New projections of 21st century climate and hydrology for Alaska and Hawaiʻi
New projections of 21st century climate and hydrology for Alaska and Hawaiʻi
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阿拉斯加和夏威夷 21 世纪气候和水文的新预测
DOI：
10.1016/j.cliser.2022.100312
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发表时间：
2022
期刊：
影响因子：
3.2
通讯作者：
Clark, Martyn P.
中科院分区：
文献类型：
--
作者：
Mizukami, Naoki;Newman, Andrew J.;Littell, Jeremy S.;Giambelluca, Thomas W.;Wood, Andrew W.;Gutmann, Ethan D.;Hamman, Joseph J.;Gergel, Diana R.;Nijssen, Bart;Clark, Martyn P.
在美国，为水资源规划开发了高分辨率的、长达一个世纪的水文气候预测数据集，重点是美国（CONUS）地区。然而，很少有阿拉斯加和夏威夷的全州水文气候预测数据集。有限的水文气候变化信息促使开发1950 - 2099年的水文情景，使用由多个统计上缩小尺度的气候预估组成的气候-水文影响建模链作为输入，用于两国的水文模式模拟。我们采用了类似于之前CONUS水文评估的方法，其中：1)我们选择来自耦合模式比对项目第5阶段的十个全球气候模式（GCM）的输出，具有代表性的浓度路径4.5和8.5；2)我们执行统计降尺度来生成水文模型的气候输入数据（阿拉斯加的栅格间距为12 km，夏威夷的栅格间距为1 km）；3)进行了基于过程的水文模型模拟。对于阿拉斯加，我们采用全水能平衡计算、冻土和一个简单的冰川模型，改进了CONUS的水文模型配置。模拟表明，强劲的变暖和降水的增加使阿拉斯加大部分地区的径流量增加，而阿拉斯加东南部目前冰川覆盖地区的径流量减少。对于夏威夷，我们制作了高分辨率（1公里）的预估，突出了该州气候变量的高空间变异性，并且在gcm上的大量降水驱动了径流在gcm上的大量分布。我们的新集合数据集有助于全州气候适应和其他水规划。
In the United States, high-resolution, century-long, hydroclimate projection datasets have been developed for water resources planning, focusing on the contiguous United States (CONUS) domain. However, there are few statewide hydroclimate projection datasets available for Alaska and Hawaiʻi. The limited information on hydroclimatic change motivates developing hydrologic scenarios from 1950 to 2099 using climate-hydrology impact modeling chains consisting of multiple statistically downscaled climate projections as input to hydrologic model simulations for both states. We adopt an approach similar to the previous CONUS hydrologic assessments where: 1) we select the outputs from ten global climate models (GCM) from the Coupled Model Intercomparison Project Phase 5 with Representative Concentration Pathways 4.5 and 8.5; 2) we perform statistical downscaling to generate climate input data for hydrologic models (12-km grid-spacing for Alaska and 1-km for Hawaiʻi); and 3) we perform process-based hydrologic model simulations. For Alaska, we have advanced the hydrologic model configuration from CONUS by using the full water-energy balance computation, frozen soils and a simple glacier model. The simulations show that robust warming and increases in precipitation produce runoff increases for most of Alaska, with runoff reductions in the currently glacierized areas in Southeast Alaska. For Hawaiʻi, we produce the projections at high resolution (1 km) which highlight high spatial variability of climate variables across the state, and a large spread of runoff across the GCMs is driven by a large precipitation spread across the GCMs. Our new ensemble datasets assist with state-wide climate adaptation and other water planning.
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DOI：
10.1016/j.envsoft.2018.03.021
发表时间：
2018-12
期刊：
Environ. Model. Softw.
影响因子：
--
作者：
J. Walsh;U. Bhatt;Jeremy S. Littell;M. Leonawicz;M. Lindgren;T. Kurkowski;P. Bieniek;R. Thoman;S. Gray;T. Scott Rupp
通讯作者：
J. Walsh;U. Bhatt;Jeremy S. Littell;M. Leonawicz;M. Lindgren;T. Kurkowski;P. Bieniek;R. Thoman;S. Gray;T. Scott Rupp
影响因子：
4.9
作者：
Chunxi Zhang;Yuqing Wang;K. Hamilton;A. Lauer
通讯作者：
Chunxi Zhang;Yuqing Wang;K. Hamilton;A. Lauer
应对生态系统转型：抵制、接受还是直接？
影响因子：
2.8
作者：
L. Thompson;A. Lynch;E. Beever;A. Engman;Jeffrey A. Falke;S. T. Jackson;Trevor J. Krabbenhoft;D. J. Lawrence;Douglas Limpinsel;R. Magill;Tracy A. Melvin;J. Morton;R. Newman;J. Peterson;Mark T. Porath;F. Rahel;S. Sethi;Jennifer L. Wilkening
通讯作者：
Jennifer L. Wilkening
对大规模大气环流和区域水文气候的新兴约束：它们在 CMIP6 中是否仍然有效？它们实际上能在多大程度上限制未来？
影响因子：
4.9
作者：
I. Simpson;K. McKinnon;F. V. Davenport;M. Tingley;F. Lehner;A. A. Fahad;Di Chen
通讯作者：
Di Chen
影响因子：
3.1
作者：
S. Kao;M. Ashfaq;B. Naz;Rocio Uria Martinez;D. Rastogi;R. Mei;Y. Jager;N. Samu;M. Salé
通讯作者：
M. Salé