Daily Series Research Articles

Simulation of Daily Precipitation from CMIP5 in the Qinghai-Tibet Plateau

As the earth's third pole, Qinghai-Tibet Plateau belongs to one of the most sensitive regions to climate change in the world. Based on the observed and the simulated daily precipitation from the Coupled Model Intercomparison Project Phase 5 (CMIP5), we evaluated the simulation performance of daily precipitation from selected CMIP5 models from 1975 to 2005 over the Qinghai-Tibet Plateau. We found that daily precipitation exhibited obvious long-range correlation characteristics using the detrended fluctuation analysis method. The scaling exponents of daily precipitation in summer and autumn are significantly larger than those in spring and winter. MIROC4H with the best performance can reproduce long-range correlation characteristic of daily precipitation series probably because of the higher resolution, which can capture small scale cloud convections. Besides there are seasonal differences in the simulation results among different regions of the Qinghai-Tibet Plateau, simulation effects of all climate models in summer and winter are better than those in spring and autumn. The performance of MIROC4H model works the best in spring. Overall, the scaling exponents of daily precipitation from BCC-CSM1-1-M, CMCC-CM and MIROC4H are close to the observations. CCSM4 and MIROC4H climate models could reproduce the internal dynamics characteristic of daily precipitation in autumn. But for winter, all climate models have exaggerated the scaling value in southeastern Qinghai-Tibet Plateau compared with the observed values.

ІНДИКАТОРИ ТЕМПЕРАТУРНИХ АНОМАЛІЙ РЕГІОНАЛЬНОГО КЛІМАТУ

Climatic indices are usually created for a particular group or sector of influence, and because of their complexity they cannot be universal and apply for more than one region. In addition, a methodology is needed that would allow studying of changes in the index over a long period of time using climatological data (both models and observations). That is why there are many metrics (or indexes) that try to provide quantitative information about the periods of extreme heat. Since there are many different climatic regimes in one region, it is necessary to focus on observations of extreme temperatures over the research territory. Thus, the anomalies of the air temperature regime that were recorded at 18 meteorological stations of Ukraine during the reference period of 1981-2010 were considered in this study. To assess the effects of hot events (especially heat waves) for a selected region, it is important to know how the climate indicator is evaluated. To assess the effects of hot events, in particular, heat waves, for a particular region, it is important to know how the climate indicator is evaluated this event because there is a certain risk that the inadequate index distorts the climatological information, which in turn leads to ineffective planning of adaptation and mitigation. Therefore, it is necessary to use the index of a climatic event, which could be easily calculated based on the regional climatic data and which could provide comprehensive information that characterizes the heat event which is observed. The latest studies show, the HWMI index is considered an alternative index of temperature regime. HWMI indices were calculated based on the analysis of the maximum daily air temperature series for the calendar year. This was achieved due to the temperature thresholds established on a single metric, which is the most important for which the heat waves were identified and estimated. The annual thresholds of maximum daily air temperature for different reference periods (1961-1990 and 1981-2010) were investigated. It was found out, there is the difference in the threshold values of maximum air temperature during the year for the indicated reference periods only in winter (January-February) and in summer (July-August) and did not exceed 3°С. The field of maximum temperature thresholds for the reference period, which focuses on the south-east of the country, is established. The spatial variability of temperature thresholds along the zonal and meridional sections was studied, which allowed optimizing the grid for the construction of the regional field of threshold extreme temperatures.

Crypto-currencies, Fiat Money or Gold Standard; An Empirical Evidence from Volatility Structure Analysis Using News Impact Curve

This study investigates whether symmetric and asymmetric volatility effects are persisted in the daily return series of Bitcoin currency compared to the gold and fiat money system using GARCH family models. The symmetric analysis shows that the three monetary systems exhibit time-varying volatility with high persistence and predictability behaviour whereas asymmetric analysis indicates that Bitcoin currency and gold are not significantly respond to asymmetric information effects in the financial markets however, the US dollar index is affected by the positive shocks. This suggesting Bitcoin and gold have the capability for hedging or safe-haven assets against market risk specifically during times of economic turmoil. Evidence suggests that cryptocurrency is a potential alternative to current fiat money system, offering benefit for policy makers and a good investment option for positional investors in terms of hedging, portfolio diversification strategy and risk management.

