Maximum Dry Spell Research Articles

Dry spells and probability of rainfall occurrence for Lake Kyoga Basin in Uganda, East Africa

The economy of Uganda depends heavily on rainfed agriculture. In this study, daily observed rainfall datasets from 9 weather stations with length varying within 1955 and 2017 were used to generate the probability of rainfall and dry spells occurrence using a Markov chain approach. The length of the maximum dry spell was obtained using the direct method based on the definition of a dry day that rainfall is less than 0.85 mm (R < 0.85 mm) and the length of a dry spell is the sum of the number of dry days in a sequence. Mann–Kendall’s statistics (MK) was used to assess the trends in the length of maximum dry spells and Sen’s slope test to estimate the magnitude of change (Q2) in days/per month. MK test results show increasing trends in the length of the maximum dry spells in March at 5 stations, while an insignificant decrease in the length of maximum dry spells is revealed for remaining stations. For the month of April and May, the length of a maximum dry spell is observed to be decreasing across most stations although not statistically significant at the 5% significance level during their respective study periods. The probability of 8 days dry spell is high across all the stations (38–69%) in March, April, and August. This could strongly be related to the changing climate in the region. Negative impacts due to increased length of dry spells could be mitigated through well-timed planting of crops, use of irrigation, and growing of heat-/drought-tolerant crop varieties to match the changing weather and climate patterns.

Analysis of Rainfall Variability for Mekelle Meteorological Station, Northern Ethiopia (1960-2009)

Nowadays rainfall variability is threatening the world. Despite this, studies related to the determination of variations, trends and fluctuations of rainfall have little local and regional specificity. Hence, this study aimed to analyze the trend and variability of the annual and seasonal rainfall occurrence at Mekelle Airport meteorological station, Northern Ethiopia. Historical rainfall data for the period 1960-2009 of the rain gauge station were obtained from National Meteorological Service Agency. Annual, seasonal and monthly rainfall trends; onset and cessation date of kiremt rainfall; growing period length and the number of rainy days and dry spell length were considered. Standardize anomaly index (SAI) and coefficient of variance was employed to analyze the rainfall data variability using INSTAT plus, SPSS version 20 and Excel. Analysis of the historical data showed extremely severe drought happened in the area in 1984 which resulted in the death of one million people, 8 million people highly affected, and 2.3 million people food in secured. 1961 was an extremely wet year. Generally, 70%, 18% and 12% years were normal, dry and wet respectively for the last 50 years. High annual, seasonal and monthly rainfall variability was observed. Though the station showed both increasing (1984-1988) and decreasing trends (1961-1968) of annual rainfall totals a declining trend was more pronounced. Besides, one to two weeks variability on the onset and cessation date was observed. The minimum and maximum kiremt season rainy days recorded in 1987 (27days) and 1986 (86 days) respectively while the maximum dry spell days recorded in the 50 years was in 1969. Thus, devising agronomic practices that retain moisture at the plant root zone and reduce crop failure due to variability of rainfall including extended dry spell could be important. Keywords Rainfall variability, trends, standard anomaly index, Mekelle DOI : 10.7176/CER/11-9-02 Publication date :October 31 st 2019

A Stochastic Approach for the Analysis of Long Dry Spells with Different Threshold Values in Southern Italy

A non-homogeneous Poisson model was proposed to analyze the sequences of dry spells below prefixed thresholds as an upgrade of a stochastic procedure previously used to describe long periods of no rainfall. Its application concerned the daily precipitation series in a 60-year time span at four rain gauges (Calabria, southern Italy), aiming at testing the different behaviors of the dry spells below prefixed thresholds in two paired periods (1951–1980 and 1981–2010). A simulation analysis performed through a Monte Carlo approach assessed the statistical significance of the variation of the mean values of dry spells observed at an annual scale in the two 30-year periods. The results evidenced that the dry spells durations increased passing from the first 30-year period to the second one for all the thresholds analyzed. For instance, for the Cassano station, an increase of about 10% of the maximum dry spell duration was detected for a threshold of 5 mm. Moreover, the return periods evaluated for fixed long dry spells through the synthetic data of the period 1981–2010 were lower than the corresponding ones evaluated with the data generated for the previous 30-year period. Specifically, the difference between the two 30-year periods in terms of the return period of long dry spells occurrence increased with the growing thresholds. As an example, for the Cosenza rain gauge with a threshold of 1 mm, the return period for a dry spell length of 70 days decreased from 20 years (in 1951–1980) to about 10 years (in 1981–2010), while for a threshold of 5 mm, the return period for the dry spell lengths of 120 days decreases from 70 years to about 20 years. These results show a higher probability of the occurrence of long dry spells in the more recent period than in the past.

