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1.
Cost and time are the two most important factors conditioning soil surveys. Since these surveys provide basic information for modelling and management activities, new methods are needed to speed the soil-mapping process with limited input data. In this study, the polypedon concept was used to extend the spatial representation of sampled pedons (point data) in order to train artificial neural networks (ANNs) for digital soil mapping (DSM). The input database contained 97 soil profiles belonging to 7 different soil series and 15 digital elevation model (DEM) attributes. Pedons were represented in raster format as one-cell areas. The corresponding polypedons were then spatially represented by neighbouring raster cells (e.g. 2 × 2, … up to 6 × 6 cells). The primary database contained 97 pedons (97 cells) that were extended up to 3492 cells (in the case of 6 × 6-cell regions). This approach employed test and validation areas to calculate the respective accuracies of data interpolation and extrapolation. The results showed increased accuracies in training and interpolation (test area) but a poor level of accuracy in the extrapolation process (validation area). However, the overall precision of all predictions increased considerably. Using only topographic attributes for extrapolation was not sufficient to obtain an accurate soil map. To improve prediction, other soil-forming factors, such as landforms and/or geology, should also be considered as input data in the ANN. The proposed method could help to improve existing soil maps by using DSM results in areas with limited soil data and to save time and money in soil survey work.  相似文献   

2.
Precipitation is a key input variable for hydrological and climate studies. Rain gauges can provide reliable precipitation measurements at a point of observations. However, the uncertainty of rain measurements increases when a rain gauge network is sparse. Satellite-based precipitation estimations SPEs appear to be an alternative source of measurements for regions with limited rain gauges. However, the systematic bias from satellite precipitation estimation should be estimated and adjusted. In this study, a method of removing the bias from the precipitation estimation from remotely sensed information using artificial neural networks-cloud classification system (PERSIANN-CCS) over a region where the rain gauge is sparse is investigated. The method consists of monthly empirical quantile mapping of gauge and satellite measurements over several climate zones as well as inverse-weighted distance for the interpolation of gauge measurements. Seven years (2010–2016) of daily precipitation estimation from PERSIANN-CCS was used to test and adjust the bias of estimation over Saudi Arabia. The first 6 years (2010–2015) are used for calibration, while 1 year (2016) is used for validation. The results show that the mean yearly bias is reduced by 90%, and the yearly root mean square error is reduced by 68% during the validation year. The experimental results confirm that the proposed method can effectively adjust the bias of satellite-based precipitation estimations.  相似文献   

3.
Quantifying rock fall hazards requires information about their frequency and volumes. Previous studies have focused on quantifying rock fall volume–frequency relationships or the weather conditions antecedent to rock fall occurrences, and their potential use as prediction tools. This paper is focused on quantifying rock fall occurrence probabilities and presents approaches for quantifying rock fall temporal distributions. In particular, von Mises distributions allow direct correlation between seasonal weather variations and rock fall occurrences. The approaches are illustrated using a rock fall database along a railway corridor in the Canadian Cordillera, in which rock fall occurrences were correlated to the morphology and lithology. A Binomial probability distribution applied to the annual rock fall frequency suggests an average daily rock fall probability of 1 × 10?2 across the study area. However, circular (von Mises) distributions associated with weather trends in the area, and fitted to monthly rock fall records, allow estimation of daily rock fall probabilities in different months. This approach allows a direct correlation between rock fall frequencies and seasonal variations in weather conditions. The results suggest daily rock fall probabilities between 4 × 10?3 and 8 × 10?3 for April through July and up to 2.1 × 10?2 in October. Moreover, local peaks in rock fall monthly records are quantitatively explained through the seasonality of weather conditions. Similar values are obtained when applying the Binomial distribution to monthly records. However, this last approach does not show strong distribution fits and does not allow a correlation between rock fall frequencies and seasonal weather variations.  相似文献   

