基于信息熵对新安江水文模型参数及预报结果不确定性的量化分析

将GLUE方法与信息熵结合,分析新安江水文模型参数及预报结果的不确定性。首先用信息熵公式初步判断参数的敏感程度,根据GLUE方法求出90％置信度下流量的不确定范围;其次应用信息熵和U不确定公式共同量化分析模型预报结果的不确定性。以资水水系的新宁站集水区为研究区域,得出结论：①GLUE方法能够用来分析新安江水文模型参数的不确定性;②信息熵公式能够初步判断模型参数的敏感程度;③模型预报结果的不确定性随着实测资料的增加而降低。     

基于GLUE的新安江模型参数不确定性分析及应用

结合新安江模型在东洋河流域的应用,提出了基于GLUE方法的新安江模型参数不确定性分析。采用GLUE算法抽样结果对东洋河流域进行不确定性预报,选用水文模拟中常用的确定性系数作为似然判据,通过设定0.7为阀值,得到的90%置信区间的流量过程,实例研究表明,以该结果进行不确定预报是可行的。     

基于GLUE的垂向混合产流模型参数不确定性分析

水文模型能将复杂的水文现象和过程进行概化,其不确定性研究已成为模型研究的重要方向之一。以半湿润地区洛河流域为研究区域,构建了基于垂向混合产流模型的流域产流模拟模型,采用GLUE方法并结合5项相关指标对构建的模型进行不确定性分析。结果表明:使用GLUE方法对垂向混合产流模型进行不确定性评估和参数优选过程中,在置信度为95%的前提下,模型预报区间的覆盖率达到87.5%,对称度S和T接近于0,数据模拟对称度良好。     

Quantifying the Uncertainty Interaction Between the Model Input and Structure on Hydrological Processes

Input error is one of the main sources of uncertainty in hydrological models. It mainly comes from the uncertainty of precipitation data, which is caused by inaccurate measurement at the point scale and imperfect representation at the regional scale. The structural error of the hydrological model is dependent on the input, and the uncertainty interaction between the model input and structural will increase the cumulative error of the hydrological process. Therefore, the objective of this study is to investigate the impacts of the uncertainties of rain gauge station input levels and hydrological models on flows with different magnitudes by setting nine input levels of rain gauge stations using three hydrological models (i.e., HyMod, XAJ and HBV). The variance decomposition method based on subsampling was used to dynamically quantify the contribution rates of rain gauge station input levels, hydrological models, and their interaction to the runoff simulation uncertainty. The results show that different rain gauge station input levels and hydrological models dynamically affected the hydrological simulation due to an uneven spatiotemporal distribution of precipitation. Moreover, the simulation accuracy was poor at low flow but better at high flow. Increasing the number of rainfall stations input under a certain threshold could significantly improve the hydrological simulation accuracy. In addition, the contributions of the uncertainties of the rain gauge station input levels and its interaction with the hydrological model to runoff were significantly enhanced in the flood season, but the contribution of the hydrological model uncertainty was still dominant. The results of this study can provide a decision-making basis and scientific guidance for the management and planning of water resources within basins under the influence of a changing environment.

     

The Improvement in GWLF Model Simulation Performance in Watershed Hydrology by Changing the Transport Framework

The correct and reasonable delineation of actual hydrologic processes is a footstone for the effective simulation of pollutants in watershed models. In this study, a simple but comprehensive semidistributed modeling approach based on the generalized watershed loading function (GWLF) was modified to enable the accurate simulation of hydrology in watersheds. The frame of the original GWLF model (ORM), with a lumped hydrological parameter, was modified by adding channel routing processes, which made it possible to introduce the concept of subbasins. Then, the revised GWLF model was applied to the Luanhe watershed (30,000 km²) on a monthly bias in comparison with the ORM and the previously revised version. The sensitivity analysis and generalized likelihood uncertainty estimation (GLUE) uncertainty analysis were individually conducted to evaluate these modifications. Eventually, we compared four extreme conditions for the daily streamflow simulations of the three model versions in the Tunxi watershed but without calibration. All of the results indicated that the stability and accuracy of the model and the validity of the parameters were all enhanced and improved by the new revised version of the model, which provided reliable simulation results and indicated that it is a prospective tool to support watershed management.

