Effect of uncertainty in temperature and precipitation inputs and spatial resolution on the crop model

Tatsumi, Kenichi; Yamashiki, Yosuke; Takara, Kaoru

doi:10.3178/hrl.5.52

Cited by 8 publications

(9 citation statements)

References 21 publications

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“…Many regional climate change studies across the world have used hydrologic models with climate projections to simulate the responses of river basins to climate change (e.g., Evans and Schreider, 2002;Milly et al, 2005;Nohara et al, 2006;IPCC, 2007;Kiem et al, 2008;Nakaegawa and Vergara, 2010;Kim et al, 2010;Ma et al, 2010;Tatsumi et al, 2011;Nakaegawa and Nakakita, 2012;Yamashiki et al, 2012;Hunukumbura and Tachikawa, 2012). For example, Milly et al (2005) and Nohara et al (2006) projected global river discharge simulated by a multimodel ensemble of atmospheric general circulation models (AGCMs) under the Special Report on Emissions Scenarios (SRES) A1B scenario.…”

Section: Introductionmentioning

confidence: 99%

Hydrological impact of regional climate change in the Chao Phraya River Basin, Thailand

Kure

Tebakari

2012

Hydrological Research Letters

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Abstract:The warming of the Earth's atmosphere system will change temperature and precipitation distributions across the globe. This will affect the hydrological cycle and, therefore, the hydrology of river basins worldwide. In this study, we model the stream flow of the Chao Phraya River Basin (CPRB), Thailand, in response to two climate change projection data sets under scenario A1B of the Special Report on Emissions Scenarios. We used Japan Meteorological Research Institute (MRI) atmospheric general circulation model 3.1 and 3.2 output data as input to a watershed hydrologic model to assess the impact of climate change for the basin. We found that, in the future, the mean annual river discharge is likely to increase in the CPRB due to increased rainfall. Furthermore, increases in annual maximum daily flows will occur toward the end of the 21st century.

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Section: Introductionmentioning

confidence: 99%

Hydrological impact of regional climate change in the Chao Phraya River Basin, Thailand

Kure

Tebakari

2012

Hydrological Research Letters

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“…This spatial averaging in turn reduces our ability to detect microclimatic conditions that constitute microrefugia for species [33]. In sum, it is clear that models, particularly those in hydrological, biodiversity and agricultural fields, would benefit from the production of finer grained input climate layers [34,35].…”

Section: Introductionmentioning

confidence: 99%

An Assessment of Methods and Remote-Sensing Derived Covariates for Regional Predictions of 1 km Daily Maximum Air Temperature

et al. 2014

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Abstract:The monitoring and prediction of biodiversity and environmental changes is constrained by the availability of accurate and spatially contiguous climatic variables at fine temporal and spatial grains. In this study, we evaluate best practices for generating gridded, one-kilometer resolution, daily maximum air temperature surfaces in a regional context, the state of Oregon, USA. Covariates used in the interpolation include remote sensing derived elevation, aspect, canopy height, percent forest cover and MODIS Land Surface Temperature (LST). Because of missing values, we aggregated daily LST values as long term (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) monthly climatologies to leverage its spatial detail in the interpolation. We predicted temperature with three methods-Universal Kriging, Geographically Weighted 8640Regression (GWR) and Generalized Additive Models (GAM)-and assessed predictions using meteorological stations over 365 days in 2010. We find that GAM is least sensitive to overtraining (overfitting) and results in lowest errors in term of distance to closest training stations. Mean elevation, LST, and distance to ocean are flagged most frequently as significant covariates among all daily predictions. Results indicate that GAM with latitude, longitude and elevation is the top model but that LST has potential in providing additional fine-grained spatial structure related to land cover effects. The study also highlights the need for more rigorous methods and data to evaluate the spatial structure and fine grained accuracy of predicted surfaces.

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“…GCM simulations of the present-day climate are forced by prescribed sea surface temperatures (SST), and are deterministically predictable in short and middle range [33,34]. Therefore, downscaled results from GCM simulations are comparable to observation data only in climatological features, as in the study by [29].…”

Section: Introductionmentioning

confidence: 61%

A Performance Evaluation of Dynamical Downscaling of Precipitation over Northern California

Jang¹,

Kavvas

Ishida

et al. 2017

Sustainability

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Abstract:It is important to assess the reliability of high-resolution climate variables used as input to hydrologic models. High-resolution climate data is often obtained through the downscaling of Global Climate Models and/or historical reanalysis, depending on the application. In this study, the performance of dynamically downscaled precipitation from the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR) reanalysis data (NCEP/NCAR reanalysis I) was evaluated at point scale, watershed scale, and regional scale against corresponding in situ rain gauges and gridded observations, with a focus on Northern California. Also, the spatial characteristics of the simulated precipitation and wind fields, with respect to various grid sizes, were investigated in order to gain insight to the topographic effect on the atmospheric state variables. To this end, dynamical downscaling was performed using the mesoscale atmospheric model MM5, and the synoptic scale reanalysis data were downscaled to a 3 km grid spacing with hourly temporal resolution. The results of comparisons at point scale and watershed scale over a 50-year time period showed that the MM5-simulated precipitation generally produced the timing and magnitude of the observed precipitation in Northern California. The spatial distributions of MM5-simulated precipitation matched the corresponding observed precipitation reasonably well. Furthermore, the statistical goodness of fit tests of the MM5-simulated precipitation against the corresponding observed precipitation showed the reliability and capability of MM5 simulations for downscaling precipitation. A comparison of the spatial characteristics of the results with respect to various grid sizes indicated that precipitation and wind fields are significantly affected by the local topography. In particular, the banded structures and orographic effects on precipitation and wind fields can be well described by a mesoscale model at 3 km and 9 km grid resolutions while 27 km and 81 km grid model simulation may not be sufficient for watershed-scale or sub-watershed-scale studies.

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Effect of uncertainty in temperature and precipitation inputs and spatial resolution on the crop model

Cited by 8 publications

References 21 publications

Hydrological impact of regional climate change in the Chao Phraya River Basin, Thailand

Hydrological impact of regional climate change in the Chao Phraya River Basin, Thailand

An Assessment of Methods and Remote-Sensing Derived Covariates for Regional Predictions of 1 km Daily Maximum Air Temperature

A Performance Evaluation of Dynamical Downscaling of Precipitation over Northern California

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