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Journal articles on the topic 'Rainfall variability'
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Stefanidis, Stefanos, and Dimitrios Stathis. "Spatial and Temporal Rainfall Variability over the Mountainous Central Pindus (Greece)." Climate 6, no. 3 (September 6, 2018): 75. http://dx.doi.org/10.3390/cli6030075.
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In this study, the authors evaluated the spatial and temporal variability of rainfall over the central Pindus mountain range. To accomplish this, long-term (1961–2016) monthly rainfall data from nine rain gauges were collected and analyzed. Seasonal and annual rainfall data were subjected to Mann–Kendall tests to assess the possible upward or downward statistically significant trends and to change-point analyses to detect whether a change in the rainfall time series mean had taken place. Additionally, Sen’s slope method was used to estimate the trend magnitude, whereas multiple regression models were developed to determine the relationship between rainfall and geomorphological factors. The results showed decreasing trends in annual, winter, and spring rainfalls and increasing trends in autumn and summer rainfalls, both not statistically significant, for most stations. Rainfall non-stationarity started to occur in the middle of the 1960s for the annual, autumn, spring, and summer rainfalls and in the early 1970s for the winter rainfall in most of the stations. In addition, the average magnitude trend per decade is approximately −1.9%, −3.2%, +0.7%, +0.2%, and +2.4% for annual, winter, autumn, spring, and summer rainfalls, respectively. The multiple regression model can explain 62.2% of the spatial variability in annual rainfall, 58.9% of variability in winter, 75.9% of variability in autumn, 55.1% of variability in spring, and 32.2% of variability in summer. Moreover, rainfall spatial distribution maps were produced using the ordinary kriging method, through GIS software, representing the major rainfall range within the mountainous catchment of the study area.
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B. AJITHKUMAR and P.P. SREEKALA. "Rainfall variability over Kerala." Journal of Agrometeorology 17, no. 2 (December 1, 2015): 273–75. http://dx.doi.org/10.54386/jam.v17i2.1027.
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Wang, Hui, Rong Fu, Arun Kumar, and Wenhong Li. "Intensification of Summer Rainfall Variability in the Southeastern United States during Recent Decades." Journal of Hydrometeorology 11, no. 4 (August 1, 2010): 1007–18. http://dx.doi.org/10.1175/2010jhm1229.1.
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Abstract The variability of summer precipitation in the southeastern United States is examined in this study using 60-yr (1948–2007) rainfall data. The Southeast summer rainfalls exhibited higher interannual variability with more intense summer droughts and anomalous wetness in the recent 30 years (1978–2007) than in the prior 30 years (1948–77). Such intensification of summer rainfall variability was consistent with a decrease of light (0.1–1 mm day−1) and medium (1–10 mm day−1) rainfall events during extremely dry summers and an increase of heavy (>10 mm day−1) rainfall events in extremely wet summers. Changes in rainfall variability were also accompanied by a southward shift of the region of maximum zonal wind variability at the jet stream level in the latter period. The covariability between the Southeast summer precipitation and sea surface temperatures (SSTs) is also analyzed using the singular value decomposition (SVD) method. It is shown that the increase of Southeast summer precipitation variability is primarily associated with a higher SST variability across the equatorial Atlantic and also SST warming in the Atlantic.
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Yang, Song, and Eric A. Smith. "Convective–Stratiform Precipitation Variability at Seasonal Scale from 8 Yr of TRMM Observations: Implications for Multiple Modes of Diurnal Variability." Journal of Climate 21, no. 16 (August 15, 2008): 4087–114. http://dx.doi.org/10.1175/2008jcli2096.1.
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Abstract This study investigates the variability of convective and stratiform rainfall from 8 yr (1998–2005) of Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) and TRMM Microwave Imager (TMI) measurements, focusing on seasonal diurnal variability. The main scientific goals are 1) to understand the climatological variability of these two dominant forms of precipitation across the four cardinal seasons and over continents and oceans separately and 2) to understand how differences in convective and stratiform rainfall variations ultimately determine how the diurnal variability of the total rainfall is modulated into multiple modes. There are distinct day–night differences for both convective and stratiform rainfall. Oceanic (continental) convective rainfall is up to 25% (50%) greater during nighttime (daytime) than daytime (nighttime). The seasonal variability of convective rainfall’s day–night difference is relatively small, while stratiform rainfall exhibits very apparent day–night variations with seasonal variability. There are consistent late evening diurnal peaks without obvious seasonal variations over ocean for convective, stratiform, and total rainfall. Over continents, convective and total rainfall exhibit consistent dominant afternoon peaks with little seasonal variations—but with late evening secondary peaks exhibiting seasonal variations. Stratiform rainfall over continents exhibits a consistent strong late evening peak and a weak afternoon peak, with the afternoon mode undergoing seasonal variability. Thus, the diurnal characteristics of stratiform rainfall appear to control the afternoon secondary maximum of oceanic rainfall and the late evening secondary peak of continental rainfall. Even at the seasonal–regional scale spatially or the interannual global scale temporally, the secondary mode can become very pronounced, but only on an intermittent basis. Overall, the results presented here demonstrate the importance of partitioning the total rainfall into convective and stratiform components and suggest that diurnal modes largely arise from distinct diurnal stratiform variations modulating convective variations.
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Singh, Vishal, and Xiaosheng Qin. "Rainfall variability in Malay Peninsula region of Southeast Asia using gridded data." E3S Web of Conferences 81 (2019): 01002. http://dx.doi.org/10.1051/e3sconf/20198101002.
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Southeast Asia is recognized as a climate-change vulnerable region as it has been significantly affected by many extreme events in the past. This study carried out a rainfall analysis over the Malay Peninsula region of Southeast Asia utilizing historical (1981-2007) gridded rainfall datasets (0.5°×0.5°). The rainfall variability was analyzed in an intra-decadal time series duration. The uncertainty involved in all datasets was also checked based on the comparison of multiple global rainfall datasets. Rainfall gap filling analysis was conducted for producing more accurate rainfall time series after testing multiple mathematical functions. Frequency-based rainfall extreme indices such as Dry Days and Wet days are generated to assess the rainfall variability over the study area. Our results revealed a notable variation existed in the rainfalls over Malay Peninsula as per the long historical duration (1981-2007).
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Alhamshry, Asmaa, Ayele Almaw Fenta, Hiroshi Yasuda, Reiji Kimura, and Katsuyuki Shimizu. "Seasonal Rainfall Variability in Ethiopia and Its Long-Term Link to Global Sea Surface Temperatures." Water 12, no. 1 (December 21, 2019): 55. http://dx.doi.org/10.3390/w12010055.
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Investigating the influence of sea surface temperatures (SSTs) on seasonal rainfall is a crucial factor for managing Ethiopian water resources. For this purpose, SST and rainfall data were used to study a wide range of inhomogeneous areas in Ethiopia with uneven distribution of rainfall for both summer (1951–2015) and spring (1951–2000) seasons. Firstly, a preliminary subdivision of rainfall grid points into zones was applied depending on spatial homogeneity and seasonality of rainfall. This introduced new clusters, including nine zones for summer rainfall peak (July/August) and five zones for spring rainfall peak (April/May). Afterward, the time series for each zone was derived by calculating the rainfall averaged over grid points within the zone. Secondly, the oceanic regions that significantly correlated with the Ethiopian rainfall were identified through cross-correlations between rainfalls averaged over every homogeneous zone and the monthly averaged SST. For summer rainfall as a main rainy season, the results indicated that the Gulf of Guinea and southern Pacific Ocean had a significant influence on rainfall zones at a lag time of 5–6 and 6–7 months. Besides, for summer rainfall zones 8 and 9 at lag time 5–6 months, the common SST regions of the southern Pacific Ocean showed the opposite sense of positive and negative correlations. Thus, the difference in SSTs between the two regions was more strongly correlated (r ≥ 0.46) with summer rainfall in both zones than others. For the spring season, the results indicated that SST of the northern Atlantic Ocean had a strong influence on spring rainfall zones (3 and 5) at a lag time 6–7 months, as indicated by a significant correlation (r ≥ −0.40). Therefore, this study suggests that SSTs of southern Pacific and northern Atlantic oceans can be used as effective inputs for prediction models of Ethiopian summer and spring rainfalls, respectively.
