Monthly Precipitation Series Research Articles

Water level changes in Polish lakes during 1976–2010

The paper presents the analysis of tendencies in water level changes in 32 lakes in Poland during 1976–2010. Series of monthly, seasonal, and annual precipitation and air temperature for 9 meteorological stations were also studied. The trend analysis for all of the studied series of water levels in lakes showed high spatial and temporal variability. Series of annual water levels in the case of 6 lakes showed statistically significant increasing tendencies, and in 7 lakes, significant decreasing trends. Series of annual amplitudes in the majority of lakes (22) showed a decreasing trend, but they were statistically significant only in three cases. The tendencies for air temperature fluctuations are more statistically significant than precipitation. The key role in determining water level changes is played by local factors, particularly including human economic activity, obscuring the effect of natural factors on water level changes. The paper describes cases of changes in water levels in lakes under anthropopressure related to among others: agricultural irrigations, hydropower infrastructure, water transfers, navigation, or mining.

Geostatistical and Spatial Statistical Modelling of Precipitation Varia-tions in Iran

This study examines the geostatistics and spatial relationships between annual, seasonal and monthly rainfall in Iran for the period 1975-2014. Precipitation variation models were com-pared in Iran deriving from six geostatis-tical, four regression and five spatial models, using monthly data. A geostatistical and spatial statistical analysis consisting of two measurement sub-models was created based on monthly accumulated precipitation; the data was the monthly and seasonal amounts for the period 1975 - 2014, and were estimated from 140 stations. The results of the new geostatistical-spatial statistical analysis model showed that average monthly precipitation se-ries in Iran were revealed to follow Gaussian distribution given their histogram plots and closeness of their mean and median values. On average, monthly precipitation ranged from 3.22 mm in April to 47.157 mm in December in Iran. The suitable interpolation of monthly precipitation indicates that the accuracy of spring precipitation interpolation (RMSE=0.558) can be applied by IDW (Cross-validation). The kriging interpolation of monthly precipitation indicates that the accuracy of autumn precipitation interpolation (RMSE=0.0822) can be applied by probability kriging of autumn precipitation. The empirical Bayesian kriging interpolation of monthly precipitation indicates that the accuracy of autumn precipitation interpolation (RMSE=0.357) can be applied by empirical Bayesian kriging of autumn precipitation. The temporal-spatial distribution of the precipitation station locations has been studied using the ANN tool of the spatial statistics toolbox of ArcGIS 10.3. Based on the calculated Moran’s Index, approximately all months’ precipitation (with the exception of February) has the monthly spatial distribution of the clustered type. The High/Low Clustering of stations’ monthly precipitation has been studied using the HLC tool. Based on the calculated g-index, approximately all months’ precipitation (except for February and March) has the monthly spatial distribution of the high-clusters type.

Beyond annual streamflow reconstructions for the Upper Colorado River Basin: A paleo‐water‐balance approach

AbstractIn this paper, we present a methodology to use annual tree‐ring chronologies and a monthly water balance model to generate annual reconstructions of water balance variables (e.g., potential evapotranspiration (PET), actual evapotranspiration (AET), snow water equivalent (SWE), soil moisture storage (SMS), and runoff (R)). The method involves resampling monthly temperature and precipitation from the instrumental record directed by variability indicated by the paleoclimate record. The generated time series of monthly temperature and precipitation are subsequently used as inputs to a monthly water balance model. The methodology is applied to the Upper Colorado River Basin, and results indicate that the methodology reliably simulates water‐year runoff, maximum snow water equivalent, and seasonal soil moisture storage for the instrumental period. As a final application, the methodology is used to produce time series of PET, AET, SWE, SMS, and R for the 1404–1905 period for the Upper Colorado River Basin.

Precipitation Complexity Measurement Using Multifractal Spectra Empirical Mode Decomposition Detrended Fluctuation Analysis

The stability of current methods of complexity measurement are generally Inefficient. In this study, multifractal spectra (MFS) analysis, which depends on empirical mode decomposition detrended fluctuation analysis (EMD–DFA), was used to measure the complexity of the monthly precipitation series from 1964 to 2013 (50 years) of 11 districts in Harbin, Heilongjiang Province, China. By comparing the anti-noise capability of MFS–EMD–DFA with that of conventional complexity measurement approaches, such as sample entropy, Lempel–Ziv complexity, and approx mate entropy, it was established that MFS–EMD–DFA has greater robustness in anti-noise jamming, and thus it could be applied more widely. The precipitation series complexity strength map of the 11 regions was drawn using a geographical information system. This study analyzed the correlation between precipitation and some meteorological factors and then ranked their strengths. The results showed that many meteorological factors have strong connections with the regional precipitation series in the study area. This study provided a solid foundation for further extraction of hydrological information in Harbin and proposed a new method for complexity analysis. The novel MFS–EMD–DFA approach could also be applied to the analysis of multifractal characteristics as well as complexity measurement in various other disciplines.

