Accuracy assessment of optimum interpolation in multichannel precipitation analysis

The accuracy of an optimum interpolation technique in filling missing values in multichannel (or multisite) hydrologic series containing time-coincident data gaps is examined. The applied methodology is based on the maximum entropy method (MEM) of spectral estimation or multivariate autoregressive modeling and heavily depends upon the properties of multichannel prediction error filter (PEF). Six precipitation time series spatially located within a hydrologic basin are used and time-coincident artificial gaps are created in all six series. The performance of the technique is assessed by comparing the filled-in series to the observed and by employing spectral analysis. The results reveal the usefulness of the method in multichannel hydrologic analysis.     

Nonlinear dynamics and recurrence analysis of extreme precipitation for observed and general circulation model generated climates

A statistical framework based on nonlinear dynamics theory and recurrence quantification analysis of dynamical systems is proposed to quantitatively identify the temporal characteristics of extreme (maximum) daily precipitation series. The methodology focuses on both observed and general circulation model (GCM) generated climates for present (1961–2000) and future (2061–2100) periods which correspond to 1xCO₂ and 2xCO₂ simulations. The daily precipitation has been modelled as a stochastic process coupled with atmospheric circulation. An automated and objective classification of daily circulation patterns (CPs) based on optimized fuzzy rules was used to classify both observed CPs and ECHAM4 GCM‐generated CPs for 1xCO₂ and 2xCO₂ climate simulations (scenarios). The coupled model ‘CP‐precipitation’ was suitable for precipitation downscaling. The overall methodology was applied to the medium‐sized mountainous Mesochora catchment in Central‐Western Greece. Results reveal substantial differences between the observed maximum daily precipitation statistical patterns and those produced by the two climate scenarios. A variable nonlinear deterministic behaviour characterizes all climate scenarios examined. Transitions’ patterns differ in terms of duration and intensity. The 2xCO₂ scenario contains the strongest transitions highlighting an unusual shift between floods and droughts. The implications of the results to the predictability of the phenomenon are also discussed. Copyright © 2013 John Wiley & Sons, Ltd.     

Variation analysis of precipitation during past 286 years in Beijing area,China, using non‐parametric test and wavelet analysis

Non‐parametric methods including Mann–Kendall (M–K) test, continuous wavelet transform (CWT) and discrete wavelet transform analysis are applied in this paper to detect the trend and periodic trait of precipitation data series in Beijing area where the data set spans nearly 300 years from 1724 to 2009. First, the trend of precipitation variables is elaborated by the M–K test (Sequential M–K test). The results show that there is an increasing trend (the value of this trend is 1.98) at the 5%‐significance level and there are not turning points in the whole data series. Then, CWT and wavelet variance are used to check for significant periodic characteristics of data series. In the plots of wavelet transform coefficients and figure of wavelet variance, some periodic events affect the trend of the annual total precipitation series in Beijing area. 85‐year, 35‐year and 21‐year periodic events are found to be the main periodic series of long‐term precipitation data, and they are all statistically significant. Moreover, the results of non‐parametric M–K test are exhibited on seven different combinations of discrete wavelet components. D5 (32‐year periodicity) periodic component is the effective and significant component on data. It is coincident with the result (35‐year periodic event as one part of main periodicity) by using CWT analysis. Moreover, approximation mode shows potential trend of the whole data set because it is the residuals as all periodicities are removed from data series. Thus, the mode A + D5 is responsible for producing a real basic structure of the trend founded on the data. Copyright © 2012 John Wiley & Sons, Ltd.     

