Wet Spell Lengths Research Articles

Comparison and Evaluation of Statistical Rainfall Disaggregation and High-Resolution Dynamical Downscaling over Complex Terrain

Abstract Representative methods of statistical disaggregation and dynamical downscaling are compared in terms of their ability to disaggregate precipitation data into hourly resolution in an urban area with complex terrain. The nonparametric statistical Method of Fragments (MoF) uses hourly data from rain gauges to split the daily data at the location of interest into hourly fragments. The high-resolution, convection-permitting Weather Research and Forecasting (WRF) regional climate model is driven by reanalysis data. The MoF can reconstruct the variance, dry proportion, wet hours per month, number and length of wet spells per rainy day, timing of the maximum rainfall burst, and intensities of extreme precipitation with errors of less than 10%. However, the MoF cannot capture the spatial coherence and temporal interday connectivity of precipitation events due to the random elements involved in the algorithm. Otherwise, the statistical method is well suited for filling gaps in subdaily historical records. The WRF Model is able to reproduce dry proportion, lag-1 autocorrelation, wet hours per month, number and length of wet spells per rainy day, spatial correlation, and 6- and 12-h intensities of extreme precipitation with errors of 10% or less. The WRF approach tends to underestimate peak rainfall of 1- and 3-h aggregates but can be used where no observations are available or when areal precipitation data are needed.

Attributing variations of temporal and spatial groundwater recharge: A statistical analysis of climatic and non-climatic factors

This paper demonstrated the benefits of statistical methods when investigating the climatic and non-climatic drivers responsible for variations in groundwater recharge with a series of up to 43 years of annual recharge for 426 bores in South-East South Australia. We identified the factors influencing groundwater recharge based on 71 climatic metrics and 17 non-climatic metrics (including groundwater abstraction). The results showed: (1) Rainfall during April to October was the most important variable influencing recharge temporal variation, with its decline identified as the most significant factor related to recharge reduction; (2) In contrast, a negative correlation between rainfall during December to February (DJF) and annual groundwater recharge was found. This suggests that a seasonal shift in rainfall (such as decreasing rainfall during April to October and an increase during DJF) can result in a decline in recharge even when the annual rainfall remains unchanged; (3) The length of wet spells (consecutive rain days) and increasing potential evapotranspiration (PET) were additional significant predictors for recharge temporal variation. It demonstrated that a simple empirical relationship (such as recharge as a fixed percentage of rainfall) is not a reliable estimation of renewable groundwater resources under changing climatic conditions; (4) There is a statistically significant correlation between mean groundwater depth and recharge, which implies that if groundwater level fall due to rainfall declines then a positive feedback loop can lead to further recharge declines; (5) Spatially the most statistically significant factors influencing groundwater recharge were soil types and land attributes. The findings of this study can identify which stressors should be included when investigating the impact of climate change on groundwater recharge.

Hydrologic Impacts of Climate Change : Quantification of Uncertainties

General Circulation Models (GCMs), which are mathematical models based on principles of fluid dynamics, thermodynamics and radiative transfer, are the most reliable tools available for projecting climate change. However, the spatial scale on which typical GCMs operate is very coarse as compared to that of a hydrologic process and hence, the output from a GCM cannot be directly used in hydrologic models. Statistical Downscaling (SD) derives a statistical or empirical relationship between the variables simulated by the GCM (predictors) and a point-scale meteorological series (predictand). In this work, a new downscaling model called CRF-downscaling model, is developed where the conditional distribution of the hydrologic predictand sequence, given atmospheric predictor variables, is represented as a conditional random field (CRF) to downscale the predictand in a probabilistic framework. Features defined in the downscaling model capture information about various factors influencing precipitation such as circulation patterns, temperature and pressure gradients and specific humidity levels. Uncertainty in prediction is addressed by projecting future cumulative distribution functions (CDFs) for a number of most likely precipitation sequences. Direct classification of dry/wet days as well as precipitation amount is achieved within a single modeling framework, and changes in the non-parametric distribution of precipitation and dry and wet spell lengths are projected. Application of the method is demonstrated with the case study of downscaling to daily precipitation in the Mahanadi basin in Orissa, with the A1B scenario of the MIROC3.2 GCM from the Center for Climate System Research (CCSR), Japan. An uncertainty modeling framework is presented in this work, which combines GCM, scenario and downscaling uncertainty using the Dempster-Shafer (D-S) evidence theory for representing and combining uncertainty. The methodology for combining uncertainties is applied to projections of hydrologic drought in terms of monsoon standardized streamflow index (SSFI-4) from streamflow projections for the Mahanadi river at Hirakud. The results from the work indicate an increasing probability of extreme, severe and moderate drought and decreasing probability of normal to wet conditions, as a result of a decrease in monsoon streamflow in the Mahanadi river due to climate change. In most studies to date, the nature of the downscaling relationship is assumed stationary, or remaining unchanged in a future climate. In this work, an uncertainty modeling framework is presented in which, in addition to GCM and scenario uncertainty, uncertainty in the downscaling relationship itself is explored by linking downscaling with changes in frequencies of modes of natural variability. Downscaling relationships are derived for each natural variability cluster and used for projections of hydrologic drought. Each projection is weighted with the future projected frequency of occurrence of that cluster, called ‘cluster-linking’, and scaled by the GCM performance…

