Distributed Hydrological Modeling and Sensitivity Analysis in Torysa Watershed, Slovakia

The spatially distributed hydrologic model WetSpa is applied to the Torysa river basin (1,297 km²) located in Slovakia. Daily hydrometeorological data from 1991 to 2000 are used as input to the model. The spatial characteristic of the basin are described by three base maps, i.e. DEM, landuse and soil type, in GIS form using 100 m cell size. Results of the simulations show a good agreement between calculated and measured hydrographs at the outlet of the basin. The model predicts the daily discharge values with a good accuracy, i.e. about 73% according to the Nash–Sutcliff criterion. Sensitivity analysis of the model parameters is performed using a model-independent parameter estimator, PEST. It is found that the correction factor for calculating the actual evapotranspiration from potential evaporation has the highest relative sensitivity. Parameter K _gm which controls the amount of evapotranspiration from the groundwater has the least relative sensitivity.     

Multi-Objective Sensitivity Analysis of a Fully Distributed Hydrologic Model WetSpa

The application of fully distributed watershed models has the advantage of providing location-specific outputs. However, the calibration of these models is very challenging due to over-parameterization. A typical strategy is to aggregate parameters and screen out insensitive parameters in order to decrease the dimension of the problem for calibration. To ensure the validity of calibration, it is important to identify important physical processes and parameter interactions, and examine how different model setups affect model simulation. In this paper, a two-step multi-objective sensitivity analysis approach is applied to a distributed hydrologic model, the WetSpa (Water and Energy Transfer between Soil, Plant and Atmosphere), with case studies in the Chaohe Basin in China and the Margecany Basin in Slovakia respectively. This two-step global sensitivity analysis technique, incorporating the Morris method and the SDP (State Dependent Parameter) method, has proved to be effective in the two case studies. The results of two case studies show that (i) a warm-up period is essential for minimizing the impact of initial state variables to the model simulation, (ii) different objective functions lead to different sensitivity results, (iii) evapotranspiration is the most sensitive process to the model result in the two study watersheds followed by the groundwater and soil water process, and (iv) the sensitivity of snowmelt process is case dependent.     

Identifiability analysis in conceptual sewer modelling

For a sufficient calibration of an environmental model not only parameter sensitivity but also parameter identifiability is an important issue. In identifiability analysis it is possible to analyse whether changes in one parameter can be compensated by appropriate changes of the other ones within a given uncertainty range. Parameter identifiability is conditional to the information content of the calibration data and consequently conditional to a certain measurement layout (i.e. types of measurements, number and location of measurement sites, temporal resolution of measurements etc.). Hence the influence of number and location of measurement sites on the number of identifiable parameters can be investigated. In the present study identifiability analysis is applied to a conceptual model of a combined sewer system aiming to predict the combined sewer overflow emissions. Different measurement layouts are tested and it can be shown that only 13 of the most sensitive catchment areas (represented by the model parameter 'effective impervious area') can be identified when overflow measurements of the 20 highest overflows and the runoff to the waste water treatment plant are used for calibration. The main advantage of this method is very low computational costs as the number of required model runs equals the total number of model parameters. Hence, this method is a valuable tool when analysing large models with a long runtime and many parameters.     

Parameter identification and global sensitivity analysis of Xin’anjiang model using meta-modeling approach

Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on (1) a screening method (Morris) for qualitative ranking of parameters, and (2) a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model (RSMSobol). First, the Morris screening method was used to qualitatively identify the parameters’ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model (RSM) were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.     

分布式水文模型EasyDHM模型（II）应用实例

本文介绍了分布式水文模型EasyDHM在汉江上游流域的应用实例。作者通过采用EasyDHM模型中的改进WetSpa产流模型、马斯京干汇流模型、LH-OAT敏感性分析方法、SCE-UA参数优化方法等，对汉江上游流域进行了水文模拟及参数率定。由参数敏感性及参数优化结果知，各产汇流参数敏感性随空间分布的不同有一定差异，而随着时间系列的延长其变化并不大，这不仅说明了按水文站进行参数分区的必要性，也说明在长系列水文模拟中，可仅对指定校正期进行参数优化。而参数优化后能较大程度提高水文模型模拟精度，则证实了参数优化的必要性以及本模型所选取参数优化算法的合理性。     

Model uncertainty--parameter uncertainty versus conceptual models.

