Assessment of the different sources of uncertainty in a SWAT model of the River Senne (Belgium)

Although rainfall input uncertainties are widely identified as being a key factor in hydrological models, the rainfall uncertainty is typically not included in the parameter identification and model output uncertainty analysis of complex distributed models such as SWAT and in maritime climate zones. This paper presents a methodology to assess the uncertainty of semi-distributed hydrological models by including, in addition to a list of model parameters, additional unknown factors in the calibration algorithm to account for the rainfall uncertainty (using multiplication factors for each separately identified rainfall event) and for the heteroscedastic nature of the errors of the stream flow. We used the Differential Evolution Adaptive Metropolis algorithm (DREAM_(zs)) to infer the parameter posterior distributions and the output uncertainties of a SWAT model of the River Senne (Belgium). Explicitly considering heteroscedasticity and rainfall uncertainty leads to more realistic parameter values, better representation of water balance components and prediction uncertainty intervals.     

Development of a recurrent Sigma-Pi neural network rainfall forecasting system in Hong Kong

At the moment, weather forecasting is still an art — the experience and intuition of forecasters play a significant role in determining the quality of forecasting. This paper describes the development of a new approach to rainfall forecasting using neural networks. It deals with the extraction of information from radar images and an evaluation of past rain gauge records to provide shortterm rainfall forecasting. All of the meteorological data were provided by the Royal Observatory of Hong Kong (ROHK). Preprocessing procedures were essential for this neural network rainfall forecasting. The forecast of the rainfall was performed every half an hour so that a storm warning signal can be delivered to the public in advance. The network architecture is based on a recurrent Sigma-Pi network. The results are very promising, and this neural-based rainfall forecasting system is capable of providing a rain storm warning signal to the Hong Kong public one hour ahead.     

Intra-seasonal NDVI change projections in semi-arid Africa

Early warning systems (EWS) tend to focus on the identification of slow onset disasters such famine and epidemic disease. Since hazardous environmental conditions often precede disastrous outcomes by many months, effective monitoring via satellite and in situ observations can successfully guide mitigation activities. Accurate short term forecasts of NDVI could increase lead times, making early warning earlier. This paper presents a simple empirical model for making 1 to 4 month NDVI projections. These statistical projections are based on parameterized satellite rainfall estimates (RFE) and relative humidity demand (RHD). A quasi-global, 1 month ahead, 1° study demonstrates reasonable accuracies in many semi-arid regions. In Africa, a 0.1° cross-validated skill assessment quantifies the technique's applicability at 1 to 4 month forecast intervals. These results suggest that useful projections can be made over many semi-arid, food insecure regions of Africa, with plausible extensions to drought prone areas of Asia, Australia and South America.     

非线性时间序列数据中的关联维提取

本文将混沌方法引入具有非线性时间序列的降雨量分析。首先对有关混沌及关联维的概念作简单介绍,在确定降雨量数据具有混沌特征后,对其进行关联维提取,得到颇具意义的结果。     

Influence of rainfall on the performance of bioretention systems modified with activated carbon and biochar

