Daily Data Series Research Articles

Distribution of rainfall probability in the Tapajos River Basin, Amazonia, Brazil

Studies on the probability of rainfall and its spatiotemporal variations are important for the planning of water resources and optimization of the calendar of agricultural activities. This study identifies the occurrence of rain by first-order Markov Chain (MC) and by two states in the Tapajos River Basin (TRB), Amazon, Brazil. Cluster analysis (CA), based on the Ward method, was used to classify homogeneous regions and select samples for checking the probability of rainfall occurrence by season. The historical series of daily rainfall data of 80 stations were used for the period 1990-2014. The CA technique identified 8 homogeneous regions and their probability of occurrence of rainfall, helping to determine which regions and periods have greater need of irrigation. Results of the probability of occurrence of dry and rainy periods in the TRB were used to define the dry (May thru September) and rainy seasons (October thru April). Elements of the matrix transition probabilities showed variability in relation to time and, in addition, the influence of geographical position of seasonal rainfall in determining dry and rainy periods at specific sites in the TRB.

Partitioning the ocean using dense time series of Earth Observation data. Regions and natural boundaries in the Western Iberian Peninsula

Biogeographic partitioning systems provide an important framework under which ocean management practices can be designed and implemented.Sea Surface Temperature (SST) provides important insights into the features and processes governing biogeography and the natural boundaries that separate regions (fronts). In this paper, a new methodology is suggested to segment the ocean at a subset of an Eastern Boundary Current system (Western Iberian Peninsula).To this end, a dense time series of daily MODIS data (Sea Surface Temperature (SST) and Chlorophyll-a concentration, 2003–2014) was analyzed using Dynamic Time Warping (DTW). The resulting dissimilarity matrix was then clustered using UPGMA to generate a set of dynamic regions. To determine whether the boundaries separating regions coincided with areas of maximum frontal activity, a matching time series was produced for the study area.The methodology successfully delivered a set of nested regions, explained by known oceanographic features and separated by natural boundaries collocated with frontal activity maxima (ANOVA, p < 0.01). The frontal activity, which was also found to be spatially organized (Moran’s I > 0.82 and z-score > 6.95, for a p < 0.01) was thus correlated with the dissimilarity patterns of SST. Together, they offer a new perspective into the biogeographic constraints influencing an important ecosystem.SST-based regions were more informative (k = 4) but both the Chlorophyll-a and the multivariate (SST and Chlorophyll-a) alternatives reproduced the coastal/oceanic dichotomy of established systems in a dynamic way. The method was also applied to yearly subsets, which generally matched the regions delineated with the full time series while depicting inter-annual variability.The DTW and the frontal activity maps contributed to the definition of a scalable and ecologically-meaningful system, which can be adopted for the monitoring of marine areas using different (annual to decadal) time scales and easily accessible datasets.

Filling Gaps in Hourly Air Temperature Data Using Debiased ERA5 Data

Missing data in hourly and daily temperature data series is a common problem in long-term data series and many observational networks. Agricultural and environmental models and climate-related tools can be used only if weather data series are complete. To support user communities, a technique for gap filling is developed based on the debiasing of ERA5 reanalysis data, the fifth generation of the European Centre for Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses of the global climate. The debiasing procedure includes in situ measured temperature. The methodology is tested for different landscapes, latitudes, and altitudes, including tropical and midlatitudes. An evaluation of results in terms of root mean square error (RMSE) obtained using hourly and daily data is provided. The study shows very low average RMSE for all gap lengths ranging from 1.1 °C (Montecristo, Italy) to 1.9 °C (Gumpenstein, Austria).

Análisis del comportamiento de la evapotranspiración de referencia durante el periodo de lluvias en cinco estaciones meteorológicas de la cuenca Lerma-Chapala