Temporal evolution of precipitation-based climate change indices across India: contrast between pre- and post-1975 features

Over the past century, extreme precipitation events have posed many problems on socio-economic status of India, a country that spans over a wide variety of climate regimes. Assessment of past changes in precipitation associated climate change indicators is very essential for effective management of the hydroclimatic risks. Present study evaluates spatio-temporal variation of eleven precipitation-based climate change indices over three 35-year epochs, i.e., 1906–1940, 1941–1975, and 1976–2010. The analysis is undertaken with the annual and monsoon season (June–September) daily precipitation data separately. Regions of significant changes are identified across the country, reflecting different characteristics (magnitude, frequency, intensity, and duration) of precipitation-based climate change indices. The results indicate a clear temporal evolution in the spatial distribution of trends over the years. During the recent epoch (1976–2010), a split is noticed with segregated increasing trends in southern region and patches of decreasing trends in northern region of the country. In contrast to the indices derived with monsoonal daily precipitation, significant trends over the country were more prominent for the indices derived with annual daily precipitation. Duration of annual maximum dry spell (wet spell) is found to significantly increase (decrease) over most of the regions. However, there is no change in total precipitation indicating an increase in short spell heavy rainfall events. The analysis and the results offer an opportunity to identify the regions of interest and to adopt revised water management policies in the future through revised water allocations, alteration of cropping pattern, etc.

How well do climate models reproduce variability in observed rainfall? A case study of the Lake Victoria basin considering CMIP3, CMIP5 and CORDEX simulations

In this study, how well the climate models reproduce variability in observed rainfall was assessed based on General Circulation Models (GCMs) from phase 3 and phase 5 of the Coupled Model Inter-comparison Project, i.e., CMIP3 and CMIP5, respectively as well as the Regional Climate Models (RCMs) of COordinated Regional Climate Downscaling EXperiment (CORDEX) over Africa. Observed and climate model based daily rainfall across the Lake Victoria Basin, which is one of the wettest parts of Africa, was considered. Temporal variability was assessed based on the coefficient of variation of daily and annual rainfall, and the maximum dry and wet spell in each year. Furthermore, variation in daily rainfall was assessed in terms of the long-range dependence. Comparison of variability results from observed and climate model based rainfall was made. It was found that the capacity to reproduce variability in observed wet and dry conditions depends on the specific GCM (of CMIP3 or CMIP5) or CORDEX RCM. However, the CORDEX RCMs replicated variability in observed daily rainfall better than the CMIP3 and CMIP5 GCMs. This was due to the spatial resolutions of the CORDEX RCMs which are higher than those of the CMIP3 and CMIP5 GCMs. The ensemble mean of the coefficients of correlation between the variability in observed and that of climate model based rainfall was close to zero for both the GCMs or RCMs. This suggests that analyses can be done on a case by case basis. In other words, GCMs or RCMs which adequately reproduce variability in observed wet and dry conditions can be considered for further statistical analysis of the changes especially on the basis of statistical methods for downscaling. For daily timescale, both the GCMs and RCMs from all the three sets of climate models generally exhibited poor performance in capturing the time of occurrences and the magnitudes of rainfall events (when considered in a combined way). To reliably assess long-term rainfall changes, it is vital to characterize natural variation in terms of the statistical dependence. With respect to natural variability of rainfall at local scales, there is room for further improvement of the climate models; however, whether theory of fractals and/or concepts of scaling behavior or self-similarity can explicitly contribute in that respect is a crucial consideration. Results from this study gave some insights in the reasonableness of the future rainfall projections.

