Global S&T Development Trend Analysis Platform of Resources and Environment
DOI | 10.1002/2017WR020876 |
Simulating Small-Scale Rainfall Fields Conditioned by Weather State and Elevation: A Data-Driven Approach Based on Rainfall Radar Images | |
Oriani, Fabio1,2; Ohana-Levi, Noa3; Marra, Francesco4; Straubhaar, Julien1; Mariethoz, Gregoire5; Renard, Philippe1; Karnieli, Amon3; Morin, Efrat4 | |
2017-10-01 | |
发表期刊 | WATER RESOURCES RESEARCH
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ISSN | 0043-1397 |
EISSN | 1944-7973 |
出版年 | 2017 |
卷号 | 53期号:10 |
文章类型 | Article |
语种 | 英语 |
国家 | Switzerland; Denmark; Israel |
英文摘要 | The quantification of spatial rainfall is critical for distributed hydrological modeling. Rainfall spatial patterns generated by similar weather conditions can be extremely diverse. This variability can have a significant impact on hydrological processes. Stochastic simulation allows generating multiple realizations of spatial rainfall or filling missing data. The simulated data can then be used as input for numerical models to study the uncertainty on hydrological forecasts. In this paper, we use the direct sampling technique to generate stochastic simulations of high-resolution (1 km) daily rainfall fields, conditioned by elevation and weather state. The technique associates historical radar estimates to variables describing the daily weather conditions, such as the rainfall type and mean intensity, and selects radar images accordingly to form a conditional training image set of each day. Rainfall fields are then generated by resampling pixels from these images. The simulation at each location is conditioned by neighbor patterns of rainfall amount and elevation. The technique is tested on the simulation of daily rainfall amount for the eastern Mediterranean. The results show that it can generate realistic rainfall fields for different weather types, preserving the temporal weather pattern, the spatial features, and the complex relation with elevation. The concept of conditional training image provides added value to multiple-point simulation techniques dealing with extremely non stationary heterogeneities and extensive data sets. |
领域 | 资源环境 |
收录类别 | SCI-E |
WOS记录号 | WOS:000418736000023 |
WOS关键词 | DAILY PRECIPITATION ; SPATIAL VARIABILITY ; STOCHASTIC-MODEL ; GENERATOR ; SPACE ; CATCHMENT ; BASIN ; IDENTIFICATION ; TEMPERATURE ; SCENARIOS |
WOS类目 | Environmental Sciences ; Limnology ; Water Resources |
WOS研究方向 | Environmental Sciences & Ecology ; Marine & Freshwater Biology ; Water Resources |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://119.78.100.173/C666/handle/2XK7JSWQ/21180 |
专题 | 资源环境科学 |
作者单位 | 1.Univ Neuchtel, Ctr Hydrogeol & Geotherm, Neuchatel, Switzerland; 2.Geol Survey Denmark & Greenland, Dept Hydrol, Copenhagen, Denmark; 3.Ben Gurion Univ Negev, Jacob Blaustein Inst Desert Res, Beer Sheva, Israel; 4.Hebrew Univ Jerusalem, Inst Earth Sci, Jerusalem, Israel; 5.Univ Lausanne, Inst Earth Surface Dynam, Lausanne, Switzerland |
推荐引用方式 GB/T 7714 | Oriani, Fabio,Ohana-Levi, Noa,Marra, Francesco,et al. Simulating Small-Scale Rainfall Fields Conditioned by Weather State and Elevation: A Data-Driven Approach Based on Rainfall Radar Images[J]. WATER RESOURCES RESEARCH,2017,53(10). |
APA | Oriani, Fabio.,Ohana-Levi, Noa.,Marra, Francesco.,Straubhaar, Julien.,Mariethoz, Gregoire.,...&Morin, Efrat.(2017).Simulating Small-Scale Rainfall Fields Conditioned by Weather State and Elevation: A Data-Driven Approach Based on Rainfall Radar Images.WATER RESOURCES RESEARCH,53(10). |
MLA | Oriani, Fabio,et al."Simulating Small-Scale Rainfall Fields Conditioned by Weather State and Elevation: A Data-Driven Approach Based on Rainfall Radar Images".WATER RESOURCES RESEARCH 53.10(2017). |
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