Hydrologic complexity and classification: a simple data reconstruction approach

Technological advances, by facilitating extensive data collection, better data sharing, formulation of sophisticated methods, and development of complex models, have brought hydrologic research to a whole new level. Despite these obvious advances, there are also concerns about their general use in practice. On the one hand, it is natural to develop more complex models than perhaps needed (i.e. representations having too many parameters and requiring too much data); on the other hand, it is often difficult to ‘translate’ results from one specific situation to another. Recent studies have addressed these concerns, albeit in different forms, such as dominant processes, thresholds, model integration, and model simplification. A common aspect in some of these studies is that they recognize the need for a globally agreed upon ‘classification system’ in hydrology. The present study explores this classification issue further from a simple phase‐space data reconstruction perspective. The reconstruction involves representation of the given multidimensional hydrologic system using only an available single‐variable series through a delay coordinate procedure. The ‘extent of complexity’ of the system (defined especially in the context of variability of relevant data) is identified by the ‘region of attraction of trajectories’ in the phase space, which is then used to classify the system as potentially low‐, medium‐ or high‐dimensional. A host of river‐related data, representing different geographic and climatic regions, temporal scales, and processes, are studied. Yielding ‘attractors’ that range from ‘very clear’ ones to ‘very blurred’ ones, depending on data, the results indicate the usefulness of this simple reconstruction concept for studying hydrologic system complexity and classification. Copyright © 2007 John Wiley & Sons, Ltd.     

Inverse flood risk modelling under changing climatic conditions

One of the most significant anticipated consequences of global climate change is the increased frequency of hydrologic extremes. Predictions of climate change impacts on the regime of hydrologic extremes have traditionally been conducted using a top‐down approach. The top‐down approach involves a high degree of uncertainty associated with global circulation model (GCM) outputs and the choice of downscaling technique. This study attempts to explore an inverse approach to the modelling of hydrologic risk and vulnerability to changing climatic conditions. With a focus targeted at end‐users, the proposed approach first identifies critical hydrologic exposures that may lead to local failures of existing water resources systems. A hydrologic model is used to transform inversely the main hydrologic exposures, such as floods and droughts, into corresponding meteorological conditions. The frequency of critical meteorological situations is investigated under present and future climatic scenarios by means of a generic weather generator. The weather generator, linked with GCMs at the last step of the proposed methodology, allows the creation of an ensemble of different scenarios, as well as an easy updating, when new and improved GCM outputs become available. The technique has been applied in Ontario, Canada. The results show significant changes in the frequency of hydro‐climatic extremes under future climate scenarios in the study area. Copyright © 2007 John Wiley & Sons, Ltd.     

水文循环模拟中下垫面参数化方法综述

针对水文循环模拟中地形、土地利用覆被等流域下垫面参数化方法众多,且模拟效果相差较大的现状。本文首先根据水文循环模拟中产汇流原理,对常用水文循环模拟中产汇流模拟方法进行汇总和分类;在此基础上,对产流模拟中的降水径流相关系数法、蓄满产流和超渗产流等及汇流模拟中的等流时线、单位线、圣维南方程、马斯京根法等主要模拟方法中地形、土地利用覆被和土壤类型参数化方法进行分析和讨论;根据其中流域地形、土地利用覆被和土壤类型参数化方法对机理过程的描述程度,将其分为无明确表示类、率定型参数类、确定型参数类、物理过程表达类;进而阐明不同参数化方法中流域地形、土地利用覆被和土壤类型对水文循环模拟结果的响应和贡献。最后回归模型本质,阐述水文循环模拟中流域下垫面参数化方法中存在经验关系对复杂机理简单表述的合理性和物理机理过程描述的欠缺性问题,并预估未来水文循环模拟中下垫面参数化方法朝着简洁实用化和复杂机理化两个方向发展。     

