Modeling tropical river runoff: A time dependent approach

Forecasting of rainfall and subsequent river runoff is important for many operational problems and applications related to hydrology. Modeling river runoff often requires rigorous mathematical analysis of vast historical data to arrive at reasonable conclusions. In this paper we have applied the stochastic method to characterize and predict river runoffofthe perennial Kulfo River in southem Ethiopia. The time series analysis based auto regressive integrated moving average （ARIMA） approach is applied to mean monthly runoff data with 10 and 20 years spans. The varying length of the input runoff data is shown to influence the forecasting efficiency of the stochastic process. Preprocessing of the runoff time series data indicated that the data do not follow a seasonal pattern. Our forecasts were made using parsimonious non seasonal ARIMA models and the results were compared to actual 10-year and 20-year mean monthly runoff data of the Kulfo River. Our results indicate that river runoff forecasts based upon the 10-year data are more accurate and efficient than the model based on the 20-year time series.     

Application of the modified Arps-Roberts discovery proces model to the 1995 U.S. National Oil and Gas Assessment

ARDS (version 4.01), a modified version of the Arps-Roberts discovery process model, was used to forecast the remaining oil and gas resources in more than 50 provinces, super-exploration plays, and individual plays in the onshore and offshore United States for the 1995 National Oil and Gas Assessment. The size distribution of oil and gas fields was estimated for the underlying distribution of fields; the size distribution for the remaining fields was calculated to be the difference between this distribution and that of discovered fields. The guidelines that govern the 1995 National Assessment require the underlying size distribution of fields to be estimated by using only data from two standard commercial data files (the NRG Associates field file and the Petroleum Information Inc. well file). However, a variety of situations required further modification of the discovery process modeling system; for example, multiple exploration plays that occurred nearly simultaneously and also displaced each other in time, and the phenomenon of field growth introduced a large bias in the forecasts produced by the discovery process models for some provinces.     

IMPACT OF CLIMATE ON SUMMER RESIDENTIAL ELECTRICITY CONSUMPTION IN HONG KONG

Ｉ.ＩｎｔｒｏｄｕｃｔｉｏｎＣｌｉｍａｔｅｈａｓｌｏｎｇｂｅｅｎｋｎｏｗｎｔｏｈａｖｅａｐｒｏｍｉｎｅｎｔｉｎｆｌｕｅｎｃｅｏｎｒｅｓｉｄｅｎｔｉａｌｅｎｅｒｇｙｃｏｎｓｕｍｐｔｉｏｎ,ｐａｒｔｉｃｕｌａｒｌｙｔｈｅｕｓｅｏｆｅｌｅｃｔｒｉｃｉｔｙｉｎｓｐａｃｅｈｅａｔｉｎｇａｎｄｃｏｏｌｉｎｇ.ＩｎＨｏｎｇＫｏｎｇ,ｔｈｅｒｅｗａｓａｎｉｎｃｒｅａｓｅｏｆ207ｐｅｒｃｅｎｔｉｎｒｅｓｉｄｅｎｔｉａｌｅｌｅｃｔｒｉｃｉｔｙｃｏｎｓｕｍｐｔｉｏｎ,ｆｒｏｍ8414ｔｅｒａｊｏｕｌｅ(ＴＪ)ｉｎ1980ｔｏ25827Ｔ…     

土地利用变化的多尺度空间自相关分析——以内蒙古翁牛特旗为例

用传统统计学方法模拟和解释土地利用变化的前提条件是研究分析的数据在统计上必须独立且均匀分布。但是空间数据相互之间通常具有依赖性 (即空间自相关),某一变量的值随着测定距离的缩小而变得更相似或更为不同。由于经典线性回归方法未能抓住数据的空间自相关特征,而空间自相关包含一些有用的信息,为了克服这一缺点,利用Moran的I系数自相关图来描述研究区土地利用变化的空间自相关,并且建立了不仅考虑回归而且又考虑空间自相关的混合回归-空间自相关回归模型 (即空间滞后模型)。研究得到：① 研究区土地利用变化模型中不但自变量之间而且因变量之间存在空间正自相关,这表明土地利用变化数据的空间自相关很强;② Moran的I系数随着尺度的变粗而减小,这是由于数据平均时的滤波特性和Moran的I系数对距离的非线性特征造成的;③ 经典线性回归模型的残差也表现出正相关,这表明标准的多元线性回归模型未能考虑土地利用数据所存在的空间依赖性;④ 混合回归-空间自相关回归模型 (即空间滞后模型) 的残差未存在空间自相关,并且有更好的拟合度;⑤ 相对于经典线性回归模型,混合回归-空间自相关回归模型 (即空间滞后模型) 对于存在空间自相关性的数据来说有着统计上的合理性,而经典线性回归模型未能考虑这些因素。     

