排序方式： 共有39条查询结果，搜索用时 31 毫秒
21.
Harald Kling 《水文科学杂志》2015,60(7-8):1374-1393
AbstractThis study is a contribution to a model intercomparison experiment initiated during a workshop at the 2013 IAHS conference in Göteborg, Sweden. We present discharge simulations with the conceptual precipitation–runoff model COSERO in 11 basins located under different climates in Europe, Africa and Australia. All of the basins exhibit some form of non-stationary conditions, due, for example, to warming, droughts or land-cover change. The evaluation of the daily discharge simulations focuses on the overall model performance and its decomposition into three components measuring temporal dynamics, mean flow volume and distribution of flows. Calibration performance is similarly high as in previous COSERO applications. However, when looking at evaluation periods independent of the calibration, the model performance drops considerably, mainly due to severely biased discharge simulations in semi-arid basins with strong non-stationarity in rainfall. Simulations are more robust in European basins with humid climates. This highlights the fact that hydrological models frequently fail when simulations are required outside of calibration conditions in basins with non-stationary conditions. As a consequence, calibration periods should be sufficiently long to include both wet and dry periods, which should yield more robust predictions. 相似文献
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Rene Westerholt Bernd Resch Franz-Benjamin Mocnik Dirk Hoffmeister 《International journal of geographical information science》2018,32(3):571-600
Spatial variance is an important characteristic of spatial random variables. It describes local deviations from average global conditions and is thus a proxy for spatial heterogeneity. Investigating instability in spatial variance is a useful way of detecting spatial boundaries, analysing the internal structure of spatial clusters and revealing simultaneously acting geographic phenomena. Recently, a corresponding test statistic called ‘Local Spatial Heteroscedasticity’ (LOSH) has been proposed. This test allows locally heterogeneous regions to be mapped and investigated by comparing them with the global average mean deviation in a data set. While this test is useful in stationary conditions, its value is limited in a global heterogeneous state. There is a risk that local structures might be overlooked and wrong inferences drawn. In this paper, we introduce a test that takes account of global spatial heterogeneity in assessing local spatial effects. The proposed measure, which we call ‘Local Spatial Dispersion’ (LSD), adapts LOSH to local conditions by omitting global information beyond the range of the local neighbourhood and by keeping the related inferential procedure at a local level. Thereby, the local neighbourhoods might be small and cause small-sample issues. In the view of this, we recommend an empirical Bayesian technique to increase the data that is available for resampling by employing empirical prior knowledge. The usefulness of this approach is demonstrated by applying it to a Light Detection and Ranging-derived data set with height differences and by making a comparison with LOSH. Our results show that LSD is uncorrelated with non-spatial variance as well as local spatial autocorrelation. It thus discloses patterns that would be missed by LOSH or indicators of spatial autocorrelation. Furthermore, the empirical outcomes suggest that interpreting LOSH and LSD together is of greater value than interpreting each of the measures individually. In the given example, local interactions can be statistically detected between variance and spatial patterns in the presence of global structuring, and thus reveal details that might otherwise be overlooked. 相似文献
23.
Zhenhong Du Sensen Wu Mei-Po Kwan Chuanrong Zhang Feng Zhang Renyi Liu 《International journal of geographical information science》2018,32(10):1927-1947
Spatiotemporal kriging (STK) is recognized as a fundamental space-time prediction method in geo-statistics. Spatiotemporal regression kriging (STRK), which combines space-time regression with STK of the regression residuals, is widely used in various fields, due to its ability to take into account both the external covariate information and spatiotemporal autocorrelation in the sample data. To handle the spatiotemporal non-stationary relationship in the trend component of STRK, this paper extends conventional STRK to incorporate it with an improved geographically and temporally weighted regression (I-GTWR) model. A new geo-statistical model, named geographically and temporally weighted regression spatiotemporal kriging (GTWR-STK), is proposed based on the decomposition of deterministic trend and stochastic residual components. To assess the efficacy of our method, a case study of chlorophyll-a (Chl-a) prediction in the coastal areas of Zhejiang, China, for the years 2002 to 2015 was carried out. The results show that the presented method generated reliable results that outperform the GTWR, geographically and temporally weighted regression kriging (GTWR-K) and spatiotemporal ordinary kriging (STOK) models. In addition, employing the optimal spatiotemporal distance obtained by I-GTWR calibration to fit the spatiotemporal variograms of residual mapping is confirmed to be feasible, and it considerably simplifies the residual estimation of STK interpolation. 相似文献
24.
飓风等自然灾害的发生会对沿海地区造成巨大的社会、经济损失,因此有必要合理评估这些区域的建筑在飓风作用下的灾害。已有研究指出,全球气候变暖会影响未来飓风的强度和发生频率。本文考虑飓风发生（随机）过程的非平稳性,提出了沿海地区飓风灾害评估的新方法。用非齐次泊松过程来描述飓风的发生,并用时变的统计参数（均值、标准差）来反映飓风强度的变化。在此基础上,给出了累积飓风灾害的均值、方差的显式公式。选取美国佛罗里达州迈阿密县进行案例分析,研究了飓风过程非平稳性对累积灾害的影响。 相似文献
25.
