Elsevier

Journal of Hydrology

Volume 395, Issues 1–2, 6 December 2010, Pages 23-38
Journal of Hydrology

Streamflow prediction using multi-site rainfall obtained from hydroclimatic teleconnection

https://doi.org/10.1016/j.jhydrol.2010.10.004Get rights and content

Summary

Simultaneous variations in weather and climate over widely separated regions are commonly known as “hydroclimatic teleconnections”. Rainfall and runoff patterns, over continents, are found to be significantly teleconnected, with large-scale circulation patterns, through such hydroclimatic teleconnections. Though such teleconnections exist in nature, it is very difficult to model them, due to their inherent complexity.

Statistical techniques and Artificial Intelligence (AI) tools gain popularity in modeling hydroclimatic teleconnection, based on their ability, in capturing the complicated relationship between the predictors (e.g. sea surface temperatures) and predictand (e.g., rainfall). Genetic Programming is such an AI tool, which is capable of capturing nonlinear relationship, between predictor and predictand, due to its flexible functional structure. In the present study, gridded multi-site weekly rainfall is predicted from El Niño Southern Oscillation (ENSO) indices, Equatorial Indian Ocean Oscillation (EQUINOO) indices, Outgoing Longwave Radiation (OLR) and lag rainfall at grid points, over the catchment, using Genetic Programming.

The predicted rainfall is further used in a Genetic Programming model to predict streamflows. The model is applied for weekly forecasting of streamflow in Mahanadi River, India, and satisfactory performance is observed.

Introduction

Basin-scale streamflow prediction is an important step in water resources management for sustainable development. The variation of basin-scale streamflow is influenced by rainfall depth, its distribution pattern, catchment characteristics and the ground water contribution to the streamflow. The rainfall distribution, over the catchment, depends on local meteorology, large scale atmospheric circulation patterns and the geography of the catchment. It may be difficult to predict weekly streamflow accurately, by just considering the rainfall of few previous weeks, because the rainfall in current week also has substantial contribution to streamflow. This is especially true for monsoon season, when the soil is saturated, leading to insignificant infiltration. In the present study, a two-step approach is proposed for weekly streamflow prediction. In first step, current (weekly) multi-gridded rainfall is predicted with Genetic Programming (GP), based on large scale atmospheric circulation patterns, OLR and lag rainfall at grid points. Current step weekly streamflow is then predicted, by using observed gridded rainfall, up to previous weekly time step and GP predicted multi-gridded rainfall, at current weekly time step.

A single step model, for streamflow forecasting, is also developed, by using same inputs and the results are compared with the performance of two step model.

The objectives of this study are summarized as following:

  • (1)

    To develop GP-based models, for weekly multi-site (multi-gridded) rainfall prediction, based on large scale atmospheric circulation patterns with hydroclimatic teleconnection, lag multi-gridded rainfall and OLR.

  • (2)

    To develop GP-based weekly basin-scale streamflow prediction model, based on observed gridded rainfall at few previous weekly time steps, GP predicted gridded rainfall at current time step and lagged streamflow at immediate previous weekly time step.

  • (3)

    To assess the improvement in streamflow predictions due to inclusion of current step (week), GP predicted rainfall in the input set, in addition to the observed gridded rainfall up to the lag-1 weekly time step.

  • (4)

    To compare the results of the aforesaid two-step model with a single-step model, that uses lag streamflow, ENSO indices, EQUINOO indices, OLR anomaly, historical avg. rainfall and rainfall at the all grid points over last six weeks.

The methodology adopted for streamflow prediction in the two-step model can be visualized in flowchart (Fig. 1a). The first step deals with the multi-gridded rainfall prediction, and the second step deals with the basin-scale streamflow prediction.

Similarly the methodology of a single-step model can be seen in flowchart (Fig. 1b).

The rainfall as well as streamflow-prediction models are developed by using Genetic Programming tool. The methodology is different from the methodologies mentioned in the literature (Eltahir, 1996, Piechota et al., 1997, Chiew et al., 1998, Chandimala and Zubair, 2007).

The novelty of this method lies in the inclusion of predicted current week rainfall in streamflow-prediction model that contributes to current-week streamflow, especially in monsoon season.

Section snippets

Influence of large scale atmospheric circulation patterns over spatio-temporal rainfall distribution

Simultaneous variations in weather and climate over widely separated regions on earth have long been noted in the meteorological literature. Such recurrent patterns are commonly referred as “teleconnections”. Rainfall distribution patterns over the continents are significantly linked with the atmospheric circulation through hydroclimatic teleconnection.

It is also established that the natural variation of rainfall is linked with these large scale atmospheric circulation patterns, through

Data and case study

Daily gridded rainfall data at a spatial resolution of 1° latitude by 1° longitude for the period 1951–2003 is obtained from India Meteorological Department (IMD). Weekly rainfall values are calculated from these daily rainfall values for the grid points, encapsulating the upper Mahanadi River basin upstream of ‘Basantpur’ stream gauging station (Fig. 2). Historical average of weekly rainfall at each grid point is then computed as climatological mean rainfall at a particular grid point for a

Multi-site weekly rainfall prediction methodology

It is mathematically difficult to use climate signals for the prediction of basin-scale hydrologic variables due to the inherent complexity of the climate systems. But such complex systems can be modeled by using the modern Artificial Intelligence (AI) tools, like Artificial Neural Networks (ANN), Genetic Algorithm (GA)-based evolutionary optimizer and Genetic Programming (GP). Applications of ANN may be found in wide range of hydrologic studies, viz., rainfall–runoff modeling (Hsu et al., 1995

Streamflow prediction by rainfall–runoff modeling approach

Genetic Programming is used to translate the gridded rainfall information into streamflow. Auto-correlations are normally found to be significant in streamflow studies. Hence, streamflow in immediate previous time step is also considered as input in the present study. The basin-scale streamflow prediction is performed using data from 1990 to 2003, among which, monsoon rainfall data of years 1990–1998 are used for training purpose. The data from 1999 to 2003 are used for testing purpose.

Three

Streamflow prediction by single-step method

The problem of weekly streamflow forecasting is solved with single-step method, for comparison, by directly relating large-scale circulation and local meteorological information with streamflow.

The revised model uses

  • (i)

    Lagged streamflow in immediate previous week

  • (ii)

    ENSO indices at weekly time steps: (t-10)–(t-1), i.e., 10 values

  • (iii)

    EQUINOO indices at weekly time steps: (t-7)–(t-1), i.e., 7 values

  • (iv)

    OLR anomaly over river basin during time step (t-1)

  • (v)

    Weekly historical avg. rainfall during particular week

Results and Discussion

First, the performance of rainfall–runoff models, developed by using weekly observed rainfall during weeks, (t-6)–(t-1) are evaluated in terms of correlation coefficient. The correlation coefficients between observed streamflow and GP-predicted streamflow are calculated. They are 0.715 during training and 0.741 during testing. (r2 = 0.514 during training and r2 = 0.555 during testing). The plots of Observed and GP-Predicted Streamflow, using this approach, during training and testing are presented

Concluding remarks

The information of large scale atmospheric circulation patterns viz. El Niño Southern Oscillation (ENSO) and Equatorial Indian Ocean Oscillation (EQUINOO), with support of lagged rainfall information at every individual grid point and basin-scale OLR anomaly are successfully used for prediction of weekly gridded monsoon rainfall, in Mahanadi catchment with reasonable accuracy.

Results of this study show that the inclusion of GP-predicted rainfall, at current time step (t), as input, for weekly

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