Changes in climate extremes over West and Central Africa at 1.5 °C and 2 °C global warming

In this study, we investigate changes in temperature and precipitation extremes over West and Central Africa (hereafter, WAF domain) as a function of global mean temperature with a focus on the implications of global warming of 1.5 °C and 2 °C according the Paris Agreement. We applied a scaling approach to capture changes in climate extremes with increase in global mean temperature in several subregions within the WAF domain: Western Sahel, Central Sahel, Eastern Sahel, Guinea Coast and Central Africa including Congo Basin.While there are several uncertainties and large ensemble spread in the projections of temperature and precipitation indices, most models show high-impact changes in climate extremes at subregional scale. At these smaller scales, temperature increases within the WAF domain are projected to be higher than the global mean temperature increase (at 1.5 °C and at 2 °C) and heat waves are expected to be more frequent and of longer duration. The most intense warming is observed over the drier regions of the Sahel, in the central Sahel and particularly in the eastern Sahel, where the precipitation and the soil moisture anomalies have the highest probability of projected increase at a global warming of 1.5 °C. Over the wetter regions of the Guinea Coast and Central Africa, models project a weak change in total precipitation and a decrease of the length of wet spells, while these two regions have the highest increase of heavy rainfall in the WAF domain at a global warming of 1.5 °C. Western Sahel is projected by 80% of the models to experience the strongest drying with a significant increase in the length of dry spells and a decrease in the standardized precipitation evapotranspiration index. This study suggests that the ‘dry gets drier, wet gets wetter’ paradigm is not valid within the WAF domain.

Projected climate over the Greater Horn of Africa under 1.5 °C and 2 °C global warming

We analyze the potential effect of global warming levels (GWLs) of 1.5 °C and 2 °C above pre-industrial levels (1861−1890) on mean temperature and precipitation as well as intra-seasonal precipitation extremes over the Greater Horn of Africa. We used a large, 25-member regional climate model ensemble from the Coordinated Regional Downscaling Experiment and show that, compared to the control period of 1971−2000, annual mean near-surface temperature is projected to increase by more than 1 °C and 1.5 °C over most parts of the Greater Horn of Africa, under GWLs of 1.5 °C and 2 °C respectively. The highest temperature increases are projected in the northern region, covering most parts of Sudan and northern parts of Ethiopia, and the lowest temperature increases are projected over the coastal belt of Tanzania. However, the projected mean surface temperature difference between 2 °C and 1. 5 °C GWLs is higher than 0.5 °C over nearly all land points, reaching 0.8 °C over Sudan and northern Ethiopia. This implies that the Greater Horn of Africa will warm faster than the global mean.While projected changes in precipitation are mostly uncertain across the Greater Horn of Africa, there is a substantial decrease over the central and northern parts of Ethiopia. Additionally, the length of dry and wet spells is projected to increase and decrease respectively. The combined effect of a reduction in rainfall and the changes in the wet and dry spells will likely impact negatively on the livelihoods of people within the coastal cities, lake regions, highlands as well as arid and semi-arid lands of Kenya, Tanzania, Somalia, Ethiopia and Sudan. The probable impacts of these changes on key sectors such as agriculture, water, energy and health sectors, will likely call for formulation of actionable policies geared towards adaptation and mitigation of the impacts of 1.5 °C and 2 °C warming.

