General circulation models (GCMs), used to predict rainfall at a seasonal lead-time, tend to simulate too many rainfall events of too low intensity relative to individual stations within a GCM grid cell. Even if bias in total rainfall is corrected relative to a target location, this distortion of frequency and intensity is expected to adversely affect simulations of crop growth and yield. We present a procedure that calibrates both the frequency and the intensity distribution of daily GCM rainfall relative to a target station, and demonstrate its application to maize yield simulation at a location in semi-arid Kenya. If GCM rainfall frequency is greater than observed frequency for a given month, averaged across years, GCM rainfall frequency is corrected by discarding rainfall events below a calibrated threshold. To correct the intensity distribution, each GCM rainfall amount above the calibrated threshold is mapped from the GCM intensity distribution onto the observed distribution. We used a gamma distribution for observed rainfall intensity, and considered both gamma and empirical distributions for GCM rainfall intensity. At the study location, the proposed correction procedure corrected both the mean and variance of monthly and seasonal GCM rainfall total, frequency and mean intensity. The empirical (GCM)-gamma (observed) transformation overestimated mean intensity slightly. A simple multiplicative shift did a better job of correcting monthly and seasonal rainfall totals, but left substantial frequency and intensity bias. All of the bias correction procedures improved maize yield simulations, but resulted in substantial negative mean bias. This bias appears to be associated with a tendency for the GCM rainfall to be more strongly autocorrelated than observed rainfall, resulting in unrealistically long dry spells during the growing season. Nonlinearity of crop response to the variability of water availability across GCM realizations may also contribute. Averaging simulated yields each year across multiple GCM realizations improved yield predictions. The proposed correction procedure provides an option for using the daily output of dynamic climate prediction models for impact studies in a manner that preserves any useful predictive information about the timing of rainfall within the season. However, its practical utility for yield forecasting at a long lead-time may be limited by the ability of GCMs to simulate rainfall with a realistic time structure.

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Seasonal climate prediction offers the potential to anticipate variations in crop production early enough to adjust critical decisions. Until recently, interest in exploiting seasonal forecasts from dynamic climate models (e.g. general circulation models, GCMs) for applications that involve crop simulation models has been hampered by the difference in spatial and temporal scale of GCMs and crop models, and by the dynamic, nonlinear relationship between meteorological variables and crop response. Although GCMs simulate the atmosphere on a sub-daily time step, their coarse spatial resolution and resulting distortion of day-to-day variability limits the use of their daily output. Crop models have used daily GCM output with some success by either calibrating simulated yields or correcting the daily rainfall output of the GCM to approximate the statistical properties of historic observations. Stochastic weather generators are used to disaggregate seasonal forecasts either by adjusting input parameters in a manner that captures the predictable components of climate, or by constraining synthetic weather sequences to match predicted values. Predicting crop yields, simulated with historic weather data, as a statistical function of seasonal climatic predictors, eliminates the need for daily weather data conditioned on the forecast, but must often address poor statistical properties of the crop-climate relationship. Most of the work on using crop simulation with seasonal climate forecasts has employed historic analogs based on categorical ENSO indices. Other methods based on classification of predictors or weather types can provide daily weather inputs to crop models conditioned on forecasts. Advances in climate-based crop forecasting in the coming decade are likely to include more robust evaluation of the methods reviewed here, dynamically embedding crop models within climate models to account for crop influence on regional climate, enhanced use of remote sensing, and research in the emerging area of 'weather within climate.'

We used a genetic algorithm (GA) to identify soil water retention θ(h) and hydraulic conductivity K(h) functions by inverting a soil‐water‐atmosphere‐plant (SWAP) model using observed near‐surface soil moisture (0‐5 cm) as search criterion. Uncertainties of parameter estimates were estimated using multipopulations in GA and considering data and modeling errors. Three hydrologic cases were considered: (1) homogenous free‐draining soil column, (2) homogenous soil column with shallow water table, and (3) heterogeneous soil column under free‐drainage condition, considering three different rainfall patterns in northern Texas. Results (cases 1 and 2) showed the identifiability of soil hydraulic parameters improving at coarse and fine scales of the soil textural class. Medium‐textured soils posed identifiability problems when the soil is dry. Nonlinearity in θ(h) and K(h) is greater at drier conditions, and some parameters are less sensitive for estimation. Flow regimes controlled by upward fluxes were found less successful, as the information content of observed near‐surface data may no longer influence the hydrologic processes in the subsurface. The identifiability of soil hydraulic parameters was found better when the soil profile is predominantly draining. In case 3, top soil layer hydraulic properties were defined using near‐surface data alone as criterion. Adding evapotranspiration (ET) improved identification of the second soil layer, although not all parameters were identifiable. Under uncertainties, θ(h) was found to be well defined while K(h) is more uncertain. Finally, we applied the method to a validation site in Little Washita watershed, Oklahoma, where derived effective soil hydraulic properties closely matched the measured ones at the field site.

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