Analysis of precipitation cycles based on MEM in the Yellow River basin

Abstract. Using the monthly precipitation series of 32 meteorological stations in the Yellow River basin from 1951 to 2003, the precipitation cycles were discussed using the Maximum Entropy Method (MEM), the spatial distribution of the precipitation cycles were analysed, and the possible driving factors of the cycles investigated. The results show that the precipitation in the Yellow River has decadal (60a), inter-decadal (25a and 14a) and inter-annual cycles (9a and 3a). The main oscillations over the whole basin are 3a and 9a. There are clearer inter-decadal variations in the riverhead area with much greater water resources, and north of the region of LanHe main stream. The decadal signals are detected in the inner area with less precipitation and Wei River basin. These differences are possibly related to some physical processes, such as the mutual action of sea and atmosphere, and solar activities.

A model for estimating rains’ area, using the dependence of the time correlation of sites’ monthly precipitation totals on the distance between sites

Based on the monthly precipitation series from 16 sites (in the Polish Carpathian Mountains), of 132 years’ length, a relatively precise scatterplot of correlation coefficients between sites versus distance between sites is obtained. The “rains” of Gaussian shape, in the spatial sense, are a good model, which produces a scatterplot very closely resembling the observed one. The essential parameter of the model is the area covered by the modeled rains, which results to be of order 30–50 km, though with about a twice lower value for the N-S direction.

The Application of Box–Cox Transformation to Determine the Standardised Precipitation Index (SPI), the Standardised Discharge Index (SDI) and to Identify Drought Events: Case Study in Eastern Kujawy (Central Poland)

Abstract The article presents the results of research into the transformation of series of hydro-meteorological data for determining dry periods with the Standardised Precipitation Index (SPI) and the Standardised Discharge Index (SDI). Time series from eight precipitation stations and five series of river discharge data in Eastern Kujawy (central Poland) were analysed for 1951–2010. The frequency distribution of the series for their convergence with the normal distribution was tested with the Shapiro–Wilk test and homogeneity with the Bartlett's test. The transformation of the series was done with the Box–Cox technique, which made it possible to homogenise the series in terms of variance. In Poland, the technique has never been used to determine the SPI. After the transformation the distributions of virtually all series complied with the normal distribution and were homogeneous. Moreover, a statistically significant correlation between the δ transformation parameter and the skewness of the series of monthly precipitation was observed. It was similar for the series of mean monthly discharges in the winter half-year and the hydrological year. The analysis indicates an alternate occurrence of dry and wet periods both in case of precipitation and run-offs. Drought periods coincided with low flow periods. Thus, the fluctuations tend to affect the development of agriculture more than long-term ones.

Spring-summer droughts in the Czech Land in 1805-2012 and their forcings

Drought is an extreme meteorological phenomenon involving serious economic consequences. In the Czech Lands, it is reflected in significant reductions in agricultural productivity, lack of water for hygiene and industry, and impacts of forest management. Mean monthly temperature and precipitation series created for the Czech Lands for 1805–2012 were used to calculate spring (MAM) and summer (JJA) drought indices (SPI-1, SPI-12, SPEI-1, SPEI-12, Z-index and PDSI), which were then used for further analyses. Fluctuations in drought indices demonstrate an increasing long-term dryness in the Czech climate, statistically significant for SPEI-12 and PDSI in MAM and JJA (in MAM as well for SPEI-1 and Z-index). A significant concentration of drought episodes before 1880 may be attributed to a lack of precipitation, whereas the droughts of recent decades (particularly 2004–2012) are more strongly related to high temperatures. The effects of droughts are reflected in significant reductions in winter wheat and spring barley yields in the eastern province of Moravia. Regression analysis of drought forcings discloses the importance of the North Atlantic Oscillation phase and the aggregate effect of anthropogenic forcing (driven largely by increases in CO2 concentration). Their magnitude of influence varies strongly with the type of drought index and season of the year. Other factors, such as solar irradiation and the Southern Oscillation phase make only minor contributions to drought variability. The effects of volcanic activity and the Atlantic Multidecadal Oscillation are even weaker and statistically insignificant.

