A Bayesian approach for multi-model downscaling: Seasonal forecasting of regional rainfall and river flows in South America

This study addresses three issues: spatial downscaling, calibration, and combination of seasonal predictions produced by different coupled ocean-atmosphere climate models. It examines the feasibility Of using a Bayesian procedure for producing combined, well-calibrated downscaled seasonal rainfall forecasts for two regions in South America and river flow forecasts for the Parana river in the south of Brazil and the Tocantins river in the north of Brazil. These forecasts are important for national electricity generation management and planning. A Bayesian procedure, referred to here as forecast assimilation, is used to combine and calibrate the rainfall predictions produced by three climate models. Forecast assimilation is able to improve the skill of 3-month lead November-December-January multi-model rainfall predictions over the two South American regions. Improvements are noted in forecast seasonal mean values and uncertainty estimates. River flow forecasts are less skilful than rainfall forecasts. This is partially because natural river flow is a derived quantity that is sensitive to hydrological as well as meteorological processes, and to human intervention in the form of reservoir management.

Land use for England and Wales: evaluation of management options to support 'good ecological status' in surface freshwaters

This paper analyses historic records of agricultural land use and management for England and Wales from 1931 and 1991 and uses export coefficient modelling to hindcast the impact of these practices on the rates of diffuse nitrogen (N) and phosphorus (P) export to water bodies for each of the major geo-climatic regions of England and Wales. Key trends indicate the importance of animal agriculture as a contributor to the total diffuse agricultural nutrient loading on waters, and the need to bring these sources under control if conditions suitable for sustaining 'Good Ecological Status' under the Water Framework Directive are to be generated. The analysis highlights the importance of measuring changes in nutrient loading in relation to the catchment-specific baseline state for different water bodies. The approach is also used to forecast the likely impact of broad regional scale scenarios on nutrient export to waters and highlights the need to take sensitive land out of production, introduce ceilings on fertilizer use and stocking densities, and controls on agricultural practice in higher risk areas where intensive agriculture is combined with a low intrinsic nutrient retention capacity, although the uncertainties associated with the modelling applied at this scale should be taken into account in the interpretation of model output. The paper advocates the need for a two-tiered approach to nutrient management, combining broad regional policies with targeted management in high risk areas at the catchment and farm scale.

Statistical downscaling of river flows

An extensive statistical ‘downscaling’ study is done to relate large-scale climate information from a general circulation model (GCM) to local-scale river flows in SW France for 51 gauging stations ranging from nival (snow-dominated) to pluvial (rainfall-dominated) river-systems. This study helps to select the appropriate statistical method at a given spatial and temporal scale to downscale hydrology for future climate change impact assessment of hydrological resources. The four proposed statistical downscaling models use large-scale predictors (derived from climate model outputs or reanalysis data) that characterize precipitation and evaporation processes in the hydrological cycle to estimate summary flow statistics. The four statistical models used are generalized linear (GLM) and additive (GAM) models, aggregated boosted trees (ABT) and multi-layer perceptron neural networks (ANN). These four models were each applied at two different spatial scales, namely at that of a single flow-gauging station (local downscaling) and that of a group of flow-gauging stations having the same hydrological behaviour (regional downscaling). For each statistical model and each spatial resolution, three temporal resolutions were considered, namely the daily mean flows, the summary statistics of fortnightly flows and a daily ‘integrated approach’. The results show that flow sensitivity to atmospheric factors is significantly different between nival and pluvial hydrological systems which are mainly influenced, respectively, by shortwave solar radiations and atmospheric temperature. The non-linear models (i.e. GAM, ABT and ANN) performed better than the linear GLM when simulating fortnightly flow percentiles. The aggregated boosted trees method showed higher and less variable R2 values to downscale the hydrological variability in both nival and pluvial regimes. Based on GCM cnrm-cm3 and scenarios A2 and A1B, future relative changes of fortnightly median flows were projected based on the regional downscaling approach. The results suggest a global decrease of flow in both pluvial and nival regimes, especially in spring, summer and autumn, whatever the considered scenario. The discussion considers the performance of each statistical method for downscaling flow at different spatial and temporal scales as well as the relationship between atmospheric processes and flow variability.

Small towns as 'sub-poles' in English rural development: Investigating rural-urban linkages using sub-regional social accounting matrices

This paper uses spatial economic data from four small English towns to measure the strength of economic integration between town and hinterland and to estimate the magnitude of town-hinterland spill-over effects. Following estimation of local integration indicators and inter-locale flows, sub-regional social accounting matrices (SAMs) are developed to estimate the strength of local employment and output multipliers for various economic sectors. The potential value of a town as a 'sub-pole' in local economic development is shown to be dependent on structural differences in the local economy, such as the particular mix of firms within towns. Although the multipliers are generally small, indicating a low level of local linkages, some sectors, particularly financial services and banking, show consistently higher multipliers for both output and employment. (c) 2007 Elsevier Ltd. All rights reserved.

