NoahMP-RAPID在高海拔山地流域的模拟检验与误差分析
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摘要
高海拔山地流域水能资源丰富、山洪易发;但产汇流机制复杂、降水数据不确定性大,准确的山地水文过程模拟面临较大困难.本研究采用中国区域地面气象要素数据集CMFD(China Meteorological Forcing Dataset)驱动NoahMP陆面过程-RAPID河网汇流耦合模式,模拟青藏高原东侧大渡河干流逐日流量,并根据铜街子、龙头石流量站观测数据率定RAPID中的波速参数,检验了NoahMP中SIMGM和NOAH两种不同的产流过程参数化方案的模拟能力,评估了CMFD降水驱动数据的系统性偏差.研究发现,基于Philip入渗模型的NOAH方案优于基于TOPMODEL的SIMGM方案,能较为准确地模拟大渡河干流流量逐日变化,相关系数大于0.85、纳什系数约为0.3;对纳什系数的数学分解发现,纳什系数较低主要是由于模拟流量显著偏低造成的,若剔除系统性偏差,NOAH方案的纳什系数可提高至约0.7.模拟流量的系统性偏差主要来自于降水驱动数据;与雨量站观测和反演数据相比,CMFD显著低估了大渡河流域平均降水量,且其系统性偏差与高程有关,在低海拔地区高估,高海拔地区低估.本研究表明,使用NOAH产流方案的NoahMP-RAPID耦合模式对大渡河流域水文过程有较好的模拟能力,可进一步应用于水库调度优化;而提高雨量站密度、降低降水数据产品的系统性偏差是进一步改进高海拔山地流域水文过程模拟的关键.
Accurate hydrological simulations in high-altitude mountainous basins are important for optimizing reservoir operations in hydropower production and flood control. However, the simulations experience substantial uncertainties from the sparse precipitation measurements over complex mountainous topography and the imperfect hydrological process representations over the heterogeneous surface. At the Daduhe river basin, which is a high-altitude mountainous river basin located in the eastern slope of the Tibetan Plateau, daily streamflow over the 2-year period of 2014 and 2015 is simulated using the Noah land surface model with multi-parameterizations(NoahMP) and Routing Application for Parallel computation of Discharge(RAPID) coupled model driven by the Chinese Meteorological Forcing Dataset(CMFD). Two different runoff parameterization schemes of NoahMP are used for the simulations: SIMGM, which is derived from TOPMODEL and parameterizes surface runoff using water table depth, and NOAH, in which surface runoff is parameterized using soil moisture content based on the Philip infiltration model. The simulated streamflow is evaluated at two mainstream gauges on the Daduhe river, Tongjiezi and Longtoushi, in terms of Nash-Sutcliffe Efficiency(NSE), correlation coefficient, bias, and standard deviation. The uncertainty of the simulated streamflow is attributed to different runoff parameterization in NoahMP, the flow wave celerity parameter of RAPID, and the forcing CMFD precipitation data. Results show that the NOAH runoff parameterization scheme outperforms the SIMGM scheme. NOAH satisfactorily reproduced the observed streamflow anomaly and flood timing at the two mainstream gauges, and the correlation coefficients between the simulated and observed streamflow are above 0.85. The NSE for NOAH is approximately 0.3. The mathematical decomposition of NSE reveals that the relatively low NSE value is mainly attributed to the significant streamflow bias. Without the bias, NSE can be improved to approximately 0.7. The bias shows little dependency on the flow wave celerity parameter of RAPID and the runoff parameterization of NoahMP, whereas it closely corresponds to the forcing CMFD precipitation data bias. Comparing the estimated precipitation from Budyko's curve and the observed streamflow, CMFD significantly underestimates the basin-averaged precipitation, which leads to the significant streamflow underestimation in the hydrological simulations. CMFD does not correctly represent the orographic effects on precipitation in the Daduhe river basin. Comparing the rain gauge measurements located in low-altitude river valleys, the CMFD precipitation is significant underestimated, suggesting a significantly underestimation in high-altitude precipitation. This study shows that streamflow bias can be reduced by densifying rain gauges at high altitudes. Despite the bias, the NoahMP RAPID coupled model can satisfactorily reproduce streamflow anomalies and flood timing in mountainous basins. As a dam model is already included, the coupled model can be further used to optimize reservoir operations for hydropower production and flood control.