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.<

Analysis of Precipitation Variability using Memory Based Artificial Neural Networks

This article analyzes the variability in precipitation of the Barak river basin using memory-based ANN models called Gamma Memory Neural Network(GMNN) and genetically optimized GMNN called GMNN-GA for precipitation downscaling precipitation. GMNN having adaptive memory depth is capable techniques in modeling time varying inputs with unknown input characteristics, while an integration of the model with GA can further improve its performances. NCEP reanalysis and HadCM3A2 (a) scenario data are used for downscaling and forecasting precipitation series for Barak river basin. Model performances are analyzed by using statistical criteria, RMSE and mean error and are compared with the standard SDSM model. Results obtained by using 24 years of daily data sets show that GMNN-GA is efficient in downscaling daily precipitation series with maximum daily annual mean error of 6.78%. The outcomes of the study demonstrate that execution of the GMNN-GA model is superior to the GMNN and similar with that of the standard SDSM.

On the Economic Significance of Stock Market Prediction and the No Free Lunch Theorem

Forecasting of stock market returns is a challenging research activity that is now expanding with the availability of new data sources, markets, financial instruments, and algorithms. At its core, the predictability of prices still raises important questions. Here, we discuss the economic significance of the prediction accuracy. To develop this question, we collect the daily series prices of almost half of the publicly traded companies around the world over a period of ten years and formulate some trading strategies based on their prediction. Proper visualization of these data together with the use of the No Free Lunch theoretical framework gives some unexpected results that show how the a priori less accurate algorithms and inefficient strategies can offer better results than the a priori best alternatives in some particular subsets of data that have a clear interpretation in terms of economic sectors and regions.

Modeling of IDF curves for stormwater design in Makkah Al Mukarramah region, The Kingdom of Saudi Arabia

Abstract Reducing the negative impacts of flooding in Makkah AL Mukarramah region in the Kingdom of Saudi Arabia (KSA) is of utmost importance. In the last decade, there are huge mega infrastructure projects in KSA in general and in Makkah AL Mukarramah region in particular. These projects require adequate stormwater drainage systems. Since, the availability of rainfall and runoff data are scarce, engineers and hydrologists rely on models developed in temperature regions that are not hydrologically similar from temperate regions. This leads to inaccurate designs of stormwater facilities. Therefore, deveoping in situ Intensity-Duration-Frequency (IDF) curves is a must in this arid region. This paper aims at modeling IDF curves for Makkah Al-Mukarramah region. Maximum annual daily rainfall series of 80 storms (with sub-hourly and hourly data) from four stations are investigated through six different probability distributions. Consequently, rainfall depth-duration-frequency models and curves are derived. Results revealed that the Gumbel Type I is the optimal one. Thus, it is used to deduce the IDF curves and relations for each station and for the region as a whole. The R2 value for fitting power-lawfunction (i = a Db) to the data is very high for the IDF parameters. The R2 for the coefficient parameter, a, is between 0.9999 and 0.9988 while it ranges between 0.8754 and 0.8039 for exponent parameter, b. High correlation coefficient (more than 0.95) has been obtained. The resulting IDF models are strongly recommended for rigorous, effective and safe design of the stormwater systems in Makkah Al-Mukarramah region.