面向“十三五”流域水环境管理的控制单元划分方法

针对国家《水污染防治行动计划》提出的对重点流域实施分流域、分区域、分阶段科学治理的要求,便于“十三五”期间基于控制单元的流域水环境管理体系实施。提出一种能有效衔接流域水自然特征和水资源三级分区、以乡镇级行政区划与流域管理相结合、综合考虑污染源分布、社会经济、土地利用等诸因素的控制单元划分方法。基于该方法将全国重点流域划分为1 784个控制单元,并促成环境保护部于2016年6月发布了“十三五”期间全国343个水质需改善控制单元信息清单。为“十三五”期间流域水环境管理进一步明确地方政府责任,实现基于控制单元的水环境精细化、差异化管理提供数据和技术支撑。     

群居蜘蛛优化算法在水文频率分析中的应用

水文频率分析在参数估计过程中常采用智能优化适线法,如蚁群算法、遗传算法、粒子群算法、模拟退火算法等,但这些算法模型参数难以有效确定,导致寻优结果存在不稳定的不足。为了克服传统优化适线法的缺陷,在系统阐述群居蜘蛛优化算法基本原理的基础上,将群居蜘蛛优化算法用于水文频率曲线的参数确定中,并与传统的参数估计方法(矩法、权函数法、概率权重矩法、遗传算法)加以比较。实例结果表明,该方法搜索效率高,寻优结果稳定,能较好获得参数的最优解。     

Estimation of submarine groundwater discharge in the Il‐Gwang watershed using water budget analysis and 222Rn mass balance

Submarine groundwater discharges (SGD) were investigated in a marine watershed in south‐eastern Korea using water budget analysis and a ²²²Rn mass balance model. Multi‐layered TOPMODEL added hydrological assumption was used to estimate groundwater components in the water budget analysis. Field observations of soil moisture, rainfall, runoff and groundwater fluctuations were used for calibration and validation of the hydrologic model. Based on observed hydrological data and terrain analyses, parameters for the hydrologic model were delineated and used to describe several hydrologic responses in the watershed. SGD estimations by ²²²Rn mass balance method were also performed at Il‐Gwang bay in July, 2010, and May, June, July and Nov. 2011. The estimated groundwater through hydrologic modeling and water balance analysis was 1.3x10⁶ m³/year, which rapidly increased during typhoon season due to heavy rainfall and permeable geologic structure. The estimated groundwater was approximately 3.7–27.1% of SGD as evaluated by ²²²Rn mass balance method ranges 3.44 and 17.45 m³m^?2year^?1. Even though SGD is predominantly influenced by tide fluctuation, the head gradient (difference) from hydrologic processes associated with heavy rainfalls can also have extra significant influences. Comprehensive understanding of SGD evaluation can be improved through a simultaneous application of both these approaches. Copyright © 2013 John Wiley & Sons, Ltd.     

Historical discharge measurements on the Middle Mississippi River,USA: no basis for ‘changing history’

This study tests the hypothesis that historical float‐based discharge measurements on the Mississippi River systematically over‐stated actual flood flows by 10% to > 30% relative to measurements using current meters. This assertion has been repeated over the past 25 years and recently has been used to adjust historical discharges used for flood‐frequency analysis. This study tests the hypothesis above using 2150 historical discharge measurements digitized from the three principal gauging stations on the Middle Mississippi River (MMR): data that include 626 float‐based discharges and 1516 meter‐based discharges, including 122 paired measurements. Multiple comparative tests show that the hypothesis above cannot be supported; if anything, the float‐based measurements slightly underestimate flows (not over‐estimate) over a broad range of discharges up to large floods. In response to the purported data bias above (‘changing history’; Dieckmann RJ, Dyhouse GR. 1998. Changing history at St. Louis—adjusting historic flows for frequency analysis. First Federal Inter‐Agency Hydrologic Modeling Conference, April 20–22, 1998. Las Vegas, NV; 4·31–4·36), historical flood discharges on the MMR have been modified, most by 10–20% and several by > 30%. These altered discharges are now being promulgated, in particular, through the Upper Mississippi River System Flow Frequency Study (UMRSFFS). New flow frequencies, flood profiles, and new flood maps from the UMRSFFS may significantly underestimate the actual flood hazard on the MMR if the original hydrologic data have been erroneously altered on the basis of an assumption of data bias. Copyright © 2010 John Wiley & Sons, Ltd.     

