Monthly Precipitation Time Research Articles

Frequency based imputation of precipitation

Changing climate and precipitation patterns make the estimation of precipitation, which exhibits two-dimensional and sometimes chaotic behavior, more challenging. In recent decades, numerous data-driven methods have been developed and applied to estimate precipitation; however, these methods suffer from the use of one-dimensional approaches, lack generality, require the use of neighboring stations and have low sensitivity. This paper aims to implement the first generally applicable, highly sensitive two-dimensional data-driven model of precipitation. This model, named frequency based imputation (FBI), relies on non-continuous monthly precipitation time series data. It requires no determination of input parameters and no data preprocessing, and it provides multiple estimations (from the most to the least probable) of each missing data unit utilizing the series itself. A total of 34,330 monthly total precipitation observations from 70 stations in 21 basins within Turkey were used to assess the success of the method by removing and estimating observation series in annual increments. Comparisons with the expectation maximization and multiple linear regression models illustrate that the FBI method is superior in its estimation of monthly precipitation. This paper also provides a link to the software code for the FBI method.

Gap Filling of Precipitation Data by SSA - Singular Spectrum Analysis

From the macroscopic standpoint, the precipitation time series is obtained from observation of natural systems rather than in the laboratory. These time series are often full of gaps (missing values) due to the conditions under which the measurements are made. Missing values give rise to various problems in spectral estimation, inhibit statistical analysis and in specifying boundary conditions for numerical models. Hence, gap filling is necessary in environmental science. The aim of this study is to highlight the application of the SSA forecasting algorithm to fill in missing values to real-life time series. It was applied to several monthly precipitation time series recorded over a large savannah area in Brazil. The results are promising and the accuracy and reliability depend on the pattern and relative length of the gaps with respect to the total length of the time series and presence of noise.

Trend analysis of precipitation in Jharkhand State, India

Jharkhand is one of the eastern states of India which has an agriculture-based economy. Uncertain and erratic distribution of precipitation as well as a lack of state water resources planning is the major limitation to crop growth in the region. In this study, the spatial and temporal variability in precipitation in the state was examined using a monthly precipitation time series of 111 years (1901–2011) from 18 meteorological stations. Autocorrelation and Mann–Kendall/modified Mann–Kendall tests were utilized to detect possible trends, and the Theil and Sen slope estimator test was used to determine the magnitude of change over the entire time series. The most probable change year (change point) was detected using the Pettitt–Mann–Whitney test, and the entire time series was sub-divided into two parts: before and after the change point. Arc-Map 9.3 software was utilized to assess the spatial patterns of the trends over the entire state. Annual precipitation exhibited a decreasing trend in 5 out of 18 stations during the whole period. For annual, monsoon and winter periods of precipitation, the slope test indicated a decreasing trend for all stations during 1901–2011. The highest variability was observed in post-monsoon precipitation (77.87 %) and the lowest variability was observed in the annual series (15.76 %) over the 111 years. An increasing trend in precipitation in the state was found during the period 1901–1949, which was reversed during the subsequent period (1950–2011).

Long term variability of the Danube River flow and its relation to precipitation and air temperature

In this work the long cycles and long range dependence of monthly discharge, precipitation and air temperature time series from the Danube River during the years 1901–2006 were analysed using wavelet analysis, with emphasis on wavelet coherence and cross wavelet spectra. All time series were deseasonalized prior to the analysis. Long cycles with 11–15 year periods during almost the whole observed period in discharge and during 1935–1975 in precipitation were found. Furthermore a reappearing four year cycle was found in all discharge time series. No significant long cycles were found in the temperature time series, which on the other hand display long term persistence. The cross-wavelet spectra and the wavelet coherence show strong correlation between the precipitation and discharge spectra in the low frequency intervals. Furthermore, a convolution of precipitation and catchment response function was used to examine the propagation of long cycles from precipitation to discharge. The results show, that the long range dependence in precipitation propagates into discharge and that the precipitation lead in the cross-wavelet spectrum increases with the increasing response time. The results indicate that especially mean monthly precipitation could be used as input variable in order to improve stochastic discharge modelling.