Modelling extreme dry spells in the Mediterranean region in connection with atmospheric circulation

Long droughts periods can affect the Mediterranean region during the winter season, when most of annual precipitation occurs, and consequently have strong impacts on agriculture, groundwater levels and water resources. The goal of this study is to model annual maximum dry spells lengths (AMDSL) that occur during the extended winter season (October to April). The spatial patterns of extreme dry spells and their relationships with large-scale atmospheric circulation were first investigated. Then, AMDSL were modelled using Generalized Extreme Value (GEV) distributions incorporating climatic covariates, to evaluate the dependences of extreme dry spells to synoptic patterns using an analogue approach. The data from a network of 160 rain gauges having daily precipitation measurements between 1960 and 2009 are considered together with the ERA-20C reanalysis of the 20th century to provide atmospheric variables (geopotential heights, humidity, winds). A regional classification of both the occurrence and the duration of AMDSL helped to distinguish three spatially contiguous regions in which the regional distributions were found homogeneous. From composite analysis, significant positive anomalies in geopotential height (Z500) and negative anomalies in zonal wind (U850) and relative and specific humidity (S850, R850) were found to be associated with AMDSL in the three regions and provided the reference to build analogue days. Finally, non-stationary GEV models have been compared, in which the location and scale parameters are related to different atmospheric indices. Results indicates, at the whole Mediterranean scale, that positives anomalies of the North Atlantic Oscillation index and to a lesser extent the Mediterranean Oscillation index are linked to longer extreme dry spells in the majority of stations. For the three regions identified, the frequency of U850 negative anomalies over North Africa is significantly associated with the magnitude of AMDSL. AMDL are also associated with the frequency of S850 negative anomalies for the southeastern region, and with positive Z500 anomalies for the Western and North-eastern Mediterranean regions.

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.

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.

Dry spell, onset and cessation of the wet season rainfall in the Upper Baro-Akobo Basin, Ethiopia

In this study, maximum dry spell length and number of dry spell periods of rainy seasons in the upper Baro-Akobo River basin which is a part of the Nile basin, Western Ethiopia, were investigated to analyse the drought trend. Daily rainfall records of the period 1972–2000 from eight rain gauge stations were used in the analysis, and Mann-Kendall test was used to test trends for significance. Furthermore, the beginning and end of the trend development in the dry spell were also tested using the sequential version of Mann-Kendall test. Results have shown that there is neither clear monotonic trend found in dry spell for the basin nor significant fluctuation in the onset, cession and duration of rainfall in the Baro-Akobo river basin. This sufficiently explains why rain-fed agriculture has suffered little in the western part of Ethiopia. The predictable nature of dry spell pattern may have allowed farmers to adjust to rainfall variability in the basin. Unlike many parts of Ethiopia, the Baro-Akobo basin climate variability is not a limiting factor for rain-fed agriculture productivity which may contribute significantly to national food security.

Regional Frequency Analysis of Extreme Dry Spells during Rainy Season in the Wei River Basin, China

Our research analyzes the regional changes of extreme dry spell, represented by the annual maximum dry spell length (noted as AMDSL) during the rainy season in the Wei River Basin (WRB) of China for 1960–2014 using the L-moments method. The mean AMDSL values increase from the west to the east of the WRB, suggesting a high dry risk in the east compared to the west in the WRB. To investigate the regional frequency more reasonably, the WRB is clustered into four homogenous subregions via theK-means method and some subjective adjustments. The goodness-of-fit test shows that the GEV, PE3, and GLO distribution can be accepted as the “best-fit” model for subregions 1 and 4, subregion 2, and subregion 3, respectively. The quantiles of AMDSL under various return levels figure out a similar spatial distribution with mean AMDSL. We also find that the dry risk in subregion 2 and subregion 4 might be higher than that in subregion 1. The relationship between ENSO events and extreme dry spell events in the rainy season with cross wavelet analysis method proves that ENSO events play a critical role in triggering extreme dry events during rainy season in the WRB.

