The Application of a Meteo-hydrological Forecasting System with Rainfall Bias Correction in a Small and Medium-sized Catchment

Meteo-hydrological forecasting models are an effective way to generate high-resolution gridded rainfall data for water source research and flood forecast. The quality of rainfall data in terms of both intensity and distribution is very important for establishing a reliable meteo-hydrological forecasting model. To improve the accuracy of rainfall data, the successive correction method is introduced to correct the bias of rainfall, and a meteo-hydrological forecasting model based on WRF and WRF-Hydro is applied for streamflow forecast over the Zhanghe River catchment in China. The performance of WRF rainfall is compared with the China Meteorological Administration Multi-source Precipitation Analysis System (CMPAS), and the simulated streamflow from the model is further studied. It shows that the corrected WRF rainfall is more similar to the CMPAS in both temporal and spatial distribution than the original WRF rainfall. By contrast, the statistical metrics of the corrected WRF rainfall are better. When the corrected WRF rainfall is used to drive the WRF-Hydro model, the simulated streamflow of most events is significantly improved in both hydrographs and volume than that of using the original WRF rainfall. Among the studied events, the largest improvement of the NSE is from -0.68 to 0.67. It proves that correcting the bias of WRF rainfall with the successive correction method can greatly improve the performance of streamflow forecast. In general, the WRF / WRF-Hydro meteo-hydrological forecasting model based on the successive correction method has the potential to provide better streamflow forecast in the Zhanghe River catchment.     

基于WRF模式的清江流域降雨-径流模拟研究

基于WRF模式,采用4层嵌套方案,选取3种积云参数化方案和7种微物理方案组成21种组合,对清江流域2016—2018年6—10月6次典型降雨事件进行数值预报,结合CMORPH卫星-地面自动站-雷达三源融合降水产品,采用TS评分和FSS评分,分析不同分辨率和云微物理方案的降雨预报效果;基于较优组合方案的WRF模式与WRF-Hydro水文模式耦合进行径流模拟,分析WRF模式在水文模拟中的应用效果。结果表明:3 km和1 km分辨率对降雨中心位置及强度预报的差别不大,对降雨落区都有较好的预报能力;在积云参数化方案中,KF方案和BMJ方案的降雨预报效果优于GF方案;在微物理方案中,WSM3、WSM5、WSM6、Thompson方案的预报结果与融合数据有较好的一致性;基于较优组合方案BMJ_WSM3,将WRF模式与WRF-Hydro模式耦合,耦合模式能较好地模拟洪水过程,径流模拟相关系数都在0.67以上,且NSE最高可达0.79。      

基于重组降水集合预报的洪水概率预报

采用条件亚正态模型方法,生成了具有包含不同可能性的降水集合预报。为了保持各子流域降水集合预报变量之间的空间相关性,采用集合预报重组方法对降水集合预报进行重新排列。使用重组后的降水集合预报驱动水文模型,实现了淮河上游大坡岭-息县、淮河上游息县-王家坝和汝河-洪河上游3个子流域的12次洪水过程的洪水概率预报,并对1988年9月7日和1991年7月31日两次洪水概率预报进行个例分析。结果表明:相对于单一确定性预报,通过条件亚正态分布模型生成降水集合预报后,再经过Schaake洗牌法空间相关性重新组合的降水集合预报,捕捉洪峰出现时间和流量的能力更强。对洪水概率预报来说,降水概率预报更能达到对未来的水文事件进行最大可能估计的目的,并尽可能综合了降水预报不确定性因素,同时也说明维持变量原有的空间相关特征对于降水概率预报具有重要意义。     

A case study of excessive rainfall forecasting

Summary Flash floods have been recognized as one of the most significant natural disaster problems in the world. Within the United States, the annual average flood death toll exceeds one hundred and property damage is on the order of a billion dollars. There has been an increased effort of the meteorological community to improve short term quantitative precipitation forecasting, principally by improving mesoscale numerical weather prediction for heavy rain events. Nevertheless, to date, numerical weather prediction has had rather limited impact on the prediction of the most damaging convective rainstorms.This study examines numerical experiments, including both coarse-mesh and fine-mesh model simulations, of the Enid, Oklahoma flood of 10–11 October 1973. Besides the great concentration of rainfall, the Enid flood was rather unique in comparison with other flash flood cases in that it was part of a much larger area of heavy rainfall which soaked the central Plains over the 24h period ending at 1200 UTC 11 October. The objective is to assess the overall usefulness and limitation of numerical weather prediction models in quantitative precipitation forecasting for this flash flood event.The model experiments reveal that the broad-scale precipitation patterns associated with the front and cyclone are well predicted, but the maximum rainfall amounts around Enid are underpredicted. The fine-mesh model is superior to the coarse-mesh model because of the former's ability to generate many significant mesoscale features in the vicinity of the front. In the fine-mesh model, many convection-related parameters (e.g., moisture flux convergence) are correlated very well temporally and spatially with the observed heavy precipitation scenario.     

