Hydrological Modeling of Upper OumErRabia Basin (Morocco), Comparative Study of the Event-Based and Continuous-Process HEC-HMS Model Methods

Human population growth and land-use changes raise demand and competition for water resources. The Upper OumErRabia River Basin is experiencing high rangeland and matorral conversion to irrigated agricultural land expansion. Given Morocco’s per capita water availability, River-basin hydrologic modelling could potentially bring together agricultural, water resources and conservation objectives. However, not everywhere have hydrological models considered events and continuous assessment of climatic data. In this study, HEC-HMS modelling approach is used to explore the event-based and continuous-process simulation of land-use and land cover change (LULCC) impact on water balance. The use of HEC-GeoHMS facilitated the digital data processing for coupling with the model. The basin’s physical characteristics and the hydro-climatic data helped to generate a geospatial database for HEC-HMS model. We analyzed baseline and future scenario changes for the 1980-2016 period using the SCS Curve-Number and the Soil Moisture Accounting (SMA) loss methods. SMA was coupled with the Hargreaves evapotranspiration method. Model calibration focused on reproducing observed basin runoff hydrograph. To evaluate the model performance for both calibration and validation, the Coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE), Root Mean Square Error (RSR) and Percent Bias (PBIAS) criteria were exploited. The average calibration NSE values were 0.740 and 0.585 for event-based (daily) and continuous-process (annual) respectively. The R2, RSR and PBIAS values were 0.624, 0.634 and +16.7 respectively. This is rated as good performance besides the validation simulations were satisfactory for subsequent hydrologic analyses. We conclude that the basin’s hydrologic response to positive and negative LULCC scenarios is significant both positive and negative scenarios. The study findings provide useful information for key stakeholders/decision-makers in water resources.

基于HEC-HMS模型的三峡区间洪水模拟

三峡区间面积在长江上游的流域面积占比5.6%,但在三峡入库洪水组成中,区间形成的洪水占比可达10%以上,可见区间暴雨洪水是水库防洪安全必须考量的重要因素。采用2007—2011年三峡入库流量,上游边界寸滩和武隆站实测流量资料,建立了基于HEC-HMS的三峡区间洪水模拟模型,用于分析区间暴雨洪水与入库洪水的关系。根据入库洪水来源组成分析和资料特点,提出分类调参、分期检验的区间洪水建模方案:对以上游来水为主型洪水,率定汇流参数;对区间降水贡献较大型洪水,率定产流参数;对2012年以后的模拟洪水过程,以三峡水库运行实录发布的洪水过程线为比对基准。结果表明:模型精度良好,率定期和验证期洪峰流量相对误差在±20%以内,峰现时间误差<3 h;经与长江三峡工程运行实录比对,模型适用于模拟2012年后的三峡入库洪水过程。以20160626场次洪水为典型,分析该场区间洪水对入库洪水的峰值贡献率达27.2%,使得峰现时间提前16 h。研究成果可用于三峡区间洪水的影响研究,也可作为区间流域洪水模拟模型建模方案的技术参考。

基于HEC-HMS的黄土高塬沟壑区流域城市化对洪水情势的影响

黄土高塬沟壑区城市化过程中下垫面硬化会影响暴雨水文情势,增大洪水模拟预报难度。为实现对流域城市洪水灾害的预警,基于HEC-HMS水文模型,分别率定砚瓦川流域城市化前后不同时期土地利用条件下的产汇流参数,开展极端暴雨洪水情景设计,揭示流域城市化对不同重现期洪水的水文效应。结果表明:建立的HEC-HMS降雨-径流模型适用于黄土高塬沟壑区洪水预报,其模拟综合合格率为81.25%,平均Nash效率系数为0.82;流域城市化对重现期短的洪水要素变化影响较大,且洪量变化幅度大于洪峰变化幅度,100 a一遇洪水的洪峰和洪量的增幅分别为4.54%和6.40%,5 a一遇洪水的洪峰和洪量的增幅分别为7.06%和9.49%;各子流域设计洪水对流域城市化的响应分布具有空间差异性,以西北部地区响应为最强,其次为南部地区,东北部地区最弱。