Accurate hydrological simulations in high-altitude mountainous basins are important for optimizing reservoir operations in hydropower production and flood control. However, the simulations experience substantial uncertainties from the sparse precipitation measurements over complex mountainous topography and the imperfect hydrological process representations over the heterogeneous surface. At the Daduhe river basin, which is a high-altitude mountainous river basin located in the eastern slope of the Tibetan Plateau, daily streamflow over the 2-year period of 2014 and 2015 is simulated using the Noah land surface model with multi-parameterizations(NoahMP) and Routing Application for Parallel computation of Discharge(RAPID) coupled model driven by the Chinese Meteorological Forcing Dataset(CMFD). Two different runoff parameterization schemes of NoahMP are used for the simulations: SIMGM, which is derived from TOPMODEL and parameterizes surface runoff using water table depth, and NOAH, in which surface runoff is parameterized using soil moisture content based on the Philip infiltration model. The simulated streamflow is evaluated at two mainstream gauges on the Daduhe river, Tongjiezi and Longtoushi, in terms of Nash-Sutcliffe Efficiency(NSE), correlation coefficient, bias, and standard deviation. The uncertainty of the simulated streamflow is attributed to different runoff parameterization in NoahMP, the flow wave celerity parameter of RAPID, and the forcing CMFD precipitation data. Results show that the NOAH runoff parameterization scheme outperforms the SIMGM scheme. NOAH satisfactorily reproduced the observed streamflow anomaly and flood timing at the two mainstream gauges, and the correlation coefficients between the simulated and observed streamflow are above 0.85. The NSE for NOAH is approximately 0.3. The mathematical decomposition of NSE reveals that the relatively low NSE value is mainly attributed to the significant streamflow bias. Without the bias, NSE can be improved to approximately 0.7. The bias shows little dependency on the flow wave celerity parameter of RAPID and the runoff parameterization of NoahMP, whereas it closely corresponds to the forcing CMFD precipitation data bias. Comparing the estimated precipitation from Budyko's curve and the observed streamflow, CMFD significantly underestimates the basin-averaged precipitation, which leads to the significant streamflow underestimation in the hydrological simulations. CMFD does not correctly represent the orographic effects on precipitation in the Daduhe river basin. Comparing the rain gauge measurements located in low-altitude river valleys, the CMFD precipitation is significant underestimated, suggesting a significantly underestimation in high-altitude precipitation. This study shows that streamflow bias can be reduced by densifying rain gauges at high altitudes. Despite the bias, the NoahMP RAPID coupled model can satisfactorily reproduce streamflow anomalies and flood timing in mountainous basins. As a dam model is already included, the coupled model can be further used to optimize reservoir operations for hydropower production and flood control.
引文
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4 Wada Y,van Beek L P H,Wanders N,et al.Human water consumption intensifies hydrological drought worldwide.Environ Res Lett,2013,8:034036
5 Meybeck M,Green P,V?r?smarty C.A new typology for mountains and other relief classes.Mt Res Dev,2001,21:34-45
6 Immerzeel W W,van Beek L P H,Bierkens M F P.Climate change will affect the Asian water towers.Science,2010,328:1382-1385
7 Freeze R A,Harlan R L.Blueprint for a physically-based,digitally-simulated hydrologic response model.J Hydrol,1969,9:237-258
