Wet-day Precipitation Research Articles

Changes in precipitation extremes over arid to semiarid and subhumid Punjab, Pakistan

Asymmetrical monsoons during the recent past have resulted into spatially variable and devastating floods in South Asia. Analysis of historic precipitation extremes record may help in formulating mitigation strategies at local level. Eleven indices of precipitation extremes were evaluated using RClimDex and daily time series data for analysis period of 1981–2010 from five representative cities across Punjab province of Pakistan. The indices include consecutive dry days, consecutive wet days, number of days above daily average precipitation, number of days with precipitation ≥10 mm, number of days with precipitation ≥20 mm, very wet days, extremely wet days, simple daily intensity index, maximum 1-day precipitation quantity, maximum 5 consecutive day precipitation quantity, and annual total wet-day precipitation. Mann-Kendall test and Sen’s slope extremes were used to detect trends in indices. Droughts and excessive precipitation were dictated by elevation from mean sea level with prolonged dry spells in southern Punjab and vice versa confirming spatial trends for precipitation extremes. However, no temporal trend was observed for any of the indices. Summer in the region is the wettest season depicting contribution of monsoons during June through August toward devastating floods in the region.

Precipitation variability in Northern Portugal: Data homogeneity assessment and trends in extreme precipitation indices

This study focuses on the spatial and temporal variability of selected extreme precipitation indices in Northern Portugal. Data were collected in the period of 1950–2000 from 39 meteorological and gauge stations. Tests of homogeneity (e.g., Pettit, SNHT, Buishand and Von Neumann tests) were performed for all the data collected from the gauge series, in order to identify and select the useful series to time variability analysis. Four extreme precipitation indices were investigated: total precipitation in wet days, with daily precipitation ≥1mm (PRCPTOT); number of days with precipitation amount≥30mm (R30mm); the maximum 5day precipitation amount (R×5day); and total precipitation amount≥95th percentile (R95p). The Mann–Kendall non-parametric test was applied to detect long term (1950–2000) trends in the aforementioned indices of extremes. Annual and seasonal trends were analyzed in this study. The results showed a decrease in extreme precipitation indices during annual scale, spring, winter and summer but showed a slight increase during autumn. For most series, there is no statistically significant trend in the parameters of extreme precipitation analyzed. Out of all of the indices examined, only three rain-gauges showed significant trends on annual scale. On a seasonal timescale, extreme precipitation indices tended to decrease in winter, spring and summer in more than 80% of the precipitation series. The opposite was observed in autumn in which the four indices studied showed that more than 70% of the series had a positive trend. However, only 15% of the series showed a significant trend in winter, 46% in spring, only 2% in summer and 4% in autumn.

Recent changes in daily extremes of temperature and precipitation over the western Tibetan Plateau, 1973–2011

Climate extremes in the Tibetan Plateau may cause serious regional and global consequences, but observation-based research over the western Tibetan Plateau is scarce. In this paper, recent changes in extremes of temperature and precipitation over the western Tibetan Plateau from 1973 to 2011 are investigated. A total of 24 indices that represent extreme climate events are selected and calculated by using daily maximum and minimum temperature and precipitation data. Results demonstrate that most cold-related indices of temperature extremes (frost and ice days, cool nights and cool days, and cold spell duration indicators) show a significant decrease, and that both the coldest night and coldest day have increased during the study period. Warm-related indices of temperature extremes such as summer days, the warmest night and the warmest day, warm nights and warm days, and warm spell duration indicators all have increased. The diurnal temperature ranges show a decreasing trend with statistical significance, whereas growing season lengths have increased significantly. The change trends of precipitation extremes are nonsignificant. Nonsignificant increasing trends are detected for most indices including annual total wet-day precipitation, maximum 1-day and 5-day precipitation, very wet and extremely wet day precipitation, and consecutive wet days. However, nonsignificant decreasing trends are found for the number of heavy precipitation days, consecutive dry days and simple daily intensity index.