Extreme Rainfall in the Eastern Region of Peninsular Malaysia

Modeling of daily rainfall data, particularly rainfall amount, is important not only for hydrological purposes but also for providing input for models of crop growth, design of urban drainage systems, land management systems and other environmental projects. This study tests the performance of Gamma and Weibull that are incorporated in the weather generator in simulating extreme rainfall and extreme dry/wet spell lengths for the eastern region of Peninsular Malaysia. Next, the monthly rainfall and the extreme rainfall are simulated using the best distribution. A weather generator known as Advanced Weather Generator (AWE-GEN) is chosen to model hourly rainfall time series in the eastern region of Peninsular Malaysia. Previously, Gamma distribution is incorporated in AWE-GEN to represent the rainfall intensity. This study proposes Weibull distribution and the performances between Gamma and Weibull distributions in generating rainfall series are compared based on Root Mean Square (RMSE) value error. Minimum RMSE value indicates the best distribution for eastern region. Results have shown that Gamma is the best distribution in simulating hourly rainfall in the east coast as compared to Weibull despite not being able to capture the peak/extreme values at several stations in the east coast of Peninsular Malaysia. The extreme wet spell length is well simulated although the extreme dry spell is under-estimated at all stations. Rainfall in November is contributing most of the annual rainfall followed by December. This study is relevant and very crucial for a tropical country like Malaysia which will benefit the policy makers in executing the strategic management of flooding in urban areas that is predominantly caused by convective activities.

Classification of precipitation regimes in Pakistan using wet and dry spells

ABSTRACTWet (dry) spells can cause extreme climatic conditions, such as floods (droughts) which can adversely influence the natural resources. A spatio‐temporal analysis of observed wet and dry spells is carried out for vulnerable and data sparse region of Pakistan (24°–38°N and 61°–78°E). Observed monthly precipitation data sets from 46 weather station are used for a length of consecutive 32 years (1976−2007). Additionally, after bias adjustment, the Asian Precipitation Highly‐Resolved Observational Data Integration Towards Evaluation (APHRODITE) precipitation data set, abbreviated as APH, is utilized to corroborate the findings. For both data sets, a threshold of 1 mm is used to define a wet spell. Decadal variability of precipitation for observed and APH data sets indicates that there is gradual decrease in wet spell length for arid and humid regimes, as compared to semi‐arid regimes where there is no change in wet spell length. Monthly dry and wet slope difference (SD), on a log–log plot between number of spells and spell length, is used to classify precipitation regimes in Pakistan, for the first time. A weather station is categorized as humid if SD is less than −2.38 in units of number of spells per length of spells. If SD lies between −2.37 and −0.51, then the weather station is classified as semi‐arid and if SD is greater than −0.51, then it is classified as arid. Thus, according to SD classification, 66% of area in Pakistan is arid, whereas 30% (4%) is semi‐arid (humid). Comparison of precipitation regimes based upon observed and APH data sets with other climate classification schemes that involve both precipitation and temperature is presented.

Rainfall analysis in the northern region of Peninsular Malaysia

Modeling of rainfall is important for assessing the possible impacts of climate change. To achieve accurate projections of rainfall events, availability of sufficient hydrological station data is critical. Precipitation is one of the most important meteorological variables for hydrological modeling. In cases where long series of observed precipitation are not available, they can be stochastically generated by weather generators. Advanced Weather Generator (AWE-GEN) has been proven to generate precipitation data at the temperate climate regions with Gamma distribution being incorporated in the model to represent rainfall intensity. However, in a tropical climate such as Malaysia, some studies disputed the incorporation of Gamma distribution. As such, in this study, Weibull a heavy tail distribution is proposed to be used. The AWE-GEN has well performed in the wetter region such as the eastern of the peninsular. However, rainfall distribution within Peninsular Malaysia is highly variable temporally and spatially. The northern region is drier especially during the southwest monsoon season. This region receives minimal rain during the northeast monsoon due to the presence of the Titiwangsa Range which obstructs the region from getting rain by the north easterly winds. Therefore, the objectives of the study are two-fold. First, this study compares the performance of Gamma and Weibull that are incorporated in the AWE-GEN in simulating rainfall series for the northern region of the peninsular. Second, the monthly rainfall and the extreme rainfall series are simulated using the better distribution. The performances of Gamma and Weibull distributions are compared using the goodness of fit test, Root Mean Square Error (RMSE). Results showed that Gamma is the better distribution in simulating rainfall at rainfall stations located at the outer parts of the northern coast whereas Weibull is the better distribution for stations located in the interior parts of the northern coast. Hourly and daily extreme rainfalls seem to be well captured at all stations. Similarly, wet spell length is well simulated while in contrast, dry spell length is slightly underestimated at all stations. Overall, Gamma and Weibull produce commendable results in simulating extreme rainfall as well as wet spell length throughout the northern region of the peninsular. (c) 2017 The Authors. Published by IASE.