Nitrogen flows from European watersheds to coastal marine waters

Nitrogen flows from European watersheds to coastal marine waters Executive summary Nature of the problem • Most regional watersheds in Europe constitute managed human territories importing large amounts of new reactive nitrogen. • As a consequence, groundwater, surface freshwater and coastal seawater are undergoing severe nitrogen contamination and/or eutrophication problems. Approaches • A comprehensive evaluation of net anthropogenic inputs of reactive nitrogen (NANI) through atmospheric deposition, crop N fixation,fertiliser use and import of food and feed has been carried out for all European watersheds. A database on N, P and Si fluxes delivered at the basin outlets has been assembled. • A number of modelling approaches based on either statistical regression analysis or mechanistic description of the processes involved in nitrogen transfer and transformations have been developed for relating N inputs to watersheds to outputs into coastal marine ecosystems. Key findings/state of knowledge • Throughout Europe, NANI represents 3700 kgN/km2/yr (range, 0–8400 depending on the watershed), i.e. five times the background rate of natural N2 fixation. • A mean of approximately 78% of NANI does not reach the basin outlet, but instead is stored (in soils, sediments or ground water) or eliminated to the atmosphere as reactive N forms or as N2. • N delivery to the European marine coastal zone totals 810 kgN/km2/yr (range, 200–4000 depending on the watershed), about four times the natural background. In areas of limited availability of silica, these inputs cause harmful algal blooms. Major uncertainties/challenges • The exact dimension of anthropogenic N inputs to watersheds is still imperfectly known and requires pursuing monitoring programmes and data integration at the international level. • The exact nature of ‘retention’ processes, which potentially represent a major management lever for reducing N contamination of water resources, is still poorly understood. • Coastal marine eutrophication depends to a large degree on local morphological and hydrographic conditions as well as on estuarine processes, which are also imperfectly known. Recommendations • Better control and management of the nitrogen cascade at the watershed scale is required to reduce N contamination of ground- and surface water, as well as coastal eutrophication. • In spite of the potential of these management measures, there is no choice at the European scale but to reduce the primary inputs of reactive nitrogen to watersheds, through changes in agriculture, human diet and other N flows related to human activity.

Anticipatory governance for social-ecological resilience

Anticipation is increasingly central to urgent contemporary debates, from climate change to the global economic crisis. Anticipatory practices are coming to the forefront of political, organizational, and citizens’ society. Research into anticipation, however, has not kept pace with public demand for insights into anticipatory practices, their risks and uses. Where research exists, it is deeply fragmented. This paper seeks to identify how anticipation is defined and understood in the literature and to explore the role of anticipatory practice to address individual, social, and global challenges. We use a resilience lens to examine these questions. We illustrate how varying forms of anticipatory governance are enhanced by multi-scale regional networks and technologies and by the agency of individuals, drawing from an empirical case study on regional water governance of Mälaren, Sweden. Finally, we discuss how an anticipatory approach can inform adaptive institutions, decision making, strategy formation, and societal resilience.

Anticipating future risk in social-ecological systems using fuzzy cognitive mapping: the case of wildfire in the Chiquitania, Bolivia

Understanding complex social-ecological systems, and anticipating how they may respond to rapid change, requires an approach that incorporates environmental, social, economic, and policy factors, usually in a context of fragmented data availability. We employed fuzzy cognitive mapping (FCM) to integrate these factors in the assessment of future wildfire risk in the Chiquitania region, Bolivia. In this region, dealing with wildfires is becoming increasingly challenging due to reinforcing feedbacks between multiple drivers. We conducted semi-structured interviews and constructed different FCMs in focus groups to understand the regional dynamics of wildfire from diverse perspectives. We used FCM modelling to evaluate possible adaptation scenarios in the context of future drier climatic conditions. Scenarios also considered possible failure to respond in time to the emergent risk. This approach proved of great potential to support decision-making for risk management. It helped identify key forcing variables and generate insights into potential risks and trade-offs of different strategies. All scenarios showed increased wildfire risk in the event of more droughts. The ‘Hands-off’ scenario resulted in amplified impacts driven by intensifying trends, affecting particularly the agricultural production. The ‘Fire management’ scenario, which adopted a bottom-up approach to improve controlled burning, showed less trade-offs between wildfire risk reduction and production compared to the ‘Fire suppression’ scenario. Findings highlighted the importance of considering strategies that involve all actors who use fire, and the need to nest these strategies for a more systemic approach to manage wildfire risk. The FCM model could be used as a decision-support tool and serve as a ‘boundary object’ to facilitate collaboration and integration of different forms of knowledge and perceptions of fire in the region. This approach has also the potential to support decisions in other dynamic frontier landscapes around the world that are facing increased risk of large wildfires.