8 Tang W,Lin Z H,Yang C G,et al.Evaluation of a hydrological simulation over the Huaihe River basin using the coupled land surface and hydrologic model system and its uncertainty analysis(in Chinese).Clim Environ Res,2014,19:463-476[唐伟,林朝晖,杨传国,等.基于陆面水文耦合模式CLHMS的淮河流域水文过程的模拟评估及其不确定性分析.气候与环境研究,2014,19:463-476]
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10 Devia G K,Ganasri B P,Dwarakish G S.A review on hydrological models.Aquat Proced,2015,4:1001-1007
11 Maidment D R.Conceptual framework for the National Flood Interoperability Experiment.J Am Water Resour Assoc,2016,53:245-257
12 McEnery J,Ingram J,Duan Q,et al.NOAA’s advanced hydrologic prediction service.Bull Am Meteorol Soc,2005,86:375-386
13 Lin P,Rajib M A,Yang Z L,et al.Spatiotemporal evaluation of simulated evapotranspiration and streamflow over Texas using the WRF-Hydro-RAPID modeling framework.J Am Water Resour Assoc,2018,54:40-54
14 Salas F R,Somos-Valenzuela M A,Dugger A,et al.Towards real-time continental scale streamflow simulation in continuous and discrete space.J Am Water Resour Assoc,2018,54:7-27
15 Fatichi S,Vivoni E R,Ogden F L,et al.An overview of current applications,challenges,and future trends in distributed process-based models in hydrology.J Hydrol,2016,537:45-60
16 Henn B,Newman A J,Livneh B,et al.An assessment of differences in gridded precipitation datasets in complex terrain.J Hydrol,2018,556:1205-1219
17 Yang K,He J,Tang W,et al.On downward shortwave and longwave radiations over high altitude regions:Observation and modeling in the Tibetan Plateau.Agric For Meteorol,2010,150:38-46
18 Lehner B,Verdin K,Jarvis A.New global hydrography derived from spaceborne elevation data.Eos Trans Am Geophys Union,2008,89:93-94
19 Lehner B,Grill G.Global river hydrography and network routing:Baseline data and new approaches to study the world’s large river systems.Hydrol Process,2013,27:2171-2186
20 Niu G Y,Yang Z L,Mitchell K E,et al.The community Noah land surface model with multiparameterization options(Noah-MP):1.Model description and evaluation with local-scale measurements.J Geophys Res Atmos,2011,116:D12109
21 Yang Z L,Niu G Y,Mitchell K E,et al.The community Noah land surface model with multiparameterization options(Noah-MP):2.Evaluation over global river basins.J Geophys Res Atmos,2011,116:D12110
22 Cai X,Yang Z L,David C H,et al.Hydrological evaluation of the Noah-MP land surface model for the Mississippi River Basin.J Geophys Res Atmos,2014,119:23-38
23 Cai X,Yang Z L,Xia Y,et al.Assessment of simulated water balance from Noah,Noah-MP,CLM,and VIC over CONUS using the NLDAS test bed.J Geophys Res Atmos,2014,119:13751-13770
24 Ma N,Niu G Y,Xia Y,et al.A systematic evaluation of Noah-MP in simulating land-atmosphere energy,water,and carbon exchanges over the continental United States.J Geophys Res Atmos,2017,122:12245-12268
25 Niu G Y,Yang Z L,Dickinson R E,et al.Development of a simple groundwater model for use in climate models and evaluation with gravity recovery and climate experiment data.J Geophys Res Atmos,2007,112:D07103
26 Beven K J,Kirkby M J.A physically based,variable contributing area model of basin hydrology.Hydrol Sci Bull,1979,24:43-69
27 Chen F,Dudhia J.Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system.Part I:Model implementation and sensitivity.Mon Weather Rev,2001,129:569-585
28 Schaake J C,Koren V I,Duan Q Y,et al.Simple water balance model for estimating runoff at different spatial and temporal scales.JGeophys Res Atmos,1996,101:7461-7475
29 Philip J R.Theory of Infiltration.In:Chow V T,ed.Advances in Hydroscience,vol.5.New York:Academic Press,1969.215-296
30 David C H,Maidment D R,Niu G Y,et al.River network routing on the NHDPlus dataset.J Hydrometeorol,2011,12:913-934
31 Cunge J A.On the subject of a flood propagation computation method(Muskingum method).J Hydraul Res,1969,7:205-230