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Brigadier, Libanda, Nkolola Barbara, and Musonda Bathsheba. "Rainfall Variability over Northern Zambia." Journal of Scientific Research and Reports 6, no. 6 (January 10, 2015): 416–25. http://dx.doi.org/10.9734/jsrr/2015/16189.
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Farmer, Graham. "Rainfall variability in tropical Africa." Land Use Policy 3, no. 4 (October 1986): 336–42. http://dx.doi.org/10.1016/0264-8377(86)90029-3.
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Macron, Clémence, Yves Richard, Thomas Garot, Miloud Bessafi, Benjamin Pohl, Adolphe Ratiarison, and Andrianaharimanana Razafindrabe. "Intraseasonal Rainfall Variability over Madagascar." Monthly Weather Review 144, no. 5 (May 2016): 1877–85. http://dx.doi.org/10.1175/mwr-d-15-0077.1.
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Using daily rain gauge records for Madagascar and nearby islands, this paper investigates rainfall intraseasonal variability at local and regional scales during the austral summer season (November–February), as well as the respective influences of recurrent convective regimes over the southwest Indian Ocean (SWIO) and the Madden–Julian oscillation (MJO). The results show a general consistency between local-scale rainfall variability in Madagascar and regional-scale features of climate variability. The influence of tropical temperate troughs in their mature phase and/or their easternmost locations is first underlined. The development of such systems over southern Africa and the Mozambique Channel can be considered as precursors for Malagasy wet spells, especially over the southern part of the island. Regional and local effects of the MJO are weaker on average, and only concern the northwest of the island and the north of the Mozambique Channel. MJO and convective regimes are finally shown to explain distinct fractions of regional rainfall variability.
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Nicholls, Neville, Wasyl Drosdowsky, and Beth Lavery. "Australian rainfall variability and change." Weather 52, no. 3 (March 1997): 66–72. http://dx.doi.org/10.1002/j.1477-8696.1997.tb06274.x.
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Jury, Mark R. "Uganda rainfall variability and prediction." Theoretical and Applied Climatology 132, no. 3-4 (April 29, 2017): 905–19. http://dx.doi.org/10.1007/s00704-017-2135-4.
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Junges, Amanda H., Carolina Bremm, and Denise C. Fontana. "Rainfall climatology, variability, and trends in Veranópolis, Rio Grande do Sul, Brazil." Revista Brasileira de Engenharia Agrícola e Ambiental 23, no. 3 (March 2019): 160–66. http://dx.doi.org/10.1590/1807-1929/agriambi.v23n3p160-166.
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ABSTRACT The objective of this study was to characterize the rainfall climatology in Veranópolis, Rio Grande do Sul, Brazil, through analyses of means, variabilities related to El Niño Southern Oscillation (ENSO), and temporal trends, using a 60-year data series (1956-2015). Descriptive statistics of annual, monthly and seasonal rainfall were used to characterize the rainfall climatology. The differences between seasons, and influence of ENSO were evaluated using analysis of variance and the Duncan’s test. Rainfall trends were evaluated by the Mann Kendall test. The local average annual rainfall is 1,683 mm and the average monthly rainfall is 140 mm, varying from 109 (May) to 182 mm (September). The annual rainfall has high interannual (standard deviation of 327 mm), monthly (60-100 mm) and seasonal (124-183 mm) variabilities, which should be considered in non-irrigated agricultural systems using rainfall as the main source of water supply to plants. Although autumn presents lower average rainfall (346 mm) than the other seasons, its average percentages were similar to the total annual rainfall (21-28%), and the rainfalls are well-distributed in the seasons. Differences between ENSO events occurred in the spring; La Niña years showed lower rainfall (385 mm) than El Niño (549 mm) and neutral (481 mm) years. The annual rainfall tended to increase by 6.3 mm per year (p < 0.01), with increases of 2.5 mm in spring and 1.9 mm in winter (p < 0.10) in the period analyzed.
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Mekanik, F., and M. A. Imteaz. "Variability of cool seasonal rainfall associated with Indo-Pacific climate modes: case study of Victoria, Australia." Journal of Water and Climate Change 9, no. 3 (February 22, 2018): 584–97. http://dx.doi.org/10.2166/wcc.2018.146.
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Abstract This study focused on diagnosing the relative and independent role of El Niño Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) on austral cool seasonal rainfall by stratifying the cool seasonal rainfall into winter (June–August) and spring (September–November). Partial regression and classification analysis was used to investigate the effect of the climate modes on rainfall in the state of Victoria in southeast Australia. Partial regression analyses revealed that when the influence of IOD is removed from ENSO, sea surface temperature (SST) anomalies in the Pacific Ocean have no significant effect on spring rainfall across Victoria and affect winter rainfall mildly in west Victoria. By removing the inter-correlations between ENSO and IOD, SST anomalies in the Indian Ocean and SLP anomalies in the Pacific Ocean showed weak relationships with Victoria's spring and winter rainfall. Classification analysis demonstrated the effects of phases of ENSO and IOD on Victoria's seasonal rainfall; the dry phases of the climate modes have more effect on spring rainfall compared to the wet phases and both show no significant effect on winter rainfalls. It is recommended that for water availability forecasting in Victoria, water managers should focus on the effect of climate modes on spring rainfalls, particularly during the dry phases of ENSO and IOD.
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WANG, Y., Z. W. SHILENJE, P. O. SAGERO, A. M. NYONGESA, and N. BANDA. "Rainfall variability and meteorological drought in the Horn of Africa." MAUSAM 68, no. 3 (December 2, 2021): 463–74. http://dx.doi.org/10.54302/mausam.v68i3.678.
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Basic rainfall characteristics and drought over the Horn of Africa (HoA) is investigated, from 1901 to 2010. Standard Precipitation Index (SPI) is used to study drought variability, mainly focusing on 3-month SPI. The dominant mode of variability of seasonal rainfall was analyzed by performing Empirical orthogonal functions (EOF) analysis. Gridded data is sourced from Climate Research Unit (CRU), spanning from 1901 to 2010. The HoA experiences predominantly bimodal rainfall distribution in time; March to May (MAM) and October to December (OND). The spatial component of the first eigenvector (EOF1) shows that the MAM and OND seasonal rainfalls are dominated by negative and positive loadings, respectively. The EOF1 explain 34.5% and 58.9% variance of MAM and OND seasonal rainfall, respectively. The EOF2, 3 and 4 are predominantly positive, explaining less than 25% in total of the seasonal rainfall variance in the two seasons. The last two decades experienced the highest negative anomaly, with OND seasonal rainfall showing higher anomalies as compared to MAM season. The OND season recorded 9% more drought events as compared to MAM season. The frequency of occurrence of moderate, severe and extreme dryness was almost the same in the two seasons. These results give a good basis for regional model validation, as well as mapping out drought hotspots and projections studies in the HoA.
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Wong, C. L., R. Venneker, S. Uhlenbrook, A. B. M. Jamil, and Y. Zhou. "Variability of rainfall in Peninsular Malaysia." Hydrology and Earth System Sciences Discussions 6, no. 4 (August 7, 2009): 5471–503. http://dx.doi.org/10.5194/hessd-6-5471-2009.
