Backcasting long-term climate data: evaluation of hypothesis   总被引：1，自引：0，他引：1  

Bahram?SaghafianEmail author  Sara?Ghasemi?Aghbalaghi  Mohsen?Nasseri 《Theoretical and Applied Climatology》2018,132(3-4):717-726

Most often than not, incomplete datasets or short-term recorded data in vast regions impedes reliable climate and water studies. Various methods, such as simple correlation with stations having long-term time series, are practiced to infill or extend the period of observation at stations with missing or short-term data. In the current paper and for the first time, the hypothesis on the feasibility of extending the downscaling concept to backcast local observation records using large-scale atmospheric predictors is examined. Backcasting is coined here to contrast forecasting/projection; the former is implied to reconstruct in the past, while the latter represents projection in the future. To assess our hypotheses, daily and monthly statistical downscaling models were employed to reconstruct past precipitation data and lengthen the data period. Urmia and Tabriz synoptic stations, located in northwestern Iran, constituted two case study stations. SDSM and data-mining downscaling model (DMDM) daily as well as the group method of data handling (GMDH) and model tree (Mp5) monthly downscaling models were trained with National Center for Environmental Prediction (NCEP) data. After training, reconstructed precipitation data of the past was validated against observed data. Then, the data was fully extended to the 1948 to 2009 period corresponding to available NCEP data period. The results showed that DMDM performed superior in generation of monthly average precipitation compared with the SDSM, Mp5, and GMDH models, although none of the models could preserve the monthly variance. This overall confirms practical value of the proposed approach in extension of the past historic data, particularly for long-term climatological and water budget studies.  相似文献   

Regional analysis of streamflow drought: a case study in southwestern Iran     

Motalleb Byzedi  Bahram Saghafian  Khosro Mohammadi  Maaroof Siosemarde 《Environmental Earth Sciences》2014,71(6):2955-2972

Droughts are complex natural hazards that, to a varying degree, affect some parts of the world every year. The range of drought impacts is related to drought occurring in different stages of the hydrological cycle and usually different types of droughts such as meteorological, agricultural, hydrological, and socio-economical are the most distinguished types. Hydrological drought includes streamflow and groundwater droughts. In this paper, streamflow drought was analyzed using the method of truncation level (at 70 % level) by daily discharges at 54 stations in southwestern Iran. Frequency analysis was carried out for annual maximum series of drought deficit volume and duration. 35 factors such as physiographic, climatic, geologic and vegetation were studied to carry out the regional analysis. According to conclusions of factor analysis, the six most effective factors include watershed area, the sum rain from December to February, the percentage of area with NDVI <0.1, the percentage of convex area, drainage density and the minimum of watershed elevation, explained 89.2 % of variance. The homogenous regions were determined by cluster analysis and discriminate function analysis. The suitable multivariate regression models were ascertained and evaluated for hydrological drought deficit volume with 2 years return period. The significance level of models was 0.01. The conclusion showed that the watershed area is the most effective factor that has a high correlation with drought deficit volume. Moreover, drought duration was not a suitable index for regional analysis.  相似文献   

Evaluating the impacts of watershed management on runoff storage and peak flow in Gav-Darreh watershed,Kurdistan, Iran     

Ata Amini  Pezhman Taherei Ghazvinei  Mitra Javan  Bahram Saghafian 《Arabian Journal of Geosciences》2014,7(8):3271-3279

Recently, water and soil resource competition and environmental degradation due to inadequate management practices have been increased and pose difficult problems for resource managers. Numerous watershed practices currently being implemented for runoff storage and flood control purposes have improved hydrologic conditions in watersheds and enhanced the establishment of riparian vegetation. The assessment of proposed management options increases management efficiency. The purpose of this study is to assess the impact of watershed managements on runoff storage and peak flow, and determine the land use and cover dynamics that it has induced in Gav-Darreh watershed, Kurdistan, Iran. The watershed area is 6.27 km² which has been subjected to non-structural and structural measures. The implemented management practices and its impact on land use and cover were assessed by integrating field observation and geographic information systems (GIS). The data were used to derive the volume of retained water and determine reduction in peak flow. The hydrology of the watershed was modeled using the Hydrologic Engineering Center–Hydrologic Modeling System (HEC–HMS) model, and watershed changes were quantified through field work. Actual storms were used to calibrate and validate HEC–HMS rainfall–runoff model. The calibrated HEC–HMS model was used to simulate pre- and post-management conditions in the watershed. The results derived from field observation and HEC–HMS model showed that the practices had significant impacts on the runoff storage and peak flow reduction.  相似文献   

Evaluation of dynamic regression and artificial neural networks models for real-time hydrological drought forecasting     

