黄河三门峡水库—小浪底水库间夏季降水量年际变化及水汽形势分析

利用1960~1999年全国600个站月平均降水资料,对黄河三门峡水库—小浪底水库间的夏季降水总量年际变化进行了统计分析,并应用功率谱诊断方法提取了夏季各月降水的振荡周期。在此基础上,选取了黄河三门峡水库—小浪底水库间降水量多与少的年份,利用NCEP再分析资料研究了其上空的水汽变化以及水汽输送量变化。结果表明,黄河三门峡水库—小浪底水库间降水量多的年份,其上空为较为明显的水汽辐合;降水量比较少的年份,其上空为明显的水汽辐散。选取多雨年(1982年)及少雨年(1997年),结合NCEP再分析资料以及TBB资料,进一步验证了上述结论。     

Mesoscale Diagnosis of a Torrential Rainfall Caused by a Tropical Depression

On August 5, 2001, Shanghai was struck by a torrential rainfall due to the passage of a tropical depression (TD). The rainfall intensity has been the strongest in recent 50 years. In this paper, a set of mesoscale re-analyses data and the planetary boundary layer observation from a wind profiler are used to understand the possible mechanism of such a heavy rain. Results show that the outburst of a southerly jet in the lower atmosphere triggered the explosive development of cyclonically vertical vorticity in the region with steep potential temperature surfaces in front of the TD; while the cyclonic vorticity increased notably at higher levels due to the small atmospheric vertical stability of westerly currents in the vicinity of Shanghai. The simultaneous sharp development of cyclonic vorticity at different levels should be the main cause for the torrential rainfall.     

The role of vegetation patterns in structuring runoff and sediment fluxes in drylands

The dynamics of vegetation‐driven spatial heterogeneity (VDSH) and its function in structuring runoff and sediment fluxes have received increased attention from both geomorphological and ecological perspectives, particularly in arid regions with sparse vegetation cover. This paper reviews the recent findings in this area obtained from field evidence and numerical simulation experiments, and outlines their implications for soil erosion assessment. VDSH is often observed at two scales, individual plant clumps and stands of clumps. At the patch scale, the local outcomes of vegetated patches on soil erodibility and hydraulic soil properties are well established. They involve greater water storage capacity as well as increased organic carbon and nutrient inputs. These effects operate together with an enhanced capacity for the interception of water and windborne resources, and an increased biological activity that accelerates breakdown of plant litter and nutrient turnover rates. This suite of relationships, which often involve positive feedback mechanisms, creates vegetated patches that are increasingly different from nearby bare ground areas. By this way a mosaic builds up with bare ground and vegetated patches coupled together, respectively, as sources and sinks of water, sediments and nutrients. At the stand scale within‐storm temporal variability of rainfall intensity controls reinfiltration of overland flow and its decay with slope length. At moderate rainfall intensity, this factor interacts with the spatial structure of VDSH and the mechanism of overland flow generation. Reinfiltration is greater in small‐grained VDSH and topsoil saturation excess overland flow. Available information shows that VDSH structures of sources and sinks of water and sediments evolve dynamically with hillslope fluxes and tune their spatial configurations to them. Rainfall simulation experiments in large plots show that coarsening VDSH leads to significantly greater erosion rates even under heavy rainfall intensity because of the flow concentration and its velocity increase. Copyright © 2005 John Wiley & Sons, Ltd.     

