The CLIVAR C20C project: skill of simulating Indian monsoon rainfall on interannual to decadal timescales. Does GHG forcing play a role?

The ability of atmospheric general circulation models (AGCMs), that are forced with observed sea surface temperatures (SSTs), to simulate the Indian monsoon rainfall (IMR) variability on interannual to decadal timescales is analyzed in a multimodel intercomparison. The multimodel ensemble has been performed within the CLIVAR International “Climate of the 20th Century” (C20C) Project. This paper is part of a C20C intercomparison of key climate time series. Whereas on the interannual timescale there is modest skill in reproducing the observed IMR variability, on decadal timescale the skill is much larger. It is shown that the decadal IMR variability is largely forced, most likely by tropical sea surface temperatures (SSTs), but as well by extratropical and especially Atlantic Multidecadal Oscillation (AMO) related SSTs. In particular there has been a decrease from the late 1950s to the 1990s that corresponds to a general warming of tropical SSTs. Using a selection of control integrations from the World Climate Research Programme’s (WCRP’s) Coupled Model Intercomparison Project phase 3 (CMIP3), it is shown that the increase of greenhouse gases (GHG) in the twentieth century has not significantly contributed to the observed decadal IMR variability.     

An Overview of Terrestrial Sequestration of Carbon Dioxide: the United States Department of Energy's Fossil Energy R&D Program

Increasing concentrations of CO₂ and other greenhouse gases (GHG) in the Earth's atmosphere have the potential to enhance the natural greenhouse effect, which may result in climatic changes. The main anthropogenic contributors to this increase are fossil fuel combustion, land use conversion, and soil cultivation. It is clear that overcoming the challenge of global climate change will require a combination of approaches, including increased energy efficiency, energy conservation, alternative energy sources, and carbon (C) capture and sequestration. The United States Department of Energy (DOE) is sponsoring the development of new technologies that can provide energy and promote economic prosperity while reducing GHG emissions. One option that can contribute to achieving this goal is the capture and sequestration of CO₂ in geologic formations. An alternative approach is C sequestration in terrestrial ecosystsems through natural processes. Enhancing such natural pools (known as natural sequestration) can make a significant contribution to CO₂ management strategies with the potential to sequester about 290 Tg C/y in U.S. soils. In addition to soils, there is also a large potential for C sequestration in above and belowground biomass in forest ecosystems.A major area of interest to DOE's fossil energy program is reclaimed mined lands, of which there may be 0.63 ×10⁶ ha in the U.S. These areas are essentially devoid of soil C; therefore, they provide an excellent opportunity to sequester C in both soils and vegetation. Measurement of C in these ecosystems requires the development of new technology and protocols that are accurate and economically viable. Field demonstrations are needed to accurately determine C sequestration potential and to demonstrate the ecological and aesthetic benefits in improved soil and water quality, increased biodiversity, and restored ecosystems.The DOE's research program in natural sequestration highlights fundamental and applied studies, such as the development of measurement, monitoring, and verification technologies and protocols and field tests aimed at developing techniques for maximizing the productivity of hitherto infertile soils and degraded ecosystems.     

Application of Large‐Scale Inversion Algorithms to Hydraulic Tomography in an Alluvial Aquifer

Large‐scale inversion methods have been recently developed and permitted now to considerably reduce the computation time and memory needed for inversions of models with a large amount of parameters and data. In this work, we have applied a deterministic geostatistical inversion algorithm to a hydraulic tomography investigation conducted in an experimental field site situated within an alluvial aquifer in Southern France. This application aims to achieve a 2‐D large‐scale modeling of the spatial transmissivity distribution of the site. The inversion algorithm uses a quasi‐Newton iterative process based on a Bayesian approach. We compared the results obtained by using three different methodologies for sensitivity analysis: an adjoint‐state method, a finite‐difference method, and a principal component geostatistical approach (PCGA). The PCGA is a large‐scale adapted method which was developed for inversions with a large number of parameters by using an approximation of the covariance matrix, and by avoiding the calculation of the full Jacobian sensitivity matrix. We reconstructed high‐resolution transmissivity fields (composed of up to 25,600 cells) which generated good correlations between the measured and computed hydraulic heads. In particular, we show that, by combining the PCGA inversion method and the hydraulic tomography method, we are able to substantially reduce the computation time of the inversions, while still producing high‐quality inversion results as those obtained from the other sensitivity analysis methodologies.     

