Application of Ordinal Set Pair Analysis in Annual Rainfall Prediction of Liao River Basin

[Objective] The research aimed to study the application of ordinal set pair analysis in the annual precipitation prediction of Liao River basin.[Method] The ordinal theory was introduced into the set pair analysis modeling,and the prediction model of set pair analysis was improved.A kind of rainfall prediction model based on the ordinal set pair analysis (OSPA) was put forward.The time sequence of annual rainfall in the hydrological rainfall station of Liao River basin during 1956-2006 was the research objective.The annual rainfall during 1998-2006 was predicted by the model,and the error analysis was given.[Result] In the relative errors of predicted results by ordinal set pair analysis,there were six relative errors within 5%,which occupied 66.7% of the total prediction number.One relative error was during 5%-10%,which occupied 11.1% of the total prediction number.Two relative errors were during 10%-15%,which occupied 22.2% of the total prediction number.All the relative errors were less than 20%,which met the precision requirement of annual rainfall prediction in Forecast Specification of Hydrological Information.[Conclusion] The rainfall prediction based on the ordinal set pair analysis model had high precision,and the prediction result was ideal.It was suitable for the annual rainfall prediction.

Effects of rainfall patterns on annual plants in Horqin Sandy Land, Inner Mongolia of China

Growth of annual plants in arid environments depends largely on rainfall pulses. An increased understanding of the effects of different rainfall patterns on plant growth is critical to predicting the potential responses of plants to the changes in rainfall regimes, such as rainfall intensity and duration, and length of dry intervals. In this study, we investigated the effects of different rainfall patterns（e.g. small rainfall event with high frequency and large rainfall event with low frequency） on biomass, growth characteristics and vertical distribution of root biomass of annual plants in Horqin Sandy Land, Inner Mongolia of China during the growing season（from May to August） of 2014. Our results showed that the rainfall patterns, independent of total rainfall amount, exerted strong effects on biomass, characteristics of plant growth and vertical distribution of root biomass. Under a constant amount of total rainfall, the aboveground biomass（AGB）, belowground biomass（BGB）, plant cover, plant height, and plant individual and species number increased with an increase in rainfall intensity. Changes in rainfall patterns also altered the percentage contribution of species biomass to the total AGB, and the percentage of BGB at different soil layers to the total BGB. Consequently, our results indicated that increased rainfall intensity in future may increase biomass significantly, and also affect the growth characteristics of annual plants.

THE IMPACT OF DIFFERENT PHYSICAL PROCESSES AND THEIR PARAMETERIZATIONS ON FORECAST OF A HEAVY RAINFALL IN SOUTH CHINA IN ANNUALLY FIRST RAINING SEASON

An ensemble prediction system based on the GRAPES model, using multi-physics, is used to discuss the influence of different physical processes in numerical models on forecast of heavy rainfall in South China in the annually first raining season(AFRS). Pattern, magnitude and area of precipitation, evolution of synoptic situation, as well as apparent heat source and apparent moisture sink between different ensemble members are comparatively analyzed. The choice of parameterization scheme for land-surface processes gives rise to the largest influence on the precipitation prediction. The influences of cumulus-convection and cloud-microphysics processes are mainly focused on heavy rainfall;the use of cumulus-convection parameterization tends to produce large-area and light rainfall. Change in parameterization schemes for land-surface and cumulus-convection processes both will cause prominent change in forecast of both dynamic and thermodynamic variables, while change in cloud-microphysics processes show primary impact on dynamic variables. Comparing simplified Arakawa-Schubert and Kain-Fritsch with Betts-Miller-Janjic schemes, SLAB with NOAH schemes, as well as both WRF single moment 6-class and NCEP 3-class with simplified explicit schemes of phase-mixed cloud and precipitation shows that the former predicts stronger low-level jets and high humidity concentration, more convective rainfall and local heavy rainfall, and have better performance in precipitation forecast. Appropriate parameterization schemes can reasonably describe the physical process related to heavy rainfall in South China in the AFRS, such as low-level convergence, latent heat release, vertical transport of heat and water vapor, thereby depicting the multi-scale interactions of low-level jet and meso-scale convective systems in heavy rainfall suitably, and improving the prediction of heavy rainfall in South China in the AFRS as a result.

Quantification of relationship between annual daily maximum temperature and annual daily maximum rainfall in South Australia

The empirical relationship between annual daily maximum temperature(ADMT)and annual daily maximum rainfall(ADMR)was investigated.The data were collected from four weather stations located in Adelaide,South Australia,from 1988 to 2017.Due to the influence of sea surface temperature on rainfall and temperature,the distance from the weather station to the sea was considered in the selection of weather stations.Two weather stations near the sea and two inland weather stations were selected.Three non-parametric statistical tests(Kruskal–Wallis,Mann–Whitney,and correlation)were applied to perform statistical analysis on the ADMT and ADMR data.It was revealed that the temperature and rainfall in South Australia varies according to weather station location.The distance from the sea to the weather station was found to have limited influence on temperature and rainfall.Meanwhile,with the 0.05 level of significance,the association between ADMT and ADMR near sea stations is not as significant as the association between the two inland weather stations.It is relatively unrealistic to use ADMR to predict ADMT,or vice versa,since their correlation is not statistically significant(Spearman’s rank correlation coefficient:−0.106).