Homogenization of monthly precipitation time series in Croatia

ABSTRACTVarious types of studies require a sufficiently long series of data processed identically over the entire area. For climate analysis, it is necessary that analysed time series are homogeneous, which means that their variations are caused only by variations in weather and climate. Unfortunately, most of the climatological series are inhomogeneous and contain outliers that may significantly affect the analysis results. The 137 stations with precipitation measurement belonging to the meteorological station network governed by the Meteorological and Hydrological Service of Croatia were selected for the present analysis. Most of the data series cover a period from the late 1940s or early 1950s through the year 2010. For quality control and homogenization, an approach based on the software ProClimDB/Anclim was applied. In this study, we describe the results from the quality control and homogenization process for monthly precipitation sums as well as the spatial relationship of precipitation in the Croatian region. The precipitation network in Croatia is fairly homogeneous as only 23% of the 137 analysed stations are found to be inhomogeneous.

Comparison of Gridded Precipitation Time Series Data in APHRODITE and Asfazari Databases within Iran’s Territory

The V1003R1 version of the monthly, seasonal and annual precipitation time series of the Middle East APHRODITE database and Asfazari database within Iranian territory in the time interval 1961 and 2004 were compared with each other. The monthly, seasonal and annual time series of the both databases in most cases show a random behavior and the time series follow a similar pattern with a significant autocorrelation of both databases. Studying cross-correlation between the time series of the two bases indicate that the zero lag significance in the monthly, seasonal and annual time series statistically confirm the coincidence of the peak and fall in the time series of both bases. Wilcoxon Test with 95% confidence confirms significance of the mean difference of the two series. However, there is not enough evidence to confirm the null hypothesis suggesting lack of difference between means of the two series. The statistical-objective analysis of the two bases’ time series indicates that although the series follow a similar course, the estimated precipitation quantity in monthly, seasonal and annual time series of APHRODITE base, except in several monthly time series and in trivial quantities, has been less than the estimated precipitation of Asfazari bases, but the amount of the difference was not constant and did not follow a regular pattern, although this difference has been narrowed in recent years.

Inter-annual temperature and precipitation variations over the Litani Basin in response to atmospheric circulation patterns

This study examines the sensitivity of a mid-size basin’s temperature and precipitation response to different global and regional climate circulation patterns. The implication of the North Atlantic Oscillation (NAO), El Nino Southern Oscillation (ENSO), Indian Monsoon and ten other teleconnection patterns of the Northern Hemisphere are investigated. A methodology to generate a basin-scale, long-term monthly surface temperature and precipitation time series has been established using different statistical tests. The Litani River Basin is the focus of this study. It is located in Lebanon, east of the Mediterranean Basin, which is known to have diverse geophysical and environmental characteristics. It was selected to explore the influence of the diverse physical and topographical features on its hydroclimatological response to global and regional climate patterns. We also examine the opportunity of conducting related studies in areas with limited long-term measured climate and/or hydrological data. Litani's monthly precipitation and temperature data have been collected and statistically extrapolated using remotely sensed data products from satellites and as well as in situ gauges. Correlations between 13 different teleconnection indices and the basin’s precipitation and temperature series are investigated. The study shows that some of the annual and seasonal temperature and precipitation variance can be partially associated with many atmospheric circulation patterns. This would give the opportunity to relate the natural climate variability with the watershed’s hydroclimatology performance and thus differentiate it from other anthropogenic induced climate change outcomes.