An approach to homogenize daily peak wind gusts: An application to the Australian series

Daily Peak Wind Gust (DPWG) time series are important for the evaluation of wind‐related hazard risks to different socioeconomic and environmental sectors. Yet, wind time series analyses can be impacted by several artefacts, both temporally and spatially, which may introduce inhomogeneities that mislead the study of their decadal variability and trends. The aim of this study is to present a strategy in the homogenization of a challenging climate extreme such as the DPWG using 548 time series across Australia for 1941–2016. This automatic homogenization of DPWG is implemented in the recently developed Version 3.1 of the R package Climatol. This approach is an advance in homogenization of climate records as it identifies 353 break points based on monthly data, splits the daily series into homogeneous subperiods, and homogenizes them without needing the monthly corrections. The major advantages of this homogenization strategy are its ability to: (a) automatically homogenize a large number of DPWG series, including short‐term ones and without needing site metadata (e.g., the change in observational equipment in 2010/2011 was correctly identified); (b) use the closest reference series even not sharing a common period with candidate series or presenting missing data; and (c) supply homogenized series, correcting anomalous data (quality control by spatial coherence), and filling in all the missing data. The NCEP/NCAR reanalysis wind speed data were also trialled in aiding homogenization given the station density was very low during the early decades of the record; however, reanalysis data did not improve the homogenization. Application of this approach found a reduced range of DPWG trends based on site data, and an increased negative regional trend of this climate extreme, compared to raw data and homogenized data using NCEP/NCAR. The analysis produced the first homogenized DPWG dataset to assess and attribute long‐term variability of extreme winds across Australia.

Variability of the Lake Onega ice coverage in the period 2000-2018 according to the satellite data

Studying of characteristics of the Lake Onega ice regime and investigating of the climate influence on the formation and destruction of the ice cover requires a continuous chronological series of data on the Lake ice coverage. Ice cover is the percentage of the ice area to the total area of the lake. In 1955–1990, calculations of the ice coverage of the Lake were based on the use of the results of airborne ice reconnaissance. On average for this period, from 5 to 15 values were annually obtained, which was not enough for a comprehensive analysis of the ice coverage variability. In this paper, for the frst time, a daily series of values of the ice coverage of the Lake Onega for the period 2000–2018 had been formed basing on the results of a combined analysis of the following satellite data sets: NSIDC, NOAA NESDIS, corrected by data of the satellite MODIS sensor. Values from November to May were grouped from the above data sets without regards for years of observations, and then the regression analysis of these values made possible to create a model (a polynomial of the 8th degree) of the chronological course of the ice coverage during the period of the ice phenomena existence on the Lake Onega. Te coefcient of determination of the model is 0.74, and the error in determining the ice coverage is 21%. Te average dates of the beginning, end, and duration of periods of the formation (from November 25th to January 19th), the destruction (from April 13th to May 17th) of the ice cover, as well as the total freeze-up time (from January 20th to April 12th) on the Lake Onega for the period 2000–2018 was determined. Te period of ice phenomena on the Lake Onega on average lasts for almost half a year (175 days), of which a signifcant part of the time is a complete freeze-up (84 days). It was found that the rate of formation of the ice cover (1.76% per day) on the Lake Onega is 1.65 times smaller than the rate of its destruction (2.90% per day), which approximately corresponds to similar results obtained for the Lake Ladoga (1.5 times).

Trends in flood events and their relationship to extreme rainfall in an urban area of Sahelian West Africa: The case study of Ouagadougou, Burkina Faso

Urban areas in Sahelian West Africa are highly vulnerable to extreme hydro‐meteorological events. In recent years, Burkina Faso has experienced several natural disasters with floods being the most frequent. This study investigates flood trends in Ouagadougou and their relationship to extreme rainfall events. Fourteen rainfall indices were analysed to characterise the frequency and intensity of extreme rainfall. A frequency analysis of annual maximum daily rainfall series was performed using three statistical distributions. The results showed that few of the rainfall indices have significant trends at 5% level over the period 1961–2015. The generalised extreme value distribution satisfactorily fit the time‐series of annual maximum daily rainfall. An analysis of media flood reports revealed that Burkina Faso experienced approximately three flood events per year throughout the period 1986–2016. In the 2000s, the number of flood events increased to five per year. Most flood events are caused by rainfall events with return periods of less than or equal to 5 years. The results indicated that the significant increase in flood risk in Ouagadougou is not only related to heavy rainfall but also due to human and environmental factors.

