Volcanic Plains using Landsat, ICESat-2 and airborne LiDAR data

Spatiotemporal dynamic information on surface water area and level is a prerequisite for effective wetland conservation and management. However, such information is either unavailable or difficult to obtain. In this study, for the first time, we leverage Landsat imagery, ICESat-2 and airborne LiDAR data to develop time series of water body dynamics over the last 35 years (1987–2021) using machine learning method on a cloud computing platform for lakes identified as international importance in the Western District Lakes Ramsar site in Victoria, Australia. Our results reveal distinct seasonal (dry and wet) variation patterns and long-term changes in trends of lake water areas and levels in response to seasonal rainfall variations and regional climate changes for the periods of before, during and after the Millennium Drought when southeast Australia experienced unprecedented dry conditions. Lake water bodies have not recovered to the status of pre-Millennium Drought, and many permanent Ramsar-listed lakes in the region have become to ephemeral lakes due to climate change. The outcome of this study provides a baseline to help understand the historical and ongoing status of the Ramsar-listed lakes in a warming and drying climate in support of the development of strategic plan to implement international obligations for wetlands protection under the Ramsar Convention.

Effects of climate change on pastoral households in the Harshin District of the Somali Region, Ethiopia.

This study was conducted in the Harshin District of the Somali Region, Ethiopia, to understand the climate change trends, their consistency with pastoralists’ perceptions and their effects on pastoral households. The study used both qualitative and quantitative data collected from 143 households through household surveys. Focus group discussions and key informant interviews were also employed to triangulate and substantiate the reports from household surveys. Data were analysed using Statistical Package for Social Sciences (SPSS) and chi-square tests to test a degree of significance between the pastoral and agropastoral households for the impact of climate change. Mann–Kendall’s trend test and Sen’s slope estimator were employed to determine climate change trends of the study area. The result showed that pastoral households perceived an increasing trend in annual temperature and a decreasing trend in annual and seasonal rainfall. Mann–Kendall’s trend analysis confirmed pastoral communities’ perceptions of higher temperatures and rainfall variability, with the exception of a long-term decline in rainfall. The findings further indicated that six droughts (one severe and five moderate) were observed for the period 1983–2017. The result indicated that the significant increase in temperature along with high interannual and seasonal rainfall variability have been causing adverse impacts on crop and livestock production. Therefore, there is a need to provide drought-tolerant and early-maturing crops and improved livestock breeds for pastoral households. Water-related interventions such as small-scale irrigation farming and water harvesting during good rainy seasons is also paramount to enhance climate resilience of the local people.

The impacts of seasonal variation and climate on food utilization in a population of critically endangered cotton-top tamarins (Saguinus oedipus) in Colombia: A 22-year longitudinal study.

To examine how precipitation patterns and climate change impact feeding choices made by a population of critically endangered cotton-top tamarins (Saguinus oedipus), we examined 22 years of feeding data (1999-2020) from 21 groups collected at Parque Natural Regional Bosque Seco El Ceibal Mono Tití in Santa Catalina, Colombia. We describe the diet and examine the role of seasonal rainfall and annual variation in rainfall on diet. Rainfall is highly seasonal (mean annual rainfall 1562 mm [range 940-2680 mm]) with a dry, early rainy, and late rainy season in each year. Over 80 species of plants formed part of the fruit, nectar, and exudate components of the diet. Fruits, although available year-round, were more commonly available and consumed during the late rainy seasons (August-November). Exudates were consumed more frequently in the dry season (December-March) and invertebrate consumption was stable across the year. Nectar feeding from a single species (Combretum fruticosum) peaked in November. Rainfall varied over the years, with 13 years exceeding the 99% confidence intervals for mean rainfall. Ten of these extreme years (both drought and extremely wet) occurred in the last 11 years. Fruit consumption did not vary between extreme and average years, but cotton-top tamarins consumed more invertebrates and exudates in wet years. Presently, cotton-top tamarins appear to be able to cope with these extreme variations in rainfall due to their highly varied diet. However, the forests that these primates depend upon for survival are threatened by human exploitation making it critically important to maintain a generalist feeding strategy for survival as many fruiting trees that compose a large proportion of the diet are removed. As conservation efforts continue, plant species consumed by cotton-top tamarins provide useful data when selecting species for habitat restoration programs.