Phytoclimatic map of Calabria (Southern Italy)

This study updates Pavari's phytoclimatic map in a region of southern Italy, Calabria (between 37° 50′ and 40° 10′ N and 15° 30′ and 17° 20′ E). A set of homogeneous monthly precipitation and temperature series registered in Calabria and relative to the period 1916–2010 were used. First, temperature data from 28 thermometric stations, over a 30-year period, were homogenized and processed. A multiple regression analysis was carried out between temperature data (annual average temperature, average of minimum temperature and average temperature of the coldest month of the year) as dependent variables and some geographical variables (altitude of the station, continentality and solar radiation) as independent variables. Then, temperature maps of the region were calculated and, finally, temperature and rainfall maps were intersected to define Pavari's phytoclimatic map. The results obtained in this study allow the identification and the delineation of three phytoclimatic belts.

The atmospheric water as a triggering factor for earthquakes in the central Virginia seismic zone

Even though central Virginia is far from the nearest plate boundaries, the region is well-known for minor-to-moderate shocks, which have occurred frequently since at least the eighteenth century. Many of its people have experienced small earthquakes, while infrequent larger ones have caused damage. The largest destructive earthquake (magnitude 5.8) in this seismic zone was recorded in August 2011. Smaller earthquakes that cause little or no damage are felt each year or two. It is difficult to link the earthquakes of this zone to known small faults which are numerous, deeply buried and do not show up at the surface. The mean earthquake depth since 1960 is 6.7 km. On the other hand, central Virginia is a big collector and transporter of precipitation water, which flows to the Atlantic Ocean through the James River and its tributaries. There are about 2,000 abandoned mining sites in Virginia with underground openings that can facilitate the interception and conveyance of surface water. This paper presents evidence that seismic activity in certain zones can be associated clearly with the hydrological effects of abundant precipitation. Such effects can increase tectonic stress, which surpasses the marginal amount when an earthquake occurs. We analyze the cross-correlation between precipitation or water discharge in the rivers and earthquake occurrence in the central Virginia seismic zone. This correlation is examined both over a long-term span (57–92 years) and with regard to individual cases in which earthquakes have followed the occurrence of intense hydrological phenomena such as torrential rainfall or hurricanes. As we probe for a correlation between earthquake time series for central Virginia and the monthly precipitation series at hydrometeorological stations located in the zone, we observe that the best cross-correlation is obtained for a time period of 3 months. The same time period applies to certain historical earthquakes that were preceded by large amounts of precipitation. These results support the hydroseismicity hypothesis, which points to the role of water in the generation of intraplate seismicity.

Global Multimodel Analysis of Drought in Runoff for the Second Half of the Twentieth Century

Abstract During the past decades large-scale models have been developed to simulate global and continental terrestrial water cycles. It is an open question whether these models are suitable to capture hydrological drought, in terms of runoff, on a global scale. A multimodel ensemble analysis was carried out to evaluate if 10 such large-scale models agree on major drought events during the second half of the twentieth century. Time series of monthly precipitation, monthly total runoff from 10 global hydrological models, and their ensemble median have been used to identify drought. Temporal development of area in drought for various regions across the globe was investigated. Model spread was largest in regions with low runoff and smallest in regions with high runoff. In vast regions, correlation between runoff drought derived from the models and meteorological drought was found to be low. This indicated that models add information to the signal derived from precipitation and that runoff drought cannot directly be determined from precipitation data alone in global drought analyses with a constant aggregation period. However, duration and spatial extent of major drought events differed between models. Some models showed a fast runoff response to rainfall, which led to deviations from reported drought events in slowly responding hydrological systems. By using an ensemble of models, this fast runoff response was partly overcome and delay in drought propagating from meteorological drought to drought in runoff was included. Finally, an ensemble of models also allows for consideration of uncertainty associated with individual model structures.

Spatiotemporal transmission and determinants of typhoid and paratyphoid fever in Hongta District, Yunnan Province, China.

BackgroundTyphoid and paratyphoid fever are endemic in Hongta District and their prevalence, at 113 per 100,000 individuals, remains the highest in China. However, the exact sources of the disease and its main epidemiological characteristics have not yet been clearly identified.Methods and FindingsNumbers of typhoid and paratyphoid cases per day during the period 2006 to 2010 were obtained from the Chinese Center of Disease Control (CDC). A number of suspected disease determinants (or their proxies), were considered for use in spatiotemporal analysis: these included locations of discharge canals and food markets, as well as socio-economic and environmental factors. Results showed that disease prevalence was spatially clustered with clusters decreasing with increasing distance from markets and discharge canals. More than half of the spatial variance could be explained by a combination of economic conditions and availability of health facilities. Temporal prevalence fluctuations were positively associated with the monthly precipitation series. Polluted hospital and residential wastewater was being discharged into rainwater canals. Salmonella bacteria were found in canal water, on farmland and on vegetables sold in markets.ConclusionDisease transmission in Hongta district is driven principally by two spatiotemporally coupled cycles: one involving seasonal variations and the other the distribution of polluted farmland (where vegetables are grown and sold in markets). Disease transmission was exacerbated by the fact that rainwater canals were being used for disposal of polluted waste from hospitals and residential areas. Social factors and their interactions also played a significant role in disease transmission.