Homogenization of daily temperature series in the European Climate Assessment &amp; Dataset

The daily maximum and minimum temperature series of the European Climate Assessment &amp; Dataset are homogenized using the quantile matching approach. As the dataset is large and the detail of metadata is generally missing, an automated method locates breaks in the series based on a comparison with surrounding series and applies adjustments which are estimated using homogeneous segments of surrounding series as reference. A total of 6,500 series have been processed and after removing duplicates and short series, about 2,100 series have been adjusted. Finally, the effect of the homogenization of daily maximum and minimum temperature on trend estimation is shown to produce a much more spatially homogeneous and then plausible picture.

Statistical classification of streamflow based on flow variability in west flowing rivers of Kerala, India

Measured streamflow and flood series of 43 gauging stations from 25 west flowing rivers in Kerala, India, were analysed for their descriptive characteristics to study their spatial and temporal variation. The spatial and temporal variations in streamflow are influenced by many factors including climatic and basin characteristics. Streamflow data from each station (length varies from 14 to 43 years) is analysed for their internal characteristics such as trend, stationarity, homogeneity, noise and periodicity to incorporate it in hydrological models, so that their predictions would be more accurate. The internal characteristics were studied along with the statistical analysis. For analysing each internal characteristic, more than one method of analysis has been used to have reliable result. The trend characteristic was analysed using Mann-Kendall (MK) test, Sen’s slope test, Spearman’s rank correlation coefficient and Pearson correlation coefficient methods. Stationarity characteristics have been tested using augmented Dickey-Fuller test (ADF), Phillips-Perron test and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) test. For identifying homogeneous nature, Pettitt test, standard normal homogeneity (SNH) test, Buishand test and von Neumann tests were used. Noise in the streamflow was verified using Box-Peirce, Ljung-Box and McLeod-Li tests. From the study, it is found that the daily series is non-homogeneous, stationary data with white noise, whereas flood series shows mixed characteristics. Based on the variations in the time series, the daily streamflow and flood series are classified into different categories such as high, average, moderate, minor and no variation stations. In most of the river basins, daily flow shows average variation. Flood series shows average variation in 44% of stations and moderate variation in 28% of stations.

Simulation of Stochastic Processes Exhibiting Any‐Range Dependence and Arbitrary Marginal Distributions

AbstractHydrometeorological processes are typically characterized by temporal dependence, short‐ or long‐range (e.g., Hurst behavior), as well as by non‐Gaussian distributions (especially at fine time scales). The generation of long synthetic time series that resembles the marginal and joint properties of the observed ones is a prerequisite in many uncertainty‐related hydrological studies, since they can be used as inputs and hence allow the propagation of natural variability and uncertainty to the typically deterministic water‐system models. For this reason, it has been for years one of the main research topics in the field of stochastic hydrology. This work presents a novel model for synthetic time series generation, termed Symmetric Moving Average (neaRly) To Anything, that holds out the promise of simulating stationary univariate and multivariate processes with any‐range dependence and arbitrary marginal distributions, provided that the former is feasible and the latter have finite variance. This is accomplished by utilizing a mapping procedure in combination with the relationship that exists between the correlation coefficients of an auxiliary Gaussian process and a non‐Gaussian one, formalized through the Nataf's joint distribution model. The generality of Symmetric Moving Average (neaRly) To Anything is stressed through two hypothetical simulation studies (univariate and multivariate), characterized by different dependencies and distributions. Furthermore, we demonstrate the practical aspects of the proposed model through two real‐world cases, one that concerns the generation of annual non‐Gaussian streamflow time series at four stations and another that involves the synthesis of intermittent, non‐Gaussian, daily rainfall series at a single location.

