A distributed water–heat coupled model for mountainous watershed of an inland river basin in Northwest China (II) using meteorological and hydrological data

A distributed water–heat coupled model (DWHC) is calibrated by using daily precipitation data from 26 hydrological and meteorological stations: daily averaged air temperature data from the 11 stations and daily pan evaporation data (E601) from the 15 stations in 2000. Six tests by using different spatial interpolation methods to calculate the above daily meteorological data in each 1 km × 1 km grid, are designed to simulate the mean daily runoff generated from the research Heihe mountainous watershed in 2000. Due to spatial sparseness and asymmetry of the hydrological and meteorological stations, the results of the six tests have little differences. The interpolation method in 3-D mode considering altitude is not better than those taking no account of altitude, nor are the model results when the daily meteorological data at the two stations far from the research watershed are complemented. At last, a nearest neighbor interpolation method in 2-D mode is used to calibrate the DWHC model, in which the revised Nash-Sutcliffe Efficiency NSE, balance error B, determinate coefficient R ², root mean square error RMSE and average absolute error MAE is about 0.61, 0.08%, 0.73, 25.0 and 15.8 m³s⁻¹, respectively. However, by using the daily data in 1999 to validate the model, the NSE, B, R ², RMSE and MAE are, respectively, 0.63, −2.98%, 0.77, 34.9 and 20.3 m³s⁻¹. The reason that the model result is not favorable is mainly because of the lack of detailed soil information, meteorological data and vegetation data; even worse, the basic equations for runoff generation processes are mainly derived from the research results in other regions and meanwhile, its flow concentration method should be improved too. The water balance of the research watershed in 2000 is also discussed in this paper. Though the runoff simulation results are not favorable, the estimated evapotranspiration and runoff components are in accordance with the usual knowledge qualitatively, parts of which meet with the field measurements. According to the model results, the runoff is mainly generated from the land surfaces and shallow soil layers in this cold mountainous watershed. The alpine meadow has evident water conservation function based on the model results, field investigation and field observation results. The DWHC model also reproduces the formation processes of the thick-layered ground ice to some extent, though it is suppositional due to lack of detailed soil, vegetation and meteorological information.     

三种插值方法在降水等值线绘制中的对比分析

为对比不同插值方法在宁夏回族自治区的适用性,基于宁夏及其周边地区77个气象台站1956—2016年平均降水量数据,运用泰森多边形法、距离平均反比法、克里金插值法对降雨量数据进行空间插值和交叉验证,从插值方法的稳健性和准确度出发进行对比分析,得出适用于宁夏回族自治区的插值方法。研究表明：（1）从准确度来看,克里金插值法与观测值走向、位置较为相似;从稳健性来看,克里金插值法MAE平均值为4.43 mm、RMSE平均值为10.36 mm,显著小于泰森多边形法和距离平均反比法。（2）整体来看,克里金插值法更为理想,可以用于宁夏回族自治区降水量空间插值分析。     

A method of characterizing the thermal relations in mountainous areas / the lower Beskid range in the Polish Carpathians as example

The method discussed in the present paper permits the determination in detail of the mesoclimatic conditions on the basis of standard data provided by a network of a measurement stations. The passage from the data available at certain points and obtained from meteorological stations to spatial conceptions leads along the determination of the relationships between the altitude, various forms of the relief, and the thermal indices of climate. Resulting from these data, the duration of the frostless period is a sensitive index in the scale of mesoclimate, besides all the other indices connected with the minimum air temperatures. The nomographs applied by the authors make it possible to construct detailed climatic maps, which — with the maps of other elements of the geographical environment — form a basis for the elaboration e.g. an agrocological map of habitats. Presented method of the evaluation of climate conditions can be applied to all mountain territories, in which a network of measurement stations enables the determination of the interdependencies between the chosen thermal parameters and the altitude, and the distinction of the impact of the convex and concave forms.     