United States Geological Survey's Reserve-Growth Models and Their Implementation

The USGS has developed several mathematical models to forecast reserve growth of fields both in the United States (U.S.) and the world. The models are based on historical reserve growth patterns of fields in the U.S. The patterns of past reserve growth are extrapolated to forecast future reserve growth. Changes of individual field sizes through time are extremely variable, therefore, the reserve growth models take on a statistical approach whereby volumetric changes for populations of fields are used in the models. Field age serves as a measure of the field-development effort that is applied to promote reserve growth. At the time of the USGS World Petroleum Assessment 2000, a reserve growth model for discovered fields of the world was not available. Reserve growth forecasts, therefore, were made based on a model of historical reserve growth of fields of the U.S. To test the feasibility of such an application, reserve growth forecasts were made of 186 giant oil fields of the world (excluding the U.S. and Canada). In addition, forecasts were made for these giant oil fields subdivided into those located in and outside of Organization of Petroleum Exporting Countries (OPEC). The model provided a reserve-growth forecast that closely matched the actual reserve growth that occurred from 1981 through 1996 for the 186 fields as a whole, as well as for both OPEC and non-OPEC subdivisions, despite the differences in reserves definition among the fields of the U.S. and the rest of the world.     

组合预测模型在女性呼气高峰流量参考值地理分布研究中的应用

随着地理学的发展以及人们对健康问题的日益关注,医学地理学得到迅速发展。鉴于目前医学参考值制定时仍存在忽略地理因素的影响,本文收集中国各地3809 例健康成年女性呼气高峰流量参考值,分析地理因素对其的影响,计算不同地区参考值的数值差异,探究地理因素对医学参考值产生影响的机理,其中,纬度、海拔高度、年平均气温、年平均相对湿度、年降水量、表土砂砾百分率、表土参考容量共7项地理因素存在显著的相关性。利用ArcGIS中的Moran's I指数对数据进行分析,确定数据与空间及地理因素存在关系。并通过岭回归分析,建立回归方程,并进行插值。研究结果表明,中国健康成年女性肺部呼气高峰流量与纬度与海拔、气候、土壤等地理因素之间存在着显著的关系,同时证明,岭回归与支持向量机组合模型的地理分布差异预测结果优于单独预测方法。     

Determinants for water consumption from improved sources in rural villages of southern Mali

Water supply must take into consideration a variety of factors, including consumer habits, options and needs. Little is known about water demands in rural areas of developing regions. Based on data from southern Mali, this paper banks on the hypothesis that water consumption in rural villages can be predicted by combining measurable variables with spatial analyses. A ten-day field campaign was carried out at the end of the dry season 2016. All improved water sources were checked for functionality, flow rate and water quality. Outcomes were coupled with those from over one-hundred household interviews and incorporated into a geographic database. Buffered network analyses were used to quantify the main spatial determinants for water use. Simple and multiple regression models were then developed to establish the main predictors for per capita and household water consumption. Multiple regression reveals that models based on the number of household members, travel time and total travel distance provide robust forecasts for water consumption (R² > 0.93). Conversely, potentially relevant parameters such as affordability or water quality were not statistically significant. This could be attributed to the presence of free water sources and to the absence of quality-monitoring, respectively. Outcomes also suggest that having access to basic means of transportation, such as pushcarts, doubles water consumption at the household level. In terms of access to improved water sources, coverage was found to be lower in practice than on paper. These outcomes contribute to the growing body of literature that challenges the widely accepted assumption that 90% of the world population has access to safe drinking supplies.     

基于多源降水数据的洞庭湖流域夏季降水与西太平洋副热带高压的关系

基于中国气象科学数据共享服务网提供的1979~2012年夏季6~8月降水实测数据（CMD）和3种不同来源的再分析降水数据,研究和比较副高特征指数与洞庭湖流域夏季降水的关系及空间差异。结果表明：① 6、7月副热带高压（副高）脊线位置偏北,洞庭湖流域大部分区域降水偏多,8月偏少;6、7月北界位置偏北使流域东南降水偏少、西北降水偏多,8月大部分地区降水偏多;6~8月西伸脊点位置东移使降水偏多范围自东向西增大;6月份副高强度增强使东部和南部降水偏多、西部和北部降水偏少,7和8月大部分地区降水减少。② 再分析降水数据大体上能反映出7月份脊线指数、6和7月北界指数、6~8月西伸脊点指数和副高强度指数对相应月份CMD降水的影响。③ 副高特征指数对CMD降水的拟合能力存在区域差异。     