A. Porporato L. Ridolfi 《Stochastic Environmental Research and Risk Assessment (SERRA)》1998,12(1):1-14
Studying the hypothetical case of a trend superimposed on a random stationary variable, we highlight the strong influence
of possible non-stationarities on exceedance probability. After a general outline, the subject is analytically developed using
the Gumbel distribution, emphasizing the quick increase of the exceedance probability over time in the presence of weak rising
trends, and its sensitive underestimation where the non-stationarity goes unnoticed or is considered negligible. Finally the
work is applied to hydrological series of rainfall and river flow.
Received: March 27, 1997 相似文献
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This study analyzed the individual and joint influences of social, urban, and physical drivers on patterns of county-scale municipal water consumption (MWC) the for the state of Texas using a cross-sectional research design on three distinct temporal slices (1990, 2000, and 2010). Global multiple linear regression models and measures of global and local spatial association were combined to determine which drivers significantly influenced county-scale per capita MWC, whether or not the statistically significant drivers varied over time, and to assess the degree to which the patterns and drivers of MWC exhibited spatial stationarity. Overall results suggested the social, urbanized, and physical environments contributed significantly to the patterns of per capita MWC to varying degrees in each year. The social and urbanized environments consistently exerted the strongest influences on per capita MWC, while the physical environment was generally less important. The social environment had the greatest cumulative influence in all three years, and the urbanized environment singly accounted for the majority of the variation in per capita MWC when the joint influences of the other significant drivers were considered. Spatial analysis of MWC patterns and drivers suggested that they both exhibited weak to moderate degrees of spatial non-stationarity in each year, as well as that MWC patterns and drivers may be sensitive to regional and climatic boundaries. Identification of temporally consistent MWC drivers merged with longitudinal and cross-sectional research designs can improve water management strategies by offering managers greater insight into the relationships between landscape change and water consumption patterns. 相似文献
28.
考虑水文趋势影响的珠江流域非一致性洪水风险分析 总被引：3，自引：0，他引：3
本文基于两参数对数正态分布和指数趋势模型建立了非一致性洪水频率计算模型,分析了珠江流域28个测站年最大日流量序列趋势性对洪水频率分析的影响。结果表明：（1）从空间分布来看,珠江流域中北部年最大日流量序列呈增加趋势,东部和南部年最大日流量序列呈减小趋势;从时间变化来看,1981-2010年年最大日流量呈显著性增加趋势的站点数较多,占总站点数的20-25%,1966-1990年年最大日流量呈显著性减小趋势的站点数较多,占总站数的25-30%;（2）珠江流域洪水放大因子(未来T年一遇设计流量与现在T年一遇设计流量比值)和重现期都受到趋势性的显著影响。西江中北部和北江洪水放大因子大于1,意味着原有的防洪工程设计标准可能无法满足未来防洪需求,存在防洪隐患;洪水放大因子较大和较小的地区集中在西江干流和东江干流;（3）非一致性条件下,同一场洪水过去、现在和未来重现期是不同的。非平稳性条件下,珠江流域近20年来20-50年一遇洪水发生站次相比平稳性条件下在减小。 相似文献
29.
Paul E. Todhunter 《水文科学杂志》2018,63(9):1275-1291
Devils Lake, a terminal lake in eastern North Dakota, rose more than 9 m between 1992 and 2013, producing a 286% increase in lake area, and causing more than US$1 billion in direct damages. An annual volumetric lake water budget is developed from monthly hydroclimatological variables for the period 1951–2010 to investigate the rapid lake expansion. The lake is an amplifier terminal lake in which long-term climatic changes are amplified by positive feedback mechanisms, causing the lake to transition from a precipitation-dominated to a runoff-dominated water budget. Factors specific to the Devils Lake Basin further amplify this positive feedback relationship. These include principles of fill–spill hydrology that operate between individual sub-basins within the closed basin, and between the innumerable wetland complexes within each sub-basin. These factors create a pronounced non-stationary precipitation–runoff relationship in the basin during both long-term wetting and drying phases. 相似文献
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AbstractIn physically-based land surface models, the parameters can all be prescribed a priori but calibration can be used to enhance the realism of the simulations in well instrumented domains. In such a case, the transferability of calibrated parameters under non-stationary conditions needs to be addressed, especially in the context of climate change. To this end, we used the Catchment Land Surface Model (CLSM) in the Upper Durance watershed located in the French Alps, which experienced a significant increase in temperature over the last century. The CLSM is forced by a 50-year meteorological dataset of good quality. Four parameters of the CLSM (one related to snow processes and three to soil properties) are calibrated against discharge observations with a multi-objective algorithm. First, the robustness of the CLSM parameterizations is tested by the Differential Split Sample Test (DSST). The simulations show good performances over a wide range of retrospective climatic conditions, except when the parameters are calibrated over a period with a large contribution of snowmelt to annual mean discharge. Then, the use of a climate change scenario reveals that the parameterizations of soil moisture processes in the CLSM are responsible for an increasing dispersion among simulations when facing dry and warm conditions. However, the differences between the simulated changes of river discharge remain very small. This work shows that calibration conveys some uncertainties, but they are moderate in the studied case, and pertain to the most conceptual parameterizations of this physically-based model. 相似文献