Wet Spell Lengths Research Articles

A comparison of three methods for downscaling daily precipitation in the Punjab region

AbstractMany downscaling techniques have been developed in the past few years for projection of station‐scale hydrological variables from large‐scale atmospheric variables simulated by general circulation models (GCMs) to assess the hydrological impacts of climate change. This article compares the performances of three downscaling methods, viz. conditional random field (CRF), K‐nearest neighbour (KNN) and support vector machine (SVM) methods in downscaling precipitation in the Punjab region of India, belonging to the monsoon regime. The CRF model is a recently developed method for downscaling hydrological variables in a probabilistic framework, while the SVM model is a popular machine learning tool useful in terms of its ability to generalize and capture nonlinear relationships between predictors and predictand. The KNN model is an analogue‐type method that queries days similar to a given feature vector from the training data and classifies future days by random sampling from a weighted set of K closest training examples. The models are applied for downscaling monsoon (June to September) daily precipitation at six locations in Punjab. Model performances with respect to reproduction of various statistics such as dry and wet spell length distributions, daily rainfall distribution, and intersite correlations are examined. It is found that the CRF and KNN models perform slightly better than the SVM model in reproducing most daily rainfall statistics. These models are then used to project future precipitation at the six locations. Output from the Canadian global climate model (CGCM3) GCM for three scenarios, viz. A1B, A2, and B1 is used for projection of future precipitation. The projections show a change in probability density functions of daily rainfall amount and changes in the wet and dry spell distributions of daily precipitation. Copyright © 2011 John Wiley & Sons, Ltd.

THE PROJECTION OF FUTURE RAINFALL CHANGE OVER KEDAH, MALAYSIA WITH THE STATISTICAL DOWNSCALING MODEL

Climate change is a crucial global issue. The trend of climate change is very important to the agriculture because its uncertainty may cause the reduction in crop yield and economic losses. Therefore, the climate model has been widely used to simulate the future climate variation based on atmospheric circulation pattern (predictors). This study utilized the Statistical Downscaling Model (SDSM). The purpose of this paper is to describe the climate variation in Malaysia’s prominent agricultural state, Kedah, in the context of climate change scenario based on the temperature, rainfall intensity, wet and dry spell length. Twenty rainfall stations were selected in Kedah as they had lesser percentage of missing data to control the quality and capability of result. Based on the SDSM simulation result, the climate trend is not much different from the historical data but it is expected to increase continuously for every interval period from the year 2010 to 2099. The increasing of temperature is predictable up to 32oC during south-west monsoon season and 40oC during north-east monsoon season. The rainfall pattern shows that the intensity is estimated to focus on the middle area of Kedah such as Kota Setar, Pendang, Yan, Kuala Muda, Sik, and Baling district and is expected to increase during north-east monsoon season even when the wet spell during that period is relatively short. The longest dry spell which is expected to assault Kedah Peak and Kodiang in the future may cause water scarcity at critical areas.

GiST: A Stochastic Model for Generating Spatially and Temporally Correlated Daily Rainfall Data

Abstract Weather generators are tools that create synthetic daily weather data over long periods of time. These tools have also been used for downscaling monthly to seasonal climate forecasts, from global and regional circulation models to daily values for use as inputs for crop and other environmental models. One main limitation of most weather generators is that they do not take into account the spatial structure of weather. Spatial correlation of daily rainfall is important when one aggregates, for example, simulated crop yields or hydrology in a watershed or region. A method was developed to generate realizations of daily rainfall for multiple sites in an area while preserving the spatial and temporal correlations among sites. A two-step method generates rainfall events at multiple sites followed by rainfall amounts at sites where generated rainfall events occur. The generation of rainfall events was based on a new orthogonal Markov chain for discrete distributions. For generating rainfall amounts, a vector of random numbers (from a uniform distribution), of order equal to the number of locations with rainfall events that were generated to occur in a day, was matrix-multiplied by the corresponding factorized correlation matrix to create spatially correlated random numbers. Elements from the resulting vector were transformed to a gamma distribution using cumulative probability functions for each location and rescaled to rainfall amounts. One study area was located in north-central Florida, where correlated rainfall data were generated for seven weather stations to evaluate its performance versus a widely used single-site weather generator. A second area was in North Carolina, where rainfall was generated for 25 weather stations to evaluate the effects of a larger number of stations in other regions. One thousand yearlong replications of daily rainfall data were generated for each area. Monthly spatial correlations of generated daily rainfall events and amounts among all pairs of weather stations closely matched their observed counterparts. For daily rainfall amounts the correlation coefficients between the observed pairwise correlation coefficients and the ones estimated from synthetic data among weather stations were 0.977 for Florida and 0.964 for North Carolina. The performance of the geospatial–temporal (GiST) weather generator was also analyzed by comparing the distributions of lengths of dry and wet spells, joint probabilities, Markov transitional probabilities, distance decay of correlation functions, and regionwide days without rainfall at any station. Multiannual mean and standard deviation of the number of rainy days per month and mean monthly rainfall were also calculated. All comparisons between observed and generated rainfall events and amounts using the GiST weather generator were highly correlated. The root-mean-square errors of pairwise correlation values ranged from 0.05 to 0.11 for rainfall events and from 0.03 to 0.06 for amounts.