中国生态系统研究网络（CERN）：概况、成就和展望

在中国科学院的支持下，中国生态系统研究网络（CERN）于1988年建立，现由代表不同生态系统的36个生态站、5个分中心和1个综合中心组成。经过10多年的努力，CERN各站和中心已按照规定的监测指标体系和操作规范，系统地开展了生态监测以及有关的研究和生态系统优化管理模式示范等方面的工作，且已取得了一系列重大成果。CERN目前已经成为我国生态学研究和人才培养的重要基地及国际生态监测与研究网络的重要组成部分。随着中国科学院知识创新工程第三期计划的启动，CERN又将进入到一个重要的发展阶段。可以预期，CERN在促进科学发展和服务社会方面都将取得更重大的成就。     

Regionalisation of the “intensity-duration-frequency” curves in Northern Algeria

The flood events observed during last years in the urban areas are subject of main interest for quantification of the hydro-climatic risks and climatic change to the regional scales. The establishment of a statistical relationship between the intensities of intense rains and the recurrence of these events allows us to determine the dimensions of the works according to a previously defined level of risk. They constitute today a leading tool for various users. This work concerns the study of the maximum annual rains, recorded at 49 stations in the northern Algeria. The objectives of this work are to determine the estimators who are the “intensity-duration-frequency” curves and to extract from these whole of information the b Montana climatic parameter to be regionalized for the calculating the river flow and for the dimensioning of the networks of cleansing in the event of insufficiency of data. Different durations going from 15 min to 24 h are studied. We utilised the collocated co-kriging as multivariate estimation method for interpolation in order to yield the space distribution maps of b Montana climatic parameter, with the benefit of using spatially correlated secondary variables, such as the digital elevation model and the distance from the coastline that are known at any localisation. All features led to choose the digital elevation model as covariate for interpolating b Montana values, yielding a better regionalisation of the studied climatic parameter. The geostatistical handling of b Montana values strictly related to auxiliary variables that constitute physical factors overcomes the data shortage in planning, managing and preventing the rain flood risk.     

结合糙率校正的河网水情数据同化

为解决河网水动力模型重要参数糙率与水力状态量水位、流量的同步校正问题,以糙率和水力状态量作为河网非线性动态系统变量,采用扩展卡尔曼滤波,构建结合糙率动态校正的河网水情数据同化模型.通过算例计算,系统分析了水位动态噪声水平、糙率动态噪声水平、糙率初始值及测站个数对模型校正的影响.结果表明:模型能够有效用于水位状态量的实时校正;靠近测站的糙率校正值趋于真值,远离测站的糙率校正值趋于初始值;通过调整糙率动态噪声水平,可以有效控制糙率的修正量,防止糙率修正过大而引起计算失效问题.     

A method of characterizing the thermal relations in mountainous areas / the lower Beskid range in the Polish Carpathians as example

The method discussed in the present paper permits the determination in detail of the mesoclimatic conditions on the basis of standard data provided by a network of a measurement stations. The passage from the data available at certain points and obtained from meteorological stations to spatial conceptions leads along the determination of the relationships between the altitude, various forms of the relief, and the thermal indices of climate. Resulting from these data, the duration of the frostless period is a sensitive index in the scale of mesoclimate, besides all the other indices connected with the minimum air temperatures. The nomographs applied by the authors make it possible to construct detailed climatic maps, which — with the maps of other elements of the geographical environment — form a basis for the elaboration e.g. an agrocological map of habitats. Presented method of the evaluation of climate conditions can be applied to all mountain territories, in which a network of measurement stations enables the determination of the interdependencies between the chosen thermal parameters and the altitude, and the distinction of the impact of the convex and concave forms.     

降雨资料Kriging与IDW插值对比分析—以漓江流域为例

降水空间化信息在很多领域都具有重要意义,而进行空间插值方法形成降水空间化信息是当代较为常用的方式。面对众多的插值方法其插值精度成为我们是否采用的关键,就kirging插值法与IDW(Inverse Distance Weighting)插值进行研究以探讨其插值效果,为此类科学研究提供依据。研究以漓江流域内各气象站点降水统计资料为基础分别采用上述两种方法进行插值处理,通过与预留实测站点比较评判方法的插值效果的优劣。结果表明,多年月平均降水量作为时间步长时,降水丰沛的月份使用kriging插值法较优于IDW插值法,而枯水月份则使用IDW插值法较优于kriging插值法。     