Spatial Modelling of Vineyard Erosion in the Neszmély Wine Region, Hungary Using Proximal Sensing

Intense soil erosion in the northern part of the Gerecse Hills, Hungary, is causing significant damage to vineyards in the area. Three vineyards in the Neszmély Wine Region were investigated to quantify the amount of eroded soils. The method was based on monitoring vineyards for one-year between July 2019 and June 2020. Every season, a set of photographs of the vineyards were taken from an unmanned aerial vehicle. The images were processed in a photogrammetric workflow to produce high-resolution digital terrain models (DTMs) and orthophotos, which were used to estimate the soil loss using the Universal Soil Loss Equation (USLE) model. Particular attention was paid to the effect of seasonal variation in vegetation cover and rainfall, and the erosion control effect of the inter-row grassing already applied in the vineyards was also modelled. The results confirm and quantify the extent to which intense summer rainfall has a more significant effect on erosion compared to autumn or winter rainfall.

Seasonal sub-basin-scale runoff predictions: A regional hydrometeorological Ensemble Kalman Filter framework using global datasets

Study regionThe São Francisco River Basin (SFRB) in Brazil Study focusIn semi-arid regions, interannual variability of seasonal rainfall and climate change is expected to stress water availability and increase the recurrence and intensity of extreme events such as droughts or floods. Local decision makers therefore need reliable long-term hydro-meteorological forecasts to support the seasonal management of water resources, reservoir operations and agriculture. In this context, an Ensemble Kalman Filter framework is applied to predict sub-basin-scale runoff employing global freely available datasets of reanalysis precipitation (ERA5-Land) as well as bias-corrected and spatially disaggregated seasonal forecasts (SEAS5-BCSD). Runoff is estimated using least squares predictions, exploiting the covariance structures between runoff and precipitation. The performance of the assimilation framework was assessed using different ensemble skill scores. New hydrological insights for the regionOur results show that the quality of runoff predictions are closely linked to the performance of the rainfall seasonal predictions and allows skillful predictions up to two months ahead in most sub-basins. The anthropogenic conditions such as in the Western Bahia state, however, must be taken under consideration, since non-stationary runoff time-series have poorer skill as such unnatural variations can not be captured by long-term covariances. In sub-basins which are dominated by little anthropogenic influence, the presented framework provides a promising and easily transferable approach for skillful operational seasonal runoff predictions on sub-basin scale.

Influence of physical environmental parameters on the abundance of Chrysichthys nigrodigitatus of the Cross River, Nigeria.

In this study, the influence of physical environmental factors (rainfall, temperature, water depth, turbidity, transparency and conductivity) on the temporal distribution, abundance and catch of C. nigrodigitatus from the Cross River was investigated. Fish catch and abundance were monitored fortnightly for 12 months. Surface water samples were collected twice monthly and analyzed using standard methods. Seasonal variations in rainfall, water depth, temperature, turbidity, transparency and conductivity were observed across the study period. Correlation analysis showed a positive and significant relationship between rainfall and fish catch (P<0.05), while temperature and conductivity were negatively correlated with fish catch (P<0.05 and 0.01 respectively). Transparency was negatively correlated with fish catch but this relationship was, however, not statistically significant (P>0.05). On the other hand, water depth and turbidity were positively correlated with fish catch but again both relationships were not statistically significant (P>0.05). These findings suggest that rainfall, temperature, conductivity and other environmental variables play important role(s) in the distribution and abundance of Chrysichthys nigrodigitatus in the Cross River, with implication of climate change impact on the species reproductive ecology and population dynamics.