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Abstract. This study analyzed and quantified the spatial patterns and time-variability of rainfall in Peninsular Malaysia on monthly, yearly and monsoon temporal scales. We first obtained an overview of rainfall patterns through the analysis of 16 point data sources. The results led to choosing three distinct regions, i.e.~the east coast, inland and west coast regions. For detailed analysis, Shepard's interpolation scheme was applied to the station data to produce daily rainfall fields on a 0.05 degree resolution grids for the period 1971–2006. The rainfall characteristics in time and space derived from a frequency analysis were found to be distinctly different in these three regions. In the east coast region, monthly rainfall shows a significant periodicity dominated by an annual cycle, followed by a half-year cycle. The inland and west coast regions show that the dominant periodic fluctuations in the monthly rainfall are dominated by a half-year cycle, followed by an annual cycle. The long-term rainfall variability analysis shows that the dry and wet conditions in Peninsular Malaysia are not primarily governed by the ENSO events. The results from the individual regions suggest that although the relative variability is influenced by ENSO, local and regional conditions have an effect on the interannual rainfall variability, which is superimposed on the large-scale weather conditions. A significant increasing trends in annual rainfall (9.3 mm/year) and northeast monsoon rainfall (6.2 mm/monsoon) were only detected in the west coast region. No trend was found in the monthly rainfall, except for November in the west coast region. The spatial variation analysis shows that the east coast region, which received substantially higher amounts of rainfall during the northeast monsoon, has lower spatial rainfall variability and a more uniform rainfall distribution than other regions. A larger range for the monthly spatial variation was observed in the west coast region.
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Salmayenti, Resti, Rahmat Hidayat, and Aris Pramudia. "Rainfall Prediction Using Artificial Neural Network." Agromet 31, no. 1 (June 10, 2017): 11. http://dx.doi.org/10.29244/j.agromet.31.1.11-21.
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Artificial neural network (ANN) is widely used for modelling in environmental science including climate, especially in rainfall prediction. Current knowledge has used several predictors consisting of historical rainfall data and El Niño Southern Oscillation (ENSO). However, rainfall variability of Indonesian is not only driven by ENSO, but Indian Ocean Dipole (IOD) could also influence variability of rainfall. Here, we proposed to use Dipole Mode Index (DMI) as index of IOD as complementary for ENSO. We found that rainfall variability in region with a monsoonal pattern has a strong correlation with ENSO and DMI. This strong correlation occurred during June-November, but a weak correlation was found for region with rainfall’s equatorial pattern. Based on statistical criteria, our model has R<sup>2</sup> 0.59 to 0.82, and RMSE 0.04-0.09 for monsoonal region. This finding revealed that our model is suitable to be applied in monsoonal region. In addition, ANN based model likely shows a low accuracy when it uses for long period prediction.
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Yilmaz, A. G., I. Hossain, and B. J. C. Perera. "Effect of climate change and variability on extreme rainfall intensity–frequency–duration relationships: a case study of Melbourne." Hydrology and Earth System Sciences 18, no. 10 (October 15, 2014): 4065–76. http://dx.doi.org/10.5194/hess-18-4065-2014.
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Abstract. The increased frequency and magnitude of extreme rainfall events due to anthropogenic climate change, and decadal and multi-decadal climate variability question the stationary climate assumption. The possible violation of stationarity in climate can cause erroneous estimation of design rainfalls derived from extreme rainfall frequency analysis. This may result in significant consequences for infrastructure and flood protection projects since design rainfalls are essential input for design of these projects. Therefore, there is a need to conduct frequency analysis of extreme rainfall events in the context of non-stationarity, when non-stationarity is present in extreme rainfall events. A methodology consisting of threshold selection, extreme rainfall data (peaks over threshold data) construction, trend and non-stationarity analysis, and stationary and non-stationary generalised Pareto distribution (GPD) models was developed in this paper to investigate trends and non-stationarity in extreme rainfall events, and potential impacts of climate change and variability on intensity–frequency–duration (IFD) relationships. The methodology developed was successfully implemented using rainfall data from an observation station in Melbourne (Australia) for storm durations ranging from 6 min to 72 h. Although statistically significant trends were detected in extreme rainfall data for storm durations of 30 min, 3 h and 48 h, statistical non-stationarity tests and non-stationary GPD models did not indicate non-stationarity for these storm durations and other storm durations. It was also found that the stationary GPD models were capable of fitting extreme rainfall data for all storm durations. Furthermore, the IFD analysis showed that urban flash flood producing hourly rainfall intensities have increased over time.
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Yilmaz, A. G., I. Hossain, and B. J. C. Perera. "Effect of climate change and variability on extreme rainfall intensity–frequency–duration relationships: a case study of Melbourne." Hydrology and Earth System Sciences Discussions 11, no. 6 (June 16, 2014): 6311–42. http://dx.doi.org/10.5194/hessd-11-6311-2014.
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Abstract. The increased frequency and magnitude of extreme rainfall events due to anthropogenic climate change, and decadal and multi-decadal climate variability question the stationary climate assumption. The possible violation of stationarity in climate can cause erroneous estimation of design rainfalls derived from extreme rainfall frequency analysis. This may result in significant consequences for infrastructure and flood protection projects since design rainfalls are essential input for design of these projects. Therefore, there is a need to conduct frequency analysis of extreme rainfall events in the context of non-stationarity, when non-stationarity is present in extreme rainfall events. A methodology consisting of, threshold selection, extreme rainfall data (peaks over threshold data) construction, trend and non-stationarity analysis, and stationary and non-stationary Generalized Pareto Distribution (GPD) models was developed in this paper to investigate trends and non-stationarity in extreme rainfall events, and potential impacts of climate change and variability on Intensity–Frequency–Duration (IFD) relationships. The developed methodology was successfully implemented using rainfall data from an observation station in Melbourne (Australia) for storm durations ranging from 6 min to 72 h. Although statistically significant trends were detected in extreme rainfall data for storm durations of 30 min, and 3 and 48 h, statistical non-stationarity tests and non-stationary GPD models did not indicate non-stationarity for these storm durations and other storm durations. It was also found that the stationary GPD models were capable of fitting extreme rainfall data for all storm durations. Furthermore, the IFD analysis showed that urban flash flood producing hourly rainfall intensities have increased over time.
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Jochum, Markus, Clara Deser, and Adam Phillips. "Tropical Atmospheric Variability Forced by Oceanic Internal Variability." Journal of Climate 20, no. 4 (February 15, 2007): 765–71. http://dx.doi.org/10.1175/jcli4044.1.
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Abstract Atmospheric general circulation model experiments are conducted to quantify the contribution of internal oceanic variability in the form of tropical instability waves (TIWs) to interannual wind and rainfall variability in the tropical Pacific. It is found that in the tropical Pacific, along the equator, and near 25°N and 25°S, TIWs force a significant increase in wind and rainfall variability from interseasonal to interannual time scales. Because of the stochastic nature of TIWs, this means that climate models that do not take them into account will underestimate the strength and number of extreme events and may overestimate forecast capability.
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Indarto, Indarto, and Askin Askin. "VARIABILITAS SPASIAL HUJAN DI WILAYAH UPT PSDA DI MALANG." Jurnal Teknik Pertanian Lampung (Journal of Agricultural Engineering) 6, no. 3 (March 28, 2018): 171. http://dx.doi.org/10.23960/jtep-l.v6i3.171-180.
