Groundwater level forecast based on principal component analysis and multivariate time series model

Predication of groundwater level is an important basis for the management of regional water resources. Based on the high randomness and hysteresis characteristics of groundwater in time series, a groundwater level prediction model that is based on principal component analysis and multivariable time series CAR model is built and used for the predication of groundwater level at Dougou irrigation area of Ji'nan. According to the results, the determination coefficient R² and the Nash-Suttcliffe coefficient E_ns of the simulated value and the measured value all reached 0.90 and the above. By taking 2011 as the base year, when precipitation reduces 10%—20%, evaporation and domestic water consumption increases 10%—20% and 273 900—1 370 000 m³ surface water is diverted for agricultural irrigation, the groundwater level at the irrigation area will be maintained at 30.99—31.29 m in 2030, increasing 0.12—0.42 m than that of the base year. Under the background of regional water resources shortage, proper diverting surface water for irrigation and reducing groundwater exploitation can gradually increase the groundwater level at irrigation area and have great significance for the sustainable development of irrigation area and the reasonable utilization of regional water resources.