Can weather generation capture precipitation patterns across different climates, spatial scales and under data scarcity?

Stochastic weather generators can generate very long time series of weather patterns, which are indispensable in earth sciences, ecology and climate research. Yet, both their potential and limitations remain largely unclear because past research has typically focused on eclectic case studies at small spatial scales in temperate climates. In addition, stochastic multi-site algorithms are usually not publicly available, making the reproducibility of results difficult. To overcome these limitations, we investigated the performance of the reduced-complexity multi-site precipitation generator TripleM across three different climatic regions in the United States. By resampling observations, we investigated for the first time the performance of a multi-site precipitation generator as a function of the extent of the gauge network and the network density. The definition of the role of the network density provides new insights into the applicability in data-poor contexts. The performance was assessed using nine different statistical metrics with main focus on the inter-annual variability of precipitation and the lengths of dry and wet spells. Among our study regions, our results indicate a more accurate performance in wet temperate climates compared to drier climates. Performance deficits are more marked at larger spatial scales due to the increasing heterogeneity of climatic conditions.

Evolution Des Indices Pluviométriques Extrêmes Par L'analyse De Modèles Climatiques Régionaux Du Programme CORDEX: Les Projections Climatiques Sur Le Sénégal

This study aims at characterizing the extreme rainfall events over West Africa particularly in the Sahel region and Senegal by 2100 (far future) under the greenhouse gas emission scenario RCP8.5 by analyzing the simulations of four (4) regional climate models (RCMs) of CORDEX (Regional COordinated climate Downscaling Experiment) program. The study of these extreme climate indices is crucial for the understanding of the impacts of climate change on some vital socio-economic sectors such as the agriculture in Sahel and Senegal. The results show that almost all the RCMs predict a decrease of the rainfall over most parts of the Sahel region particularly over the Western Sahel. The analysis of the climate indices such as the highest one day precipitation amount, the 99th percentile and the maximum dry spell length (CDD) shows that the RCMs (except CanRCM4) project an increase of these exceptional rainfall events over the Sahel (especially over the Western Sahel) by 2100. In Senegal, the RCMs (except RCA4) agree on a decrease of the precipitation and the number of wet days by 2100. When considering the evolution of rainfall events intensity, the highest one day precipitation amount and the 99th percentile, the RCMs (except CanRCM4) predict an increase of the extreme events which may translate into strong floods in Senegal. As for the dry and wet sequences, the RCMs projections (except those of RCA4) show an increase (respectively a decrease) of the maximum dry spell length (respectively of the maximum wet spell length) in Senegal. This increase in extreme rainfall indices may translate into a strengthening of natural disasters such as floods and drought. This work can be considered as a support for the policymakers in West Africa and particularly in Senegal for the better long-term planning of water resources and disaster management as wells as the build of a resilient agricultural system.

A stochastic rainfall generator model for simulation of daily rainfall events in Kurau catchment: model testing

Reservoirs play a substantial role in meeting water demands in arid and semi-arid regions especially with increasing changes in global rainfall patterns. Kurau catchment located at Northwest Perak, Malaysia, is the largest source of water to Bukit Merah Reservoir. Based on climate change effects on rainfall pattern, it is important to develop a model to simulate rainfall occurrence and amount that flow into the reservoir. To address this problem, a stochastic rainfall generator model based on first-order two-state Markov chain approach was developed to simulate long-term daily rainfall series. Rainfall time series for a 30-year period (1976-2006) was assessed. The observed time series data were used as input to the stochastic model to generate a new set of daily time series data. The statistical properties of the new set of data including monthly mean, standard deviation, dry and wet spell lengths were compared with the observed data to gauge the model accuracy. The results obtained were satisfactory, giving motivation in applying the model for generating future rainfall series under different climate change scenarios.