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This study show the spatial variabilit of rainfall (monthly and annual) rainfall in the area of technical implementation unit of water resources management (UPT-PSDA) in Malang. Administrative area of UPT PSDA in Malang include Malang regency, Malang city, Batu, Blitar Regency, Tulungagung Regency, and Trenggalek Regency. Daily rainfall data from 88 pluviometers spread around the areas are used as main input. The research procedures consist of : (1) data pre-analysis; (2) the analyses using ESDA tools (Histogram, voronoi, QQ-Plot); (3) interpolation by using IDW method; (4) producing a thematic map; and (5) interpretation. Analysis using the histogram, voronoi–maps and normal QQ-plots tools illustrates more detail the spatial variability of the monthly and annual rainfall around the regions. Interpolation produces a thematic map of mean monthly-rainfall, between 100 – 400 mm/month. The spatial distribution of annual rainfall was illustrated by a thematic show the average-annual-range from 1000 – 4000 mm/year. Keywords: spatial variability, rainfall, ESDA, IDW, monthly, annual rainfall
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Jiang, Xingwen, and Mingfang Ting. "Intraseasonal Variability of Rainfall and Its Effect on Interannual Variability across the Indian Subcontinent and the Tibetan Plateau." Journal of Climate 32, no. 8 (April 1, 2019): 2227–45. http://dx.doi.org/10.1175/jcli-d-18-0319.1.
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AbstractIntraseasonal variability of rainfall over the Indian subcontinent (IS) and the Tibetan Plateau (TP) has been discussed widely but often separately. In this study, we investigate the covariability of rainfall across the IS and the TP on intraseasonal time scales and its impact on interannual variability of regional rainfall. The most dominant mode of rainfall intraseasonal variability across the region features a dipole pattern with significant out-of-phase rainfall anomalies between the southeastern TP and the central and northern IS. This dipole rainfall pattern is associated with intraseasonal oscillations (ISOs) of 10–20 days and 30–60 days, especially the latter. An active spell of rainfall in the central and northern IS (southeastern TP) is associated with the strengthening (northward shift) of water vapor transport of the Indian summer monsoon, resulting in more water vapor entering into the central and northern IS (southeastern TP) and thus more rainfall. The 10–20-day ISO of the dipole rainfall pattern is caused by the 10–20-day atmospheric ISO in both the tropics and the extratropics, whereas the 30–60-day ISO of the dipole rainfall pattern is only associated with atmospheric ISO in the tropics. The dipole rainfall pattern resembles the most dominant mode of interannual variability of July–August mean rainfall. The 30–60-day ISO of the dipole rainfall pattern has an important contribution to the dipole pattern of July–August mean rainfall anomalies on an interannual time scale due to the different frequencies of occurrence of the active and break phases.
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Peleg, Nadav, Frank Blumensaat, Peter Molnar, Simone Fatichi, and Paolo Burlando. "Partitioning the impacts of spatial and climatological rainfall variability in urban drainage modeling." Hydrology and Earth System Sciences 21, no. 3 (March 14, 2017): 1559–72. http://dx.doi.org/10.5194/hess-21-1559-2017.
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Abstract. The performance of urban drainage systems is typically examined using hydrological and hydrodynamic models where rainfall input is uniformly distributed, i.e., derived from a single or very few rain gauges. When models are fed with a single uniformly distributed rainfall realization, the response of the urban drainage system to the rainfall variability remains unexplored. The goal of this study was to understand how climate variability and spatial rainfall variability, jointly or individually considered, affect the response of a calibrated hydrodynamic urban drainage model. A stochastic spatially distributed rainfall generator (STREAP – Space-Time Realizations of Areal Precipitation) was used to simulate many realizations of rainfall for a 30-year period, accounting for both climate variability and spatial rainfall variability. The generated rainfall ensemble was used as input into a calibrated hydrodynamic model (EPA SWMM – the US EPA's Storm Water Management Model) to simulate surface runoff and channel flow in a small urban catchment in the city of Lucerne, Switzerland. The variability of peak flows in response to rainfall of different return periods was evaluated at three different locations in the urban drainage network and partitioned among its sources. The main contribution to the total flow variability was found to originate from the natural climate variability (on average over 74 %). In addition, the relative contribution of the spatial rainfall variability to the total flow variability was found to increase with longer return periods. This suggests that while the use of spatially distributed rainfall data can supply valuable information for sewer network design (typically based on rainfall with return periods from 5 to 15 years), there is a more pronounced relevance when conducting flood risk assessments for larger return periods. The results show the importance of using multiple distributed rainfall realizations in urban hydrology studies to capture the total flow variability in the response of the urban drainage systems to heavy rainfall events.
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Liu, Zhenzhen, Qiang Dai, and Lu Zhuo. "Relationship between Rainfall Variability and the Predictability of Radar Rainfall Nowcasting Models." Atmosphere 10, no. 8 (August 12, 2019): 458. http://dx.doi.org/10.3390/atmos10080458.
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Radar rainfall nowcasts are subject to many sources of uncertainty and these uncertainties change with the characteristics of a storm. The predictive skill of a radar rainfall nowcasting model can be difficult to understand as sometimes it appears to be perfect but at other times it is highly inaccurate. This hinders the decision making required for the early warning of natural hazards caused by rainfall. In this study we define radar spatial and temporal rainfall variability and relate them to the predictive skill of a nowcasting model. The short-term ensemble prediction system model is configured to predict 731 events with lead times of one, two, and three hours. The nowcasting skill is expressed in terms of six well-known indicators. The results show that the quality of radar rainfall nowcasts increases with the rainfall autocorrelation and decreases with the rainfall variability coefficient. The uncertainty of radar rainfall nowcasts also shows a positive connection with rainfall variability. In addition, the spatial variability is more important than the temporal variability. Based on these results, we recommend that the lead time for radar rainfall nowcasting models should change depending on the storm and that it should be determined according to the rainfall variability. Such measures could improve trust in the rainfall nowcast products that are used for hydrological and meteorological applications.
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Zhang, Jun, Dawei Han, Yang Song, and Qiang Dai. "Study on the effect of rainfall spatial variability on runoff modelling." Journal of Hydroinformatics 20, no. 3 (March 22, 2018): 577–87. http://dx.doi.org/10.2166/hydro.2018.129.
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Abstract Rainfall spatial variability was assessed to explore its influence on runoff modelling. Image size, coefficient of variation (Cv) and Moran's I were chosen to assess for rainfall spatial variability. The smaller the image size after compression, the less complex is the rainfall spatial variability. The results showed that due to the drawing procedure and varied compression methods, a large uncertainty exists for using image size to describe rainfall spatial variability. Cv quantifies the variability between different rainfall values without considering rainfall spatial distribution and Moran's I describes the spatial autocorrelation between gauges rather than the values. As both rainfall values and spatial distribution have an influence on runoff modelling, the combination of Cv and Moran's I was further explored. The results showed that the combination of Cv and Moran's I is reliable to describe rainfall spatial variability. Furthermore, with the increase of rainfall spatial variability, the hydrological model performance decreases. Moreover, it is difficult for a lumped model to cope with rainfall events assigned with complex rainfall spatial variability since spatial information is not taken into consideration (i.e. the VIC model used in this study). Therefore, it is recommended to apply distributed models that can deal with more spatial input information.
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Cobon, David H., Louis Kouadio, Shahbaz Mushtaq, Chelsea Jarvis, John Carter, Grant Stone, and Peter Davis. "Evaluating the shifts in rainfall and pasture-growth variabilities across the pastoral zone of Australia during 1910–2010." Crop and Pasture Science 70, no. 7 (2019): 634. http://dx.doi.org/10.1071/cp18482.