A Novel Hybrid Approach for Predicting Western Australia’s Seasonal Rainfall Variability

In this paper, 100 years of uninterrupted rainfall data for 12 rainfall stations (four rainfall stations from each region) in Western Australia were analyzed against respective dominant climate indices, and representative prediction models were developed using ARIMAX, GEP, and a hybrid technique (GEP-ARIMAX). Statistical performance evaluators such as Pearson correlation (r), root mean square error (RMSE), mean absolute error (MAE), and refined Willmot index of agreement ({d}_{r}) were used to evaluate the prediction performance of the developed models. These models demonstrated their capability to predict up to four months in advance with Pearson correlation (r) values ranging from 0.53 to 0.83, 0.75 to 0.85, and 0.87 to 0.95 for ARIMAX, GEP, and hybrid (GEP-ARIMAX) models respectively. While compared, the hybrid (GEP-ARIMAX) model showed superior prediction performance in both calibration and validation periods with Pearson correlation (r) and refined Willmot index of agreement ({d}_{r}) values were as high as 0.96 and 0.84 respectively. This paper demonstrated a novel hybrid GEP-ARIMAX model showing significantly good rainfall forecasting capability than conventional linear and non-linear models.

Rainfall variability and trends over Rwanda

Rainfall is regarded as one of the foundational concepts for comprehending climate variability and/or change. This paper aimed at examining the seasonal and annual rainfall variability and its associated trends over Rwanda between 1981 and 2017. The Coefficient of Variation (CV) was used to determine the rainfall variability. Mann-Kendall test and Sen’s slope estimator techniques were used to detect trends and to quantify the magnitude of change. High rainfall variability was observed over eastern Rwanda, around Kigali city and central plateau, while the south-western, western and the north highland revealed lower rainfall variability. Trend analysis of annual and March-May (MAM) rainfall season depicted a significant decrease of -6.7% over south-western region (Kamembe-Aero station) and -9.8% over eastern parts (Kibungo-Kazo) respectively. A significant increase in trend of 4.3% over North-western (Gisenyi-Aero station) during September-December (SOND) season was also obtained. The rest of the stations registered a non-significant trend on both seasonal and annual time scales. Spatially, areas of Kigali City, eastern and central parts of Rwanda revealed a decreasing trend whereas an increasing trend was observed over the western, northern highland, north eastern and southern region except for the areas towards the south-east. The high rainfall variability and significant nature of changing trends demand that rain-dependent sectors of the economy link climate science and policy in order to make proper planning. With proper climate homogeneity zoning, the agricultural sector in particular needs to develop effective techniques that optimize food production including water use rationalisation.

Variability and time series trend analysis of rainfall in the mid-hill sub humid zone: a case study of Nauni.

The spatiotemporal variability of rainfall, particularly in the context of climate change, has been imperative for examining the cropping patterns, farming sustainable crop production, and food security in rainfed areas. To that end, trend analysis was done to study the change in rainfall patterns in the mid-hills of Himachal Pradesh. The study investigated the historical rainfall data from 1971 to 2020 on a monthly, annual, seasonal, and decadal basis by using the variability analysis methods,viz., standard deviation (SD), coefficient of variance (CV), and transformed annual precipitation departure (Z). The trend analysis was also done by Mann-Kendall (MK) and Sen's slope estimator (SSE) test and linear regression model. The annual rainfall in the region was 1115.1mm, which showed a decreasing trend (Z = - 0.79mm/year). Based on the linear regression model, the decrease in annual rainfall was about - 2.28mm/year. The monthly and seasonal variability of rainfall exhibited a sensitivity to change. The months of January, April, July, and September showed an increasing trend, whereas the rest of the other months showed a decreasing trend. The seasonal rainfall (summer, monsoon, and post-monsoon) showed a decreasing trend, whereas the winter season depicted an increasing trend. During the entire study period, 1988 recorded as the wettest year, with highest annual rainfall of about 2205.0mm and monsoon rainfall of about 1653.0mm. The highest annual (2205.0mm) and monsoon (1653.0mm) rainfall was recorded in the year 1988. The decadal analysis of the rainfall on an annual basis revealed a decrease in rainfall during the periods 1971-1980, 2001-2010, and 2011-2020 as compared to 1981-1990 and 1991-2000. The rainfall over the study region confirms the strength of the change in trend.Thus, the erratic rainfall pattern makes the cropping calendar shorter and affects the agricultural productivity.