Drought Hazard Mapping from Regional Climate Multimodel Ensemble over Spain

&lt;p&gt;Improving the knowledge about the impacts of climate change on extreme drought events at basin scale, is important for decision makers in order to develop drought contingency plans which are the leading edge of adaptive management strategy. Considering high-resolution grids of observed daily rainfall and information provided by latest-generation Regional Climate Models (RCMs), the changes in the spatio-temporal patterns of extreme droughts in peninsular Spain are assessed. The non-stationarity character of time series, due to climate and anthropogenic changes, is represented by probabilistic models considering the time evolution of probability density function (PDF) parameters fitted to annual maximum lengths of dry spells time series. By a PDF ensemble from 17 RCMs, the spatio-temporal variability exhibited by the RCMs is represented. Scoring of models is based in the goodness-of-fit to CDFs (cumulative distribution functions) of observed annual maximum dry spells lengths. The reliability and skills of RCMs are assessed, for building the PDF ensemble, at grid site for the study area. Therefore, by adjusting PDF to series of annual maximum dry spells lengths, applying GAMLSS and bootstrapping techniques, the assessment of regional changes and trends associated to high returns periods (&lt;em&gt;Tr&lt;/em&gt; = 25 and 50 yr.) is assessed. In general, an intensification of drought events for 2050 horizon, in contrast with 1990, is expected. By increasing return periods, the length of the annual maximum dry spells rises, albeit with a smaller number of areas with significant differences. The areas prone to extreme droughts in mainland Spain are identified.&lt;/p&gt;

Assessing Nonstationary Spatial Patterns of Extreme Droughts from Long-Term High-Resolution Observational Dataset on a Semiarid Basin (Spain)

In basins of South-eastern Spain; such as the semiarid Segura River Basin (SRB), a strong decrease in runoff from the end of the 1970s has been observed. However, in the SRB the decreasing trend is not only related with climate variability and change, also with intensive reforestation aimed at halting desertification and erosion, whichever the reason is, the default assumption of stationarity in water resources systems cannot be guaranteed. Therefore there is an important need for improvement in the ability of monitoring and predicting the impacts associated with the change of hydrologic regimes. It is thus necessary to apply non-stationary probabilistic models, which are able to reproduce probability density functions whose parameters vary with time. From a high-resolution daily gridded rainfall dataset of more than five decades (1950−2007), the spatial distribution of lengths of maximum dry spells for several thresholds are assessed, applying Generalized Additive Models for Location Scale and Shape (GAMLSS) models at the grid site. Results reveal an intensification of extreme drought events in some headbasins of the SRB important for water supply. The identification of spatial patterns of drought hazards at basin scale, associated with return periods; contribute to designing strategies of drought contingency preparedness and recovery operations, which are the leading edge of adaptation strategies.

Rainfall Simulation Using RegCM4 Model in Kalimantan during El Nino Southern Oscillation

The performance of Regional Climate Model 4 (RegCM4) in simulating rainfall is evaluated through a comparison of several observations using Asian Precipitation-Highly Resolved Observational Data Integration towards Evaluation (APHRODITE) and Climate Research Unit (CRU) in 19-year simulation period of 1982-2000 in Kalimantan. The analysis includes the perormance of RegCM4 in capturing the rainfall type, the rainfall characteristics when ENSO, the error and the diversity of data, and dominant cycles in Kalimantan. The analysis is continued with a calculation of Maximum Dry Spell Length (MDSL) and Probability Distribution Function (PDF). We found that RegCM4 produces overestimated value, but it is capable in capturing rainfall type, rainfall characteristics during ENSO, and dominant cycles in Kalimantan.

Dry spell characteristics over India based on IMD and APHRODITE datasets

Selected characteristics of dry spells and associated trends over India during the 1951–2007 period is studied using two gridded datasets: the Indian Meteorological Department (IMD) and the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of the water resources (APHRODITE) datasets. Two precipitation thresholds, 1 and 3 mm, are used to define a dry day (and therefore dry spells) in this study. Comparison of the spatial patterns of the dry spell characteristics (mean number of dry days, mean number of dry spells, mean and maximum duration of dry spells) for the annual and summer monsoon period obtained with both datasets agree overall, except for the northernmost part of India. The number of dry days obtained with APHRODITE is larger for this region compared to IMD, which is consistent with the smaller precipitation for the region in APHRODITE. These differences are also visible in the spatial patterns of mean and maximum dry spell durations. Analysis of field significance associated with trends, at the level of 34 predefined meteorological subdivisions over the mainland, suggests better agreement between the two datasets in positive trends associated with number of dry days for the annual and summer monsoon period, for both thresholds. Important differences between the two datasets are noted in the field significance associated with the negative trends. While negative trends in annual maximum duration of dry spells appear field significant for the desert regions according to both datasets, they are found field significant for two regions (Punjab and South Interior Karnataka) for the monsoon period for both datasets. This study, in addition to providing information on the spatial and temporal patterns associated with dry spell characteristics, also allows identification of regions and characteristics where the two datasets agree/disagree.