Quantitative assessment on the influence of heavy rainfall on the crystalline schist landslide by monitoring system -case study on Zentoku landslide, Japan

On Shikoku Island, which is one of the four main islands of Japan, a large number of large-scale crystalline schist landslides have been revealed and are being monitored by an observation system. Seasonal heavy rainfall is the most active meteorological factor that can threaten the stability of this kind of site-specific landslide. In this paper, on the basis of the study of the rainfall-related behavior of a typical crystalline schist landslide, the Zentoku landslide, by analyzing the precisely and continuously observed piezometric and movement data, a method was developed to quantitatively assess the effect of heavy rainfall on a large-scale landslide. The results indicated that heavy rainfall-induced landslide displacement shows good correlation with the variation of groundwater levels. Variations of groundwater level have been simulated with the use of a tank model. The simulation using this model permits the change in water levels for future rainfall events to be predicted. By combining the predicted results with the empirical relation between displacements and water levels, rainfall-induced landslide movement during extreme rainfall events can be estimated in advance. The effect of heavy rainfall on sliding behavior can be quantified in terms of the change in displacement. Thus warning information or advisories for the local residents can be provided.     

Statistical identification of orographic effects in the regional analysis of extreme rainfall

Regional models of extreme rainfall must address the spatial variability induced by orographic obstacles. However, the proper detection of orographic effects often depends on the availability of a well‐designed rain gauge network. The aim of this study is to investigate a new method for identifying and characterizing the effects of orography on the spatial structure of extreme rainfall at the regional scale, including where rainfall data are lacking or fail to describe rainfall features thoroughly. We analyse the annual maxima of daily rainfall data in the Campania region, an orographically complex region in Southern Italy, and introduce a statistical procedure to identify spatial outliers in a low order statistic (namely the mean). The locations of these outliers are then compared with a pattern of orographic objects that has been a priori identified through the application of an automatic geomorphological procedure. The results show a direct and clear link between a particular set of orographic objects and a local increase in the spatial variability of extreme rainfall. This analysis allowed us to objectively identify areas where orography produces enhanced variability in extreme rainfall. It has direct implications for rain gauge network design criteria and has led to promising developments in the regional analysis of extreme rainfall. Copyright © 2015 John Wiley & Sons, Ltd.     

Critical rainfall statistics for predicting watershed flood responses: rethinking the design storm concept

Recent advances have been made to modernize estimates of probable precipitation scenarios; however, researchers and engineers often continue to assume that rainfall events can be described by a small set of event statistics, typically average intensity and event duration. Given the easy availability of precipitation data and advances in desk‐top computational tools, we suggest that it is time to rethink the ‘design storm’ concept. Design storms should include more holistic characteristics of flood‐inducing rain events, which, in addition to describing specific hydrologic responses, may also be watershed or regionally specific. We present a sensitivity analysis of nine precipitation event statistics from observed precipitation events within a 60‐year record for Tompkins County, NY, USA. We perform a two‐sample Kolmogorov–Smirnov (KS) test to objectively identify precipitation event statistics of importance for two related hydrologic responses: (1) peak outflow from the Six Mile Creek watershed and (2) peak depth within the reservoir behind the Six Mile Creek Dam. We identify the total precipitation depth, peak hourly intensity, average intensity, event duration, interevent duration, and several statistics defining the temporal distribution of precipitation events to be important rainfall statistics to consider for predicting the watershed flood responses. We found that the two hydrologic responses had different sets of statistically significant parameters. We demonstrate through a stochastic precipitation generation analysis the effects of starting from a constrained parameter set (intensity and duration) when predicting hydrologic responses as opposed to utilizing an expanded suite of rainfall statistics. In particular, we note that the reduced precipitation parameter set may underestimate the probability of high stream flows and therefore underestimate flood hazard. Copyright © 2016 John Wiley & Sons, Ltd.     

The use of the exponent K(q) function to delimit homogeneous regions in regional frequency analysis of extreme annual daily rainfall

The regional frequency analysis of extreme annual rainfall data is a useful methodology in hydrology to obtain certain quantile values when no long data series are available. The most crucial step in the analysis is the grouping of sites into homogeneous regions. This work presents a new grouping criterion based on some multifractal properties of rainfall data. For this purpose, a regional frequency analysis of extreme annual rainfall data from the Maule Region (Chile) has been performed. Daily rainfall data series of 53 available stations have been studied, and their empirical moments scaling exponent functions K(q) have been obtained. Two characteristics parameters of the K(q) functions (γ_max and K(0)) have been used to group the stations into three homogeneous regions. Only five sites have not been possible to include into any homogenous regions, being the local frequency analysis of extreme daily rainfall the most appropriate method to be used at these locations. Copyright © 2014 John Wiley & Sons, Ltd.     

