Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Statistics for Spatial Data.

The purpose of this paper is to provide a comprehensive presentation and interpretation of the Ensemble Kalman Filter (EnKF) and its numerical implementation. The EnKF has a large user group, and numerous publications have discussed applications and theoretical aspects of it. This paper reviews the important results from these studies and also presents new ideas and alternative interpretations which further explain the success of the EnKF. In addition to providing the theoretical framework needed for using the EnKF, there is also a focus on the algorithmic formulation and optimal numerical implementation. A program listing is given for some of the key subroutines. The paper also touches upon specific issues such as the use of nonlinear measurements, in situ profiles of temperature and salinity, and data which are available with high frequency in time. An ensemble based optimal interpolation (EnOI) scheme is presented as a cost-effective approach which may serve as an alternative to the EnKF in some applications. A fairly extensive discussion is devoted to the use of time correlated model errors and the estimation of model bias.

The Ensemble Kalman Filter: Theoretical formulation and practical implementation

The erosion, transport and redeposition of sediments shape the Earth's surface, and affect the structure and function of ecosystems and society(1,2). The Yellow River was once the world's largest carrier of fluvial sediment, but its sediment load has decreased by approximately 90% over the past 60 years(3). The decline in sediment load is due to changes in water discharge and sediment concentration, which are both influenced by regional climate change and human activities. Here we use an attribution approach to analyse 60 years of runoff and sediment load observations from the traverse of the Yellow River over China's Loess Plateau - the source of nearly 90% of its sediment load. We find that landscape engineering, terracing and the construction of check dams and reservoirs were the primary factors driving reduction in sediment load from the 1970s to 1990s, but large-scale vegetation restoration projects have also reduced soil erosion from the 1990s onwards. We suggest that, as the ability of existing dams and reservoirs to trap sediments declines in the future, erosion rates on the Loess Plateau will increasingly control the Yellow River's sediment load.

Reduced sediment transport in the Yellow River due to anthropogenic changes

Drought is a complex natural hazard that impacts ecosystems and society in many ways. Many of these impacts are associated with hydrological drought (drought in rivers, lakes, and groundwater). It is, therefore, crucial to understand the development and recovery of hydrological drought. In this review an overview is given of the current state of scientific knowledge of definitions, processes, and quantification of hydrological drought. Special attention is given to the influence of climate and terrestrial properties (geology, land use) on hydrological drought characteristics and the role of storage. Furthermore, the current debate about the use and usefulness of different drought indicators is highlighted and recent advances in drought monitoring and prediction are mentioned. Research on projections of hydrological drought for the future is summarized. This review also briefly touches upon the link of hydrological drought characteristics with impacts and the issues related to drought management. Finally, four challenges for future research on hydrological drought are defined that relate international initiatives such as the Intergovernmental Panel on Climate Change (IPCC) and the ‘Panta Rhei’ decade of the International Association of Hydrological Sciences (IAHS). WIREs Water 2015, 2:359–392. doi: 10.1002/wat2.1085

For further resources related to this article, please visit the WIREs website.

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Hydrological drought explained

[1] Both climate change and human activities are known to have induced changes to hydrology. Consequently, quantifying the net impact of human contribution to the streamflow change is a challenge. In this paper, a decomposition method based on the Budyko hypothesis is used to quantify the climate (i.e., precipitation and potential evaporation change) and direct human impact on mean annual streamflow (MAS) for 413 watersheds in the contiguous United States. The data for annual precipitation, runoff, and potential evaporation are obtained from the international Model Parameter Estimation Experiment (MOPEX), which is often assumed to only include gauges unaffected by human interferences. The data are split into two periods (1948–1970 and 1971–2003) to quantify the change over time. Although climate is found to affect MAS more than direct human impact, the results show that assuming the MOPEX data set to be unaffected by human activities is far from realistic. Climate change causes increasing MAS in most watersheds, while the direct human-induced change is spatially heterogeneous in the contiguous United States, with strong regional patterns, e.g., human activities causing increased MAS in the Midwest and significantly decreased MAS in the High Plains. The climate- and human-induced changes are found to be more severe in arid regions, where water is limited. Comparing the results to a collection of independent data sets indicates that the estimated direct human impacts on MAS in this largely nonurban set of watersheds might be attributed to several human activities, such as cropland expansion, irrigation, and the construction of reservoirs.