Majid Dehghani  Bahram Saghafian  Firoozeh Rivaz  Ahmad Khodadadi 《Arabian Journal of Geosciences》2017,10(12):266

In this study, application of a class of stochastic dynamic models and a class of artificial intelligence model is reported for the forecasting of real-time hydrological droughts in the Black River basin in the USA. For this purpose, the Standardized Hydrological Drought Index (SHDI) was adopted in different time scales to represent the hydrological drought index. Six probability distribution functions (PDF) were fitted to the discharge time series to obtain the best fit for SHDI calculation. Then, a dynamic linear spatio-temporal model (DLSTM) and artificial neural network (ANN) were used to forecast SHDI. Although results indicated that both models were able to forecast SHDI in different time scales, the DLSTM was far superior in longer lead times. The DLSTM could forecast SHDI up to 6 months ahead while ANN was only capable of forecasting SHDI up to 2 months ahead appropriately. For short lead times (1–6 months), the DLSTM has performed nearly perfect in test phase and CE oscillates between 0.97 and 0.86 while for ANN modeling, CE is between 0.72 and 0.07. However, the performance of DLSTM and ANN reduced considerably in medium lead times (7–12 months). Overall, the DLSTM is a powerful tool for appropriately forecasting SHDI at short time scales; a major advantage required for drought early warning systems.  相似文献   

Seismic reliability assessment of a steel moment-resisting frame with two different ductility levels using a cloud analysis approach     

Aval  Seyed Bahram Beheshti  Verki  Amir Masoumi 《地震工程与工程振动(英文版)》2019,18(1):171-185

A cloud method for generating percentile engineering demand parameter versus intensity measure(EDP-IM) curves of a structure subjected to a set of synthetic ground motions is presented. To this end, an ensemble of synthetic ground motions based on available real ones is generated. This is done by using attenuation relationships, duration and suitable Gutenberg-Richter relations attributed to the considered seismic hazard at a given site by estimating a suitable distribution of magnitude and site to source distance. The study aims to clarify the significance of the duration and frequency content on the seismic performance of structures, which were not considered in developing percentile incremental dynamic analysis(IDA) curves. The collapse probabilities of two steel moment-resisting frames with different ductility levels generated by IDA and the proposed cloud method are compared at different intensity levels. When compared with conventional IDA, the suggested cloud analysis(SCA) methodology with the same run number of dynamic analyses was able to develop response hazard curves that were more consistent with site-specific seismic hazards. Eliminating the need to find many real records by generating synthetic records consistent with site-specific seismic hazards from a few available recorded ground motions is another advantage of using this scheme over the IDA method..  相似文献   

Monitoring surface changes in discontinuous permafrost terrain using small baseline SAR interferometry,object-based classification,and geological features: a case study from Mayo,Yukon Territory,Canada     

Fariba Mohammadimanesh  Bahram Salehi  Masoud Mahdianpari  Jerry English  Joseph Chamberland  Pierre-Jean Alasset 《地理信息系统科学与遥感》2019,56(4):485-510

Permafrost-induced deformation of ground features is threating infrastructure in northern communities. An understanding of permafrost distribution is therefore critical for sustainable adaptation planning and infrastructure maintenance. Considering the large area underlain by permafrost in the Yukon Territory, there is a need for baseline information to characterize the permafrost in this region. In this study, the Differential Interferometric Synthetic Aperture Radar (DInSAR) technique was used to identify areas of ground movement likely caused by changes in permafrost. The DInSAR technique was applied to a series of repeat-pass C-band RADARSAT-2 observations collected in 2015 over the Village of Mayo, in central Yukon Territory, Canada. The conventional DInSAR technique demonstrated that ground deformation could be detected in this area, but the resulting deformation maps contained errors due to a loss of coherence from changes in vegetation and atmospheric phase delay. To address these limitations, the Small BAseline Subset (SBAS) InSAR technique was applied to reduce phase error, thus improving the deformation maps. To understand the relationship between the deformation maps and land cover types, an object-based Random Forest classification was developed to classify the study area into different land cover types. Integration of the InSAR results and the classification map revealed that the built-up class (e.g., airport) was affected by subsidence on the order of ?2 to ?4 cm. The spatial extent of the surface displacement map obtained using the SBAS InSAR technique was then correlated with the surficial geology map. This revealed that much of the main infrastructure in the Village of Mayo is underlain by interbedded glaciofluvial and glaciolacustrine sediments, the latter of which caused the most damage to human made structures. This study provides a method for permafrost monitoring that builds upon the synergistic use of the SBAS InSAR technique, object-based image analysis, and surficial geology data.  相似文献