Three dimensional numerical simulation of residential building on shrink–swell soils in response to climatic conditions

Shrink–swell soils can cause distresses in buildings, and every year, the economic loss associated with this problem is huge. This paper presents a comprehensive system for simulating the soil–foundation–building system and its response to daily weather conditions. Weather data include rainfall, solar radiation, air temperature, relative humidity, and wind speed, all of which are readily available from a local weather station or the Internet. These data are used to determine simulation flux boundary conditions. Different methods are proposed to simulate different boundary conditions: bare soil, trees, and vegetation. A coupled hydro‐mechanical stress analysis is used to simulate the volume change of shrink–swell soils due to both mechanical stress and water content variations. Coupled hydro‐mechanical stress‐jointed elements are used to simulate the interaction between the soil and the slab, and general shell elements are used to simulate structural behavior. All the models are combined into one finite element program to predict the entire system's behavior. This paper first described the theory for the simulations. A site in Arlington, Texas, is then selected to demonstrate the application of the proposed system. Simulation results are shown, and a comparison between measured and predicted movements for four footings in Arlington, Texas, over a 2‐year period is presented. Finally, a three‐dimensional simulation is made for a virtual residential building on shrink–swell soils to identify the influence of various factors. Copyright © 2015 John Wiley & Sons, Ltd.     

Estimation of daily stream water temperatures with a Bayesian regression approach

Stream water temperature plays a significant role in aquatic ecosystems where it controls many important biological and physical processes. Reliable estimates of water temperature at the daily time step are critical in managing water resources. We developed a parsimonious piecewise Bayesian model for estimating daily stream water temperatures that account for temporal autocorrelation and both linear and nonlinear relationships with air temperature and discharge. The model was tested at 8 climatically different basins of the USA and at 34 sites within the mountainous Boise River Basin (Idaho, USA). The results show that the proposed model is robust with an average root mean square error of 1.25 °C and Nash–Sutcliffe coefficient of 0.92 over a 2‐year period. Our approach can be used to predict historic daily stream water temperatures in any location using observed daily stream temperature and regional air temperature data.     

Including spatial distribution in a data‐driven rainfall‐runoff model to improve reservoir inflow forecasting in Taiwan

Multi‐step ahead inflow forecasting has a critical role to play in reservoir operation and management in Taiwan during typhoons as statutory legislation requires a minimum of 3‐h warning to be issued before any reservoir releases are made. However, the complex spatial and temporal heterogeneity of typhoon rainfall, coupled with a remote and mountainous physiographic context, makes the development of real‐time rainfall‐runoff models that can accurately predict reservoir inflow several hours ahead of time challenging. Consequently, there is an urgent, operational requirement for models that can enhance reservoir inflow prediction at forecast horizons of more than 3 h. In this paper, we develop a novel semi‐distributed, data‐driven, rainfall‐runoff model for the Shihmen catchment, north Taiwan. A suite of Adaptive Network‐based Fuzzy Inference System solutions is created using various combinations of autoregressive, spatially lumped radar and point‐based rain gauge predictors. Different levels of spatially aggregated radar‐derived rainfall data are used to generate 4, 8 and 12 sub‐catchment input drivers. In general, the semi‐distributed radar rainfall models outperform their less complex counterparts in predictions of reservoir inflow at lead times greater than 3 h. Performance is found to be optimal when spatial aggregation is restricted to four sub‐catchments, with up to 30% improvements in the performance over lumped and point‐based models being evident at 5‐h lead times. The potential benefits of applying semi‐distributed, data‐driven models in reservoir inflow modelling specifically, and hydrological modelling more generally, are thus demonstrated. Copyright © 2012 John Wiley & Sons, Ltd.