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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. [ABSTRACT FROM AUTHOR]

This paper points out two procedures for verifying the statistical independence between the historical and actual data series. They are based respectively on the computation of an empirical distance correlation coefficient, and the use of Hamming distance between binary encoded phase signals corresponding to Log-Gabor filtered time-series. Both procedures are applied to the monthly precipitation series recorded during 480 successive months at 49 meteorological stations in Dobrogea region (Romania) and on 4 and 5-valued fuzzified versions of the initial data represented using linguistic labels. The results show that the crisp data and their fuzzified versions cannot support the hypothesis of history-based predictability from their history. Hence, the two statistical independence tests are robust with respect to the k-means fuzzification of data and cross-validate each other, being applicable to any long-term analysis of precipitation series, to any other signals in general, and to their fuzzified versions, as well. [ABSTRACT FROM AUTHOR]

Translating changes in land surface conditions and climate into changes in precipitation, runoff and flood frequencies over a range of catchment scales is a pressing challenge for hydrologic design and flood risk management today. In this paper, we describe a novel approach for modeling areal runoff frequency from heavy-to-extreme precipitation in large urban areas using a simple but general stochastic model for runoff at point scale and bi- and univariate parametric probability distributions for positive point precipitation and areal runoff, respectively. We apply the approach to the Dallas–Fort Worth area using a 22-year historical multisensor precipitation dataset from the National Weather Service to characterize how the different factors that specify the second-order statistics of precipitation, imperviousness and soil water holding capacity at point scale may shape areal runoff frequency, and to assess how changes in precipitation climatology and land surface conditions may change areal runoff frequency as a function of catchment scale and magnitude of precipitation. The results indicate that areal runoff frequency is impacted most significantly by changes in climatological mean and coefficient of variation of positive point precipitation, water holding capacity of soil, imperviousness, and spatial correlation scale of positive point precipitation given the probability of occurrence of heavy-to-extreme precipitation, and that a very small number of low-order statistics of point precipitation may describe areal runoff frequency given the conditional probability distribution models for point precipitation and areal runoff. The approach presented hence offers a parsimonious physically-based alternative to purely numerical approaches based on integrated modeling, or empirical approaches based solely on statistical modeling toward predictive modeling of areal precipitation and runoff frequencies under changing hydroclimatological conditions. [ABSTRACT FROM AUTHOR]

Interannual variability is an important modulator of synoptic and intraseasonal variability in South America. This paper seeks to characterize the main modes of interannual variability of seasonal precipitation and some associated mechanisms. The impact of this variability on the frequency of extreme rainfall events and the possible effect of anthropogenic climate change on this variability are reviewed. The interannual oscillations of the annual total precipitation are mainly due to the variability in austral autumn and summer. While autumn is the dominant rainy season in the northern part of the continent, where the variability is highest (especially in the northeastern part), summer is the rainy season over most of the continent, thanks to a summer monsoon regime. In the monsoon season, the strongest variability occurs near the South Atlantic Convergence Zone (SACZ), which is one of the most important features of the South American monsoon system. In all seasons but summer, the most important source of variability is ENSO (El Niño Southern Oscillation), although ENSO shows a great contribution also in summer. The ENSO impact on the frequency of extreme precipitation events is also important in all seasons, being generally even more significant than the influence on seasonal rainfall totals. Climate change associated with increasing emission of greenhouse gases shows potential to impact seasonal amounts of precipitation in South America, but there is still great uncertainty associated with the projected changes, since there is not much agreement among the models' outputs for most regions in the continent, with the exception of southeastern South America and southern Andes. Climate change can also impact the natural variability modes of seasonal precipitation associated with ENSO. [ABSTRACT FROM AUTHOR]

African precipitation trends are commonly analyzed using short-term data observed over small areas. This study analyzed changes in long-term (1901-2015) annual and seasonal precipitation of high spatial (0.5° × 0.5° grid) resolution covering the entire African continent. To assess an acceleration/deceleration of the precipitation increase/decrease, trend magnitude (mm/year) over the period 1991-2015 was subtracted from that of 1965-1990 to obtain Slope Difference (SD, mm/year). Co-variation of precipitation sub-trends with changes in large-scale ocean-atmosphere conditions was investigated. Regardless of the trend significance, in most parts of Africa, annual precipitation exhibited negative (positive) trends over the period 1965-1990 (1991-2015). Thus, the continent was, on average, recently (from 1991 to 2015) wetter than it was over the period 1965-1990. From 1901 to 2015, the null hypothesis H0 (no trend) was rejected (p < 0.05) for annual precipitation decrease over West Africa especially along the coastal areas near the Gulf of Guinea. The H0 was also rejected (p < 0.05) for the increase in annual and September-November precipitation of some areas along the Equatorial region (such as in Gabon and around Lake Victoria). For both annual and seasonal precipitation, the least SD values in the range − 1 to 1 mm/year were obtained in areas north of 10° N. The SD value went up to about 20 mm/year over the Sahel belt especially for the peak monsoon (June-August season). For the March-May precipitation, positive SD values were obtained in the Western part of Southern Africa. However, negative SD values (around − 5 mm/year) were obtained in the Horn of Africa. Variation in sub-trends of the East African precipitation was found to be driven by changes in Sea Surface Temperature (SST) of the Indian and Atlantic Oceans. Variability in sub-trends of the West African precipitation is linked to changes in SST of the Atlantic Ocean. Changes in sub-trends of the South African precipitation correspond to anomalies in SST from the Pacific and Indian Oceans. Knowledge of precipitation changes and possible drivers is vital for predictive adaptation regarding the impacts of climate variability on hydro- or agro-meteorology. [ABSTRACT FROM AUTHOR]

