Treatment of input uncertainty in hydrologic modeling: Doing hydrology backward with Markov chain Monte Carlo simulation

There is increasing consensus in the hydrologic literature that an appropriate framework for streamflow forecasting and simulation should include explicit recognition of forcing and parameter and model structural error. This paper presents a novel Markov chain Monte Carlo (MCMC) sampler, entitled differential evolution adaptive Metropolis (DREAM), that is especially designed to efficiently estimate the posterior probability density function of hydrologic model parameters in complex, high-dimensional sampling problems. This MCMC scheme adaptively updates the scale and orientation of the proposal distribution during sampling and maintains detailed balance and ergodicity. It is then demonstrated how DREAM can be used to analyze forcing data error during watershed model calibration using a five-parameter rainfall-runoff model with streamflow data from two different catchments. Explicit treatment of precipitation error during hydrologic model calibration not only results in prediction uncertainty bounds that are more appropriate but also significantly alters the posterior distribution of the watershed model parameters. This has significant implications for regionalization studies. The approach also provides important new ways to estimate areal average watershed precipitation, information that is of utmost importance for testing hydrologic theory, diagnosing structural errors in models, and appropriately benchmarking rainfall measurement devices

Equifinality of formal (DREAM) and informal (GLUE) Bayesian approaches in hydrologic modeling?

In recent years, a strong debate has emerged in the hydrologic literature regarding what constitutes an appropriate framework for uncertainty estimation. Particularly, there is strong disagreement whether an uncertainty framework should have its roots within a proper statistical (Bayesian) context, or whether such a framework should be based on a different philosophy and implement informal measures and weaker inference to summarize parameter and predictive distributions. In this paper, we compare a formal Bayesian approach using Markov Chain Monte Carlo (MCMC) with generalized likelihood uncertainty estimation (GLUE) for assessing uncertainty in conceptual watershed modeling. Our formal Bayesian approach is implemented using the recently developed differential evolution adaptive metropolis (DREAM) MCMC scheme with a likelihood function that explicitly considers model structural, input and parameter uncertainty. Our results demonstrate that DREAM and GLUE can generate very similar estimates of total streamflow uncertainty. This suggests that formal and informal Bayesian approaches have more common ground than the hydrologic literature and ongoing debate might suggest. The main advantage of formal approaches is, however, that they attempt to disentangle the effect of forcing, parameter and model structural error on total predictive uncertainty. This is key to improving hydrologic theory and to better understand and predict the flow of water through catchment

Integrated Land Use Planning and Sustainable Watershed Management

This paper discusses the key issues and concerns regarding sustainable Philippine watershed management. Emphasis is made on the various requisites of a sustainable management with a focus on the critical roles of land use planning.land use planning, land management, watershed

Scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers

Scene parsing, or semantic segmentation, consists in labeling each pixel in an image with the category of the object it belongs to. It is a challenging task that involves the simultaneous detection, segmentation and recognition of all the objects in the image. The scene parsing method proposed here starts by computing a tree of segments from a graph of pixel dissimilarities. Simultaneously, a set of dense feature vectors is computed which encodes regions of multiple sizes centered on each pixel. The feature extractor is a multiscale convolutional network trained from raw pixels. The feature vectors associated with the segments covered by each node in the tree are aggregated and fed to a classifier which produces an estimate of the distribution of object categories contained in the segment. A subset of tree nodes that cover the image are then selected so as to maximize the average "purity" of the class distributions, hence maximizing the overall likelihood that each segment will contain a single object. The convolutional network feature extractor is trained end-to-end from raw pixels, alleviating the need for engineered features. After training, the system is parameter free. The system yields record accuracies on the Stanford Background Dataset (8 classes), the Sift Flow Dataset (33 classes) and the Barcelona Dataset (170 classes) while being an order of magnitude faster than competing approaches, producing a 320 \times 240 image labeling in less than 1 second.Comment: 9 pages, 4 figures - Published in 29th International Conference on Machine Learning (ICML 2012), Jun 2012, Edinburgh, United Kingdo