Specification of wet-day daily rainfall quantiles from the mean value

ABSTRACTThe characteristics associated with an exponential distribution is used as a yard stick to assess distributions for 24-hr precipitation records from 13 771 rain gauges from mainly the USA and Europe. It is shown that the daily accumulated precipitation amount is approximately similar to an exponential distribution, but not identical, and that quantiles of the distributions can to a zeroth order be specified from the mean of the wet-day precipitation. However, the data have a thicker upper tail than the exponential distribution. We propose a simple method for making a crude estimate for quantiles of wet-day 24-hr precipitation distribution, and a refinement of the exponential distribution based on principal component analysis. We also show that the high quantiles are related to the wet-day mean. The associations between the wet-day mean and quantiles from the observations are compared with results from regional climate model simulations, taken from a number of regional climate models. Similar tendencies are seen in the models as in the rain gauge data.

A Comparison of Bangladesh Climate Surfaces from the Geostatistical Point of View

This study analyses the degree and margin of differences among the surfaces of annual total precipitation in wet days (PRCPTOT) and the yearly maximum value of the daily maximum temperature (TXx) of Bangladesh, produced by thin plate spline (TPS), inverse distance weighting (IDW), ordinary kriging (OK), and universal kriging (UK) methods of spatial interpolation. From the surface differences, the maximum and minimum differences are observed between the surfaces produced by TPS and IDW, and OK and UK, respectively. The residual plots from cross-validation depict that IDW and OK methods mostly under predict and TPS and UK methods mostly overpredict the observed climate indices’ values. Both the tendency of methods’ over and underprediction and the surface-differences decrease with the increase in the number of available spatial point observations. Finally, two performance measures—the index of agreement (d) and the coefficient of variation of prediction (ρf)—imply that there is a little difference in the prediction ability of the four different methods. The performance of the spatial interpolation improves with the increase in the number of available spatial points, and eventually the predicted climate surfaces get similar. However, UK shows better interpolation performance in most of the years.

Recent changes in daily precipitation and surface air temperature extremes in mainland Portugal, in the period 1941–2007

Changes in the climatology of precipitation and surface air temperature are being investigated worldwide, searching for changes in variability, the mean and extreme events (maximum and minimum). By exploring recent adjustments in the climate of mainland Portugal, particularly in the intensity, frequency and duration of extreme events, this study investigates trends in selected specific indices that are calculated from daily precipitation data from 57 and surface air temperature data from 23 measuring stations scattered across the territory. Special attention is paid to regional differences and variations in seasonality. The data cover the periods 1941–2007 for precipitation, and 1941–2006 for temperature. They are explored at the annual and seasonal scales and for different sub-periods.Results show that trends in annual precipitation indices are generally weak and, overall, not statistically significant at the 5% level. Nevertheless, a decreasing trend is revealed by regional indices of total wet-day precipitation and extreme precipitation (above the 99th percentile). Seasonal precipitation exhibits significant decreasing trends in spring precipitation, while extreme heavy precipitation events, in terms of both magnitude and frequency, have become more pronounced in autumn. Results for winter and summer suggest that the extremes have not suffered any significant aggravation.Trends for air temperature are statistically more significant and marked than for precipitation and indicate general warming across the territory. This warming trend is revealed very consistently by the time series of individual stations and regional mean temperature, and is also consistent with the findings reported in other studies for Portugal and at the European scale.