Precipitation is an important part of the hydrologic cycle, and its complexity is closely related to surface runoff and changing groundwater dynamics, which in turn influences the accuracy of precipitation forecasts. In this study, we used the Lempel-Ziv algorithm (LZA) and a multi-scaling approach to assess precipitation complexity for 1958-2011 by analyzing time series data from 28 gauging stations located throughout Jilin province, China. The spatial distribution of normalized precipitation complexity was measured by LZA, a symbolic dynamics algorithm, and by a multi-scaling approach, which is described by fractals. In addition, the advantages and limitations of these two methods were investigated. The results indicate that both methods are applicable and consistent for calculating precipitation complexity, and that the degree of relief is a primary factor controlling precipitation complexity in the mountainous area; in the plain terrain, however, the prominent influencing factor is climate. [ABSTRACT FROM AUTHOR]

This article deals with the right-tail behavior of a response distribution $$F_Y$$ conditional on a regressor vector $${\mathbf {X}}={\mathbf {x}}$$ restricted to the heavy-tailed case of Pareto-type conditional distributions $$F_Y(y|\ {\mathbf {x}})=P(Y\le y|\ {\mathbf {X}}={\mathbf {x}})$$ , with heaviness of the right tail characterized by the conditional extreme value index $$\gamma ({\mathbf {x}})>0$$ . We particularly focus on testing the hypothesis $${\mathscr {H}}_{0,tail}:\ \gamma ({\mathbf {x}})=\gamma _0$$ of constant tail behavior for some $$\gamma _0>0$$ and all possible $${\mathbf {x}}$$ . When considering $${\mathbf {x}}$$ as a time index, the term trend analysis is commonly used. In the recent past several such trend analyses in extreme value data have been published, mostly focusing on time-varying modeling of location or scale parameters of the response distribution. In many such environmental studies a simple test against trend based on Kendall's tau statistic is applied. This test is powerful when the center of the conditional distribution $$F_Y(y|{\mathbf {x}})$$ changes monotonically in $${\mathbf {x}}$$ , for instance, in a simple location model $$\mu ({\mathbf {x}})=\mu _0+x\cdot \mu _1$$ , $${\mathbf {x}}=(1,x)'$$ , but the test is rather insensitive against monotonic tail behavior, say, $$\gamma ({\mathbf {x}})=\eta _0+x\cdot \eta _1$$ . This has to be considered, since for many environmental applications the main interest is on the tail rather than the center of a distribution. Our work is motivated by this problem and it is our goal to demonstrate the opportunities and the limits of detecting and estimating non-constant conditional heavy-tail behavior with regard to applications from hydrology. We present and compare four different procedures by simulations and illustrate our findings on real data from hydrology: weekly maxima of hourly precipitation from France and monthly maximal river flows from Germany. [ABSTRACT FROM AUTHOR]

Reservoir storage plays an important role in water supply during the dry season when precipitation is insufficient. In a watershed where the streams are controlled by reservoirs, drought occurrences depend on not only precipitation variations but also reservoir regulation. In this study, the joint dependence structure of the reservoir storage and its relevant variables of precipitation and/or upstream outflow were analyzed for two cascade reservoirs in a headwater basin of the Huaihe River, China. Correlation analysis indicates that the reservoir storage in October (the end of the wet season) depends highly on the regional precipitation at time scales of several months, e.g., 7 months for the upstream and 9 months for the downstream. Additionally, the downstream storage is correlated with outflow from the upstream reservoir at the 5-month timescale significantly. For estimation of the joint probability of pairs of the storage and its relevant variables, univariate marginal distributions and bivariate copula were appropriately selected in terms of statistical tests. The bivariate return period of $$T(X < x,Y < y)$$ and $$T(X \le x,Y \ge y)$$ and the conditional probability of $$P(Y \ge y|X \le x)$$ were estimated by using the selected Clayton copula. The results from contour lines of the bivariate return period demonstrate that the probability of drought occurrences affected by both reservoir storage and precipitation/outflow is smaller than that by either of the variables. Meanwhile, the concurrent drought probability between precipitation and reservoir storage in the upstream is higher than that in the downstream. The estimated conditional probability offers useful information on how much the regular storage could be remained under some specified drought levels of precipitation/upstream outflow. Therefore, the results are helpful for improving the operation strategies of the cascade reservoirs for the adaptive management of drought under different climate variations. [ABSTRACT FROM AUTHOR]

