Yasushi Ishigooka

ELPIS-JP: a dataset of local-scale daily climate change scenarios for Japan

We developed a dataset of local-scale daily climate change scenarios for Japan (called ELPIS-JP) using the stochastic weather generators (WGs) LARS-WG and, in part, WXGEN. The ELPIS-JP dataset is based on the observed (or estimated) daily weather data for seven climatic variables (daily mean, maximum and minimum temperatures; precipitation; solar radiation; relative humidity; and wind speed) at 938 sites in Japan and climate projections from the multi-model ensemble of global climate models (GCMs) used in the coupled model intercomparison project (CMIP3) and multi-model ensemble of regional climate models form the Japanese downscaling project (called S-5-3). The capability of the WGs to reproduce the statistical features of the observed data for the period 1981–2000 is assessed using several statistical tests and quantile–quantile plots. Overall performance of the WGs was good. The ELPIS-JP dataset consists of two types of daily data: (i) the transient scenarios throughout the twenty-first century using projections from 10 CMIP3 GCMs under three emission scenarios (A1B, A2 and B1) and (ii) the time-slice scenarios for the period 2081–2100 using projections from three S-5-3 regional climate models. The ELPIS-JP dataset is designed to be used in conjunction with process-based impact models (e.g. crop models) for assessment, not only the impacts of mean climate change but also the impacts of changes in climate variability, wet/dry spells and extreme events, as well as the uncertainty of future impacts associated with climate models and emission scenarios. The ELPIS-JP offers an excellent platform for probabilistic assessment of climate change impacts and potential adaptation at a local scale in Japan.

Dependency of parameter values of a crop model on the spatial scale of simulation

Reliable regional-scale representation of crop growth and yields has been increasingly important in earth system modeling for the simulation of atmosphere-vegetation-soil interactions in managed ecosystems. While the parameter values in many crop models are location specific or cultivar specific, the validity of such values for regional simulation is in question. We present the scale dependency of likely parameter values that are related to the responses of growth rate and yield to temperature, using the paddy rice model applied to Japan as an example. For all regions, values of the two parameters that determine the degree of yield response to low temperature (the base temperature for calculating cooling degree days and the curvature factor of spikelet sterility caused by low temperature) appeared to change relative to the grid interval. Two additional parameters (the air temperature at which the developmental rate is half of the maximum rate at the optimum temperature and the value of developmental index at which point the crop becomes sensitive to the photoperiod) showed scale dependency in a limited region, whereas the remaining three parameters that determine the phenological characteristics of a rice cultivar and the technological level show no clear scale dependency. These results indicate the importance of using appropriate parameter values for the spatial scale at which a crop model operates. We recommend avoiding the use of location-specific or cultivar-specific parameter values for regional crop simulation, unless a rationale is presented suggesting these values are insensitive to spatial scale.

Development of a method for estimating total CH4 emission from rice paddies in Japan using the DNDC-Rice model.

Methane (CH4) is a greenhouse gas, and paddy fields are one of its main anthropogenic emission sources. To mitigate this emission based on effective management measures, CH4 emission from paddy fields must be quantified at a national scale. In Japan, country-specific emission factors have been applied since 2003 to estimate national CH4 emission from paddy fields. However, this method cannot account for the effects of weather conditions and temporal variability of nitrogen fertilizer and organic matter application rates; thus, the estimated emission is highly uncertain. To improve the accuracy of national-scale estimates, we calculated country-specific emission factors using the DeNitrification-DeComposition-Rice (DNDC-Rice) model. First, we calculated CH4 emission from 1981 to 2010 using 986 datasets that included soil properties, meteorological data, and field management data. Using the simulated site-specific emission, we calculated annual mean emission for each of Japans seven administrative regions, two water management regimes (continuous flooding and conventional mid-season drainage), and three soil drainage rates (slow, moderate, and fast). The mean emission was positively correlated with organic carbon input to the field, and we developed linear regressions for the relationships among the regions, water management regimes, and drainage rates. The regression results were within the range of published observation values for site-specific relationships between CH4 emission and organic carbon input rates. This suggests that the regressions provide a simplified method for estimating CH4 emission from Japanese paddy fields, though some modifications can further improve the estimation accuracy.

Modeling of continental-scale crop water requirement and available water resources

The relationship between agricultural water demand and supply has been of interest to government decision makers and scientists because of its importance in water resources management. We developed a water cycle model for eastern Eurasia that can estimate water requirements for crop growth and evaluate the demand–supply relationships of agricultural water use on a continental scale. To produce an appropriate water cycle, the model was constructed based on small drainage basins. To validate the model performance with respect to simulated runoff, which is here considered as the available water resource, we compared our outputs with those of other models and with observed river discharges. The results show that this model is comparable to other models and that it is applicable for the evaluation of water cycles at continental scale. We defined two types of crop water deficits (CWDs) as indicators of agricultural water demand. These were formulated by considering the physical processes of crop water use; we did not include water consumption that is dependent on cultivation management practices, such as water losses in irrigation systems. We assessed the reliability of our indicators by comparison with indicators from other studies and with published statistics related to agricultural water use. These comparisons suggest that our indicators are consistent with independent data and can provide a reasonable representation of water requirements for crop growth.

Features of the AFFRC model for evaluating the relationship between the water cycle and rice production

This paper introduces the Agriculture, Forestry and Fisheries Research Council of Japan (AFFRC) model, an integrated model that predicts future rice production in the Mekong River basin by taking into account the effect of global warming on both the water cycle and the rice economy. The model focuses especially on the water balance of paddy fields for different farmland water use systems. We defined six categories of irrigated paddies and three categories of rain-fed paddies on the basis of their systems of water usage. We included a process-based model to predict future rice production, accounting for daily changes in available water resources such as precipitation. Many models of crop production treat rice in the same way as other crops; the particular characteristics of rice farming are considered in more detail in our model. Our results show that it is possible to estimate future rice production in the Mekong River basin by taking into account changes in available water, and to model the resultant effects on the grain market.

Projection of Effects of Climate Change on Rice Yield and Keys to Reduce Its Uncertainties

The increase in atmospheric CO2 concentration and accompanying global warming should affect crop productivity. A number of experiments and simulations have been conducted to predict the impacts of climate change on rice yield. When conducting large-scale evaluation of rice yield, there are large uncertainties, which resulted from a number of sources, such as those in the greenhouse gas (GHG) emission scenarios, global climate models (GCMs) and its gaps between global and local climates. In addition, the rice development models themselves include uncertainties. In this paper, we present our recent studies on large-scale evaluation by crop models and trials to elucidate and reduce uncertainties accompanied with each aspect of evaluation. In modeling technique aspect, statistical approach for model parameters and the use of multi-scenarios and multi-GCMs are reviewed. In field experiment aspect, we present a field survey on spikelet sterility in the hot summer of 2007 and some insights from free-air CO2 enrichment (FACE) experiment. They strongly suggest the necessity for developing a process-based rice development model including heat balance. The synthesized process-based model study in tandem with FACE experiments contributes not only for reducing the evaluation uncertainties, but also for validating the adapting or avoiding studies of heat stress or negative influence on rice under projected climate change.