MV-WG: a new multi-variable weather generator

This paper presents a new stochastic multi-variable weather generator (MV-WG) and compares its performance with LARS-WG version 4.0. Daily data of 109 meteorological stations from a North American database were used in a twofold comparison of the two generators: (1) the capability of reproducing the mean and variance of annual, seasonal and monthly values, and (2) the capability of reproducing extreme weather events were compared. Both generators did very well on imitating the mean and the variance of the monthly values of the investigated variables, but both showed a more moderate performance as far as the generation of extreme events was concerned. The three-parameter Weibull function, which is first introduced in MV-WG, was found to be a powerful tool to describe not only the distribution of the daily precipitation amounts, but also the distribution of dry and wet spell lengths, as well.

Transient responses to increasing CO2 and climate change in an unfertilized grass–clover sward

Mechanisms controlling transient responses to elevated CO2 concentration and climate change in an unfertilized grassland on the Swiss Plateau were examined in light of simulations with PROGRASS, a process-based model of grass-clover interactions. Daily weather for a series of tran- sient climate scenarios spanning the 21st century were developed for the study site with the help of the LARS-WG weather generator. Changes in the length of dry and wet spells, temperature, precip- itation and solar radiation defining the scenarios were obtained from regional climate simulations carried out in the framework of the PRUDENCE project. Compared to 1961-1990, the latter indicated that for 2071-2100 there would be a noticeable increase in temperature (roughly 3°C in winter and 5°C in summer), a significant drop in summer precipitation (of the order of -30%) and a nearly 2-fold increase in the length of dry spells. Assuming that clover is less drought-tolerant than grass, we examined whether clover abundance would decrease as a consequence of increasing heat and water stress, or be promoted on account of CO2 stimulation. Results indicated that: (1) at our site, climate change alone did not curtail biological N2 fixation to the point as to alter the composition of the sward but nevertheless entailed a decrease in grassland productivity; (2) increasing CO2 clearly promoted clover growth (via feedbacks on nitrogen acquisition) and grassland productivity; and (3) even with CO2 stimulation, the additional N input from symbiotic N2 fixation was not sufficient to substantially improve the mineral N status of the system and promote grass competitiveness.

Stochastic modeling of the occurrence of rainfall over some districts of Assam during 1987-1992

The Primary aim in this paper is to find an alternative approach that consists of modeling the pattern of dry and wet spell over some districts of Assam. The Markov Chain Model is used to predict the length of dry and wet spells during the Indian summer monsoon season (June to September). This information may help the agronomists and agricultural scientists in crop planning. Five districts viz., Dibrugarh, Kamrup, Sonitpur, Dhemaji and North­ Lakhimpur are considered here for this study. Markov Chain Model is fitted for each of the district and the results of the five districts are pooled. This pooled result reveals that during the period 1987-1992, the probability for the day being wet when the immediately preceding day is dry for different years varies from 0.44 to 0.54 while the probability of the day being wet when the immediately preceding day is wet for different years varies from 0.74 to 0.86. It is also found that in the Indian summer monsoon season after about every consecutive 4 - 7 wet days a dry day is expected to occur whereas alter about consecutive 2 dry days, a wet day is expected to occur. The number of days required for the process to reach the state of equilibrium varies from 4 - 7 days.