EVALUATING THE COST EFFECTIVENESS OF LAND RETIREMENT PROGRAMS

This paper extends an integrated framework that combines economic, environmental and GIS modeling to evaluate the cost effectiveness of land retirement programs. The modeling framework is applied to the Lower Sangamon Watershed in Cass County of Illinois to examine the economic costs and environmental benefits of three land retirement scenarios: land actually enrolled in the Illinois CREP, land selected by a land rental cap mechanism and land identified by a least cost model. We find that land retirement in the watershed successfully achieved the program goal of 20% sediment abatement. However, in achieving the same level of sediment abatement, the costs of actual land retirement are 1.3 times and 2.1 times of those in a land rental cap mechanism and a least cost model respectively. The model results also reveal that cost effective land retirement parcels are more sloping, close to river, with higher upland sediment inflow, more on-site erosion and lower quasi-rents. The results indicate that governments may improve the cost effectiveness of land retirement program through targeting. And there is a need to modify current Illinois CREP eligibility criteria to include sloping cropland adjacent to the river in the program. Furthermore our results suggest that in the program implementation land retirement contracts could be selected based on several measurable parameters such as distance from the river and slope.Land Economics/Use,

The use of vegetation series to assess á and â vegetation diversity and their relationships with geodiversity in the province of Almeria (Spain) with watersheds as operational geographic units

Abstract With this paper we suggest that vegetation series is a useful conceptual tool to identify a clear level of biodiversity of land systems among the many possible logical levels. The suggestion is supported by the results of a case study carried out for the province of Almeria (Spain) using the watersheds as operational geographic units. The application of standard correlation analysis, simple and partial, the Mantel?s test, and the cluster analysis has shown that ? and ? vegetation diversities, based on vegetation series, are significantly predictive with respect to environmental heterogeneity expressed by pedodiversity, lithodiversity, and some parameters of digital elevation model. Being a product of the Braun Blanquet?s floristic approach, vegetation series could be the key to enter into vegetation databases for biodiversity analysis of land systems at many other levels of knowledge. Keywords: data mining, Mantel, nestedness, partial correlation, pedodiversity, Simpson, knowledge generatio

Surface networks

© Copyright CASA, UCL. The desire to understand and exploit the structure of continuous surfaces is common to researchers in a range of disciplines. Few examples of the varied surfaces forming an integral part of modern subjects include terrain, population density, surface atmospheric pressure, physico-chemical surfaces, computer graphics, and metrological surfaces. The focus of the work here is a group of data structures called Surface Networks, which abstract 2-dimensional surfaces by storing only the most important (also called fundamental, critical or surface-specific) points and lines in the surfaces. Surface networks are intelligent and “natural ” data structures because they store a surface as a framework of “surface ” elements unlike the DEM or TIN data structures. This report presents an overview of the previous works and the ideas being developed by the authors of this report. The research on surface networks has fou

Species from different taxonomic groups show similar invasion traits

Invasion ecology tends to treat taxonomic groups separately. However, given that all invasive species go through the same stages of the invasion process (transport, escape, establishment, spread), it is likely that – across taxa – comparable traits help to successfully complete this process ("invasion traits"). Perhaps not all invasive species have the same invasion traits, but different combinations of invasion traits can be found among invaders, corresponding to different possibilities to become a successful invader. These combinations of invasion traits might be linked to taxonomic affiliation, but this is not necessarily the case. We created a global dataset with 201 invasive species from seven major taxonomic groups (animals, green plants, fungi, heterokonts, bacteria, red algae, alveolates) and 13 invasion traits that are applicable across all taxa. The dataset was analysed with cluster analysis to search for similarities in combinations of invasion traits. Three of the five clusters, comprising 60% of all species, contain several major taxonomic groups. While some invasion trait frequencies were significantly related to taxonomic affiliation, the results show that invasive species from different taxonomic groups often share similar combinations of invasion traits. A post-hoc analysis suggests that combinations of traits characterizing successful invaders can be associated with invasion stages across taxa. Our findings suggest that there are no universal invasion traits which could explain the invasion success of all invaders, but that invaders are successful for different reasons which are represented by different combinations of invasion traits across taxonomic groups