Iwan Supit

A simple method for generating daily rainfall data

Abstract In recent years, agricultural scientists have developed considerable interest in modeling and simulation of rainfall as new ways of analyzing rainfall data and assessing its impact on agriculture. Among the proposed methods, a combination of Markov chain and gamma distribution function is recognized as a simple approach and is demonstrated to be effective in generating daily rainfall data for many environments. Unfortunately, this method requires that many years of daily weather records be available for estimating the model parameters. Thus the availability of the weather data limits the applicability of the simulation method. When these model parameters are evaluated over time and at different places, however, certain general characteristics are revealed. First, the transitional probability of a wet day followed by a wet day tends to be greater but parallel to the transitional probability of a dry day followed by a wet day. This phenomenon leads to a linear relationship of the transitional probabilities to the fraction of wet days per month. Second, the beta parameter in a gamma distribution function, which is used to describe the amount of rainfall, is closely related to the amount of rain per wet day owing to the positive skewness of the rainfall distribution. Based on these relationships, a simple method is introduced, by which model parameters can be estimated from monthly summaries instead of from daily values. The suggested method, therefore, provides a convenient vehicle for applying weather simulation models to areas in which its use had been impossible because of the unavailability of long series of daily weather data.

Multimodel ensembles of wheat growth: many models are better than one.

Crop models of crop growth are increasingly used to quantify the impact of global changes due to climate or crop management. Therefore, accuracy of simulation results is a major concern. Studies with ensembles of crop models can give valuable information about model accuracy and uncertainty, but such studies are difficult to organize and have only recently begun. We report on the largest ensemble study to date, of 27 wheat models tested in four contrasting locations for their accuracy in simulating multiple crop growth and yield variables. The relative error averaged over models was 24-38% for the different end-of-season variables including grain yield (GY) and grain protein concentration (GPC). There was little relation between error of a model for GY or GPC and error for in-season variables. Thus, most models did not arrive at accurate simulations of GY and GPC by accurately simulating preceding growth dynamics. Ensemble simulations, taking either the mean (e-mean) or median (e-median) of simulated values, gave better estimates than any individual model when all variables were considered. Compared to individual models, e-median ranked first in simulating measured GY and third in GPC. The error of e-mean and e-median declined with an increasing number of ensemble members, with little decrease beyond 10 models. We conclude that multimodel ensembles can be used to create new estimators with improved accuracy and consistency in simulating growth dynamics. We argue that these results are applicable to other crop species, and hypothesize that they apply more generally to ecological system models.

The uncertainty of crop yield projections is reduced by improved temperature response functions

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections.

Climate change and maize yield in southern Africa: what can farm management do?

There is concern that food insecurity will increase in southern Africa due to climate change. We quantified the response of maize yield to projected climate change and to three key management options - planting date, fertilizer use and cultivar choice - using the crop simulation model, agricultural production systems simulator (APSIM), at two contrasting sites in Zimbabwe. Three climate periods up to 2100 were selected to cover both near- and long-term climates. Future climate data under two radiative forcing scenarios were generated from five global circulation models. The temperature is projected to increase significantly in Zimbabwe by 2100 with no significant change in mean annual total rainfall. When planting before mid-December with a high fertilizer rate, the simulated average grain yield for all three maize cultivars declined by 13% for the periods 2010-2039 and 2040-2069 and by 20% for 2070-2099 compared with the baseline climate, under low radiative forcing. Larger declines in yield of up to 32% were predicted for 2070-2099 with high radiative forcing. Despite differences in annual rainfall, similar trends in yield changes were observed for the two sites studied, Hwedza and Makoni. The yield response to delay in planting was nonlinear. Fertilizer increased yield significantly under both baseline and future climates. The response of maize to mineral nitrogen decreased with progressing climate change, implying a decrease in the optimal fertilizer rate in the future. Our results suggest that in the near future, improved crop and soil fertility management will remain important for enhanced maize yield. Towards the end of the 21st century, however, none of the farm management options tested in the study can avoid large yield losses in southern Africa due to climate change. There is a need to transform the current cropping systems of southern Africa to offset the negative impacts of climate change.

