分布式水文概念性模型及应用

在遥感与GIS支持下，建立地理空间信息库，提取了流域土地利用、土壤类型和植被类型等地理信息及相应的水空间参数，采用已有的水概念性模型，建立了一个分布式水概念性模型．模型结构简单，所需参数较少，易于率定，可操作性强．实例应用表明，该模型对雁栖河流域径流量的模拟是有效的，可用于评估流域水资源量、气候变化以及土地利用变化对水资源的影响．     

A Genetic Programming Approach to Rainfall-Runoff Modelling

Planning for sustainable development of water resources relies crucially on the data available. Continuous hydrologic simulation based on conceptual models has proved to be the appropriate tool for studying rainfall-runoff processes and for providing necessary data. In recent years, artificial neural networks have emerged as a novel identification technique for the modelling of hydrological processes. However, they represent their knowledge in terms of a weight matrix that is not accessible to human understanding at present. This paper introduces genetic programming, which is an evolutionary computing method that provides a transparent and structured system identification, to rainfall-runoff modelling. The genetic-programming approach is applied to flow prediction for the Kirkton catchment in Scotland (U.K.). The results obtained are compared to those attained using two optimally calibrated conceptual models and an artificial neural network. Correlations identified using data-driven approaches (genetic programming and neural network) are surprising in their consistency considering the relative size of the models and the number of variables included. These results also compare favourably with the conceptual models.     

Season Algorithm-Multigene Genetic Programming: A New Approach for Rainfall-Runoff Modelling

Genetic programming (GP) is recognized as a robust machine learning method for rainfall-runoff modelling. However, it may produce lagged forecasts if autocorrelation feature of runoff series is not taken carefully into account. To enhance timing accuracy of GP-based rainfall-runoff models, the paper proposes a new rainfall-runoff model that integrates season algorithm (SA) with multigene-GP (MGGP). The proposed SA-MGGP model was trained and validated for single- and two- and three-day ahead streamflow forecasts at Haldizen Catchment, Trabzon, Turkey. Timing and prediction accuracy of the proposed model were assessed in terms of different efficiency criteria. In addition, the efficiency results were compared to those of monolithic GP, MGGP, and SA-GP forecasting models developed in the present study as the benchmarks. The outcomes indicated that SA augments timing accuracy of GP-based models in the range 250% to 500%. It is also found that MGGP may identify underlying structure of the rainfall-runoff process slightly better than monolithic GP at the study catchment.     

Comparison of the Lumped and Quasi-distributed Clark Runoff Models in Simulating Flood Hydrographs on a Semi-arid Watershed

In applied hydrology, predicting peak flow for a stream or river is so complex due to temporal and spatial dependency of hydrological variables such as meteorological parameters, variations in soil type and land use. Either advanced distributed hydrological models or simple Lump models can be used for simulating these situations. This paper compares the performance of the quasi-distributed model ModClark versus lumped parameter model Clark in simulating the process of transformation of rainfall to runoff. The aim of this comparison is to identify whether using a complex model which takes into account spatial and temporal distribution parameters, which are hard to prepare and use, will lead to more precise results or not. For the purpose of this study, historical data of Randan basin situated in semi-arid region of Iran in North West of Tehran was used. The size of the catchment is 67.76 km². Reviewing the results of calibration and accuracy of models revealed that both models are able to simulate the hydrology of the catchment in an acceptable way.     

A Study on Grid Based Distributed Rainfall Runoff Models

Two distributed rainfall-runoff models were developed separately by using linear and nonlinear storage approaches for simulating the heterogeneity of hydrological processes over the catchment. A 1 × 1 km grid-based mesh was used to abstract the geometry of the catchment. The hydrological processes and geographic information were modeled on this scale. Both linear and nonlinear models were calibrated by global optimization technique. The models to simulate spatial hydrological processes over a catchment were verified using the observed hydrographs at two stream gauged stations within the catchment. The nonlinear model has better performance than the linear model for two verification storm events.     