Changes in daily climate extremes in the arid area of northwestern China

There has been a paucity of information on trends in daily climate and climate extremes, especially for the arid region. We analyzed the changes in the indices of climate extremes, on the basis of daily maximum and minimum air temperature and precipitation at 59 meteorological stations in the arid region of northwest China over the period 1960–2003. Twelve indices of extreme temperature and six indices of extreme precipitation are examined. Temperature extremes show a warming trend with a large proportion of stations having statistically significant trends for all temperature indices. The regional occurrence of extreme cool days and nights has decreased by −0.93 and −2.36 days/decade, respectively. Over the same period, the occurrence of extreme warm days and nights has increased by 1.25 and 2.10 days/decade, respectively. The number of frost days and ice days shows a statistically significant decrease at the rate of −3.24 and −2.75 days/decade, respectively. The extreme temperature indices also show the increasing trend, with larger values for the index describing variations in the lowest minimum temperature. The trends of Min Tmin (Tmax) and Max Tmin (Tmax) are 0.85 (0.61) and 0.32 (0.17) °C/decade. Most precipitation indices exhibit increasing trends across the region. On average, regional maximum 1-day precipitation, annual total wet-day precipitation, and number of heavy precipitation days and very wet days show insignificant increases. Insignificant decreasing trends are also found for consecutive dry days. The rank-sum statistic value of most temperature indices exhibits consistent or statistically significant trends across the region. The regional medians after 1986 of Min Tmin (Tmax), Max Tmin (Tmax), warm days (nights), and warm spell duration indicator show statistically more larger than medians before 1986, but the frost days, ice days, cool days (nights), and diurnal temperature range reversed. The medians of precipitation indices show insignificant change except for consecutive dry days before and after 1986.

Downscaling precipitation extremes

A new method for predicting the upper tail of the precipitation distribution, based on empirical–statistical downscaling, is explored. The proposed downscaling method involves a re-calibration of the results from an analog model to ensure that the results have a realistic statistical distribution. A comparison between new results and those from a traditional analog model suggests that the new method predicts higher probabilities for heavy precipitation events in the future, except for the most extreme percentiles for which sampling fluctuations give rise to high uncertainties. The proposed method is applied to the 24-h precipitation from Oslo, Norway, and validated through a comparison between modelled and observed percentiles. It is shown that the method yields a good approximate description of both the statistical distribution of the wet-day precipitation amount and the chronology of precipitation events. An additional analysis is carried out comparing the use of extended empirical orthogonal functions (EOFs) as input, instead of ordinary EOFs. The results were, in general, similar; however, extended EOFs give greater persistence for 1-day lags. Predictions of the probability distribution function for the Oslo precipitation indicate that future precipitation amounts associated with the upper percentiles increase faster than for the lower percentiles. Substantial random statistical fluctuations in the few observations that make up the extreme upper tail implies that modelling of these is extremely difficult, however. An extrapolation scheme is proposed for describing the trends associated with the most extreme percentiles, assuming an upper physical bound where the trend is defined as zero, a gradual variation in the trend magnitude and a function with a simple shape.

Evaluation of daily precipitation characteristics in the CLM and their sensitivity to parameterizations

This paper presents a detailed evaluation study of the regional climate model CLM (climate version of the Lokal-Modell), driven by ERA-40 re-analysis data, with respect to summer precipitation characteristics over Germany. Since extremes might become more important in a changing climate, characteristics other than mean values need to be analyzed in more detail. Two aspects are investigated, namely the capability of the CLM to simulate different statistical characteristics of daily precipitation and the sensitivity of these characteristics to changes in the physical parameterizations. Our evaluation is based on 132 model runs, encompassing 11 summers with 12 different model configurations each, and relates to observations of a dense station network. In addition to its frequency distribution, different characteristics of daily precipitation, including extremes, are assessed by means of several indices. We analyze these characteristics in four different regions of Germany with a size of about 220x220 km 2 , by assessing the model skill with respect to both the spatial average and the spatial structure within these regions. The range of the model uncertainty due to modified parameterizations is derived for all indices and regions. Our results indicate that: (i) the CLM underestimates both observed total precipitation and wet-day frequency, although the wet-day precipitation intensity is simulated quite well, (ii) the model deviations from observations of the indices describing extreme events are comparable to those of the total precipitation amount, (iii) the frequency distribution is reasonably well simulated by the CLM, (iv) the CLM is unable to adequately reproduce the observed spatial structure of precipitation indices within the selected regions, (v) in the southernmost region which is characterized by complex terrain the CLM performance is generally low, and (vi) the uncertainty due to the modified physical parameterizations is considerable. All in all, the model performance differs for different characteristics, regions or model configurations, which leads to the conclusion that application-specific evaluation studies are indispensable prior to using the model results from climate change projections.

