Amor Valeriano M. Ines

Application of GIS and crop growth models in estimating water productivity

Abstract Tighter competition in water use is projected in the future. As water demand increases, water related problems could happen along the way. Accordingly, issues on water availability and use could be crucial to study to search for ways and means on how to cope up with the present trend. Sound water management practices could play a key role to the solution of problems relating to water availability and use. Water use in agriculture is considered the highest among other water users because of the water intensive processes involved in it. Aside from the crop water requirements, water loss, which are not beneficial to crop processes can add a huge volume to the total water usage in agriculture. Base from this argument, there could be greater possibility to save water from agriculture, which can be used for other purposes thereafter. To explore this option, analysis at the crop level could be beneficial. However, the issue of scaling should be also considered because the knowledge on the field scale could not be generally true in the basin scale. The objective of the study was to apply crop growth simulation models coupled with geographic information system (GIS) to analyze water productivity, which is an indicator of water use efficiency, at the basin scale. The methodology was applied to Laoag River Basin in Ilocos Norte, Philippines to study water productivity in spatial and temporal dimensions. Three crops were considered in the analysis: rice, maize and peanut. Simulations were done for both existing and potential agricultural areas. The potential productions of the selected crops from October 1996–September 1997 were used as bases in determining water productivity for the three cropping seasons (CS) being considered in the study. Water-limited productions were simulated for each of the crops, for each of the CS in the basin. Moreover, a marginal productivity analysis was done to determine the potential of water for crop production in the basin. Subsequently, the significance of irrigation was emphasized in the analysis when availability of water, and the combination of water and nitrogen (N) are limiting, respectively. The results showed that the spatio-temporal analysis of water productivity could provide substantial information for water saving opportunities and, hence, strategies in irrigated agriculture.

Downscaling of seasonal precipitation for crop simulation

A nonhomogeneous hidden Markov model (NHMM) is used to make stochastic simulations of March– August daily rainfall at 10 stations over the southeastern United States, 1923–98. Station-averaged observed daily rainfall amount is prescribed as an input to the NHMM, which is then used to disaggregate the rainfall in space. These rainfall simulations are then used as inputs to a Crop Estimation through Resource and Environment Synthesis (CERES) crop model for maize. Regionally averaged yields derived from the NHMM rainfall simulations are found to correlate very highly (r 0.93) with those generated by the crop model using observed rainfall; stationwise correlations range between 0.44 and 0.74. Rainfall and crop simulations are then constructed under increasing degrees of temporal smoothing applied to the regional rainfall input to the NHMM, designed to exclude the submonthly weather details that would be unpredictable in seasonal climate forecasts. Regional yields are found to be remarkably insensitive to this temporal smoothing; even with 90-day low-pass-filtered inputs to the NHMM, resulting yields are still correlated at 0.85 with the baseline simulation, whereas stationwise correlations range between 0.18 and 0.68. From these findings, it is expected that regional maize yields over the southeastern United States will be largely insensitive to year-to-year details of subseasonal rainfall variability; they should be downscalable, in principle, using an NHMM from climate forecasts archived at daily resolution, with the important caveat that the latter need to be skillful enough at the 90-day time scale. As a by-product of the analysis, subseasonalto-interdecadal summer rainfall variability over the southeastern United States is interpretable in terms of six discrete weather states indicative of a monsoonlike climate regime. Low-simulated-yield years are found to be associated with delayed summer rainfall onset.

Prediction of Rice Production in the Philippines Using Seasonal Climate Forecasts

Predictive skills of retrospective seasonal climate forecasts (hindcasts) tailored to Philippine rice production data at national, regional, and provincial levels are investigated using precipitation hindcasts from one uncoupled general circulation model (GCM) and two coupled GCMs, as well as using antecedent observations of tropical Pacificseasurfacetemperatures,warmwatervolumes(WWV),and zonalwinds(ZW).Contrastingcross-validated predictive skills are found between the ‘‘dry’’ January‐June and ‘‘rainy’’ July‐December crop-production seasons. For the dry season, both irrigated and rain-fed rice production are shown to depend strongly on rainfall in the previous October‐December. Furthermore, rice-crop hindcasts based on the two coupled GCMs, or on the observed WWV and ZW, are each able to account for more than half of the total variance of the dry-season national detrended rice production with about a 6-month lead time prior to the beginning of the harvest season. At regional and provincial levels, predictive skills are generally low. The relationships are found to be more complex for rainyseason rice production. Area harvested correlates positively with rainfall during the preceding dry season, whereas the yield has positive and negative correlations with rainfall in June‐September and in October‐December of the harvested year, respectively. Tropical cyclone activity isalso shown to be a contributing factor in the latter 3-month season. Hindcasts based on the WWV and ZW are able to account for almost half of the variance of the detrended rice production data in Luzon with a few months’ lead time prior to the beginning of the rainy season.