Estimating rainfall erosivity from daily precipitation records: A comparison among methods using data from the Ebro Basin (NE Spain)

Summary Among the major factors controlling soil erosion, as vegetation cover or soil erodibility, rainfall erosivity has a paramount importance since it is difficult to predict and control by humans. Accurate estimation of rainfall erosivity requires continuous rainfall data; however, such data rarely demonstrate good spatial and temporal coverage. Daily weather records are now commonly available, providing good coverage that better represents rainfall intensity behavior than do more aggregated rainfall data. In the present study annual rainfall erosivity was estimated from daily rainfall records, and compared to data obtained employing the RUSLE R factor procedure. A spatially-dense precipitation database of high temporal resolution (15 min) was used. Two methodologies were applied: (i) daily rainfall erosivity estimated using several parametric models, and, (ii) annual rainfall erosivity estimated by regression-based techniques employing several intensity precipitation indices and the modified Fournier index. To determine the accuracy of estimates, several goodness-of-fit and error statistics were computed in addition to a spatial distribution comparison. The daily rainfall erosivity models accurately predicted annual rainfall erosivity. Parametric models with few combined parameters and a periodic function simulating intra-annual rainfall behavior provided the best results. Where daily rainfall records were not available, good estimates of annual rainfall erosivity were also obtained using regression-based techniques based on 5-day maximum precipitation events, the maximum wet spell duration, and the ratio between the lengths of average wet and dry spells. Inherent limitations remain in the use of daily weather records for estimating rainfall erosivity. Future research should focus on incorporating measures of natural rainfall properties of the particular region, including kinetic energy and intensity, and their effects on the soil.

마코프 연쇄를 이용한 서울지점 일강우의 발생특성 변화 연구

본 논문에서는 연속자료로서 세계 최장의 기록을 보유하고 있는 서울지점의 강우량 자료를 이용하여 강우 발생특성의 장기 변동성을 분석하였다. 우선 마코프 연쇄에 근거한 전이확률 및 발생특성을 분석하여 측우기 자료의 정확성을 강우의 발생확률적 측면에서 평가하였다. 그리고 2차원 LOWESS 회귀방법을 이용하여 전이확률의 월간 장기변화특성을 분석하였다. 전이확률 및 발생특성 분석결과 원자료 계열의 CWK와 MRG는 발생특성이 다르게 나타났다. 강우사상의 특성은 과거에 비해 강우사상의 발생빈도가 높아지고 있으며 각 강우사상의 지속기간은 짧아지고 있는 것으로 나타났다. 그리고 전이확률의 월간 장기 변화특성을 분석한 결과, M20을 기준으로 CWK와 MRG의 무강우지속기간은 크게 차이를 보이지 않고 있으며, 강우지속기간은 1830년대 이후 지속적으로 감소하는 경향을 보이고 있는 것으로 나타났다. 특히 최근 9월 강우지속기간의 감소 경향이 두드러지게 나타났다. 이러한 결과를 최근 강우량의 증가양상과 더불어 고려하면 강우사상의 빈도와 심도(강우강도)가 증가하는 추세라고 해석할 수 있다. In this study, long-term variabilities of rainfall-occurrence characteristics are analyzed using rainfall data at Seoul, which is the longest data record existing in world. first, the accuracy of Chukwooki data set (CWK) are evaluated in view of rainfall-occurrence probability by analyzing the transition probabilities and occurrence characteristics based on Markov chain. And long-term inter-monthly variabilities of transition probabilities are analyzed using two dimensional LOWESS regression. From the results of analyzed transition probabilities and occurrence characteristics, it is different that rainfall-occurrence characteristics between CWK and modern rain gage data set (MRG) for original rainfall data sets (M00). For characteristics of rainfall series, occurrences probabilities of rainfall are increased and durations of each rainfall are shorter than past. And from the results of analyzing the long-term inter-monthly variabilities of transition probabilities, in case of M20, lengths of dry spells between CWK and MRG are not different significantly and lengths of wet spells are decreased persistently after A.D. 1830. Especially, decreasing trend for lengths of wet spells at recent september are appeared significantly. These results are considered with increasing trend of recent rainfall, it is concluded that recent frequencies and intensities of rainfall are increasing.