Statistical analysis of precipitation time series in Dobrudja region

Temporal characteristics of precipitation evolution in Dobrudja, a region situated in the Southeastern part of Romania, are analyzed in this article, using a data base of ten monthly series, collected in the period January 1965-December 2005. This paper describes different methods to detect the break points existence in order to detect changes in evolution of the monthly precipitation series. The study indicates a constant trend of precipitation before 2000 and an increasing one after 2000, in concordance with the predictions for this region.

Avaliação das condições hídricas por meio do panorama de mudanças climáticas em cabaceiras-pb

The aim of this study was to evaluate the behavior of water conditions due to global warming, examining future water availability in the municipality of Cabaceiras. To investigate the water behavior we performed the calculation of climatic water balance using the methods of Thornthwaite and Mather using monthly precipitation series for the period 1926 to 2011 and temperature data estimated by the software T-Esteem. The results were applied to the medium conditions and then used to scenarios monthly precipitation (reduction of 10.0% and 20.0%) and temperature (increase of 1.0 ° C to 4.0 ° C) to extreme scenarios CO2 emission, optimistic and pessimistic at the same time. It was observed that under drought stress occurs on average every month for the cases of the climatic water balance and simulations reductions of 10 and 20%. The value of annual potential evapotranspiration is approximately four times that of precipitation, evaporation and the value of real equals the annual precipitation. In the water balance simulation with reduced rainfall by 10% and increased by 1.0 º C in the average temperature there is a reduction in rainfall and evaporative, while potential evapotranspiration continues at extreme elevation, the same description can be redone for the scenario with rainfall reduction by 20% and increase of 4.0 ° C mean temperature.

Analysis of precipitation in Central México: Trends, self-affinity and important frequencies

Long–term monthly precipitation series registered at 56 meteorological stations located within Mexico’s state of Zacatecas were analyzed in order to identify their trends. We analyzed the power spectrum signals of precipitation anomaly series in order to identify their important frequencies and its possible connection with periodic phenomena. We found negative linear trends for 28 out of 56 precipitation series and 28 of the 56 were increasing linear trends. Only one of the increasing trends was significant at p<0.05. Noise in 24 out of 56 of the monthly precipitation anomaly series was found to have persistent behavior, anti–persistent behavior was found in 26 out of 56 of the monthly precipitation anomaly series, and random walk noise was found in 6 out of 56 of monthly precipitation anomaly series. Additionally, we found important frequencies that could be related with yearly cycle, quasi-biennial cycle, ‘EL Nino Sothern Oscillation’ phenomena and sunspot cycle. Key words: Linear trends, fractal dimension, power spectrum density, yearly cycle, quasi–Biennial cycle, ENSO, sunspot cycle.

Baseline Probabilities for the Seasonal Prediction of Meteorological Drought

AbstractThe inherent persistence characteristics of various drought indicators are quantified to extract predictive information that can improve drought early warning. Predictive skill is evaluated as a function of the seasonal cycle for regions within North America. The study serves to establish a set of baseline probabilities for drought across multiple indicators amenable to direct comparison with drought indicator forecast probabilities obtained when incorporating dynamical climate model forecasts. The emphasis is on the standardized precipitation index (SPI), but the method can easily be applied to any other meteorological drought indicator, and some additional examples are provided. Monte Carlo resampling of observational data generates two sets of synthetic time series of monthly precipitation that include, and exclude, the annual cycle while removing serial correlation. For the case of no seasonality, the autocorrelation (AC) of the SPI (and seasonal precipitation percentiles, moving monthly averages of precipitation) decays linearly with increasing lag. It is shown that seasonality in the variance of accumulated precipitation serves to enhance or diminish the persistence characteristics (AC) of the SPI and related drought indicators, and the seasonal cycle can thereby provide an appreciable source of drought predictability at regional scales. The AC is used to obtain a parametric probability density function of the future state of the SPI that is based solely on its inherent persistence characteristics. In addition, a method is presented for determining the optimal persistence of the SPI for the case of no serial correlation in precipitation (again, the baseline case). The optimized, baseline probabilities are being incorporated into Internet-based tools for the display of current and forecast drought conditions in near–real time.

