Search

This paper presents the methods, procedure and results in studying spatial and temporal characteristics of rainfall in Malawi, a data scarce region, between 1960 and 2006. Rainfall variables and indicators from rainfall readings at 42 stations in Malawi, excluding Lake Malawi, were analysed at monthly, seasonal and annual scales. In the study, the data were firstly subjected to quality checks through the cumulative deviations test and the standard normal homogeneity test. Spatial rainfall variability was investigated using the spatial correlation function. Temporal trends were analysed using Mann-Kendall and linear regression methods. Heterogeneity of monthly rainfall was investigated using the precipitation concentration index (PCI). Finally, inter-annual and intra-annual rainfall variability were tested using normalized precipitation anomaly series of annual rainfall series (| AR|) and the PCI (| APCI|), respectively. The results showed that (1) most stations revealed statistically non-significant decreasing rainfall trends for annual, seasonal, monthly and the individual months from March to December at the 5% significance level. The months of January and February (the highest rainfall months), however, had overall positive but statistically non-significant trends countrywide, suggesting more concentration of the seasonal rainfall around these months. (2) Spatial analysis results showed a complex rainfall pattern countrywide with annual mean of 1,095 mm centred to the south of the country and mean inter-annual variability of 26%. (3) Spatial correlation amongst stations was highest only within the first 20 km, typical of areas with strong small-scale climatic influence. (4) The country was further characterised by unstable monthly rainfall regimes, with all PCIs more than 10. (5) An increase in inter-annual rainfall variability was found. [ABSTRACT FROM AUTHOR]

Summary: Bayesian revision is widely used in hydrological model uncertainty assessment. With respect to model calibration, parameter estimation and analysis of uncertainty sources, various regression and probabilistic approaches have been used in different models calibrated for either daily or monthly time step. None of these applications however includes a comparison of uncertainty analysis in hydrological models with respect to the time periods, at which the models are operated. This study pursues a comprehensive inter-comparison and evaluation of uncertainty assessments by Bayesian revision using the Metropolis Hasting (MH) algorithm with the hydrological model WASMOD with daily and monthly time step. In the daily step model three likelihood functions are used in combination with Bayesian revision: (i) the AR (1) plus Normal time period independent model (Model 1), (ii) the AR (1) plus Multi-Normal model (Model 2), and (iii) the AR (1) plus Normal time period dependent model (Model 3). In addition an index called the percentage of observations bracketed by the Unit Confidence Interval (PUCI) was used for uncertainty evaluation. The results reveal that it is more important to consider the autocorrelation in daily WASMOD rather than monthly WASMOD. Firstly, the resulting goodness of fit of the daily model vs. observations as measured by the Nash–Sutcliffe efficiency value is comparable with that calculated by the optimization algorithm in monthly WASMOD. Secondly, the AR (1) model is not sufficiently adequate to estimate the distribution of residuals in daily WASMOD since PUCI shows that Model 2 outperforms Model 1. Furthermore, the maximum Nash–Sutcliffe efficiency value of Model 2 is the largest. Thirdly, Model 3 performs best over the entire flow range, while Model 2 outperforms Model 3 for high flows. This shows that additional statistical parameters reflect the statistical characters of the residuals more efficiently and accurately. Fourthly, by considering the difference in terms of application and computational efficiency it becomes evident that Model 3 performs best for daily WASMOD. Model 2 on the other hand is superior for daily time step WASMOD if the auto-correlation of parameters is considered. [Copyright &y& Elsevier]

Statistical downscaling is useful for managing scale and resolution problems in outputs from global climate models (GCMs) for climate change impact studies. To improve downscaling of precipitation occurrence, this study proposes a revised regression-based statistical downscaling method that couples a support vector classifier (SVC) and first-order two-state Markov chain to generate the occurrence and a support vector regression (SVR) to simulate the amount. The proposed method is compared to the Statistical Downscaling Model (SDSM) for reproducing the temporal and quantitative distribution of observed precipitation using 10 meteorological indicators. Two types of calibration and validation methods were compared. The first method used sequential split sampling of calibration and validation periods, while the second used odd years for calibration and even years for validation. The proposed coupled approach outperformed the other methods in downscaling daily precipitation in all study periods using both calibration methods. Using odd years for calibration and even years for validation can reduce the influence of possible climate change-induced nonstationary data series. The study shows that it is necessary to combine different types of precipitation state classifiers with a method of regression or distribution to improve the performance of traditional statistical downscaling. These methods were applied to simulate future precipitation change from 2031 to 2100 with the CMIP5 climate variables. The results indicated increasing tendencies in both mean and maximum future precipitation predicted using all the downscaling methods evaluated. However, the proposed method is an at-site statistical downscaling method, and therefore this method will need to be modified for extension into a multisite domain. [ABSTRACT FROM AUTHOR]