     

The Utility of Land-Surface Model Simulations to Provide Drought Information in a Water Management Context Using Global and Local Forcing Datasets

Drought diagnosis and forecasting are fundamental issues regarding hydrological management in Spain, where recurrent water scarcity periods are normal. Land-surface models (LSMs) could provide relevant information for water managers on how drought conditions evolve. Here, we explore the usefulness of LSMs driven by atmospheric analyses with different resolutions and accuracies in simulating drought and its propagation to precipitation, soil moisture and streamflow through the system. We perform simulations for the 1980-2014 period with SASER (5 km resolution) and LEAFHYDRO (2.5 km resolution), which are forced by the Spanish SAFRAN dataset (at 5km and 30km resolutions), and the global eartH2Observe datasets at 0.25 degrees (including the MSWEP precipitation dataset). We produce standardized indices for precipitation (SPI), soil moisture (SSMI) and streamflow (SSI). The results show that the model structure uncertainty remains an important issue in current generation large-scale hydrological simulations based on LSMs. This is true for both the SSMI and SSI. The differences between the simulated SSMI and SSI are large, and the propagation scales for drought regarding both soil moisture and streamflow are overly dependent on the model structure. Forcing datasets have an impact on the uncertainty of the results but, in general, this impact is not as large as the uncertainty due to model formulation. Concerning the global products, the precipitation product that includes satellite observations (MSWEP) represents a large improvement compared with the product that does not.

     

GLUE Based Assessment on the Overall Predictions of a MIKE SHE Application

The generalised likelihood uncertainty estimation (GLUE) approach was applied to assess the performance of a distributed catchment model and to estimate prediction limits after conditioning based on observed catchment-wide streamflow. Prediction limits were derived not only for daily streamflow but also for piezometric levels and for extreme events. The latter analysis was carried out considering independent partial duration time series (PDS) obtained from the observed daily streamflow hydrograph. Important data uncertainties were identified. For streamflow the stage-discharge data analysis led to estimate an average data uncertainty of about 3 m³ s^− 1. For piezometric levels, data errors were estimated to be in the order of 5 m in average and 10 m at most. The GLUE analysis showed that most of the inspected parameters are insensitive to model performance, except the horizontal and vertical components of the hydraulic conductivity of one of the geological layers that have the most influence on the streamflow model performance in the application catchment. The study revealed a considerable uncertainty attached to the simulation of both high flows and low flows (i.e., in average terms 5 m³ s^− 1 before the Bayesian updating of the prediction limits). Similarly, wide prediction intervals were obtained for the piezometric levels in relevant wells, in the order of 3.3 and 1.5 m before and after the Bayesian updating of the prediction limits, respectively. Consequently, the results suggest that, in average terms, the model of the catchment predicts overall outputs within the limitations of the errors in the input variables.     

基于SODA方法的HyMOD模型不确定性分析

为了改进水文建模过程中的不确定性处理,采用一种融合全局优化和数据同化(Simultaneous Optimization and Data Assimilation, SODA)的混合框架,对HyMOD模型进行了不确定性分析,并与经典SCEM-UA方法进行了比较。SODA方法具有如下特点①具备较高的参数搜索效率和寻优能力;②明确考虑包括输入、输出、参数以及模型结构在内的重要不确定性来源。SODA方法在渭河流域的实例应用结果表明与SCEM-UA方法相比,SODA方法不仅显著提高了预报精度,而且推求出了性质更为优良的预报区间。SODA方法的成功应用,有助于模型概念的改进及对水文系统功能的理解。     

利用 GLUE 方法不同算法分析 TOPMODEL模型不确定性

以丹江的西峡流域为研究对象,采用GLUE方法探讨TOPMODEL模型不确定性。分析不同似然函数算法、调节参数、模型参数和结果预测区间的不确定性,结果表明不同算法的似然函数值既有差异又有相似性,值变化会引起不同似然函数分布的变化,也影响模型参数与似然值的关系。分析不同阈值的90％置信区间的实测值覆盖率、预测区间宽度及预测区间对称性等特征,结果说明阈值越小,预测区间的可靠性越高,但不确定性范围越大;阈值越大则情况相反。说明选定似然函数和阈值时需综合考虑多方面因素。     