Rainfall-Runoff Modeling: Comparison of Two Approaches with Different Data Requirements

Among several hydrological models developed over the years, the most widely used technique for estimating direct runoff depth from storm rainfall i.e., the United States Department of Agriculture (USDA) Soil Conservation Service’s (SCS) Curve Number (CN) method was adopted in the present study. In addition, the Muskingum method, which continues to be popular for routing of runoff in river network, was used in the developed model to route surface runoffs from different subbasin outlet points up to the outlet point of the catchment. SCS CN method in combination with Muskingum routing technique, however, required a detailed knowledge of several important properties of the watershed, namely, soil type, land use, antecedent soil water conditions, and channel information, which may not be readily available. Due to this complexity of semi-distributed conceptual approach (SCS CN method) and non-linearity involved in rainfall-runoff modeling, researchers also attempted another less data requiring approach for runoff prediction, i.e., the neural network approach, which is inherently suited to problems that are mathematically difficult to describe. The purpose of this study was to compare the rainfall-runoff modeling performance of semi-distributed conceptual SCS CN method (in combination with Muskingum routing technique) with that of empirical ANN technique. The models were coded in C language and to make them user friendly, a Graphical User Interface (GUI) was also developed in Visual Basic 6.0. The developed models were tested for Kangsabati catchment, situated in the western part of West Bengal, India. Monsoon data of 1996 to 1999 were used for calibration of the models whereas they were validated for another four years (1987, 1989, 1990, and 1993) monsoon data. Modeling efficiency (ME) and coefficient of residual mass (CRM) were used as performance indicators. Results indicated that for Kangsabati catchment, the empirical runoff prediction approach (ANN technique), in spite of requiring much less data, predicted daily runoff values more accurately than semi-distributed conceptual runoff prediction approach (SCS CN method).     

TRMM雨量在汉江上游大尺度水文模型中的应用

将TRMM（Tropic Rainfall Measurement Mission）卫星收集分析的降雨资料,利用大尺度水文模型对汉江白河以上流域进行流量模拟与预测,研究TRMM降雨资料应用于大尺度水文模型（LSHM）进行流量预测的可行性。对比分析了TRMM年、月、日系列降雨资料与气象站网降水观测资料,以及将其作为输入数据源的大尺度模型降雨径流模拟预测结果,分析表明：大尺度水文模型概念基本适合汉江上游的流量预测,水库对模拟预测结果影响显著,TRMM日降雨资料尚不满足利用该模型进行洪峰流量预报精度的要求。     

Calibration of a Distributed Hydrological Model (VIC-3L) Based on Global Water Resources Reanalysis Datasets

The lack of observed streamflow datasets for calibration of rainfall-runoff models imposes substantial problems for their applicability, especially in poorly gauged or ungauged river basins. Developing satellite technologies and increasing computational powers over the past decades, have provided an environment for researchers to simulate several water balance components globally using these datasets and assimilation techniques. Due to importance of accurate hydrologic modeling, this study aims to investigate the applicability of global water resources reanalysis (GWRR) datasets including surface soil moisture (SSM), evapotranspiration (ET), and surface runoff (SR) components for calibration of the macro-scale hydrological model (VIC-3L) over the SefidRood basin (SRB) in Iran at different calibration scenarios. Results show that in the case of using SSM datasets, the model’s performance in the simulation of streamflow hydrograph, with the NSE value higher than 0.65, is better than using other datasets. Among different datasets, the SSM based on LISFLOOD and HBV are the best ones for calibration of VIC-3L model over SRB. In contrast, using ET datasets aren’t so reliable for hydrological calibration in the study area. Furthermore, in the cases of using SSM and surface runoff datasets, the model tends to overestimation of low-flows, while, ET datasets are more reliable for simulation of such these flows. Also, findings displayed that the combination of ET and SSM datasets for hydrological calibration performed better than using only one dataset. In conclusion, this research gives useful and applied insights in the applicability of GWRR data sources for hydrological modeling and water resources studies, especially in data limited regions.

     

TOPMODEL for Streamflow Simulation of a Tropical Catchment Using Different Resolutions of ASTER DEM: Optimization Through Response Surface Methodology

The unavailability of proper hydrological data quality combined with the complexity of most physical based hydrologic models limits research on rainfall-runoff relationships, particularly in the tropics. In this paper, an attempt has been made to use different resolutions of DEM generated from freely available 30 m-based ASTER imagery as primary input to the topographically-based hydrological (TOPMODEL) model to simulate the runoff of a medium catchment located in the tropics. Response surface methodology (RSM) was applied to optimize the most sensitive parameters for streamflow simulation. DEM resolutions from 30 to 300 m have been used to assess their effects on the topographic index distribution (TI) and TOPMODEL simulation. It is found that changing DEM resolutions reduces the TOPMODEL simulation performance as the resolutions are varied from 30 to 300 m. The study concluded that the ASTER 30 m DEM can be used for reasonable streamflow simulation of a data scarce tropical catchment compared with the resampled DEMs.     

