An Efficient Prediction Model for Water Discharge in Schoharie Creek, NY

Abstract

Flooding normally occurs during periods of excessive precipitation or thawing in the winter period (ice jam). Flooding is typically accompanied by an increase in river discharge. This paper presents a statistical model for the prediction and explanation of the water discharge time series using an example from the Schoharie Creek, New York (one of the principal tributaries of the Mohawk River). It is developed with a view to wider application in similar water basins. In this study a statistical methodology for the decomposition of the time series is used. The Kolmogorov-Zurbenko filter is used for the decomposition of the hydrological and climatic time series into the seasonal and the long and the short term component. We analyze the time series of the water discharge by using a summer and a winter model. The explanation of the water discharge has been improved up to 81%. The results show that as water discharge increases in the long term then the water table replenishes, and in the seasonal term it depletes. In the short term, the groundwater drops during the winter period, and it rises during the summer period. This methodology can be applied for the prediction of the water discharge at multiple sites.

Time Series Regression for Forecasting Flood Events in Schenectady, New York

“…where m = 2k + 1 A flowchart describes the methodology for the decomposition of the time series of all the variables and the application of the time series regression model in the long-term, seasonal-term, and short-term component ( Figure 4). To stabilize the variance of the data for the series to be stationary, we use the log transform of the variable [14]. For our study, the log transformation of the water discharge time series is named as the raw data ( Figure 4).…”

“…Ice jams occur when floes accumulate at the base of bridge piers, locks, and dam structures, impeding the downstream water flow causing an upstream rise in the water level [10,11]. Flood forecasting research has been conducted in the past at Schenectady in which flood forecasting and damage evaluation has been surveyed [12][13][14][15][16][17]. In this study, we use hydrogeological and climatic data to predict the water discharge time series in Mohawk River at Schenectady, New York.…”

“…The reason that the TSR model has been chosen was due to the advantage of physical interpretation for the variables which may contribute to a flood event. Other models may provide higher or lower forecasting power, but they may lack for the physical interpretation of the variables included in the model [14,[18][19][20]. The climatic and hydrogeological data consists of a sufficient amount of daily records and frequent extreme events among natural seasonal phenomena.…”

“…To isolate the different cycles in the time series and eliminate the interferences between the cycles, we decompose the time series of the water discharge and the hydrogeological and climatic variables into the long, seasonal and short-term component [14]. Several studies have shown that the decomposition of the variables into different components may isolate the noise from the data and separate the different signals in the time series [14,21,22]. The multiple linear regression model (MLR) uses only the current values for the explanation of the water discharge.…”

“…The TSR model is a statistical model that includes current and past observations of the predictors' variables and can be used for forecasting an event applied in several fields [26][27][28][29]. The hydrogeological and climatic data have shown in a previous analysis [14] seasonality and periodicities ranging from a couple of weeks to an annual cycle. The application on the data of a time series model (i.e., ARIMA model) or a multiple regression model without the decomposition of the time series may lead to inaccurate results and lack of physical interpretation due to the mixed cycles on the data [30,31].…”