The use of bioretention areas is common in urban stormwater management, but their performance varies significantly depending on rainfall characteristics and design conditions. In this study, a pilot experiment using bioretention columns with different media (commercial activated carbon and river sediment-derived biochar) investigated the influence of rainfall on bioretention performance. The results indicated that the runoff volume retention ratio (R_v), which included the runoff purified and discharged at the bottom of the column, and the runoff retained in media during rainfall event, decreased significantly with increases in the rainfall event return period (p < 0.05). The R_v of the activated carbon and biochar columns decreased with a 2-yr return period and then fell further with a 50-yr return period. Porous material has been shown to improve the water-holding capacity of bioretention media, but it did not result in an improved R_v under heavy rain that exceeded the 2-yr return period. With the increase of the return period from two to 50 yr, the mass removal efficiency (R_L) of total phosphorus and phosphate illustrated a clear decreasing trend in all columns. The total nitrogen, ammonia and nitrate removal did not show a clear trend with return periods because of transformations among different forms of nitrogen and similar saturation periods during the different rainfall events. The influence of the return period on chemical oxygen demand (COD) removal was related to whether the inflow COD reached maximum COD removal capacity of the bioretention media. Under a rainfall event with a specific return period, there were no significant differences in the R_L of all nitrogen species and COD among the different columns (p > 0.05). The addition of adsorptive material, such as activated carbon and biochar, may not be the key factor for improving nitrogen and COD removal under heavy rain that exceeds the 2-yr return period. The bioretention performance of phosphorus removal from urban stormwater runoff could be improved by replacing or adding media with high adsorption capacity, but these improvements would not be significant under heavy rain that exceeds the 2-yr return period. The results provide some reference for evaluating bioretention performance and optimizing bioretention design in the future.     

组合模型在降雨量预测中的应用

研究降雨量准确预测问题,降水量的变化既受大气环流、地形、气压、气候带等各种环境因子的影响,降水量的动态特征呈现复杂非线性和各种干扰因素,预测不可能准确。传统预测模型难以对其进行准确预测,预测精度低。为提高降雨量的预测精度,提出一种组合模型的降雨量预测模型。首先采用小波分析将降雨量数据进行分解成线性和非线性部分,然后分别采用ARIMA和RBF神经网络模型对其进行预测,最后采用小波重构线性和非线性预测结果,得到降雨量最终预测结果。仿真结果表明,相对于传统预测模型,组合模型提高了降雨量预测精度,预测结果可以帮助农业、水利部门提高防治旱涝灾害的科学依据。     

Numerical analysis of rainfall effects in external overburden dump

The effect of slope angle for external overburden dump in response to average and heavy rainfall has been analyzed using a two dimensional finite difference method of transient water flow through unsaturated–saturated soil. The external dump stability is evaluated for five geomaterial types on the basis of globally accepted safety factor analysis technique, based on shear strength reduction approach using finite difference method. The results obtained from the finite difference method of analysis indicate that the external dump with more than 30° slope angle is greatly influenced by the rainfall under the studied conditions for geomaterial 3, 4 and 5, whereas dumps with geomaterial 1 and 2 remain safe. The analysis shows that major slope failure is out of preview for the studied rainfall conditions.     

Comparing interpolation techniques for monthly rainfall mapping using multiple evaluation criteria and auxiliary data sources: A case study of Sri Lanka

Interpolating climatic variables such as rainfall is challenging due to the highly variable nature of meteorological processes, the effects of terrain and geography, and the difficulty in establishing a representative network of stations. While interpolation models are being adapted to include these effects, often the rainfall data contain significant gaps in coverage. In this paper, we evaluated rainfall data from an agro-ecological monitoring network for producing maps of total monthly rainfall in Sri Lanka. We compared four spatial interpolation techniques: inverse distance weighting, thin-plate splines, ordinary kriging, and Bayesian kriging. Error metrics were used to validate interpolations against independent data. Satellite data were used to assess the spatial pattern of rainfall. Results indicated that Bayesian kriging and splines performed best in low and high rainfall, respectively. Rainfall maps generated from the agro-ecological network were found to have accuracies consistent with previous studies in Sri Lanka.     

雨洪资源利用进展与利用模式探索

对雨洪及雨洪资源利用的概念进行了界定,明确了雨洪资源开发利用的对象。在明确雨洪资源概念的基础上,概括了近20年来国内外在雨洪资源利用方面的研究和进展,并对国外在此利用技术和管理方面所取得的经验进行了总结。接下来用较多的篇幅对雨洪资源利用工程和非工程的措施进行了的介绍,在对每种利用方式加以剖析之后,分别引用了相应利用方式中比较典型的实例。最后对这些利用方式在未来的发展趋势做了初步的展望。