贵州望谟河流域径流量的延伸期预报试验

由于极端天气事件导致灾害频发,为延长洪水预见期,以望谟河流域为例,利用DEM数字高程资料、土地利用数据、土壤数据、气象数据等驱动SWAT水文模型,对流域水文循环过程进行了模拟,并采用2016～2018年逐日和2010～2018年逐月望谟水文监测站实测径流数据进行了率定和验证。同时基于CFSv2模式,采用双线性插值法得到延伸期时段望谟站2019年6月1日起报的未来45d的降水预报产品,与实况数据作对比分析,并与SWAT模型耦合进行了延伸期时段的径流量耦合预报。结果表明:(1)望谟河流域日尺度模拟中,率定期确定系数R2和Nash-Sutcliffe系数NSE均为0.75,验证期R2=0.61,NSE=0.55,月尺度模拟中,率定期R2=0.85,NSE=0.81,验证期R2=0.80,NSE=0.74,无论日尺度或月尺度,百分比偏差PBIAS的绝对值均在5%以内,模拟效果较好,可满足应用要求;(2)以2019年6月1日为起报日得到的CFSv2未来10～45d降水数据,CFSv2降水预报过程与实况趋势总体一致,强降水过程时段偏差在1～3d左右,但日降水量级的预报值偏小,说明需对CFSv2模式产品进行系统误差订正。基于SWAT模型与CFSv2降水预报产品的径流量耦合预报在未来10～15d内的变化趋势与实测值一致,尤其在未来10d左右模拟趋势效果最好;(3)对比6月10～13日不同起报日的降水数据,4个起报时刻对于未来10d强降雨过程均有稳定的预报信号,以6月10日作为起报日的径流量耦合预报于提前10～20d效果较为稳定,但由于降水预报量级偏小,致使径流量的模拟量级也偏小。研究成果为延伸期时段水文气象耦合模式的洪水预报试验研究提供了参考。      

Validating the Runoff from the PRECIS Model Using a Large-Scale Routing Model

The streamflow over the Yellow River basin is simulated using the PRECIS （Providing REgional Climates for Impacts Studies） regional climate model driven by 15-year （1979-1993） ECMWF reanalysis data as the initial and lateral boundary conditions and an off-line large-scale routing model （LRM）. The LRM uses physical catchment and river channel information and allows streamflow to be predicted for large continental rivers with a 1°×1° spatial resolution. The results show that the PRECIS model can reproduce the general southeast to northwest gradient distribution of the precipitation over the Yellow River basin, The PRECIS- LRM model combination has the capability to simulate the seasonal and annual streamflow over the Yellow River basin. The simulated streamflow is generally coincident with the naturalized streamflow both in timing and in magnitude.     

汉江丹江口流域水文气象预报系统

汉江丹江口流域水文气象预报系统在GIS技术的支持下,以水文气象监测网、定量降水估算、定量降水预报、洪水预报技术为基础,通过雷达估算降水技术、中尺度数值模式预报技术获取高时空分辨率的降水信息输入水文模型来进行水文气象预报。以Web形式为基础的汉江丹江口流域水文气象预报系统平台在2010年7月以及2011年9月汉江丹江口两次洪水过程中及时、准确地显示了流域实况降水、预报降水,准确地预报了洪水入库过程。目前系统已成功移植到三峡区间、清江水布垭、淮河王家坝、漳河水库等流域开展汛期试验与服务,取得了较好的应用效果。     