Analysis of precipitation appearance in time

This paper analyses precipitation occurrence in time. The calculations were made with the data from continuous precipitation measurements by two automatic float‐type rainfall recorders (Hellmann type) during the 10‐year period 1984–1993. The measurement increment was 5 minutes with 0.1 mm resolution. The effect of different time increments on precipitation duration in a year has been researched. Calculations show that a smaller time increment diminishes the duration of precipitation in a year. If a 5‐minute time increment is used for calculation, the precipitation duration is about 3% of the year. If a 24‐hour time increment is used, the precipitation duration is 33% of the year. The real mean duration of yearly precipitation has been evaluated as 216 hours, that is 2.47% of the year. The appearance of a precipitation intensity higher than 0·2 mm/min has been researched during the year and over 24 hours. Analyses show that intensive precipitation appears during the warmer part of the year, from June to August. The precipitation distribution is not uniform over a day. In the city of Zagreb, where both rain gauge stations are situated, in 90% of the cases, the precipitation intensity higher than 1·2 mm/min falls during the night, from 9 p.m. to 1 a.m., at the same time causing floods. Copyright © 1999 John Wiley & Sons, Ltd.     

GRACE月时变模型缺失值的填补方法与精度分析

长时间序列的GRACE时变重力场对研究全球地表质量变化具有重要的意义.部分月份的GRACE卫星观测数据质量不佳导致了相应月份的时变重力场模型缺失,为了保持时变重力场模型的连续性,可采用一定的插值方法填补.本文以ITSG-Grace2016时变重力场模型序列为研究对象,详细分析了三次样条插值、三次埃尔米特插值和三次多项式插值等3种方法用于填补GRACE时变模型序列的精度,实验结果表明:(1)利用3种插值方法获取空缺1个月或连续空缺2个月的时变重力场模型时,插值时变模型与实测时变模型比较,阶误差均较小,且三次埃尔米特插值的精度稍好;(2)利用插值时变模型分析区域质量变化时,在空缺1个月数据的情况下,插值时变模型与实测时变模型符合度较高,但在连续空缺2个月数据的情况下,插值时变模型与实测时变模型的计算结果差异较大,说明利用阶误差评定模型精度具有一定局限性;(3)对区域质量变化的趋势项进行分析时,区域质量变化的复杂程度决定了模型内插的精度,当时间序列的长度在3年或3年以上时,插值时变模型的精度对区域质量变化分析的影响较小.在分析区域质量变化时,三次样条插值方法的插值结果与实测结果更为接近,建议...     

Investigation of changes in characteristics of hydrological time series by Bayesian methods

A review of literature reveals the inadequacy of Intervention analysis and spectrum based methods to adequately quantify changes in hydrologic times series. A Bayesian method is used to investigate the statistical significance of observed changes in hydrologic times series and the results are reported herein. The Bayesian method is superior to the previous methods.     

Spatio-temporal interpolation of precipitation during monsoon periods in Pakistan

Spatio-temporal estimation of precipitation over a region is essential to the modeling of hydrologic processes for water resources management. The changes of magnitude and space–time heterogeneity of rainfall observations make space–time estimation of precipitation a challenging task. In this paper we propose a Box–Cox transformed hierarchical Bayesian multivariate spatio-temporal interpolation method for the skewed response variable. The proposed method is applied to estimate space–time monthly precipitation in the monsoon periods during 1974–2000, and 27-year monthly average precipitation data are obtained from 51 stations in Pakistan. The results of transformed hierarchical Bayesian multivariate spatio-temporal interpolation are compared to those of non-transformed hierarchical Bayesian interpolation by using cross-validation. The software developed by [11] is used for Bayesian non-stationary multivariate space–time interpolation. It is observed that the transformed hierarchical Bayesian method provides more accuracy than the non-transformed hierarchical Bayesian method.     