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Interannual rainfall variability in Australia is a source of risk within agricultural industries. Insights into changes to rainfall and pasture-growth variabilities are essential to inform adaptation strategies for climate risk management within the grazing industry. We investigated shifts in rainfall and pasture-growth variabilities between the periods 1910–1960 and 1961–2010 for the pastoral zone in Australia. Rainfall variability was also assessed for the high-rainfall and wheat–sheep zones. An index of variability was calculated by using gridded rainfall and pasture-growth data for both periods. The percentage change was then calculated as the difference in variation between the two periods. Overall, the variability of annual rainfall has significantly increased (P < 0.01) between the two periods for the pastoral zone. Pastoral regions in the Northern Territory had the greatest increases in pasture-growth variability, with 62–85% of the area affected by a significant increase in variability. Between the periods 1910–1960 and 1961–2010 across the wheat–sheep zone, annual rainfall variability significantly decreased (P < 0.01), with 70% of the area having a negative change, whereas for the high-rainfall zone, the variability did not change significantly. Monitoring ongoing trends in rainfall and pasture-growth variability is important to inform strategic grazing management. Management practices to mitigate the impacts of increased variability in pastoral regions are discussed.
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Guntu, Ravi Kumar, and Ankit Agarwal. "Investigation of Precipitation Variability and Extremes Using Information Theory." Environmental Sciences Proceedings 4, no. 1 (November 13, 2020): 14. http://dx.doi.org/10.3390/ecas2020-08115.
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Quantifying the spatiotemporal variability of rainfall is the principal component for the assessment of the impact of climate change on the hydrological cycle. A better understanding of the quantification of variability and its trend is vital for water resources planning and management. Therefore, a multitude of studies has been dedicated to quantifying the rainfall variability over the years. Despite their importance for modelling rainfall variability, the studies mainly focused on the amount of rainfall and its spatial patterns. The studies investigating the spatial and temporal variability of rainfall across Central India, in general, and at multiscale, in particular, are limited. In this study, we used a Standardized Variability Index (SVI), based on information theory to investigate the spatiotemporal variability of rainfall. SVI is independent of the temporal scale, length of the data and can compare the rainfall variability at multiple timescales. Distinct spatial patterns were observed for information entropies at the monthly and seasonal scale. Grid points with statistically significant trends were observed and vary from monthly to seasonal scale. There is an increase in the variability of rainfall amount from South to North, indicating that spread of the rainfall is high in the South when compared to North of Central India. Trend analysis revealed there is changing behavior in the rainfall amount as well as rainy days, showing an increase in variability of rainfall over Central India, hence the high probability of occurrence of extreme events in the near future.
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Nouaceur, Zeineddine, Ovidiu Murărescu, and George Murătoreanu. "Rainfall Variability and Trend Analysis of Multiannual Rainfall in Romanian Plain." Annals of Valahia University of Targoviste, Geographical Series 17, no. 2 (October 1, 2017): 124–44. http://dx.doi.org/10.1515/avutgs-2017-0012.
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AbstractThe IPCC climate models predict, for the Central Europe, are for climate changes, being seen variability of temperature, with a growing trend of 1-2,5° C (with 1° C for alpine zone – Carpathians and 2-2,5° C for plains). Current observations in the Romanian plain are not consistent, with an existence of a multiannual variability of temperature and precipitations depending on cyclonal and anti-ciclonal activity. The research is based on calculation of reduced centered index, also the graphical chronological method in information processing (MGCTI) of „Bertin Matrix” type, to show current trends of the spatio-temporal variability of precipitation in the context of global climate change. These are in line with the movement of air masses in Europe in general, and implicitly in Romania, with particular regard to the southern region of the country where the Romanian Plain. The variability of short-term global climate is generally associated with coupling phases of oceanic and atmospheric phenomena including El Niño Southern Oscillation (ENSO) and the North Atlantic Oscillation (NAO). While El Niño Southern Oscillation (ENSO) affects climate variability in the world, the North Atlantic Oscillation (NAO) is the climate model dominant in the North Atlantic region. The latter cyclic oscillation whose role is still under debate could explain the variability of rainfall in much of the, central Europe area, and support the hypothesis of a return of the rains marking the end of years of drought in Romanian plain. Faced with such great changes that today affect the central Europe region and given the complexity of spatial and temporal dimensions of the climatic signal, a more thorough research of causes and retroactions would allow for a better understanding of the mechanisms behind this new trend.
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Nhu Le, D., and A. John Petkau. "The variability of rainfall acidity revisited." Canadian Journal of Statistics 16, no. 1 (March 1988): 15–38. http://dx.doi.org/10.2307/3315061.
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USHA, BALAMBAL, and BV MUDGAL. "RAINFALL VARIABILITY OVER SMALLER SPATIAL SCALE." MAUSAM 65, no. 2 (April 1, 2014): 270–76. http://dx.doi.org/10.54302/mausam.v65i2.1010.
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Saini, Divya, Pankaj Bhardwaj, and Omvir Singh. "Recent rainfall variability over Rajasthan, India." Theoretical and Applied Climatology 148, no. 1-2 (January 29, 2022): 363–81. http://dx.doi.org/10.1007/s00704-021-03904-6.
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Moten, Subramaniam. "Multiple time scales in rainfall variability." Journal of Earth System Science 102, no. 1 (March 1993): 249–63. http://dx.doi.org/10.1007/bf02839194.
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Ash, Caroline. "Child growth sensitivity to rainfall variability." Science 355, no. 6325 (February 9, 2017): 592.4–593. http://dx.doi.org/10.1126/science.355.6325.592-d.
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Eshel, Gidon, and Brian F. Farrell. "Mechanisms of Eastern Mediterranean Rainfall Variability." Journal of the Atmospheric Sciences 57, no. 19 (October 2000): 3219–32. http://dx.doi.org/10.1175/1520-0469(2000)057<3219:moemrv>2.0.co;2.
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Eshel, Gidon, and Brian F. Farrell. "Thermodynamics of Eastern Mediterranean Rainfall Variability." Journal of the Atmospheric Sciences 58, no. 1 (January 2001): 87–92. http://dx.doi.org/10.1175/1520-0469(2001)058<0087:toemrv>2.0.co;2.
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Mapande, Amin T., and C. J. C. Reason. "Interannual rainfall variability over Western Tanzania." International Journal of Climatology 25, no. 10 (2005): 1355–68. http://dx.doi.org/10.1002/joc.1193.
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Aditya, Fanni, Evi Gusmayanti, and Jajat Sudrajat. "Pengaruh Perubahan Curah Hujan terhadap Produktivitas Padi Sawah di Kalimantan Barat." Jurnal Ilmu Lingkungan 19, no. 2 (June 25, 2021): 237–46. http://dx.doi.org/10.14710/jil.19.2.237-246.