Trends of precipitation intensity and frequency in hydrological regions of China from 1956 to 2005

Daily precipitation data of 741 meteorological stations ranging from 1956 to 2005 were used to run a preliminary investigation of changes in temporal and spatial distribution of precipitation intensity and frequency in ten hydrological regions in China. Average annual and seasonal spatial values of indices of precipitation characteristics (i.e. precipitation amount, intensity and frequency) were obtained using a Kriging interpolation method. Temporal tendencies were calculated by Mann-Kendall's method. The trends of extreme rainfall events and precipitation-based droughts were also discussed in ten hydrological regions using the maximum daily precipitation and dry spell duration in a year. Results show that precipitation intensity has significant increasing trends while precipitation frequency has significant decreasing trends over China. Meanwhile precipitation has a major decline in autumn and a slight increase in winter. Both extreme rainfall events and precipitation-based droughts show a general increasing trend. The aggravated spatial and temporal unevenly distributed precipitation leads to more water shortage as well as floods in China.

On the Current and Future Dry Spell Characteristics over Africa

Changes in precipitation frequency and intensity distribution over Africa will have a direct impact on dry spells and, therefore, will affect various climate sensitive sectors. In this study, the ability of the fifth generation of the Canadian Regional Climate Model (CRCM5) in simulating annual and seasonal dry spell characteristics is assessed for four precipitation thresholds (0.5 mm, 1 mm, 2 mm and 3 mm) over Africa. The dry spell characteristics considered are the number of dry days, number of dry spells and five-year return levels of maximum dry spell durations. The performance errors are assessed by comparing ERA-Interim driven CRCM5 with the Global Precipitation Climatology Project (GPCP) dataset, for the common 1997–2008 period. Lateral boundary forcing errors, i.e., errors in the CRCM5 simulation created by errors in the driving Canadian Earth System model (CanESM2) data—as well as the added value—of CRCM5 over CanESM2 are also assessed for the current climate. This is followed by an assessment of projected changes to dry spell characteristics for two future climates (2041–2070 and 2071–2100) simulated by both CRCM5 driven by CanESM2 and CanESM2 itself, for Representative Concentration Pathway (RCP) 4.5. Results suggest that CRCM5 driven by ERA-Interim has a tendency to overestimate the annual mean number of dry days and the five-year return level of the maximum dry spell duration in a majority of the regions while it slightly underestimates the number of dry spells. In general, the CRCM5 performance errors associated with the annual and seasonal dry spell characteristics are found to be larger in magnitude compared to the lateral boundary forcing errors. Projected changes to the dry spell characteristics for the 2041–2070 and 2071–2100 periods, with respect to the 1981–2010 period suggests significant changes in the tropics, with the mean number of dry days and the five-year return levels of maximum dry spell duration increasing, while the mean number of dry spell days decreases.

Stochastic generation of multi-site daily precipitation for applications in risk management

Unlike single-site precipitation generators, multi-site precipitation generators make it possible to reproduce the space–time variation of precipitation at several sites. The extension of single-site approaches to multiple sites is a challenging task, and has led to a large variety of different model philosophies for multi-site models. This paper presents an alternative semi-parametric multi-site model for daily precipitation that is straightforward and easy to implement. Multi-site precipitation occurrences are simulated with a univariate Markov process, removing the need for individual Markov models at each site. Precipitation amounts are generated by first resampling observed values, followed by sampling synthetic precipitation amounts from parametric distribution functions. These synthetic precipitation amounts are subsequently reshuffled according to the ranks of the resampled observations in order to maintain important statistical properties of the observation network. The proposed method successfully combines the advantages of non-parametric bootstrapping and parametric modeling techniques. It is applied to two small rain gauge networks in France (Ubaye catchment) and Austria/Germany (Salzach catchment) and is shown to well reproduce the observations. Limitations of the model relate to the bias of the reproduced seasonal standard deviation of precipitation and the underestimation of maximum dry spells. While the lag-1 autocorrelation is well reproduced for precipitation occurrences, it tends to be underestimated for precipitation amounts. The model can generate daily precipitation amounts exceeding the ones in the observations, which can be crucial for risk management related applications. Moreover, the model deals particularly well with the spatial variability of precipitation. Despite its straightforwardness, the new concept makes a good alternative for risk management related studies concerned with producing daily synthetic multi-site precipitation time series.