Precipitation pattern modeling using cross-station perception: regional investigation

Establishing robust models for predicting precipitation processes can yield a significant aspect for many applications in water resource engineering and environmental prospective. In particular, understanding precipitation phenomena is crucial for managing the effects of flooding in watersheds. In this research, a regional precipitation pattern modeling was undertaken using three intelligent predictive models incorporating artificial neural network (ANN), support vector machine (SVM) and random forest (RF) methods. The modeling was carried out using monthly time scale precipitation information in a semi-arid environment located in Iraq. Twenty weather stations covering the entire region were used to construct the predictive models. At the initial stage, the region was divided into three climatic districts based on documented research. Initially, modeling was carried out for each district using historical information from regionally distributed meteorological stations for calibration. Subsequently, cross-station modeling was undertaken for each district using precipitation data from other districts. The study demonstrated that cross-station modeling was an effective means of predicting the spatial distribution of precipitation in watersheds with limited meteorological data.     

内陆河高寒山区流域分布式水热耦合模型（Ⅱ）：地面资料驱动结果

以黑河出山日平均流量作为对比，利用26个降水站点、11个气温站点和14个潜在蒸发站点2000年日资料，模型设计了6套气象因子空间分布方案，进行数值模拟试验，结果表明，在黑河流域现有观测站点的情况下，利用各种空间插值方法所得结果基本相当，考虑地面高程的三维插值与不考虑地面高程的二维插值结果相差不大，补充距离研究区较远的站点观测资料，模型结果反而变差。最终模型采用基于二维算法的最近距离法（nearest），利用2000年资料校正模型，计算与实测黑河日出山平均流量序列的效率系数为0.6101，平衡误差为0.0808％。以1999年资料验证模型，效率系数和平衡误差分别为0.6270和-2.9824％。模型基于水热连续方程模拟了黑河山区流域水热交换和耦合过程，探讨了流域的水量平衡，分析了水量平衡因子的时空分布，其模拟结果表明，内陆河高寒山区流域主要为浅表产流，高山草甸具有拦蓄降水和水源涵养作用，并反映了高山地区浅表土壤地下厚层冰的聚集过程。各种模型结果与本区野外实际调查结果基本一致，也符合当前对寒区流域水文循环过程的定性认识。     

GaRiRO: Gradient and residual integrated rank ordering of stations in rainfall monitoring network

This paper presents a new method that integrates gradient and residual values for rank ordering of stations in a monitoring network (GaRiRO). The innovation is derived from the fact that the parameter (dependent variable) gauged through the monitoring network is modelled using independent variables that influence its measured quantity. And the dependent variable exhibit non-stationary spatial gradient with respect to the independent variables, particularly in complex terrain. GaRiRO technique was developed to prioritize the rain gauge stations for optimizing the existing network and selection of the best locations for relocation or installation of gauges. Although initially aimed to assist hydrologists with a ranking scheme for rain gauge stations, it can be applied to any environmental, meteorological or hydro-meteorological monitoring network. The new procedure is based on deriving gradient and residual value at each station by modeling the spatial relationship of dependent-independent variable using geographically weighted regression (GWR) technique. For the prospective stations with no record, the gradient value is estimated using GWR model and the residual value is derived from the residual map generated by applying kriging technique on the residual derived at all gauged locations. The method combines the decision factor with analytical strength of GIS for prioritizing the stations which results in limited number of trials for installation or relocation of gauges to yield optimized network configuration.     