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Variabilitas curah hujan sangat erat kaitannya dengan perubahan iklim di suatu wilayah dan analisisnya sangat berguna dalam mengukur ketersediaan air untuk pertanian khususnya padi sawah. Penelitian ini bertujuan menganalisis variabilitas curah hujan dan hubungan curah hujan tahunan terhadap produktivitas padi di Kalimantan Barat. Lokasi penelitian difokuskan di wilayah Kabupaten Mempawah dan Kubu Raya dengan menggunakan data yang tersedia pada tahun 2000-2019. Analisis datanya menggunakan persamaan variabilitas dan dilanjutkan dengan analisis korelasi dan komposit. Hasil analisis menunjukkan bahwa variabilitas curah hujan tahunan di Mempawah dan Kubu Raya termasuk dalam kategori rendah. Nilai variabilitas bulanan menunjukkan rentang yang bervariasi dari rendah hingga ekstrem di setiap lokasi. El Nino memiliki dampak negatif yang kuat terhadap curah hujan pada periode Juni-Juli-Agustus (JJA) dan September-Oktober-November (SON), sedangkanLa Nina memiliki dampak positif yang kuat terhadap curah hujan pada periode Juni-Juli-Agustus. Pada periode Desember-Januari-Februari (DJF) dan Maret-April-Mei (MAM), El Nino (La Nina) memiliki efek terhadap peningkatan (pengurangan) curah hujan. Dipole Mode Positif memberikan dampak pengurangan curah hujan pada periode SON dan MAM. Dipole Mode Negatif memberikan dampak bervariasi pada curah hujan pada periode JJA, SON dan DJF. Hubungan signifikan antara curah hujan tahunan dan produktivitas padi hanya ditunjukkan di Sungai Kunyit dan Sungai Kakap. Hal ini mengindikasikan bahwa curah hujan tahunan secara umum tidak berpengaruh signifikan terhadap produktivitas padi di sebagian besar wilayah penelitian. ABSTRACTRainfall variability is closely related to climate change in a particular region and it is useful in estimating the water availability for agriculture, especially lowland rice. This study examines the rainfall variability and correlation between annual rainfall and rice productivity in West Kalimantan. The research location is focused on the Mempawah and Kubu Raya districts in 2000-2019. The variability equation accompanied by correlation and composite analysis was used in the analysis. The result shows that the variability of annual rainfall in Mempawah and Kubu Raya falls in the low category. Monthly rainfall variability values mark a range that varies from low to extreme at each location. El Nino had a substantial negative impact on rainfall in the June-July-August (JJA) and September-October-November SON period. While, La Nina had a positive impact on rainfall only in the JJA period. In the December-January-February (DJF) and March-April-May (MAM) period, El Nino (La Nina) has an anomalous effect on increasing (reducing) rainfall. Positive Dipole Mode gives the negative impact in the SON dan MAM period. Negative Dipole Mode has a varied impact on rainfall in the JJA, SON and DJF periods. The significant corellation between annual rainfall and rice productivity was shown only at Sungai Kunyit and Sungai Kakap. This indicates that the annual rainfall generally has no significant effect on rice productivity in most areas.
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SRIVASTAVA, A. K., G. P. SINGH, and O. P. SINGH. "Variability and trends in extreme rainfall over India." MAUSAM 67, no. 4 (December 8, 2021): 745–66. http://dx.doi.org/10.54302/mausam.v67i4.1406.
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This study has been attempted to investigate the seasonal and annual trends and variations in the occurrence of extreme rainfall over different Indian region and India as a whole. Trends and variations are examined on the basis of following parameters (i) frequency and magnitude of extreme rainfall intensity (ERI) and its contribution in total rainfall (ii) highest rainfall events (iii) frequency of extreme rainfall events and days (iv) frequency of rainfall events and days with daily rainfall above 100 mm and 200 mm in a grid box (1° × 1°) over different Indian regions and India as a whole. Daily gridded rainfall data from India Meteorological Department (IMD) available at 1° × 1° resolution has been used to examine trends and variations associated with extreme rainfall events. Based on the long term 95 and 99 percentile values of daily total /maximum rainfall as a threshold for extreme rainfall intensity/events of category 1 and category 2 respectively, the trends and variations in above mentioned parameters are analyzed for the periods 1951-2007, 1951-1980 and 1981-2007.
The magnitude of highest intensity rainfall is increased over country as a whole and over peninsular India; it is found to be increased by 1% during 1981-2007 as compared to period 1951-1980. The frequency of extreme rainfall intensity (ERI) days of category 1 is found to be significant increasing (0.4 days/decade) over north central region and significant decreasing trend is found over north east region (0.5 days/decade) during the pre-monsoon season. The magnitude of 24 hours highest rainfall in a grid box is found to be significant increasing over all regions under consideration except over north east and south peninsular regions. Over the last ten years period of the present study, most of the 24 hours highest rainfall events in a grid box are seen over west peninsular region. Generalized extreme value (GEV) distribution fitted with annual highest rainfall event over the country as a whole and over different Indian region indicates an increase in magnitude of most probable 24 hours highest rainfall in a grid box during second half of the study period over north central region of the country. Analysis also reveals an increase in frequency and severity of extreme rainfall over north west, north central and west peninsular regions during the period of 1981-2007 as compared to 1950-1980.
Annual frequency of days and events with extreme rainfall of both categories is increased most significantly over country during the period of present study (1951-2007). Significant increasing trends in frequency of days with extreme rainfall of both categories is noticed only during the monsoon season while extreme rainfall events showed increasing trends during monsoon and winter season over country as a whole. Number of days and events with daily rainfall in any grid box above 100 mm and 200 mm is observed to be significantly increased over the country. Out of six regions, significant increasing trends in annual number of days with rainfall above 100 mm in a grid box is observed over north central and north east regions and for rainfall above 200 mm significant increase is observed over north west and north central regions.
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Braimah, Mohammed, Vincent A. Asante, Maureen A. Ahiataku, Samuel O. Ansah, Frederick Otu-Larbi, Bashiru Yahaya, John B. Ayabilah, and Francis Nkrumah. "Variability of the Minor Season Rainfall over Southern Ghana (1981–2018)." Advances in Meteorology 2022 (April 22, 2022): 1–14. http://dx.doi.org/10.1155/2022/1861130.
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The monitoring of rainfall variability over recent decades has become a necessity due to its devastating effects such as floods and droughts, which render humans vulnerable across different parts of the West African region. The current study seeks to provide a good understanding of variability within the minor rainfall season over southern Ghana by employing statistical tools to quantify variability in rainfall. Daily rainfall data from 1981 to 2018 for seventeen (17) synoptic weather stations across southern Ghana are used for this analysis. We perform trend and descriptive statistics of rainfall amount and extreme indices intending to identify the areas with the greatest variability in rainfall. Further, for five recent years (2014–2018), we do an interpolation of the ground station rainfall data and compute anomalies. We find increasing trends of rainfall in the minor rainy season for 16 out of the 17 stations, with rainfall increasing between 0.10 mm and 4.30 mm each season. For extreme rainfall indices, the 17 stations show nonsignificant trends of very wet and extremely wet days. We also find that the middle parts of Ghana have the highest rainfall amounts (262.7 mm/season–400.2 mm/season), while the East Coast has the lowest (125.2 mm/season–181.8 mm/season). Over the whole of southern Ghana, we find high variability in rainfall amount with the coefficient of variations (CV) between 25.3% and 70.8% and moderate to high variability in rainfall frequency (CV = 14.0%–48.8%). The results of rainfall anomalies show that the middle parts had an above-normal rainfall amount. In the same period, the transition areas experienced below-normal rainfall. Our finding of high variability in the minor rainfall season has implications for agricultural productivity in Ghana and countries in the West African region, which rely heavily on rain-fed agriculture. Hence, this study recommends more research to understand the causes of variability in the West African monsoon and how this will change in the region.
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Espinosa, Luis Angel, Maria Manuela Portela, João Dehon Pontes Filho, and Martina Zelenakova. "Bivariate Modelling of a Teleconnection Index and Extreme Rainfall in a Small North Atlantic Island." Climate 9, no. 5 (May 19, 2021): 86. http://dx.doi.org/10.3390/cli9050086.