Regional frequency analysis and spatial pattern characterization of Dry Spells in Iran

ABSTRACTThis paper presents a methodology for regional frequency analysis and spatial pattern features of the Annual Maximum Dry Spell Length (AMDSL) as an indicator of drought conditions using the well‐known L‐moments approach and statistical‐based methods. Applying Ward's cluster‐analysis method identifies eight regions with distinctive AMDSL behaviours for Iran. Homogeneity testing indicates that most of these regions are homogenous. The goodness‐of‐fit test ZDist shows that Generalized Logistic; Generalized Extreme Value and Pearson type III, distributions fit best for most regions. The spatial pattern of L‐Moment statistics demonstrates that although the northwestern and northern parts of the country experience short dry spells, these periods are inconstant, and extreme dry spell events may happen in these areas. Almost all spatial mapping of AMDSLs at different probabilistic levels demonstrates that dry spells increase gradually from west to east and from north to south, and the southern parts (especially along the Persian Gulf and Oman Sea) and central areas, including most agricultural lands, stand out as the most sensitive to soil moisture deficits, because of longer lasting droughts. © 2013 Royal Meteorological Society

Estimation of dry spells in three Brazilian regions — Analysis of extremes

The aim of this study was to model the occurrence of extreme dry spells in the Midwest, Southeast and Southern regions of Brazil and estimate the return period of the phenomenon indicating the time when the occurrence is more severe. The generalized extreme value distribution was the best fit for a series of maximum dry spell number and the parameters estimated by the maximum likelihood method. The data series adherence to the probability distribution was verified by the Kolmogorov–Smirnov test and the percentile–percentile charts. The positive trend of dry spells was verified by the Mann–Kendall test and non-stationarity rejected by Dickey–Fuller and augmented Dickey–Fuller tests. The irregular distribution of rainfall in the growing season for the Midwest region has increased the number of dry spells. The increase of rainy days in the Southeast and the South resulted in a decrease of dry spells in these regions. Regarding the return period of one year, dry spells occurred from 5 to 25days in the Midwest region meaning a loss of productivity for Brazilian agriculture if it happened between the flowering and grain filling phases, making it, therefore the region with the largest agricultural risk. When the intensity of the dry spells was analyzed for different return periods, the Southern region was the most vulnerable.

Assessment of RegCM4 simulated inter-annual variability and daily-scale statistics of temperature and precipitation over Mexico

The skill of a regional climate model (RegCM4) in capturing the mean patterns, interannual variability and extreme statistics of daily-scale temperature and precipitation events over Mexico is assessed through a comparison of observations and a 27-year long simulation driven by reanalyses of observations covering the Central America CORDEX domain. The analysis also includes the simulation of tropical cyclones. It is found that RegCM4 reproduces adequately the mean spatial patterns of seasonal precipitation and temperature, along with the associated interannual variability characteristics. The main model bias is an overestimation of precipitation in mountainous regions. The 5 and 95 percentiles of daily temperature, as well as the maximum dry spell length are realistically simulated. The simulated distribution of precipitation events as well as the 95 percentile of precipitation shows a wet bias in topographically complex regions. Based on a simple detection method, the model produces realistic tropical cyclone distributions even at its relatively coarse resolution (dx = 50 km), although the number of cyclone days is underestimated over the Pacific and somewhat overestimated over the Atlantic and Caribbean basins. Overall, it is assessed that the performance of RegCM4 over Mexico is of sufficient quality to study not only mean precipitation and temperature patterns, but also higher order climate statistics.