Chuvas na região Centro-Oeste e no Estado do Tocantins: análise histórica e tendência futura

Meteorological phenomena are the result of climatic variations which can be observed during a series in order to detect trends or changes in hydrometeorological time series. Aiming to analyze the rainfall and the future trend of rainfall in the Midwest and the state of Tocantins, a study was carried out with linear regression and measures of central tendency and dispersion of rainfall from rain. We used 261 rainfall stations with 30 years of data. We used the average monthly and annual precipitation series of historical rainfall data obtained by the National Hydrometeorology, submitted to a preliminary analysis. The statistic calculated the mean, median and standard deviation of rainfall time needed to check the parameters, and observed the dispersion of the sample. There is a downward trend in average rainfall between 1977 and 2006 for the Midwest region and the State of Tocantins, in the order of 6.5%. Only the months of March and April showed growth of precipitation. Throughout this paper presents the temporal distribution, linear regression analysis and statistics, future trends, and monthly and annual variation of rainfall from the historical average for the Midwest region and the State of Tocantins.

Computation of Drought Index SPI with Alternative Distribution Functions

The Standardized Precipitation Index (SPI) is widely used as drought meteorological index, to identify the duration and/or severity of a drought. The SPI is usually computed by fitting the gamma probability distribution to the observed precipitation data. In this work, the possibility to calculate SPI by fitting to the precipitation data the normal and the log-normal probability distributions was studied. For this purpose, 19 time series of monthly precipitation of 76 years were used, and the assumption that the gamma probability distribution would provide better representation of the precipitation data than log-normal and normal distributions, at various time scales (1, 3, 6, 12 and 24 months) was tested. It is concluded that for SPI of 12 or 24 months, the log-normal or the normal probability distribution can be used for simplicity, instead of gamma, producing almost the same results.

Temperature and precipitation fluctuations in the Czech Republic during the period of instrumental measurements

The history of early meteorological observations using instruments in the Czech Lands is described (the longest temperature series for Prague-Klementinum starts in 1775, precipitation series for Brno in 1803). Using the PRODIGE method, long-term monthly temperature and precipitation series from selected secular stations were homogenised (for 10 and 12 stations, respectively). All the seasonal and annual temperature series for the common period 1882–2010 show a significant positive linear trend with accelerated warming from the 1970s onwards. No significant linear trends were disclosed in the series of seasonal and annual precipitation totals. Correlation coefficients between the Czech series analysed decrease as distances between measuring stations increase. A sharper decrease of correlations for precipitation totals displays much weaker spatial relationships than those for mean temperatures. The highest correlations between all stations appeared in 1921–1950, the lowest in 1891–1920 (temperature) and 1981–2010 (precipitation). Wavelet analysis reveals that very distinct annual cycles as well as the slightly weaker semi-annual ones are better expressed for temperature series than for precipitation. Statistically significant cycles longer than 1 year are temporally unstable and sporadic for precipitation, while in the temperature series cycles of 7.4–7.7 and 17.9–18.4 years were recorded as significant by all stations in 1882–2010 (quasi-biennial cycle of 2.1–2.2 years for half the stations). Czech homogenous temperature series correlate best with those of the Northern Hemisphere for annual, spring and summer values (with significant correlation coefficients between 0.60 and 0.70), but this relation is temporally unstable. Circulation indices, such as the North Atlantic Oscillation Index (NAOI) and the Central European Zonal Index (CEZI), may explain the greater part of Czech temperature variability, especially from December to March and for the winter; however, this relationship is much weaker, or even random, for precipitation series. Further, relationships with the Southern Oscillation Index (SOI) are weak and random. Relatively weak coincidences exist between statistically significant cycles in the Czech series and those detected in NAOI, CEZI and SOI series.

A monthly precipitation database for Spain (1851–2008): reconstruction, homogeneity and trends

Abstract. The compilation and reconstruction of a dataset integrated by 66 long monthly precipitation series, covering mainland Spain and the Balearic Islands, is presented. The reconstruction is based on the hypothesis that the cessation of data recording at one observatory is followed by the establishment of a new observatory very close to the closed one. In order to detect and adjust for possible multiple change points or shifts that could exist in the precipitation series, the R-package CLIMATOL V2.0 is used. This method enables to take advantage of the whole historical Spanish precipitation network in the detection and correction of inhomogeneities. The analysis of annual precipitation trends indicate a high temporal variability. Negative trends dominate for the period 1951–2008 but not for all observatories. On the other hand, positive trends can be detected in the northern Spain for 1902–2008.