Kei Ishida

Physically Based Estimation of Maximum Precipitation over Three Watersheds in Northern California: Atmospheric Boundary Condition Shifting

AbstractMaximum precipitation during a historical period is estimated by means of a physically based regional atmospheric model over three watersheds in Northern California: the American River watershed (ARW), the Yuba River watershed (YRW), and the Upper Feather River watershed (UFRW). In Northern California, severe storm events are mostly caused by a high-moisture atmospheric flow from the Pacific Ocean, referred to as atmospheric river (AR). Therefore, a method to maximize the contribution of an AR on precipitation over each of the targeted watersheds is proposed. The method shifts the atmospheric boundary conditions of the regional atmospheric model in space with latitude and longitude so that the AR strikes each of the targeted watersheds in an optimal direction and location to maximize the precipitation over these watersheds. For this purpose, the fifth generation Penn State/National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) is used as the regional atmospheric model, and the NCAR/...

New Methodology to Develop Future Flood Frequency under Changing Climate by Means of Physically Based Numerical Atmospheric-Hydrologic Modeling

AbstractEffect of climate change on hydrologic flow regimes, particularly extreme events, necessitates modeling of future flows in order to best inform water resources management. This study simulated future flows in the Cache Creek watershed in California over the 21st century using a hydro-climate model (Watershed Environmental Hydrology Hydro-Climate Model; WEHY-HCM) forced by future climate projections. The future climate projections, based on four emission scenarios simulated by two global climate models (GCMs), the fifth-generation atmospheric global climate model and third-generation community climate model (ECHAM5 and CCSM3), under several initial conditions, were dynamically-downscaled using the fifth-generation mesoscale atmospheric model (MM5), a regional climate model. The downscaled future precipitation data were bias-corrected before being input into the WEHY model to simulate the detailed flow at hourly intervals along the main Cache Creek branch and its tributaries during 2010–2099. The re...

Assessment of 21st century drought conditions at Shasta Dam based on dynamically projected water supply conditions by a regional climate model coupled with a physically-based hydrology model

Along with socioeconomic developments, and population increase, natural disasters around the world have recently increased the awareness of harmful impacts they cause. Among natural disasters, drought is of great interest to scientists due to the extraordinary diversity of their severity and duration. Motivated by the development of a potential approach to investigate future possible droughts in a probabilistic framework based on climate change projections, a methodology to consider thirteen future climate projections based on four emission scenarios to characterize droughts is presented. The proposed approach uses a regional climate model coupled with a physically-based hydrology model (Watershed Environmental Hydrology Hydro-Climate Model; WEHY-HCM) to generate thirteen equally likely future water supply projections. The water supply projections were compared to the current water demand for the detection of drought events and estimation of drought properties. The procedure was applied to Shasta Dam watershed to analyze drought conditions at the watershed outlet, Shasta Dam. The results suggest an increasing water scarcity at Shasta Dam with more severe and longer future drought events in some future scenarios. An important advantage of the proposed approach to the probabilistic analysis of future droughts is that it provides the drought properties of the 100-year and 200-year return periods without resorting to any extrapolation of the frequency curve.

Climate change analysis on historical watershed‐scale precipitation by means of long‐term dynamical downscaling

A methodology based on long-term dynamical downscaling to analyse climate change effects on watershed-scale precipitation during a historical period is proposed in this study. The reliability and applicability of the methodology were investigated based on the long-term dynamical downscaling results. For an application of the proposed methodology, two study watersheds in Northern California were selected: the Upper Feather River watershed and the Yuba River watershed. Then, precipitation was reconstructed at 3-km spatial resolution and hourly intervals over the study watersheds for 141 water years from 1 October 1871 to 30 September 2012 by dynamically downscaling a long-term atmospheric reanalysis dataset, 20th century global reanalysis version 2 by means of a regional climate model. The reconstructed precipitation was compared against observed precipitation, in order to assess the applicability of the proposed methodology for the reconstruction of watershed-scale precipitation and to validate this methodology. The validation shows that the reconstructed precipitation is in good agreement with observation data. Moreover, the differences between the reconstructed precipitation and the corresponding observations do not significantly change through the historical period. After the validation, climate change analysis was conducted based on the reconstructed precipitation. Through this analysis, it was found that basin-average precipitation has increased significantly over both of the study watersheds during the historical period. An upward trend in monthly basin-average precipitation is not significant in wet months except February while it is significant in dry months of the year. Furthermore, peak values of basin-average precipitation are also on an upward trend over the study watersheds. The upward trend in peak basin-average precipitation is more significant during a shorter duration. Copyright

