Empirically-based modeling of spatial sampling uncertainties associated with rainfall measurements by rain gauges

In the quantitative evaluation of radar-rainfall products (maps), rain gauge data are generally used as a good approximation of the true ground rainfall. However, rain gauges provide accurate measurements for a specific location, while radar estimates represent areal averages. Because these sampling discrepancies could introduce noise into the comparisons between these two sensors, they need to be accounted for. In this study, the spatial sampling error is defined as the ratio between the measurements by a single rain gauge and the true areal rainfall, defined as the value obtained by averaging the measurements by an adequate number of gauges within a pixel. Using a non-parametric scheme, the authors characterize its full statistical distribution for several spatial (4, 16 and 36 km²) and temporal (15 min and hourly) scales.     

Estimation of rainfall fields using commercial microwave communication networks of variable density

The use of commercial microwave radio networks which are a part of cellular communication infrastructure for mapping of the near-the-ground rainfall is challenging for many reasons: the network geometry in space is irregular, the distribution of links by frequencies and polarizations is inhomogeneous, and measurements of rain-induced attenuation are distorted by quantization. A non-linear tomographic model over a variable density grid is formulated, and its applicability and performance limits are studied by means of a simulated experiment using a model of a real microwave network. It is shown that the proposed technique is capable to accurately measure integrated near-the-ground rainfall amounts over the area of 3200 km² with a bias smaller than 10%. In urban area, where the density of microwave links is high, the average correlation in space between the simulated model and reconstructed rainfall fields reaches 0.89 over the variable density grid with average cell size of 5.7 km² and 0.74 when interpolated into the rectangular grid with pixel size 0.775 × 0.775 km², for the quantization interval of 0.1 dB.     

Movement of Heavy Metals in Soil through Preferential Flow Paths under Different Rainfall Intensities

Preferential flow in soils deserves attention due to its potential role in accelerating the movement of contaminants to groundwater. This study investigates the movement of Cd, Cu, and Pb through preferential flow paths under different applied rainfall intensities. Artificial acid rain (pH of 4.1) containing CdCl₂, CuCl₂, and Pb(NO₃)₂ was applied to undisturbed soil and repacked sand columns at low and high intensities, and leachate metals and chloride concentrations were measured. Cd was found in the leachate at both low and high rates in all columns, while the increase in Cu concentrations in the leachate was detected only at the high rate of the undisturbed columns. Pb was retained in both columns. For undisturbed columns, the breakthrough curves of Cd and Cu were similar to those of Cl, showing early initial breakthrough by preferential flow and dependency on rainfall intensities. The Cd concentrations were detected in the leachate from repacked columns for high rate rainfall, implying that even homogeneous soil may not be perfectly able to retain metals and the initially wet condition is more harmful for subsurface contaminant transport. In conclusion, the study demonstrated that, despite its highly sorptive nature, the transport of some metals may be as fast as that of a tracer under preferential flow conditions, and the rainfall intensity is a significant factor for the degree of transport.     

Variability in rainfall threshold for debris flow after the Chi-Chi earthquake in central Taiwan, China

The purpose of this study is to analyze variability in rainfall threshold for debris flow （critical rainfall for debris flow triggering） after the ML 7.3 Chi-Chi earthquake in central Taiwan in 1999. Two study sites with different geological conditions were surveyed in the earthquake area. Streambed surveys were conducted to continuously monitor debris flows between 1999 and 2006. During the 7-year study period, every debris flow event was identified, and the streambed characterized. Results show that the rainfall threshold for debris flow was remarkably lower just after the Chi-Chi Earthquake, but gradually recovered. To date, this rainfall threshold is still lower than the original level prior to the earthquake. This variability in rainfall threshold is closely related to the mount of sediment material in the initiation area of debris flow, which increased rapidly due to landslides resulting from the earthquake. With the increase in sediment material, the rainfall threshold was lowered severely during the first year following the Chi-Chi earthquake. However, heavy rainfalls mobilized the sediment material, causing debris flows and transporting sediment downstream. With the decrease in sediment material, the rainfall threshold recovered gradually over time. Furthermore, debris flows occurred only in the subbasins that had sufficient sediment material to cause significant movement. Hence, these results confirm that the sediment material in the initiation area of debris flow is a crucial component of the rainfall threshold for debris flow.     

A non-parametric automatic blending methodology to estimate rainfall fields from rain gauge and radar data

Quantitative estimation of rainfall fields has been a crucial objective from early studies of the hydrological applications of weather radar. Previous studies have suggested that flow estimations are improved when radar and rain gauge data are combined to estimate input rainfall fields. This paper reports new research carried out in this field. Classical approaches for the selection and fitting of a theoretical correlogram (or semivariogram) model (needed to apply geostatistical estimators) are avoided in this study. Instead, a non-parametric technique based on FFT is used to obtain two-dimensional positive-definite correlograms directly from radar observations, dealing with both the natural anisotropy and the temporal variation of the spatial structure of the rainfall in the estimated fields. Because these correlation maps can be automatically obtained at each time step of a given rainfall event, this technique might easily be used in operational (real-time) applications. This paper describes the development of the non-parametric estimator exploiting the advantages of FFT for the automatic computation of correlograms and provides examples of its application on a case study using six rainfall events. This methodology is applied to three different alternatives to incorporate the radar information (as a secondary variable), and a comparison of performances is provided. In particular, their ability to reproduce in estimated rainfall fields (i) the rain gauge observations (in a cross-validation analysis) and (ii) the spatial patterns of radar fields are analyzed. Results seem to indicate that the methodology of kriging with external drift [KED], in combination with the technique of automatically computing 2-D spatial correlograms, provides merged rainfall fields with good agreement with rain gauges and with the most accurate approach to the spatial tendencies observed in the radar rainfall fields, when compared with other alternatives analyzed.     