Extraction of information content from stochastic disaggregation and bias corrected downscaled precipitation variables for crop simulation

We applied a simple statistical downscaling procedure for transforming daily global climate model (GCM) rainfall to the scale of an agricultural experimental station in Katumani, Kenya. The transformation made was two-fold. First, we corrected the rainfall frequency bias of the climate model by truncating its daily rainfall cumulative distribution into the station’s distribution based on a prescribed observed wet-day threshold. Then, we corrected the climate model rainfall intensity bias by mapping its truncated rainfall distribution into the station’s truncated distribution. Further improvements were made to the bias corrected GCM rainfall by linking it with a stochastic disaggregation scheme to correct the time structure problem inherent with daily GCM rainfall. Results of the simple and hybridized GCM downscaled precipitation variables (total, probability of occurrence, intensity and dry spell length) were linked with a crop model for a more objective evaluation of their performance using a non-linear measure based on mutual information based on entropy. This study is useful for the identification of both suitable downscaling technique as well as the effective precipitation variables for forecasting crop yields using GCM’s outputs which can be useful for addressing food security problems beforehand in critical basins around the world.

Inverse modeling to quantify irrigation system characteristics and operational management

Remotely sensed (RS) data is a major source to obtain spatialdata required for hydrological models. The challenge for thefuture is to obtain besides the more direct observable data(landcover, leaf area index, digital elevation model andevapotranspiration), non-visible data such as soilcharacteristics, groundwater depth and irrigation practices.In this study we have explore the option of using inversemodeling to obtain these non-RS-visible data. For a commandarea in Haryana, India, we applied for the 2000–2001 rabiseason a RS-GIS-combined inverse modeling approach to derivenon-RS-visible data required in the regional application ofhydrological models. A Genetic Algorithm loaded stochasticphysically based soil-water-atmosphere-plant model (SWAP) wasdeveloped for the inverse problem and used in the study. Theresults showed good agreement with the inventoried data suchas soil hydraulic properties, sowing dates, groundwaterdepths, irrigation practices and water quality. The deriveddata could be used to predict the state of the system at anytime in the cropping season, which can be used to evaluateoperational management strategies.

Soil hydraulic parameters estimated from satellite information through data assimilation

Leaf area index (LAI) and actual evapotranspiration (ETa) from satellite observations were used to estimate simultaneously the soil hydraulic parameters of four soil layers down to 60 cm depth using the combined soil water atmosphere plant and genetic algorithm (SWAP–GA) model. This inverse model assimilates the remotely sensed LAI and/or ETa by searching for the most appropriate sets of soil hydraulic parameters that could minimize the difference between the observed and simulated LAI (LAIsim) or simulated ETa (ETasim). The simulated soil moisture estimates derived from soil hydraulic parameters were validated using values obtained from soil moisture sensors installed in the field. Results showed that the soil hydraulic parameters derived from LAI alone yielded good estimations of soil moisture at 3 cm depth; LAI and ETa in combination at 12 cm depth, and ETa alone at 28 cm depth. There appeared to be no match with measurement at 60 cm depth. Additional information would therefore be needed to better estimate soil hydraulic parameters at greater depths. Despite this inability of satellite data alone to provide reliable estimates of soil moisture at the lowest depth, derivation of soil hydraulic parameters using remote sensing methods remains a promising area for research with significant application potential. This is especially the case in areas of water management for agriculture and in forecasting of floods or drought on the regional scale.

Combining Crop Models and Remote Sensing for Yield Prediction: Concepts, Applications and Challenges for Heterogeneous Smallholder Environments

JRC and CCAFS jointly organized a workshop on June 13-14, 2012 in Ispra, Italy with the aim to advance the state-of- knowledge of data assimilation for crop yield forecasting in general, to address challenges and needs for successful applications of data assimilation in forecasting crop yields in heterogeneous, smallholder environments, and to enhance collaboration and exchange of knowledge among data assimilation and crop forecasting groups. The workshop showed that advances made in crop science are widely applicable to crop forecasting. The presentations of the participants approached the challenge from many sides, leading to ideas for improvement that can be implemented in real-time, operational crop yield forecasting. When applied, this knowledge has the potential to benefit the livelihoods of smallholder farmers in the developing world.

