INTAMAP: the design and implementation of an interoperable automated interpolation web service

INTAMAP is a Web Processing Service for the automatic spatial interpolation of measured point data. Requirements were (i) using open standards for spatial data such as developed in the context of the Open Geospatial Consortium (OGC), (ii) using a suitable environment for statistical modelling and computation, and (iii) producing an integrated, open source solution. The system couples an open-source Web Processing Service (developed by 52°North), accepting data in the form of standardised XML documents (conforming to the OGC Observations and Measurements standard) with a computing back-end realised in the R statistical environment. The probability distribution of interpolation errors is encoded with UncertML, a markup language designed to encode uncertain data. Automatic interpolation needs to be useful for a wide range of applications and the algorithms have been designed to cope with anisotropy, extreme values, and data with known error distributions. Besides a fully automatic mode, the system can be used with different levels of user control over the interpolation process.

Development and Evaluation of Statistical Downscaling Models for Monthly Precipitation

Several statistical downscaling models have been developed in the past couple of decades to assess the hydrologic impacts of climate change by projecting the station-scale hydrological variables from large-scale atmospheric variables simulated by general circulation models (GCMs). This paper presents and compares different statistical downscaling models that use multiple linear regression (MLR), positive coefficient regression (PCR), stepwise regression (SR), and support vector machine (SVM) techniques for estimating monthly rainfall amounts in the state of Florida. Mean sea level pressure, air temperature, geopotential height, specific humidity, U wind, and V wind are used as the explanatory variables/predictors in the downscaling models. Data for these variables are obtained from the National Centers for Environmental Prediction-National Center for Atmospheric Research (NCEP-NCAR) reanalysis dataset and the Canadian Centre for Climate Modelling and Analysis (CCCma) Coupled Global Climate Model, version 3 (CGCM3) GCM simulations. The principal component analysis (PCA) and fuzzy c-means clustering method (FCM) are used as part of downscaling model to reduce the dimensionality of the dataset and identify the clusters in the data, respectively. Evaluation of the performances of the models using different error and statistical measures indicates that the SVM-based model performed better than all the other models in reproducing most monthly rainfall statistics at 18 sites. Output from the third-generation CGCM3 GCM for the A1B scenario was used for future projections. For the projection period 2001-10, MLR was used to relate variables at the GCM and NCEP grid scales. Use of MLR in linking the predictor variables at the GCM and NCEP grid scales yielded better reproduction of monthly rainfall statistics at most of the stations (12 out of 18) compared to those by spatial interpolation technique used in earlier studies.

Evaluating Methods to Predict Streamflow at Ungauged Sites using Regional Flow Duration Curves: A Case Study

Precise information on streamflows is of major importance for planning and monitoring of water resources schemes related to hydro power, water supply, irrigation, flood control, and for maintaining ecosystem. Engineers encounter challenges when streamflow data are either unavailable or inadequate at target locations. To address these challenges, there have been efforts to develop methodologies that facilitate prediction of streamflow at ungauged sites. Conventionally, time intensive and data exhaustive rainfall-runoff models are used to arrive at streamflow at ungauged sites. Most recent studies show improved methods based on regionalization using Flow Duration Curves (FDCs). A FDC is a graphical representation of streamflow variability, which is a plot between streamflow values and their corresponding exceedance probabilities that are determined using a plotting position formula. It provides information on the percentage of time any specified magnitude of streamflow is equaled or exceeded. The present study assesses the effectiveness of two methods to predict streamflow at ungauged sites by application to catchments in Mahanadi river basin, India. The methods considered are (i) Regional flow duration curve method, and (ii) Area Ratio method. The first method involves (a) the development of regression relationships between percentile flows and attributes of catchments in the study area, (b) use of the relationships to construct regional FDC for the ungauged site, and (c) use of a spatial interpolation technique to decode information in FDC to construct streamflow time series for the ungauged site. Area ratio method is conventionally used to transfer streamflow related information from gauged sites to ungauged sites. Attributes that have been considered for the analysis include variables representing hydrology, climatology, topography, land-use/land- cover and soil properties corresponding to catchments in the study area. Effectiveness of the presented methods is assessed using jack knife cross-validation. Conclusions based on the study are presented and discussed. (C) 2015 The Authors. Published by Elsevier B.V.