Spatial Variability and Cartography of Maximum Annual Daily Rainfall under Different Return Periods in Northern Algeria

The estimation of precipitation quantiles has always been an area of great importance to meteorologists, hydrologists, planners and managers of hydrotechnical infrastructures. In many cases, it is necessary to estimate the values relating to extreme events for the sites where there is little or no measurement, as well as their return periods. A statistical approach is the most used in such cases. It aims to find the probability distribution that best fits the maximum daily rainfall values. In our study, 231 rainfall stations were used to regionalize and find the best distribution for modeling the maximum daily rainfall in Northern Algeria. The L-moments method was used to perform a regionalization based on discordance criteria and homogeneity test. It gave rise to twelve homogeneous regions in terms of LCoefficient of variation(L-CV), L-Skewness(L-CS) and L-Kurtosis(L-CK). This same technique allowed us to select the regional probability distribution for each group using the Z statistic. The generalized extreme values distribution(GEV) was selected to model the maximum daily rainfall of 10 groups located in the north of the steppe region and the generalized logistic distribution(GLO) for groups representing the steppes of Central and Western Algeria. The study of uncertainty by the bias and RMSE showed that the regional approach is acceptable. We have also developed maximum daily rainfall maps for 2, 5, 10, 20, 50 and 100 years return periods. We relied on a network of 255 rainfall stations. The spatial variability of quantiles was evaluated by semi-variograms. All rainfall frequency models have a spatial dependence with an exponential model adjusted to the experimental semi-variograms. The parameters of the fitted semi-variogram for different return periods are similar, throughout, while the nugget is more important for high return periods. Maximum daily rainfall increases from South to North and from West to East, and is more significant in the coastal areas of eastern Algeria where it exceeds 170 mm for a return period of 100 years. However, it does not exceed 50 mm in the highlands of the west.

Extensive identification of landslide boundaries using remote sensing images and deep learning method

The frequent occurrence of extreme weather events has rendered numerous landslides to a global natural disaster issue.It is crucial to rapidly and accurately determine the boundaries of landslides for geohazards evaluation and emergency response.Therefore,the Skip Connection DeepLab neural network(SCDnn),a deep learning model based on 770 optical remote sensing images of landslide,is proposed to improve the accuracy of landslide boundary detection.The SCDnn model is optimized for the over-segmentation issue which occurs in conventional deep learning models when there is a significant degree of similarity between topographical geomorphic features.SCDnn exhibits notable improvements in landslide feature extraction and semantic segmentation by combining an enhanced Atrous Spatial Pyramid Convolutional Block(ASPC)with a coding structure that reduces model complexity.The experimental results demonstrate that SCDnn can identify landslide boundaries in 119 images with MIoU values between 0.8and 0.9;while 52 images with MIoU values exceeding 0.9,which exceeds the identification accuracy of existing techniques.This work can offer a novel technique for the automatic extensive identification of landslide boundaries in remote sensing images in addition to establishing the groundwork for future inve stigations and applications in related domains.

STUDY ON THE RELATIONSHIP BETWEEN THE DECADAL VARIATIONS OF ANNUALLY FIRST RAINY SEASON PRECIPITATION OF GUANGXI AND SEA SURFACE TEMPERATURE OF INDIAN OCEAN IN SOUTHERN HEMISPHERE

Decadal circulation differences between more and less rainfall periods in the annually first rainy season of Guangxi and their association with sea surface temperature (SST) of the austral Indian Ocean are investigated by using the NCEP/NCAR reanalysis data. The results are shown as follows. A pattern in which there is uniform change of the Guangxi precipitation shows a 20-year decadal oscillation and a 3-year interannual change. In contrast, a pattern of reversed-phase change between the north and the south of Guangxi has a 6-year interannual periodicity and quasi-biennial oscillation. In the period of more precipitation, the surface temperature in Eurasia is positively anomalous so as to lead to stronger low pressure systems on land and larger thermal contrast between land and ocean. Therefore, the air column is more unstable and ascending flows over Guangxi are intensified while the Hadley cell is weakened. Furthermore, the weaker western Pacific subtropical high and South Asia High, together with a stronger cross-equatorial flow, result in the transportation of more humidity and the appearance of more precipitation. The correlation analysis indicates that the Indian Ocean SST in Southern Hemisphere is closely associated with the variation of the seasonal precipitation of Guangxi on the decadal scale by influencing the Asian monsoon through the cross-equatorial flow.

Drought Forecasting in a Semi-arid Watershed Using Climate Signals:a Neuro-fuzzy Modeling Approach

Large-scale annual climate indices were used to forecast annual drought conditions in the Maharlu-Bakhtegan watershed,located in Iran,using a neuro-fuzzy model.The Standardized Precipitation Index(SPI) was used as a proxy for drought conditions.Among the 45 climate indices considered,eight identified as most relevant were the Atlantic Multidecadal Oscillation(AMO),Atlantic Meridional Mode(AMM),the Bivariate ENSO Time series(BEST),the East Central Tropical Pacific Surface Temperature(NINO 3.4),the Central Tropical Pacific Surface Temperature(NINO 4),the North Tropical Atlantic Index(NTA),the Southern Oscillation Index(SOI),and the Tropical Northern Atlantic Index(TNA).These indices accounted for 81% of the variance in the Principal Components Analysis(PCA) method.The Atlantic surface temperature(SST:Atlantic) had an inverse relationship with SPI,and the AMM index had the highest correlation.Drought forecasts of neuro-fuzzy model demonstrate better prediction at a two-year lag compared to a stepwise regression model.