新疆OLR基本特征及强震前的异常现象

利用美国国家环境监测中心的NOAA卫星长波辐射(OLR)月际资料进行数据的月距平、涡度场计算等处理,总结分析了新疆地区长波辐射场的基本特征.对2003年以来新疆及其周边地区发生的中强震进行研究,发现在部分地震前震中及附近地区出现较明显的高值辐射异常,认为这种现象可能与强震前的地一气系统变化有关.应用OLR资料进行新疆地区的地震趋势预测研究可在一定程度上弥补前兆台网监测能力的不足.     

Spatial rainfall model using a pattern classifier for estimating missing daily rainfall data

Missing data in daily rainfall records are very common in water engineering practice. However, they must be replaced by proper estimates to be reliably used in hydrologic models. Presented herein is an effort to develop a new spatial daily rainfall model that is specifically intended to fill in gaps in a daily rainfall dataset. The proposed model is different from a convectional daily rainfall generation scheme in that it takes advantage of concurrent measurements at the nearby sites to increase the accuracy of estimation. The model is based on a two-step approach to handle the occurrence and the amount of daily rainfalls separately. This study tested four neural network classifiers for a rainfall occurrence processor, and two regression techniques for a rainfall amount processor. The test results revealed that a probabilistic neural network approach is preferred for determining the occurrence of daily rainfalls, and a stepwise regression with a log-transformation is recommended for estimating daily rainfall amounts.     

Slopewash,surface runoff and fine-litter transport in forest and landslide scars in humid-tropical steeplands,luquillo experimental forest,Puerto Rico

Rainfall, slopewash (the erosion of soil particles), surface runoff and fine-litter transport steepland sites in the Luquillo Experimental Forest, Puerto Rico (18° 20’ N, 65° 45’ W) were measured from 1991 to 1995. Hillslopes underlain by (1) Cretaceous tuffaceous sandstone and silstone in subtropical rain (tanonuco) forest with vegetation recovering from Hurricane Hugo (1989), and (2) Tertiary quartz diorite in subtropical lower mantone wet (colorado and dwarf) forest with undisturbed forest canopy were compared to recent landslide scars. Monthly surface runoff on these very steep hillslopes (24° to 43°) was only 0·2 to 0·5 per cent of monthly rainfall. Slopewash was higher in sandy loam soils whose parent material is quartz diorite (averaging 46 g m⁻² a⁻¹) than in silty clay loam soils derived from tuffaceous sandstone and siltstone where the average was 9 g m⁻² a⁻¹. Annual slopewash of 100 to 349 g m⁻² on the surfaces of two recent, small landslide scars was measured initially but slopewash decreased to only 3 to 4 g m⁻² a⁻¹ by the end of the study. The mean annual mass of fine litter (mainly leaves and twigs) transported downslope at the forested sites ranged from 5 to 8 g m⁻² and was lower at the tabonuco forest site, where post-Hurricane Hugo recovery is still in progress. Mean annual fine-litter transport was 2·5 g m⁻² on the two landslide scars. Copyright © 1999 John Wiley & Sons, Ltd.     

A semi‐distributed parallel‐type linear reservoir rainfall‐runoff model and its application in Taiwan

The main purpose of this paper is to introduce a semi‐distributed parallel surface rainfall‐runoff conceptual model. In this paper, a general solution of the instantaneous unit hydrograph (IUH) has been derived successfully for N linearly connected reservoirs, each having a different storage constant. The solution is a function of geomorphologic parameters, meteorologic factors and roughness coefficients. The model also takes into account the hydrologic response which is influenced by outflow downstream of a reservoir. For calibration, the shuffled complex evolution (SCE) algorithm is used to search for the global optimal parameters of the model. Because of the parallel structure, the mean roughness parameter of the channel becomes a “conceptual” parameter without a real physical meaning. To evaluate the adaptability of the model adopted, three watersheds around the city of Taipei in Taiwan were chosen to test the effectiveness of the model. The study provides an appropriate rainfall‐runoff model for planning flood mitigation in Taiwan. Copyright © 1999 John Wiley & Sons, Ltd.     

Characteristics of seasonal and spatial variations of primary production over the southeastern Bering Sea shelf

In situ primary production data collected during 1978–1981 period and 1997–2000 period were combined to improve understanding of seasonal and spatial distribution of primary production in the southeastern Bering Sea. Mean daily primary production rates showed an apparent seasonal cycle with high rates in May and low rates in summer over the entire shelf of the southeastern Bering Sea except for oceanic region due to lack of data. There was also an increasing trend of primary production rates in the fall over the inner shelf and the middle shelf. There was a decreasing trend of primary production rates between late April and mid-May over the inner shelf while there was an abrupt increase between late April and mid-May over the middle shelf and the outer shelf. In the shelf break region, there was an increasing pattern in late May. These suggest that there was a gradual progression of the development of the spring phytoplankton bloom from the inner shelf toward the shelf break region. There was also a latitudinal variability of primary production rate over the middle shelf, probably due to either spatial variations of the seasonal advance and retreat of sea ice or horizontal advection of saline water in the bottom layer. Annual rates of primary production across the southeastern Bering Sea shelf were 121, 150, 145, 110, and 84 g C m⁻² yr⁻¹ in the inner shelf, the middle shelf, the outer shelf, the shelf break, and oceanic region, respectively. High annual rates of primary production over the inner shelf can be attributed to continuous summer production based on regenerated nitrogen and/or a continuous supply of nitrogen at the inner front region, and to fall production. There were some possibilities of underestimation of annual primary production over the entire shelf due to lack of measurement in early spring and fall, which may be more apparent over the shelf break and oceanic region than the inner shelf, the middle, and the outer shelf. This study suggests that the response of primary production by climate change in the southeastern Bering Sea shelf can be misunderstood without proper temporal and seasonal measurement.     