Causes of systematic over‐ or underestimation of low streamflows by use of index‐streamgage approaches in the United States

Low‐flow characteristics can be estimated by multiple linear regressions or the index‐streamgage approach. The latter transfers streamflow information from a hydrologically similar, continuously gaged basin (‘index streamgage’) to one with a very limited streamflow record, but often results in biased estimates. The application of the index‐streamgage approach can be generalized into three steps: (1) selection of streamflow information of interest, (2) definition of hydrologic similarity and selection of index streamgage, and (3) application of an information‐transfer approach. Here, we explore the effects of (1) the range of streamflow values, (2) the areal density of streamgages, and (3) index‐streamgage selection criteria on the bias of three information‐transfer approaches on estimates of the 7‐day, 10‐year minimum streamflow (Q_{7, 10}). The three information‐transfer approaches considered are maintenance of variance extension, base‐flow correlation, and ratio of measured to concurrent gaged streamflow (Q‐ratio invariance). Our results for 1120 streamgages throughout the United States suggest that only a small portion of the total bias in estimated streamflow values is explained by the areal density of the streamgages and the hydrologic similarity between the two basins. However, restricting the range of streamflow values used in the index‐streamgage approach reduces the bias of estimated Q_{7, 10} values substantially. Importantly, estimated Q_{7, 10} values are heavily biased when the observed Q_{7, 10} values are near zero. Results of the analysis also showed that Q_{7, 10} estimates from two of the three index‐streamgage approaches have lower root‐mean‐square error values than estimates derived from multiple regressions for the large regions considered in this study. Published in 2011 by John Wiley & Sons, Ltd.     

Distributed modelling of future changes in hydrological processes of Spencer Creek watershed

The hydrologic impact of climate change has been largely assessed using mostly conceptual hydrologic models. This study investigates the use of distributed hydrologic model for the assessment of the climate change impact for the Spencer Creek watershed in Southern Ontario (Canada). A coupled MIKE SHE/MIKE 11 hydrologic model is developed to represent the complex hydrologic conditions in the Spencer Creek watershed, and later to simulate climate change impact using Canadian global climate model (CGCM 3·1) simulations. Owing to the coarse resolution of GCM data (daily GCM outputs), statistical downscaling techniques are used to generate higher resolution data (daily precipitation and temperature series). The modelling results show that the coupled model captured the snow storage well and also provided good simulation of evapotranspiration (ET) and groundwater recharge. The simulated streamflows are consistent with the observed flows at different sites within the catchment. Using a conservative climate change scenario, the downscaled GCM scenarios predicted an approximately 14–17% increase in the annual mean precipitation and 2–3 °C increase in annual mean maximum and minimum temperatures for the 2050s (i.e., 2046–2065). When the downscaled GCM scenarios were used in the coupled model, the model predicted a 1–5% annual decrease in snow storage for 2050s, approximately 1–10% increase in annual ET, and a 0·5–6% decrease in the annual groundwater recharge. These results are consistent with the downscaled temperature results. For future streamflows, the coupled model indicated an approximately 10–25% increase in annual streamflows for all sites, which is consistent with the predicted changes in precipitation. Overall, it is shown that distributed hydrologic modelling can provide useful information not only about future changes in streamflow but also changes in other key hydrologic processes such as snow storage, ET, and groundwater recharge, which can be particularly important depending on the climatic region of concern. The study results indicate that the coupled MIKE SHE/MIKE 11 hydrologic model could be a particularly useful tool for understanding the integrated effect of climate change in complex catchment scale hydrology. Copyright © 2010 John Wiley & Sons, Ltd.