Introduction: Estimating evapotranspiration of cultivated areas is commonly based on the calculation of reference evapotranspiration (ETo), and is essential in climatic and hydrological studies, as well as in projects related to water management in agriculture. Objectives: To evaluate ETo and analyze its behavior during the rainy season on a daily, monthly and seasonal basis. Methodology: Daily data series on temperature, radiation, atmospheric pressure, relative humidity and wind speed from five weather stations in the Lerma-Chapala basin were used. The ETo calculation was based on the application of the Penman-Monteith method. Results: ETo during the rainy season followed a decreasing pattern, characterized by being non-linear. At the beginning of the season, values higher than 6 mm∙day-1 were observed, and at the end they were lower than 2 mm∙day-1. Correlation analysis showed that ETo depends on relative humidity and radiation. Study limitations: The number of stations used was very low for the size of the basin because the network of automatic weather stations is still under development. Originality: There are very few studies focused on evapotranspiration variation as a function of elevation and meteorological variables, in addition to a specific analysis in the rainy season. Conclusions: The seasonal behavior of ETo shows important changes related to the behavior of relative humidity and radiation, which can have a significant impact on crop water needs.<

Does Sea Surface Temperature Contribute to Determining Range Limits and Expansion of Mangroves in Eastern South America (Brazil)?

Low Sea Surface Temperature (SST) is a climate barrier because it may inhibit and reduce seedling growth of mangrove propagules upon dispersal through seawater. Our objective is to analyze the spatio-temporal series of daily SST data from the Multi-scale Ultra-high Resolution (MUR)-SST in order to identify the occurrence of chilling events for mangrove plants at the Eastern South America mangrove limit and beyond. We focus our study on three key sites: (i) the Rhizophora mangle L. distribution limit (Praia do Sonho: 27°53′S), (ii) the Eastern South America mangrove limit (Laguna: 28°30′S) and (iii) one beyond mangrove areas, in Araranguá (28°55′S). Our results show that, in Araranguá, chilling events are more intense and occur more frequently than in the other two sites that have a mangrove cover. We conclude that, the chilling events of SST may play a role in restricting mangroves within their actual limits. In this sense, higher occurrences of chilling events of SST could be an explanation for the absence of R. mangle in Laguna. However, Laguncularia racemosa (L.) C.F. Gaertn. was reported to be tolerant to low temperatures, and yet it is absent from the southernmost study site. This may be an indication of the role of other factors than SST in determining a mangrove range expansion, such as dispersal constraints.

Relating Spatiotemporal Patterns of Forest Fires Burned Area and Duration to Diurnal Land Surface Temperature Anomalies

Forest fires are a major source of ecosystem disturbance. Vegetation reacts to meteorological factors contributing to fire danger by reducing stomatal conductance, thus leading to an increase of canopy temperature. The latter can be detected by remote sensing measurements in the thermal infrared as a deviation of observed land surface temperature (LST) from climatological values, that is as an LST anomaly. A relationship is thus expected between LST anomalies and forest fires burned area and duration. These two characteristics are indeed controlled by a large variety of both static and dynamic factors related to topography, land cover, climate, weather (including those affecting LST) and anthropic activity. To investigate the predicting capability of remote sensing measurements, rather than constructing a comprehensive model, it would be relevant to determine whether anomalies of LST affect the probability distributions of burned area and fire duration. This research approached the outlined knowledge gap through the analysis of a dataset of forest fires in Campania (Italy) covering years 2003–2011 against estimates of LST anomaly. An LST climatology was first computed from time series of daily Aqua-MODIS LST data (product MYD11A1, collection 6) over the longest available sequence of complete annual datasets (2003–2017), through the Harmonic Analysis of Time Series (HANTS) algorithm. HANTS was also used to create individual annual models of LST data, to minimize the effect of varying observation geometry and cloud contamination on LST estimates while retaining its seasonal variation. LST anomalies where thus quantified as the difference between LST annual models and LST climatology. Fire data were intersected with LST anomaly maps to associate each fire with the LST anomaly value observed at its position on the day previous to the event. Further to this step, the closest probability distribution function describing burned area and fire duration were identified against a selection of parametric models through the maximization of the Anderson-Darling goodness-of-fit. Parameters of the identified distributions conditional to LST anomaly where then determined along their confidence intervals. Results show that in the study area log-transformed burned area is described by a normal distribution, whereas log-transformed fire duration is closer to a generalized extreme value (GEV) distribution. The parameters of these distributions conditional to LST anomaly show clear trends with increasing LST anomaly; significance of this observation was verified through a likelihood ratio test. This confirmed that LST anomaly is a covariate of both burned area and fire duration. As a consequence, it was observed that conditional probabilities of extreme events appear to increase with increasing positive deviations of LST from its climatology values. This confirms the stated hypothesis that LST anomalies affect forest fires burned area and duration and highlights the informative content of time series of LST with respect to fire danger.