基于HEC-HMS的资料匮乏山区洪水模拟研究

以白花河小流域为例,探讨缺资料地区HEC-HMS模型的适用效果。通过流域DEM等下垫面数据获取流域信息,采用SCS-CN曲线法、SCS单位线法、指数衰退法、马斯京根演算法进行流域产汇流计算及河道洪水演算,构建了HEC-HMS降雨径流模型,结合广东省综合单位线法与推理公式法的结果对模型参数进行率定。结果表明,设计洪水计算结果的误差均小于20%且平均相对误差均小于10%;实测洪水模拟的洪峰流量相对误差小于10%,Nash系数为0.90。HEC-HMS模型进行径流模拟效果较好,可适用于研究区山洪预报。

HEC-HMS水文模型不同降雨损失方法对比研究

为对比分析HEC-HMS模型三种降雨损失方法在沁河流域的适用性。借助Morris筛选法识别降雨损失方法的关键参数,选用流域内5场雨洪资料进行参数率定和模拟精度分析。结果表明:(1)SCS CN值曲线法、Green-Ampt法、Initial and Uniform法主要敏感性参数分别为CN值、土壤饱和导水率、恒定损失率。(2)选取洪峰流量、洪水总量、峰现时刻误差以及Nash系数对模型模拟精度进行评价,SCS CN值曲线法和Initial and Uniform法模拟结果达到乙等精度,Green-Ampt法模拟结果达到丙等精度。研究成果可为半湿润地区中小流域降雨损失方法的选择提供参考。

HEC-HMS模型在江西省山区中小河流洪水预报中的适用性研究

山区洪水暴涨陡落,常造成严重经济损失和人员伤亡,实现中小河流洪水预报在防洪减灾中十分重要。为探究HEC-HMS分布式水文模型在江西省山区中小河流的适用性,以蜀水流域为研究区,构建了基于HEC-HMS的蜀水流域分布式水文模型,选用2013-2019年间的12场降雨对模型进行参数率定、敏感性分析和洪水过程模拟。结果表明:模型参数中CN值为最敏感参数;模型预测值与实测值结果显示洪峰流量和峰现时间合格率均为91.67%,径流深合格率为100%,确定性系数均高于0.7;HEC-HMS模型在蜀水流域中有较好的模拟效果,可为江西省山区中小河流域洪水预报提供参考。

基于HEC-HMS水文模型的中小流域洪水模拟预报分析

水文预报对于我国中小型流域的抗洪减灾和水资源管理十分重要。以渠江流域为例,基于DEM数字高程信息建立流域数字模型,利用HEC-HMS水文模型分别对选定的20组率定期洪水和5组验证期洪水进行模拟,并在参数率定及模型验证的基础上对研究区域进行洪水预报分析,得出任一区域由降雨所引起的最大洪峰流量及峰现时间,可为流域防洪度汛提供一定的科学依据。

基于HEC-HMS模型的太行山小流域山洪模拟研究——以邢台路罗川流域为例

太行山南段东坡路罗川流域山洪灾害频发,然而相关的模拟研究较少。采用HEC-HMS模型,以罗川流域1982—2016年间8场山洪进行参数率定,2场洪水进行验证,并对比两套参数组合方案((1)初损常损法+Sndyer单位线法+指数衰退法+马斯京根法;(2)SCS曲线数法+SCS单位线法+指数衰退法+马斯京根法)。结果表明:两套方案合格率均为90%,平均Nash效率系数分别为0.83和0.79,预报精度均到达乙级水平,方案一优于方案二。产流初损和CN值均与起涨流量密切相关,分别为对数和指数关系。在验证期,基于经验关系的动态推求法模拟的洪峰流量平均相对误差为12.35%,洪量平均相对误差为12.35%,平均Nash系数为0.82,均优于固定参数法(17.26%、13.35%和0.77),可有效提高模拟精度。

HEC-HMS模型在不同水文气象分区中的改进

采用前期影响雨量Pa替换前5 d降水量以评估前期土壤湿润程度、线性内插增加CN值的分级以及根据不同水文气象分区选用适宜初损率λ等改进措施对HEC-HMS半分布式水文模型产流模块中的SCS-CN方法进行改进,并于县北沟、呈村、平和3个中小流域进行验证,探讨了反算法率定SCS模型参数的局限性;通过率定不同流域前期影响雨量下的最适宜CN值(C_(x))并回归C_(x)-P_(a)关系,分析了实际湿润程度随前期影响雨量变化的规律。研究结果表明,当湿润区和半湿润区流域初损率取值0.05和0.20时HEC-HMS模型能取得较好的模拟精度。

