Prediction of SPI Drought Class Transitions Using Markov Chains

Using the SPI relative to 67 years data sets, a Markov chains approach has been utilized for several locations in Alentejo, southern Portugal, to characterize the stochasticity of droughts, which allowed predicting the transition from a class of severity to another up to 3 months ahead. Markov models were applied using both the homogeneous and non-homogeneous formulations. The results of the application of the Markov models are presented and discussed, showing in particular the usefulness of adopting a non-homogeneous formulation, which allows to differentiate predictions in relation to the initial month considered, thus understanding the probable evolution of a drought as influenced by the climate and, in particular, the seasonality of rainfall. However, these results, which are promising in view of drought management, require further developments and to be associated with other predictive tools of stochastic or physical nature. Possible approaches on using predictions of drought class transitions in view of drought risk management are also discussed.     

Regional Drought Modes in Iran Using the SPI: The Effect of Time Scale and Spatial Resolution

In the present paper, regional drought modes in Iran are identified applying the Principal Component Analysis (PCA) and Varimax rotation to the Standardized Precipitation Index (SPI) computed on different time scales. Data used include gridded monthly precipitation covering the period 1951–2007 retrieved from the Global Precipitation Climatology Centre (GPCC) archive with different spatial resolutions (2.5, 1 and 0.5° resolution). The objective of the study is twofold: (i) Investigate the stability of drought spatial modes as a function of the SPI time scales used for monitoring the different kinds of drought, (ii) Evaluate the impact of the spatial resolution of gridded data on drought regionalization. For the coarse spatial resolution of 2.5°, results show four drought modes of distinct variability, which remain quite stable when the SPI time scale is varied from 1- to 24-month. Differently, for higher spatial resolutions drought modes appear more sensitive to the index time scale and become less spatially homogeneous as the time scale is increased. Moreover, the number of identified modes (sub-regions) may reduce to three or two, but in all cases the most well defined sub-region appears to be the southern one. This suggests that both the spatial resolution of precipitation data and the time scale may affect drought regionalization, i.e. the number of drought modes and their spatial homogeneity.     

Influence of Precipitation Changes on the SPI and Related Drought Severity. An Analysis Using Long-Term Data Series

Drought indices, such as the Standardized Precipitation Index (SPI) are used to quantify drought severity. Due to the SPI probabilistic and standardized nature, a given value of SPI computed in distinct time periods or locations indicates the same relative drought severity but corresponds to different amounts of precipitation. Thus, the present study aims at contributing for a comprehensive analysis of the influence of long-term precipitation variability on drought assessment by the SPI. Long records of monthly precipitation, spanning from 1863 to 2007 in several locations across Portugal, were divided into 30 years sub-periods and the SPI with 12-month time scale (SPI-12) was computed for each sub-period and for the entire period of records. The probability distributions adjusted to precipitation in those different time periods were compared envisaging to detect the SPI sensitivity to the reference period and, therefore, to changes in precipitation. Precipitation thresholds relative to the upper limits of SPI-12 drought categories were obtained and the influence of the time period was investigated. Results have shown that when SPI values derived from the full data record for a recent time period are lower/higher than the SPI values derived from data of the considered time period a recent downward/upward shift of precipitation has occurred. Coherently, a common pattern of drought aggravation from the initial until the more recent period was not detected. However, in southern locations, lower precipitation thresholds of the SPI drought categories were generally found in the more recent period, particularly for more severe drought categories, whereas in the northern locations Porto and Montalegre, an increase was detected. The impacts of the reference period on the computed SPI drought severity and frequency are shown, bringing to discussion the need for updating ´normal´ conditions when long term precipitation records are available and precipitation changes are observed.     