Seasonal precipitation trends in the Mediterranean Iberian Peninsula in second half of 20th century

AbstractThis is a study of the changes in annual and seasonal precipitation amounts and variability, during the period 1951–2000, using MOPREDAMES. This dataset includes 1113 complete and homogeneous monthly precipitation time series from the Mediterranean Iberian Peninsula (IP), and corresponds to the five official Spanish hydrological divisions which drain into the Mediterranean Sea. The time series of annual and seasonal precipitation were used to test for trends. The absolute value of the anomaly time series was also tested for trends to identify changes in interannual variability of precipitation. The significance of these changes was assessed using the non‐parametric Spearman rank test. The intensities of observed changes, both on mean values and variability, were estimated by using linear regression techniques. Finally, we analysed the area affected by different trends by using raster maps and spatial statistics, in addition to calculating the global balances for the five hydrological divisions. We detected high variability in precipitation regimes and conditions in the study area; nevertheless, a decrease in seasonal and annual precipitation has predominated in the east of the IP during the second half of the 20th century. On an annual scale, precipitation has diminished over 90.1% of the study area. Additionally, a high percentage of the territory was affected by diminishing precipitation at a seasonal level: 85% (of territory) in summer, 82% in spring, 64% during winter and 61% in autumn. Taking the study area as a whole, seasonal precipitation decreases are ranked as follows: summer (−22.5%), spring (−19.3%), winter (−7.3%), and autumn (−5.2%), with a decrease in the value of the global mean annual precipitation − 12.4%. We also detected an increase of precipitation variability in winter (+23.5%) and summer (+11.4%), and a decrease in autumn and spring (−14.9 and − 16.8%, respectively) with a global mean value of + 7.8%. Copyright © 2008 Royal Meteorological Society

Hydrologic Effects of NDVI Time Series in a Context of Climatic Variability in an Upstream Catchment of the Minjiang River1

Abstract: Increasing regional vegetation activity has been frequently found especially in middle and high latitude and alpine areas, but the effects of which on regional hydrology is still highly uncertain. The Upstream Catchment of Minjiang River is a large mountainous catchment covering 22,919 km2 with a diverse vegetation distribution pattern, including alpine group (A), subalpine group (SA), and temperate and subtropical group (T/ST). The Seasonal Mann‐Kendall test, a nonparametric trend test method, detected consistent upward trends in all groups in monthly accumulated growing degree days (AGDDM) time series from 1982 to 2003, but no significant trend in mean monthly precipitation (MMP) time series in any group. The alpine group had a significant (p = 0.024) upward trend in monthly Normalized Difference of Vegetation Index (NDVI) time series from 1982 to 2003, in contrast, the SA and T/ST groups had decreasing (although not significant) trends. AGDDM plays more important role than MMP in affecting NDVI change in alpine areas, indicating temperature was the main climatic driver. In contrast, water was the main driver for the T/ST group, as indicated by the significant correlation between NDVI and MMP and a weak correlation with AGDDM. Correlation coefficients of NDVI and river flow varied with seasons, mostly negative, especially during the growing season (April to October). A significant (p = 0.025) correlation was found only in August, indicating that an increase in peak‐NDVI decreased high flow significantly. TI‐NDVIc, which was developed in an attempt to track the vegetation change at the catchment scale, accounted for more than 40% of the evapotranspiration increase (r2 = 0.43).

Quality control and homogeneity of Turkish precipitation data

AbstractOutlier trimming and homogeneity checking/correction were performed on the monthly precipitation time series of various lengths from 267 stations in Turkey. Outlier values are usually found during dry summer months, and are concentrated mostly over the southern parts of the country, where the dry period is most pronounced, implying natural extremes rather than wrong measurements. Homogeneity analysis was done using the Standard Normal Homogeneity Test, on an individual monthly basis, which led to many non‐testable series due to lack of reference stations, especially during summer months. Yet, remaining testable months were usually helpful for the assessment of homogenity, revealing a well distributed set of stations that proved to be homogeneous. There were still a number of stations which either could not be tested efficiently, or were classified as inhomogeneous. Lack of metadata is argued to be largely responsible for inefficient homogeneity testing. Copyright © 2008 John Wiley & Sons, Ltd.