We introduce a stochastic weather generator for the variables of minimum temperature, maximum temperature and precipitation occurrence. Temperature variables are modeled in vector autoregressive framework, conditional on precipitation occurrence. Precipitation occurrence arises via a probit model, and both temperature and occurrence are spatially correlated using spatial Gaussian processes. Additionally, local climate is included by spatially varying model coefficients, allowing spatially evolving relationships between variables. The method is illustrated on a network of stations in the Pampas region of Argentina where nonstationary relationships and historical spatial correlation challenge existing approaches. [ABSTRACT FROM AUTHOR]

In this study, we used the statistical downscaling model (SDSM) to estimate mean and extreme precipitation indices under present and future climate conditions for Shikoku, Japan. Specifically, we considered the following mean and extreme precipitation indices: mean daily precipitation, R10 (number of days with precipitation >10 mm/day), R5d (annual maximum precipitation accumulated over 5 days), maximum dry-spell length (MaDSL), and maximum wet-spell length (MaWSL). Initially, we calibrated the SDSM model using the National Center for environmental prediction (NCEP) reanalysis dataset and daily time series of precipitation for ten locations in Shikoku which were acquired from the surface weather observation point dataset. Subsequently, we used the validated SDSM, using data from NCEP and outputs form general circulation models (GCM), to predict future precipitation indices. Specifically, the HadCM3 GCM was run under the special report on emissions scenarios (SRES) A2 and B2 scenarios, and the CGCM3 GCM was run under the SRES A2 and A1B scenarios. The results showed that: (1) the SDSM can reasonably be used to simulate mean and extreme precipitation indices in the Shikoku region; (2) the values of annual R10 were predicated to decrease in the future in northern Shikoku under all climate scenarios; conversely, the values of annual R10 were predicted to increase in the future in the range of 0-15 % in southern and western Shikoku. The values of annual MaDSL were predicted to increase in northern Shikoku, and the values of annual MaWSL were predicted to decrease in northeastern Shikoku; (3) the spatial variation of precipitation indices indicated the potential for an increased occurrence of drought across northeastern Shikoku and an increased occurrence of flood events in the southwestern part of Shikoku, especially under the A2 and A1B scenarios; (4) characteristics of future precipitation may differ between the northern and southern sides of the Shikoku Mountains. Regional variations in extreme precipitation indices were not notably evident in the B2 scenario compared to the other scenarios. [ABSTRACT FROM AUTHOR]

This study examines the temporal patterns of precipitation and the influence of large-scale climate anomalies in the Pearl River basin (South China), with particular focus on sub-basin scale. Three popular data analysis techniques are employed: (1) wavelet analysis; (2) principal component analysis (PCA); and (3) rank correlation method. With due consideration to hydrologic factors, water resources activities, and large-scale climate data, the entire basin is divided into ten sub-basins and the analysis is performed on monthly data. The wavelet analysis reveals discernible differences in temporal scales of fluctuation embedded in the monthly precipitation anomalies over the basin. The PCA delineates three coherent regions exhibiting similar distribution of variability across scales. Analysis of linkages between precipitation and teleconnection patterns using cross-wavelet transform and wavelet coherence reveals that the dominant variabilities of precipitation are essentially depicted by the Indian Ocean Dipole (IOD), especially for the central and eastern part of the Pearl River basin. On the influence of El Niño-Southern Oscillation (ENSO) signal on precipitation, more significant correlation is detected for the eastern part of the basin, long-term relationships (within 4-8 years band) are found for the western part of the basin, while the central part seems to be acting as a transition zone. Rank correlations of scale-averaged wavelet power between regional precipitation and climate indices for the dominant low-frequency variability band (0.84-8.40 years) provide further support to the different precipitation-climate relationships for different regions over the basin. The present results provide valuable information towards: (1) improving predictions of extreme hydroclimatic events in the Pearl River basin, based on their relationships with IOD or ENSO; and (2) devising better adaptation and mitigation strategies under a future changing climate. [ABSTRACT FROM AUTHOR]

Among other sources of uncertainties in hydrologic modeling, input uncertainty due to a sparse station network was tested. The authors tested impact of uncertainty in daily precipitation on streamflow forecasts. In order to test the impact, a distributed hydrologic model (PRMS, Precipitation Runoff Modeling System) was used in two hydrologically different basins (Animas basin at Durango, Colorado and Alapaha basin at Statenville, Georgia) to generate ensemble streamflows. The uncertainty in model inputs was characterized using ensembles of daily precipitation, which were designed to preserve spatial and temporal correlations in the precipitation observations. Generated ensemble flows in the two test basins clearly showed fundamental differences in the impact of input uncertainty. The flow ensemble showed wider range in Alapaha basin than the Animas basin. The wider range of streamflow ensembles in Alapaha basin was caused by both greater spatial variance in precipitation and shorter time lags between rainfall and runoff in this rainfall dominated basin. This ensemble streamflow generation framework was also applied to demonstrate example forecasts that could improve traditional ESP (Ensemble Streamflow Prediction) method. [ABSTRACT FROM AUTHOR]