Use of Modern Portfolio Theory to Evaluate Diversification of Agricultural Land Use as an Adaptation to Climate Risks in the Tisza River Basin

Adaptation is gaining attention as an inevitable answer to the challenges posed by climate change. The increasingly uncertain climatic conditions to which actors are exposed are becoming a constraint for their well-being. This chapter looks at diversification of agricultural land use as a key factor in reducing risk and as a means of coping with an uncertain climate. Borrowing from economic theory, this chapter illustrates how cropping patterns influence the expected revenue and risk. The standard deviation of the land use revenue is used as a proxy for climate risk.

Multimodel ensembles improve predictions of crop–environment–management interactions

A recent innovation in assessment of climate change impact on agricultural production has been to use crop multimodel ensembles (MMEs). These studies usually find large variability between individual models but that the ensemble mean (e-mean) and median (e-median) often seem to predict quite well. However, few studies have specifically been concerned with the predictive quality of those ensemble predictors. We ask what is the predictive quality of e-mean and e-median, and how does that depend on the ensemble characteristics. Our empirical results are based on five MME studies applied to wheat, using different data sets but the same 25 crop models. We show that the ensemble predictors have quite high skill and are better than most and sometimes all individual models for most groups of environments and most response variables. Mean squared error of e-mean decreases monotonically with the size of the ensemble if models are added at random, but has a minimum at usually 2-6 models if best-fit models are added first. Our theoretical results describe the ensemble using four parameters: average bias, model effect variance, environment effect variance, and interaction variance. We show analytically that mean squared error of prediction (MSEP) of e-mean will always be smaller than MSEP averaged over models and will be less than MSEP of the best model if squared bias is less than the interaction variance. If models are added to the ensemble at random, MSEP of e-mean will decrease as the inverse of ensemble size, with a minimum equal to squared bias plus interaction variance. This minimum value is not necessarily small, and so it is important to evaluate the predictive quality of e-mean for each target population of environments. These results provide new information on the advantages of ensemble predictors, but also show their limitations.

Author Correction: The uncertainty of crop yield projections is reduced by improved temperature response functions

Nature Plants3, 17102 (2017); published online 17 July 2017; corrected online 27 September 2017.

The Mekong's future flows under multiple drivers: How climate change, hydropower developments and irrigation expansions drive hydrological changes

The river flow regime and water resources are highly important for economic growths, flood security, and ecosystem dynamics in the Mekong basin - an important transboundary river basin in South East Asia. The river flow, although remains relatively unregulated, is expected to be increasingly perturbed by climate change and rapidly accelerating socioeconomic developments. Current understanding about hydrological changes under the combined impacts of these drivers, however, remains limited. This study presents projected hydrological changes caused by multiple drivers, namely climate change, large-scale hydropower developments, and irrigated land expansions by 2050s. We found that the future flow regime is highly susceptible to all considered drivers, shown by substantial changes in both annual and seasonal flow distribution. While hydropower developments exhibit limited impacts on annual total flows, climate change and irrigation expansions cause changes of +15% and -3% in annual flows, respectively. However, hydropower developments show the largest seasonal impacts characterized by higher dry season flows (up to +70%) and lower wet season flows (-15%). These strong seasonal impacts tend to outplay those of the other drivers, resulting in the overall hydrological change pattern of strong increases of the dry season flow (up to +160%); flow reduction in the first half of the wet season (up to -25%); and slight flow increase in the second half of the wet season (up to 40%). Furthermore, the cumulative impacts of all drivers cause substantial flow reductions during the early wet season (up to -25% in July), posing challenges for crop production and saltwater intrusion in the downstream Mekong Delta. Substantial flow changes and their consequences require careful considerations of future development activities, as well as timely adaptation to future changes.