Spatial and Time Variability of Drought Based on SPI and RDI with Various Time Scales

The spatial and temporal variability of droughts were studied for the Northeast Algeria using SPI and RDI computed with monthly precipitation data from 123 rainfall stations and CFSR reanalysis monthly temperature data covering the period 1979–80 to 2013–14. The gridded temperature data was interpolated to all the locations having precipitation data, thus providing to compute SPI and RDI with the time scales of 3-, 6- and 12-month with the same observed rainfall data. Spatial and temporal patterns of droughts were obtained using Principal Component Analysis in S-Mode with Varimax rotation applied to both SPI and RDI. For all time scales of both indices, two principal components were retained identifying two sub-regions that are similar and coherent for all SPI and RDI time scales. Both components explained more than 70% and 74% of drought spatial variability of SPI and RDI, respectively. The identified sub-regions are similar and coherent for all SPI and RDI time scales. The Modified Mann-Kendall test was used to assess trends of the RPC scores, which have shown non-significant trends for decreasing drought occurrence and severity in both identified drought sub-regions and all time scales. Both indices have shown a coherent and similar behavior, however with RDI likely showing to identify more severe and moderate droughts in the southern and more arid sub-region which may be due to its ability to consider influences of global warming. Results for RDI are quite uniform relative to time scales and show smaller differences among the various climates when compared with SPI. Further assessments covering the NW and NE of Algeria using longer time series should be performed to better understand the behavior of both indices.     

水文时间序列预报的人工神经网络模型

1　引言由于水文现象本身的复杂性 ,目前还很难用物理方法对水文现象进行描述 ,人们主要借助数理统计方法以及其它的一些不确定性方法来描述水文现象 ,以弥补物理方法的不足。不确定性方法主要运用概率统计、时间序列、模糊数学和灰色理论等来探索水文现象。但这些理论和方法都需要具有显式函数 ,而它们往往难以找到 ,或只能以近似表达式进行描述 ,一般都有比较大的误差 ,因此 ,寻找更合理与灵活的理论和方法来充实水文学的研究成了水文工作者多年来不懈的追求 ,人工神经网络途径便是一种尝试。近年来 ,人工神经网络技术已被广泛应用于水文…     

Correction to: Spatial and Time Variability of Drought Based on SPI and RDI with Various Time Scales

Water Resources Management - Due to an oversight, Figure 1 image and Figure 4 caption were incorrectly captured in the original publication.     

A Hybrid ANFIS-GA Approach for Estimation of Hydrological Time Series

Water Resources Management - River flow modeling plays a leading role in the management of water resources and ensuring sustainability. The complex nature of hydrological systems and the difficulty...     

宜昌水文站年径流量演变多时间尺度分析

水文时间序列往往在时域中存在多层次时间尺度结构和局部化特征。运用小波分析的多分辨率功能,对宜昌水文站1882-2006年年径流量时间序列资料进行了多时间尺度分析。研究结果表明：宜昌水文站年径流量存在明显的年际变化特征,主要存在30～40年、10～20年及10年以下3类尺度的周期变化规律,变化主周期为14年,第二周期为7年,第三周期为34年;自2006年以后的3年左右,宜昌站年径流量将处于一个相对偏枯期,之后又将进入一个丰水期。     

东江流域水文时问序列的周期分析

利用频谱分析法对东江流域控制性水文站博罗站的年径流序列进行了计算与分析,得出的东江流域年径流量存在约2．2年、4．5年、2．4年、12．5年和8．3年的周期。了解年径流序列的周期特征,对进一步合理开发利用东江流域水资源具有一定的参考作用。     

Application of Heuristic Approaches for Prediction of Hydrological Drought Using Multi-scalar Streamflow Drought Index

Water Resources Management - Quantification and prediction of drought events are important for planning and management of water resources in coping with climate change scenarios at global and local...     