Uncertainties in model structures have been recognised often to be the main source of uncertainty in predictive model simulations. Despite this knowledge, uncertainty studies are traditionally limited to a single deterministic model and the uncertainty addressed by a parameter uncertainty study. The extent to which a parameter uncertainty study may encompass model structure errors in a groundwater model is studied in a case study. Three groundwater models were constructed on the basis of three different hydrogeological interpretations. Each of the models was calibrated inversely against groundwater heads and streamflows. A parameter uncertainty analysis was carried out for each of the three conceptual models by Monte Carlo simulations. A comparison of the predictive uncertainties for the three conceptual models showed large differences between the uncertainty intervals. Most discrepancies were observed for data types not used in the model calibration. Thus uncertainties in the conceptual models become of increasing importance when predictive simulations consider data types that are extrapolates from the data types used for calibration.     

基于 UQ-PyL 的 SWAT 模型参数不确定性 分析综合评估

为解决 SWAT?(soil?and?water?assessment?tool) 模型在复杂情形下的参数不确定性分析问题，引入参数不确定 性分析平台 UQ-PyL(Uncertainty?Quantification?Python?Laboratory)，开发 UQ-PyL 与 SWAT 模型的耦合模块，使得 UQ-PyL 中的各种算法能够方便快捷地应用于 SWAT 模型的参数不确定性分析。为验证 UQ-PyL 用于 SWAT 模 型参数不确定性分析的效果，在我国不同气候条件下的 4 个流域构建 SWAT 模型，综合对比评估 UQ-PyL 与 SWAT-CUP 对模型参数的不确定性分析结果。结果表明：UQ-PyL 多种敏感性分析方法筛选出的敏感参数比 SWAT-CUP 单一方法筛选的结果更加合理；使用 UQ-PyL 率定的参数在 4 个流域应用中都表现良好，优化后模拟 结果的纳什效率系数均在 0.55 以上，收敛次数在 550 次以内；在 4 个流域的模拟中，UQ-PyL 能提供计算效率更高 的算法 ASMO，也能提供模拟结果更准确的算法 SCE。综上，与 SWAT 模型相耦合的 UQ-PyL 能够支持 SWAT 模 型用户在不同系统下对模型参数进行更高效的不确定性分析研究。     

基于LH-OAT分布式水文模型参数敏感性分析

为了有效进行分布式水文模型参数的优选,消除模型计算过程中的不确定性,更好地理解参数对水文模拟的影响,开展了模型参数敏感性分析。使用LH-OAT方法,对比分析了3个不同的流域中多个目标函数下的分布式物理水文模型——流溪河模型的参数敏感性,将其参数敏感性归为：极敏感,敏感,一般敏感和不敏感4类。研究表明,模型参数的敏感性并不是一成不变的,在不同流域,不同评价目标下,会发生一定程度的改变。     

Predicting the Frequency of Water Quality Standard Violations Using Bayesian Calibration of Eutrophication Models