Streamflow and Hydrological Drought Trend Analysis and Forecasting in Cyprus

The persistent water shortage in Cyprus has been alleviated by importing freshwater from neighbouring countries, and severe droughts have been met with financial reimbursement from the EU at least twice. The goal of this research is to investigate and perform short-term forecasting of both streamflow and hydrological drought trends over the island. Eleven hydrometric stations with a 34-year common record length of the mean daily discharge from 10/1979 to 09/2013 are used for this purpose, with the relevant upstream catchments considered to represent pristine conditions. The Streamflow Drought Index (SDI) successfully captures the hydrological drought conditions over the island, and the performance of the index is validated based on both the historic drought archives and results from other drought indices for the island. The Mann–Kendall (M-K) test reveals that the annual and seasonal time series of the discharge volumes always illustrate a decreasing but insignificant trend at a significance level of a?=?0.05; additionally, the decrease per decade in the average annual streamflow volume based on Sen’s slope statistic is approximately ?9.4%. The M-K test on the SDI reveals that drought conditions intensified with time. Ten autoregressive integrated moving average (ARIMA) models are built and used to forecast the mean monthly streamflow values with moderate accuracy; the best ARIMA forecast model in each catchment is derived by comparing two model-performance statistical measures for the different (p,d,q) model parameters. The predicted discharge values are processed by the SDI-3 index, revealing that non-drought conditions are expected in most catchments in the upcoming three months, although mild-drought conditions are anticipated for catchments 7, 8 and 9.     

How uncertain is model-based prediction of copper loads in stormwater runoff?

In this paper, we conduct a systematic analysis of the uncertainty related with estimating the total load of pollution (copper) from a separate stormwater drainage system, conditioned on a specific combination of input data, a dynamic conceptual pollutant accumulation-washout model and measurements (runoff volumes and pollutant masses). We use the generalized likelihood uncertainty estimation (GLUE) methodology and generate posterior parameter distributions that result in model outputs encompassing a significant number of the highly variable measurements. Given the applied pollution accumulation-washout model and a total of 57 measurements during one month, the total predicted copper masses can be predicted within a range of +/-50% of the median value. The message is that this relatively large uncertainty should be acknowledged in connection with posting statements about micropollutant loads as estimated from dynamic models, even when calibrated with on-site concentration data.     

基于月水量平衡模型的年径流模拟方法设计及应用

为了实现长时段径流的准确模拟,基于abcd、TWBM、VWBM和DWBM四个月水量平衡模型的结构框架,设计了一组年径流模拟方法.其基本思路为:以L(L为12的约数)个月的累积实测降水量和潜在蒸散发量作为模型输入,通过模型模拟首先得到相应时长的累积模拟径流量,然后每个时长12/L顺次分段相加得到相应模拟年径流量.设计的年...     

Modelling Impacts of Climate Change on a River Basin: Analysis of Uncertainty Using REA & Possibilistic Approach

In the context of climate change, the uncertainty associated with Global Climate Models (GCM) and scenarios needs to be assessed for effective management practices and decision-making. The present study focuses on modelling the GCM and scenario uncertainty using Reliability Ensemble Averaging (REA) and possibility theory in projecting streamflows over Wainganga river basin. A macro scale, semi-distributed, grid-based hydrological model is used to project the streamflows from 2020 to 2094. The observed meteorological data are collected from the India Meteorological Department (IMD) and the streamflow data is obtained from Central Water Commission (CWC) Hyderabad. In REA, meteorological data are weighted based on the performance and convergence criteria (GCM uncertainty). Whereas in possibility theory, based on the projection of different GCMs and scenarios during recent past (2006–2015) possibility values are assigned. Based on the possibility values most probable experiment and weighted mean possible CDF for the future periods are obtained. The result shows that there is no significant difference in the outcomes is observed between REA and possibility theory. The uncertainty associated with GCM is more significant than the scenario uncertainty. An increasing trend in the low and medium flows is predicted in annual and monsoon period. However, flows during the non-monsoon season are projected to increase significantly. Moreover, it is observed that streamflow generation not only depends on the change in precipitation but also depends on the previous state of physical characteristics of the region.     

通过流量放大减小水文模拟与预报不确定性的应用研究

应用TOPMODEL模型,采用普适似然不确定估计(GLUE)方法,以属于半干旱地区的东湾流域为例,探讨径流系数在水文模拟与预报中的影响作用。通过其影响来提高径流系数较低地区的模拟精度,减小水文模拟与预报的不确定性。研究发现,模拟精度随着径流系数增大而提高,并在径流系数为0.5附近达到峰值后逐渐降低。基于此通过流量放大的改进方法进行东湾流域的水文模拟,改进后的结果有较大改善。     

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.

