786 research outputs found

    Atmospheric Downscaling using Multi-Objective Genetic Programming

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    Numerical models are used to simulate and to understand the interplay of physical processes in the atmosphere, and to generate weather predictions and climate projections. However, due to the high computational cost of atmospheric models, discrepancies between required and available spatial resolution of modeled atmospheric data occur frequently. One approach to generate higher-resolution atmospheric data from coarse atmospheric model output is statistical downscaling. The present work introduces multi-objective Genetic Programming (MOGP) as a method for downscaling atmospheric data. MOGP is applied to evolve downscaling rules, i.e., statistical relations mapping coarse-scale atmospheric information to the point scale or to a higher-resolution grid. Unlike classical regression approaches, where the structure of the regression model has to be predefined, Genetic Programming evolves both model structure and model parameters simultaneously. Thus, MOGP can flexibly capture nonlinear and multivariate predictor-predictand relations. Classical linear regression predicts the expected value of the predictand given a realization of predictors minimizing the root mean square error (RMSE) but in general underestimating variance. With the multi-objective approach multiple cost/fitness functions can be considered which are not solely aimed at the minimization of the RMSE, but simultaneously consider variance and probability distribution based measures. Two areas of application of MOGP for atmospheric downscaling are presented: The downscaling of mesoscale near-surface atmospheric fields from 2.8 km to 400 m grid spacing and the downscaling of temperature and precipitation series from a global reanalysis to a set of local stations. (1) With growing computational power, integrated modeling platforms, coupling atmospheric models to land surface and hydrological/subsurface models are increasingly used to account for interactions and feedback processes between the different components of the soil-vegetation-atmosphere system. Due to the small-scale heterogeneity of land surface and subsurface, land surface and subsurface models require a small grid spacing, which is computationally unfeasible for atmospheric models. Hence, in many integrated modeling systems, a scale gap occurs between atmospheric model component and the land surface/subsurface components, which potentially introduces biases in the estimation of the turbulent exchange fluxes at the surface. Under the assumption that the near surface atmospheric boundary layer is significantly influenced by land surface heterogeneity, MOGP is used to evolve downscaling rules that recover high-resolution near-surface fields of various atmospheric variables (temperature, wind speed, etc.) from coarser atmospheric data and high-resolution land surface information. For this application MOGP does not significantly reduce the RMSE compared to a pure interpolation. However, (depending on the state variable under consideration) large parts of the spatial variability can be restored without any or only a small increase in RMSE. (2) Climate change impact studies often require local information while the general circulation models used to create climate projections provide output with a grid spacing in the order of approximately 100~km. MOGP is applied to estimate the local daily maximum, minimum and mean temperature and the daily accumulated precipitation at selected stations in Europe from global reanalysis data. Results are compared to standard regression approaches. While for temperature classical linear regression already achieves very good results and outperforms MOGP, the results of MOGP for precipitation downscaling are promising and outperform a standard generalized linear model. Especially the good representation of precipitation extremes and spatial correlation (with the latter not incorporated in the objectives) are encouraging.Numerische Modelle, welche für Wettervorhersagen und Klimaprojektionen verwendet werden, simulieren das Zusammenspiel physikalischer Prozesse in der Atmosphäre. Bedingt durch den hohen Rechenaufwand atmosphärischer Modelle treten jedoch häufig Diskrepanzen zwischen benötigter und verfügbarer Auflösung atmosphärischer Daten auf. Ein möglicher Ansatz, höher aufgelöste atmosphärische Daten aus vergleichsweise grobem Modelloutput zu generieren, ist statistisches Downscaling. Die vorliegende Arbeit stellt multi-objektives Genetic Programming (MOGP) als Methode für das Downscaling atmosphärischer Daten vor. MOGP wird verwendet, um Downscaling Regeln (statistische Beziehungen) zu generieren, welche grobskalige atmosphärische Daten auf die Punktskala oder ein höher aufgelöstes Gitter abbilden. Im Gegensatz zu klassischen Regressionsansätzen, in welchen die Struktur des Regressionsmodells vorgegeben wird, entwickelt MOGP Modellstruktur und Modellparameter simultan. Dieses erlaubt es, auch nicht lineare und multivariate Beziehungen zwischen Prädiktoren und Prädiktand zu berücksichtigen. Ein klassisches lineares Regressionsmodel schätzt den Erwartungswert des Prädiktanden, eine Realisierung von Prädiktoren gegeben, und minimiert somit den mittleren quadratischen Fehler (root mean square error, RMSE), aber unterschätzt im Allgemeinen die Varianz. Mit einem multi-objektiven Ansatz können multiple Kostenfunktionen berücksichtigt werden, welche nicht ausschließlich auf die Minimierung des RMSE ausgelegt sind, sondern simultan auch Varianz und Wahrscheinlichkeitsverteilung berücksichtigen. In dieser Arbeit werden zwei verschiedene Anwendungen von MOGP für atmosphärisches Downscaling präsentiert: Das Downscaling mesoskaliger oberflächennaher atmosphärischer Felder von einem 2.8km auf ein 400 m Gitter und das Downscaling von Temperatur- und Niederschlagszeitreihen von globalen Reanalysedaten auf lokale Stationen. (1) Mit wachsender Rechenleistung werden integrierte Modellplattformen, welche Atmosphären-modelle mit Landoberflächenmodellen und hydrologischen Bodenmodellen koppeln, immer häufiger verwendet, um auch die Interaktionen und Feedbacks zwischen den Komponenten des Boden-Vegetations-Atmosphären Systems zu berücksichtigen. Aufgrund kleinskaliger Heterogenitäten in Landoberfläche und Boden benötigen die Landoberflächen- und Bodenmodelle eine hohe Gitterauflösung. Für atmosphärische Modelle hingegen ist eine solch hohe Auflösung rechnerisch nicht praktikabel. Daher findet sich typischerweise ein Skalenunterschied zwischen atmosphärischer und Landoberflächen-/hydrologischer Modellkomponente. Solch ein Skalensprung kann jedoch zu Problemen bei der Schätzung der turbulenten Flüsse zwischen Atmosphäre und Boden führen, da die turbulenten Flüsse in nichtlinearer Weise vom Zustand des Bodens und der bodennahen Atmosphäre abhängen. Die mit MOGP entwickelten Downscaling Regeln verwenden grob aufgelöste atmosphärische Daten und hoch aufgelöste Landoberflächen-Informationen, um hoch aufgelöste Felder verschiedener bodennaher atmosphärischer Variablen (Temperatur, Windgeschwindigkeit etc.) generieren. Die Regeln basieren somit auf der Annahme, dass die bodennahe atmosphärische Grenzschicht signifikant von der Heterogenität der Landoberfläche beeinflusst wird. Zwar erreicht MOGP für diese Anwendung nur selten eine signifikante Reduktion des RMSE gegenüber einer reinen Interpolation, jedoch kann, abhängig von der betrachteten atmosphärischen Variablen, ein großer Teil der räumlichen Variabilität wiederhergestellt werden ohne oder mit nur sehr geringem Anstieg des RMSE. (2) Studien zur Auswirkung des Klimawandels benötigen oft hochaufgelöste oder lokale atmosphärische Daten. Der Output globaler Klimamodelle, mit Hilfe derer Klimaprojektionen erstellt werden, ist gemeinhin zu grob. MOGP wird verwendet, um Tagesmaximum, -minimum und -mittel der Temperatur sowie den täglich akkumulierten Niederschlag an lokalen Stationen in Europa zu schätzen. Die Resultate werden mit linearen Regressionsmethoden verglichen. Für das Downscaling von Temperatur liefert eine klassische lineare Regression bereits sehr gute Resultate, welche MOGP im Allgemeinen an Qualität übertreffen. Für Niederschlag hingegen sind die MOGP Resultate vielversprechend, auch im Vergleich zu generalisierten linearen Modellen. Insbesondere die Repräsentation von Niederschlagsextremen und räumlicher Korrelation (letzteres ist nicht Bestandteil der Kostenfunktionen) sind vielversprechend