Quantifying and predicting the accuracy of radar-based quantitative precipitation forecasts

On the basis that hydrological users need to know the forecast uncertainty at the time that the forecast is issued, we computed distributions of radar rainfall forecast uncertainty as a function of forecast lead time, basin size, and forecasted rainfall intensity using data from the US 3-D National Mosaic of radar data. We document how exceptional forecasts such as those of heavy rainfall are generally biased. Since forecast uncertainty is also weather dependent, we tried to find good predictors to help either reduce the forecast uncertainty or better define it. These predictors were based either on characteristics of the current precipitation field or on the performance of the nowcast in the immediate past. The value of some predictors, especially those based on the properties of large-scale rainfall patterns, was significant though modest, the predictors being generally more skillful at characterizing forecast uncertainty than at improving forecast accuracy.     

Copula-based mixed models for bivariate rainfall data: an empirical study in regression perspective

A comprehensive parametric approach to study the probability distribution of rainfall data at scales of hydrologic interest (e.g. from few minutes up to daily) requires the use of mixed distributions with a discrete part accounting for the occurrence of rain and a continuous one for the rainfall amount. In particular, when a bivariate vector (X, Y) is considered (e.g. simultaneous observations from two rainfall stations or from two instruments such as radar and rain gauge), it is necessary to resort to a bivariate mixed model. A quite flexible mixed distribution can be defined by using a 2-copula and four marginals, obtaining a bivariate copula-based mixed model. Such a distribution is able to correctly describe the intermittent nature of rainfall and the dependence structure of the variables. Furthermore, without loss of generality and with gain of parsimony this model can be simplified by some transformations of the marginals. The main goals of this work are: (1) to empirically explore the behaviour of the parameters of marginal transformations as a function of time scale and inter-gauge distance, by analysing data from a network of rain gauges; (2) to compare the properties of the regression curves associated to the copula-based mixed model with those derived from the model simplified by transformations of the marginals. The results from the investigation of transformations’ parameters are in agreement with the expected theoretical dependence on inter-gauge distance, and show dependence on time scale. The analysis on the regression curves points out that: (1) a copula-based mixed model involves regression curves quite close to some non-parametric models; (2) the performance of the parametric regression decreases in the same cases in which non-parametric regression shows some instability; (3) the copula-based mixed model and its simplified version show similar behaviour in term of regression for mid-low values of rainfall. An erratum to this article can be found at     

重庆金佛山岩溶作用驱动因素初探

走向北东— 南西向的向斜和近东西向的横张大节理等地质构造控制了金佛山岩溶作用发生的空间展布方向。下二叠统的中厚层块状灰岩及生物碎屑灰岩给金佛山岩溶作用的发生提供了坚实的物质基础。4～ 10月这一温暖湿润的气候特征加大了金佛山岩溶作用发生的强度和速度。茂密的森林植被和及其植被分带性在进一步促进了金佛山岩溶作用发生的同时,也导致了金佛山岩溶作用强度在时空分布上的差异。      

长江下游夏季降水与东亚夏季风及春季太平洋海温的关系

利用NCEP/NCAR逐日再分析资料、NOAA月平均海表温度资料及中国站点逐日降水资料,研究了长江下游夏季降水、东亚夏季风(区分南海热带夏季风和副热带夏季风)及春季太平洋海温之间的关系。结果表明,南海夏季风强度与长江下游夏季降水量呈反相关,而副热带夏季风强度与长江下游夏季降水量呈正相关;春季赤道东太平洋海温与当年长江下游夏季降水存在正相关,是夏季长江下游夏季降水变化趋势的较好前期预测信号;南海夏季风和副热带夏季风强度对春季赤道东太平洋海温异常的响应是相反的。     

GRAPES千米尺度模式在西南复杂地形区降水预报偏差与成因初探

利用2019年夏季（6—8月）西南复杂地形区地面观测站逐时和逐日降水量观测数据,从降水量和降水频率入手,对同期GRAPES-Meso 3 km业务模式短期（36 h以内）降水预报性能,特别是在不同典型地貌区—四川盆地子区、云贵高原北部子区和南部子区、青藏高原东缘山地子区的预报偏差进行细致评估与分析。结果表明:（1）GRAPES-Meso 3 km模式能合理地刻画出西南复杂地形区夏季日降水和日内尺度降水的主要特征,以及小时降水频次-强度的基本关系。（2）在各子区,模式日降水量（频率）预报表现为清晰的正偏差,正偏差在盆地子区最显著,为观测值的1.1倍（0.3倍）;日降水量正偏差主要由强降水日降水量预报偏大引起,但频率正偏差在云贵高原南、北子区与其他两个子区不同,主要是中小雨日数预报偏多的贡献;强降水（中小雨）落区预报存在明显（轻微）偏大倾向,强降水预报落区偏大频率在青藏高原东缘山地子区最高,达82.8%,在云贵高原南部子区最低,为53.6%。（3）日循环上,各时次小时降水量（频率）预报整体偏大,且主要正偏差出现在观测的夜雨峰值时段,其中海拔1200 m以下区域的降水频率正偏差从夜间峰值区延续到中午,模式偏强的日降水量预报往往表现为日内偏长的降水时长或小时降水空报。（4）诊断分析显示,模式在四川盆地区突出的夏季日降水预报正偏差是模式对流层低层在云贵高原南-东南侧偏强的西南风预报与西南地区特殊地形结合的产物。