WRF模式与Topmodel模型在洪水预报中的耦合预报试验研究

基于空间分辨率90 m×90 m的湖北荆门漳河水库数字高程模型（DEM）地形数据,并从2012-2015年选取了20场洪水过程（其中16场用于模拟,4场用于检验）,将华中区域数值天气预报业务模式WRF提供的三重嵌套空间分辨率3 km×3 km、9 km×9 km和27 km×27 km预报降雨与集总式新安江模型以及半分布式水文模型Topmodel耦合进行洪水预报试验。通过对比试验得到以下结论：当流域降雨的时、空分布比较均匀时,集总式新安江模型可以较准确地预报出洪峰流量和峰现时间,而当降雨时、空分布差异较大时,预报误差也会随之增大。基于DEM数据建立的Topmodel模型可以反映不同降雨时、空分布下洪水预报结果的差异,试验结果表明,3 km×3 km和9 km×9 km洪水预报的输出结果比较接近,且在确定性系数和洪峰相对误差上要优于27 km×27 km的洪水预报结果,而在峰现时差的预报上,则是27 km×27 km的洪水预报结果与实测较吻合。通过研究还发现,虽然当流域降雨的时、空分布存在一定差异时,3种空间分辨率的WRF预报降雨均无法预报出与实测一致的降雨分布,但是在某些情况下,当降雨的时间分布误差和空间分布误差相抵消时,仍然可以得到较为准确的洪水预报结果。因此,高时、空分辨率的模式预报降雨并不一定就能对洪水预报结果产生正贡献,需要通过反复尝试寻找水文模型和数值模式耦合的最佳时、空分辨率。     

集合数值预报方法在山洪预报中的初步应用

选取湖南4个典型山洪个例,分析了嵌套模式不同分辨率的预报效果,并采用多物理过程组合的集合预报方法,研究区域集合数值预报技术在山洪预报中的应用。研究指出,单一模式条件下,使用模式嵌套技术提高分辨率,并不能有效地改进对引发山洪灾害的突发强降水过程的预报能力,且这种单一的确定性预报,对暴雨等灾害性天气的预报存在不确定性。集合预报是解决单一预报不确定性提出的新方法,与单一模式提供的确定性预报结果相比,集合预报可为山洪预报提供多种定量预报产品。利用集合平均预报、极值预报,可以引导对山洪采取分类应对措施;集合预报在给出降水分布的同时,还给出伴随预报结果的可信度;概率定量降水预报(PQPF)与水文预报模型结合,可以用于概率水文预报。     

暴雨致洪预报系统及其评估

该文研发了基于CREST V2.1分布式水文模型的暴雨致洪预报系统,应用中国气象局降水业务产品,开展全国0.125°×0.125°的逐日洪水预报和区域30"×30"的逐时洪水预报。其中,全国洪水预报以松花江、辽河、海河、黄河、淮河、长江、东南诸河、珠江、西南诸河和西北诸河十大水资源分区的典型流域为研究对象,区域洪水预报以淮河流域为研究对象。以模拟和观测流量之间的效率系数为目标函数,采用SCE-UA方法分别对全国和区域洪水预报模型的参数分流域进行率定。评估参数率定前后模型对效率系数、相关系数和相对偏差的改进程度,并对参数率定后的模型进行检验。结果表明:率定后的模型能够重现控制水文站的实测洪水过程,与率定前相比,效率系数和相对偏差有显著改进,相关系数有较大改进。系统符合业务需求,具有较好的预报精度和时效性,具备业务应用能力。     

Basic principles of automatic calibration of multi-parameter models used in operational systems of flash flood forecasting

This paper initiates a series of three articles on automatic calibration of operational multi-parameter models used for flash flood forecasting in automated regime. The main point expressed in this series is that proper calibration of even a single model for a single basin is not a trivial problem, but in automated forecasting systems, when thousands or even millions of basins should be parametrized simultaneously, priorities change. Now the computational efficiency becomes the most critical; an implemented calibration procedure must be fast and efficient rather than giving “the best“ parameters yet computationally expensive. The first article of the series contains critical analysis of the “mainstream“ in hydrological models calibration and presents basic principles of the Stepwise Line Search algorithm and its modifications, practically feasible and robust parametrization approaches that would be suitable for automated systems used for flash flood forecasting.     