Updated world map of the Köppen-Geiger climate classification

Abstract. Although now over 100 years old, the classification of climate originally formulated by Wladimir Köppen and modified by his collaborators and successors, is still in widespread use. It is widely used in teaching school and undergraduate courses on climate. It is also still in regular use by researchers across a range of disciplines as a basis for climatic regionalisation of variables and for assessing the output of global climate models. Here we have produced a new global map of climate using the Köppen-Geiger system based on a large global data set of long-term monthly precipitation and temperature station time series. Climatic variables used in the Köppen-Geiger system were calculated at each station and interpolated between stations using a two-dimensional (latitude and longitude) thin-plate spline with tension onto a 0.1°×0.1° grid for each continent. We discuss some problems in dealing with sites that are not uniquely classified into one climate type by the Köppen-Geiger system and assess the outcomes on a continent by continent basis. Globally the most common climate type by land area is BWh (14.2%, Hot desert) followed by Aw (11.5%, Tropical savannah). The updated world Köppen-Geiger climate map is freely available electronically in the Supplementary Material Section.

한강과 낙동강 유역평균 월강수량의 기후 특성: I. 유역평균 시계열의 변동

The climatological characteristics of the area averaged monthly precipitation over the Han- and Nakdong-river basins were investigated. The data used for this study is monthly precipitation data from 51 meteorological stations for the period of 1954 to 2002. The magnitude of area averaged precipitation in the Han-river basin was about 10% larger than that in the Nakdong-river basin. However, the variability of two monthly precipitation time series exhibited similar characteristics: April precipitation tends to decrease and August precipitation increase significantly, while there was no significant trend for the other months. There were some indications of abrupt change around the 1970`s in the periodicity of precipitation and relationship with El Nino index. September precipitation showed negative correlation with NINO3 index but November precipitation, positive correlation with NINO3 index, indicating a possible connection with the global-scale phenomena.㘮㔰㔻ἀ䍯浰畴敲⁓捩敮捥…⁉湦潲浡瑩潮

SPATIAL SYNCHRONY OF SPRUCE BUDWORM OUTBREAKS IN EASTERN NORTH AMERICA

We investigated the spatial synchrony of outbreaks of the spruce budworm, Choristoneura fumiferana, over much of its outbreak range in eastern North America during the period 1945–1988. Spatial synchrony decreased with distance between local populations and approached zero near 2000 km. Investigation of the synchrony of local population time series with cluster analysis revealed a pattern of geographically distinct blocks of clusters oriented along an east–west axis. Spatial synchrony also was identified in monthly temperature and precipitation time series at 18 weather stations over the same time period and geographical range as the spruce budworm outbreaks. Cross correlations decreased linearly with distance between stations and approached zero near 3000 km and 1800 km, respectively. We developed a spatially explicit lattice model for a single species occupying multiple patches. Within patches, the model had first order logistic dynamics, and patches were linked by dispersal that depended upon their separation distances. Both local and regional stochasticity (i.e., a Moran effect) were present. The modeled lattice had the same spatial configuration as the outbreak region to facilitate investigating the relative effects of a Moran effect and dispersal on spatial synchrony. Simulations with and without a simple region-wide Moran effect and three levels of dispersal did not produce the decrease in spatial synchrony with distance observed with spruce budworm time series. However, when run at the highest dispersal rate, those simulations produced cluster maps similar to that observed for spruce budworm defoliation. Simulations with a spatially autocorrelated disturbance that had either zero or high local variability and three levels of dispersal produced decreases in spatial synchrony with distance similar to that observed in the historical data. When run at the highest dispersal rate, simulations yielded cluster maps similar to the cluster map for defoliation. We discuss the potential significance of the spatially autocorrelated disturbance factor in understanding regional insect outbreaks. We also consider the plausibility of dispersal rates used in our simulations. We suggest in conclusion that spruce budworm outbreaks were synchronized by a combination of a spatially autocorrelated Moran effect and a high dispersal rate.

Interannual variability in western US precipitation

Low-frequency (interannual or longer period) climatic variability is of interest, because of its significance for the understanding and prediction of protracted climatic anomalies. Since precipitation is one of the key variables driving various hydrologic processes, it is useful to examine precipitation records to better understand long-term climate dynamics. Here, we use the multi-taper method of spectral analysis to analyze the monthly precipitation time series (both occurrence and amount) at a few stations along a meridional transect from Priest River, ID to Tucson, AZ. We also examine spectral coherence between monthly precipitation and widely used atmospheric indices, such as the central Northern Pacific (CNP) and southern oscillation index (SOI). This analysis reveals statistically significant ‘signals’ in the time series in the 5–7 and 2–3 year bands. These interannual signals are consistent with those related to El-Niño southern oscillation (ENSO) and quasi-biennial variability identified by others.