Uncertainty-Driven Characterization of Climate Change Effects on Drought Frequency Using Enhanced SPI

The Standardized Precipitation Index (SPI) is a well-established drought index that is based on transforming the interannual distribution of precipitation to a standard normal distribution. Because of its robust statistical basis, SPI is readily applicable to different regions making comparisons between locations and time windows possible. Nevertheless, the usability of SPI results is undermined by shortcomings that are partly resultant from data and model uncertainties. One such shortcoming is the inability of the existing SPI model to include change in variability of interannual precipitation from non-stationary normal – mostly caused by climate change. In addition, epistemic uncertainty in the form of incompleteness in station-wide precipitation records results in heterogeneity and inconsistency in SPI results. The effects of such epistemic uncertainty on the accuracy of estimations of long-term changes in drought frequency are mostly unknown. Given such deficiency, SPI’s procedure and subsequent results remain deterministic and inadequately informative. Here, we introduce modifications to the traditional SPI using Dempster-Shafer theory (DST) to enable modeling and propagation of variability and epistemic uncertainty with the regular SPI procedure. By generalizing the SPI model from a deterministic setting to an “uncertainty-driven setting” provided by DST, this work makes possible: (a) efficiently propagating data uncertainty in interpolation of station-wide precipitation and SPI, and (b) modeling the effects of shift in precipitation normals (due to e.g., climate change) on drought frequency. In addition, the significance of this shift may then be evaluated with respect to the epistemic uncertainty by measuring how much of the surrounding epistemic uncertainty this shift encloses (i.e., “probability of enclosing”). The latter is especially important due to large unknowns already associated with climate change modeling. We implement the model on summer extreme drought for the Okanagan Basin, BC, Canada. For a single general circulation model and scenario (CGCM3 A2) a maximum 7 % increase in summer extreme drought (for 2080s, as per current definition) is estimated with a maximum probability of enclosing of 36 %.     

水文时间序列分析计算方法的研究进展与展望

综合分析了国内外关于水文时间序列分析计算方法的研究成果和进展情况,指出了各自的优越性和不足,对未来发展趋势进行了展望.     

Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series

In this study, a nonparametric technique to set up a river stage forecasting model based on empirical mode decomposition (EMD) is presented. The approach is based on the use of the EMD and artificial neural networks (ANN) to forecast next month’s monthly streamflows. The proposed approach is applied to a real case study. The data from station on the Kizilirmak River in Turkey was used. The mean square errors (MSE), mean absolute errors (MAE) and correlation coefficient (R) statistics were used for evaluating the accuracy of the EMD-ANN model. The accuracy of the EMD-ANN model was then compared to the artificial neural networks (ANN) model. The results showed that EMD-ANN approach performed better than the ANN in predicting stream flows. The most accurate EMD-ANN model had MSE?=?0.0132, MAE?=?0.0883 and R?=?0.8012 statistics, respectively.     

应用频谱法分析水文时间序列的代表性

水文时间序列的代表性分析是水文频率计算的一项重要内容,水文频率分析成果的优劣在很大程度上取决于水文序列的代表性。目前对水文序列代表性分析仍缺乏有效的方法。本文尝试引入频谱分析法分析水文序列的代表性,根据频域中水文序列周期数,判断研究的序列是否含有丰、平、枯或大、中、小各特征量级的序列值,从而确定水文序列代表性的优劣。通过对实际测站年最高水位序列的频谱分析去判断其周期,由此推断该序列具有较高的代表性,其频率计算设计成果具有较高的精度。     

Karst Spring Discharges Analysis in Relation to Drought Periods,Using the SPI

Based on the long hydrological time series, the correlation between karst spring discharge series and rainfall has been analysed, using the Standard Precipitation Index (SPI). Analysis has been focused on the drought periods. Data come from a large karst system (Campania, Southern Italy), in an area characterised by a distribution of the precipitation prevalently during autumn-winter period. Insufficient recharge due to poor rainfall results in flat spring hydrographs (with no peak during spring season) that indicate a continuously decreasing discharge. Specifically, it has been found that 12 months cumulative rainfall, expressed by SPI₁₂, and spring discharge have similar trend. When SPI₁₂ will be equal or less that − 1, springs reduce the discharge, and a flat spring hydrograph will be produced when SPI reaches value less than − 1.5. In these cases, the prolonged shortage of accumulated rainfall causes a reduction in spring discharge also during the following year as well, pointing out a memory effect of the karst aquifer, and more complex rainfall–discharge relationship is observed.     