Combined Effects of the Southern Oscillation Index and the Pacific Decadal Oscillation on a Stochastic Daily Precipitation Model*

AbstractThe combined effects of the Southern Oscillation index (SOI) and the Pacific decadal oscillation (PDO) on a second-order Markov chain mixed exponential daily precipitation model were determined for 15 stations in Nevada, Arizona, New Mexico, and Washington. The linkage between these monthly indices and daily precipitation was investigated by perturbing logits of Markov chain transition probabilities and the mean of the mixed exponential distribution by linear functions of a time-lagged SOI and concurrent PDO. The combination of linear coefficients and lags that resulted in the maximum log-likelihood functions was identified, and the Akaike information criterion (AIC) was used to determine the best model. The SOI effect was consistent with previous studies in that negative SOI (El Niño) leads to more frequent precipitation and greater amounts of precipitation given a wet day in Nevada and the Southwest, with the opposite effect in the northwest. SOI and PDO interactions were most significant for the Nevada stations where a positive PDO increases both the probability of a wet day for three transition probabilities and also increases the mean of wet-day precipitation. The PDO had little effect for the monsoonal (Arizona and New Mexico) stations except for Hobbs, New Mexico, where a positive PDO would increase the mean depth on a wet day. The Washington stations showed an increase in the frequency and mean depth of precipitation given a positive SOI, but PDO had little effect. Identification of combined SOI–PDO effects allows more realistic simulations of daily precipitation and provides insight into the reliability of short records.

Adjustment of CLIGEN parameters to generate precipitation change scenarios in southeastern Australia

Global climate model predictions are often downscaled with stochastic weather generators to produce suitable climate change scenarios for impact analysis. Proportional adjustment to generated daily precipitation and direct adjustment to parameter values for weather generators have been used for assessing the impact of climate change on runoff and soil loss. Little is known of how these parameter values should be realistically adjusted, the amount of adjustment, and whether the adjustments are correlated among different parameters. Rainfall in southeastern Australia has significantly increased since the late 1940s. Rainfall records in Sydney show a similar trend. Long term daily and 6-min intensity data from Sydney have made it possible to examine how CLIGEN parameter values have changed in relation to the underlying significant increase in rainfall. This study shows that for Sydney, most of the increase in rainfall is a result of the increase in wet day precipitation. The increase in the standard deviation of wet-day precipitation is greater than that in the mean, implying a greater rainfall variability during wetter periods. The wet-following-wet transition probability, and maximum 30-min rainfall intensity are all positively and significantly correlated with the change in wet-day precipitation. The change in peak intensity is about half the change in rainfall. No significant relationship can be established between the changes in mean monthly rainfall and those in the skewness coefficient for wet day precipitation and wet following dry transition probability for the site. Simultaneous adjustment of all these parameters is needed for generation of precipitation change scenarios for the region. Using simple proportional adjustment to generated precipitation sequences would lead to maximum impacts on runoff and soil loss predicted with WEPP, while attributing precipitation change equally to the change in wet day precipitation and the number of wet days would under-estimate the magnitude of the impacts considerably for the site.

On the Choice of the Temporal Aggregation Level for Statistical Downscaling of Precipitation

Abstract The merits of daily and monthly downscaling models for precipitation are compared using data from Bern, Switzerland; Deuselbach, Germany; and De Bilt, the Netherlands. For each station, generalized linear models are developed to describe rainfall occurrence, the wet-day precipitation amounts, and the monthly precipitation totals. The predictor dataset includes dynamical variables and atmospheric moisture (relative humidity for rainfall occurrence and specific humidity for rainfall amount). Fitting a generalized linear model to daily rainfall data generally results in larger regression coefficients than fitting the same model to monthly data. For rainfall occurrence this can be attributed mostly to the nonlinearity of the function that links the wet-day probabilities to the predictor variables, whereas for rainfall amounts there is, apart from nonlinearity, also a bias in the estimated regression coefficients of the monthly models caused by averaging predictor variables over both wet and dry days....

