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  • 1
    Publication Date: 2021-10-15
    Description: Cities are the drivers of socioeconomic innovation and are also forced to address the accelerating risk of failure in providing essential services such as water supply today and in the future. Here, we investigate the resilience of urban water supply security, which is defined in terms of the services that citizens receive. The resilience of services is determined by the availability and robustness of critical system elements or “capitals” (water resources, infrastructure, finances, management efficacy, and community adaptation). We translate quantitative information about this portfolio of capitals from seven contrasting cities on four continents into parameters of a coupled system dynamics model. Water services are disrupted by recurring stochastic shocks, and we simulate the dynamics of impact and recovery cycles. Resilience emerges under various constraints, expressed in terms of each city's capital portfolio. Systematic assessment of the parameter space produces the urban water resilience landscape, and we determine the position of each city along a continuous gradient from water insecure and nonresilient to secure and resilient systems. In several cities stochastic disturbance regimes challenge steady-state conditions and drive system collapse. While water insecure and nonresilient cities risk being pushed into a poverty trap, cities which have developed excess capitals risk being trapped in rigidity and crossing a tipping point from high to low services and collapse. Where public services are insufficient, community adaptation improves water security and resilience to varying degrees. Our results highlight the need for resilience thinking in the governance of urban water systems under global change pressures.
    Keywords: 333.9 ; systems dynamics modeling ; coupled natural-human-engineered systems (CNHES) ; adaptive capacity ; water management ; stochastic shocks ; Capital Portfolio Approach (CPA)
    Language: English
    Type: map
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  • 2
    Electronic Resource
    Electronic Resource
    Springer
    Plant and soil 67 (1982), S. 35-43 
    ISSN: 1573-5036
    Keywords: Agro-ecosystems ; Mathematical models ; N-cycling ; Simulation modeling
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Description / Table of Contents: Resumen Los modelos de simulación del comportamiento en agro-ecosistemas difieren en dos características: (i)entereza o minuciosidad conceptual, en términos del número de procesos que considera, y (ii)el nivel de detalle en el cual cada proceso es modelado. Estas características del modelo son determinadas por los objetivos para los cuales el modelo es diseñado y por el grado acutal del conocimiento de los procesos incluídos en el modelo. Los niveles de entereza conceptual y de detalle en un modelo determinan las aplicaciones potenciales para las cuales dicho modelo puede ser utilizado. Las aplicationes pueden ser orientadas a la investigación, manejo o planificación. Un modelo adecuado para una aplicación determinada debería tener un nivel apropiado de entereza y detalle para lograr el objetivo establecido. Los criterios de ayuda en la selección y evaluación de los modelos de simulación del nitrógeno para una aplicación particular incluyen: (i) la disponibilidad de facilidades computacionales, (ii) las escalas espacial y temporal de aplicación, (iii) el uso deseado de las simulaciones, (iv) la disponibilidad de datos de entrada al modelo, y (v) los ámbitos de confianza asociadas con las salidas del modelo.
    Notes: Abstract Available simulation models for describing nitrogen behavior in agro-ecosystems vary in two characteristics:(i) conceptual completeness in terms of the number of processes considered, and(ii) thelevel of detail at which each process is modeled. These model characteristics are determined by both the objectives that the model is designed to meet and the current state-of-the-art understanding of the various processes included in the model. The levels of conceptual completeness and detail in a model govern the potential applications for which the model may be used. Applications of models may be research-oriented, management-oriented, or planning-oriented. A model suitable for a given application should have an appropriate level of completeness and detail to accomplish the stated objective. Criteria to aid in the selection and evaluation of nitrogen simulation models for a particular application include: i) the availability of computational facilities, ii) the spatial and temporal scales of application, iii) the intended use of the simulations, iv) the availability of model input data, and v) the confidence regions associated with the model output.
    Type of Medium: Electronic Resource
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  • 3
    Electronic Resource
    Electronic Resource
    Springer
    Plant and soil 106 (1988), S. 159-169 
    ISSN: 1573-5036
    Keywords: allophane ; first-order kinetics ; mineralization ; nitrate
    Source: Springer Online Journal Archives 1860-2000
    Topics: Agriculture, Forestry, Horticulture, Fishery, Domestic Science, Nutrition
    Notes: Abstract Nitrogen mineralization kinetics of Costa Rican soils developed from volcanic ash were investigated. Two aspects of the aerobic incubation method proposed by Stanford and Smith (1972) were examined: the addition of—N nutrient leaching solution, and air-drying samples prior to incubation. Net cumulative N mineralized over 210 days was analyzed in terms of three kinetic models of N mineralization: single term, first-order (FO); two term, first-order (DFO); and combined first-order, zero-order (FOZ). Net cumulative N mineralization was significantly increased both by air-drying and by—N nutrient solution addition. The effect of air-drying was to induce a ‘flush’ of mineralization during the first 30 to 45 days of incubation, while the effect of the—N nutrient solution was more apparent after that time. The 16 mineralization curves generated were qualitatively similar. The FO model provided the worst fit with the data and deviated from it in a consistent manner. Both the DFO and the FOZ models fit the data closely but the precision of parameter estimation using the DFO model was relatively poor. The first-order term of the FOZ model was interpreted as accounting for pretreatment effects (e.g., air-drying) and the zero-order term as describing the ‘basal mineralization rate’ of the soil. The success of the zero-order term in describing mineralization over long times suggests that incubation methods cannot be used to isolate a ‘mineralizable fraction’ of soil organic N.
    Type of Medium: Electronic Resource
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