Evaluation of ERA-Interim, MERRA, NCEP-DOE R2 and CFSR Reanalysis precipitation Data using Gauge Observation over Ethiopia for a period of 33 years

The vital demand of reliable climatic and hydrologic data of fine spatial and temporal resolution triggered the employment of reanalysis datasets as a surrogate in most of the hydrological modelling exercises. This study examines the performance of four widely used reanalysis datasets: ERA-Interim, NCEP-DOE R2, MERRA and CFSR, in reproducing the spatio-temporal characteristics of observed daily precipitation of different stations spread across Ethiopia, East Africa. The appropriateness of relying on reanalysis datasets for hydrologic modelling, climate change impact assessment and regional modelling studies is assessed using various statistical and non-parametric techniques. ERA-Interim is found to exhibit higher correlation and least root mean square error values with observed daily rainfall, which is followed by CFSR and MERRA in most of the stations. The variability of daily precipitation is better captured by ERA, CFSR and MERRA, while NCEP-DOE R2 overestimated the spread of the precipitation data. While ERA overestimates the probability of moderate rainfall, it is seemingly better in capturing the probability of low rainfall. CFSR captures the overall distribution reasonable well. NCEP-DOE R2 appears to be outperforming others in capturing the probabilities of higher magnitude rainfall. Climatological seasonal cycle and the characteristics of wet and dry spells are compared further, where ERA seemingly replicates the pattern more effectively. However, observed rainfall exhibits higher frequency of short wet spells when compared to that of any reanalysis datasets. MERRA relatively underperforms in simulating the wet spell characteristics of observed daily rainfall. CFSR overestimates the mean wet spell length and mean dry spell length. Spatial trend analysis indicates that the northern and central western Ethiopia show increasing trends, whereas the Central and Eastern Ethiopia as well as the Southern Ethiopia stations show either no trend or decreasing trend. Overall, ERA-Interim and CFSR are better in depicting various characteristics of daily rainfall in Ethiopian region.

The characteristics of wet and dry spells for the diverse climate in China

Using a large precipitation dataset from 692 gauge stations across China for the period of 1960–2013, this study analyzed the characteristics of wet/dry spells related to four types of climate including arid, semiarid, semiarid to subhumid, and humid climate. A wet/dry spell is defined as the consecutive days with precipitation amount greater/less than a threshold. The frequency of wet/dry spells, the contributions of wet/dry spells with different lengths to the total number of wet/dry days and total precipitation amount were analyzed for different climate types. The wet/dry spells with the greatest contributions to the total wet/dry days or total precipitation amount vary with climate. For drier climate, long-duration dry spells contribute more to the total number of dry days, while short-duration wet spells contribute more to the total number of wet days and the total precipitation amount. The characteristics of wet/dry spells are closely related to local climate. Good regression relationships were obtained for the number of dry/wet spell versus spell length, and precipitation amount versus wet spell length. Although the correlation between precipitation amount and wet spell length has rarely been considered in stochastic precipitation generation, the relationships identified in this study justify the necessity of taking it into account.

Performance comparison of three predictor selection methods for statistical downscaling of daily precipitation

Predictor selection is a critical factor affecting the statistical downscaling of daily precipitation. This study provides a general comparison between uncertainties in downscaled results from three commonly used predictor selection methods (correlation analysis, partial correlation analysis, and stepwise regression analysis). Uncertainty is analyzed by comparing statistical indices, including the mean, variance, and the distribution of monthly mean daily precipitation, wet spell length, and the number of wet days. The downscaled results are produced by the artificial neural network (ANN) statistical downscaling model and 50 years (1961–2010) of observed daily precipitation together with reanalysis predictors. Although results show little difference between downscaling methods, stepwise regression analysis is generally the best method for selecting predictors for the ANN statistical downscaling model of daily precipitation, followed by partial correlation analysis and then correlation analysis.