Verification of the motion fields over Southeast Asia by the Florida State University (FSU) superensemble forecast

The Florida State University (FSU) multimodel superensemble forecast is evaluated against several other operational weather models for the Southeast Asia region. The superensemble technique has demonstrated its exceptional skills in forecasting precipitation, motion and mass fields compared to either individual global operational or ensemble mean forecasts. The motion field investigation for the season of 2001 reveals that the superensemble forecasts are closer to the observed data compared to the other global member operational models through its low systematic errors at the 850 hPa level. The FSU multimodel superensemble forecasts exhibit the lowest root mean square errors (RSMEs), the highest correlation against the best observed data and the lowest systematic errors compared to the other operational model members. These forecasts have the potential to provide better daily weather predictions over the Southeast Asia region, particularly during the early northeast monsoon that often causes heavy rainfall in the equatorial part of the Southeast Asia region.     

Sediment yield variability in Spain: a quantitative and semiqualitative analysis using reservoir sedimentation rates

An existing dataset of area-specific sediment yield (SSY) for 60 catchments in Spain that was retrieved from sediment deposition rates in reservoirs [Avendaño Salas, C., Sanz Montero, E., Rayán, C., Gómez Montaña, 1997. Sediment yield at Spanish reservoirs and its relationship with the drainage basin area. In: Proceedings of the 19th Symposium of Large Dams, Florence, 1997. ICOLD (International Committee on Large Dams), pp. 863–874] reveals that catchment area alone explains only 17% of the variability in SSY. In this study, an attempt to explain the remaining variability in SSY was made using a quantitative and a semiqualitative approach for 22 catchments. During a field survey, the 22 selected catchments were characterised by topography, vegetation cover, lithology, shape and the presence of gullies in the broad vicinity of the reservoir. This information was used to develop a factorial scoring index model that provides a fairly accurate and reliable prediction of SSY. A classical multiple regression model using climatic, topographic and land use properties derived from regional datasets could not explain as much variance as the qualitative index model, nor did it appear to be as reliable. The same conclusion could be drawn when using the CORINE soil erosion risk map of southern Europe. The low prediction capability of the multiple regression models and the CORINE soil erosion risk map could be attributed mainly to the fact that these methods do not incorporate gully erosion and that the land cover data are not a good representation of soil cover. Both variables have been shown to be of great importance during the field surveys. Future assessments of SSY could be quickly and efficiently made using the proposed factorial scoring index model. In comparison with other models, which demand more data, the index model offers an alternative prediction tool.     

中国能源消费碳排放的空间计量分析(英文)

Based on energy consumption data of each region in China from 1997 to 2009 and using ArcGIS9.3 and GeoDA9.5 as technical support,this paper made a preliminary study on the changing trend of spatial pattern at regional level of carbon emissions from energy con-sumption,spatial autocorrelation analysis of carbon emissions,spatial regression analysis between carbon emissions and their influencing factors.The analyzed results are shown as follows.(1) Carbon emissions from energy consumption increased more than 148% from 1997 to 2009 but the spatial pattern of high and low emission regions did not change greatly.(2) The global spatial autocorrelation of carbon emissions from energy consumption in-creased from 1997 to 2009,the spatial autocorrelation analysis showed that there exists a "polarization" phenomenon,the centre of "High-High" agglomeration did not change greatly but expanded currently,the centre of "Low-Low" agglomeration also did not change greatly but narrowed currently.(3) The spatial regression analysis showed that carbon emissions from energy consumption has a close relationship with GDP and population,R-squared rate of the spatial regression between carbon emissions and GDP is higher than that between carbon emissions and population.The contribution of population to carbon emissions in-creased but the contribution of GDP decreased from 1997 to 2009.The carbon emissions spillover effect was aggravated from 1997 to 2009 due to both the increase of GDP and population,so GDP and population were the two main factors which had strengthened the spatial autocorrelation of carbon emissions.     

Forecasting the Ethiopian Coffee Price Using Kalman Filtering Algorithm

Ethiopian coffee price is highly fluctuated and has significant effect on the economy of the country. Conducting a research on forecasting coffee price has theoretical and practical importance.This study aims at forecasting the coffee price in Ethiopia. We used daily closed price data of Ethiopian coffee recorded in the period 25 June 2008 to 5 January 2017 obtained from Ethiopia commodity exchange (ECX) market to analyse coffee prices fluctuation. Here, the nature of coffee price is non-stationary and we apply the Kalman filtering algorithm on a single linear state space model to estimate and forecast an optimal value of coffee price. The performance of the algorithm for estimating and forecasting the coffee price is evaluated by using root mean square error (RMSE). Based on the linear state space model and the Kalman filtering algorithm, the root mean square error (RMSE) is 0.000016375, which is small enough, and it indicates that the algorithm performs well.     