Development of an operational rainfall data quality-control scheme based on radar-raingauge co-kriging analysis

Abstract

Automatic raingauge data often serve as an important input to hydrological and weather warning operations. They are not only fundamental in quantitative rainfall analysis, but also act as the ground truth in warning operation and forecast validation. Quality control is required before the data can be used quantitatively due to systematic and random errors. Extremely large random errors and unreasonably small or false zero values can hamper effective monitoring of heavy rain. Yet both are difficult to detect in real-time by objective means. In an attempt to address these problems, a rainfall data quality-control scheme based on radar-raingauge co-kriging analysis was developed. The important threshold values required in the data quality control of 60-min raingauge rainfall were determined from a detailed analysis of the distributions of rainfall residuals defined as the arithmetic difference and the logarithm of the ratio between a raingauge measurement and its co-kriging estimate. The scheme has been developed and is in real-time use in Hong Kong, a coastal city of about 1100 km² area with more than 150 raingauges installed. Geographically, it is located in the subtropics and dominated by heavy convective rainfall in the summer. As a basis of the quality-control scheme, the co-kriging rainfall analysis was shown through a verification exercise to be superior to those obtained by the Barnes analysis and ordinary kriging of raingauge data. The performance of the quality-control algorithm was assessed using selected cases and controlled tests, and was found to be satisfactory, with a high error detection rate for the two targeted types of error. Limitations and operational issues identified during a real-time trial of the quality-control scheme are also discussed.

Citation Yeung, H.Y., Man, C., Chan, S.T., and Seed, A., 2014. Development of an operational rainfall data quality-control scheme based on radar-raingauge co-kriging analysis. Hydrological Sciences Journal, 59 (7), 1285–1299. http://dx.doi.org/10.1080/02626667.2013.839873     

A comparative study of extensive machine learning models for predicting long-term monthly rainfall with an ensemble of climatic and meteorological predictors

Rainfall prediction is of vital importance in water resources management. Accurate long-term rainfall prediction remains an open and challenging problem. Machine learning techniques, as an increasingly popular approach, provide an attractive alternative to traditional methods. The main objective of this study was to improve the prediction accuracy of machine learning-based methods for monthly rainfall, and to improve the understanding of the role of large-scale climatic variables and local meteorological variables in rainfall prediction. One regression model autoregressive integrated moving average model (ARIMA) and five state-of-the-art machine learning algorithms, including artificial neural networks, support vector machine, random forest (RF), gradient boosting regression, and dual-stage attention-based recurrent neural network, were implemented for monthly rainfall prediction over 25 stations in the East China region. The results showed that the ML models outperformed ARIMA model, and RF relatively outperformed other models. Local meteorological variables, humidity, and sunshine duration, were the most important predictors in improving prediction accuracy. 4-month lagged Western North Pacific Monsoon had higher importance than other large-scale climatic variables. The overall output of rainfall prediction was scalable and could be readily generalized to other regions.     

Temporal and spatial rainfall analysis across a humid tropical catchment

Temporal and spatial rainfall patterns were analysed to describe the distribution of daily rainfall across a medium‐sized (379km²) tropical catchment. Investigations were carried out to assess whether a climatological variogram model was appropriate for mapping rainfall taking into consideration the changing rainfall characteristics through the wet season. Exploratory, frequency and moving average analyses of 30 years' daily precipitation data were used to describe the reliability and structure of the rainfall regime. Four phases in the wet season were distinguished, with the peak period (mid‐August to mid‐September) representing the wettest period. A low‐cost rain gauge network of 36 plastic gauges with overflow reservoirs was installed and monitored to obtain spatially distributed rainfall data. Geostatistical techniques were used to develop global and wet season phase climatological variograms. The unscaled climatological variograms were cross‐validated and compared using a range of rainfall events. Ordinary Kriging was used as the interpolation method. The global climatological variogram performed better, and was used to optimize the number and location of rain gauges in the network. The research showed that although distinct wet season phases could be established based on the temporal analysis of daily rainfall characteristics, the interpolation of daily rainfall across a medium‐sized catchment based on spatial analysis was better served by using the global rather than the wet season phase climatological variogram model. Copyright © 2001 John Wiley & Sons, Ltd.     

Vegetation influences on water yields from grassland and shrubland ecosystems in the Chihuahuan Desert

This study examines runoff generated under simulated rainfall on Summerford bajada in the Jornada Basin, New Mexico, USA. Forty‐five simulation experiments were conducted on 1 m² and 2 m² runoff plots on grassland, degraded grassland, shrub and intershrub environments located in grassland and shrubland communities. Average hydrographs generated for each environment show that runoff originates earlier on the vegetated plots than on the unvegetated plots. This early generation of runoff is attributed to soil infiltration rates being overwhelmed by the rapid concentration of water at the base of plants by stemflow. Hydrographs from the degraded grassland and intershrub plots rise continuously throughout the 30 min simulation events indicating that these plots do not achieve equilibrium runoff. This continuously rising form is attributed to the progressive development of raindrop‐induced surface seals. Most grassland and shrub plots level out after the initial early rise indicating equilibrium runoff is achieved. Some shrub plots, however, display a decline in discharge after the early rise. The delayed infiltration of water into macropores beneath shrubs with vegetation in their understories is proposed to explain this declining form. Water yields predicted at the community level indicate that the shrubland sheds 150 per cent more water for a given storm event than the grassland. Copyright © 2002 John Wiley & Sons, Ltd.