4.
The study on the stream-flow change associated with future climate change scenarios has a practical significance for local socio-economic development and eco-environmental protection. A study on the Jianzhuangcuan catchments was carried out to quantify the expected impact of climate change on the stream-flow using a multi-model ensemble approach. Climate change scenarios were developed by ensemble four Global Climate Models, which showed good performance for Jianzhuangcuan catchment. Soil and Water Assessment Tool (SWAT), a physically based distributed hydrological model, was used to investigate the impacts on stream-flow under climate change scenarios. The model was calibrated and validated using daily stream-flow records. The calibration and validation results showed that the SWAT model was able to simulate the daily stream-flow well, with Nash–Sutcliffe efficiency >0.83 for Yaoping Long station, for calibration and validation at daily and monthly scales. Their difference in simulating the stream-flow under future climate scenarios was also investigated. The results indicate a 0.6–0.9 °C increase in annual temperature and changes of 12.6–18.9 mm in seasonal precipitation corresponded to a change in stream-flow of about 0.62–3.67 for 2020 and 2030 scenarios. The impact of the climate change increased in both scenarios.  相似文献   

5.
SWAT模型中天气发生器与数据库构建及其验证   总被引：2，自引：0，他引：2  
庞靖鹏  徐宗学  刘昌明 《水文》2007,27(5):25-30
提出了通过日照时数和太阳辐射量的相关关系来估算逐日辐射量的方法。采用日平均温度和日平均湿度来计算日露点温度，以建立SWAT模型天气发生器。采用插值方法对土壤粒径进行转换，并利用SPAW程序估算土壤水特性参数，建立了SWAT模型土壤属性库。将构建的SWAT模型应用于潮河上游下会水文站以上流域的水文过程模拟，月效率系数≥0．91，确定性系数≥0．93，取得了非常好的模拟效果。结果表明在缺乏详细的气象和土壤数据情况下，可以构建SWAT模型进行水文模拟研究。  相似文献   

6.
降水、 气温的空间分布是影响流域水量平衡模拟的关键因素, 运用距离权重反比法(IDW)、 梯度距离权重反比法(GIDW)、 样条函数法(Spline)和克里金插值法(Kriging)对青海湖流域及周边地区43个气象站1995-2009年逐日气温和降水进行了空间插值, 并以气象要素空间插值数据驱动模型, 进行布哈河流域径流模拟. 选用布哈河口月平均流量, 以Nash-Suttclife系数(Ens)、 相关系数(R)和相对误差(RE)为评价指标, 进行校准期(2000-2004年)和验证期(2005-2009年)的径流模拟效果比较. 结果表明: 径流模拟精度较高, GIDW和IDW更适合于布哈河流域的气象要素空间化, 并且气象要素空间插值数据误差是引起模型模拟不确定性和参数据不确定性的原因之一.  相似文献   

7.
Salt marsh elevation and geomorphic stability depends on mineral sedimentation. Many Mediterranean-climate salt marshes along southern California, USA coast import sediment during El Niño storm events, but sediment fluxes and mechanisms during dry weather are potentially important for marsh stability. We calculated tidal creek sediment fluxes within a highly modified, sediment-starved, 1.5-km2 salt marsh (Seal Beach) and a less modified 1-km2 marsh (Mugu) with fluvial sediment supply. We measured salt marsh plain suspended sediment concentration and vertical accretion using single stage samplers and marker horizons. At Seal Beach, a 2014 storm yielded 39 and 28 g/s mean sediment fluxes and imported 12,000 and 8800 kg in a western and eastern channel. Western channel storm imports offset 8700 kg exported during 2 months of dry weather, while eastern channel storm imports augmented 9200 kg imported during dry weather. During the storm at Mugu, suspended sediment concentrations on the marsh plain increased by a factor of four; accretion was 1–2 mm near creek levees. An exceptionally high tide sequence yielded 4.4 g/s mean sediment flux, importing 1700 kg: 20 % of Mugu’s dry weather fluxes. Overall, low sediment fluxes were observed, suggesting that these salt marshes are geomorphically stable during dry weather conditions. Results suggest storms and high lunar tides may play large roles, importing sediment and maintaining dry weather sediment flux balances for southern California salt marshes. However, under future climate change and sea level rise scenarios, results suggest that balanced sediment fluxes lead to marsh elevational instability based on estimated mineral sediment deficits.  相似文献   

