黄河流域气候与农业种植面积变化对径流的影响

根据黄河流域1960—2005年5个水文站逐日流量、77个气象站1959—2013年逐日降水数据,结合流域内主要农作物种植面积及大型水库资料,全面探讨气候与农业面积变化及人类活动对黄河流域径流变化的影响.研究表明:黄河流域所有流量分位数总体呈下降趋势,并于1980s中后期到1990s中期发生突变.降水变化是黄河流域径流变化的主要影响因素.在考虑不同流量分位数情况下,农作物种植面积变化对不同分位数径流变化的影响也有差异性.花园口站农作物种植面积变化对径流量量级和可变性均有显著影响;其余4站各项气候变化与农作物种植指标参数较大,虽均未达到10%的显著性水平,但仍会对径流的量级变化产生影响.对唐乃亥站,农作物耕作面积的下降减少了灌溉用水,在0.5流量分位数时有高达60%增加径流量的间接作用.对于头道拐站,农作物耕作面积的增加使得流域总蒸发量增加,灌溉用水增加,在0.3流量分位数时有高达40%减少径流量的间接作用.该研究为气候变化与人类活动影响下黄河流域水资源优化配置提供重要理论依据.     

CNOP-P-based parameter sensitivity for double-gyre variation in ROMS with simulated annealing algorithm

Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model,while simulating double-gyre variation in Regional Ocean Modeling System(ROMS).Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P)is an effective method of studying the parameters sensitivity,which represents a type of parameter error with maximum nonlinear development at the prediction time.Intelligent algorithms have been widely applied to solving Conditional Nonlinear Optimal Perturbation(CNOP).In the paper,we proposed an improved simulated annealing(SA)algorithm to solve CNOP-P to get the optimal parameters error,studied the sensitivity of the single parameter and the combination of multiple parameters and verified the effect of reducing the error of sensitive parameters on reducing the uncertainty of model simulation.Specifically,we firstly found the non-period oscillation of kinetic energy time series of double gyre variation,then extracted two transition periods,which are respectively from high energy to low energy and from low energy to high energy.For every transition period,three parameters,respectively wind amplitude(WD),viscosity coefficient(VC)and linear bottom drag coefficient(RDRG),were studied by CNOP-P solved with SA algorithm.Finally,for sensitive parameters,their effect on model simulation is verified.Experiments results showed that the sensitivity order is WD>VC>>RDRG,the effect of the combination of multiple sensitive parameters is greater than that of single parameter superposition and the reduction of error of sensitive parameters can effectively reduce model prediction error which confirmed the importance of sensitive parameters analysis.     

Optimal precursors of double-gyre regime transitions with an adjoint-free method

In this paper,we find the optimal precursors which can cause double-gyre regime transitions based on conditional nonlinear optimal perturbation(CNOP)method with Regional Ocean Modeling System(ROMS).Firstly,we simulate the multiple-equilibria regimes of double-gyre circulation under different viscosity coefficient and obtain the bifurcation diagram,then choose two equilibrium states(called jet-up state and jet-down state)as reference states respectively,propose Principal Component Analysisbased Simulated Annealing(PCASA)algorithm to solve CNOP-type initial perturbations which can induce double-gyre regime transitions between jet-up state and j et-down state.PCASA algorithm is an adj oint-free method which searches optimal solution randomly in the whole solution space.In addition,we investigate CNOP-type initial perturbations how to evolve with time.The results show:(1)the CNOP-type perturbations present a two-cell structure,and gradually evolves into a three-cell structure at predictive time;(2)by superimpo sing CNOP-type perturbations on the j et-up state and integrating ROMS,double-gyre circulation transfers from jet-up state to jet-down state,and vice versa,and random initial perturbations don't cause the transitions,which means CNOP-type perturbations are the optimal precursors of double-gyre regime transitions;(3)by analyzing the transition process of double-gyre regime transitions,we find that CNOPtype initial perturbations obtain energy from the background state through both barotropic and baroclinic instabilities,and barotropic instability contributes more significantly to the fast-growth of the perturbations.The optimal precursors and the dynamic mechanism of double-gyre regime transitions revealed in this paper have an important significance to enhance the predictability of double-gyre circulation.     

Optimal precursors of double-gyre regime transitions with an adjoint-free method

In this paper, we find the optimal precursors which can cause double-gyre regime transitions based on conditional nonlinear optimal perturbation (CNOP) method with Regional Ocean Modeling System (ROMS). Firstly, we simulate the multiple-equilibria regimes of double-gyre circulation under different viscosity coefficient and obtain the bifurcation diagram, then choose two equilibrium states (called jet-up state and jet-down state) as reference states respectively, propose Principal Component Analysis-based Simulated Annealing (PCASA) algorithm to solve CNOP-type initial perturbations which can induce double-gyre regime transitions between jet-up state and jet-down state. PCASA algorithm is an adjoint-free method which searches optimal solution randomly in the whole solution space. In addition, we investigate CNOP-type initial perturbations how to evolve with time. The results show:(1) the CNOP-type perturbations present a two-cell structure, and gradually evolves into a three-cell structure at predictive time;(2) by superimposing CNOP-type perturbations on the jet-up state and integrating ROMS, double-gyre circulation transfers from jet-up state to jet-down state, and vice versa, and random initial perturbations don't cause the transitions, which means CNOP-type perturbations are the optimal precursors of double-gyre regime transitions;(3) by analyzing the transition process of double-gyre regime transitions, we find that CNOP-type initial perturbations obtain energy from the background state through both barotropic and baroclinic instabilities, and barotropic instability contributes more significantly to the fast-growth of the perturbations. The optimal precursors and the dynamic mechanism of double-gyre regime transitions revealed in this paper have an important significance to enhance the predictability of double-gyre circulation.     

CNOP-P-based parameter sensitivity for double-gyre variation in ROMS with simulated annealing algorithm

Reducing the error of sensitive parameters by studying the parameters sensitivity can reduce the uncertainty of the model, while simulating double-gyre variation in Regional Ocean Modeling System(ROMS). Conditional Nonlinear Optimal Perturbation related to Parameter(CNOP-P) is an effective method of studying the parameters sensitivity, which represents a type of parameter error with maximum nonlinear development at the prediction time. Intelligent algorithms have been widely applied to solving Conditional Nonlinear Optimal Perturbation(CNOP). In the paper, we proposed an improved simulated annealing(SA) algorithm to solve CNOP-P to get the optimal parameters error,studied the sensitivity of the single parameter and the combination of multiple parameters and verified the effect of reducing the error of sensitive parameters on reducing the uncertainty of model simulation. Specifically, we firstly found the non-period oscillation of kinetic energy time series of double gyre variation, then extracted two transition periods, which are respectively from high energy to low energy and from low energy to high energy. For every transition period, three parameters, respectively wind amplitude(WD), viscosity coefficient(VC)and linear bottom drag coefficient(RDRG), were studied by CNOP-P solved with SA algorithm. Finally,for sensitive parameters, their effect on model simulation is verified. Experiments results showed that the sensitivity order is WDVCRDRG, the effect of the combination of multiple sensitive parameters is greater than that of single parameter superposition and the reduction of error of sensitive parameters can effectively reduce model prediction error which confirmed the importance of sensitive parameters analysis.