Environmental flow assessment for rivers feeding a coastal wetland complex in the Pacific coast of northwest Mexico

AbstractHydrological variability is one of the main drivers for rivers structure, and functionality, making the environmental flow (EF) assessment a key element to understand some ecological processes depending on it. Therefore, we aimed to evaluate the EF regime for the main rivers feeding a coastal wetland complex in northwest Mexico, following the hydrologic methodology officially approved by the Mexican National Water Commission based on daily data series (1975–2014). A mean annual runoff around 11 556 Mm3 was estimated to feed the Marismas Nacionales (MN) wetlands system from the discharge of seven rivers. It is expected that the estimated flows, regarded as necessary for the MN system to function properly as a whole, will change because of regional hydraulic infrastructure plans, threatening the discharge of the San Pedro and Baluarte rivers by reducing the flow significantly with negative effects on the MN wetlands.

Funções de estimativa do armazenamento de água no solo sob diferentes níveis de capacidade de água disponível

The estimative of soil water storage is essential for representation of soil water relations for Brazil agricultural planning purposes. We had to aim evaluate the performance of five equations used to estimate soil water storage, through simulations of a daily water balance to the cities of Goiânia-GO and Ponta Grossa-PR, considering different amounts of total available water in the soil. For that purpose, were used daily data series of precipitation, reference evapotranspiration, crop coefficient, p fraction and initial soil water storage. It was considered total available water from 20 mm to 320 mm, with an interval of 30 mm. Potential functions of First and Second Order were approximately equal to the Cosine function in a daily water balance simulations, under conditions of supplementary irrigation and non-irrigation over the year. The Exponential function was not adequate in the estimative of soil water storage. Braga function was not adequate when considering additional irrigations in a daily water balance. We recommend the improvement of the functions to estimate soil water storage from replacement of “negative cumulative” function by other parameters, which could better explain the variation of soil water storage available for plants.

Delimitation of Agricultural Areas with Natural Constraints in Greece: Assessment of the Dryness Climatic Criterion Using Geostatistics

The Less Favored Areas (LFAs) scheme has existed in various forms since 1975 and it is a broad mechanism supporting rural development in agricultural areas with natural constraints (ANC). Within the programme period 2014–2020, the European Commission developed a common set of biophysical criteria (soil, climate, and terrain) to meet the requirement for a robust and harmonized approach of delimiting ANC throughout the EU Member States. Soil and terrain criteria can be derived directly from soil maps using geospatial analysis techniques based on the provided guidelines. However, the assessment of climatic criteria can be challenging especially in regions characterized by increased spatial variability and data scarcity. In this paper, the assessment of the dryness climatic criterion in a data-scarce region (Greece) as well as the challenges, limitations, and solutions are presented. Daily data-series from 140 meteorological stations for a 30-year reference period were analyzed and the spatial distribution of the precipitation and the potential evapotranspiration for each year were estimated in order to make the final assessment of the dryness criterion. Climate variability and the presence of trends were investigated as well. The obtained results indicated that most of the utilized agricultural area is affected by dryness due to a combination of low precipitation and high evapotranspiration rates. The extreme spatial variability especially in precipitation was also highlighted. An important temporal variability was observed as well, including indications of decreasing trends in precipitation and aridity index. Climate variability and possible trends should be investigated in more detail using longer time series in order to evaluate their impact in agricultural production.

A Novel Hybrid Data-Driven Model for Daily Land Surface Temperature Forecasting Using Long Short-Term Memory Neural Network Based on Ensemble Empirical Mode Decomposition.

