There is an established evidence of climatic teleconnection between El Niño–Southern Oscillation (ENSO) and Indian summer monsoon rainfall (ISMR) during June through September. Against the long‐recognized negative correlation between ISMR and ENSO, unusual experiences of some recent years motivate the search for some other causal climatic variable, influencing the rainfall over the Indian subcontinent. Influence of recently identified Equatorial Indian Ocean Oscillation (EQUINOO, atmospheric part of Indian Ocean Dipole mode) is being investigated in this regard. However, the dynamic nature of cause‐effect relationship burdens a robust and consistent prediction. In this study, (1) a Bayesian dynamic linear model (BDLM) is proposed to capture the dynamic relationship between large‐scale circulation indices and monthly variation of ISMR and (2) EQUINOO is used along with ENSO information to establish their concurrent effect on monthly variation of ISMR. This large‐scale circulation information is used in the form of corresponding indices as exogenous input to BDLM, to predict the monthly ISMR. It is shown that the Indian monthly rainfall can be modeled in a better way using these two climatic variables concurrently (correlation coefficient between observed and predicted rainfall is 0.82), especially in those years when negative correlation between ENSO and ISMR is not well reflected (i.e., 1997, 2002, etc.). Apart from the efficacy of capturing the dynamic relationship by BDLM, this study further establishes that monthly variation of ISMR is influenced by the concurrent effects of ENSO and EQUINOO.
Abstract:In the recent past, a variety of statistical and other modelling approaches have been developed to capture the properties of hydrological time series for their reliable prediction. However, the extent of complexity hinders the applicability of such traditional models in many cases. Kernel-based machine learning approaches have been found to be more popular due to their inherent advantages over traditional modelling techniques including artificial neural networks(ANNs ). In this paper, a kernel-based learning approach is investigated for its suitability to capture the monthly variation of streamflow time series. Its performance is compared with that of the traditional approaches. Support vector machines (SVMs) are one such kernelbased algorithm that has given promising results in hydrology and associated areas. In this paper, the application of SVMs to regression problems, known as support vector regression (SVR), is presented to predict the monthly streamflow of the Mahanadi River in the state of Orissa, India. The results obtained are compared against the results derived from the traditional Box-Jenkins approach. While the correlation coefficient between the observed and predicted streamflows was found to be 0Ð77 in case of SVR, the same for different auto-regressive integrated moving average (ARIMA) models ranges between 0Ð67 and 0Ð69. The superiority of SVR as compared to traditional Box-Jenkins approach is also explained through the feature space representation.
This study borrows the measures developed for the operation of water resources systems as a means of characterizing droughts in a given region. It is argued that the common approach of assessing drought using a univariate measure (severity or reliability) is inadequate as decision makers need assessment of the other facets considered here. It is proposed that the joint distribution of reliability, resilience, and vulnerability (referred to as RRV in a reservoir operation context), assessed using soil moisture data over the study region, be used to characterize droughts. Use is made of copulas to quantify the joint distribution between these variables. As reliability and resilience vary in a nonlinear but almost deterministic way, the joint probability distribution of only resilience and vulnerability is modeled. Recognizing the negative association between the two variables, a Plackett copula is used to formulate the joint distribution. The developed drought index, referred to as the drought management index (DMI), is able to differentiate the drought proneness of a given area when compared to other areas. An assessment of the sensitivity of the DMI to the length of the data segments used in evaluation indicates relative stability is achieved if the data segments are 5 years or longer. The proposed approach is illustrated with reference to the Malaprabha River basin in India, using four adjoining Climate Prediction Center grid cells of soil moisture data that cover an area of approximately 12,000 km 2 .
[1] Drought triggers are patterns in hydroclimatic variables that herald upcoming droughts and form the basis for mitigation plans. This study develops a new method for identification of triggers for hydrologic droughts by examining the association between the various hydroclimatic variables and streamflows. Since numerous variables influence streamflows to varying degrees, principal component analysis (PCA) is utilized for dimensionality reduction in predictor hydroclimatic variables. The joint dependence between the first two principal components, that explain over 98% of the variability in the predictor set, and streamflows is computed by a scale-free measure of association using asymmetric Archimedean copulas over two study watersheds in Indiana, USA, with unregulated streamflows. The M6 copula model is found to be suitable for the data and is utilized to find expected values and ranges of predictor hydroclimatic variables for different streamflow quantiles. This information is utilized to develop drought triggers for 1 month lead time over the study areas. For the two study watersheds, soil moisture, precipitation, and runoff are found to provide the fidelity to resolve amongst different drought classes. Combining the strengths of PCA for dimensionality reduction and copulas for building joint dependence allows the development of hydrologic drought triggers in an efficient manner.Citation: Maity, R., M. Ramadas, and R. S. Govindaraju (2013), Identification of hydrologic drought triggers from hydroclimatic predictor variables, Water Resour. Res., 49,[4476][4477][4478][4479][4480][4481][4482][4483][4484][4485][4486][4487][4488][4489][4490][4491][4492]
In this study, an index, named as standardized precipitation anomaly index (SPAI), is proposed for the meteorological drought quantification in the context of the monsoon-dominated climatology, where the precipitation is strongly seasonal and periodic. In the computation of SPAI, the anomalies of the precipitation are normalized rather than normalizing the raw precipitation series. The SPAI is compared with the standardized precipitation index (SPI), with respect to certain shortcomings of the latter. It is shown that the SPAI, owing to its design, is able to successfully differentiate between the consequences of shortages/surplus in rainfall in the monsoon and nonmonsoon months which is not possible through SPI. The unique suitability of SPAI for monsoon dominated regions is also illustrated by comparing its premise of development with that of the standardized nonstationary precipitation index (SnsPI). Further, drought quantification through the SPAI is shown to be applicable for both periodic and nonperiodic precipitation series. This is demonstrated using a typical strongly periodic precipitation series (from India) and a typical nonperiodic precipitation series (from Arkansas, United States of America). As compared with SPI, the SPAI is found to have a better coherence with the consequences of droughts and wet spells faced by the country (India, as the study area) in the past.
This paper characterizes the long-term, spatiotemporal variation of drought propensity through a newly proposed, namely Drought Management Index (DMI), and explores its predictability in order to assess the future drought propensity and adapt drought management policies for a location. The DMI was developed using the reliability-resilience-vulnerability (RRV) rationale commonly used in water resources systems analysis, under the assumption that depletion of soil moisture across a vertical soil column is equivalent to the operation of a water supply reservoir, and that drought should be managed not simply using a measure of system reliability, but should also take into account the readiness of the system to bounce back from drought to a normal state. Considering India as a test bed, 5 year long monthly gridded (0.5 Lat 3 0.5 Lon) soil moisture data are used to compute the RRV at each grid location falling within the study domain. The Permanent Wilting Point (PWP) is used as the threshold, indicative of transition into water stress. The association between resilience and vulnerability is then characterized through their joint probability distribution ascertained using Plackett copula models for four broad soil types across India. The joint cumulative distribution functions (CDF) of resilience and vulnerability form the basis for estimating the DMI as a five-yearly time series at each grid location assessed. The status of DMI over the past 50 years indicate that drought propensity is consistently low toward northern and north eastern parts of India but higher in the western part of peninsular India. Based on the observed past behavior of DMI series on a climatological time scale, a DMI prediction model comprising deterministic and stochastic components is developed. The predictability of DMI for a lead time of 5 years is found to vary across India, with a Pearson correlation coefficient between observed and predicted DMI above 0.6 over most of the study area, indicating a reasonably good potential for drought management in the medium term water resources planning horizon.
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