Autocalibration of HSPF for Simulation of Streamflow Using a Genetic Algorithm

Hydrologic models are essential to watershed planning and management, particularly in the San Antonio River watershed where competition for scarce water resources is a challenge. As a result, the calibration and validation of hydrologic models are essential steps for their successful application. In this study, we examined the use of a loosely coupled genetic algorithm (GA) as an autocalibration tool for optimization of model parameters for the Hydrologic Simulation Program - Fortran (HSPF), a model frequently used in surface hydrology and water quality modeling. The GA-HSPF model is a more objective and less time-consuming alternative to traditional trial-and-error methods. The objective function was optimized by minimizing the mean absolute error (MAE) between corresponding simulated and observed average daily streamflow in the San Antonio River watershed. The MAE was used to evaluate the fitness of the parameter set in the GA. The calibrated model parameters (LZSN, INFILT, AGWRC, UZSN, DEEPFR, LZETP, and INTFW) were selected based on a sensitivity analysis from a previous study. Goodness-of-fit of the GA calibrated model was evaluated using the Nash-Sutcliffe coefficient of efficiency, MAE, root mean square error, flow duration curves, wavelet analysis, and total volume error. Overall simulation time with 2000 model simulations was 11 days, which can be improved significantly under parallel computing, as GA-HSPF simulations are highly independent. The objective function ceased improvement after approximately 250 simulations, with a minimized MAE of 25.8 m 3 /s. With the exception of DEEPFR, all optimized model parameter values were within the range cited in the literature. Nash-Sutcliffe coefficients in all simulations were above 0.5, suggesting that the simulated flows were in good agreement with the observed flows. Visual comparison between observed and simulated stream flow using time series and flow duration curves showed that the GA calibrated model was unable to simulate peak flow events accurately, particularly in the 0% to 10% exceedence range. It is hypothesized that the storage-based routing scheme in HSPF limits its ability to predict peak flows in this watershed. Comparison between observed and simulated flows in the wavelet domain indicated that the GA calibrated model was not able to preserve the scale and location of some high frequencies, but the scale and location of lower frequencies were preserved. The cyclic nature of the streamflow in this watershed was more prominent in lower frequencies. While overall flow rates were adequately predicted using a GA-HSPF approach, future work in this watershed needs to focus on multi-objective optimization that optimizes both volumes and peak flows. The GA-HSPF model offers an objective and efficient method for calibration and validation, a useful tool in watershed planning efforts.

Climate-Agriculture-Modeling and Decision Tool (CAMDT): A software framework for climate risk management in agriculture

Seasonal climate forecasts (SCFs) have received a lot of attention for climate risk management in agriculture. The question is, how can we use SCFs for informing decisions in agriculture? SCFs are provided in formats not so conducive for decision-making. The commonly issued tercile probabilities of most likely rainfall categories i.e., below normal (BN), near normal (NN) and above normal (AN), are not easy to translate into metrics useful for decision support. Linking SCF with crop models is one way that can produce useful information for supporting strategic and tactical decisions in crop production e.g., crop choices, management practices, insurance, etc. Here, we developed a decision support system (DSS) tool, Climate-Agriculture-Modeling and Decision Tool (CAMDT), that aims to facilitate translations of probabilistic SCFs to crop responses that can help decision makers adjust crop and water management practices that may improve outcomes given the expected climatic condition of the growing season.

The Regional Hydrologic Extremes Assessment System: A software framework for hydrologic modeling and data assimilation

The Regional Hydrologic Extremes Assessment System (RHEAS) is a prototype software framework for hydrologic modeling and data assimilation that automates the deployment of water resources nowcasting and forecasting applications. A spatially-enabled database is a key component of the software that can ingest a suite of satellite and model datasets while facilitating the interfacing with Geographic Information System (GIS) applications. The datasets ingested are obtained from numerous space-borne sensors and represent multiple components of the water cycle. The object-oriented design of the software allows for modularity and extensibility, showcased here with the coupling of the core hydrologic model with a crop growth model. RHEAS can exploit multi-threading to scale with increasing number of processors, while the database allows delivery of data products and associated uncertainty through a variety of GIS platforms. A set of three example implementations of RHEAS in the United States and Kenya are described to demonstrate the different features of the system in real-world applications.