Daily land surface temperature (LST) forecasting is of great significance for application in climate-related, agricultural, eco-environmental, or industrial studies. Hybrid data-driven prediction models using Ensemble Empirical Mode Composition (EEMD) coupled with Machine Learning (ML) algorithms are useful for achieving these purposes because they can reduce the difficulty of modeling, require less history data, are easy to develop, and are less complex than physical models. In this article, a computationally simple, less data-intensive, fast and efficient novel hybrid data-driven model called the EEMD Long Short-Term Memory (LSTM) neural network, namely EEMD-LSTM, is proposed to reduce the difficulty of modeling and to improve prediction accuracy. The daily LST data series from the Mapoling and Zhijiang stations in the Dongting Lake basin, central south China, from 1 January 2014 to 31 December 2016 is used as a case study. The EEMD is firstly employed to decompose the original daily LST data series into many Intrinsic Mode Functions (IMFs) and a single residue item. Then, the Partial Autocorrelation Function (PACF) is used to obtain the number of input data sample points for LSTM models. Next, the LSTM models are constructed to predict the decompositions. All the predicted results of the decompositions are aggregated as the final daily LST. Finally, the prediction performance of the hybrid EEMD-LSTM model is assessed in terms of the Mean Square Error (MSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE), Pearson Correlation Coefficient (CC) and Nash-Sutcliffe Coefficient of Efficiency (NSCE). To validate the hybrid data-driven model, the hybrid EEMD-LSTM model is compared with the Recurrent Neural Network (RNN), LSTM and Empirical Mode Decomposition (EMD) coupled with RNN, EMD-LSTM and EEMD-RNN models, and their comparison results demonstrate that the hybrid EEMD-LSTM model performs better than the other five models. The scatterplots of the predicted results of the six models versus the original daily LST data series show that the hybrid EEMD-LSTM model is superior to the other five models. It is concluded that the proposed hybrid EEMD-LSTM model in this study is a suitable tool for temperature forecasting.

Spatiotemporal Variations of Extreme Precipitation under a Changing Climate in the Three Gorges Reservoir Area (TGRA)

The Three Gorges Dam (TGD) is one of the largest hydroelectric projects in the world. Monitoring the spatiotemporal distribution of extreme precipitation offers valuable information for adaptation and mitigation strategies and reservoir management schemes. This study examined variations in extreme precipitation over the Three Gorges Reservoir area (TGRA) in China to investigate the potential role of climate warming and Three Gorges Reservoir (TGR). The trends in extreme precipitation over the TGRA were investigated using the iterative-based Mann–Kendall (MK) test and Sen’s slope estimator, based on weather station daily data series and TRMM (Tropical Rainfall Measuring Mission) data series. The mean and density distribution of extreme precipitation indices between pre-dam and post-dam, pre-1985 and post-1985, and near and distant reservoir area were assessed by the Mann–Whitney test and the Kolmogorov–Smirnov test. The ratio of extreme precipitation to non-extreme precipitation became larger. The precipitation was characterized by increases in heavy precipitation as well as decreases in light and moderate rain. Comparing extreme precipitation indices between pre-1985 (cooling) and post-1985 (warming) indicated extreme precipitation has changed to become heavier. Under climate warming, the precipitation amount corresponding to more than the 95th percentile increased at the rate of 6.48%/°C. Results from comparing extreme precipitation for the pre- and post-dam, near reservoir area (NRA) and away from the reservoir area (ARA) imply an insignificant role of the TGR on rainfall extremes over the TGRA. Moreover, the impoundment of TGR did not exert detectable impacts on the surface relative humidity (RH) and water vapor pressure (WP).

Erosive rainfall in the Rio do Peixe Valley: Part III - Risk of extreme events

ABSTRACT Understanding the risks of extreme events related to soil erosion is important for adequate dimensioning of erosion and runoff control structures. The objective of this study was to determine the rainfall erosivity with different return periods for the Valley of the Rio do Peixe in Santa Catarina state, Brazil. Daily pluviographic data series from 1984 to 2014 from the Campos Novos, and Videira meteorological stations and from 1986 to 2014 from the Caçador station were used. The data series of maximum annual rainfall intensity in 30 min, maximum annual erosive rainfall, and total annual erosivity were analyzed for each station. The Gumbel-Chow distributions were adjusted and their adhesions were evaluated by the Kolmogorov-Smirnov test at a significance level of 5%. The Gumbel-Chow distribution was adequate for the estimation of all studied variables. The mean annual erosivity corresponds to the return period of 2.25 years. The data series of the annual maximum individual rainfall erosivity coefficients varied from 47 to 50%.

Stochastic sensitivity analysis of nitrogen pollution to climate change in a river basin with complex pollution sources.