Inteligência computacional aplicada na geração de respostas impulsivas bi-auriculares e em aurilização de salas

Neste trabalho é apresentada uma nova abordagem para obter as respostas impulsivas biauriculares (BIRs) para um sistema de aurilização utilizando um conjunto de redes neurais artificiais (RNAs). O método proposto é capaz de reconstruir as respostas impulsivas associadas à cabeça humana (HRIRs) por meio de modificação espectral e de interpolação espacial. A fim de cobrir todo o espaço auditivo de recepção, sem aumentar a complexidade da arquitetura da rede, uma estrutura com múltiplas RNAs (conjunto) foi adotada, onde cada rede opera uma região específica do espaço (gomo). Os três principais fatores que influenciam na precisão do modelo arquitetura da rede, ângulos de abertura da área de recepção e atrasos das HRIRs são investigados e uma configuração ideal é apresentada. O erro de modelagem no domínio da frequência é investigado considerando a natureza logarítmica da audição humana. Mais ainda, são propostos novos parâmetros para avaliação do erro, definidos em analogia com alguns dos bem conhecidos parâmetros de qualidade acústica de salas. Através da metodologia proposta obteve-se um ganho computacional, em redução do tempo de processamento, de aproximadamente 62% em relação ao método tradicional de processamento de sinais utilizado para aurilização. A aplicabilidade do novo método em sistemas de aurilização é reforçada mediante uma análise comparativa dos resultados, que incluem a geração das BIRs e o cálculo dos parâmetros acústicos biauriculares (IACF e IACC), os quais mostram erros de magnitudes reduzidas.

土壤稳定入渗速率空间插值不确定性研究

以黄土丘陵沟壑第三副区的藉河流域为研究区,根据65个实测点数据,采用普通克里格法、反距离权重法、样条函数法等插值方法,分析了测点数量变化、栅格像元尺寸变化及插值方法的差异对土壤稳定入渗速率空间插值结果的影响,剖析了空间插值中的不确定性。结果表明:(1)参与插值站点越多,所得插值结果不确定性越小;(2)像元尺寸在25~800 m间变化对土壤稳定入渗速率的插值结果影响微弱;(3)不同插值方法对插值结果的精度影响较大,说明插值方法的差异对插值结果的不确定性有较大影响。

陕西省水蚀土壤因子指标插值方法比较研究

基于地统计学原理和半变异理论,采用地理信息系统技术定量研究了陕西省水蚀土壤因子指标的空间变异特征。分析比较了反距离权重法、样条函数法与普通克吕格法对陕西省土壤因子指标空间插值的精度。结果表明,研究区土壤抗冲系数具有中等强度的空间相关性,块金系数为32.29%,而稳渗速率、崩解速率、抗剪强度均表现为强烈的空间相关性,块金系数分别为13.19%,11.61%和12.98%。综合考虑平均相对误差、均方差及插值效果,认为普通克吕格法最好,更能反映土壤参数的空间特征并符合区域水土流失模型对数据的要求。对于普通克吕格法,稳渗速率的Lag步长为30 000 m,半方差理论模型为指数模型;抗冲系数、崩解速率、抗剪强度的Lag步长为55 000,半方差理论模型均为高斯模型。在空间分布上,各指标随土壤类型由北到南呈现明显的地带性规律。

区域水土流失研究中降雨量空间插值方法选取

在区域水土流失模型研究中,空间插值可提供每个计算栅格的气象要素资料。考虑到研究区域降雨与高程相关性很弱,不宜采用梯度距离反比法(GIDS),故采用距离反比法(IDW)和普通克里格法(Kriging),对延安示范区及其周围共50个站点2000—2003年的5—10月逐月降雨量进行插值。交叉验证结果表明:对2种插值方法,二者经过对数变换后平均相对误差(MRE)为8.30%和7.67%,分别比原始数据插值后的MRE下降了23.17%和23.50%,说明插值精度得到了提升,对研究区域某一年逐月降水的插值Kriging方法比IDW方法更加精确。

Fate Hazardous elements in soils surrounding a coal-fired power plant complex

Proceedings of the 13th International UFZ-Deltares Conference on Sustainable Use and Management of Soil, Sediment and Water Resources - 9–12 June 2015 • Copenhagen, Denmark

Evaluation of satellite-based and model re-analysis rainfall estimates for Uganda

The dependence of much of Africa on rain fed agriculture leads to a high vulnerability to fluctuations in rainfall amount. Hence, accurate monitoring of near-real time rainfall is particularly useful, for example in forewarning possible crop shortfalls in drought-prone areas. Unfortunately, ground based observations are often inadequate. Rainfall estimates from satellite-based algorithms and numerical model outputs can fill this data gap, however rigorous assessment of such estimates is required. In this case, three satellite based products (NOAA-RFE 2.0, GPCP-1DD and TAMSAT) and two numerical model outputs (ERA-40 and ERA-Interim) have been evaluated for Uganda in East Africa using a network of 27 rain gauges. The study focuses on the years 2001 to 2005 and considers the main rainy season (February to June). All data sets were converted to the same temporal and spatial scales. Kriging was used for the spatial interpolation of the gauge data. All three satellite products showed similar characteristics and had a high level of skill that exceeded both model outputs. ERA-Interim had a tendency to overestimate whilst ERA-40 consistently underestimated the Ugandan rainfall.