Streamflow response to regional climate model output in the mountainous watershed: a case study from the Swiss Alps

Regional climate model (RCM) outputs are often used in hydrological modeling, in particular for streamflow forecasting. The heterogeneity of the meteorological variables such as precipitation, temperature, wind speed and solar radiation often limits the ability of the hydrological model performance. This paper assessed the sensitivity of RCM outputs from the PRUDENCE project and their performance in reproducing the streamflow. The soil and water assessment tool was used to simulate the streamflow of the Rhone River watershed located in the southwestern part of Switzerland, with the climate variables obtained from four RCMs. We analyzed the difference in magnitude of precipitation, maximum and minimum air temperature, and wind speed with respect to the observed values from the meteorological stations. In addition, we also focused on the impact of the grid resolution on model performance, by analyzing grids with resolutions of 50 × 50 and 25 × 25 km². The variability of the meteorological inputs from various RCMs is quite severe in the studied watershed. Among the four different RCMs, the Danish Meteorological Institute provided the best performance when simulating runoff. We found that temperature lapse rate is significantly important in the mountainous snow and glacier dominated watershed as compared to other variables like precipitation, and wind speed for hydrological performance. Therefore, emphasis should be given to minimum and maximum temperature in the bias correction studies for downscaling climatic data for impact modeling in the mountainous snow and glacier dominated complex watersheds.     

中国大陆森林线空间分布特征及其与多年冻土线、气候雪线的关系

在自然植被垂直带谱上一般以暗针叶林带的上限代表森林线,它的海拔高度取决于温度及其年较差。本文主要根据我国一些地区现代暗针叶林带上限的海拔高程和102个气象台站的气象资料,计算出我国的森林线的理论海拔高度,并编绘出我国的森林线高程等值线图以及多年冻土线与森林线高差等值线图和现代气候雪线与森林线高差等值线图。在此基础上研究了我国森林线的空间分布特征及其与多年冻土线、气候雪线的关系。      

Thermal comfort and forecast of energy consumption in Northwest Iran

Assessing the climatic characteristics and identifying the climatic parameters of a specific region can play a major role in human welfare. Thermal comfort conditions are among the most significant factors of climatic variables in the northwestern regions of Iran due to the considerable spatial and temporal variations and are vital for environmental, energy and economic management. It is therefore necessary to advance our knowledge of the climatic conditions in order to provide an appropriate tool for managing climatic extremes. This requires charting of the range of clusters of the thermal comfort conditions in this region. In this study, the general atmosphere circulation model HADCM3 and the A1 scenario, downscaled by the LARS-WG model, were employed to simulate the climatic conditions in Iran during the period 2011–2040. The data obtained were compared with sampled data from six Iranian climatic stations for the 30-year period (1961–1990). In order to tabulate this comparison, six clusters per climatic station were defined based on intrinsic similarity of data. Results show an increase in the annual average temperature of these six stations by 1.69 °C for the predicted years, projected from the base years 1961–1990. This factor has resulted in an increment of the annual average thermal comfort temperature inside buildings by a magnitude of 0.52 °C in future decades. When the thermal requirements of the studied region were evaluated based on the real temperature difference and the degree of thermal comfort, it becomes clear that apart from cluster 1, the energy required to reach thermal comfort inside buildings will increase in the future. As a result of this temperature increase, an increase of the energy required to reach the thermal comfort is expected. This new methodology is an interesting tool and needs to be seriously considered by engineers and architects in designing buildings of the future.     

Relationship of drought frequency and severity with range of annual temperature variation

The frequency and severity of occurrence of meteorological droughts in different climatic regions depend on regional climatic factors. This study has made an effort to explore the relationship of range of annual temperature variation at a given place with the frequency of occurrence of drought and the maximum magnitude of seasonal rainfall deficit (i.e., severity). The seasonal rainfall refers to sum of monsoon season (rainy season) rainfall in India. The monthly precipitation data of 113 years (1901–2013) for 256 stations in different parts of India have been used to estimate the return period of meteorological drought at different stations. The daily normal values of observed maximum and minimum temperatures from 40 years of records have been utilized to estimate range of temperature variation (θ_R) during the year at each stations. In various parts of India, the θ_R ranges from 10 °C in humid regions to 40 °C in arid regions. The various climatic regions have been experiencing maximum deficiency of annual rainfall ranging from 30% (humid) to 90% (arid). The results reveal that places exhibiting θ_R values between 40 to 30 °C face more frequent droughts with average frequency of once in 3 to once in 6 years. The occurrence of extreme and severe drought events is more frequent in the regions with higher values of θ_R compare to that in lesser values of θ_R. The regions with θ_R values between 30 to 25 °C mostly face severe and moderate events having the average drought return period of 6–9 years, and the occurrence of extreme droughts in these regions is rare. Furthermore, regions with θ_R?<?20 °C face moderate droughts only with an average return period of 14 years. This study divulges that the average return period and magnitude of deficiency of drought events have notable relationship with the range of temperature variation during the year at a given place.