8.
The objective of this study is to evaluate the hydrological impacts of climate change on rainfall, temperature and streamflow in a west flowing river originating in the Western Ghats of India. The long-term trend analysis for 110 yr of meteorological variables (rainfall and temperature) was carried out using the modified Mann–Kendall trend test and the magnitude of the trend was quantified using the Sen’s slope estimator. The Regional Climate Model (RCM), COordinated Regional climate Downscaling EXperiment (CORDEX) simulated daily weather data of baseline (1951–2005) and future RCP 4.5 scenarios (2006–2060) were used to run the hydrological model, Soil and Water Assessment Tool (SWAT), in order to evaluate the effect of climate change on rainfall, temperature and streamflow. Significant changes were observed with regard to rainfall, which have shown decreasing trend at the rate of 2.63 mm per year for the historical and 8.85 mm per year for RCP 4.5 future scenarios. The average temperature was found to be increasing at \(0.10\,^{\circ }\hbox {C}\) per decade for both historical and future scenarios. The impact of climate change on the annual streamflow yielded a decreasing trend at the rate of \(1.2\,\hbox {Mm}^{3}\) per year and 2.56 \(\hbox {Mm}^{3},\) respectively for the past and future scenarios. The present work also investigates the capability of SWAT to simulate the groundwater flow. The simulated results are compared with the recession limb of the hydrograph and were found to be reasonably accurate.  相似文献   

9.
An extreme heat wave hit Egypt in summer 2015. Abnormal hot weather conditions existed over Egypt for the entire summer season. The present paper investigates the relationship between the intertropical convergence zone (ITCZ) over Africa and a scorching heat wave that existed over Egypt in summer 2015. The National Centers for Environmental Prediction/National Center for Atmospheric Research (NCEP/NCAR) reanalysis data of mean surface air temperature for the domain of Egypt for the summer season from 1948 to 2015 were used in this study. In addition, data of the daily maximum and daily minimum temperature used for the summer season of the year 2015 were also used. Time cross-section analysis of the daily operational data of geopotential height at level 500 hPa over Egypt from 1 June to 31 August 2015 was done. Moreover, the African ITCZ, both the western and the eastern ITCZ, data for summer of 2015 were used for the said period. The time series, time cross-section, anomaly, and correlation coefficient techniques were used to analyze the datasets. The results revealed that a new climate change record of heat wave over Egypt existed in summer 2015. Moreover, there is an outstanding significant positive correlation between the abrupt shift of African ITCZ position and heat wave occurrence over Egypt in summer 2015. In particular, the southerly movement of the eastern African ITCZ controls the weather over Egypt and led to the extreme heat wave in summer 2015.  相似文献   

10.
The impacts of floods and droughts are intensified by climate change, lack of preparedness, and coordination. The average rainfall in study area is ranging from 200 to 400 mm per year. Rain gauge generally provides very accurate measurement of point rain rates and the amounts of rainfall but due to scarcity of the gauge locations provides very general information of the area on regional scale. Recognizing these practical limitations, it is essential to use remote sensing techniques for measuring the quantity of rainfall in the Middle Indus. In this research, Tropical Rainfall Measuring Mission (TRMM) estimation can be used as a proxy for the magnitude of rainfall estimates from classical methods (rain gauge), quantity, and its spatial distribution for Middle Indus river basin. In order to use TRMM satellite data for discharge measurement, its accuracy is determined by statistically comparing it with in situ gauged data on daily and monthly bases. The daily R 2 value (0.42) is significantly lower than monthly R 2 value (0.82), probably due to the time of summation of TRMM 3-hourly precipitation data into daily estimates. Daily TRMM data from 2003 to 2012 was used as input forcing in Soil and Water Assessment Tool (SWAT) hydrological model along with other input parameters. The calibration and validation results of SWAT model give R 2 = 0.72 and 0.73 and Nash-Sutcliffe coefficient of efficiency = 0.69 and 0.65, respectively. Daily and monthly comparison graphs are generated on the basis of model discharge output and observed data.  相似文献   