Multifractal behaviour of long‐term karstic discharge fluctuations

Karstic watersheds are highly complex hydrogeological systems that are characterized by a multiscale behaviour corresponding to the different pathways of water in these systems. The main issue of karstic spring discharge fluctuations consists in the presence and the identification of characteristic time scales in the discharge time series. To identify and characterize these dynamics, we acquired, for many years at the outlet of two karstic watersheds in South of France, discharge data at 3‐mn, 30‐mn and daily sampling rate. These hydrological records constitute to our knowledge the longest uninterrupted discharge time series available at these sampling rates. The analysis of the hydrological records at different levels of detail leads to a natural scale analysis of these time series in a multifractal framework. From a universal class of multifractal models based on cascade multiplicative processes, the time series first highlights two cut‐off scales around 1 and 16 h that correspond to distinct responses of the aquifer drainage system. Then we provide estimates of the multifractal parameters α and C₁ and the moment of divergence q_D corresponding to the behaviour of karstic systems. These results constitute the first estimates of the multifractal characteristics of karstic spingflows based on 10 years of high‐resolution discharge time series and should lead to several improvements in rainfall‐karstic springflow simulation models. Copyright © 2012 John Wiley & Sons, Ltd.     

Detection of changes in precipitation and runoff over eastern Canada and U.S. using a Bayesian approach

Inference about the existence and characteristics of changes in mean level of hydrometeorological sequences that may be generated by climatic variability is an important step before developing management rules in water resources systems. This paper presents a Bayesian approach, based on a single shifting model, which can be used to study a change in the mean level of a set of independent normal random variables. Two different problems are considered: the first is the detection of a change, while the second is the estimation of the change-point and its amplitude under the assumption that a change has occurred. This method is applied to precipitation and runoff data series over eastern Canada and U.S. during the twentieth century. The main results show an increase in the late sixties in the Eastern North American precipitation. This supports conclusions drawn from a number of studies which identified the late sixties to early seventies as a period of possible change.     

Spectral analysis of climate data

The complexity of climate variability on all time scales requires the use of several refined tools to unravel its primary dynamics from observations. Indeed, ideas from the theory of dynamical systems have provided new ways of interpreting the information contained in climatic time series.We review the properties of several modern time series analysis methods. Those methods belong to four main classes: Fourier techniques (Blackman-Tukey and Multi-Taper), Maximum Entropy technique, Singular-spectrum techniques and wavelet analysis. Their respective advantages and limitations are illustrated by numerical experiments on synthetic time series. As climate data can be irregularly spaced in time, we also compare three interpolating methods on those time series. Those tests are aimed at showing the pitfalls of the blind use of mathematical or statistical techniques on climate data.We apply those methods to real climatic data from temperature variations over the last century, and the Vostok ice core deuterium record over the last glacial cycle. Then we show how interpretations on the dynamics of climate can be derived on those time scales.     

Prediction of river temperature surges is dependent on precipitation method

Urban river systems are particularly sensitive to precipitation‐driven water temperature surges and fluctuations. These result from rapid heat transfer from low‐specific heat capacity surfaces to precipitation, which can cause thermally polluted surface run‐off to enter urban streams. This can lead to additional ecological stress on these already precarious ecosystems. Although precipitation is a first‐order driver of hydrological response, water temperature studies rarely characterize rain event dynamics and typically rely on single gauge data that yield only partial estimates of catchment precipitation. This paper examines three precipitation measuring methods (a statutory automatic weather station, citizen science gauges, and radar estimates) and investigates relationships between estimated rainfall inputs and subhourly surges and diurnal fluctuations in urban river water temperature. Water temperatures were monitored at 12 sites in summer 2016 in the River Rea, in Birmingham, UK. Generalized additive models were used to model the relationship between subhourly water temperature surges and precipitation intensity and subsequently the relationship between daily precipitation totals and standardized mean water temperature. The different precipitation measurement sources give highly variable precipitation estimates that relate differently to water temperature fluctuations. The radar catchment‐averaged method produced the best model fit (generalized cross‐validation score [GCV] = 0.30) and was the only model to show a significant relationship between water temperature surges and precipitation intensity (P < 0.001, R² = 0.69). With respect to daily metrics, catchment‐averaged precipitation estimates from citizen science data yielded the best model fit (GCV score = 0.20). All precipitation measurement and calculation methods successfully modelled the relationship between standardized mean water temperature and daily precipitation (P < 0.001). This research highlights the potential for the use of alternative precipitation datasets to enhance understanding of event‐based variability in water quality studies. We conclude by recommending the use of spatially distributed precipitation data operating at high spatial (<1 km²) and temporal (<15 min) resolutions to improve the analysis of event‐based water temperature and water quality studies.     