Evaluating the impact of lightning data assimilation on mesoscale model simulations of a flash flood inducing storm

Flash floods are associated with highly localized convective storms producing heavy rainfall. Quantitative precipitation forecasting of such storms will potentially benefit from explicit representations of deep moist convection in numerical weather prediction models. However, explicit representation of moist convection is still not viable in operational mesoscale models, which rely on convective parameterizations for issuing short to medium-range forecasts. In this study we evaluate a technique that uses regional Cloud-to-Ground (CG) lightning observations to define areas of deep moist convection in thunderstorms and adjust the model-generated precipitation fields in those regions. The study focuses on a major flash flood inducing storm in central Europe (23 August 2005) that was simulated with the aid of an operational weather forecasting system (POSEIDON system based on Eta/NCEP model). The performance of the technique is assessed using as reference distributed rainfall estimates from a network of radar observations. The results indicate that CG lightning data can offer sufficient information to increase the mesoscale model skill in reproducing local convective precipitation that leads to flash floods. The model error correction is shown to be proportional to the density of lightning occurrence, making the technique potentially suitable for operational forecasting of flash flood inducing thunderstorms.     

阿克苏河流域防洪气象服务保障技术分析

阿克苏河流域防汛抗旱气象服务的首要任务是夏季汛期防洪服务，其次是春季到夏初的抗旱服务。根据阿克苏河流域各类型洪水的气象成因，提出了该流域防洪气象服务保障技术包括：流域内山区积雪遥感监测，融雪气象条件分析，面融雪量分析，面雨量分析，流域内夏季强降水监测和短时短期预报，平原区大风天气过程的中短期预报等。环流特征分析是做好引发洪水的各类气象要素变化的分析和预报的基础，给出了阿克苏河流域洪水主要类型的环流特征。     

The 12–13 November 1999 flash flood in southern France

The flash flood that occurred on 12–13 November 1999 in meridional France is documented. This event caused 35 fatalities and severe damage to property as rainfall totals locally exceeded 550 mm in 24 h and 620 mm in 48 h.The main issue of the present study is to discuss how realistically the spatial and temporal rainfall distribution of this flash flood event can be represented with present state-of-the-art operational and research modeling frameworks. The predictability of the present event for different forecast ranges is investigated and sensitivity studies are conducted in order to discuss the influence of model physics (convection, microphysics), atmospheric moisture analysis and Mediterranean sea surface temperature forcing on the quality of the results. It is shown that the present event could be reasonably predicted on forecast ranges of 2–3 days as it was essentially determined by strong moisture advection from the Mediterranean coupled with frontal and orographical lifting. However, precipitation scores show significant sensitivity to both analysis errors and model physics.     

Atmospheric moisture budget and floods in the Yangtze River basin, China

In this paper, we explored the trends of the atmospheric moisture budget, precipitation, and streamflow in summer during 1961 to 2005 and possible correlations between them by using the linear regression method in the Yangtze River basin, China. The results indicate that: (1) increasing tendencies can be detected in the atmospheric moisture budget, precipitation and streamflow in the Yangtze River basin; however, the significant increasing trends occur only in the atmospheric moisture budget and precipitation in the middle and lower Yangtze River basin; (2) both the ratio of summer moisture budget to annual moisture budget and the ratio of summer precipitation to annual precipitation exhibit a significant increasing trend in the Yangtze River basin. The ratio of summer streamflow to annual streamflow is in a significant increasing trend in Hankou station. Significant increasing summer precipitation can be taken as the major controlling factor responsible for the higher probability of flood hazard occurrences in the Yangtze River basin. The consecutively increasing summer precipitation is largely due to the consistently increasing moisture budget; (3) the zonal geopotential height anomaly between 1991 and 2005 and 1961 and 1990 is higher from the south to the north, which to a large degree, limits the northward propagation of the summer monsoon to north China. As a result, the summer moisture budget increases in the middle and lower Yangtze River basin, which leads to more summer precipitation. This paper sheds light on the changing properties of precipitation and streamflow and possible underlying causes, which will be greatly helpful for better understanding of the changes of precipitation and streamflow in the Yangtze River basin.     