Revisiting Major Dry Periods by Rolling Time Series Analysis for Human-Water Relevance in Drought

Drought is increasingly gaining importance for society, humans, and the environment. It is analyzed commonly by the use of available hydroclimatic or hydrologic data with little in-depth consideration of specific major dry periods experienced over a region. Also, it is not a common practice to assess the probability of drought categories with a rolling time series and hence the changing knowledge for operational drought monitoring. A combination of such quantitative analysis with a comprehensive qualitative assessment of drought as a human-water relation aimed to fill this gap performing a case study in the Seyhan River Basin, Turkey. Six major dry periods were identified from the precipitation time series of 19 meteorological stations. Major dry periods were analyzed by rolling time series and full time series, and they were also analyzed individually. A major dry period could be important in terms of its duration while another in terms of its severity or intensity, and each with its own impact on the human-water relations that can be influential on the drought mitigation, management and governance. Significantly higher probabilities were calculated for extreme droughts with the use of individual major dry periods. An important outcome from the study is that drought is underestimated in practice with the sole use of the whole data record.

     

Palmer指数和SPI指数对广西干旱的评估分析

利用广西地区2009-2010年20个气象站逐日气温和降水资料,计算逐月帕默尔指数(PDSI)和标准化降雨指数(SPI)。采用ANUSPLIN插值软件对降雨、气温和两种指数的空间分布进行差值分析,并分析了降雨量和温度对干旱指数的影响。研究表明,降雨量的大小和温度的高低影响着该地区干旱过程,在两种干旱指数中,PDSI指数更加符合广西的实际情况,更适合于广西地区的干旱评估。     

应用MQ Series实现广域网接收北斗卫星水情数据

在简要介绍基于卫星通信的水情自动测报系统和MQ Series的基本概念、原理及特点的基础上，叙述了应用MQ Series实现广域网接收该水情自动测报系统数据的方法。     

Hydro-Climatological Drought Analyses and Projections Using Meteorological and Hydrological Drought Indices: A Case Study in Blue River Basin, Oklahoma

Understanding the characteristics of historical droughts will benefit water resource managers because it will reveal the possible impacts that future changes in climate may have on drought, and subsequently, the availability of water resources. The goal of this study was to reconstruct historical drought occurrences and assess future drought risk for the drought-prone Blue River Basin in Oklahoma, under a likely changing climate using three types of drought indices, i.e., Standardized Precipitation Index (SPI), Palmer Drought Severity Index (PDSI) and Standardized Runoff Index (SRI). No similar research has been conducted in this region previously. Monthly precipitation and temperature data from the observational period 1950?C1999 and over the projection period 2010?C2099 from 16 statistically downscaled Global Climate Models (GCM) were used to compute the duration, severity, and extent of meteorological droughts. Additionally, soil moisture, evapotranspiration (ET), and runoff data from the well-calibrated Thornthwaite Monthly Water Balance Model were used to examine drought from a hydrological perspective. The results show that the three indices captured the historical droughts for the past 50?years and suggest that more severe droughts of wider extent are very likely to occur over the next 90?years in the Blue River Basin, especially in the later part of the 21st century. In fact, all three indices display lower minimum values than those ever recorded in the past 50?years. This study also found that SRI and SPI (PDSI) had a correlation coefficient of 0.81 (0.78) with a 2-month (no appreciable) lag time over the 1950?C2099 time period across the basin. There was relatively lower correlation between SPI and PDSI over the same period. Although this study recommends that PDSI and SRI are the most suitable indices for assessing future drought risks under an increasingly warmer climate, more drought indices from ecological and socioeconomic perspectives should be investigated and compared to provide a complete picture of drought and its potential impacts on the dynamically coupled nature-human system.     

水文时间序列AR(P)模型阶数的识别

介绍水文时间序列AR(P)模型的建立及其在中长期水文预报中的应用，并对确定模型阶数P的方法进行了总结和分析，力求进一步提高中长期预报的精度。