Long-distance air pressure differences correlate with European rain

Precipitation in Europe shows natural fluctuations that differ considerably between seasons and geographical regions. A number of studies have linked local or seasonal rainfall variability to various long-distance air pressure differences in north–south or west–east direction. This paper presents the first continent-wide analysis of European rainfall variability on a month-by-month and country-by-country basis. We calculated Pearson r values for unsmoothed monthly rainfall data of 39 European countries over the period 1950–2019 with five potential climatic drivers, namely the North Atlantic Oscillation (NAO), the Arctic Oscillation (AO), the North Sea Caspian Pattern (NCP), and two indices of Mediterranean Oscillation (MOI2, WeMOI). For a variety of countries and months we found substantial and statistically significant r values of up to r = 0.7 and more. The dynamic temporal-spatial evolution of the Pearson correlations was mapped out across the continent, tracking the gradual or abrupt expansion, displacement and subsequent waning of the various effects over the course of the year. The correlation analysis was complemented by best subset multiple regression, controlling for intercorrelation of the potential drivers. Our results may help to improve short- to midterm rainfall prognoses in Europe and provide important calibration data for the further refinement of climate models.

Burden, Clinical Characteristics, Risk Factors, and Seasonality of Adenovirus 40/41 Diarrhea in Children in Eight Low-Resource Settings

BackgroundThe application of molecular diagnostics has identified enteric group adenovirus serotypes 40 and 41 as important causes of diarrhea in children. However, many aspects of the epidemiology of adenovirus 40/41 diarrhea have not been described.MethodsWe used data from the 8-site Etiology, Risk Factors, and Interactions of Enteric Infections and Malnutrition and the Consequences for Child Health and Development Project birth cohort study to describe site- and age-specific incidence, risk factors, clinical characteristics, and seasonality.ResultsThe incidence of adenovirus 40/41 diarrhea was substantially higher by quantitative polymerase chain reaction than enzyme immunoassay and peaked at ∼30 episodes per 100 child-years in children aged 7–15 months, with substantial variation in incidence between sites. A significant burden was also seen in children 0–6 months of age, higher than other viral etiologies with the exception of rotavirus. Children with adenovirus 40/41 diarrhea were more likely to have a fever than children with norovirus, sapovirus, and astrovirus (adjusted odds ratio [aOR], 1.62; 95% CI, 1.16–2.26) but less likely than children with rotavirus (aOR, 0.66; 95% CI, 0.49–0.91). Exclusive breastfeeding was strongly protective against adenovirus 40/41 diarrhea (hazard ratio, 0.64; 95% CI, 0.48–0.85), but no other risk factors were identified. The seasonality of adenovirus 40/41 diarrhea varied substantially between sites and did not have clear associations with seasonal variations in temperature or rainfall.ConclusionsThis study supports the situation of adenovirus 40/41 as a pathogen of substantial importance, especially in infants. Fever was a distinguishing characteristic in comparison to other nonrotavirus viral etiologies, and promotion of exclusive breastfeeding may reduce the high observed burden in the first 6 months of life.

The Effect of Social Networks on Smallholder Farmers’ Decision to Join Farmer-Base Seed Producer Cooperatives (FBSc): The Case of Hararghe, Oromia, Ethiopia

The paper presents the results of analysis conducted to empirically establish the role of social network in smallholder farmers’ decision to join seed producer cooperative, in Hararghe, Oromia, Ethiopia. We used a ‘random matching within sample’ technique to generate data on social links and resources shared like information among their links. Average treatment effects were used to limit ‘non-awareness’ or ‘selection bias’ on participation rate. An econometric strategy was employed to isolate endogenous effect from correlated and contextual social network effects and analyzed by Probit model. Rainfall data was obtained from NMA for 1986–2018 years and analyzed by coefficient of variation (CV) and standardized anomalies index (Z). The result CV and Z shows intra- and inter seasonal variability of rainfall and, the severity and frequency of drought have increase which works against a single optimum seed source. Rate of membership to FBSc was 75% for exposed sub-sample against 70% for the full sample. This shows the existence of exposure bias. The size of social network, linkage with relatives and extension agents influence farmers’ decision to join FBSc and thus, the presence of endogenous effects of social networks. Thus, participation decision is more likely to be affected by characters and structure of social network. Access to off-farm income and perception on profit influences farmer participation decision where weather-indexed seed insurance can service as an effective strategy to ensure sustained membership. Therefore, any intervention in promotion of FBSc should consider the characters’ and structure of social network and emphasis on indexed-based seed insurance and climate information services as strategy to promote seed producers cooperatives.