Trend analysis of watershed-scale precipitation over Northern California by means of dynamically-downscaled CMIP5 future climate projections

The impacts of climate change on watershed-scale precipitation through the 21st century were investigated over eight study watersheds in Northern California based on dynamically downscaled CMIP5 future climate projections from three GCMs (CCSM4, HadGEM2-ES, and MIROC5) under the RCP4.5 and RCP8.5 future climate scenarios. After evaluating the modeling capability of the WRF model, the six future climate projections were dynamically downscaled by means of the WRF model over Northern California at 9km grid resolution and hourly temporal resolution during a 94-year period (2006-2100). The biases in the model simulations were corrected, and basin-average precipitation over the eight study watersheds was calculated from the dynamically downscaled precipitation data. Based on the dynamically downscaled basin-average precipitation, trends in annual depth and annual peaks of basin-average precipitation during the 21st century were analyzed over the eight study watersheds. The analyses in this study indicate that there may be differences between trends of annual depths and annual peaks of watershed-scale precipitation during the 21st century. Furthermore, trends in watershed-scale precipitation under future climate conditions may be different for different watersheds depending on their location and topography even if they are in the same region.

Long-term trend analysis on total and extreme precipitation over Shasta Dam watershed

Californias interconnected water system is one of the most advanced water management systems in the world, and understanding of long-term trends in atmospheric and hydrologic behavior has increasingly being seen as vital to its future well-being. Knowledge of such trends is hampered by the lack of long-period observation data and the uncertainty surrounding future projections of atmospheric models. This study examines historical precipitation trends over the Shasta Dam watershed (SDW), which lies upstream of one of the most important components of Californias water system, Shasta Dam, using a dynamical downscaling methodology that can produce atmospheric data at fine time-space scales. The Weather Research and Forecasting (WRF) model is employed to reconstruct 159years of long-term hourly precipitation data at 3km spatial resolution over SDW using the 20th Century Reanalysis Version 2c dataset. Trend analysis on this data indicates a significant increase in total precipitation as well as a growing intensity of extreme events such as 1, 6, 12, 24, 48, and 72-hour storms over the period of 1851 to 2010. The turning point of the increasing trend and no significant trend periods is found to be 1940 for annual precipitation and the period of 1950 to 1960 for extreme precipitation using the sequential Mann-Kendall test. Based on these analysis, we find the trends at the regional scale do not necessarily apply to the watershed-scale. The sharp increase in the variability of annual precipitation since 1970s is also detected, which implies an increase in the occurrence of extreme wet and dry conditions. These results inform long-term planning decisions regarding the future of Shasta Dam and Californias water system.

Projected 21st century climate change on snow conditions over Shasta Dam watershed by means of dynamical downscaling

Abstract Snow is an important component of the Earths climate system and is particularly vulnerable to global warming. It has been suggested that warmer temperatures may cause significant declines in snow water content and snow cover duration. In this study, snowfall and snowmelt were projected by means of a regional climate model that was coupled to a physically based snow model over Shasta Dam watershed to assess changes in snow water content and snow cover duration during the 21st century. This physically based snow model requires both physical data and future climate projections. These physical data include topography, soils, vegetation, and land use/land cover, which were collected from associated organizations. The future climate projections were dynamically downscaled by means of the regional climate model under 4 emission scenarios simulated by 2 general circulation models (fifth‐generation of the ECHAM general circulation model and the third‐generation atmospheric general circulation model). The downscaled future projections were bias corrected before projecting snowfall and snowmelt processes over Shasta Dam watershed during 2010–2099. This studys results agree with those of previous studies that projected snow water equivalent is decreasing by 50–80% whereas the fraction of precipitation falling as snowfall is decreasing by 15% to 20%. The obtained projection results show that future snow water content will change in both time and space. Furthermore, the results confirm that physical data such as topography, land cover, and atmospheric–hydrologic data are instrumental in the studies on the impact of climate change on the water resources of a region.