HEC-HMS模型和NAM模型在降雨径流模拟中的应用研究

HEC-HMS(Hydrologic Engineering Center-Hydrologic Modeling System)模型是半分布式水文模型,NAM(Nedbør-Afstrømnings-Model)模型是集总式概念性模型,通过对两种模型计算原理和模型架构及参数组成进行对比,并选取滁河沙河集子流域的5场日径流进行模拟,通过模拟结果对两种模型在降雨径流模拟性能上进行比较研究。研究结果表明,两种模型均有较好的模拟效果,预报的径流深均在许可误差范围内。两种模型在降雨径流模拟过程中均存在一定的局限性,HEC-HMS模型计算功能强大,部分参数能直接反映流域的具体特性,但是其建模过程较繁琐,且内部的观测值难以进行检验,NAM建模过程简便,所需参数较少且物理意义明确,但是模型计算过程忽略了流域的空间变异及产流的空间分布。

HEC-HMS模型和TOPMODEL模型在东庄流域山洪预报的应用研究

水文模型是山洪预报的理论依据,不同模型间的结构、适用性、精度均不同。为探究HEC-HMS模型和TOPMODEL模型的特性及其模拟效果的差异,选取东庄流域10场代表性洪水,以峰现时间误差、径流深误差、时间误差和确定性系数为检验指标,进行场次洪水模拟。结果表明,两模型的预报方案在东庄流域模拟中各评定指标均达标,率定期均为甲等精度,验证期均为丙等精度。HEC-HMS模型和TOPMODEL模型对蒸散发的差异性参考,使得在半干旱区HEC-HMS模型的适用性更好。研究成果对于HEC-HMS模型和TOPMODEL模型在该区域的应用及防灾减灾有重要意义。

基于HEC-HMS模型的大水峪水库洪水预报研究

为有效解决潮白河流域中小水库洪水监测、预报调度的实际问题,本研究选取潮白河流域内暴雨频发、山洪沟道密集、防洪库容偏小的大水峪水库为研究对象,基于HEC-HMS水文模型,构建大水峪水库洪水预报模型,为水库科学优化洪水调度提供依据。HEC-HMS模型是一个具有物理概念的分布式降雨径流模型,在洪水预报中具有广泛的应用。通过大水峪水库上游4场降雨洪水过程,对该模型进行了率定验证,洪峰流量场次模拟合格率为75%,满足乙级预报精度要求。分析表明该模型在大水峪水库洪水预报模拟上有很好的应用。

HEC-HMS模型在漓江上游流域洪水预报中的应用

漓江上游流域洪水灾害频发,是广西山洪灾害多发区中较严重的流域之一。本文以漓江上游流域为研究对象,通过HEC-HMS水文模型,采用SCS-CN法计算降水损失、Snyder单位线法计算坡面汇流、退水曲线法模拟基流、马斯京根法进行河道洪水演算等4种研究方法模拟降雨-径流过程,基于实测水文、气象数据资料选取2008—2013年的27场洪水作为率定期洪水,2014—2015年8场洪水作为验证期洪水进行降雨径流模拟,经过模拟计算得到结果:率定期27场洪水合格率为81.5%,平均确定性系数为0.809;验证期8场洪水合格率为75%达到乙级精度,平均确定性系数为0.76,达到乙级精度。模拟结果的分析表明,HEC-HMS模型适用于漓江流域上游且取得较好的模拟结果,可为漓江流域上游洪水作业预报提供参考。

耦合优化蚁群算法与P-Median model的选址模型设计

为节省城建部门对于公共体育设施的投入成本以及提高城市人民生活质量,以运动场所优化选址为例,提出一种新型设施选址模型。该模型主要基于P-Median model(最小化阻抗模型)根据需求点数量从全部候选设施选址中选择设施空间位置,让用户达到离自己最近设施距离成本总和最小的目的,对选址的基本原则和实际情况提出要求,构造目标函数用于优化后蚁群算法求解进行选址工作。优化蚁群算法实现基于Python语言模块,通过改进蚁群原始信息素,提升原有算法的收敛速度,求出目标函数最优解,可以很好地模拟对于运动场所的选址。用二者耦合进行优势互补所设计的选址模型来搜寻研究区蚁群信息素浓度残留最大的栅格像元,从而确定未被已有设施点服务半径覆盖的最佳设施点建立位置。实验结果表明,该新型选址模型相较于最小化阻抗模型与最大化覆盖模型,新增优化设施点使整体服务半径覆盖率分别高出10.42%和6.95%,适合求解较为精确且小规模空间下的选址问题。

