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Rain‐fed crop yields are heavily influenced by seasonal rainfall patterns and temperature, particularly during vegetative and reproductive growth stages. This study was conducted to investigate the effects of rainfall distribution indices (monthly, seasonal, and annual) on rain‐fed wheat and barley yields using polynomial regression analysis across six different locations with varying elevations in Chaharmahal and Bakhtiari province, Iran. Additionally, the economic feasibility of rain‐fed wheat and barley in all locations was evaluated. Results showed that the monthly rainfall distribution index could not accurately predict wheat/barley yield, where elevation exceeds 2000 m and the average annual minimum temperature is below 4°C (such as in Koohrang, Borujen, Shahrekord, and Farsan). Conversely, the monthly rainfall distribution index was able to predict the wheat/barley yield with high accuracy (R2 > 0.75) in locations with lower elevation and higher average annual minimum temperature (such as Lordegan and Ardal). Compared to seasonal rainfall indices, annual rainfall indices showed weaker predictive accuracy in all locations. Furthermore, a significant relationship (p‐value < 0.0001) with a high coefficient of determination (R2 > 0.80) was found between spring rainfall index, spring minimum temperature, and wheat/barley yield in all locations. Therefore, incorporating minimum mean air temperature with the spring rainfall index is recommended for yield prediction for all locations. Economic analysis revealed that the internal return rates in Borujen, Farsan, Lordegan and Ardal exceeded the bank interest rate (14%), indicating that cultivating wheat and barley in these four locations was profitable and economic. Moreover, an exponential relationship between the average annual temperature and internal return rate was also established, offering a useful tool for farmers and planners to estimate the internal return rate based on only the average annual temperature. The relationships between measured and predicted barley yield in all locations. Dash line represents 1:1 line.

In small mountain catchments, the spatial and temporal resolution of rainfall can vary significantly across the catchment. However, rainfall gauging stations can be sparse in these regions, and collected data may not reflect the real rainfall distribution across the catchment. When modelling flash floods, finding a suitable approach to estimate the actual rainfall distribution is nontrivial. In this study, the effectiveness of different methods for obtaining a spatial and temporal rainfall distribution for use in numerical modelling of flash floods was investigated using a full two‐dimensional depth‐averaged shallow‐water hydrodynamic model. It was demonstrated that the Thiessen polygon method and the inverse distance weighted interpolation method (IDW), with appropriate empirical coefficients, produce results in agreement with observed stage and discharge hydrographs. We show that the uniform distribution method cannot be used to represent realistic spatial and temporal variability of rainfall for flash flood events in small mountain catchments. By combining available data with the common IDW method, missing rainfall timeseries data in a small catchment can be estimated, even for short‐duration time scales, such as a single flash flood event.

Understanding the occurrence of natural disasters in regions where the occurrence is high is very important, and it is known that the occurrence of disasters associated with intense rains is a source of research in different locations around the globe, being important not only for increasing accuracy of weather forecasting models, but important information for civil defense, where lives can be saved. The increase in the occurrence of natural disasters related to extreme rainfalls has become a problem of large urban centers, such as the city of Rio de Janeiro (CRJ). Thus, the identification of homogeneous regions for rainfall distribution (HRRD) becomes essential to identify regions at risks of floods and mass movements. The aim of this research was to identify HRRD in CRJ associated with the risk of natural disasters. The identification of homogeneous regions was carried out with the use of monthly rainfall data from 14 pluviometric stations spatially distributed in the study area between 1997 and 2018. Data derived from the MOD13A3 product were also used to identify the monthly temporal behavior of the vegetation areas in the CRJ during the period 2001 to 2020. Rainfall data were submitted to descriptive statistical analysis, and subsequently to Cluster Analysis. Cluster analysis identified 4 homogeneous groups regarding annual rainfall distribution. The nonparametric test analysis identified vegetation growth during the dry season and decay during the rainy season. These vegetation results found by the nonparametric Mann–Kendall test evidence the reduction in rainfall in the CRJ. The result showed relevance regarding physiographic aspects that characterize the rainfall dynamics in CRJ, highlighting areas favorable to the occurrence of natural disasters.