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This paper explores practical applications of bivariate modelling via copulas of two likely dependent random variables, i.e., of the North Atlantic Oscillation (NAO) coupled with extreme rainfall on the small island of Madeira, Portugal. Madeira, due to its small size (∼740 km2), very pronounced mountain landscape, and location in the North Atlantic, experiences a wide range of rainfall regimes, or microclimates, which hamper the analyses of extreme rainfall. Previous studies showed that the influence of the North Atlantic Oscillation (NAO) on extreme rainfall is at its largest in the North Atlantic sector, with the likelihood of increased rainfall events from December through February, particularly during negative NAO phases. Thus, a copula-based approach was adopted for teleconnection, aiming at assigning return periods of daily values of an NAO index (NAOI) coupled with extreme daily rainfalls—for the period from December 1967 to February 2017—at six representative rain gauges of the island. The results show that (i) bivariate copulas describing the dependence characteristics of the underlying joint distributions may provide useful analytical expressions of the return periods of the coupled previous NAOI and extreme rainfall and (ii) that recent years show signs of increasing climate variability with more anomalous daily negative NAOI along with higher extreme rainfall events. These findings highlight the importance of multivariate modelling for teleconnections of prominent patterns of climate variability, such as the NAO, to extreme rainfall in North Atlantic regions, especially in small islands that are highly vulnerable to the effects of abrupt climate variability.
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Askin, Askin, Indarto Indarto, Dimas Ghufron Ash-Shiddiq, and Sri Wahyuningsih. "Variabilitas Spasial Hujan Tahunan di Wilayah UPT PSDA di Pasuruan, Jawa Timur : Analisis Histogram dan Normal QQ-Plot." Rona Teknik Pertanian 11, no. 1 (April 1, 2018): 35–49. http://dx.doi.org/10.17969/rtp.v11i1.9981.
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Abstrak. Penelitian ini bertujuan untuk menganalisis variabilitas spasial hujan di wilayah UPT PSDA di Pasuruan. Wilayah studi mencakup kabupaten Probolinggo, kota Probolinggo, Kabupaten Pasuruan dan Kota Pasuruan di Jawa Timur. Data hujan tahunan rerata (Hthn_rrt) dan hujan tahunan maksimal (HthnMaks) dihitung dari kumulatif data hujan harian pada 93 stasiun dan dijadikan sebagai input utama untuk analisis. Panjang periode rekaman data yang digunakan dari tahun 1980 sampai dengan 2015 (35 tahun). Tahap penelitian mencakup: (1) pra-pengolahan data, (2) analisis pendahuluan, (3) analisis menggunakan tool histogram dan voronoi map, (4) interpolasi data dan pembuatan peta tematik. Pra-pengolahan data dilakukan menggunakan excel. Analisis histogram dan QQ-Plot dilakukan untuk melihat variabilitas spasial lebih detail per sub-wilayah. Selanjutnya, metode interpolasi digunakan untuk membuat peta tematik hujan tahunan. Peta tematik menunjukkan hujan tahunan rerata (Hthn_rrt) yang terjadi di wilayah tersebut selama 35 tahun terakhir berkisar antara 1200 sd 2600 mm/tahun. Hujan tahunan maksimal yang terjadi berkisar antara 2100 sd 4500 mm/tahun. Penelitian juga menunjukkan adanya korelasi positif antara lokasi stasiun hujan (elevasi) dengan jumlah hujan tahunan yang diterima. Spatial Variability of Annual Rainfall in The Administrative Area of UPT PSDA at Pasuruan, East Java : Analysis Using Histogram and QQ-Plot Abstract. This research aims to analyze the spatial variability of annual rainfall. Daily rainfall data from 93 rain gauge in the administrative area of UPT PSDA Pasuruan were used as the main input. The average annual rainfall and the maximum annual rainfall obtained from the daily rainfall data. Histograms, and QQ-Plot were used to describe the spatial variability in each sub-regions. Next, interpolation methods is used to create a thematic map of the annual rainfall. The results shows that local spatial variability of rainfall can be visualized more detail for each sub-region by means of histogram and QQ-Plot. The thematic map showed that the distribution of average annual rainfall in the region range from 1,200 mm/year up to 2,600 mm/year. Maximum annual rainfall range between 2,100 mm/year up to 4,500 mm/year. The result also show the positif correlation between the altitude of the rain gauge and local annual rainfall received.
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Peleg, N., M. Ben-Asher, and E. Morin. "Radar subpixel-scale rainfall variability and uncertainty: a lesson learned from observations of a dense rain-gauge network." Hydrology and Earth System Sciences Discussions 10, no. 1 (January 2, 2013): 1–32. http://dx.doi.org/10.5194/hessd-10-1-2013.
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Abstract. Hydrological models for runoff estimations and flash-flood predictions are very sensitive to rainfall's spatial and temporal variability. The increasing use of radar and satellite data in hydrological applications, due to the sparse distribution of rain gauges over most catchments worldwide, requires improving our knowledge of the uncertainties of these data. In 2011, a new super-dense network of rain gauges, containing 27 gauges covering an area of about 4 km2, was installed near Kibbutz Galed in northern Israel. This network was established for a detailed exploration of the uncertainties and errors regarding rainfall variability in remote-sensing at subpixel-scale resolution. In this paper, we present the analysis of the first year's record collected from this network and from the Shacham weather radar. The gauge–rainfall spatial correlation and uncertainty were examined along with the estimated radar error. The zero-distance correlation between rain gauges was high (0.92 on the 1-min scale) and increased as the time scale increased. The variance of the differences between radar pixel rainfall and averaged point rainfall (the variance reduction factor – VRF) was 1.6%, as measured for the 1-min scale. It was also found that at least four uniformly distributed rain stations are needed to represent the rainfall on the radar pixel scale. The radar–rain gauge error was mainly derived from radar estimation errors as the gauge sampling error contributed up to 22% to the total error. The radar rainfall estimations improved with increasing time scale and the radar-to-true rainfall ratio decreased with increasing time scale. Rainfall measurements collected with this network of rain gauges in the coming years will be used for further examination of rainfall's spatial and temporal variability.
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Mondal, M. Shahjahan, Sara Nowreen, and Mostofa Najmus Sakib. "Scale-Dependent Reliability of Projected Rainfalls over Bangladesh with the PRECIS Model." Climate 8, no. 2 (January 27, 2020): 20. http://dx.doi.org/10.3390/cli8020020.
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The regional climate model, Providing REgional Climates for Impact Studies (PRECIS), has been widely used throughout the world to generate climate change projections for impact studies and adaptations. Its recent application in South Asia also includes the projection of rainfall extremes. In spite of its wide application, a stringent validation of the model is yet to be reported. In this study, we assessed the performance of the model in simulating annual, monthly and extreme rainfalls over Bangladesh by using a number of statistical techniques, e.g., pattern (both spatial and temporal) correlation, root mean square difference (RMSD), mean absolute difference (MAD), Student’s t-test for significance, probability density functions, etc. The results indicated that the PRECIS model could capture the overall spatial pattern of mean annual and monthly rainfalls very well. However, the inter-annual variability was poorly simulated by the model. In addition, the model could not capture the rainfall extremes. A spatial aggregation of rainfall data did not improve the reliability of the model as far as variability and extremes are concerned. Therefore, further improvements of the model and/or its driving global climate model are warranted for its practical use in the generation of rainfall scenarios.
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Bartolini, Paolo, and Juan B. Valdés. "Representation of Spatial Variability of Rainfall in Aggregated Rainfall‐Runoff Models." Journal of Hydraulic Engineering 120, no. 10 (October 1994): 1199–219. http://dx.doi.org/10.1061/(asce)0733-9429(1994)120:10(1199).
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Nouaceur, Zeineddine, and Ovidiu Murărescu. "Rainfall Variability and Trend Analysis of Annual Rainfall in North Africa." International Journal of Atmospheric Sciences 2016 (October 30, 2016): 1–12. http://dx.doi.org/10.1155/2016/7230450.