Analysis and interpretation of patterns within and between hydroclimatological time series in an alpine glacier basin

This paper explores patterns within and between climatological and hydrological time series from an alpine glacier basin. Time series recorded in the basin of the Haut Glacier d'Arolla over the 1989 ablation season are subdivided into five subperiods. Box-Jenkins ARIMA (AutoRegressive Integrated Moving Average) and TFN (Transfer Function-Noise) models are estimated for each of the five subperiods and differences between the models are interpreted in the context of changing glacier hydrology, particularly the changing nature and extent of the glacier drainage network.     

Trend analysis in Turkish precipitation data

This study aims to determine trends in the long‐term annual mean and monthly total precipitation series using non‐parametric methods (i.e. the Mann–Kendall and Sen's T tests). The change per unit time in a time series having a linear trend was estimated by applying a simple non‐parametric procedure, namely Sen's estimator of slope. Serial correlation structure in the data was accounted for determining the significance level of the results of the Mann–Kendall test. The data network used in this study, which is assumed to reflect regional hydroclimatic conditions, consists of 96 precipitation stations across Turkey. Monthly totals and annual means of the monthly totals are formed for each individual station, spanning from 1929 to 1993. In this case, a total of 13 precipitation variables at each station are subjected to trend detection analysis. In addition, regional average precipitation series are established for the same analysis purpose. The application of a trend detection framework resulted in the identification of some significant trends, especially in January, February, and September precipitations and in the annual means. A noticeable decrease in the annual mean precipitation was observed mostly in western and southern Turkey, as well as along the coasts of the Black Sea. Regional average series also displayed trends similar to those for individual stations. Copyright © 2005 John Wiley & Sons, Ltd.     

Lessons learnt from checking the quality of openly accessible river flow data worldwide

ABSTRACT

Advances in open data science serve large-scale model developments and, subsequently, hydroclimate services. Local river flow observations are key in hydrology but data sharing remains limited due to unclear quality, or to political, economic or infrastructure reasons. This paper provides methods for quality checking openly accessible river-flow time series. Availability, outliers, homogeneity and trends were assessed in 21 586 time series from 13 data providers worldwide. We found a decrease in data availability since the 1980s, scarce open information in southern Asia, the Middle East and North and Central Africa, and significant river-flow trends in Africa, Australia, southwest Europe and Southeast Asia. We distinguish numerical outliers from high-flow peaks, and integrate all investigated quality characteristics in a composite indicator. We stress the need to maintain existing gauging networks, and highlight opportunities in extending existing global databases, understanding drivers for trends and inhomogeneity, and in innovative acquisition methods in data-scarce regions.     

Impact of precipitation characteristics on soil hydrology in tile‐drained landscapes

Understanding the variables regulating tile‐flow response to precipitation in the US Midwest is critical for water quality management. This study (1) investigates the relationship between precipitation characteristics, antecedent water table depth and tile‐flow response at a high temporal resolution during storms; and (2) determines the relative importance of macropore flow versus matrix flow in tile flow in a tile‐drained soya bean field in Indiana. In spring, although variations in antecedent water table depth imparted some variation in tile‐flow response to precipitation, bulk precipitation was the best predictor of mean tile flow, maximum tile flow, time to peak, and run‐off ratio. The contribution of macropore flow to total flow significantly increased with precipitation amount, and macropore flow represented between 11 and 50% of total drain flow, with peak contributions between 15 and 74% of flow. For large storms (>6 cm bulk precipitation), cations data indicated a dilution of groundwater with new water as discharge peaked. Although no clear dilution or concentration patterns for Mg²⁺ or K⁺ were observed for smaller tile flow generating events (<3 cm bulk precipitation), macropore flow still contributed between 11 and 17% of the total flow for these moderate size storms. Inter‐drain comparison stressed the need to use triplicate or duplicate tile drain experiments when investigating tile drainage impact on water and N losses at the plot scale. These results significantly increase our understanding of the hydrological functioning of tile‐drained fields in spring, when most N losses to streams occur in the US Midwest. Copyright © 2010 John Wiley & Sons, Ltd.     