Novel evaluation metrics for sparse spatio-temporal point process hotspot predictions - a crime case study

Many physical and sociological processes are represented as discrete events in time and space. These spatio-temporal point processes are often sparse, meaning that they cannot be aggregated and treated with conventional regression models. Models based on the point process framework may be employed instead for prediction purposes. Evaluating the predictive performance of these models poses a unique challenge, as the same sparseness prevents the use of popular measures such as the root mean squared error. Statistical likelihood is a valid alternative, but this does not measure absolute performance and is therefore difficult for practitioners and researchers to interpret. Motivated by this limitation, we develop a practical toolkit of evaluation metrics for spatio-temporal point process predictions. The metrics are based around the concept of hotspots, which represent areas of high point density. In addition to measuring predictive accuracy, our evaluation toolkit considers broader aspects of predictive performance, including a characterisation of the spatial and temporal distributions of predicted hotspots and a comparison of the complementarity of different prediction methods. We demonstrate the application of our evaluation metrics using a case study of crime prediction, comparing four varied prediction methods using crime data from two different locations and multiple crime types. The results highlight a previously unseen interplay between predictive accuracy and spatio-temporal dispersion of predicted hotspots. The new evaluation framework may be applied to compare multiple prediction methods in a variety of scenarios, yielding valuable new insight into the predictive performance of point process-based prediction.     

Assessing the Ecological Carrying Capacity of Countries along the Belt and Road

The Belt and Road Initiative (B&R Initiative) is a crucial strategy to promote regional sustainable development in the new era. However, the realization of the B&R Initiative faces huge challenges because of the dual characteristics of a fragile eco-environment and strong dependence on ecological resources for economic development in the Belt and Road (B&R) countries. The ecological carrying capacity (ECC) is a crucial indicator for evaluating regional sustainable development. From the perspective of the relationship between the supply and consumption of ecological resources, this study uses net primary productivity data to measure the supply capacity of ecological resources, and it uses the agricultural production and trade data provided by the United Nations Food and Agriculture Organization to measure the level of ecological resource consumption. These supply and consumption data are then used to assess the ECC and ecological carrying status (ECS) of the B&R countries in 2017. The results show that: (1) The ECC of the B&R is 11.097 billion people; the ecosystem can also support 6.433 billion people, and the ECC is in a state of rich and surplus. (2) The ECS is polarized among the regions and countries along the B&R. Of the 65 countries, the ECC of 40 countries is in a rich and surplus state, mainly in Mid-East Europe and Southeast Asia, while the ECC of 19 countries is in severe overload, mainly in West Asia/Middle East. (3) Although the ecosystems still have ample carrying space in countries along the B&R, ecological protection is still facing enormous challenges during the implementation of the B&R Initiative combined with the internationally recognized ecological protection standards as well as the forecasts of the population and economic development. As the core content of building a new international trade network, the B&R Initiative will help to solve the spatial mismatch between the supply and consumption of ecological resources, which provides a new opportunity to coordinate the contradiction between the ecological protection and social demands of the B&R countries.     

香港降水的短期气候预测

利用正规化周期回归分析方法和门限自回归理论对香港1853-1995年月降水资料进行了建模和拟合，并对1996年进行了预报试验，正规化周期回归拟合效果较好，对香港7个时距的试报准确率为71%，建立了7个时距的10年滑动平均序列的门限自回归模型，模型对序列拟合效果较理想，预报误差较小，试报准确率主国100%。     

Surface Heat Balance and Spatially Distributed Ablation Modelling at Koryto Glacier, Kamchatka Peninsula, Russia

To investigate the characteristics of ablation at Koryto Glacier, a mountain glacier under maritime climate in Kamchatka Peninsula, Russia, we made field observations from August to early September 2000. At a site near the equilibrium line, the 31‐day average net radiation, sensible heat flux, and latent heat flux were 43, 59 and 31 W^?2, respectively. We developed a new distributed ablation model, which only needs measurements of air temperature and global radiation at one site. Hourly ablation rates at this site obtained by the energy balance method are related to measured air temperature and global radiation by linear multiple regression. A different set of multiple regression coefficients is fitted for snow and ice surfaces. Better estimates of ablation rate can be obtained by this approach than by other temperature index models. These equations are then applied to each grid cell of a digital elevation model to estimate spatially distributed hourly melt. Air temperature is extrapolated using a constant temperature lapse rate and global radiation is distributed considering topographic effects. The model enables us to calculate the hourly spatial distribution of ablation rates within the glacier area and could well provide a realistic simulation of ablation over the whole glacier.     