Daily precipitation data from 590 stations in China covering a period of 1960-2005 are analysed using copulas, the modified Mann-Kendall (MK) trend test and linear regression. Changing characteristics of eight precipitation indices are investigated in both time and space. Results indicate that (1) the regions west of 100°E, particularly northwest China, exhibit a wetting tendency as reflected by increasing/decreasing number of consecutive rain/non-rain days; (2) the drying tendency is observed mainly in the regions covered by the Yellow River basin, the Huaihe River basin, and the Haihe River basin, and relatively moderate changes in precipitation indices are found in northeast China; (3) precipitation extremes are intensifying in the regions east of 100°E, particularly in case of south China, specifically the lower Yangtze River basin, the southeast rivers and the Pearl River basin. The intensification of precipitation extremes in south China is mirrored mainly by the decreasing number of rain days and increasing number of consecutive non-rain days. Besides an increasing percentage of P90 to the annual total precipitation, (4) the intensification of precipitation extremes has the potential to increase the probability of occurrence of natural hazards, particularly floods and droughts. The spatial distribution of flood- and drought-affected crop areas is in agreement with that of precipitation extremes, showing considerable impacts of precipitation extremes on meteor-hydrological hazards. An increasing number of consecutive non-rain days in south China will cause a higher risk of droughts. The regions east of 100°E are heavily populated and are economically developed. Food security, water security, and sustainable socioeconomy in China urgently call for effective water resource management policy. Copyright © 2012 Royal Meteorological Society [ABSTRACT FROM AUTHOR]

Using daily precipitation data spanning 1960-2005 from 51 meteorological stations in Xinjiang province, China, spatial and temporal changes in consecutive maximum wet days in the year, summer, and winter were investigated. Fifteen precipitation extreme indices, which reflect the attributes of consecutive maximum wet days, were defined, and the modified Mann-Kendall test was applied to detect the tendencies, and changes in the indices were evaluated through linear regression with the F test. Results showed that: (1) two consecutive wet days occurred most frequently in the year and summer, and the fractional contributions and precipitation intensities decreased as the duration increased; in winter, one wet day had the maximum possibility, fractional contributions decreased and intensities increased as the duration increased. (2) The possibility of consecutive wet days which had short durations reduced, while those of long durations increased; annual fractional contributions of short durations decreased, while those of long durations increased; summer and winter fractional contribution of all durations decreased first and then increased; the intensities of all durations increased. (3) The wet tendency was identified in Xinjiang; the wet trend in Southern Xinjiang was more significant than Northern Xinjiang in summer, while in winter the wet tendency in Northern Xinjiang was more pronounced. [ABSTRACT FROM AUTHOR]

Hydrological models have been widely used to predict runoff in regions with observed discharge data, and regionalization methods have been extensively discussed for providing runoff predictions in ungauged basins (PUB), especially during the PUB decade (2003-2012). Great progress has been achieved in the field of regionalization in a large number of studies, in which different hydrological models have been coupled with various regionalization methods. However, most of these studies have concentrated their evaluations on either one hydrological model or one regionalization method. In our study, we assess the performance of three regionalization methods (spatial proximity, physical similarity and regression methods) and four daily lumped rainfall-runoff models (GR4J, WASMOD, HBV and XAJ) at the same time. Our aim is to evaluate how the performance depends on the inter-relationship between the hydrological model and regionalization method. This investigation uses data from 86 evenly distributed catchments in Norway, covering three different climate zones (oceanic, continental and polar tundra) according to the Köppen-Geiger classification. The results show that (a) the output averaging option for distance-based methods performs better for all hydrological models and the regression method performs worst in most cases; (b) the difference between the parameter averaging option and output averaging option is positively related to the number of hydrological model parameters, i.e. the more the number of parameters, the larger the difference between the two options; (c) models with fewer parameters tend to produce similar performance for the different regionalization methods; and (d) clear differences in the performance of regionalization methods exist between the three climate classes. This study provides insight to the influence from hydrological models on the performance of regionalization methods in various climate regions. [ABSTRACT FROM AUTHOR]