The water quality standard setting process usually relies on mathematical models with strong mechanistic basis, as this provides assurance that the model will more realistically project the effects of alternative management schemes. From an operational standpoint, the interpretation of model results should be coupled with rigorous error analysis and explicit consideration of the predictive uncertainty and natural variability. In this study, our main objective is to attain effective model calibration and rigorous uncertainty assessment by integrating environmental mathematical modeling with Bayesian analysis. We use a complex aquatic biogeochemical model that simulates multiple elemental cycles (org. C, N, P, Si, O), multiple functional phytoplankton (diatoms, green algae and cyanobacteria) and zooplankton (copepods and cladocerans) groups. The Bayesian calibration framework is illustrated using three synthetic datasets that represent oligo-, meso- and eutrophic lake conditions. Scientific knowledge, expert judgment, and observational data were used to formulate prior probability distributions and characterize the uncertainty pertaining to a subset of the model parameters, i.e., a vector comprising the 35 most influential parameters based on an earlier sensitivity analysis of the model. Our study also underscores the lack of perfect simulators of natural system dynamics using a statistical formulation that explicitly accounts for the discrepancy between mathematical models and environmental systems. The model reproduces the key epilimnetic temporal patterns and provides realistic estimates of predictive uncertainty for water quality variables of environmental management interest. Our analysis also demonstrates how the Bayesian parameter estimation can be used for assessing the exceedance frequency and confidence of compliance of different water quality criteria. The proposed methodological framework can be very useful in the policy-making process and can facilitate environmental management decisions in the Laurentian Great Lakes region.     

Estimation of kinetic parameters of a model for deammonification in biofilms and evaluation of the model.

A systematic approach to estimate and evaluate parameters for deammonification in biofilms from available experimental data was evaluated. Parameter estimation was based on a regional steady state sensitivity analysis to select relevant parameters and design of experiments based on a local identifiability analysis. The calibrated model was evaluated under different experimental conditions. Nine of the 16 kinetic and stoichiometric parameters had a significant influence on model predictions. Of these nine parameters only six kinetic parameters were identifiable from batch experiments regardless of the experimental design. More parameters were not identifiable due to high correlations between growth rates and the corresponding affinity constant for oxygen. Data from a batch experiment at 2 mg/L dissolved oxygen (DO) were used to estimate inactivation rates and affinity constants for oxygen for ammonium oxidisers (AO), nitrite oxidisers (NO) and anaerobic ammonium oxidisers (AN). In addition, it was found that not only kinetic and stoichiometric parameters but also the external mass transfer resistance significantly affected model predictions. The resulting model was able to reproduce batch test and continuous reactor operation where DO concentrations were similar to those in the batch experiment used for parameter estimation. However, the model overestimated deammonification for a batch experiment at a much higher DO concentration (5 mg/L). Thus, parameter values that are identifiable and are estimated for given environmental conditions may not necessarily be valid for significantly different experimental conditions.     

基于智能聚类识别的 SWAT 模型参数优化方法 −以石头口门水库流域为例

为探究 SWAT 模型参数优化过程与方法,降低参数估计不确定性,采用敏感性分析方法遴选关键参数,针对 关键参数采用拉丁超立方抽样构建参数样本集,进而结合各组关键参数组合下的模拟精度指标构建聚类指标集, 采用 SOM 聚类算法进行聚类,并基于模拟精度较高且波动较小类别识别各关键参数取值范围,形成一种 SWAT 模型关键参数优化系统方法。以石头口门水库流域为例,选取 1980—2016 年（1980—1986 年为预热期,1987— 2009 年为率定期,2010—2016 年为验证期）的月径流实测资料,建立流域 SWAT 模型,引入 SOM 聚类算法进行参 数优化,不断缩小模型关键参数合理取值区间,并应用 SUFI-2 算法进行模拟结果对比。结果表明：SWAT 模型适 用于石头口门水库流域,且参数优化前验证期的决定系数 R2为 0.79,纳什效率系数 ENS为 0.74,P-factor 为 0.65,R-factor 为 0.56;参数优化后验证期 R 2为 0.88,ENS为 0.83,P-factor 为 0.70,R-factor 为 0.50,模拟效果较好。故 应用 SOM 算法进行 SWAT 模型参数优化可以降低模型不确定性,提高径流模拟精度,为水文模型参数优化算法 的选择提供思路,对水资源管理政策制定与水库优化调度具有重要意义。     