     

Climatic classification of the Tibet Autonomous Region using multivariate statistical methods

A climatic regionalization of the Tibet (Xizang) Autonomous Region (TAR) is developed based on a multivariate analysis of temperature and precipitation records of two data sets from the periods 1971–1980 and 1980–1989. For these two decades 15 selected variables of the 29 meteorological stations — in total more than 50,000, partly handwritten original data — were computed. Three different statistical approaches, Cluster Analysis (CA), Factor Analysis (FA) and Discriminant Analysis (DA) were followed up. The vast expanse (1.2 million km²) and the pronounced relief of the TAR (between 500 m and 8,848 m in elevation), the low density and unequal distribution of meteorological stations, as well as uncertainties inherent in the selection of statistical methods, restricted and hampered the separation of coherent climatic subregions using multivariate statistics. However, a solution comprising of nine clusters, representing the different climatic conditions of Tibet, was found.The results of the multivariate statistical analyses were compared with traditional classification schemes published by the Chinese Academy of Sciences (CAS). The Chinese classifications from the years 1982 (CAS 82) and 1984 (CAS 84) are here presented to the scientific community of the western countries for the first time (Fig 2 and 3). The main aim of these Chinese climatic classifications in Tibet was the delineation of areas with the best climatic conditions for agricultural land-use. In comparison, the aim of this paper was to establish a new classification of Tibet's climate, mainly based on measurements of climatic elements analyzed with multivariate statistical procedures following up the approach of McGregor (1993). The Factor Analysis (FA) included the temperature factor, which explains 46% of the total variance, whereas 32% is explained by the winter moisture and 12% by the moisture factor.For temperatur zonation and, in particular, for the distribution of precipitation, the high mountain topography of Tibet is interpreted as the determining factor. The new climatic classification derived from multivariate statistics allows a more dynamic interpretation of the climatic cluster pattern. First, it shows the channelized influence of the summer monsoon along the southeast-northwest- and east-west-trending broad valleys north of the Gulf of Bengal. This is also obvious in the pattern of the temperature and moisture factors. Second, it can be supposed that a general south-trending influence of the winter monsoon, in combination with a northwest-trending influence from the Bay of Bengal leads to more irregular relief- and wind-dependent pattern, which can be estimated from the distribution of the winter moisture factor.     

Data-driven topo-climatic mapping with machine learning methods

Automatic environmental monitoring networks enforced by wireless communication technologies provide large and ever increasing volumes of data nowadays. The use of this information in natural hazard research is an important issue. Particularly useful for risk assessment and decision making are the spatial maps of hazard-related parameters produced from point observations and available auxiliary information. The purpose of this article is to present and explore the appropriate tools to process large amounts of available data and produce predictions at fine spatial scales. These are the algorithms of machine learning, which are aimed at non-parametric robust modelling of non-linear dependencies from empirical data. The computational efficiency of the data-driven methods allows producing the prediction maps in real time which makes them superior to physical models for the operational use in risk assessment and mitigation. Particularly, this situation encounters in spatial prediction of climatic variables (topo-climatic mapping). In complex topographies of the mountainous regions, the meteorological processes are highly influenced by the relief. The article shows how these relations, possibly regionalized and non-linear, can be modelled from data using the information from digital elevation models. The particular illustration of the developed methodology concerns the mapping of temperatures (including the situations of Föhn and temperature inversion) given the measurements taken from the Swiss meteorological monitoring network. The range of the methods used in the study includes data-driven feature selection, support vector algorithms and artificial neural networks.     