A multisite daily rainfall generator driven by bivariate copula‐based mixed distributions

This work describes a simple and parsimonious multisite Markov model deduced from bivariate copula‐based mixed distributions useful for modeling and generating daily rainfall series. The structure of the model (involving only joint probabilities of wet/dry condition, a copula, the distribution of positive rainfall values, and a spatial cross‐correlation matrix) allows preserving spatial and temporal intermittency and several other properties of hydrologic interest (e.g., wet spell length, daily rainfall amount, rainfall accumulated on wet spells, first steps of the autocorrelation function, and spatial Kendall's correlation coefficient). The model does not require marginal transformations of the observed values, and non‐Gaussian temporal structures of dependence can be introduced by copulas. The spatial dependence is accounted for by simulating temporally independent but spatially correlated standard uniform random sequences. Application to observed rainfall time series and Monte Carlo simulations are carried out to assess the model performances. The approach appears to be a viable way to simulate daily rainfall sequences in a fairly easy way.

The variability of daily precipitation totals in Poland (1951–2000)

The analysis concerned the variability of daily precipitation totals observed during the second half of the 20th century at five stations in Poland. The elements examined were number of days with precipitation exceeding given thresholds, lengths of wet and dry spells and precipitation amounts in a single spell. There is an upward trend for the number of spells and days with precipitation, and a downward trend for mean precipitation during a given spell. Changes in peak precipitation are not uniform, there being negative trends at some stations and positive ones at others. However the lack of a trend for precipitation totals combines with the climatic warming now to be observed to imply risk of a water deficit.

On the Proper Order of Markov Chain Model for Daily Precipitation Occurrence in the Contiguous United States

Abstract Markov chains are widely used tools for modeling daily precipitation occurrence. Given the assumption that the Markov chain model is the right model for daily precipitation occurrence, the choice of Markov model order was examined on a monthly basis for 831 stations in the contiguous United States using long-term data. The model order was first identified using the Bayesian information criteria (BIC). The maximum-likelihood estimates of the Markov transition probabilities were computed from 100 bootstrapped samples and were then used to generate 50-yr precipitation occurrence series. The distributions of dry- and wet-spell lengths in the resulting series were then compared with observations using a two-sample Kolmogorov–Smirnov (K-S) test. The results suggest that the most parsimonious model, as identified by the BIC, usually (in approximately 68% of the cases) reproduced the wet- and dry-spell length distributions. However, the K-S test often indicated a second-order model when the BIC indicated a first-order model. In a smaller number of cases, the BIC indicated a higher-order model than the K-S test. In both cases, the differences were found to be due to the distribution of wet spells rather than dry spells. It is concluded that models chosen on the basis of the BIC may not adequately reproduce the distributions of wet and dry spells for some locations and times of year.

Understanding the surface hydrology of the Lake Eyre Basin: Part 1—Rainfall

This is the first of two papers that describe the surface hydrology of the Lake Eyre Basin (LEB) (1,140,000 km 2) in central Australia and compares some key characteristics with those observed from arid regions globally. This paper concentrates on annual rainfall, whereas the second paper is devoted to streamflow. The first part describes the LEB's climate (arid to semi-arid), which is dominated by a subtropical high pressure ridge stretching latitudinally across central Australia. Then follow major analyses that include the characteristics of rainfall, wet and dry spell lengths and cumulative surpluses and deficiencies, rainfall trends and intra- and inter-decadal fluctuations, and the relationship between rainfall and El Niño-Southern Oscillation (ENSO). The paper concludes with six conclusions, the key ones being: the variability of the annual rainfall (based on the coefficient of variation) in the LEB is approximately 60% greater than that found for stations located in arid regions in the rest of the world; there is a bias towards longer lengths of dry years than observed in the rest of Australia; and, there is a significant lag correlation between rainfall and ENSO, particularly in the east and in the latter part of a year.