32 Zheng H,Yang Z L.Effects of soil-type datasets on regional terrestrial water cycle simulations under different climatic regimes.J Geophys Res Atmos,2016,121:14387-14402
33 Gupta H V,Kling H,Yilmaz K K,et al.Decomposition of the mean squared error and NSE performance criteria:Implications for improving hydrological modelling.J Hydrol,2009,377:80-91
34 Chen Y,Ebert E E,Walsh K J E,et al.Evaluation of TRMM 3B42 precipitation estimates of tropical cyclone rainfall using PACRAINdata.J Geophys Res Atmos,2013,118:2184-2196
35 Budyko M I.Climate and Life.New York:Academic Press,1974
36 Mu Q,Zhao M,Running S W.Improvements to a MODIS global terrestrial evapotranspiration algorithm.Remote Sens Environ,2011,115:1781-1800
37 Gentine P,D’Odorico P,Lintner B R,et al.Interdependence of climate,soil,and vegetation as constrained by the Budyko curve.Geophys Res Lett,2012,39:L19404
38 Adam J C,Clark E A,Lettenmaier D P,et al.Correction of global precipitation products for orographic effects.J Clim,2006,19:15-38
39 Peng G K.Statistical analysis of summer-time upper wind over Ya’An(in Chinese).Plateau Mt Meteorol Res,1992,1:3-7[彭贵康.夏季雅安高空风的统计分析.高原山地气象研究,1992,1:3-7]
40 David C H,Yang Z L,Hong S.Regional-scale river flow modeling using off-the-shelf runoff products,thousands of mapped rivers and hundreds of stream flow gauges.Environ Model Softw,2013,42:116-132
2 Archfield S A,Clark M P,Arheimer B,et al.Accelerating advances in continental domain hydrologic modeling.Water Resour Res,2015,51:10078-10091
3 Clark M P,Fan Y,Lawrence D M,et al.Improving the representation of hydrologic processes in Earth System Models.Water Resour Res,2015,51:5929-5956
4 Wada Y,van Beek L P H,Wanders N,et al.Human water consumption intensifies hydrological drought worldwide.Environ Res Lett,2013,8:034036
5 Meybeck M,Green P,V?r?smarty C.A new typology for mountains and other relief classes.Mt Res Dev,2001,21:34-45
6 Immerzeel W W,van Beek L P H,Bierkens M F P.Climate change will affect the Asian water towers.Science,2010,328:1382-1385
7 Freeze R A,Harlan R L.Blueprint for a physically-based,digitally-simulated hydrologic response model.J Hydrol,1969,9:237-258
8 Tang W,Lin Z H,Yang C G,et al.Evaluation of a hydrological simulation over the Huaihe River basin using the coupled land surface and hydrologic model system and its uncertainty analysis(in Chinese).Clim Environ Res,2014,19:463-476[唐伟,林朝晖,杨传国,等.基于陆面水文耦合模式CLHMS的淮河流域水文过程的模拟评估及其不确定性分析.气候与环境研究,2014,19:463-476]
9 Li M,Lin Z H,Shao Y P,et al.Improvement of a coupled land surface-hydrological model with calibrated hydraulic parameters(in Chinese).Clim Environ Res,2015,20:141-153[李敏,林朝晖,绍亚平,等.陆面-水文耦合模式的参数率定及改进研究.气候与环境研究,2015,20:141-153]
10 Devia G K,Ganasri B P,Dwarakish G S.A review on hydrological models.Aquat Proced,2015,4:1001-1007
11 Maidment D R.Conceptual framework for the National Flood Interoperability Experiment.J Am Water Resour Assoc,2016,53:245-257
12 McEnery J,Ingram J,Duan Q,et al.NOAA’s advanced hydrologic prediction service.Bull Am Meteorol Soc,2005,86:375-386
13 Lin P,Rajib M A,Yang Z L,et al.Spatiotemporal evaluation of simulated evapotranspiration and streamflow over Texas using the WRF-Hydro-RAPID modeling framework.J Am Water Resour Assoc,2018,54:40-54
14 Salas F R,Somos-Valenzuela M A,Dugger A,et al.Towards real-time continental scale streamflow simulation in continuous and discrete space.J Am Water Resour Assoc,2018,54:7-27
15 Fatichi S,Vivoni E R,Ogden F L,et al.An overview of current applications,challenges,and future trends in distributed process-based models in hydrology.J Hydrol,2016,537:45-60
16 Henn B,Newman A J,Livneh B,et al.An assessment of differences in gridded precipitation datasets in complex terrain.J Hydrol,2018,556:1205-1219