基于HEC-HMS模型的洪水模拟研究

由于山西省内防洪预警系统、水资源规划调度等相关措施尚不完善,因此以洮水河流域为研究区,建立HEC-HMS洪水预报模型,针对不同级别的洪水得到较为合理科学的模型参数,以期为省内的洪水调度、其他水文模型的建立、洮水河流域的水资源合理开发利用及规划调度等提供一定的参考依据。对洮水河流域水文地质、测站分布、流域概况进行汇总,从该地区1976—2017年降雨洪水资料中优选出代表性较好的25场洪水,进行降雨径流数据资料的插补延长,建立HEC-HMS模型,进行洪水模拟,按照大、中、小洪水形成3套参数,对参数进行率定与验证,模拟结果在该地区精度达到了乙级,对洮水河流域适用性良好。

Mshpy23:a user-friendly,parameterized model of magnetosheath conditions

Lunar Environment heliospheric X-ray Imager(LEXI)and Solar wind−Magnetosphere−Ionosphere Link Explorer(SMILE)will observe magnetosheath and its boundary motion in soft X-rays for understanding magnetopause reconnection modes under various solar wind conditions after their respective launches in 2024 and 2025.Magnetosheath conditions,namely,plasma density,velocity,and temperature,are key parameters for predicting and analyzing soft X-ray images from the LEXI and SMILE missions.We developed a userfriendly model of magnetosheath that parameterizes number density,velocity,temperature,and magnetic field by utilizing the global Magnetohydrodynamics(MHD)model as well as the pre-existing gas-dynamic and analytic models.Using this parameterized magnetosheath model,scientists can easily reconstruct expected soft X-ray images and utilize them for analysis of observed images of LEXI and SMILE without simulating the complicated global magnetosphere models.First,we created an MHD-based magnetosheath model by running a total of 14 OpenGGCM global MHD simulations under 7 solar wind densities(1,5,10,15,20,25,and 30 cm)and 2 interplanetary magnetic field Bz components(±4 nT),and then parameterizing the results in new magnetosheath conditions.We compared the magnetosheath model result with THEMIS statistical data and it showed good agreement with a weighted Pearson correlation coefficient greater than 0.77,especially for plasma density and plasma velocity.Second,we compiled a suite of magnetosheath models incorporating previous magnetosheath models(gas-dynamic,analytic),and did two case studies to test the performance.The MHD-based model was comparable to or better than the previous models while providing self-consistency among the magnetosheath parameters.Third,we constructed a tool to calculate a soft X-ray image from any given vantage point,which can support the planning and data analysis of the aforementioned LEXI and SMILE missions.A release of the code has been uploaded to a Github repository.

24Model与LCM原因因素定义对比研究

为探究损失致因模型(LCM)原因因素定义与事故致因“2-4”模型(24Model)存在的异同和优缺点,梳理2个模型各层面原因和结果的定义,对比定义内容及其对事故原因分析等安全实务的指导作用,并以一起瓦斯爆炸事故为例加以实证分析,获得二者分析结果之间的差异。研究结果表明:LCM是首个将管理因素纳入事故致因分析的一维事件序列模型,可明确各层面原因因素的定义和因素间的逻辑关系,但部分定义存在交叉重复的问题,并没有揭示安全工作指导思想等深层次事故致因因素;24Model作为系统性事故致因模型,对各类因素的定义均以组织为主体,描述事件、事故、安全的概念内涵,划分个体安全动作、安全能力和组织安全管理体系的类别并给出含义解析,探究组织安全文化层面的问题并以32个元素体现;2个模型的事故原因分析方法均建立在对各层级原因因素定义的基础上,并适用于模型理论体系本身。

Assessing the Performance of CMIP6 Models in Simulating Droughts across Global Drylands