基于多重网格的地表水文与二维水动力动态双向耦合模型研究

针对流域洪涝模拟模型的计算精度、格式稳定性及计算效率等问题,本文提出基于多重网格技术的地表水文与二维水动力动态双向耦合模型(M-DBCM)。地表水文模型采用非线性水库法模拟降雨产流和径流;二维水动力模型采用浅水方程模拟洪水演进过程。采用不同分辨率的网格划分计算区域,在粗网格区域采用地表水文模型模拟降雨径流过程;在细网格区域采用二维水动力模型模拟洪涝积水区的水流运动。地表水文和二维水动力模型通过内部耦合移动界面(Coupling Moving Interface, CMI)实现无缝连接,保证通过CMI的水量和动量等通量守恒,提高模型的模拟精度。采用时间显式格式同时求解地表水文和水动力模型,在不同区域采用不同的计算时间步长,以提高模型的计算效率。通过典型案例验证本文构建的耦合模型的性能,结果表明本文提出的动态双向耦合模型能够在保证模拟精度的同时提高计算效率。     

深度学习水文预报研究进展综述Ⅰ——常用模型与建模方法

数据密集研究范式主导的数据水文学正在成为水文研究的一个重要方向,而善于从大量数据中挖掘规律的深度学习理论推动了近年来数据驱动水文预报的研究热潮,并不断和水文学科融合,逐步成为数据水文学的重要研究方法体系。从深度学习与水文预报学科交叉的角度,简要介绍水文领域常用深度学习模型的原理与结构及其应用于水文预报中的一般建模方法,在此基础上进一步介绍深度学习与水文物理机制整合的基本方法,以期为相关研究人员开展深度学习水文预报研究提供有益参考。     

A Modified Hortonian Overland Flow Model Based on Laboratory Experiments

Overland flow, which is examined here, is an important hydrological response to storms of a catchment. And it is also a main component of hillslope hydrology. Through controlling of rainfall intensities and changing of artificial slope angles, 49 overland flow hydrographs are obtained from rainfall-runoff simulation experiments conducted on a manmade hillside. Based on the measured overland flow processes, the relationship between the average water depth on the hillslope and that at the outlet, which is nearly a quadratic curve, is obtained. The relationship is different from the assumption, in which the average water depth on the hillslope is equal to that at the outlet, proposed by Horton. Finally, a regression equation is also presented, which provides both the theoretical and experimental basis to modify the time delay of peak flow for a distributed rainfall-runoff modeling.     

重要水源地小流域水环境动态模拟及调控研究

本研究选取北京密云水库上游曹家路小流域为对象,通过野外监测和模型计算的方法对小流域水环境进行动态模拟及调控研究。综合运用水文学、水环境学、数理统计和地理信息系统等方法,并结合野外监测、实验室分析和计算机模拟等手段,建立了小流域半分布式水文模型和一维河道水质模型。运用该模型对曹家路小流域的水质状况进行了模拟计算,得到了流域出口断面NH3-N、Os、BOD、COD和TN等水质因子的总体特征和全年全时段变化过程。根据1956~2000年的水文数据,分析了流域特征水文年水环境的动态变化,提出了相应的水质保护和水环境容量利用综合调控方案,并依据区域的经济发展进行了水环境动态规划。     

水文模型中植被截留计算的误差分析

在众多植被截留模型中,针对场次降雨建立的概念性指数模型由于具有一定的物理基础,且参数通过常规观测资料即可确定,在构建物理性流域水文模型时被广泛采用。但在水文模型的应用中,计算是逐时段进行的,这造成模型计算时段和适用时段之间的不匹配,是植被截留计算误差的原因之一。分析表明,无论在一个计算时段内有多个降雨过程发生,还是一场降雨过程被划分为多个计算时段,逐时段计算均将过高估计植被截留量。     