Role of Snowmelt in Determining whether the Maximum Precipitation Always Results in the Maximum Flood

AbstractIn snow-dominated regions surface air temperature is expected to have a substantial effect on the magnitude of a flood during a storm event. It is risky to estimate the design flood based only on the maximum precipitation while excluding other atmospheric variables like temperature and radiation. To overcome this problem, a methodology to estimate the maximum flood is proposed based on a physically based hydrologic model with input from physically maximized storm events by means of a numerical atmospheric model. As a case study, the probable maximum floods are simulated for the Upper Feather River watershed, the Yuba River watershed, and the American River watershed that are located in a mountainous region in Northern California, from the most severe 60 historical precipitation events during 1951–2010 for each watershed. The results show that this methodology can explain the underlying physical causes for the occurrence of maximum precipitation. It also shows that the maximum precipitation, determ...

Characterization of Extreme Storm Events Using a Numerical Model–Based Precipitation Maximization Procedure in the Feather, Yuba, and American River Watersheds in California

AbstractImprovements on nonhydrostatic atmospheric models such as MM5 in the last few decades have enhanced our understanding of the precipitation mechanism affected by topography and nonlinear dynamics of the atmosphere. This study addresses the use of such a regional atmospheric model to estimate physical maximum precipitation rates for the next generation of flood management strategies under evolving climate conditions. First, 48 significant historical storm events were selected based on the continuous reconstructed precipitation conditions on the Feather, Yuba, and American River watersheds in California. Then, the boundary conditions of the numerical atmospheric model were modified with the fully saturated atmospheric layers (100% relative humidity) to generate the atmospheric conditions that maximize the precipitation over the three watersheds. Surprisingly, maximizing the atmospheric moisture supply at the model boundary does not always increase the precipitation in the watersheds of interest. A ra...

Assessing the impacts of future climate change on the hydroclimatology of the Gediz Basin in Turkey by using dynamically downscaled CMIP5 projections

The Gediz Basin is a Mediterranean watershed along the Aegean coast of Turkey, in which the most important economic activity is agriculture. Over the last few decades, this basin has been experiencing water-related problems such as water scarcity and competing use of water. This study assesses the impact of future climate change on the availability of water resources in the Gediz Basin during the 21st century by investigating the inflows into the major reservoir in the basin, Demirkopru Reservoir, which is the major source of irrigation water to the basin. The analysis in this study involves setting up a coupled hydro-climate model over the Gediz Basin by coupling the Weather Research and Forecasting (WRF) model to the physically-based Watershed Environmental Hydrology (WEHY) model. First, the WRF model is used to reconstruct the historical climatic variables over the basin by dynamically downscaling the ERA-Interim reanalysis dataset. The calibrated and validated WRF model is then used to dynamically downscale eight different future climate projections over the Gediz Basin to a much finer resolution (6 km), which is more appropriate for the hydrologic modeling of the basin. These climate projections are from four Coupled Model Intercomparison Project Phase 5 (CMIP5) Global Climate Models (GCMs), namely, CCSM4, GFDL-ESM2M, HadGEM2-ES, and MIROC5, under two IPCC (The Intergovernmental Panel on Climate Change) representative concentration pathway scenarios (RCP4.5 and RCP8.5). The outputs from the WRF model are then input into the WEHY model, which is calibrated and validated over the basin, to simulate the hydrological processes within the basin and to obtain the projected future inflows into the Demirkopru Reservoir. Results of the future analysis over the 21st century (2017-2100) are then compared to the historical values (1985-2012) to investigate the impacts of future climate change on the hydroclimatology of the Gediz Basin.