GIS-based applications of sensitivity analysis for sewer models

Sensitivity analysis (SA) evaluates the impact of changes in model parameters on model predictions. Such an analysis is commonly used when developing or applying environmental models to improve the understanding of underlying system behaviours and the impact and interactions of model parameters. The novelty of this paper is a geo-referenced visualization of sensitivity indices for model parameters in a combined sewer model using geographic information system (GIS) software. The result is a collection of maps for each analysis, where sensitivity indices (calculated for model parameters of interest) are illustrated according to a predefined symbology. In this paper, four types of maps (an uncertainty map, calibration map, vulnerability map, and design map) are created for an example case study. This article highlights the advantages and limitations of GIS-based SA of sewer models. The conclusion shows that for all analyzed applications, GIS-based SA is useful for analyzing, discussing and interpreting the model parameter sensitivity and its spatial dimension. The method can lead to a comprehensive view of the sewer system.     

目标函数对滦河流域SWAT模型径流模拟不确定性的影响

针对水文模型参数和径流模拟结果不确定性问题,选取2Nash-Sutcliffe效率系数(NSE)、改进的决定系数(R_m²)、相对误差(PBIAS)、Kling-Gupta效率系数(KGE)4种目标函数,对构建的滦河流域潘家口水库上游SWAT模型进行参数率定及验证,分析了不同目标函数下模型参数的敏感性差异及径流模拟的不确定性。结果表明：参数敏感性会随迭代次数增加和抽样范围变化发生改变,不同目标函数下率定的参数范围和最优值显著不同;NSE和KGE作为目标函数在各站点径流模拟中更稳健,分别表现出较高的模拟精度和较低的模拟不确定性。     

Uncertainty Analysis of a Two-dimensional Hydrodynamic Model

First-Order Second Moment (FOSM) and Monte Carlo analysis were applied to characterize the uncertainty in selected water levels and velocities simulated by a two-dimensional hydrodynamic model of the Upper St. Lawrence River downstream from Lake Ontario. The analysis utilized an application of the Resource Management Associates’ RMA2 model. Both FOSM and Monte Carlo analysis provided similar estimates of uncertainty, with Monte Carlo analysis results being 15% less than FOSM. Based on the findings of this work, the FOSM is preferred. FOSM provides a conservative estimate of the uncertainty and it is simpler to apply than Monte Carlo analysis, requiring less information and fewer model executions. FOSM also provides an immediate indication of the primary contributors to the uncertainty in the output, where Monte Carlo analysis requires additional effort to do the same. Results indicate that the parameter describing bottom resistance using Manning's n contributed more to model uncertainty than other factors investigated. The uncertainty in and sensitivity in Manning's n is large which results in a significant amount of uncertainty in the model outputs is contributed by this parameter. The calculations described in this study show that uncertainty analysis is a practical addition to the two-dimensional hydrodynamic modelling process. It provides insight to the model developer, quantifying how good the model actually is. It also provides a measure of the accuracy of the model for future model developers or clients using hydrodynamic modeling outputs     

水文模型参数敏感性分析方法评述

针对水文模型敏感性分析中存在的诸多问题,分析水文模型参数敏感性分析在模型构建及应用过程中的主要作用及其与不确定性分析和参数优化之间的联系,总结敏感性分析方法的3种分类,并探讨水文模型中常用的筛选法、回归分析法、基于方差的分析方法及基于代理模型技术的分析方法等4种关键技术方法,剖析水文模型参数敏感性分析方法的适用条件及优缺点,回顾各种方法在水文模型中的研究现状,提出水文模型参数敏感性分析的研究框架与步骤,指出水文模型参数敏感性分析的计算效率、可靠性和参数的相关性是未来的主要研究方向。     

