Ensemble prediction of regional droughts using climate inputs and the SVM–copula approach

In this study, the climate teleconnections with meteorological droughts are analysed and used to develop ensemble drought prediction models using a support vector machine (SVM)–copula approach over Western Rajasthan (India). The meteorological droughts are identified using the Standardized Precipitation Index (SPI). In the analysis of large‐scale climate forcing represented by climate indices such as El Niño Southern Oscillation, Indian Ocean Dipole Mode and Atlantic Multidecadal Oscillation on regional droughts, it is found that regional droughts exhibits interannual as well as interdecadal variability. On the basis of potential teleconnections between regional droughts and climate indices, SPI‐based drought forecasting models are developed with up to 3 months' lead time. As traditional statistical forecast models are unable to capture nonlinearity and nonstationarity associated with drought forecasts, a machine learning technique, namely, support vector regression (SVR), is adopted to forecast the drought index, and the copula method is used to model the joint distribution of observed and predicted drought index. The copula‐based conditional distribution of an observed drought index conditioned on predicted drought index is utilized to simulate ensembles of drought forecasts. Two variants of drought forecast models are developed, namely a single model for all the periods in a year and separate models for each of the four seasons in a year. The performance of developed models is validated for predicting drought time series for 10 years' data. Improvement in ensemble prediction of drought indices is observed for combined seasonal model over the single model without seasonal partitions. The results show that the proposed SVM–copula approach improves the drought prediction capability and provides estimation of uncertainty associated with drought predictions. Copyright © 2013 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.     

Efficient estimation of flood forecast prediction intervals via single‐ and multi‐objective versions of the LUBE method

Prediction intervals (PIs) are commonly used to quantify the accuracy and precision of a forecast. However, traditional ways to construct PIs typically require strong assumptions about data distribution and involve a large computational burden. Here, we improve upon the recent proposed Lower Upper Bound Estimation method and extend it to a multi‐objective framework. The proposed methods are demonstrated using a real‐world flood forecasting case study for the upper Yangtze River Watershed. Results indicate that the proposed methods are able to efficiently construct appropriate PIs, while outperforming other methods including the widely used Generalized Likelihood Uncertainty Estimation approach. Copyright © 2016 John Wiley & Sons, Ltd.     

Piecewise prediction model for watershed‐scale erosion and sediment yield of individual rainfall events on the Loess Plateau,China

Establishing a universal watershed‐scale erosion and sediment yield prediction model represents a frontier field in erosion and soil/water conservation. The research presented here was conducted on the Chabagou watershed, which is located in the first sub‐region of the hill‐gully area of the Loess Plateau, China. A back‐propagation artificial neural model for watershed‐scale erosion and sediment yield was established, with the accuracy of the model, then compared with that of multiple linear regression. The sensitivity degree of various factors to erosion and sediment yield was quantitatively analysed using the default factor test. On the basis of the sensitive factors and the fractal information dimension, the piecewise prediction model for erosion and sediment yield of individual rainfall events was established and further verified. The results revealed the back‐propagation artificial neural network model to perform better than the multiple linear regression model in terms of predicting the erosion modulus, with the former able to effectively characterize dynamic changes in sediment yield under comprehensive factor conditions. The sensitivity of runoff erosion power and runoff depth to the erosion and sediment yield associated with individual rainfall events was found to be related to the complexity of surface topography. The characteristics of such a hydrological response are thus closely related to topography. When the fractal information dimension is greater than the topographic threshold, the accuracy of prediction using runoff erosion power is higher than that of using runoff depth. In contrast, when the fractal information dimension is smaller than the topographic threshold, the accuracy of prediction using runoff depth is higher than that of using runoff erosion power. The developed piecewise prediction model for watershed‐scale erosion and sediment yield of individual rainfall events, which introduces runoff erosion power and runoff depth using the fractal information dimension as a boundary, can be considered feasible and reliable and has a high prediction accuracy. Copyright © 2013 John Wiley & Sons, Ltd.     

Effect of rainfall uncertainty on the performance of physically based rainfall–runoff models

This paper analyses the effect of rain data uncertainty on the performance of two hydrological models with different spatial structures: a semidistributed and a fully distributed model. The study is performed on a small catchment of 19.6 km² located in the north‐west of Spain, where the arrival of low pressure fronts from the Atlantic Ocean causes highly variable rainfall events. The rainfall fields in this catchment during a series of storm events are estimated using rainfall point measurements. The uncertainty of the estimated fields is quantified using a conditional simulation technique. Discharge and rain data, including the uncertainty of the estimated rainfall fields, are then used to calibrate and validate both hydrological models following the generalized likelihood uncertainty estimation (GLUE) methodology. In the storm events analysed, the two models show similar performance. In all cases, results show that the calibrated distribution of the input parameters narrows when the rain uncertainty is included in the analysis. Otherwise, when rain uncertainty is not considered, the calibration of the input parameters must account for all uncertainty in the rainfall–runoff transformation process. Also, in both models, the uncertainty of the predicted discharges increase in similar magnitude when the uncertainty of rainfall input increase.     