Analysis of Climate Variability and Its Impact on Crop Yield Over Waghemra Zone, Ethiopia

Climate variability manifested by seasonal rainfall and temperature variability greatly affects agricultural crop production under rain-fed conditions (main source of income for rural communities). Therefore, this study has focused on analyzing the impacts of climate variability on rain-fed wheat and sorghum production in Waghemra Zone, Ethiopia. Daily rainfall and temperature data were collected from National Meteorological Agency of Ethiopia and crop yield data was collected from Central Statics Agency of Ethiopia. Temporal variability of rainfall, temperature and crop yield was analyzed using coefficient of variation (CV) and standardized anomaly method. The rainfall and temperature trends were assessed via Mann-Kendall test and Sen’s slope estimator. The impacts of climate variability on crop yields were evaluated using correlation and regression analysis. The rainfall analysis exhibited large inter-annual and seasonal variation both in amount and distribution. The rainfall has showed decreasing trends in Belg and increasing in both annual and Kiremt season. Moreover, the detected trends are not significant. In contrast, minimum and maximum temperature showed significantly increasing trends at annual and seasonal time scales. The number of warm days and nights has increased and the number of cold days and nights has decreased. The regression analysis revealed that climate variability over the study area has differential effects on the yield of Wheat and Sorghum with coefficient of determination 86% and 84%, respectively. The observed rainfall and temperature variability has posed major risks to rain-fed agriculture and specific adaptation strategies are needed to cope with the risks, sustain farming and improve food security.

Effects of tillage and seasonal variation of rainfall on soil water content and root growth distribution of winter wheat under rainfed conditions of the Loess Plateau, China

Drought is the leading cause of low and unstable wheat yield in dryland. The field experiment was performed from 2009 to 2017 in the eastern part of Loess Plateau, China, to study the effect of tillage (deep ploughing, DP and no-tillage, NT) and seasonal variation in precipitation on soil moisture and root traits and grain yield of wheat. In dry seasons, soil water content, root length surface density (RLSD), and root diameter peaked at 60 cm depth, but in the normal/wet seasons, peaked at 40 cm depth. The reduction of RLSD at anthesis by drought was 44% under NT and 29% with DP, while the reduction in above-ground dry weight was 67% under NT and 56% under DP. More soil water was depleted from the deep soil layer (80-100 cm depth) than that in the shallow layer (20-60 cm depth) from jointing in the dry seasons, and from anthesis in the normal/wet seasons. The higher amount of effective in-seasonal rainfall in the dry seasons increased RLSD at anthesis by 44% with NT and 21% with DP and increased grain yield by 24.1% under NT and 14.2% under DP. The average grain yield in the dry seasons was 28–41% lower than in the normal/wet seasons. The average water use efficiency (WUE) in the dry seasons was 42–54% lower than that in the normal/wet seasons. WUE with DP was 32% higher than that with NT in the dry seasons, while it was 15% higher than that with NT in the normal/wet seasons. The average contribution rates of soil water content at sowing to grain yield under DP and NT were 23–25% in the dry seasons, and 52–53% in the normal/wet seasons. Thus, deep ploughing in summer fallow should be adopted for high water storage and yield stability.

Evaluation of Spatial-Temporal Characteristics of Rainfall Variations over Thailand Inferred from Different Gridded Datasets

