Rajib Maity

Bayesian dynamic modeling for monthly Indian summer monsoon rainfall using El Niño–Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO)

There is an established evidence of climatic teleconnection between El Nino–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.

Regional Rainfall Forecasting using Large Scale Climate Teleconnections and Artificial Intelligence Techniques

This paper presents an Artificial Intelligence approach for regional rainfall forecasting for Orissa state, India on monthly and seasonal time scales. The possible relation between regional rainfall over Orissa and the large scale climate indices like El-Nino Southern Oscillation (ENSO), EQUitorial INdian Ocean Oscillation (EQUINOO) and a local climate index of Ocean-Land Temperature Contrast (OLTC) are studied first and then used to forecast monsoon rainfall. To handle the highly non-linear and complex behavior of the climatic variables for forecasting the rainfall, this study employs Artificial Neural Networks (ANNs) methodology. To optimize the ANN architecture, Genetic Optimizer (GO) is used. After identifying the lagged relation between climate indices and monthly rainfall, the rainfall values are forecast for the summer monsoon months of June, July, August, and September (JJAS) individually, as well as for total monsoon rainfall. The models are trained individually for monthly and for seasonal rainfall forecasting. Then the trained models are tested to evaluate the performance of the model. The results show reasonably good accuracy for monthly and seasonal rainfall forecasting. This study emphasizes the value of using large-scale climate teleconnections for regional rainfall forecasting and the significance of Artificial Intelligence approaches like GO and ANNs in predicting the uncertain rainfall.

Characterizing Drought Using the Reliability-Resilience-Vulnerability Concept

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 char- acterize 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 . DOI: 10.1061/ (ASCE)HE.1943-5584.0000639.

REVIEW OF HYDROCLIMATIC TELECONNECTION BETWEEN HYDROLOGIC VARIABLES AND LARGE-SCALE ATMOSPHERIC CIRCULATION PATTERNS WITH INDIAN PERSPECTIVE

ABSTRACT Hydroclimatic teleconnection between hydrologic variables and large-scale atmospheric circulation phenomena is being studied worldwide and gaining more and more interest in recent years due to its potential use in hydrologic time series analysis and forecasting. In this paper a review of such related work is presented. First, characteristics of major large-scale atmospheric circulation phenomena from tropical Pacific Ocean and Indian Ocean region are explained. El Niño-Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) mode from tropical Pacific Ocean and Indian Ocean respectively are selected and their global influences on hydrologic variables through hydroclimatic teleconnection are elaborated. Potential predictive power of such large-scale indices for hydrologic variables is explained based on the established research work across the world. Research opportunities, in this direction, are then explained in Indian perspective. A preliminary analysis is also presented in this regard. Predictive potential of such large-scale indices is of immense use to water resources community.

Spatiotemporal variation of long-term drought propensity through reliability-resilience-vulnerability based Drought Management Index

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 × 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.

Hydroclimatic streamflow prediction using Least Square-Support Vector Regression

In last two decades, many Artificial Intelligence-based models have gained recognition in the field of hydrologic forecasting. In this study, potential of least square support vector regression model is explored in the context of streamflow prediction using hydroclimatic inputs. This study is conducted in Narmada river basin up to Sandia gauging station and Mahanadi river basin up to Basantpur gauging station. Four different hydroclimatic variables, namely rainfall, maximum temperature, minimum temperature and streamflow values of previous day are used as input variables. Prediction performances are assessed in terms of different statistical measures, namely – correlation coefficient, root mean square error and Nash–Sutcliffe efficiency. Whereas these statistics indicate an overall impressive result, in particular, low and medium ranges of streamflows are found to have better correspondence between observed and predicted values.

Meteorological Drought Quantification with Standardized Precipitation Anomaly Index for the Regions with Strongly Seasonal and Periodic Precipitation

AbstractIn 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 an...

Short-Term Basin-Scale Streamflow Forecasting Using Large-Scale Coupled Atmospheric–Oceanic Circulation and Local Outgoing Longwave Radiation

Abstract This paper investigates the use of large-scale circulation patterns (El Nino–Southern Oscillation and the equatorial Indian Ocean Oscillation), local outgoing longwave radiation (OLR), and previous streamflow information for short-term (weekly) basin-scale streamflow forecasting. To model the complex relationship between these inputs and basin-scale streamflow, an artificial intelligence approach—genetic programming (GP)—has been employed. Research findings of this study indicate that the use of large-scale atmospheric circulation information and streamflow at previous time steps, along with OLR as a local meteorological input, potentially improves the performance of weekly basin-scale streamflow prediction. The genetic programming approach is found to capture the complex relationship between the weekly streamflow and various inputs. Different input variable combinations were explored to come up with the best one. The observed and predicted streamflows were found to correspond well with each othe...

A hydrometeorological approach for probabilistic simulation of monthly soil moisture under bare and crop land conditions

This study focuses on the probabilistic estimation of monthly soil moisture variation by considering (a) the influence of hydrometeorological forcing to model the temporal variation and (b) the information of Hydrological Soil Groups (HSGs) and Agro-Climatic Zones (ACZs) to capture the spatial variation. The innovative contributions of this study are: (i) development of a Combined Hydro-Meteorological (CHM) index to extract the information of different influencing hydrometeorological variables, (ii) consideration of soil-hydrologic characteristics (through HSGs) and climate regime-based zoning for agriculture (through ACZs), and (iii) quantification of uncertainty range of the estimated soil moisture. Usage of Supervised Principal Component Analysis (SPCA) in the development of the CHM index helps to eliminate the “curse of dimensionality,” typically arises in the multivariate analysis. The usage of SPCA also ensures the maximum possible association between the developed CHM index and soil moisture variation. The association between these variables is modeled through their joint distribution which is obtained by using the theory of copula. The proposed approach is also spatially transferable, since the information on HSGs and ACZs is considered. The “leave-one-out” cross-validation (LOO-CV) approach is adopted for stations belong to a particular HSG to examine the spatial transferability. The simulated soil moisture values are also compared with a few existing soil moisture data sets, derived from different Land Surface Models (LSMs) or retrieved from different satellite-based missions. The potential of the proposed approach is found to be promising and even applicable to crop land also, though with a lesser degree of efficiency as compared to bare land conditions.

Probabilistic simulation of surface soil moisture using hydrometeorological inputs

Soil moisture is an important parameter in hydrometeorological as well as terrestrial geochemical processes. Near surface soil moisture is found to be critical for crop yield, occurrence of drought, soil erosion, regional weather prediction etc. However, in situ measurement of this important variable is difficult because of its high spatial and temporal variability. Variability of soil moisture can be attributed to heterogeneity in soil properties and distribution of hydrometeorological factors like precipitation, temperature, relative humidity etc. In this article, a hydrometeorological approach to probabilistically simulate soil moisture, at the monthly scale using a combined hydrometeorological (CHM) index, is proposed. A principal component analysis (PCA)–based approach is adopted to derive the CHM index from several meteorological variables. The joint probability distribution between CHM index and soil moisture is determined by a bivariate copula function. The proposed model is able to estimate soil moisture along with the quantification of associated uncertainty for a new location having a hydrometeorological data set and information on predominant soil type at that location. Simulated soil moisture is compared with soil moisture simulated by H96 Climate Prediction Center (CPC) model, which is based on the leaky bucket model. Advantages of proposed model for 10 soil moisture–monitoring stations in India are discussed.