The climate of Europe 6000 years ago

Palaeoclimates across Europe for 6000 y BP were estimated from pollen data using the modern pollen analogue technique constrained with lake-level data. The constraint consists of restricting the set of modern pollen samples considered as analogues of the fossil samples to those locations where the implied change in annual precipitation minus evapotranspiration (P–E) is consistent with the regional change in moisture balance as indicated by lakes. An artificial neural network was used for the spatial interpolation of lake-level changes to the pollen sites, and for mapping palaeoclimate anomalies. The climate variables reconstructed were mean temperature of the coldest month (T c ), growing degree days above 5  °C (GDD), moisture availability expressed as the ratio of actual to equilibrium evapotranspiration (α), and P–E. The constraint improved the spatial coherency of the reconstructed palaeoclimate anomalies, especially for P–E. The reconstructions indicate clear spatial and seasonal patterns of Holocene climate change, which can provide a quantitative benchmark for the evaluation of palaeoclimate model simulations. Winter temperatures (T c ) were 1–3 K greater than present in the far N and NE of Europe, but 2–4 K less than present in the Mediterranean region. Summer warmth (GDD) was greater than present in NW Europe (by 400–800 K day at the highest elevations) and in the Alps, but >400 K day less than present at lower elevations in S Europe. P–E was 50–250 mm less than present in NW Europe and the Alps, but α was 10–15% greater than present in S Europe and P–E was 50–200 mm greater than present in S and E Europe.

Combining Satellite And Gauge Precipitation Data With Co-Kriging Method For Jakarta Region

Jakarta is vulnerable to flooding mainly caused by prolonged and heavy rainfall and thus a robust hydrological modeling is called for. A good quality of spatial precipitation data is therefore desired so that a good hydrological model could be achieved. Two types of rainfall sources are available: satellite and gauge station observations. At-site rainfall is considered to be a reliable and accurate source of rainfall. However, the limited number of stations makes the spatial interpolation not very much appealing. On the other hand, the gridded rainfall nowadays has high spatial resolution and improved accuracy, but still, relatively less accurate than its counterpart. To achieve a better precipitation data set, the study proposes cokriging method, a blending algorithm, to yield the blended satellite-gauge gridded rainfall at approximately 10-km resolution. The Global Satellite Mapping of Precipitation (GSMaP, 0.1⁰×0.1⁰) and daily rainfall observations from gauge stations are used. The blended product is compared with satellite data by cross-validation method. The newly-yield blended product is then utilized to re-calibrate the hydrological model. Several scenarios are simulated by the hydrological models calibrated by gauge observations alone and blended product. The performance of two calibrated hydrological models is then assessed and compared based on simulated and observed runoff.

Meteomap: Generation Of Meteorological Variables For Distributed Physically-Based Hydrological Modeling

Distributed energy and water balance models require time-series surfaces of the meteorological variables involved in hydrological processes. Most of the hydrological GIS-based models apply simple interpolation techniques to extrapolate the point scale values registered at weather stations at a watershed scale. In mountainous areas, where the monitoring network ineffectively covers the complex terrain heterogeneity, simple geostatistical methods for spatial interpolation are not always representative enough, and algorithms that explicitly or implicitly account for the features creating strong local gradients in the meteorological variables must be applied. Originally developed as a meteorological pre-processing tool for a complete hydrological model (WiMMed), MeteoMap has become an independent software. The individual interpolation algorithms used to approximate the spatial distribution of each meteorological variable were carefully selected taking into account both, the specific variable being mapped, and the common lack of input data from Mediterranean mountainous areas. They include corrections with height for both rainfall and temperature (Herrero et al., 2007), and topographic corrections for solar radiation (Aguilar et al., 2010). MeteoMap is a GIS-based freeware upon registration. Input data include weather station records and topographic data and the output consists of tables and maps of the meteorological variables at hourly, daily, predefined rainfall event duration or annual scales. It offers its own pre and post-processing tools, including video outlook, map printing and the possibility of exporting the maps to images or ASCII ArcGIS formats. This study presents the friendly user interface of the software and shows some case studies with applications to hydrological modeling.

Dengue: padrões ambientais, conhecimento da população e cenários potenciais à transmissão em duas regiões de Jaboticabal, SP

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

As chuvas no Paraná: Variabilidade, teleconexões, e impactos de eventos extremos

Pós-graduação em Geografia - FCT