The fidelity of gridded rainfall datasets is important for the characterization of rainfall features across the globe. This study investigates the climatology, interannual variability, and spatial-temporal variations of seasonal rainfall over Thailand during the 1970–2007 period using station data obtained from the Thai Meteorological Department (TMDstn). In addition, the performance of three gridded rainfall datasets, namely APHRODITE, CRU, and GPCC, in reproducing these seasonal rainfall features were intercompared and further validated with the results derived from the TMDstn. Results show that the gridded datasets can reproduce the spatial distribution of the TMDstn’s summer mean rainfall. However, large systematic underestimation is seen in APHRODITE, while GPCC shows better agreement with TMDstn as compared to others. In the winter, the spatial distribution of the seasonal mean of rainfall is well captured by all gridded data, especially in the upper part of Thailand, while they failed to capture high rainfall intensity in the south and the eastern parts of Thailand. Meanwhile, all the gridded datasets underestimated the interannual variability of summer and winter season rainfall. Using EOF analysis, we demonstrate that all the gridded datasets captured the first two dominant modes of summer rainfall, while they underestimated the explained variance of EOF-1. In the winter season, a good agreement is found between the first two modes of the TMDstn and the gridded datasets for both the spatial pattern and temporal variation. Overall, the GPCC data show relatively better performance in reproducing the spatial distribution of rainfall climatology and their year-to-year variation over Thailand. Furthermore, the performance of the gridded datasets over Thailand is largely dependent on the season and the complexity of the topography. However, this study indicates the existence of systematic bias in the gridded rainfall datasets when compared with TMDstn. Therefore, this indicates the need for users to pay attention to the reliability of gridded rainfall datasets when trying to identify possible mechanisms responsible for the interannual variability of seasonal rainfall over Thailand.

Seasonal extreme rainfall variability over India and its association with surface air temperature

Seasonal extreme rainfall variability over India and its association with surface air temperature

Atmospheric circulation patterns that trigger heavy rainfall in West Africa

AbstractClassification of atmospheric circulation patterns (CP) is a common tool for downscaling rainfall, but it is rarely used for West Africa. In this study, a two‐step classification procedure is proposed for this region, which is applied from 1989 to 2010 for the Sudan‐Sahel zone (Central Burkina Faso) with a focus on heavy rainfall. The approach is based on a classification of large‐scale atmospheric CPs (e.g., Saharan Heat Low) of the West African Monsoon using a fuzzy rule‐based method to describe the seasonal rainfall variability. The wettest CPs are further classified using meso‐scale monsoon patterns to better describe the daily rainfall variability during the monsoon period. A comprehensive predictor screening for the seasonal classification indicates that the best performing predictor variables (e.g., surface pressure, meridional moisture fluxes) are closely related to the main processes of the West African Monsoon. In the second classification step, the stream function at 700 hPa for identifying troughs and ridges of tropical waves shows the highest performance, providing an added value to the overall performance of the classification. Thus, the new approach can better distinguish between dry and wet CPs during the rainy season. Moreover, CPs are identified that are of high relevance for daily heavy rainfall in the study area. The two wettest CPs caused roughly half of the extremes on about 6.5% of days. Both wettest patterns are characterized by an intensified Saharan Heat Low and a cyclonic rotation near the study area, indicating a tropical wave trough. Since the classification can be used to condition other statistical approaches used in climate sciences and other disciplines, the presented classification approach opens many different applications for the West African Monsoon region.

Annual, Seasonal and Monthly Rainfall Variability at Dharwad in Karnataka

Rainfall is one of the important weather factors deciding the economy, food habits, industries and work force of the area. Distribution of rainfall throughout the year further decides cropping pattern, variety to be cultivated and duration of the crop and management practices for efficient rainfall utility. In this regard 51 year past rainfall data (1970-2020) of Dharwad district of Karnataka was collected from Main Agricultural Research Station (MARS), UAS, Dharwad for analysing seasonal, annual and monthly variabilities. From the analysis we found that the rainfall varied in the range of 417 to 1316 mm with the average of 748 mm and with the variation of 22 per cent. Five years moving rainfall average showed declining trend. July month recorded highest average rainfall of 136 mm with the lowest variation (39 per cent). Though January month recorded lowest rainfall (one mm), the highest variation was observed in February. Southwest (Jun-Sept), Northeast (Oct-Dec), Winter (Jan-Feb) and Post monsoon (March-May) contributed 65 per cent, 19 per cent, 1per cent and 15 per cent, respectively of the annual rainfall of Dharwad district. The lowest variation in rainfall over the years was observed in Southwest monsoon (27 per cent) while winter season showed highest variation. Farmers can be advised to take long duration crops like Cotton and Maize in kharif because of nearly even distributed rainfall for the months from June- October and early sowing in case of rabi to harness the more moisture for better yield. Summer sowing should be avoided because of more uncertainty in rainfall.