Using comprehensive and sequential vegetation classification system to predict the influence of climate change on vegetation succession of alpine grassland of Qinghai Plateau

Distribution of grassland vegetation is highly associated with climatic conditions and varied with climatic change. The tendency of climatic changes on Qinghai Plateau was analyzed, based on the meteorological data from 1961 to 2007 collected from 50 meteorological stations distributed throughout the whole plateau. The vegetation distribution of alpine grassland under past and future climatic change was estimated by using the approach of Comprehensive and Sequential Classification system. Results show that the climate varied greatly before and after 1987. The temperature increased 0.16°C/10a before 1987 and 0.64°C/10a after 1987. The precipitation increased 0.14 mm/10a before 1987 and 3.92 mm/10a after 1987. There were 12 types of grassland vegetation between 1961 and 1987, while there were 11 types of grassland vegetation between 1988 and 2007 on the Plateau. When climatic warming continued with CO₂ doubling in the future, the vegetation of alpine grassland will shrink into nine types.     

Evaluation of Interpolation Accuracy of Neural Kriging with Application to Temperature-Distribution Analysis

An interpolation method based on a multilayer neural network (MNN), has been examined and tested for the data of irregular sample locations. The main advantage of MNN is in that it can deal with geoscience data with nonlinear behavior and extract characteristics from complex and noisy images. The training of MNN is used to modify connection weights between nodes located in different layers by a simulated annealing algorithm (one of the optimization algorithms of the network). In this process, three types of errors are considered: differences in values, semivariograms, and gradients between sample data and outputs from the trained network. The training is continued until the summation of these errors converges to an acceptably small value. Because the MNN trained by this learning criterion can estimate a value at an arbitrary location, this method is a form of kriging and termed Neural Kriging (NK). In order to evaluate the effectiveness of NK, a problem on restoration ability of a defined reference surface from randomly chosen discrete data was prepared. Two types of surfaces, whose semivariograms are expressed by isotropic spherical and geometric anisotropic gaussian models, were examined in this problem. Though the interpolation accuracy depended on the arrangement pattern of the sample locations for the same number of data, the interpolation errors of NK were shown to be smaller than both those of ordinary MNN and ordinal kriging. NK can also produce a contour map in consideration of gradient constraints. Furthermore, NK was applied to distribution analysis of subsurface temperatures using geothermal investigation loggings of the Hohi area in southwest Japan. In spite of the restricted quantity of sample data, the interpolation results revealed high temperature zones and convection patterns of hydrothermal fluids. NK is regarded as an interpolation method with high accuracy that can be used for regionalized variables with any structure of spatial correlation.     

Return period estimates of extreme sea level along the east coast of India from numerical simulations

Estimates of return periods of extreme sea level events along the coast are useful for impact assessment. In this study, a vertically integrated 2D model was developed for the simulation of storm surges in the Bay of Bengal. The bathymetry for the model was derived from an improved ETOPO-5 data set, which was prepared in our earlier work. The meteorological forcing for the model was obtained from the cyclone model of Holland using the data available for 136 low-pressure systems that occurred during 1974–2000 in the Bay of Bengal. The simulated total sea level and the surge component were obtained for each event. The simulated peak levels showed good agreement with the observations available at few stations. The annual maxima of sea levels, extracted from the simulations, were fitted with Gumbel distribution using r-largest annual maxima method to estimate the 5- and 50-year return periods of extreme events at 26 stations along the east coast of India. The return periods estimated from simulated sea levels showed good agreement with those obtained from observations. The 5- and 50-year return levels of total sea level along the east coast of India show a considerable increase from south to north, with the 50-year return total sea levels being as high as 6.9 and 8.7 m at stations along the north eastern coast such as Sagar Island and Chandipur, respectively. The high return levels are expected at these stations as the cyclones developed in the Bay of Bengal generally move north or north-west, producing extreme events in the northern part, and moreover, these stations are characterized by high tidal ranges. However, at some regions in the southern part such as Surya Lanka and Machilipatnam, though 50-year return levels of total sea level are not very high (2.98 and 2.97 m, respectively) because of the relatively lower tidal ranges, high return levels of surges (0.84 and 0.57 m, respectively) are found. In addition to the role of shallow depths (5.0 and 6.1 m, respectively) at the two stations, the high return levels of surges are attributed to the effect of geometrical configuration at Surya Lanka and width (100 km) and orientation of continental shelf at Machilipatnam.