Sulfur Hexafluoride and Potassium Bromide as Groundwater Tracers for Managed Aquifer Recharge

Sulfur hexafluoride (SF₆) is an established tracer for use in managed aquifer recharge projects. SF₆ exsolves from groundwater when it encounters trapped air according to Henry's law. This results in its retardation relative to groundwater flow, which can help determine porous media saturation and flow dynamics. SF₆ and the conservative, nonpartitioning tracer, bromide (Br⁻ added as KBr), were introduced to recharge water infiltrated into stacked glacial aquifers in Thurston County, Washington, providing the opportunity to observe SF₆ partitioning. Br⁻, which is assumed to travel at the same velocity as the groundwater, precedes SF₆ at most monitoring wells (MWs). Average groundwater velocity in the unconfined aquifer in the study area ranges from 3.9 to 40 m/d, except in the southwestern corner where it is slower. SF₆ in the shallow aquifer exhibits an average retardation factor of 2.5 ± 3.8, suggesting an air-to-water ratio on the order of 10⁻³ to 10⁻² in the pore space. Notable differences in tracer arrival times at adjacent wells indicate very heterogeneous conductivity. One MW exhibits double peaks in concentrations of both tracers with different degrees of retardation for the first and second peaks. This suggests multiple flowpaths to the well with variable saturation. The confining layer between the upper two aquifers appears to allow intermittent connection between aquifers but serves as an aquitard in most areas. This study demonstrates the utility of SF₆ partitioning for evaluating hydrologic conditions at prospective recharge sites.     

Risk-adjusted approaches for planning sustainable agricultural development

In this paper we show that explicit treatment of risks and uncertainties in agricultural production planning may considerably alter strategies for achieving robust outcomes with regard to sustainable agricultural developments. We discuss production planning models under uncertainties and risks that may assist in planning location-specific production expansion within environmental and health risk indicators and constraints. The proposed approaches are illustrated with the example of spatially explicit livestock production allocation in China to 2030.     

Validation of official erosion modelling based on high‐resolution radar rain data by aerial photo erosion classification

The universal soil loss equation (USLE) is the most frequently applied erosion prediction model and it is also implemented as an official decision‐making instrument for agricultural regulations. The USLE itself has been already validated using different approaches. Additional errors, however, arise from input data and interpolation procedures that become necessary for field‐specific predictions on a national scale for administrative purposes. In this study, predicted event soil loss using the official prediction system in Bavaria (Germany) was validated by comparison with aerial photo erosion classifications of 8100 fields. Values for the USLE factors were mainly taken from the official Bavarian high‐resolution (5 × 5 m²) erosion cadastre. As series of erosion events were examined, the cover and management factor was replaced by the soil loss ratio. The event erosivity factor was calculated from high‐resolution (1 × 1 km², 5 min), rain gauge‐adjusted radar rain data (RADOLAN). Aerial photo erosion interpretation worked sufficiently well and average erosion predictions and visual classifications correlated closely. This was also true for data broken down to individual factors and different crops. There was no reason to assume a general invalidity of the USLE and the official parametrization procedures. Event predictions mainly suffered from errors in the assumed crop stage period and tillage practices, which do not reflect interannual and farm‐specific variation. In addition, the resolution of radar data (1 km²) did not seem to be sufficient to predict short‐term erosion on individual fields given the strong spatial gradients within individual rains. The quality of the input data clearly determined prediction quality. Differences between USLE predictions and observations are most likely caused by parametrization weaknesses but not by a failure of the model itself. Copyright © 2017 John Wiley & Sons, Ltd.     

2021 JGS best paper award and the editors’ choice paper volume 24(1)

Journal of Geographical Systems -     

Learning in neural spatial interaction models: A statistical perspective

 In this paper we view learning as an unconstrained non-linear minimization problem in which the objective function is defined by the negative log-likelihood function and the search space by the parameter space of an origin constrained product unit neural spatial interaction model. We consider Alopex based global search, as opposed to local search based upon backpropagation of gradient descents, each in combination with the bootstrapping pairs approach to solve the maximum likelihood learning problem. Interregional telecommunication traffic flow data from Austria are used as test bed for comparing the performance of the two learning procedures. The study illustrates the superiority of Alopex based global search, measured in terms of Kullback and Leibler's information criterion. Received: December 2001 / Accepted: August 2002 The author gratefully thanks Martin Reismann (Department of Economic Geography & Geoinformatics) for his valuable research assistance.