It is increasingly recognized that climate change could impose both direct and indirect impacts on the quality of the water environment. Previous studies have mostly concentrated on evaluating the impacts of climate change on non-point source pollution in agricultural watersheds. Few studies have assessed the impacts of climate change on the water quality of river basins with complex point and non-point pollution sources. In view of the gap, this paper aims to establish a framework for stochastic assessment of the sensitivity of water quality to future climate change in a river basin with complex pollution sources. A sub-daily soil and water assessment tool (SWAT) model was developed to simulate the discharge, transport, and transformation of nitrogen from multiple point and non-point pollution sources in the upper Huai River basin of China. A weather generator was used to produce 50years of synthetic daily weather data series for all 25 combinations of precipitation (changes by -10, 0, 10, 20, and 30%) and temperature change (increases by 0, 1, 2, 3, and 4°C) scenarios. The generated daily rainfall series was disaggregated into the hourly scale and then used to drive the sub-daily SWAT model to simulate the nitrogen cycle under different climate change scenarios. Our results in the study region have indicated that (1) both total nitrogen (TN) loads and concentrations are insensitive to temperature change; (2) TN loads are highly sensitive to precipitation change, while TN concentrations are moderately sensitive; (3) the impacts of climate change on TN concentrations are more spatiotemporally variable than its impacts on TN loads; and (4) wide distributions of TN loads and TN concentrations under individual climate change scenario illustrate the important role of climatic variability in affecting water quality conditions. In summary, the large variability in SWAT simulation results within and between each climate change scenario highlights the uncertainty of the impacts of climate change and the need to incorporate extreme conditions in managing water environment and developing climate change adaptation and mitigation strategies.

A rainfall analysis and forecasting tool

MENSEI-L is a stand-alone software tool for the automatic analysis of pluviometric networks, that also provides three-day rainfall forecasts based on weather types. The software tool, implemented in Python and R, is able to fill missing values in original daily data series and to generate synthetic pluviometers in ungauged locations, by means of kriging techniques. MENSEI-L also characterizes punctual and spatial, average and extreme distributions of precipitation for the complete pluviometric network. Tenerife (Canary Islands, Spain) is used as study site to evaluate the capabilities of MENSEI-L and the implicit rainfall analysis methodology that it implements. MENSEI-L proves to be a useful tool to extract information from dense observation networks where manual analysis is not practical.

A Bayesian encompassing test using combined value-at-risk estimates

The Value at Risk (VaR) is a risk measure that is widely used by financial institutions in allocating risk. VaR forecast estimation involves the conditional evaluation of quantiles based on the currently available information. Recent advances in VaR evaluation incorporate conditional variance into the quantile estimation, yielding the Conditional Autoregressive VaR (CAViaR) models. However, the large number of alternative CAViaR models raises the issue of identifying the optimal quantile predictor. To resolve this uncertainty, we propose a Bayesian encompassing test that evaluates various CAViaR models predictions against a combined CAViaR model based on the encompassing principle. This test provides a basis for forecasting combined conditional VaR estimates when there are evidences against the encompassing principle. We illustrate this test using simulated and financial daily return data series. The results demonstrate that there are evidences for using combined conditional VaR estimates when forecasting quantile risk.

Determination of the soil moisture recession constant from satellite data: a case study of the Yucatan peninsula

ABSTRACTEstimation of the recession constant for soil moisture can assist in soil and water management. This article estimates soil moisture recession velocity from satellite data, thereby taking advantage of extensive data coverage in a metric that is more commonly used with point data for rivers. Retrieval from satellites of the surface soil moisture has produced global coverage of multiannual time series data, thereby allowing the application of techniques that require long time series of daily data. We applied two techniques from river hydrology to soil moisture data from the advanced scatterometer aboard the meteorological operational satellite: (1) baseflow separation; and (2) master recession curve (MRC) with the correlation method. The former filtered the data and extracted those for the base soil moisture (BSM), which is considered the water that circulates in the soil by capillarity. The latter technique allowed the estimation of recession constants by the extraction of continuously decreasing BSM segments. The use of MRC for a large range of BSM provides a recession constant representative of all the moisture decrease for each pixel, thereby permitting the identification of drought-sensitive zones. The recession constant, a metric that had not been used for soil moisture, allowed us to determine potential temporal evolution of drought in the Yucatan peninsula. Government agencies could use the approach applied in this study to improve water management and drought prevention.