Heavy rainfall has consistently acted as the primary catalyst for floods, resulting in numerous casualties and significant economic losses globally. Rainfall forecasting is accomplished by analysing existing rainfall data, which is then used to analyse the hydraulic system’s features. Gaining an understanding of rainfall requirements is a crucial challenge for every location, particularly in the case of India, given its diverse geographical area, population, and other influencing factors that impact various demands. This study evaluated the rainfall data for a span of 1990-2021 in six districts of Kerala State, India. To match the rainfall data from all districts, we utilized both Kaumarasamy-distribution and Dagum-distributions. Various Probabilistic tests, were employed to comparing these distributions. The results revealed that, in Kasargod, the Kumarasamy distribution demonstrates superior goodness-of-fit with the lowest Kolmogorov-Smirnov statistic (0.0597) and Anderson-darling statistic (2.271). However, in Wayanad, Malappuram, Palakkad, Idukki, and Trivandrum, the Dagum distribution consistently exhibits the most accurate fit, evident from its lowest Kolmogorov-Smirnov statistics (0.07447, 0.05435, 0.0556, 0.03636, 0.04291) and favourable Chi-Squared statistics (19.471, 8.4907, 19.239, 5.7318, 7.5297). These results emphasize the regional variation in precipitation data and the suitability of specific distribution models for accurate representation across differentlocations.

The spatial and temporal patterns of rainfall over the Baram River Basin (BR) in Sarawak (Malaysian Borneo) were characterised through cluster analysis and multivariate statistics for the 25 year period from 1990 to 2014. Baram River Basin recorded an average annual rainfall of 3654 mm with high spatial and temporal variations. Ward’s method based analytical hieratical clustering (AHC) identified three homogeneous clusters (HC’s) of rain gauging stations (HC-I, HC-II, and HC-III). Rain gauges in HC-I are located in high rainfall domain, HC-II gauges are in moderate rainfall domain and HC-III gauges are in low rainfall domains. The moderate elevation regions in the Baram basin recorded the highest amounts of rainfall compared to lower and upper elevated regions. Multivariate statistics show variation in the distribution of monthly and annual rainfall within and between individual HC’s. Mean annual rainfall recorded at individual HC’s varies by around 1000 mm. Though the mean monthly rainfall distribution in HC’s varies by more than 70 mm, all clusters show a common pattern of high and low rainfall seasons related to monsoon characteristics of the region. The northeast monsoon (NEM) is the wettest period with highest mean rainfall falling in the months of November and December at all stations. In comparison, the southwest monsoon (SWM) is the driest season with the lowest mean rainfall falling in July. It was also noted that the inter-monsoon periods are actually wetter than the SWM season. Overall, rainfall in the Baram shows high inter-annual variability with dominant low rainfall domain in the lower (coastal) and upper (plateau) reach with highly localised high rainfall domain surrounded by fluctuating moderate rainfall areas in the middle reaches of the river basin. The localised nature of high rainfall domain in the central (south and southwest) part of the basin suggests that the surrounding elevated mountains and hills in that region influence the high rainfall recorded. The findings of the present research will aid in formulating water resource management plans, large scale plantation and agricultural practices and any other hydrological development projects in the region.

Changes in rainfall distribution, generally predicted by many climate models, can affect resource dynamics and ecosystem function. While little studied, intra-annual rainfall distribution may have particularly strong effects on competitive interactions. Here, we test whether increased rainfall event size and decreased frequency within a growing season can influence competitive dynamics related to the invasion of exotic annual grasses in California coastal sage scrub (CSS). We hypothesized that larger rainfall events and decreased frequency will increase the competitive ability of native CSS species: a deeper root system will permit greater water use during dry periods between pulses and enhance their resource depletion effect on more shallow-rooted grasses. We planted grass and CSS seedlings in an additive competition design under three rainfall treatments: frequent small events, infrequent large events, and infrequent small events. The first two treatments had the same total rainfall but different frequency, while the second and third treatments had the same frequency but different total rainfall. Rainfall treatment altered the competitive interactions between CSS and grasses. In the first year, the competitive effect of annual grasses on shrub seedlings was strongest under the frequent small rainfall regime where they reduced deep soil moisture and light. In year two, the established shrubs began to exert strong competitive effects on grasses, and these effects were strongest under the infrequent small rainfall regime (low total rain) where they reduced shallow soil moisture and decreased grass stomatal conductance. Results suggest that reductions in both rainfall frequency and total rainfall may be important to competitive interactions, and can alter plant community composition and invasion when species have different rooting depths and different responses to soil moisture.