Reconstruction of Lamb weather type series back to the eighteenth century

The Lamb weather type series is a subjective catalogue of daily atmospheric patterns and flow directions over the British Isles, covering the period 1861–1996. Based on synoptic maps, meteorologists have empirically classified surface pressure patterns over this area, which is a key area for the progression of Atlantic storm tracks towards Europe. We apply this classification to a set of daily pressure series from a few stations from western Europe, in order to reconstruct and to extend this daily weather type series back to 1781. We describe a statistical framework which provides, for each day, the weather types consistent enough with the observed pressure pattern, and their respective probability. Overall, this technique can correctly reconstruct almost 75% of the Lamb daily types, when simplified to the seven main weather types. The weather type series are described and compared to the original series for the winter season only. Since the low frequency variability of synoptic conditions is directly related to the North Atlantic Oscillation (NAO), we derive from the weather type series an NAO index for winter. An interesting feature is a larger multidecadal variability during the nineteenth century than during the twentieth century.

Vegetation phenology and its variations in the Tibetan Plateau, China

ABSTRACTUnderstanding the vegetation phenology and its variations in the Tibetan Plateau is critical to the study of ecological responses to global climate change. In this study, several pre-processed methods or techniques were applied to filter the Global Inventory Modelling and Mapping Study’s Normalized Difference Vegetation Index (GIMMS NDVI) data from 1982 to 2006, and construct the daily NDVI series. Firstly, vegetation and non-vegetation were determined by NDVI quantity contour, and cloud-covered pixels were also eliminated by NDVI change characteristics in a year. Then, the NDVI series were filtered by three-standard deviation and Savitzky-Golay method. Finally, the Savitzky–Golay method was employed to fit and construct the daily NDVI series. These methods guarantee a more reliable subsequent calculation of subsequent vegetation phenology. The vegetation phenology parameters including the start of growth season (SOG), the end of growth season (EOG), the lengths of growth season (LOG) and the absolute increase in vegetation (AIV), defined as the difference between the maximum NDVI and the NDVI for SOG in a year, were derived from the daily NDVI series based on the maximum ratio threshold method and their variations were analysed. The results showed that the SOGs were gradually delayed from the southeast to the northwest of the Tibetan Plateau, but the distribution pattern of the EOGs was opposite to that of the SOGs. From 1982 to 2006, SOGs were advanced approximately 3–18 days and EOGs delayed around 0–24 days in the southeast, whereas AIVs decreased around 0–0.3. In the northwest, these phenology parameters followed inverse trends compared with those of the southeast. Over the 25-year period, LOG changes had no constructive or active effects on the vegetation absolute increase. These complex phenological shifts were mainly due to the spatial differences in the environmental changes. However, in some extent, they might be related to the vegetation itself, such as its fractional cover. These findings may help to understand the alpine vegetation responds to climate change in the Tibetan Plateau.

A perfect prognosis approach for daily precipitation series in consideration of space–time correlation structure

Downscaling techniques are the required tools to link the global climate model outputs provided at a coarse grid resolution to finer scale surface variables appropriate for climate change impact studies. Besides the at-site temporal persistence, the downscaled variables have to satisfy the spatial dependence naturally observed between the climate variables at different locations. Furthermore, the precipitation spatial intermittency should be fulfilled. Because of the complexity in describing these properties, they are often ignored, which can affect the effectiveness of the hydrologic process modeling. This study is a continuation of the work by Khalili and Nguyen (Clim Dyn 49(7–8):2261–2278. https://doi.org/10.1007/s00382-016-3443-6 , 2017) regarding the multi-site statistical downscaling of daily precipitation series. Different approach of multi-site statistical downscaling based on the concept of the spatial autocorrelation is presented in this paper. This approach has proven to give effective results for multi-site multivariate statistical downscaling of daily extreme temperature time series (Khalili et al. in Int J Climatol 33:15–32. https://doi.org/10.1002/joc.3402 , 2013). However, more challenges are presented by the precipitation variable because of the high spatio-temporal variability and intermittency. The proposed approach consists of logistic and multiple regression models, linking the global climate predictors to the precipitation occurrences and amounts respectively, and using the spatial autocorrelation concept to reproduce the spatial dependence observed between the precipitation series at different sites. An empirical technique has also been involved in this approach in order to fulfill the precipitation intermittency property. The proposed approach was performed using observed daily precipitation data from ten weather stations located in the southwest region of Quebec and southeast region of Ontario in Canada, and climate predictors from the NCEP/NCAR (National Centers for Environmental Prediction/National Centre for Atmospheric Research) reanalysis dataset. The results have proven the ability of the proposed approach to adequately reproduce the observed precipitation occurrence and amount characteristics, temporal and spatial dependence, spatial intermittency and temporal variability.