17 Yang K,He J,Tang W,et al.On downward shortwave and longwave radiations over high altitude regions:Observation and modeling in the Tibetan Plateau.Agric For Meteorol,2010,150:38-46
18 Lehner B,Verdin K,Jarvis A.New global hydrography derived from spaceborne elevation data.Eos Trans Am Geophys Union,2008,89:93-94
19 Lehner B,Grill G.Global river hydrography and network routing:Baseline data and new approaches to study the world’s large river systems.Hydrol Process,2013,27:2171-2186
20 Niu G Y,Yang Z L,Mitchell K E,et al.The community Noah land surface model with multiparameterization options(Noah-MP):1.Model description and evaluation with local-scale measurements.J Geophys Res Atmos,2011,116:D12109
21 Yang Z L,Niu G Y,Mitchell K E,et al.The community Noah land surface model with multiparameterization options(Noah-MP):2.Evaluation over global river basins.J Geophys Res Atmos,2011,116:D12110
22 Cai X,Yang Z L,David C H,et al.Hydrological evaluation of the Noah-MP land surface model for the Mississippi River Basin.J Geophys Res Atmos,2014,119:23-38
23 Cai X,Yang Z L,Xia Y,et al.Assessment of simulated water balance from Noah,Noah-MP,CLM,and VIC over CONUS using the NLDAS test bed.J Geophys Res Atmos,2014,119:13751-13770
24 Ma N,Niu G Y,Xia Y,et al.A systematic evaluation of Noah-MP in simulating land-atmosphere energy,water,and carbon exchanges over the continental United States.J Geophys Res Atmos,2017,122:12245-12268
25 Niu G Y,Yang Z L,Dickinson R E,et al.Development of a simple groundwater model for use in climate models and evaluation with gravity recovery and climate experiment data.J Geophys Res Atmos,2007,112:D07103
26 Beven K J,Kirkby M J.A physically based,variable contributing area model of basin hydrology.Hydrol Sci Bull,1979,24:43-69
27 Chen F,Dudhia J.Coupling an advanced land surface-hydrology model with the Penn State-NCAR MM5 modeling system.Part I:Model implementation and sensitivity.Mon Weather Rev,2001,129:569-585
28 Schaake J C,Koren V I,Duan Q Y,et al.Simple water balance model for estimating runoff at different spatial and temporal scales.JGeophys Res Atmos,1996,101:7461-7475
29 Philip J R.Theory of Infiltration.In:Chow V T,ed.Advances in Hydroscience,vol.5.New York:Academic Press,1969.215-296
30 David C H,Maidment D R,Niu G Y,et al.River network routing on the NHDPlus dataset.J Hydrometeorol,2011,12:913-934
31 Cunge J A.On the subject of a flood propagation computation method(Muskingum method).J Hydraul Res,1969,7:205-230
32 Zheng H,Yang Z L.Effects of soil-type datasets on regional terrestrial water cycle simulations under different climatic regimes.J Geophys Res Atmos,2016,121:14387-14402
33 Gupta H V,Kling H,Yilmaz K K,et al.Decomposition of the mean squared error and NSE performance criteria:Implications for improving hydrological modelling.J Hydrol,2009,377:80-91
34 Chen Y,Ebert E E,Walsh K J E,et al.Evaluation of TRMM 3B42 precipitation estimates of tropical cyclone rainfall using PACRAINdata.J Geophys Res Atmos,2013,118:2184-2196
35 Budyko M I.Climate and Life.New York:Academic Press,1974
36 Mu Q,Zhao M,Running S W.Improvements to a MODIS global terrestrial evapotranspiration algorithm.Remote Sens Environ,2011,115:1781-1800
37 Gentine P,D’Odorico P,Lintner B R,et al.Interdependence of climate,soil,and vegetation as constrained by the Budyko curve.Geophys Res Lett,2012,39:L19404
38 Adam J C,Clark E A,Lettenmaier D P,et al.Correction of global precipitation products for orographic effects.J Clim,2006,19:15-38
39 Peng G K.Statistical analysis of summer-time upper wind over Ya’An(in Chinese).Plateau Mt Meteorol Res,1992,1:3-7[彭贵康.夏季雅安高空风的统计分析.高原山地气象研究,1992,1:3-7]
40 David C H,Yang Z L,Hong S.Regional-scale river flow modeling using off-the-shelf runoff products,thousands of mapped rivers and hundreds of stream flow gauges.Environ Model Softw,2013,42:116-132