Monthly temperature and precipitation fields on a storm‐facing mountain front: Statistical structure and empirical parameterization

This paper presents an analysis of monthly temperature (T) and precipitation (P) time series at 28 climatologic stations on the storm‐facing slope of the Wasatch Range, Utah. The goal is to examine the space‐time structure of T and P and to develop an empirical model incorporating both seasonal and elevation effects. Each time series (T or P) is decomposed into the sum of a long‐term mean, a seasonal cycle, and a residual random process. The seasonal cycle is well determined by the amplitude and phase of a few harmonics, and the residual noise is approximated by a power law form of the variance spectrum. Empirical correlations are found relating the temporal moments of altitude, allowing the construction of a parametric T‐P model as a function of altitude and season. The observed correlations are discussed within the context of the region's synoptic weather patterns. When combined with digital elevation data, the model can be used to estimate seasonal temperature and precipitation fields as input to mountain front hydrologic studies.

Bayesian relative information measure: A tool for analyzing the outputs of general circulation models

Mathematical models that generate scenarios containing no temporal correspondence to time series of actual occurrences are difficult to evaluate. One such class of models consists of atmospheric General Circulation Models (GCM), which have an additional drawback that the temporal and spatial scales of their outputs do not match those of the actual observations of the simulated phenomena. The problem of disparate scales can be ameliorated by aggregating both the model output and the observed data to commensurate scales. However, this approach does not permit quantitative testing at scales less than the least common level of aggregation. The lack of paired observations in the aggregated time series makes standard statistical methods either invalid or ineffective in testing the validity of a GCM. One approach to resolving this quandary is the use of a relative information measure, which is based on the uncertainties contained in the histograms of the aggregations of both the model output and the data base. Each interval of each histogram is analyzed, from a Bayesian perspective, as a binomial probability. For the data‐based histogram, the reciprocal of the sum of the variances of the posterior distributions of probability in each interval is denoted as its information content. For the model‐based histogram, the reciprocal of the sum of the expected mean squared errors of the posterior distributions in each interval likewise is its information content. The expected mean squared error, which accounts for potential biases in the GCM, is computed as the expectation of the squared differences between the data‐based and the model‐based posterior distributions for each interval. The ratio of the information content of the model‐based histogram to that of the data‐based histogram is the relative information of the model. A 5‐year monthly precipitation time series for January at a single node of the current version of the Community Climate Model including the Biosphere Atmosphere Transfer Scheme contains 7.5 % of the information in the most recent 30 years of data in the Climatological Data Set assembled by the National Climate Data Center (NCDC). If the 5‐year simulation were extended, its relative information content could be expected to approach 11.5 %. For July monthly precipitation, the 5‐year simulation contained 13.7 % of the information of the NCDC data base and had a limit of 39.3 % for extremely long simulations. Aggregation of two adjacent cells at the same latitude showed some improvements in relative information, but longitudinal aggregation with two additional adjacent cells showed improvement for January precipitation, but degraded information for July precipitation.

Investigation of Precipitation Droughts by Use of Kriging Method

Precipitation droughts were defined using monthly precipitation time series and different truncation levels—namely, 70%, 80%, 90%, and 95%—in which a 70% truncation level means that 70% of the recorded monthly precipitations are greater than or equal to the value. Conditional probabilities that droughts of certain truncation levels will advance to their next higher levels were estimated at each gage location. There are 21 precipitation stations selected in the Scioto River Basin, in Ohio, for the study of regional drought characteristics. The kriging method based on the minimum unbiased estimation was used to obtain a regional distribution of conditional drought probabilities at each truncated level. Contour lines of conditional probabilities for each truncation level were constructed to show possible drought‐prone distributions for the ungaged region. This reflects the areal severity of water shortage in case of drought.