Bias-corrected regional climate projections of extreme rainfall in south-east Australia

This study presents future changes in extreme precipitation as projected within the New South Wales and Australian Capital Territory Regional Climate Modelling (NARCliM) project’s regional climate ensemble for south-east Australia. Model performance, independence and projected future changes were considered when designing the ensemble. We applied a quantile mapping bias correction to the climate model outputs based on theoretical distribution functions, and the implications of this for the projected precipitation extremes is investigated. Precipitation extremes are quantified using several indices from the Expert Team on Climate Change Detection and Indices set of indices. The bias correction was successful in removing most of the magnitude bias in extreme precipitation but does not correct biases in the length of maximum wet and dry spells. The bias correction also had a relatively small effect on the projected future changes. Across a range of metrics, robust increases in the magnitude of precipitation extreme indices are found. While these increases are often in-line with a continuation of the trends present over the last century, they are not found to be statistically significant within the ensemble as a whole. The length of the maximum consecutive wet spell is projected to remain at present-day levels, while the length of the maximum dry spell is projected to increase into the future. The combination of longer dry spells and increases in extreme precipitation magnitude indicate an important change in the character of the precipitation time series. This could have considerable hydrological implications since changes in the sequencing of events can be just as important as changes in event magnitude for hydrological impacts.

Environment and cotton fibre quality

Along with historical climate data, the daily outputs of Commonwealth Scientific and Industrial Research Organisation (CSIRO) Conformal Cubic Atmospheric Model driven by four general circulation models (GCMs) were used by a stochastic weather generator, LARS-WG, to construct local climate scenarios at nine key cotton production areas in eastern Australia. These local climate scenarios were then used by established crop-level empirical relationships that firstly estimated the time and average temperature when the majority of cotton bolls in a crop are thickening their fibres, and secondly related this estimate of temperature to fibre micronaire. Micronaire is an indirect measure of fibre fineness and maturity. It is an important attribute of cotton fibre quality influencing spinning and dying processes. A significant change in micronaire occurs when it changes by 0.1 of a unit, which can move it into, or out of the optimum range of 3.8–4.5. Monthly mean rainfall and average wet spells for autumn months (i.e. March, April and May) were also analysed to examine their potential impact on cotton fibre grade (e.g. colour). Included in the micronaire impact assessment, we considered four planting times: normal planting, 15 days earlier and later than normal planting, and 30 days later than normal planting. Research data showed that when compared to the baseline (current climate and normal planting) (1) climate change with normal planting increased mean micronaire 0.04 ~ 0.33 in 2030 across all locations with Hillston increasing the most (from 4.06 to 4.39) and St George and Bourke with negligible increases (from 4.56 to 4.60, from 4.60 to 4.64, respectively) and decreased the chances of attaining optimum micronaire (3.8–4.5) in 2030 at most of the locations; (2) earlier planting (15 days) increased mean micronaire (0.03–0.34) across locations with Hillston increasing the most (from 4.06 to 4.40) and St George with negligible increase (4.56–4.59) and decreased the chances for micronaire falling in the optimum range at most of the locations; and (3) later 15 and 30 days plantings also increased the mean micronaire at all locations with the increase of ≥0.1 at three locations, and decreased the chances for optimum micronaire at most of study locations (7–100 % and 1–100 % for 15 and 30 days late planting respectively). However, there was an improvement in the frequency of obtaining optimal micronaire at five locations due to late plantings compared with early planting even though they could not offset the negative impacts of increased temperature on micronaire in comparison with baseline. This suggests that other management options such as plant breeding need to be put in place to counteract the impact of increased temperature on micronaire. Increases in monthly mean rainfall and the average length of wet spells in autumn were found indicating negative impacts on fibre quality with greater risk found at Hillston and Narrabri, followed by Goondiwindi and then the others. However, when the risks of rainfall were investigated on a monthly basis, there was reduced probability of increased rainfall and wet spells associated with May. This may assist with crops potentially grown for longer under the warmer conditions in Australia. This study provides useful information for the cotton industry to adapt to a changing climate from the perspective of developing strategies that may assist in maintaining cotton fibre quality.