11.
Method for prediction of landslide movements based on random forests   总被引：4，自引：3，他引：1  
Prediction of landslide movements with practical application for landslide risk mitigation is a challenge for scientists. This study presents a methodology for prediction of landslide movements using random forests, a machine learning algorithm based on regression trees. The prediction method was established based on a time series consisting of 2 years of data on landslide movement, groundwater level, and precipitation gathered from the Kostanjek landslide monitoring system and nearby meteorological stations in Zagreb (Croatia). Because of complex relations between precipitations and groundwater levels, the process of landslide movement prediction is divided into two separate models: (1) model for prediction of groundwater levels from precipitation data and (2) model for prediction of landslide movements from groundwater level data. In a groundwater level prediction model, 75 parameters were used as predictors, calculated from precipitation and evapotranspiration data. In the landslide movement prediction model, 10 parameters calculated from groundwater level data were used as predictors. Model validation was performed through the prediction of groundwater levels and prediction of landslide movements for the periods from 10 to 90 days. The validation results show the capability of the model to predict the evolution of daily displacements, from predicted variations of groundwater levels, for the period up to 30 days. Practical contributions of the developed method include the possibility of automated predictions, updated and improved on a daily basis, which would be an important source of information for decisions related to crisis management in the case of risky landslide movements.  相似文献   

12.
Hydrological models play vital roles in understanding and management of surface water resources. The physically based distributed model Soil and Water Assessment Tool (SWAT) was applied to a small catchment in south eastern Australia to determine its ability to mimic low and high streamflows. The model was successfully calibrated using 1993–2002 streamflow data and validated using 2003–2011 data with a combination of manual and auto-calibration techniques for both monthly and daily time steps. Sensitivity analysis indicated that curve number for moisture condition II (CN2) is the most sensitive parameter for both time steps. In general, the model performance statistics indicated “very good” agreement between measured and simulated discharges for both calibration and validation periods. The model was able to satisfactorily simulate both low and high flows of the Yass River. Analysis of water balance components indicated that more than 90 % of the rainfall is lost as evapotranspiration and about 45 % of the streamflow is base flow. The calibrated and validated SWAT model can be used to analyze the effect of climate and land use changes on catchment wide hydrologic process.  相似文献   

13.
The changes in annual runoff of the three original rivers and the mainstream of Tarim River were analyzed by the non-parametric tests based on the hydrologic data during the period of 50 years. Using hydrologic data, meteorological data and the fitted equation, the impacts of climate change and human activities on annual runoff of the mainstream were assessed. Based on the analysis, the following conclusions can be drawn: (1) headstream runoff has increased in the past 50 years, and has sharply jumped after 1990; (2) mainstream runoff decreased progressively in the past 50 years, which indicated that interference from human activities was the main cause for the decreasing runoff. This had greater negative influence than positive influence, which caused the mainstream average runoff to decrease by 5.4 × 108 m3 from 1990 to 2008 as compared to 1957–2008; (3) if human activities remained at pre-1990 levels, climate change alone would have caused the runoff of mainstream of Tarim River to increase by 5.4 × 108 m3 annually in the past 20 years; (4) if the climate had remained at pre-1990 conditions, human activities alone would have caused the runoff of mainstream of Tarim River to increase by 5.4 × 108 m3 annually over the past 20 years. However, mainstream average runoff was 42.6 × 108 m3 from 1990 to 2008 with the negative effects of human activities masked by the larger, positive effect of climate changes. The results in this paper provide a scientific basis for conservation strategies, sustainable management, and ecological restoration of the Tarim River Basin.  相似文献   