Prediction of the South China Sea Dipole Using SSA-MEM

ABSTRACT

A dipole structure appears in the sea surface height off the central coast of Vietnam during boreal summer in the South China Sea. This dipole, which possesses a chlorophyll signature associated with higher phytoplankton concentrations arising from nutrient upwelling, is important for the productivity of local fisheries. Multi-satellite sea level anomalies are used to investigate the life cycle of the dipole structure. By applying empirical orthogonal function (EOF) analysis, the third EOF mode (EOF 3) is found to represent the major variations of the dipole structure. By removing the temporal noise of EOF 3, a South China Sea dipole index is defined. This index captures the life cycle of the dipole including its generation, mature strength, and final termination. Both one-dimensional and two-dimensional forecasts are generated using a statistical forecasting method that combines singular-spectrum analysis and the maximum entropy method. The appearance of the dipole structure can be predicted with an accuracy of 78% at one-month lead times and an accuracy of 61% at one-year lead times.     

Revised runoff curve number for runoff prediction in the Loess Plateau of China

The soil conservation service (now Natural Resources Conservation Service) Curve Number (SCS-CN), one of the most commonly used methods for surface runoff prediction. The runoff calculated by this method was very sensitive to CN values. In this study, CN values were calculated by both arithmetic mean (CN_C) and least square fit method (CN_F) using observed rainfall-runoff data from 43 sites in the Loess Plateau region, which are considerably different from the CN₂ values obtained from the USDA-SCS handbook table (CN_T). The results showed that using CN_C instead of CN_T for each watershed produce little improvement, while replacing CN_T with CN_F improves the performance of the original SCS-CN method, but still performs poorly in most study sites. This is mainly due to the SCS-CN method using a constant CN value and discounting of the temporal variation in rainfall-runoff process. Therefore, three factors—soil moisture, rainfall depth and intensity—affecting the surface runoff variability are considered to reflect the variation of CN in each watershed, and a new CN value was developed. The reliability of the proposed method was tested with data from 38 watersheds, and then applied to the remaining five typical watersheds using the optimized parameters. The results indicated that the proposed method, which boosted the model efficiencies to 81.83% and 74.23% during calibration and validation cases, respectively, performed better than the original SCS-CN and the Shi and Wang (2020b) method, a modified SCS-CN method based on tabulated CN value. Thus, the proposed method incorporating the influence of the temporal variability of soil moisture, rainfall depth, and intensity factors suggests an accurate runoff prediction for general applications under different hydrological and climatic conditions on the Loess Plateau region.     

基于GIS的城市空间扩展预测

高速的城市扩展给社会发展带来了无比的活力。但是,也带来了一系列影响社会经济可持续发展的问题。因此,建立城市扩展预测模型对城市空间扩展预测有着实际的意义。本文主要是根据射线预测法的相关理论,使用Map Basic编程和Map Info软件进行相关操作,对济南市进行城市空间扩展预测并对预测进行分析,验证射线预测法的准确性。     

基于Logistic和Gompertz曲线模型拟合的建筑物沉降分析

重大工程建设一般会有定期的沉降和变形监测,本研究利用具有规律变化的Logistic和Gompertz曲线模型进行拟合,并以某大型发电厂为研究对象,利用近15年的监测数据,建立预测模型并进行精度评估。研究结果表明,若监测数据具有一定程度的稳定性,并对计算时监测数据进行合理取舍,对采取的全区、分区平均值或单一点高度值的检测数据,运用Logistic和Gompertz曲线模型来预测大型建筑物的沉降情况是可行的。     

基于NGM(1,1,k,c)模型和改进切线角的危岩变形预测

针对危岩变形预测问题,本文以非齐次指数序列的灰色模型(NGM)作为危岩变形预测的基本模型,通过对望霞危岩变形的分析结果显示,NGM(1,1,k,c)模型拟合效果明显优于GM(1,1)模型,说明危岩变形趋势更接近于非齐次指数序列。利用NGM(1,1,k,c)模型结合改进切线角可对危岩变形趋势进行分析预测,可作为危岩稳定性和发展趋势的评估依据。