Chronic Pain, Sleep, and Cognition in Older Adults With Insomnia: A Daily Multilevel Analysis.

The goal of this study was to examine daily associations between sleep and cognition in older adults suffering from insomnia, with or without a history of chronic pain. Sixty older adults with insomnia and a history of chronic pain (HxCP; n = 33, mean age = 69.5 years, standard deviation = 7.8) or no history of chronic pain (NCP; n = 27, mean age = 69.7 years, standard deviation = 7.9) completed 14 days of diaries and actigraphy, measuring sleep onset latency (SOL), wake after sleep onset (WASO), sleep efficiency (SE), and sleep quality. Participants completed daily cognitive measures of processing speed (ie, symbol digit modalities test, SDMT), reasoning (ie, letter series), and verbal memory (ie, word list delayed recall). For HxCP and NCP, associations between sleep parameters, daily pain, depressive symptoms (ie, Beck Depression Inventory, Second Edition scores), and daily cognition, controlling for age, and global cognition were examined through multilevel modeling. For HxCP, greater self-reported WASO was associated with worse next-day SDMT performance, whereas greater actigraphic WASO was associated with better next-day SDMT performance. Greater depression was associated with worse daily letter series performance. Greater self-reported WASO and SE were associated with better next-day delayed recall. For NCP, greater self-reported WASO and depression were associated with better daily SDMT performance, whereas worse daily pain was associated with worse SDMT and delayed recall performance. In older adults with HxCP, improving sleep may benefit lower level cognition, whereas reducing depression may affect higher level cognition. Discrepancies in sleep parameters promote assessment of objective and subjective sleep outcomes when investigating effects of insomnia on cognition. Title: Intraindividual Variability in Sleep and Cognitive Performance in Older Adults (REST), Registry: ClinicalTrials.gov, Identifier: NCT02967185, URL: https://clinicaltrials.gov/ct2/show/NCT02967185.

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.

Spatio-Temporal Interpolated Echo State Network for Meteorological Series Prediction.

Spatio-temporal series prediction has attracted increasing attention in the field of meteorology in recent years. The spatial and temporal joint effect makes predictions challenging. Most of the existing spatio-temporal prediction models are computationally complicated. To develop an accurate but easy-to-implement spatio-temporal prediction model, this paper designs a novel spatio-temporal prediction model based on echo state networks. For real-world observed meteorological data with randomness and large changes, we use a cubic spline method to bridge the gaps between the neighboring points, which results in a pleasingly smooth series. The interpolated series is later input into the spatio-temporal echo state networks, in which the spatial coefficients are computed by the elastic-net algorithm. This approach offers automatic selection and continuous shrinkage of the spatial variables. The proposed model provides an intuitive but effective approach to address the interaction of spatial and temporal effects. To demonstrate the practicality of the proposed model, we apply it to predict two real-world datasets: monthly precipitation series and daily air quality index series. Experimental results demonstrate that the proposed model achieves a normalized root-mean-square error of approximately 0.250 on both datasets. Similar results are achieved on the long short-term memory model, but the computation time of our proposed model is considerably shorter. It can be inferred that our proposed neural network model has advantages on predicting meteorological series over other models.

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.