Uncertainty Propagation of Regional Climate Model Precipitation Forecasts to Hydrologic Impact Assessment

Abstract A Monte Carlo framework is adopted for propagating uncertainty in dynamically downscaled seasonal forecasts of area-averaged daily precipitation to associated streamflow response calculations. Daily precipitation is modeled as a mixture of two stochastic processes: a binary occurrence process and a continuous intensity process, both exhibiting serial correlation. The parameters of these processes (e.g., the proportion of wet days and the average wet-day precipitation intensity in a month) are derived from the forecast record. Parameter uncertainty is characterized via an empirical Bayesian model, whereby such parameters are modeled as random with a specific joint probability distribution. The hyperparameters specifying this probability distribution are derived from historical precipitation records at the study basin. Simulated parameter values are then generated using the Bayesian model, leading to alternative synthetic daily precipitation records simulated via the stochastic precipitation model....

Changes in average and extreme precipitation in two regional climate model experiments

Two regional climate model experiments for northern and central Europe are studied focussingon greenhouse gas-induced changes in heavy precipitation. The average yearly maximum onedayprecipitation Pmax shows a general increase in the whole model domain in both experiments, although the mean precipitation Pmean decreases in the southern part of the area, especially inone of the experiments. The average yearly maximum six-hour precipitation increases evenmore than the one-day Pmax, suggesting a decrease in the timescale of heavy precipitation. Thecontrast between the Pmean and Pmax changes in the southern part of the domain and the lackof such a contrast further north are affected by changes in wet-day frequency that stem, at leastin part, from changes in atmospheric circulation. However, the yearly extremes of precipitationexhibit a larger percentage increase than the average wet-day precipitation. The signal-to-noiseaspects of the model results are also studied in some detail. The 44 km grid-box-scale changesin Pmax are very heavily affected by inter-annual variability, with an estimated standard errorof about 20% for the 10-year mean changes. However, the noise in Pmax decreases sharplytoward larger horizontal scales, and large-area mean changes in Pmax can be estimated withsimilar accuracy to those in Pmean. Although a horizontal averaging of model results smoothsout the small-scale details in the true climate change signal as well, this disadvantage is, in thecase of Pmax changes, much smaller than the advantage of reduced noise.

Dependence of precipitation on temperature at Florence and Livorno (Italy)

Papers dealing with precipitation scenarios for a potentially warmer climate have often ignored the relationship between precipitation and temperature. Although the atmosphere has a larger capacity to hold water vapour at higher temperatures, plots of the mean wet-day precipitation amounts versus temperature may not reveal a clear positive relationship. This phenomenon is studied in detail for Florence and Livorno (Italy). Vorticity and the difference between the land and sea surface temper- atures are 2 important factors that determine precipitation at these sites. The wet-day precipitation amount is explored as a function of these 2 variables and temperature using an iterative smoothing technique. The estimated functions show a marked increase in the mean wet-day precipitation amount with increasing temperature (~6% °C -1 ). The differences between the smoothed relations for Florence and Livorno are small and the smooths for the winter and summer halves of the year are also quite sim- ilar. Besides the mean wet-day precipitation amounts, the mean wet-hour amounts (mean rainfall intensities) of each day with rain are also analysed. These show a much stronger increase with increas- ing temperature (~11% °C -1 ), which is partly due to a decrease of rainfall duration with increasing tem-

CALIBRATION AND TESTING OF A DAILY RAINFALL EROSIVITY MODEL

ABSTRACT A general procedure is presented for calibrating a model for rainfall erosivity based on daily rainfall. The approach is based on probability distributions of wet-day precipitation amount and monthly erosivities which are inferred from published data summaries. The calibrated model was tested by comparisons with erosivities computed from hourly precipitation data. Model results were generally consistent with values based on hourly data and explained over 85% and 70%, respectively, of the variations in annual and event erosivities. Model results for extreme values (annual erosivities exceeded in 5% of the years and l-in-20-year event erosivities) often substantially exceeded values computed for hourly data. To facilitate general use of the daily model, calibration coefficients were calculated for 33 sites in the eastem and central U.S.