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The IPCC climate models predict, for the Maghreb countries, lower rainfall and increased aridity. Current observations in the three countries of central Maghreb (Morocco, Algeria, and Tunisia) are not consistent with these predictions. To demonstrate this new trend, a detailed regional analysis of rainfall evolution is conducted. This investigation is based on the calculation of the reduced centered index and the chronological graphical method of processing information (MGCTI) of “Bertin matrix” type. The results show extreme variability of this parameter and the severe past drought (more intense for Morocco, in which the drastic conditions from the seventies are observed). The results also show the beginning of a gradual return to wetter conditions since the early 2000s in Algeria and Tunisia and from 2008 for Morocco (this trend is confirmed by recent agricultural production data in 2011/2012 and 2012/2013).
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Cahyadi, Ahmad, Eko Haryono, Tjahyo Nugroho Adji, Margaretha Widyastuti, Indra Agus Riyanto, Dzakwan Taufiq Nur Muhammad, and Naufal Fattah Tastian. "RAINFALL VARIABILITY IN GUNUNGSEWU KARST AREA, JAVA ISLAND, INDONESIA." Indonesian Journal of Forestry Research 8, no. 1 (April 30, 2021): 23–35. http://dx.doi.org/10.20886/ijfr.2021.8.1.23-35.
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Karst area is highly susceptible to changes to climate parameters. One of the parameters is rainfall variability. In addition to shaping the condition of water resources, rainfall in the Gunung Sewu karst area determines the nature of crop and livestock of the agriculture sectors―the local population's main economic activities, warranting the significance of the rainfall variability studies. Rainfall variability in karst areas also affects disaster conditions such as drought and floods. However, due to insufficient meteorological data in quality and quantity, there has been no rainfall variability studies conducted in this locality. The research intended to analyze rainfall variability in the Gunung Sewu karst area in 1979‒2013 by utilizing rainfall predictions from satellite images that many scholars had tested in different locations and recognized as having good quality. In the analysis, mean monthly rainfall was calculated, and the trends of annual rainfall and average rainfall intensity, dry and rainy seasons, the number of rainy days, and the effect of ENSO (El Niño Southern Oscillation) on rainfall were analyzed. The research data were 35 years of daily rainfall records derived from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Reanalysis (CFSR). The analysis results showed that the mean rainfall, number of rainy days, and rainfall intensity had an increasing trend. Also, El Niño quantitatively influenced the rain in the Gunung Kidul karst area.
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Eddenjal, Ali Salem. "Spatio Temporal Variability of Rainfall Across Western Libya from 1979 to 2009." Current Environmental Management 6, no. 3 (January 7, 2020): 235–44. http://dx.doi.org/10.2174/2212717806666191022175547.
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Aims: To understand the links between climate variability and hydrology in western Libya. Background: This study represents the first comprehensive assessment of rainfall variability in western Libya at a regional scale. Objective: To assess temporal and spatial variability of rainfall in western Libya, based on data (1979-2009) from 16 rain gauges. Methods: The non-parametric Mann-Kendall method and Sen’s slop estimator were used to define changes in rainfall series and their statistical significance. Results: Coastal and mountainous time series showed decreasing trends at the annual, autumn, and spring scales, with very few exceptions. Notably, winter showed increasing trends, with the significant values of 1.94 and 0.88 mm/year at Sirt and Nalut, respectively. Desert stations showed increasing trends, especially at the annual scale, with the greatest significant increase on the order of 1.19 mm/year in Ghadames. For the regional rainfall trend analysis, annual, spring and autumn rainfalls decreased in the coastal and mountainous zones, with the highest significant decrease of 1.94 mm/year. Again, winter rainfall showed increasing trend over the whole study domain. Conclusion: Although most time series showed a tendency towards more drier conditions, most of the detected trends were statistically non-significant. This study will provide guidance for policy makers in their future planning to mitigate the impact of drought.
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Sakazaki, Takatoshi. "Tropical Rainfall Variability Accompanying Global Normal Mode Oscillations." Journal of the Atmospheric Sciences 78, no. 4 (April 2021): 1295–316. http://dx.doi.org/10.1175/jas-d-20-0288.1.
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AbstractUsing global precipitation datasets (GSMaP, TRMM) and the latest reanalysis data (ERA5), we performed a comprehensive analysis of the tropical rainfall variability that accompanies global-scale, low-frequency normal modes: Rossby, Rossby–gravity, and Kelvin modes. Cross-spectral analysis and lag-regression analysis both showed that coherent rainfall variations accompany not only the wavenumber-1 gravest Rossby mode (“5-day” wave) but other low-frequency modes. The normal mode rainfall variations are enhanced in regions such as the Amazon basin, but also include circumglobally traveling structures with substantial amplitude over the open ocean. These results are remarkably consistent among the three datasets including even ERA5 rainfall data. The circumglobal rainfall signals may be considered primarily as a response to the normal mode dynamical variations. We found that the phase relationship between rainfall and dynamical field variability is strongly dependent on the type of mode and even on the zonal wavenumber. We suggest that this is explained by the difference in relative importance of two underlying processes: 1) moisture-flux convergence and 2) rainfall enhancement associated with adiabatic cooling. Our determined rainfall signals are the response to quasi-monochromatic, periodic waves that have a simple vertical structure and represent one special case of tropospheric wave–rainfall coupling. Implications for the mechanism of 12-h rainfall oscillations believed to be forced by the atmospheric tide are also considered.
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Zhou, Yan, Zhongmin Liang, Binquan Li, Yixin Huang, Kai Wang, and Yiming Hu. "Seamless Integration of Rainfall Spatial Variability and a Conceptual Hydrological Model." Sustainability 13, no. 6 (March 23, 2021): 3588. http://dx.doi.org/10.3390/su13063588.
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Rainfall is an important input to conceptual hydrological models, and its accuracy would have a considerable effect on that of the model simulations. However, traditional conceptual rainfall-runoff models commonly use catchment-average rainfall as inputs without recognizing its spatial variability. To solve this, a seamless integration framework that couples rainfall spatial variability with a conceptual rainfall-runoff model, named the statistical rainfall-runoff (SRR) model, is built in this study. In the SRR model, the exponential difference distribution (EDD) is proposed to describe the spatial variability of rainfall for traditional rain gauging stations. The EDD is then incorporated into the vertically mixed runoff (VMR) model to estimate the statistical runoff component. Then, the stochastic differential equation is adopted to deal with the flow routing under stochastic inflow. To test the performance, the SRR model is then calibrated and validated in a Chinese catchment. The results indicate that the EDD performs well in describing rainfall spatial variability, and that the SRR model is superior to the Xinanjiang model because it provides more accurate mean simulations. The seamless integration framework considering rainfall spatial variability can help build a more reasonable statistical rainfall-runoff model.
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DAS, H. P., and A. CHOWDHURY. "Variability in rainfall dispersion in Madhya Pradesh." MAUSAM 43, no. 1 (December 30, 2021): 29–36. http://dx.doi.org/10.54302/mausam.v43i1.3314.
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An attempt has been made to examine distribution and dispersion in rainfall variability in Madhya Pradesh by applying Gamma distribution probability model, The spatial and regional distribution of shape and scale parameters of the Gamma distribution have been examined, Periods of water surpluses and deficiencies have been identified by comparing the probability rainfall with the water requirement. Regression equations have been developed to find probabilitistic rainfall from the mean rainfall. Agronomic practices have been evaluated for efficient utilization of water resources for crop planning.
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BISWAS, B., and K. GUPTA. "Variability of southwest monsoon rainfall over West Bengal." MAUSAM 44, no. 4 (January 1, 2022): 353–58. http://dx.doi.org/10.54302/mausam.v44i4.3925.
Full textAbstract:
Monthly and seasonal variations of southwest monsoon rainfall over the districts of Gangetic and Sub-Himalayan West Bengal are presented and their differences discussed. Latitudinal variations of monsoon rainfall are brought out. Decadal means of seasonal rainfall over plains are compared with those at higher elevations and northern latitudes. An attempt is made to study long term rainfall trends.