Long‐range reservoir inflow forecasts using large‐scale climate predictors

Abstract

Identifying significant large‐scale climate indicators has the potential to improve long‐range streamflow forecasts. In this research, we develop streamflow forecasts for Lake Urmia basin, Iran, specifically for inflow into the Boukan and Mahabad reservoirs. In doing so, two types of inflow forecast models are considered: a single site univariate model ignoring the cross correlation between streamflow at different stations, and a multi‐site multivariate forecast model which takes into consideration the cross correlations among stations. Predictor selection is performed through a principal component analysis and an adaptive‐network‐based fuzzy inference system is used to forecast streamflow. Forecast performance is investigated by employing different combinations of large‐scale climatic information and hydrologic data. We found that gridded ocean‐atmospheric circulation variables, including surface precipitation rate and omega (pressure vertical velocity), have the highest correlations (about 0.7) with annual streamflow. In general, multivariate models are able to better preserve the annual cross‐correlations between streamflow at different stations, as expected, without sacrificing forecast skill as compared to the univariate forecast model approach. Additionally, as compared with the baseline feed‐forward artificial neural network‐ and traditional multiple linear regression‐forecast models, the results were approximately the same. This similarity in the forecast performance between the linear and nonlinear models is likely due to the short of data (44‐sample record).

Assessing Climatic Drivers of Spring Mean and Annual Maximum Flows in Western Canadian River Basins

“…Statistical modelling techniques such as multiple linear regression (MLR) have been widely used in hydrology, e.g., for establishing predictor-predictand relationships and identifying predictors' relative importance such as in spring freshet and peak flow prediction [21][22][23][24]. Such statistical predictions of hydrologic time series mostly depend on historic observations and are based on the correlations between the predictand and predictor variables that manifest the influence of large-scale climate on the hydrologic regime [21].…”

“…While hydrologic processes have nonlinear characteristics, a number of previous studies have shown that MLR between selected predictors and predictand variables can explain most of the variance in catchment responses [21][22][23][24]. In most of the cases, predictors were selected based on an understanding of the physical processes, relevant literature and initial exploratory data analysis.…”

Short-Term River Flow Forecasting Framework and Its Application in Cold Climatic Regions

“…ANFIS has shown the highest accuracy among the process-and data-driven techniques presented in the literature. In general, artificial intelligence (AI) techniques such as ANFIS have been broadly applied to hydrological modelling for their high performance [26,27]. At the same time, AI models are often complex to calibrate due to numerous calibration parameters, requiring specialized personnel to properly operate the software.…”

Simulating the Impact of Climate Change with Different Reservoir Operating Strategies on Sedimentation of the Mangla Reservoir, Northern Pakistan

“…A number of researchers predicted the hydrological parameter by developing artificial neural network (ANN)-based models [47,48]. For many years, ANN-based models have been used for rainfall estimation, runoff estimation, reservoir inflow prediction, suspended sediment prediction, reservoir level estimation, and reservoir operation [39,[49][50][51][52]. In this study, missing and future suspended sediment data were predicted by ANN-based data-driven technique for the efficient estimation of incoming suspended sediment.…”