Rainfall Regions (rainfall + regions)

Distribution by Scientific Domains


Selected Abstracts


Modelling daily precipitation features in the Volta Basin of West Africa

INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 7 2009
P. Laux
Abstract The combination of a conventional Markov chain model (zero and first order) and a gamma distribution model are found to be applicable to derive meaningful agricultural features from precipitation in the Volta Basin (West Africa). Since the analysis of the monthly or annual precipitation amount does not provide any adequate information on rainfall timing and sufficiency of crop water requirement, rainfall modelling was performed on a daily time scale for 29 rainfall stations. The modelled rainfall features follow distinct spatial patterns, which will be presented as maps of(1) rainfall occurrence probabilities and (2) recommendations of optimal planting dates. In addition, the effective drought index (EDI) working on daily time scales is calculated in order to assess drought properties of five different rainfall regions within the Volta Basin. Apart from the common way of separately modelling the duration and intensity due to their different distributions, a copula approach is chosen in this study to construct a bivariate drought distribution. Application of the measures derived to agricultural decision support will be discussed briefly. Copyright © 2009 Royal Meteorological Society [source]


Identification of three dominant rainfall regions within Indonesia and their relationship to sea surface temperature

INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 12 2003
Edvin Aldrian
Abstract The characteristics of climatic rainfall variability in Indonesia are investigated using a double correlation method. The results are compared with empirical orthogonal function (EOF) and rotated EOF methods. In addition, local and remote responses to sea-surface temperature (SST) are discussed. The results suggest three climatic regions in Indonesia with their distinct characteristics. Region A is located in southern Indonesia from south Sumatera to Timor island, southern Kalimantan, Sulawesi and part of Irian Jaya. Region B is located in northwest Indonesia from northern Sumatra to northwestern Kalimantan. Region C encompasses Maluku and northern Sulawesi. All three regions show both strong annual and, except Region A, semi-annual variability. Region C shows the strongest El Niño,southern oscillation (ENSO) influence, followed by Region A. In Region B, the ENSO-related signal is suppressed. Except for Region B, there are significant correlations between SST and the rainfall variabilities, indicating a strong possibility for seasonal climate predictions. March to May is the most difficult season to predict the rainfall variability. From June to November, there are significant responses of the rainfall pattern to ENSO in Regions A and C. A strong ENSO influence during this normally dry season (June to September) is hazardous in El Niño years, because the negative response means that higher SST in the NIÑO3 of the Pacific region will lower the rainfall amount over the Indonesian region. Analyses of Indonesian rainfall variability reveal some sensitivities to SST variabilities in adjacent parts of the Indian and Pacific Oceans. Copyright © 2003 Royal Meteorological Society [source]


Intra-seasonal rainfall characteristics and their importance to the seasonal prediction problem

INTERNATIONAL JOURNAL OF CLIMATOLOGY, Issue 9 2002
Warren J. Tennant
Abstract Daily station rainfall data in South Africa from 1936 to 1999 are combined into homogeneous rainfall regions using Ward's clustering method. Various rainfall characteristics are calculated for the summer season, defined as December to February. These include seasonal rainfall total, region-average number of station rain days exceeding 1 and 20 mm, region-average of periods between rain days at stations >1 and >20 mm, region-average of wet spell length (sequential days of station rainfall >1 and >20 mm), correlation of daily station rainfall within a region and correlation of seasonal station rainfall anomalies within a region. Rank-ordered rainfall characteristic data generally form an s-shaped curve, and significance testing of discontinuities in these curves suggests that normal rainfall conditions in South Africa consist of a combined middle three quintiles separated from the outer quintiles, rather than the traditional middle tercile. The relationships between the various rainfall characteristics show that seasons with a high total rainfall generally have a higher number of heavy rain days (>20 mm) and not necessarily an increase in light rain days. The length of the period between rain days has a low correlation to season totals, demonstrating that seasons with a high total rainfall may still contain prolonged dry periods. These additional rainfall characteristics are important to end-users, and the analysis undertaken here offers a valuable starting point for seeking physical relationships between rainfall characteristics and the general circulation. Preliminary studies show that the vertical mean wind is related to rainfall characteristics in South Africa. Given that general circulation models capture this part of the circulation adequately, seasonal forecasts of rainfall characteristics become plausible. Copyright © 2002 Royal Meteorological Society. [source]


Convergence towards higher leaf mass per area in dry and nutrient-poor habitats has different consequences for leaf life span

JOURNAL OF ECOLOGY, Issue 3 2002
Ian J. Wright
Summary 1,Leaf life span (LL) and leaf mass per area (LMA) are fundamental traits in the carbon economy of plants, representing the investment required per unit leaf area (LMA) and the duration of the resulting benefit (LL). Species on dry and infertile soils converge towards higher LMA. It has been generally assumed that this allows species from low-resource habitats to achieve longer average leaf life spans, as LMA and LL are often correlated. 2,Leaf life span and LMA were measured for 75 perennial species from eastern Australia. Species were sampled from nutrient-rich and nutrient-poor sites within high and low rainfall regions. LL and LMA were positively correlated across species within each site. In addition, evolutionary divergences in LL and LMA were correlated within each site, indicating that cross-species relationships were not simply driven by differences between higher taxonomic groups. 3,Within a rainfall zone, LL,LMA combinations shifted as expected along common axes of variation such that species on poorer soils had higher LMA and longer LL, but significantly so only at high rainfall. 4,Low rainfall species were expected to have shorter LL at a given LMA or, equally, require higher LMA to achieve a given LL, i.e. shift to a parallel axis of variation, and this was observed on both nutrient-rich and nutrient-poor soils. On average, 30% higher LMA was seemingly required at dry sites to achieve a given LL. Thus, convergence towards higher LMA has different consequences for leaf life span in dry and nutrient-poor habitats. 5,The broad shifts in LL,LMA combinations between site types were also seen when comparing closely related species-pairs (phylogenetically independent contrasts) occurring on nutrient-rich and nutrient-poor soils (within each rainfall zone), and at high- and low-rainfall sites (at each soil nutrient level). [source]


Spatial variability of rainfall regions in West Africa during the 20th century

ATMOSPHERIC SCIENCE LETTERS, Issue 1 2009
Zéphirin Yepdo Djomou
Abstract Four regions spread in five areas are identified in West Africa for the base period 1901,1940. Trends have a magnitude of up to 1.5 K per century, with a decreasing of the precipitation since the year 1970. The application of a segment of 15 years with overlap going from 1901 to 1940 (P0), and 1961 to 1998 (P4) throughout the periods 1916,1955 (P1), 1931,1970 (P2) and 1946,1985 (P3), shows important spatiotemporal modifications of rainfall regions south of 15°N. From P0 to P4, the surface of semiarid land doubles while wetland is reduced to half. Copyright © 2008 Royal Meteorological Society [source]