Assessment of first-order-moment-based sample reconstruction method for design flood estimation in changing environment

Estimating the design flood under nonstationary conditions is challenging. In this study, a sample reconstruction approach was developed to transform a nonstationary series into a stationary one in a future time window (FTW). In this approach, the first-order moment (EFTW) of an extreme flood series in the FTW was used, and two possible methods of estimating EFTW values in terms of point values and confidence intervals were developed. Three schemes were proposed to analyze the uncertainty of design flood estimation in terms of sample representativeness, uncertainty from EFTW estimation, and both factors, respectively. To investigate the performance of the sample reconstruction approach, synthesis experiments were designed based on the annual peak series of the Little Sugar Creek in the United States. The results showed that the sample reconstruction approach performed well when the high-order moment of the series did not change significantly in the specified FTW. Otherwise, its performance deteriorated. In addition, the uncertainty of design flood estimation caused by sample representativeness was greater than that caused by EFTW estimation.

The improved local linear prediction of chaotic time series

Based on the Bayesian information criterion, this paper proposes the improved local linear prediction method to predict chaotic time series. This method uses spatial correlation and temporal correlation simultaneously. Simulation results show that the improved local linear prediction method can effectively make multi-step and one-step prediction of chaotic time series and the multi-step prediction performance and one-step prediction accuracy of the improved local linear prediction method are superior to those of the traditional local linear prediction method.

A scalable software package for time series reconstruction of remote sensing datasets on the Google Earth Engine platform

Spatiotemporal residual noise in terrestrial earth observation products,often caused by unfavorable atmospheric conditions,impedes their broad applications.Most users prefer to use gap-filled remote sensing products with time series reconstruction(TSR)algorithms.Applying currently available implementations of TSR to large-volume datasets is time-consuming and challenging for non-professional users with limited computation or storage resources.This study introduces a new open-source software package entitled‘HANTS-GEE’that implements a well-known and robust TSR algorithm,i.e.Harmonic ANalysis of Time Series(HANTS),on the Google Earth Engine(GEE)platform for scalable reconstruction of terrestrial earth observation data.Reconstruction tasks can be conducted on user-defined spatiotemporal extents when raw datasets are available on GEE.According to site-based and regional-based case evaluation,the new tool can effectively eliminate cloud contamination in the time series of earth observation data.Compared with traditional PC-based HANTS implementation,the HANTS-GEE provides quite consistent reconstruction results for most terrestrial vegetated sites.The HANTS-GEE can provide scalable reconstruction services with accelerated processing speed and reduced internet data transmission volume,promoting algorithm usage by much broader user communities.To our knowledge,the software package is thefirst tool to support full-stack TSR processing for popular open-access satellite sensors on cloud platforms.

RECONSTRUCTION OF PRECIPITATION SERIES AND ANALYSIS OF CLIMATE CHANGE OVER PAST 500 YEARS IN NORTHERN CHINA

It is important and necessary to get a much longer precipitation series in order to research features of drought/flood and climate change. Based on dryness and wetness grades series of 18 stations in Northern China of 533 years from 1470 to 2002, the Moving Cumulative Frequency Method （MCFM） was developed, moving average precipitation series from 1499 to 2002 were reconstructed by testing three kinds of average precipitation, and the features of ell mate change and dry and wet periods were researched by using reconstructed precipitation series in the present paper. The results showed that there were good relationship between the reconstructed precipitation series and the observation precipiration series sincc 1954 and their relative root mean-square error were below 1.89%, that the relation between reconstructed series and the dryness and wetness grades series were nonlinear and this nonlinear relation implied that reconstructed series were reliable and could became foundation data for researching evolution of the drought and flood. Analysis of climate change upon reconstructed precipitation series revealed that although drought intensity of recent dry period from mid- dle 1970s of 20th century until early 21st century was not the strongest in historical climate of Northern China, intensity and duration of wet period was a great deal decreasing and shortening respectively, climate evolve to aridification situa- tion in Northern China.

RECONSTRUCTION OF THE EIGHTEENTH CENTURY SUMMER MONTHLY PRECIPITATION SERIES OF NANJING,SUZHOU,AND HANGZHOU USING THE CLEAR AND RAIN RECORDS OF QING DYNASTY”

In this study the Qing weather records 'Clear and Rain Records' were used to reconstruct the 18th century summer (June-August) precipitation and monthly precipitation series of May, June, July, and August. The nature of these records and the techniquc of transforming them into numerical codes are described. The procedure of converting these descriptive records into quantitative monthly precipitation and its scientific basis are given in detail. The reliability of the obtaind precipitation series is also ex- amined.

CHARACTERISTICS OF THE INTERANNUAL AND INTERDECADAL TIMESCALES OF EVAPORATION IN NORTHERN CHINA IN THE RECENT 115 YEARS

Evaporation plays an important role in water balance on the land surface. In Northern China, the evaporation series of the recent 115 years was reconstructed on the basis of Yhornthwaite evaporation formula. The characteristics of the reconstructed series were analyzed using 10-year running mean filter, power spectrum method, and running t-text of abrupt changes on the paper. The results showed that there were remarkable interannual fluctuations with timescales of about quasi-8-year, 3-4-year, and quasi-2-year, and interdecadal oscillations with timescales of 57.5-year, quasi-23-year, and l l.5-year. Meanwhile, the abrupt changes of the evaporation series were also of interdecadal timescale. Either interannual fluctuations or interdecadal oscillations of evaporation were closely related to variations in air temperatures and precipitation.