Seasonal and Interannual Variability of Wet and Dry Spells over Two Urban Regions in the Western Maritime Continent

Abstract Daily rainfall data from two urban regions in Southeast Asia are analyzed to study seasonal and interannual variability of wet and dry spells. The analysis is carried out using 35 years of data from Singapore and 23 years of data from Jakarta. The frequency distribution of wet (dry) spells and their relative contribution to the total number of wet (dry) days and to the total rainfall are studied using 15 statistical indicators. At the annual scale, Singapore has a greater number of wet spells and a larger mean wet spell length compared to Jakarta. However, both cities have equal probability of extreme wet spells. Seasonal-scale analysis shows that Singapore is drier (wetter) than Jakarta during boreal winter (summer). The probability of extreme wet spells is lower (higher) for Singapore than Jakarta during boreal winter (summer). The results show a stronger contrast between Singapore and Jakarta during boreal summer. The study also examined the time series of Singapore wet and dry spell indicators for the presence of interannual trends. The results indicate statistically significant upward trends for a majority of wet spell indicators. The wet day percentage and mean wet spell length are increasing at 2.0% decade−1 and 0.18 days decade−1, respectively. Analysis of dynamic and thermodynamic variables from ERA-Interim during the study period indicates a strengthening of low-level convergence and vertical motion and an increase in specific humidity and atmospheric instability (convective available potential energy), which explain the increasing trends observed in Singapore wet spell indicators.

Markovian Model of Rainfall Pattern with Application

In this study, we model the occurrence and length of wet, medium wet and dry spells by Markov chain that best describes the rainfall pattern of Bungoma County (Western Kenya).This is achieved by Markov chain theory and estimation of probabilities of the chain by MLE. Also computed is the distribution of the length of each spells; wet, medium wet and dry from which the central moments of the rainfall pattern are computed. The model developed is applied to rainfall data from Bungoma meteorological station. A three by three transition matrix is obtained and used to predict the weather pattern. It is observed that if everything remains constant, prediction can be certain at the twelfth year as the matrix show stationarity. The three states are recurrent, non-null and a periodic hence forming an ergodic chain. Keywords: Markov chain, Wet spell, Medium wet spell, Dry spell, Prediction, Stationary distribution

Changes in spatial and temporal trends in wet, dry, warm and cold spell length or duration indices in Kansas, USA

ABSTRACTExtended periods with excessive or no rainfall or high or low temperatures have important implications on the water cycle, can stress ecosystems and can be detrimental to the economy of a region. These periods are generally studied using spell length indicators or duration indices (SDIs). Fourteen SDIs are calculated to study the changes in wet/dry/warm/cold spells using daily precipitation and maximum and minimum air temperature from 23 centennial weather stations spread across Kansas during four time periods (through 1920, 1921–1950, 1951–1980 and 1981–2009) and two temporal scales (annual and seasonal). Among the SDIs, 3 represent wet spells [wet spell length (WetSL); AvWetSL; MaxWetSL]; 3 for dry spells [dry spell length (DrySL); AvDrySL; MaxDrySL]; 4 for warm spells [warm spell length (WarmSL); average warm spell length (AvWarmSL); maximum warm spell length (MaxWarmSL); WarmSDI] and 4 are for cold spells [cold spell length (ColdSL); average cold spell length (AvColdSL); maximum cold spell length (MaxColdSL); ColdSDI]. In general, we observe that Kansas has 57–64 days year−1 in a wet spell; 302–309 days year−1 in a dry spell; ∼47 days year−1 in each warm and cold spells. The average length of a wet/dry spell is ∼1.5 days, while the warm/cold spells are for 2 days. The maximum length of a wet spell is ∼4.4 days, a dry spell is ∼35 days and warm/cold spells is ∼6 days. We found the number of wet days increasing annually. Interestingly, the warm days during winter are increasing with an overall decrease in the days in warm and cold spells across both temporal scales.

Trends in precipitation indices at Akola

Climate warming observed over the past several years is consistently associated with changes in a number of components of the hydrological cycle. Precipitation is one of the important component of this cycle. Changes in mean and extreme values of precipitation will impact on rivers, agriculture, environment etc., which may be the serious issue. Therefore predicted trends of precipitation indices can be used to take proper decision for future. In this study the trends in different rainfall indices for the period 1974-2013 were examined for Akola rain gauge station. The meteorological data of rainfall were collected from Agro-Meteorological Observatory, Dr. PDKV Akola. The trends in monthly and annual precipitation indices were calculated statistically and represented graphically. The results showed that there were no significant changes in annual total precipitation within the last few years. In one day maximum rainfall, the decreasing trend was observed during months of monsoon and winter seasons, whereas increasing trends were found during summer months. Maximum number of months showed decreasing trend, especially during monsoon months in respect of consecutive 5days maximum rainfall. The precipitation decrease in August was compensated by a precipitation increase in September. The lengths of wet spells were increased with increase in length of dry spells.