/pdf/quantifying-the-relative-contribution-of-the-climate-and-41xvd9jah2.pdf

Quantifying the relative contribution of the climate and direct human impacts on mean annual streamflow in the contiguous United States

Marginal agricultural land is estimated for biofuel production in Africa, China, Europe, India, South America, and the continental United States, which have major agricultural production capacities. These countries/regions can have 320-702 million hectares of land available if only abandoned and degraded cropland and mixed crop and vegetation land, which are usually of low quality, are accounted. If grassland, savanna, and shrubland with marginal productivity are considered for planting low-input high-diversity (LIHD) mixtures of native perennials as energy crops, the total land availability can increase from 1107-1411 million hectares, depending on if the pasture land is discounted. Planting the second generation of biofuel feedstocks on abandoned and degraded cropland and LIHD perennials on grassland with marginal productivity may fulfill 26-55% of the current world liquid fuel consumption, without affecting the use of land with regular productivity for conventional crops and without affecting the current pasture land. Under the various land use scenarios, Africa may have more than one-third, and Africa and Brazil, together, may have more than half of the total land available for biofuel production. These estimations are based on physical conditions such as soil productivity, land slope, and climate.

Land Availability for Biofuel Production

Separating the impacts of climate change and human activities on runoff using the Budyko-type equations with time-varying parameters

Coastal responses to sea level rise (SLR) include inundation of wetlands, increased shoreline erosion, and increased flooding during storm events. Hydrodynamic parameters such as tidal ranges, tidal prisms, tidal asymmetries, increased flooding depths and inundation extents during storm events respond nonadditively to SLR. Coastal morphology continually adapts toward equilibrium as sea levels rise, inducing changes in the landscape. Marshes may struggle to keep pace with SLR and rely on sediment accumulation and the availability of suitable uplands for migration. Whether hydrodynamic, morphologic, or ecologic, the impacts of SLR are interrelated. To plan for changes under future sea levels, coastal managers need information and data regarding the potential effects of SLR to make informed decisions for managing human and natural communities. This review examines previous studies that have accounted for the dynamic, nonlinear responses of hydrodynamics, coastal morphology, and marsh ecology to SLR by implementing more complex approaches rather than the simplistic “bathtub” approach. These studies provide an improved understanding of the dynamic effects of SLR on coastal environments and contribute to an overall paradigm shift in how coastal scientists and engineers approach modeling the effects of SLR, transitioning away from implementing the “bathtub” approach. However, it is recommended that future studies implement a synergetic approach that integrates the dynamic interactions between physical and ecological environments to better predict the impacts of SLR on coastal systems.

https://agupubs.onlinelibrary.wiley.com/doi/epdf/10.1002/2015EF000298

The dynamic effects of sea level rise on low-gradient coastal landscapes: A review

Hydrologic models can be categorized as being either Newtonian or Darwinian in nature. The Newtonian approach requires a thorough understanding of the individual physical processes acting in a watershed in order to build a detailed hydrologic model based on the conservation equations. The Darwinian approach seeks to explain the behavior of a hydrologic system as a whole by identifying simple and robust temporal or spatial patterns that capture the relevant processes. Darwinian-based hydrologic models include the Soil Conservation Service (SCS) curve number model, the “abcd” model, and the Budyko-type models. However, these models were developed based on widely differing principles and assumptions and applied to distinct time scales. Here, we derive a one-parameter Budyko-type model for mean annual water balance which is based on a generalization of the proportionality hypothesis of the SCS model and therefore is independent of temporal scale. Furthermore, we show that the new model is equivalent to the key equation of the “abcd” model. Theoretical lower and upper bounds of the new model are identified and validated based on previous observations. Thus, we illustrate a temporal pattern of water balance amongst Darwinian hydrologic models, which allows for synthesis with the Newtonian approach and offers opportunities for progress in hydrologic modeling.

/pdf/a-one-parameter-budyko-model-for-water-balance-captures-4vdag7sl1y.pdf

Dingbao Wang

Papers

Quantifying the relative contribution of the climate and direct human impacts on mean annual streamflow in the contiguous United States

Land Availability for Biofuel Production

Separating the impacts of climate change and human activities on runoff using the Budyko-type equations with time-varying parameters

The dynamic effects of sea level rise on low-gradient coastal landscapes: A review

A one-parameter Budyko model for water balance captures emergent behavior in darwinian hydrologic models