Weather Types and Rainfall over Senegal. Part II: Downscaling of GCM Simulations

Abstract Four methods of downscaling daily rainfall sequences from general circulation model (GCM) simulations are intercompared over Senegal, using a 13-station network of daily observations during July–September 1961–98. The local scaling method calibrates raw GCM daily rainfall at the closest grid point to a given station so that the climatological distribution of rainfall matches the observed one. The k-nearest neighbor and weather classification schemes resample historical station rainfall observations according to the similarity between the daily wind fields from an ensemble of GCM simulations and a historical library of reanalysis [from 40-yr ECMWF Re-Analysis (ERA-40)] daily wind fields. The nonhomogenous hidden Markov model uses a small set of hidden states to describe the relationship between daily station rainfall observations and low-pass-filtered simulated winds to simulate stochastic sequences of daily rainfall. The four methods are assessed in terms of seasonal statistics of daily rainfall, including seasonal amount, rainfall frequency, and the mean length of wet and dry spells. Verification measures used are mean bias error, interannual anomaly correlation, root-mean-square error, and ranked probability skill score. The k-nearest neighbor and weather-type classification are shown to perform similarly well in reproducing the mean seasonal cycle, interannual variability of seasonal amount, daily rainfall frequency, as well as the mean length of dry and wet spells, and generally slightly better than the nonhomogeneous hidden Markov model. All three methods are shown to outperform the simple local scaling method. This is due to (i) the ability of the GCM to reproduce remarkably well the mean seasonal cycle and the transitions between weather types defined from reanalysis and (ii) the GCM’s moderate-to-strong skill in reproducing the interannual variability of the frequency occurrence of the weather types that strongly influence the interannual variability of rainfall in Senegal. In contrast, the local scaling exaggerates the length of wet and dry spells and reproduces less accurately the interannual variability of the seasonal-averaged amounts, occurrences, and dry/wet spells. This failure is attributed primarily to systematic errors in the GCM’s precipitation simulation.

Validation of the weather generator CLIGEN with daily precipitation data from the Loess Plateau, China

Weather generators are widely used in hydrological, ecological, and crop-yield modeling. CLIGEN is a frequently adopted weather generator. Although it has been tested in many parts of the world, it still needs to be validated before being applied to the Loess Plateau, China, because of the unique climate of the region. Only the generated daily precipitation are validated in this paper, for it is assumed to be the most important climatic factor affecting the precision of SWAT, which was selected as the main tool to do a regional assessment of soil erosion. Five sites, which have 30 years of relatively complete daily precipitation records along an aridity gradient, are selected for the validation of CLIGEN. The main purposes of this study are to test the usefulness of CLIGEN in precipitation simulation and to determine whether the performance varies with the aridity gradient. Means and standard deviations of five rainfall variables between measured and each of the 10 sets of CLIGEN generated precipitation are compared for each site. The precipitation variables analyzed are: yearly total precipitation, yearly number of rainy days, monthly total precipitation, log-transformed monthly average wet-spell length, and log-transformed monthly average dry-spell length. T-tests and F-tests are used to determine if there are significant differences in the means and standard deviations, respectively, between observed data and CLIGEN-generated data. Results show that CLIGEN preserves the means quite well, and preserves standard deviations reasonably well, although there are considerable variations among sites, among precipitation variables, and among seasons. There is no obvious trend of performance along the aridity gradient.

Potential future changes in the characteristics of daily precipitation in Europe simulated by the HIRHAM regional climate model

In this study the potential future changes in various aspects of daily precipitation events over Europe as a consequence of the anticipated future increase in the atmospheric greenhouse gas concentrations are investigated. This is done by comparing two 3-member ensembles of simulations with the HIRHAM regional climate model for the period 1961–1990 and 2071–2100, respectively. Daily precipitation events are characterized by their frequency and intensity, and heavy precipitation events are described via 30-year return levels of daily precipitation. Further, extended periods with and without rainfall (wet and dry spells) are studied, considering their frequency and length as well as the average and extreme amounts of precipitation accumulated during wet spells, the latter again described via 30-year return levels. The simulations show marked changes in the characteristics of daily precipitation in Europe due to the anticipated greenhouse warming. In winter, for instance, the frequency of wet days is enhanced over most of the European continent except for the region on the Norwegian west coast and the Mediterranean region. The changes in the intensity and the 30-year return level of daily precipitation are characterized by a similar pattern except for central Europe with a tendency of decreased 30-year return levels and increased precipitation intensity. In summer, on the other hand, the frequency of wet days is decreased over most of Europe except for northern Scandinavia and the Baltic Sea region. In contrast, the precipitation intensity and the 30-year return level of daily precipitation are increased over entire Scandinavia, central and eastern Europe. The changes in the 30-year return level of daily precipitation are generally stronger than the corresponding changes in the precipitation intensity but can have opposite signs in some regions. Also the distribution of wet days is changed in the future. During summer, for instance, both the frequency and the length of dry spells are substantially increased over most of the European continent except for the Iberian Peninsula. The frequency and the length of wet spells, on the other hand, are generally reduced during summer and increased during winter, again, with the exception of the Iberian Peninsula. The future changes in the frequency of wet days in winter are related to a change in the large-scale flow over the North Atlantic and a corresponding shift of the North Atlantic storm track. The reduction in the frequency of wet days in summer is related to a northward extension of the dry subtropical region in the future, with a reduction of the convective activity because of the large-scale sinking motion in the downward branch of the Hadley cell. Because the atmosphere contains more moisture in the warmer future climate, the amount of precipitation associated with individual low-pressure systems or with individual convective events is increased, leading to a general increase in the intensity of individual precipitation events. Only in regions, where all the moisture evaporates from the ground already in spring, the intensity of precipitation events is reduced in summer.

