LANDHOLDER COOPERATION FOR SUSTAINABLE UPLAND WATERSHED MANAGEMENT: A THEORETICAL REVIEW OF THE PROBLEMS AND PROSPECTS

Despite national legislation and substantial donor investments, watershed degradation continues to threaten the sustained economic development and social welfare of millions of citizens in the developing world. Past efforts have largely concentrated on the physical rather than institutional aspects of watersheds, and have often relied on external incentives to coerce or persuade individuals to adopt conservation practices. In contrast to this conventional "physical" perspective, watersheds can be considered as sets of vested interests (and social relations) within a physically defined space. In essence, watersheds are physically defined subsets of rural society. Actors with vested interests within watersheds are interdependent because of water flow across political boundaries. From this perspective, the achievement of watershed management is a question of social relations, and cooperation between individual actors. Though there is growing realization for an expanded role of local, cooperative institutions in watershed management, theories on how such institutions might be identified, evolve or be promoted are limited. Toward this end, this paper examines some of the theoretical aspects of landholder cooperation for watershed management: the socio-political setting of upland watersheds; the physical attributes of watersheds influencing cooperation; the nature of externalities and incentives in watersheds; and the economic and socio-cultural factors affecting the emergence of collective action units. The processes by which collective action groups actually form are also reviewed. The paper concludes with a synthesis of the prospects for landholder cooperation approaches, the appropriate role of policy and a proposed process for promoting such cooperation.Resource /Energy Economics and Policy,

Modeling Nonlinear Dynamic Systems Using BSS-ANOVA Gaussian Process

Nonlinear dynamic systems are some of the most common variety of systems encountered in the sciences, but are the potentially more onerous to model through system identification than static systems due to their added complexity, sensitivity to initial conditions, and the potential application of new dynamic and nonlinear behavior through any time dependent forcing functions. The BSS-ANOVA Gaussian Process is a Machine Learning method for dynamic system ID that possesses several attributes that make it a natural candidate for this variety of problem. BSS-ANOVA is fully Bayesian, works best for continuous tabular datasets, and fast training and inference times and high model fidelity. The BSS-ANOVA GP is based upon a Karhunen- Loeve (KL) expansion of the basis set of the BSS-ANOVA kernel. This produces ordered, non-parametric, and spectral terms that are capable of utilizing Gibbs sampling during model building. Each of the model’s terms can be viewed as either a main effect of an input or as the interaction between two to three inputs coupled with a weight. These terms can be added iteratively to the model using forward variable selection, beginning with the lowest order effects, until the model’s accuracy is balanced by its complexity via a cost function. For dynamic systems, the GP creates a model of the static derivatives of the system, then integrates. This approach is carried out for three sets of data. The first creates a model of a benchmark dataset concerning the heights of a pair of cascaded tanks fed by a pump provided a random current, viewed here as a forcing function. The second models a synthetic dataset of SIR disease transmission, which has a constant transmissibility input during training and a variable transmissibility for inference. The third models the degradation rate of a solid oxide fuel cell as a function of its operating temperature, current density, and overpotential. The former two datasets are compared to a variety of alternative ML methods. The static derivative problem is compared to the results of a Random Forest, a Residual Neural Network, and an Orthogonalized Additive Kernel (OAK) inducing point GP. The dynamic time series predictions are compared to the GRU and LSTM Recurrent Neural Networks (RNN), as well as SINDy. For the static predictions, the BSS-ANOVA model exceeds the accuracy of the Residual Neural Network and the Random Forest, with lower accuracy than the the OAK GP while being several orders of magnitude faster. SINDy outperformed the BSS-ANOVA model for the synthetic SIR dataset, but was less accurate for the experimental data. Both were more accurate than the RNNs, which failed to capture the behavior of the SIR case entirely. The experimental fuel cell data was used to demonstrate the GP’s capability to identify regions of the input space that would benefit the most from additional data, a method that will continue to be utilized as more fuel cell experiments are conducted