Both the attribution of historical change and future projections of droughts rely heavily on climate modeling. However,reasonable drought simulations have remained a challenge, and the related performances of the current state-of-the-art Coupled Model Intercomparison Project phase 6(CMIP6) models remain unknown. Here, both the strengths and weaknesses of CMIP6 models in simulating droughts and corresponding hydrothermal conditions in drylands are assessed.While the general patterns of simulated meteorological elements in drylands resemble the observations, the annual precipitation is overestimated by ~33%(with a model spread of 2.3%–77.2%), along with an underestimation of potential evapotranspiration(PET) by ~32%(17.5%–47.2%). The water deficit condition, measured by the difference between precipitation and PET, is 50%(29.1%–71.7%) weaker than observations. The CMIP6 models show weaknesses in capturing the climate mean drought characteristics in drylands, particularly with the occurrence and duration largely underestimated in the hyperarid Afro-Asian areas. Nonetheless, the drought-associated meteorological anomalies, including reduced precipitation, warmer temperatures, higher evaporative demand, and increased water deficit conditions, are reasonably reproduced. The simulated magnitude of precipitation(water deficit) associated with dryland droughts is overestimated by 28%(24%) compared to observations. The observed increasing trends in drought fractional area,occurrence, and corresponding meteorological anomalies during 1980–2014 are reasonably reproduced. Still, the increase in drought characteristics, associated precipitation and water deficit are obviously underestimated after the late 1990s,especially for mild and moderate droughts, indicative of a weaker response of dryland drought changes to global warming in CMIP6 models. Our results suggest that it is imperative to employ bias correction approaches in drought-related studies over drylands by using CMIP6 outputs.

Genetically modified non-human primate models for research on neurodegenerative diseases

Neurodegenerative diseases(NDs)are a group of debilitating neurological disorders that primarily affect elderly populations and include Alzheimer's disease(AD),Parkinson's disease(PD),Huntington's disease(HD),and amyotrophic lateral sclerosis(ALS).Currently,there are no therapies available that can delay,stop,or reverse the pathological progression of NDs in clinical settings.As the population ages,NDs are imposing a huge burden on public health systems and affected families.Animal models are important tools for preclinical investigations to understand disease pathogenesis and test potential treatments.While numerous rodent models of NDs have been developed to enhance our understanding of disease mechanisms,the limited success of translating findings from animal models to clinical practice suggests that there is still a need to bridge this translation gap.Old World nonhuman primates(NHPs),such as rhesus,cynomolgus,and vervet monkeys,are phylogenetically,physiologically,biochemically,and behaviorally most relevant to humans.This is particularly evident in the similarity of the structure and function of their central nervous systems,rendering such species uniquely valuable for neuroscience research.Recently,the development of several genetically modified NHP models of NDs has successfully recapitulated key pathologies and revealed novel mechanisms.This review focuses on the efficacy of NHPs in modeling NDs and the novel pathological insights gained,as well as the challenges associated with the generation of such models and the complexities involved in their subsequent analysis.

Large animal models for Huntington's disease research

Huntington'sdisease(HD)isahereditary neurodegenerative disorder for which there is currently no effectivetreatmentavailable.Consequently,the development of appropriate disease models is critical to thoroughly investigate disease progression.The genetic basis of HD involves the abnormal expansion of CAG repeats in the huntingtin(HTT)gene,leading to the expansion of a polyglutamine repeat in the HTT protein.Mutant HTT carrying the expanded polyglutamine repeat undergoes misfolding and forms aggregates in the brain,which precipitate selective neuronal loss in specific brain regions.Animal models play an important role in elucidating the pathogenesis of neurodegenerative disorders such as HD and in identifying potential therapeutic targets.Due to the marked species differences between rodents and larger animals,substantial efforts have been directed toward establishing large animal models for HD research.These models are pivotal for advancing the discovery of novel therapeutic targets,enhancing effective drug delivery methods,and improving treatment outcomes.We have explored the advantages of utilizing large animal models,particularly pigs,in previous reviews.Since then,however,significant progress has been made in developing more sophisticated animal models that faithfully replicate the typical pathology of HD.In the current review,we provide a comprehensive overview of large animal models of HD,incorporating recent findings regarding the establishment of HD knock-in(KI)pigs and their genetic therapy.We also explore the utilization of large animal models in HD research,with a focus on sheep,non-human primates(NHPs),and pigs.Our objective is to provide valuable insights into the application of these large animal models for the investigation and treatment of neurodegenerative disorders.