基于STIRPAT模型的中国能源压力分析——基于空间计量经济学模型的视角

21世纪的中国已成为世界第一大能源消费国,迅猛而粗放的经济发展对能源的需求和依赖倍增,这给能源生产和供给带来巨大压力。由于产业结构和区位的异同,各省的能耗呈现空间异质和空间趋同;部分能耗通过空间效应来解释,可以改正传统能耗模型的估计偏差。采用能源消费总量作为环境压力的衡量指标,以STIRPAT模型为基础,将能源消费的空间效应纳入到STIRPAT模型进行空间计量分析。结果发现,中国省域能源消费在空间上存在依赖性,人口、社会富裕度和第二产业比重与能源消费皆为正相关,随着人口、社会富裕度和第二产业比重的增加,对能源消费的弹性系数逐渐增加。适当地控制人口、社会倡导低碳生活以及节能降耗均能缓解能源压力,同时制定差异化的区域能源消费调控措施也很有必要。     

Empirical models to predict the volumes of debris flows generated by recently burned basins in the western U.S.

Recently burned basins frequently produce debris flows in response to moderate-to-severe rainfall. Post-fire hazard assessments of debris flows are most useful when they predict the volume of material that may flow out of a burned basin. This study develops a set of empirically-based models that predict potential volumes of wildfire-related debris flows in different regions and geologic settings.The models were developed using data from 53 recently burned basins in Colorado, Utah and California. The volumes of debris flows in these basins were determined by either measuring the volume of material eroded from the channels, or by estimating the amount of material removed from debris retention basins. For each basin, independent variables thought to affect the volume of the debris flow were determined. These variables include measures of basin morphology, basin areas burned at different severities, soil material properties, rock type, and rainfall amounts and intensities for storms triggering debris flows. Using these data, multiple regression analyses were used to create separate predictive models for volumes of debris flows generated by burned basins in six separate regions or settings, including the western U.S., southern California, the Rocky Mountain region, and basins underlain by sedimentary, metamorphic and granitic rocks.An evaluation of these models indicated that the best model (the Western U.S. model) explains 83% of the variability in the volumes of the debris flows, and includes variables that describe the basin area with slopes greater than or equal to 30%, the basin area burned at moderate and high severity, and total storm rainfall. This model was independently validated by comparing volumes of debris flows reported in the literature, to volumes estimated using the model. Eighty-seven percent of the reported volumes were within two residual standard errors of the volumes predicted using the model. This model is an improvement over previous models in that it includes a measure of burn severity and an estimate of modeling errors. The application of this model, in conjunction with models for the probability of debris flows, will enable more complete and rapid assessments of debris flow hazards following wildfire.     

Swelter in the city: Urban greenery and its effects on temperature in Guwahati,India

Changing urban landscape with multistoried high rises, roads and pavements is continuously reducing urban green space. These structures result in high surface temperature variation within cities. To explore the relationship between surface temperature and normalized difference vegetation index (NDVI), this study estimates two models—geographically weighted regression (GWR) and a fixed effect panel data model in relation to the Guwahati Metropolitan Area (GMA), a secondary city in north east India. The results indicate the superiority of GWR regression in presence of spatial dependence. Panel data analysis shows that the densely populated urban areas in the GMA with less than 10 per cent greenery are 1°C warmer than the sub-urban areas with 50 per cent greenery.     

钾肥需求模拟研究

旨在模拟预测中国钾肥需求,为钾肥市场参与者和决策者提供基本的数据和理论支持。运用主成份分析法和多元回归计量经济模型模拟我国钾肥需求。首先,运用主成份分析法对钾肥需求影响因子进行降维处理;其次,采用多元统计非线性回归计量经济学模型模拟钾肥需求。结果表明:(1)钾肥需求与人均耕地、受灾面积、钾肥价格呈负相关关系;(2)钾肥需求与耕地、粮食用地、农产品价格存正相关关系;(3)钾肥需求预测可由较易得的人均耕地、耕地、粮食用地、受灾面积、农产品价格和钾肥价格数据代入模型模拟获得,且模拟数据与现实数据吻合较好;(4)此方法可较广泛运用,不需大量的野外试验和较大的工作量,且无空间跨度差异;(5)政府行为对钾肥需求有着极大的影响作用,政府应发挥其积极性,制定出一套有序的制度安排,以弥补市场本身潜在的问题,引导市场机制不断完善。