14.
The Eastern Mediterranean and the Middle East (EMME) is suffering from abnormal cooling of weather conditions and existence of an extreme weather phenomenon known as ice storm Alexa. The present paper investigates the weather conditions over Europe that causes this abnormal weather over the EMME through December of 2013. Daily data sets of several meteorological elements (temperature, precipitation, relative humidity, sea level pressure, and geopotential height at level 500 hPa, etc.) over the northern hemisphere, including Europe and EMME of December of 2013, have been used through the present work. In addition, to that, a time cross section analysis of the daily operational data for meteorological elements (mean surface temperature, temperature and geopotential height at level 500 hPa, relative humidity, precipitation rate, and sea surface pressure) was done over the EMME for December 2013. The methodology of anomaly and correlation coefficient techniques for the data sets has been used. The results uncovered that the EMME has abnormal and very cold weather conditions due to the inference of meridional blocking persisted over Europe and the existence of the extremely negative geopotential height anomaly aloft over Eastern Europe throughout this month.  相似文献   

15.
The sea level change is a crucial indicator of our climate. The spatial sampling offered by satellite altimetry and its continuity during the last 18 years are major assets to provide an improved vision of the sea level changes. In this paper, we analyze the University of Colorado database of sea level time series to determine the trends for 18 large ocean regions by means of the automatic trend extraction approach in the framework of the singular spectrum analysis technique. Our global sea level trend estimate of 3.19 mm/year for the period from 1993 to 2010 is comparable with the 3.20-mm/year sea level rise since 1993 calculated by AVISO Altimetry. However, the trends from the different ocean regions show dissimilar patterns. The major contributions to the global sea level rise during 1993–2010 are from the Indian Ocean (3.78?±?0.08 mm/year).  相似文献   

16.
This study presented herein compares the bivariate and multivariate landslide susceptibility mapping methods and presents the landslide susceptibility map of the territory of Western Carpathians in small scale. This study also describes pioneer work for the territory of Western Carpathians, overreaching state borders, using verified sophisticated statistical methods. In the susceptibility mapping, digital elevation model was first constructed using a GIS software, and parameter maps affecting the slope stability such as geology, seismicity, precipitation, topographical elevation, slope angle, slope aspect and land cover were considered. In the last stage of the analyses, landslide susceptibility maps were produced using bivariate and multivariate analyses, and they were then compared by means of their validations. The validation of the bivariate analysis data was performed using the results of bivariate analysis for landslide areas of Slovakia containing five classes of susceptibility in scale 1:500,000. The validation area is the area of Western Carpathians within Slovakia. Eighty-two per cent of area does not differ in more than one class. The validation of the multivariate analysis data was performed using the results from the Kysuce region in the northern part of Slovakia in scale 1:10,000. The raster calculator was used to express the difference between each pair of pixels within these two layers. Seventy-seven per cent of the pixels do not differ in more than 25 %, 94 % of the pixels do not differ in more than 50 %. The maximal possible difference is 100 % (one pixel with value 0 and other with value 1, or vice versa). Receiver operating characteristic analysis was also performed, the area under curve value for bivariate model was calculated to be 0.735, while it was 0.823 for multivariate. The results of the validation can be considered as satisfactory.  相似文献   

17.
This paper proposes a new ensemble-based algorithm that assimilates the vertical rain structure retrieved from microwave radiometer and radar measurements in a regional weather forecast model, by employing a Bayesian framework. The goal of the study is to evaluate the capability of the proposed technique to improve track prediction of tropical cyclones that originate in the North Indian Ocean. For this purpose, the tropical cyclone Jal has been analyzed by the community mesoscale weather model, weather research and forecasting (WRF). The ensembles of prognostic variables such as perturbation potential temperature (θ, k), perturbation geopotential (?, m2/s2), meridional (U) and zonal velocities (V) and water vapor mixing ratio (q v , kg/kg) are generated by the empirical orthogonal function technique. An over pass of the tropical rainfall-measuring mission (TRMM) satellite occurred on 06th NOV 0730 UTC over the system, and the observations from the radiometer and radar on board the satellite(1B11 data products) are inverted using a combined in-home radiometer-radar retrieval technique to estimate the vertical rain structure, namely the cloud liquid water, cloud ice, precipitation water and precipitation ice. Each ensemble is input as a possible set of initial conditions to the WRF model from 00 UTC which was marched in time till 06th NOV 0730 UTC. The above-mentioned hydrometeors from the cloud water and rain water mixing ratios are then estimated for all the ensembles. The Bayesian filter framework technique is then used to determine the conditional probabilities of all the candidates in the ensemble by comparing the retrieved hydrometeors through measured TRMM radiances with the model simulated hydrometeors. Based on the posterior probability density function, the initial conditions at 06 00 UTC are then corrected using a linear weighted average of initial ensembles for the all prognostic variables. With these weighted average initial conditions, the WRF model has been run up to 08th Nov 06 UTC and the predictions are then compared with observations and the control run. An ensemble independence study was conducted on the basis of which, an optimum of 25 ensembles is arrived at. With the optimum ensemble size, the sensitivity of prognostic variables was also analyzed. The model simulated track when compared with that obtained with the corrected set of initial conditions gives better results than the control run. The algorithm can improve track prediction up to 35 % for a 24 h forecast and up to 12 % for a 54 h forecast.  相似文献   