流域水文过程与植被相互作用研究评述

从流域水文过程与植被相互作用规律、流域生态水文模型、流域生态水文对气候变化的响应三个方面，对国内外相关研究现状进行了总结分析。研究发现，当前流域水文学正在从以单一水循环过程为主要研究对象发展成以研究水分、能量与物质耦合循环以及水文过程与生态过程的相互作用等为主要内容的综合性和交叉性学科。现关于流域水文过程与植被之间的关系研究多集中于单向作用的研究，如水文过程对植被的影响研究和植被变化对水文过程的影响研究，而对水文过程与植被之间的反馈机制以及对流域内土壤-植被-大气复杂系统的整体研究不足，缺乏对流域水文过程与植被相互作用机理的全面认识。研究表明气候变化已经影响到流域水文过程和植被生长，气候变化下的流域生态水文响应是水文学研究的前沿课题。水文学需要强化对流域生态水文机理的研究，只有在充分揭示流域生态水文机理的前提下，才能预测未来气候变化下的流域生态水文响应。生态最优性原理能够解释植被对外界环境变化的响应，从而被应用于描述植被与水文过程的相互作用。基于生态最优性原理的流域生态水文模型已逐渐成为生态水文学的研究热点之一。     

National classification of surface–groundwater interaction using random forest machine learning technique

Characterization of surface–groundwater interaction is an increasingly useful skill for riverine ecologists and water resource managers interested in the dynamics of water, nutrient, and micro‐organism exchange at the reach scale, as it can be used to better represent point‐scale processes within larger catchment‐scale models. This study describes a method for predicting the nature of reach‐scale surface–groundwater interaction, using the random forest (RF) machine learning technique with national‐scale geology, hydrology, and land use data. Observed stream flow depletion and accretion surveys from riparian areas and spring‐line flow accretion surveys were also used. The RF model allowed prediction of observed losing and gaining reaches with a high prediction accuracy (“out‐of‐bag” error < 10%). The performance of the model, however, was found to be dependent on the geographic administrative region. The model was also found to be sensitive to slope, distance to headwater, distance to coast, and underlying geological characteristics. When applied in New Zealand, this approach yielded a realistic conceptual representation of national surface–groundwater dynamics that are subsequently being used to inform a national‐scale hydrological model.     

新一代水文模拟系统的流域嵌套式强化观测方案设计 —以新安江水文水资源实验改建项目为例

气候变化和高强度的人类活动对水文循环及其时空演变规律产生了重要影响,原有研究成果已不能真实反映现代条件下的水文变化规律.作为概念性模型的代表,新安江模型已不能适应变化环境下流域综合水文要素的模拟,因此有大量应用基础研究工作亟需加强.为解决这一难题,文章以新安江水文水资源实验站改建项目为例,阐述嵌套式强化观测流域设计方案的应用,在原有设施基础上,嵌套增建了原型小流域及坡地水文综合要素观测场、嵌套式强化观测流域、水文综合实验与分析测试中心及远程接收中心.结果表明该设计方案能够有效的提升数据资源类型及涵盖面,为发展新一代水文模型系统提供数据支撑,为建设流域大气-水文-生态综合模型提供科学依据.     

概念性水文模型遗传算法多目标参数优选研究

简要介绍了概念性降水—径流模型的多目标参数优选方法,以新安江模型为例,从Pareto支配法（Pareto Domination Approach）原理出发讨论了四目标函数情形下Pareto最优参数空间（Pareto Optimal Set）的Pareto优先排序（Pareto Preference Ordering）求解策略。通过对汉江上游江口流域降水—径流的新安江模型的模拟检验,证明该方法能够为模型提供全局最优参数,好于传统的单目标参数优选结果。     

城市有效不透水下垫面及其识别研究进展

城市中不透水下垫面,尤其是有效不透水下垫面的增加,会对城市水文循环的各个环节产生影响。针对城市有效不透水下垫面及其识别的问题,剖析了不透水下垫面的水文效应,从水文学的角度辨析了采用总不透水面积和有效不透水面积衡量城市不透水性的优缺点,系统梳理了现场调查与测定法、遥感及影像资料分析法、土地利用类型确定法、经验公式法以及降雨-径流关系法等国内外常用的有效不透水下垫面识别方法的优点与不足,指出未来需深入分析城市不同种类不透水下垫面之间的关系及其对城市雨洪过程的影响,从而提高城市雨洪模拟精度,为城市洪涝防治和海绵城市建设提供技术支撑。