Stochastic Modeling of Daily Summertime Rainfall over the Southwestern United States. Part II: Intraseasonal Variability

Abstract The intraseasonal variability of summertime precipitation over the southwestern United States is examined using stochastic daily occurrence models combined with empirical daily rainfall distributions to document 1) the seasonal evolution of the frequency and intensity of rainfall events across the summertime monsoon season and 2) the climatological evolution of wet spells, dry spells, and storm events. Study results indicate that the evolution of the North American monsoon system (NAMS) is most apparent in the occurrence of daily rainfall events, which exhibit clear time dependence across the summer season over the southwestern United States and can be principally portrayed by stochastic models. In contrast, the seasonal evolution of NAMS is largely absent in the averaged daily rainfall amount time series. There is also a significant seasonal evolution in the length of dry spells. In the central area of the domain (approximately 39 out of 78 stations) dry-spell lengths tend to increase over the course of the summer season, while on the western fringe (8 out of 78 stations) dry-spell lengths tend to decrease. In contrast, wet spells tend to exhibit constant lengths over the course of the season (44 out of 78 stations). The seasonal trend for storms indicates that the number and duration of storms tend to decrease in September; however, the storm depths tend to be more intense, particularly over the western portion of the domain. Overall, 90% of the area-averaged variance for dry-spell lengths can be explained by the random daily evolution of the stochastic model alone. For wet-spell lengths, the area-averaged variance explained by the stochastic models is 98% and for storm amounts it is 92%. These results suggest that the characteristics of most intraseasonal events over this region (i.e., spell lengths and storm amounts) can be captured by the random evolution of daily rainfall models, even with constant year-to-year statistical parameters, indicating that systematic variations in the background climatic conditions from one year to the next may contribute little to the characteristics of these events.

Influence of similarity measures on the performance of the analog method for downscaling daily precipitation

This study examines the performance of the analog method for downscaling daily precipitation. The evaluation is performed for (1) a number of similarity measures for searching analogs, (2) various ways to include the past atmospheric evolution, and (3) different truncations in EOF space. It is carried out for two regions with complex topographic structures, and with distinct climatic characteristics, namely, California’s Central Valley (together with the Sierra Nevada) and the European Alps. NCEP/NCAR reanalysis data are used to represent the large scale state of the atmosphere over the regions. The assessment is based on simulating daily precipitation for 103 stations for the month of January, for the years 1950–2004 in the California region, and for 70 stations in the European Alps (January 1948–2004). Generally, simulated precipitation is in better agreement with observations in the California region than in the European Alps. Similarity measures such as the Euclidean norm, the sum of absolute differences and the angle between two atmospheric states perform better than measures which introduce additional weightings to principal components (e.g., the Mahalanobis distance). The best choice seems dependent upon the target variable. Lengths of wet spells, for instance, are best simulated by using the angular similarity measure. Overall, the Euclidean norm performs satisfactorily in most cases and hence is a reasonable first choice, whereas the use of Mahalanobis distance is less advisable. The performance of the analog method improves by including large-scale information for bygone days, particularly, for the simulation of wet and dry spells. Optimal performance is obtained when about 85–90% of the total predictor variability is retained.