18.
This work describes the climate change impact study on rainfall patterns in Macta watershed, located in the northwest of Algeria. The monthly rainfall data collection, verification and validation have built a database with 42 stations, each with 42 years of observations from 1970 to 2011. The study of annual total rainfall has identified a downward trend and quantifies the deficits that are within the observation time series. The division of the annual rainfall series into four periods allowed to highlighting the increase in inter-year temporal variability with the coefficient of variation increases from 17 to 27%. The study shows an annual rainfall deficit increment from 13 to 25%. The standard deviation values decrease significantly for the last two periods showing a spatial variability. Multivariate statistical study by the hierarchical clustering method resulted in the formation of station groups indicating the unification of monthly rainfall patterns.  相似文献   

19.
The growth of early rice is often threatened by a phenomenon known as Grain Buds Cold, a period of anomalously cold temperatures during the booting and flowering stage. As a high yield loss due to Grain Buds Cold will lead to increasing insurance premiums, quantifying the impact of weather on crop yield is crucial to the design of weather index insurance. In this study, we propose a new approach to the estimation of premium rates of Grain Buds Cold weather index insurance. A 2-year artificial controlled experiment was utilized to develop logarithmic and linear yield loss models. Additionally, incorporating 51 years of meteorological data, an information diffusion model was used to calculate the probability of different durations of Grain Buds Cold, ranging from 3 to 20 days. The results show that the pure premium rates determined by a logarithmic yield loss model exhibit lower risk and greater efficiency than those determined by a linear yield loss model. The premium rates of Grain Buds Cold weather index insurance were found to fluctuate between 7.085 and 10.151% at the county level in Jiangxi Province, while the premium rates based on the linear yield loss model were higher (ranging from 7.787 to 11.672%). Compared with common statistical methods, the artificial controlled experiment presented below provides a more robust, reliable and accurate way of analyzing the relationship between yield and a single meteorological factor. At the same time, the minimal data requirements of this experimental approach indicate that this method could be very important in regions lacking historical yield and climate data. Estimating weather index insurance accurately will help farmers address extreme cold weather risk under changing climatic conditions.  相似文献   

20.
In this study a Wenchuan earthquake-induced landslide susceptibility assessment was carried out in the Longnan area in northwestern China using a GIS-based logistic regression model. This region has frequently been affected by landslides in the past, and was intensively affected by the 5.12 Wenchuan earthquake which received considerable international attention. The data used for this study consist of the landslides triggered by the Wenchuan earthquake and a landslide pre-disposing factor database. Information regarding the landslide causative factors came from additional data sources, such as a digital elevation model (DEM) with a 30 × 30 m2 resolution, orthophotos, geological and land-use maps, precipitation records, and information on peak ground acceleration data from the 2008 earthquake. The statistical analysis of the relationship between the Wenchuan earthquake-triggered landslides and pre-disposing factors showed the great influence of lithological and topographical conditions on slope failures. The quality of susceptibility mapping was validated by splitting the study area into training and validation sections. The prediction capability analysis demonstrated that the landslide susceptibility map could be used for land planning as well as emergency planning by local authorities.  相似文献   

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