Estimation of the effective precipitation recharge coefficient in an unconfined aquifer using stochastic analysis

Effectively managing groundwater relies heavily on estimating the amount of precipitation that may infiltrate the subsurface and supply groundwater. In this study, we present a novel estimation method based on a stochastic approach to evaluate the quantity of precipitation that may recharge groundwater. The precipitation recharge coefficient is also investigated based on an unconfined aquifer with an unbound, infinitely extended boundary condition. Moreover, a spectrum's relationship to the precipitation and groundwater level variation is also derived. The precipitation recharge coefficient can be obtained from the solution of the spectrum equation. Furthermore, sensitivity analysis is performed in order to determine the key variable on the precipitation recharge coefficient. Analysis results indicate that the location of an observation well affects the estimated precipitation recharge coefficient. If the precipitation recharge area is large enough, the precipitation recharge coefficient becomes insensitive to the location of the observation well. The spectrum's relationship between the precipitation recharge and groundwater level variation is also applied when estimating the precipitation recharge coefficient upstream of the Cho‐Shui River alluvial fan. According to those results, the precipitation recharge coefficient is 0·03 and the amount of groundwater recharge from precipitation is 35 million tons of water annually upstream of the Cho‐Shui River alluvial fan. Copyright © 2000 John Wiley & Sons, Ltd.     

Stochastic models in hydrology

A stochastic approach to the analysis of hydrologic processes is defined along with a discussion of causes of tendency, periodicity and stochasticity in hydrologic series. Sources of temporal non-stationarity are described along with objectives and methods of analysis of processes and, in general, of information extraction from data. Transferred information as measured by correlation coefficients is compared with the transferable information as measured by entropy coefficients. Various multivariate approaches to hydrologic stochastic modeling are classified in light of complexities of spatial/temporal hydrologic processes. Alternatives of time series structural decomposition and modeling are compared. A special approach to modeling of space properties further contributes to approximate simulations of spatial/temporal processes over large areas. Several aspects of stochastic models in hydrology are concisely reviewed.     

Reconstruction of precipitation variation from tree rings in recent 1000 years in Delingha, Qinghai

A major attempt of the studies on past global changes (PAGES) and climate variability and predict- ability (CLIVAR) is to reconstruct climate change us- ing high-resolution proxies[1―3]. Tree-ring data have played an important role in such studies. To date, tree rings have been extensively used to reconstruct tem- perature variations in the recent 1000 and even 2000 years of the Northern Hemisphere[4―7], to assess the effect of volcanic eruptions on temperature varia-tions[8], and to in…     

Spatial interpolation using nonlinear mathematical programming models for estimation of missing precipitation records

Abstract

New mathematical programming models are proposed, developed and evaluated in this study for estimating missing precipitation data. These models use nonlinear and mixed integer nonlinear mathematical programming (MINLP) formulations with binary variables. They overcome the limitations associated with spatial interpolation methods relevant to the arbitrary selection of weighting parameters, the number of control points within a neighbourhood, and the size of the neighbourhood itself. The formulations are solved using genetic algorithms. Daily precipitation data obtained from 15 rain gauging stations in a temperate climatic region are used to test and derive conclusions about the efficacy of these methods. The developed methods are compared with some naïve approaches, multiple linear regression, nonlinear least-square optimization, kriging, and global and local trend surface and thin-plate spline models. The results suggest that the proposed new mathematical programming formulations are superior to those obtained from all the other spatial interpolation methods tested in this study.

Editor D. Koutsoyiannis; Associate editor S. Grimaldi

Citation Teegavarapu, R.S.V., 2012. Spatial interpolation using nonlinear mathematical programming models for estimation of missing precipitation records. Hydrological Sciences Journal, 57 (3), 383–406.