Stochastic Space-Time Modeling for Agricultural Decision Support in the Argentine Pampas

This dissertation presents three statistical models and applies them to the predominantly rain-fed Argentine Pampas, one of the most productive agricultural regions in the world. The Argentine Pampas experienced an upward trend in annual precipitation since the 1960s; global soybean prices surged shortly thereafter, which provided an optimal combination of climate, economics, and technology, and motivated vast agricultural expansion to semi-arid regions. Annual precipitation totals have declined since the turn of the century, which begs the question: "Are the existing agricultural production systems viable in a drier future?" Stochastic weather generators have long been used to produce synthetic daily weather series to drive process based models, which in turn are used to assess likely impacts on climate-sensitive sectors of society, and to evaluate the outcomes of alternative adaptive actions. Unfortunately, many traditional approaches of stochastic weather generation are limited in their ability to generate space-time weather (i.e., at unobserved locations), or values outside the range of the historical record, which is particularly important for climate change applications in rural agricultural regions, such as the Argentine Pampas. To this end, we developed a coupled stochastic weather generator (GLMGEN), which takes advantage of the flexibility of generalized linear models (GLMs) to model skewed and discrete variables (i.e., precipitation intensity and occurrence, respectively). Spatial process models estimate the GLM parameters in space to simulate at arbitrary locations, such as on a regular grid. Subsequent application of GLMGEN within a nonstationary context, such as climate change studies, is presented for the Salado A sub-basin of the Argentine Pampas. The inclusion of large-scale climate indices as covariates enables the simulation of daily weather ensembles that exhibit the traits and trends of seasonal forecasts and climate model projections. Regional climate model, experiment RCP8.5, and two IRI seasonal forecasts are used to condition the output of GLMGEN, thus translating this coarse scale climate information into more salient information for decision makers. In addition, we present a Bayesian stochastic weather generator (BayGEN), which quantifies and preserves the uncertainty associated with all model parameters. Uncertainty will subsequently propagate to synthetic daily weather ensembles and their respective uses, such as to drive crop simulation and hydrologic models, properly quantifying risk for decision making and climate change adaptation strategies. Direct comparison of BayGEN with GLMGEN will illustrate the benefit of propagating this uncertainty to simulation space. Finally, a statistical space-time hierarchical metamodel for monthly actual evapotranspiration (ET) and monthly water table depth (WTD) was developed as a complementary tool for near real-time decision support. In the first level of hierarchy, ET is modeled as a function of climate and land use decision variables; the second level models WTD as a function of climate and predicted ET. The metamodel was conditioned on and validated by a calibrated hydrologic model (i.e., MIKE-SHE) for the Salado A sub-basin, and is shown to adequately capture the dominant mechanisms of spatial and temporal variability. Use of the metamodel with output from a weather generator, as well as with ensembles of different land uses, can identify regions of high risk by producing distributions of WTD and its response to climate and land use change scenarios

Statistical Methods for Blending Satellite and Ground Observations to Improve High-Resolution Precipitation Estimates

Drought and flood management practices require accurate estimates of precipitation in space and time. However, data is sparse in regions with complicated terrain, often in valleys, and of poor quality. Consequently, extreme precipitation events are poorly represented. Satellite-derived rainfall data is an attractive alternative in such regions and is being widely used, though it too fails in representing extreme events due to its dependency on retrieval algorithms and the indirect relationship between satellite infrared observations and precipitation intensities. Thus, it seems appropriate to blend satellite-derived rainfall data of extensive spatial coverage with rain gauge data in order to provide a more robust estimate of precipitation. To this end, in this research we offer four techniques to blend rain gauge data and the Climate Hazards group InfraRed Precipitation (CHIRP) satellite-derived precipitation estimate for Central America and Colombia. In the first two methods, the gauge data is assigned to the closest CHIRP grid point, where the error is defined as r(s) = Yobs(s) - Ysat(s). The spatial structure of r(s) is then modeled using physiographic information (easting, northing, and elevation) by two methods (i) a traditional Co-kriging approach which utilizes a variogram that is calculated in Euclidean space and (ii) a nonparametric method based on local polynomial functional estimation. The models are used to estimate r at all grid points, which is then added to the CHIRP, thus creating an improved satellite estimate. We demonstrate these methods by applying them to pentadal and monthly total precipitation fields during 2009. The models' predictive abilities and their ability to capture extremes are investigated. These blending methods significantly improve upon the satellite-derived estimates and are also competitive in their ability to capture extreme precipitation. The above methods assume satellite-derived precipitation to be unbiased estimates of gauge precipitation, which is far from being the case. Thus the third method, Bayesian Hierarchical Modeling (BHM), is offered. In this approach, first, the gauge observations are modeled as a function of satellite-derived estimates and other variables such as elevation (the satellite estimate coefficient is in effect a bias correction factor). The residual from this first hierarchical model is then subjected to a spatial kriging model. The posterior distributions of all the model parameters are estimated simultaneously in Markov Chain Monte Carlo framework -- consequently, the posterior distributions and uncertainties of the blended precipitation estimates are attained. This approach provides a robust treatment of the uncertainties and the hierarchy enables incorporating all relevant covariates. While the BHM provides a robust confidence interval of the bias correction factor for CHIRP, it is reasonable to assume this bias is not uniform over the domain. Therefore a fourth method is proposed, wherein a GLM is fit to the time series at each point (Yobs(s,t) = &beta;(s)&nbsp;Ysat(s,t) + ϵ(s,t)), and the satellite coefficients are interpolated using a Co-kriging model similar to the first two methods. This provides a unique bias correction factor for every time frame (pentad, month), and therefore may be applied in near-real-time. To obtain the error field (i.e. residuals ϵ(s,t)) for a specific time frame t, the residuals corresponding to the appropriate time frame are extracted from the GLMs and interpolated, again using a physiographic Co-kriging model. These blended products provide more accurate and representative initial conditions for hydrologic modeling applications that are crucial for modeling and mitigatin</p

Managing cross boundary collaboration for value creation: lessons from a 'Blue Growth' initiative

New growth initiatives require collaboration across traditional sectors to develop eco-systems that can deliver sustainable sources of value. Collaborating across academic disciplines, the authors analyse a recent case of unprecedented collaboration between academia, government and industry in Ireland concerning marine innovation. Our analysis investigates: the effects of organizational structure and the characteristics of innovators; interpersonal dynamics that frustrate or enable cross-boundary teaming; and the role of senior executives in detecting, and responding to small deviations in expectations to avoid large-scale organizational failures. To leverage the potential benefits of cross-sector collaboration, we argue that leaders must engage in strategic error management. This paper extracts the value from the Irish case to find ways to avoid the mistakes of the relatively short-lived collaboration in future innovation efforts

Cystic lymphangioma of the mesentery

peer reviewedCystic lymphangioma of the mesentery is a benign condition, probably of malformative origin, and frequently appearing in infancy. Its symptomatology can be very polymorphic. Its diagnosis is suspected by ultrasonography and computed tomography, and definitely confirmed by pathology. About a recent case of cystic lymphangioma of the mesentery diagnosed and operated on at the university hospital of Liege in an adult patient, the authors review its classification and its therapeutic strategy. Surgical resection is indicated in symptomatic cystic lymphangioma