基于不同降水产品的WRF-Hydro模式径流模拟——以漳河流域为例

以漳河流域为研究区域,以CMORPH卫星-地面自动站-雷达三源降水融合数据和ERA5再分析降水资料为WRF-Hydro模式的输入,进行径流模拟,对比分析模拟径流与实测径流的差异,探讨基于两种降水数据的WRF-Hydro模式在漳河流域的径流模拟效果。结果表明:三源降水融合数据的径流模拟效果较优,纳什系数均达到0.7以上,模拟径流与实测径流过程吻合较好;采用三源降水融合数据和ERA5再分析降水资料率定WRF-Hydro模式,以ERA5再分析降水资料为输入,径流模拟结果都不佳;总体而言,三源降水融合数据与WRF-Hydro模式耦合能够较好地模拟漳河流域径流过程。      

人类活动对1961～2016年长江流域降水变化的可能影响

人类活动造成的温室气体浓度增加对气候变化的加剧做出了贡献,降水作为重要的气象要素和水循环组成部分,人类活动对其时空变化特征的影响也是当下研究的重要课题。本文以长江流域为例,利用1961～2016年CN05.1逐日降水数据和20世纪气候检测归因计划（C20C+D&A Project）中CAM5.1-1degree模式的逐日降水结果,分析了人类活动对长江流域年降水量及三个极端降水指数时空变化的影响。结果表明:包含人类活动及自然强迫因素的现实情景（All-Hist）的模拟结果与观测结果较为相近。All-Hist情景下的多试验集合平均结果对长江流域降水的模拟能力较为可靠。通过对比两种情景下模拟的长江流域降水量时空变化特征发现:考虑人类活动影响后,长江流域平均降水相对于仅考虑自然强迫情景下时呈现减少趋势,且减少趋势随时间推移加剧;极端降水受人类活动的影响随时间呈现出的增加趋势有所削弱;对平均降水及极端降水变化趋势的影响存在空间差异性,其中受人类活动影响最严重的是上游中部、东南部及中下游东南部地区,均呈现减少趋势;但在长江上游西南部极端降水受人类活动影响显著增加,需要加强该区域洪涝预防工作。另外,人类活动对平均降水的减少贡献最大的时段为2000～2009年,影响最明显季节为秋冬两季;人类活动对极端降水的影响与降水的极端程度成正相关,降水极端性越强,受人类活动影响的变化程度更大,且空间分布上的差异性也更加显著。     

Development and Application of an Atmospheric-Hydrologic-Hydraulic Flood Forecasting Model Driven by TIGGE Ensemble Forecasts

A coupled atmospheric-hydrologic-hydraulic ensemble flood forecasting model,driven by The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) dat...     

水文气象研究进展

从面向流域的定量降水估测与预报、流域水文模型、水文气象耦合预报三个方面系统介绍水文气象研究进展。研究指出,融合天气雷达、卫星遥感及实况降水等多源信息是精细化定量降水估测产品的主要发展方向;采用多模式降水预报集成技术是提高定量降水预报精度的重要途径;分布式水文模型是流域水文模型的发展方向;引入定量降水预报的水文气象耦合预报模式可以延长洪水预报预见期,水文集合预报是水文预报方法的有效解决途径,而数值预报模式与水文模型的双向耦合模式是另一重要发展方向。     

Evaluation of different downscaling techniques for hydrological climate-change impact studies at the catchment scale

Hydrological modeling for climate-change impact assessment implies using meteorological variables simulated by global climate models (GCMs). Due to mismatching scales, coarse-resolution GCM output cannot be used directly for hydrological impact studies but rather needs to be downscaled. In this study, we investigated the variability of seasonal streamflow and flood-peak projections caused by the use of three statistical approaches to downscale precipitation from two GCMs for a meso-scale catchment in southeastern Sweden: (1) an analog method (AM), (2) a multi-objective fuzzy-rule-based classification (MOFRBC) and (3) the Statistical DownScaling Model (SDSM). The obtained higher-resolution precipitation values were then used to simulate daily streamflow for a control period (1961–1990) and for two future emission scenarios (2071–2100) with the precipitation-streamflow model HBV. The choice of downscaled precipitation time series had a major impact on the streamflow simulations, which was directly related to the ability of the downscaling approaches to reproduce observed precipitation. Although SDSM was considered to be most suitable for downscaling precipitation in the studied river basin, we highlighted the importance of an ensemble approach. The climate and streamflow change signals indicated that the current flow regime with a snowmelt-driven spring flood in April will likely change to a flow regime that is rather dominated by large winter streamflows. Spring flood events are expected to decrease considerably and occur earlier, whereas autumn flood peaks are projected to increase slightly. The simulations demonstrated that projections of future streamflow regimes are highly variable and can even partly point towards different directions.