Simulation of daily rainfall scenarios with interannual and multidecadal climate cycles for South Florida
Simulation of daily rainfall scenarios with interannual and multidecadal climate cycles for South Florida
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南佛罗里达州年际和数十年气候循环的每日降雨情景模拟
DOI：
10.1007/s00477-008-0270-2
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发表时间：
2009
影响因子：
4.2
通讯作者：
J. Obeysekera
中科院分区：
文献类型：
--
作者：
H. Kwon;Upmanu Lall;J. Obeysekera
对人为气候变化的潜在影响的关注导致了对气候如何在长期内变化的更密切的研究，以及这种变化如何影响每日到季节性时间尺度上的降雨变化。特别是对南佛罗里达来说，低频气候现象，如厄尔尼诺-南方涛动（ENSO）和大西洋多年代际涛动（AMO）的影响已被确定与年或季节总降雨量变化有关。由于这些变化的综合影响表现为降雨量的持续多年变化，因此在时间和空间尺度上对这些变化进行建模的问题已经出现，以便与南佛罗里达水管理区（SFWMD）使用的每日时间步长驱动的水文模型相结合。为了解决这一问题，本文提出并说明了在多个雨量计位置对低频率和高频现象进行分层建模的一般方法。基本策略是使用区域气候的长期代用物，首先开发区域气候的随机情景，其中包括驱动区域降雨过程的低频变化，然后使用这些指标来调节所有考虑的雨量计的日降雨量并行模拟。在确定合适的区域降雨气候代用物后，第一步使用一种新开发的方法，称为小波自回归模型（WARM）。这些代用物通常有一个世纪到四个世纪的可用数据，因此可以更可靠地确定感兴趣的长期准周期气候模式。利用非齐次隐马尔可夫模型（NHMM），利用与该地区季节性降雨的相关性分析来确定作为后续日降雨属性调节候选的特定代理。5 - 6 - 7月（MJJ）赛季说明了这种组合策略。本研究详细介绍了MJJ赛季的建模方法和结果。
Concerns about the potential effects of anthropogenic climate change have led to a closer examination of how climate varies in the long run, and how such variations may impact rainfall variations at daily to seasonal time scales. For South Florida in particular, the influences of the low-frequency climate phenomena, such as the El Nino Southern Oscillation (ENSO) and the Atlantic Multi-decadal Oscillation (AMO), have been identified with aggregate annual or seasonal rainfall variations. Since the combined effect of these variations is manifest as persistent multi-year variations in rainfall, the question of modeling these variations at the time and space scales relevant for use with the daily time step-driven hydrologic models in use by the South Florida Water Management District (SFWMD) has arisen. To address this problem, a general methodology for the hierarchical modeling of low- and high-frequency phenomenon at multiple rain gauge locations is developed and illustrated. The essential strategy is to use long-term proxies for regional climate to first develop stochastic scenarios for regional climate that include the low-frequency variations driving the regional rainfall process, and then to use these indicators to condition the concurrent simulation of daily rainfall at all rain gauges under consideration. A newly developed methodology, called Wavelet Autoregressive Modeling (WARM), is used in the first step after suitable climate proxies for regional rainfall are identified. These proxies typically have data available for a century to four centuries so that long-term quasi-periodic climate modes of interest can be identified more reliably. Correlation analyses with seasonal rainfall in the region are used to identify the specific proxies considered as candidates for subsequent conditioning of daily rainfall attributes using a Non-homogeneous hidden Markov model (NHMM). The combined strategy is illustrated for the May–June–July (MJJ) season. The details of the modeling methods and results for the MJJ season are presented in this study.