为了弥补缺资料地区实际蒸散发模拟的不足,本研究以黄河流域内蒙古段典型植被恢复区——浑河流域为对象,建立基于Budyko理论与互补模型的耦合模型(BC_(2021)),实现对缺少实测蒸散发资料地区1982—2020年实际蒸散发的模拟,并分析其时空...为了弥补缺资料地区实际蒸散发模拟的不足,本研究以黄河流域内蒙古段典型植被恢复区——浑河流域为对象,建立基于Budyko理论与互补模型的耦合模型(BC_(2021)),实现对缺少实测蒸散发资料地区1982—2020年实际蒸散发的模拟,并分析其时空变化特征与影响因子。结果表明:(1)建立的BC_(2021)模型对浑河流域年际与年内不同时间尺度的实际蒸散发模拟具有良好的效果,其参数与降水呈显著正相关,与日照时数呈显著负相关;(2)浑河流域近40年平均实际蒸散发为413.24 mm, 1982—1999年以0.44 mm·a^(-1)的不显著下降,而自1999年大规模植树造林以来,该流域平均实际蒸散发则以2.53 mm·a^(-1)的速率显著上升;年内实际蒸散发以春季变化最为显著,而年际变化呈现先下降后上升的趋势。此外,受地形、气候、植被覆盖的综合影响,浑河流域实际蒸散发呈现由西北部向东南部递增的地带性特征;(3)降水是影响浑河流域年际实际蒸散发变化的主要因素,而影响年内实际蒸散发变化的驱动因素存在一定差异,降水、NDVI与日照时数分别是影响夏季、秋季与冬季实际蒸散发变化的主要因素。研究结果为缺乏实测蒸散发资料地区实际蒸散发的长时间模拟提供了可靠的依据,且对于植被恢复显著的干旱半干旱区水资源合理利用与管理具有重要的科学价值。展开更多
Hydrologic models generally represent the most dominant processes since they are mere simplifications of physical reality and thus are subject to many significant uncertainties. As such, a coupling strategy is propose...Hydrologic models generally represent the most dominant processes since they are mere simplifications of physical reality and thus are subject to many significant uncertainties. As such, a coupling strategy is proposed. To this end, the coupling of the artificial neural network （ANN） with the Xin＇anjiang conceptual model with a view to enhance the quality of its flow forecast is presented. The approach uses the latest observations and residuals in runoff/discharge forecasts from the Xin＇anjiang model. The two complementary models （Xin＇anjiang ＆ ANN） are used in such a way that residuals of the Xin＇anjiang model are forecasted by a neural network model so that flow forecasts can be improved as new observations come in. For the complementary neural network, the input data were presented in a patterned format to conform to the calibration regime of the Xin＇anjiang conceptual model, using differing variants of the neural network scheme. The results show that there is a substantial improvement in the accuracy of the forecasts when the complementary model was operated on top of the Xin＇anjiang conceptual model as compared with the results of the Xin＇anjiang model alone.展开更多
文摘为了弥补缺资料地区实际蒸散发模拟的不足,本研究以黄河流域内蒙古段典型植被恢复区——浑河流域为对象,建立基于Budyko理论与互补模型的耦合模型(BC_(2021)),实现对缺少实测蒸散发资料地区1982—2020年实际蒸散发的模拟,并分析其时空变化特征与影响因子。结果表明:(1)建立的BC_(2021)模型对浑河流域年际与年内不同时间尺度的实际蒸散发模拟具有良好的效果,其参数与降水呈显著正相关,与日照时数呈显著负相关;(2)浑河流域近40年平均实际蒸散发为413.24 mm, 1982—1999年以0.44 mm·a^(-1)的不显著下降,而自1999年大规模植树造林以来,该流域平均实际蒸散发则以2.53 mm·a^(-1)的速率显著上升;年内实际蒸散发以春季变化最为显著,而年际变化呈现先下降后上升的趋势。此外,受地形、气候、植被覆盖的综合影响,浑河流域实际蒸散发呈现由西北部向东南部递增的地带性特征;(3)降水是影响浑河流域年际实际蒸散发变化的主要因素,而影响年内实际蒸散发变化的驱动因素存在一定差异,降水、NDVI与日照时数分别是影响夏季、秋季与冬季实际蒸散发变化的主要因素。研究结果为缺乏实测蒸散发资料地区实际蒸散发的长时间模拟提供了可靠的依据,且对于植被恢复显著的干旱半干旱区水资源合理利用与管理具有重要的科学价值。
文摘Hydrologic models generally represent the most dominant processes since they are mere simplifications of physical reality and thus are subject to many significant uncertainties. As such, a coupling strategy is proposed. To this end, the coupling of the artificial neural network （ANN） with the Xin＇anjiang conceptual model with a view to enhance the quality of its flow forecast is presented. The approach uses the latest observations and residuals in runoff/discharge forecasts from the Xin＇anjiang model. The two complementary models （Xin＇anjiang ＆ ANN） are used in such a way that residuals of the Xin＇anjiang model are forecasted by a neural network model so that flow forecasts can be improved as new observations come in. For the complementary neural network, the input data were presented in a patterned format to conform to the calibration regime of the Xin＇anjiang conceptual model, using differing variants of the neural network scheme. The results show that there is a substantial improvement in the accuracy of the forecasts when the complementary model was operated on top of the Xin＇anjiang conceptual model as compared with the results of the Xin＇anjiang model alone.