Diagnostic statistics of daily rainfall variability in an evolving climate

Abstract. To investigate the character of daily rainfall variability under present and future climate described via global warming a suite of diagnostic statistics was used. The rainfall was modeled as a stochastic process coupled with atmospheric circulation. In this study we used an automated objective classification of daily patterns based on optimized fuzzy rules. This kind of classification method provided circulation patterns suitable for downscaling of General Circulation Model (GCM)-generated precipitation. The precipitation diagnostics included first and second order moments, wet and dry-day renewal process probabilities and spell lengths as well as low-frequency variability via the standard deviation of monthly totals. These descriptors were applied to nine elevation zones and entire area of the Mesochora mountainous catchment in Central Greece for observed, 1×CO2 and 2×CO2 downscaled precipitation. The statistics' comparison revealed significant differences in the most of the daily diagnostics (e.g. mean wet-day amount, 95th percentile of wet-day amount, dry to wet probability), spell statistics (e.g. mean wet/dry spell length), and low-frequency diagnostic (standard deviation of monthly precipitation total) between warm (2×CO2) and observed scenario in a progressive rate from lower to upper zone. The differences were very greater for the catchment area. In the light of these results, an increase in rainfall occurrence with diminished rainfall amount and a sequence of less consecutive dry days could describe the behaviour of a possible future climate on the examined catchment.

Seasonality properties of four statistical-downscaling methods in central Sweden

Daily precipitation in northern Europe has different statistical properties depending on season. In this study, four statistical downscaling methods were evaluated in terms of their ability to capture statistical properties of daily precipitation in different seasons. Two of the methods were analogue downscaling methods; one using principal component analysis (PCA) and one using gradients in the pressure field (Teweles-Wobus scores, TWS) to select the analogues in the predictor field. The other two methods were conditional-probability methods; one using classification of weather patterns (MOFRBC) and the other using a regression method conditioning a stochastic weather generator (SDSM). The two analogue methods were used as benchmark methods. The study was performed on seven precipitation stations in south-central Sweden and the large-scale predictor was gridded mean-sea-level pressure over Northern Europe. The four methods were trained and calibrated on 25 years of data (1961–1978, 1994–2000) and validated on 15 years (1979–1993). Temporal and spatial limitations were imposed on the methods to find the optimum predictor settings for the downscaling. The quality measures used for evaluating the downscaling methods were the residuals of a number of key statistical properties, and the ranked probability scores (RPS) for precipitation and maximum length of dry and wet spells. The results showed that (1) the MOFRBC and SDSM outperformed the other methods for the RPS, (2) the statistical properties for the analogue methods were better during winter and autumn; for SDSM and TWS during spring; and for MOFRBC during summer, (3) larger predictor areas were needed for summer and autumn precipitation than winter and spring, and (4) no method could well capture the difference between dry and wet summers.

Uncertainty analysis of statistical downscaling methods

Three downscaling models namely Statistical Down-Scaling Model (SDSM), Long Ashton Research Station Weather Generator (LARS-WG) model and Artificial Neural Network (ANN) model have been compared in terms various uncertainty assessments exhibited in their downscaled results of daily precipitation, daily maximum and minimum temperatures. In case of daily maximum and minimum temperature, uncertainty is assessed by comparing monthly mean and variance of downscaled and observed daily maximum and minimum temperature at each month of the year at 95% confidence level. In addition, uncertainties of the monthly means and variances of downscaled daily temperature have been calculated using 95% confidence intervals, which are compared with the observed uncertainties of means and variances. In daily precipitation downscaling, in addition to comparing means and variances, uncertainties have been assessed by comparing monthly mean dry and wet spell lengths and their confidence intervals, cumulative frequency distributions (cdfs) of monthly mean of daily precipitation, and the distributions of monthly wet and dry days for observed and downscaled daily precipitation. The study has been carried out using 40 years of observed and downscaled daily precipitation, daily maximum and minimum temperature data using NCEP (National Center for Environmental Prediction) reanalysis predictors starting from 1961 to 2000. The uncertainty assessment results indicate that the SDSM is the most capable of reproducing various statistical characteristics of observed data in its downscaled results with 95% confidence level, the ANN is the least capable in this respect, and the LARS-WG is in between SDSM and ANN.