     

Effect of calibration data series length on performance and optimal parameters of hydrological model

In order to assess the effects of calibration data series length on the performance and optimal parameter values of a hydrological model in ungauged or data-limited catchments (data are non-continuous and fragmental in some catchments), we used non-continuous calibration periods for more independent streamflow data for SIMHYD (simple hydrology) model calibration. Nash-Sutcliffe efficiency and percentage water balance error were used as performance measures. The particle swarm optimization (PSO) method was used to calibrate the rainfall-runoff models. Different lengths of data series ranging from one year to ten years, randomly sampled, were used to study the impact of calibration data series length. Fifty-five relatively unimpaired catchments located all over Australia with daily precipitation, potential evapotranspiration, and streamflow data were tested to obtain more general conclusions. The results show that longer calibration data series do not necessarily result in better model performance. In general, eight years of data are sufficient to obtain steady estimates of model performance and parameters for the SIMHYD model. It is also shown that most humid catchments require fewer calibration data to obtain a good performance and stable parameter values. The model performs better in humid and semi-humid catchments than in arid catchments. Our results may have useful and interesting implications for the efficiency of using limited observation data for hydrological model calibration in different climates.     

Effect of DEM Resolution,Source, Resampling Technique and Area Threshold on SWAT Outputs

Application of an inappropriate Digital Elevation Model (DEM) might lead to uncertainty in modelling of the hydrological cycle. The novelty of this work is the development of a comprehensive framework to evaluate the effect of DEM resolution (12 to 500 m), source (TanDEM-X, SRTM, AW3D30 and ASTER GDEM2), resampling technique (nearest neighbour, bilinear interpolation, cubic convolution and majority) and area threshold (1000 to 50,000 ha) on Soil and Water Assessment Tool (SWAT) outputs based on five criteria: (1) river network extraction, (2) streamflow simulation, (3) topography, slope and basin characteristics, (4) hydrological and (5) water quality simulations. Kelantan River Basin, a tropical basin in Peninsular Malaysia was selected as study area. The major findings are summarized as follows: (1) TanDEM-X had better river network extraction capability than ASTER GDEM2, (2) better monthly streamflow simulations were obtained between 20 m and 60 m DEM resolutions, with the smallest area threshold (1000 ha), (3) TanDEM-X and SRTM DEMs outperformed ASTER GDEM2 on monthly streamflow simulation, (4) DEM resolution, source and resampling technique were insensitive to most of the hydrological components, except the lateral flow, (5) area threshold was sensitive to SWAT-simulated surface runoff, soil water content and evapotranspiration, (6) DEM scenarios had a larger impact on sediment yield simulations compared to the total nitrogen and total phosphorus simulations. We recommend a preliminary assessment of DEM uncertainties on SWAT outputs to obtain more reliable modelling outputs.     

The Quest for Hydrological Signatures: Effects of Data Transformation on Bayesian Inference of Watershed Models

Hydrological models contain parameters, values of which cannot be directly measured in the field, and hence need to be meaningfully inferred through calibration against historical records. Although much progress has been made in the model inference literature, relatively little is known about the effects of transforming calibration data (or error residual) on the identifiability of model parameters and reliability of model predictions. Such effects are analyzed herein using two hydrological models and three watersheds. Our results depict that calibration data transformations significantly influence parameter and predictive uncertainty estimates. Those transformations that distort the temporal distribution of calibration data, such as flow duration curve, normal quantile transform, and Fourier transform, considerably deteriorate the identifiability of model parameters derived in a formal Bayesian framework with a residual-based likelihood function. Other transformations, such as wavelet, BoxCox and square root, while demonstrating some merits in identifying specific model parameters, would not consistently improve predictive capability of hydrological models in a single objective inverse problem. Multi-objective optimization schemes, however, may present a more rigorous basis to extract several independent pieces of information from different data transformations. Finally, data transformations might offer a greater potential to evaluate model performance and assess specific sections of model behavior, rather than to calibrate models in a single objective framework. Findings of this study shed light on the importance and impacts of data transformations in search of hydrological signatures.     

流域水文模型识别方法研究与应用

基于模型产汇流机理,分析对比水文模型适用性,建立考虑气候特征、下垫面条件和人类活动影响的流域水文模型识别的指标体系,并以辽宁东部各中小流域为研究对象进行实例研究。分析了研究区域的气候特征,将新安江模型、大伙房模型和TOPMODEL模型作为备选模型,利用主成分分析法确定流域面积/主河道长度、河道比降、森林覆盖率、地形指数和气候类型作为流域水文模型识别的输入指标,进而采用层次分析法识别适用于各研究流域洪水模拟的水文模型。洪水模拟结果表明:所建立的流域水文模型识别指标体系有代表性,所识别的水文模型可很好地反映流域的产汇流特性。     

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。