基于WetSpa模型的设计洪水计算

分别根据长期的流量资料、广东省推理公式法,基于WetSpa分布式水文模型,推求广东省流溪河水库流域设计洪水。基于WetSpa模型推求设计洪水结果与广东省推理公式法相比,基于WetSpa模型推求设计洪水结果比根据长期流量资料计算结果更接近,说明基于WetSpa模型推求设计洪水是可行的。利用WetSpa模型推求设计洪水只需要知道流域的DEM、土地利用类型、土壤类型以及少量的降雨洪水资料。流域的DEM、土地利用类型、土壤类型数据可通过互联网免费下载,所以WetSpa模型为短缺资料地区推求设计洪水提供了新的参考。     

一种SWAT模型参数识别的改进方法

参数的敏感性分析和不确定性分析是分布式水文模型构建的先决条件。在辽河流域建立SWAT模型,利用SWAT-CUP中的SUFI-2算法进行参数的率定,在此基础上提出一种更为简便的参数识别方法。将研究区域辽河干流的主要支流分别进行参数识别,再将SWAT-CUP中率定的最佳参数的从TXINOUT文件中提取出来,分别覆盖到SWAT模型中各对应支流子流域的TXINOUT文件中,即可得到按主要支流经过参数识别后的SWAT模型,避免了SWAT-CUP调参工具涉及众多子流域导致参数识别过于复杂的问题。结果表明,辽河干流主要支流招苏台河、清河、柴河等子流域主要水文断面率定期的平均纳什效率系数分别为0.60、0.65、0.68,验证期分别为0.60、0.72、0.77,参数率定的结果相对于全局调参有较大的改进。采用本文提出的参数识别方法,可以解决SWAT-CUP全局调参时上下游断面结果难以同时匹配或伪匹配的问题,又可以避免分区参数识别时对不同子流域的土地利用类型、土壤类型以及坡度等参数的繁琐设定,同时降低了SWAT模型手动调参的复杂程度,可较好地应用到SWAT模型参数识别过程中。     

WEP模型参数自动优化及在汉江流域上游的应用

本文将全局参效自动优化算法-复形进化算法,引入WEP-L模型,并成功应用于汉江上游流域.通过复形进化算法参数自动优化,可找到WEP-L模型的一套较优的参数.同时,为比较不同目标函数对参数敏感性与自动率定的影响,分别对8种目标函数进行了参数敏感性分析及自动率定.结果显示,如果以水量误差和纳什效率系数为评价标准,排序后的残差平方和及其变化形式的效果较好.WEP-L模型参数敏感性分析及参数自动化率定的实现,可提高WEP-L模型参数率定的科学性和客观性,从而方便WEP-L模型的推广与应用.     

Uncertainty in predicting riverbed erosion caused by urban stormwater discharge.

Ecologically based criteria require an integrated modeling approach. Due to the complexity of the system, the stochastic nature of loads, and the model abstractions, many uncertainties are involved. In this study, a simple integrated model is applied, which Swiss engineers employ to assess the impact of urban stormwater discharges on riverbed stability. In the course of a case study, an uncertainty analysis is carried out focusing on parameter uncertainties. The underlying context of the uncertainties is evaluated, and a variance-based sensitivity analysis is presented estimating the local uncertainty contribution of each parameter. The results reveal that the largest contributions stem from the model components describing the natural system. An experimental design is proposed that manages to reduce the output uncertainty significantly. Finally, we discuss the benefits of following the proposed procedure.     

SWAT和WetSpa Extension在罗玉沟流域的应用比较

近年来,运用水文模型来研究流域水文状况越来越普遍,本文以黄土高原丘陵沟壑区第三副区的典型小流域为例,基于SWAT和WetSpa Extension两个模型,从模型的输入数据、模型的参数和模型模拟结果等方面进行比较,以期为该区水沙模拟选择合适水文模型并为将来模型性能的改进提供参考。连续15年的校准验证表明:两个模型都能得到较好的结果,而SWAT模型能应用于水文及泥沙等模拟过程,相对来说功能更全面;WetSpa Extension模型目前仅能提供水文模拟的模块,优点在于模拟时间步长较任意,可进行洪水模拟。