Ethiopia’s agriculture is mostly dependent on rain, though the rainfall distribution and amount are varied in spatiotemporal context. The study was conducted to analyze the distribution, trends, and variability of monthly, seasonal, and annual rainfall data over the Wollo area from 1981 to 2022. To accomplish this, the study utilized the Climate Hazards Group Infrared Precipitation with Stations version two (CHIRPS-v2) data. Standard Rainfall Anomaly Index (SRA) and Coefficient of Variation (CV) were employed to examine rainfall variability and develop drought indices over southern Ethiopia. The Modified Mann Kendall (MMK) test, Sen’s slope estimator and the innovative trend analysis (ITA) were employed to detect temporal changes in rainfall trends over the study period. The study found that the area experienced considerable rainfall variability and change, resulting in extended drought and flood events within the study period. Results from SRA and CV revealed interannual and seasonal rainfall variability, with the proportions of years below and above the long-term mean being estimated at 56% and 44%, respectively. The MMK test showed that the annual rainfall during the Kiremt (summer-main rainy season) had an increasing trend. On the other hand, rainfall for the Belg (short rain season for the study area) season and the Bega (winter) season showed a significantly decreasing trend (p < 0.05). Results from the innovative trend analysis (ITA) also revealed that the annual and seasonal rainfall trends exhibited different trends in varied magnitude for different stations. On a spatial basis, the eastern and northeastern regions of the study area showed trends of increasing rainfall during the Kiremt (JJA). Decision-makers and development planners need to design strategies to mitigate the risks posed by changes in rainfall variability and distribution and enhance community adaptation and mitigation capacities in Wollo, Ethiopia.

This paper presents a detailed spatiotemporal analysis of the rainfall variability, seasonality, and the extreme characteristics of Tehri catchment located in the lower Himalayan region in India. To this end, the daily rainfall data is extracted from 22 grids for 117 years (1901–2017) from the high-resolution (0.25° × 0.25°) gridded observation dataset. Monthly rainfall distribution is evaluated using precipitation concentration index (PCI) and seasonality index. The extreme rainfall indices, viz., maximum 1-day rainfall (Rx1Day), maximum 5-day rainfall (Rx5Day), number of rainy days (NxRainy), total precipitation in rainy days (PRCPTOT), number of heavy rainfall events (NxHeavy), maximum consecutive wet days (CWD), and simple daily intensity index (SDII) are computed for each year considering the thresholds suggested by India Meteorological Department (IMD). The Mann–Whitney-Pettitt test when applied to the annual rainfall time series revealed the year 1958 to be the statistically significant change point. The non-parametric modified Mann–Kendall and Sen’s slope tests are employed to detect the trend in monthly, seasonal, annual rainfall time series, extreme precipitation indices, and seasonality indices for both the pre- and post-1958 periods. The annual rainfall over the grids mostly possessed higher negative trends during 1959–2017 than those during 1901–1958, mainly due to the decreasing trends in post-monsoon and winter seasons. Compared to 1901–1958, NxRainy, CWD, and PRCPTOT exhibited a remarkable decreasing trend whereas NxHeavy, Rx1Day, Rx5Day, and SDII exhibited higher positive trends during 1959–2017, indicating intensification of precipitation. The precipitation over the catchment has been more concentrated in the latter epochs of monsoon season and annual rainfall and it is also evident from the increasing trends of the seasonality indices. There is no such study dealing comprehensively with identification of extreme characteristics, seasonality/concentration characteristics, and various categorical trends of precipitation in a Himalayan region reported in literature. This study will be useful in understanding the decreasing trend of precipitation volume coupled with increasing intensity and concentration and it is quite critical for a Himalayan catchment.

The primary objective here is the study of the urban rainfall anomaly patterns, particularly the positive/negative dipole reported in the literature as well as their temporal/spatial evolution due to rapid urban development. The spatial changes in the annual rainfall distribution, eastward and downwind of the largest coastal urban area of Israel, i.e., the Greater Tel Aviv region, in relation to the rapid expansion of the urban area are analyzed. This provides a unique opportunity, as shown here, to study the effect of a most rapid urban expansion on the potential for urban rainfall anomalies. Tel-Aviv probably serves as a case study for other fast-growing Mediterranean cities. Rain gauges’ data (over 100) collected over a period of 70 years (1948–2018), are divided into six sub-periods of 20 years and plotted on top of the urban area growth in those years. The average precipitation spatial distributions and their anomalies are both calculated for each sub-period. The results were examined along three geographic lines of particularly rapid urban expansion over the area, towards the northeast, east, and southeast. Increases of the precipitation downwind of the urban area are noticed when progressing along with the urban development. In addition, an upwind decrease over the coastal region is found. These findings are well correlated with the expansion of the urban area and the rainfall urban anomalies, P d e v , are of the order of 50–100 mm/y. Other potential explanations to these anomalies are discussed and suggested to be less feasible.