A note on modeling world equity markets with nonsynchronous data

We investigate information transmission in world equity markets using lengthy time series of daily data for nine developed equity markets over the 1999–2014 (post-Euro) time interval. Three nine-variable systems are examined, including stock indexes in both local currency and US dollars plus foreign exchange rates. After finding only weak evidence of cointegration in the three nine-variable systems, vector autoregression models are used to identify which price indexes drive the others. Our initial tests are consistent with earlier findings; that is, the US market appears to be the clear price leader. However, after constructing alternative models with the US data series lagged one period (making it the first market reported each day rather than the last), the results are reversed; the US is no longer the price leader. The US market is driven by nearly all other markets, while making little or no impact on the others. Thus, in earlier studies using nonsynchronous data reported by calendar date, it only appears that the US drives other stock markets, when in fact the US is merely an equal participant in information transmission around the world.

Sub-daily runoff predictions using parameters calibrated on the basis of data with a daily temporal resolution

Concentration times in small and medium-sized basins (∼10–1000km2) are commonly less than 24h. Flood-forecasting models are thus required to provide simulations at high temporal resolutions (1h–6h), although time-series of input and runoff data with sufficient lengths are often only available at the daily temporal resolution, especially in developing countries. This has led to study the relationships of estimated parameter values at the temporal resolutions where they are needed from the temporal resolutions where they are available. This study presents a methodology to treat empirically model-parameter dependencies on the temporal resolution of data in two small basins using a bucket-type hydrological model, HBV-light, and the generalised likelihood uncertainty estimation approach for selecting its parameters. To avoid artefacts due to the numerical resolution or numerical method of the differential equations within the model, the model was consistently run using modelling time-steps of one-hour regardless of the temporal resolution of the rainfall-runoff data. The distribution of the parameters calibrated at several temporal resolutions in the two basins did not show model-parameter dependencies on the temporal resolution of data and the direct transferability of calibrated parameter sets (e.g., daily) for runoff simulations at other temporal resolutions for which they were not calibrated (e.g., 3h or 6h) resulted in a moderate (if any) decrease in model performance, in terms of Nash-Sutcliffe and volume-error efficiencies. The results of this study indicate that if sub-daily forcing data can be secured, flood forecasting in basins with sub-daily concentration times may be possible with model-parameter values calibrated from long time series of daily data. Further studies using more models and basins are required to test the generality of these results.

Impacts of wildfires on interannual trends in land surface phenology: an investigation of the Hayman Fire

Land surface phenology (LSP) derived from satellite data has been widely associated with recent global climate change. However, LSP is frequently influenced by land disturbances, which significantly limits our understanding of the phenological trends driven by climate change. Because wildfire is one of the most significant disturbance agents, we investigated the influences of wildfire on the start of growing season (SOS) and the interannual trends of SOS in the Hayman Fire area that occurred in 2002 in Colorado using time series of daily MODIS data (2001–2014). Results show that the Hayman Fire advanced the area-integrated SOS by 15.2 d and converted SOS from a delaying trend of 3.9 d/decade to an advancing trend of −1.9 d/decade during 2001–2014. The fire impacts on SOS increased from low burn severity to high burn severity. Moreover, the rate of increase of annual maximum and minimum EVI2 from 2003–2014 reflects that vegetation greenness could recover to pre-fire status in 2022 and 2053, respectively, which suggests that the fire impacts on the satellite-derived SOS variability and the interannual trends should continue in the next few decades.

Obtaining Homogeneous Regions by Determining the Generalized Fractal Dimensions of Validated Daily Rainfall Data Sets

Extreme rainfall data are widely used in several hydrological models and civil engineering design. Despite high temporal resolution rainfall data are not commonly available, daily rainfall data series are easily found. When these available data series are short in length the Regional Frequency Analysis (RFA) is a good tool to enlarge them by joining stations into homogeneous regions. This is by far, the most complicated step in RFA. This work presents a new method to form homogeneous regions of extreme annual daily rainfall data series. Daily rainfall data series from 53 weather stations in the Maule Region (Chile) have been used. Their fractal dimensions spectra have been obtained by applying the box counting method. Each station has been characterized by the fractal dimensions D1 and D2. A cluster analysis has been carried out based on these at-site characteristics and three regions have been obtained. After performing a RFA of extreme daily annual rainfall data series within each region they have shown as homogeneous. Only one of the available stations has not been possible to be included into any homogeneous regions, being the local frequency analysis the only suitable method to be applied at this location.