    Conjunctive Management of Water Resources under Climate Change Projection Uncertainty

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    Goal of this study is to investigate the impacts of climate change projection uncertainty on conjunctive use of water resources. To pursue this goal first, a conjunctive-use model is developed for management of groundwater and surface water resources via mixed integer linear fractional programming (MILFP). The conjunctive management model maximizes the ratio of groundwater usage to reservoir water usage. A conditional head constraint is imposed to maintain groundwater sustainability. A transformation approach is introduced to transform the conditional head constraint into a set of mixed integer linear constraints in terms of groundwater head. A supply network is proposed to apply the conjunctive-use model to northern Louisiana and southern Arkansas. Then, simple model averaging (SMA), reliability ensemble averaging (REA), and hierarchical Bayesian model averaging (HBMA) are utilized as ensemble averaging methods to provide a thorough understanding of the impacts of climate change on future runoff for the study area. An ensemble of 78 hydroclimate models is formed by forcing HELP3 with climate data from combinations of 13 GCMs, 2 RCPs, and 3 downscaling methods. Runoff projections obtained from SMA, REA, and HBMA are compared. The Analysis of Variance (ANOVA) is used to quantify the sources of uncertainty of SMA projection and compare to the estimations made by HBMA. Both methods show similar contribution of uncertainty indicating that GCMs are the dominant source of uncertainty. At last, the proposed conjunctive use model is applied to optimize the conjunctive use of future surface water and groundwater resources under climate change projection. Future inflows to the reservoirs are estimated from the future runoffs projected through hydroclimate modeling, where the Variable Infiltration Capacity (VIC) model and 11 GCM RCP8.5 downscaled climate outputs are considered. Bayesian model averaging (BMA) is adopted to quantify uncertainty in future runoff projections and reservoir inflow projections due to uncertain future climate projections. The results from the developed conjunctive management model indicate that the future reservoir water even with low inflow projections at 2.5% cumulative probability would be able to counterbalance groundwater pumping reduction to satisfy demands while improving the Sparta aquifer through conditional groundwater head constraint

    A new model to downscale urban and rural surface and air temperatures evaluated in Shanghai, China

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    A simple model, TsT2m (Surface Temperature and near surface air Temperature (at 2 m) model), is developed to downscale numerical model output (such as from ECMWF) to obtain higher temporal and spatial resolution surface and near surface air temperature. It is evaluated in Shanghai, China. Surface temperature (TS) and near surface air temperature (Ta) sub-models account for variations in land covers and their different thermal properties, resulting in spatial variations of surface and air temperature. The Net All Wave Radiation Parameterization (NARP) scheme is used to compute net wave radiation for the surface temperature sub-model, the Objective Hysteresis Model (OHM) is used to calculate the net storage heat fluxes, and the surface temperature is obtained by the force-restore method. The near surface air temperature sub-model considers the horizontal and vertical energy changes for a column of well mixed air above the surface. Modeled surface temperatures reproduce the general pattern of MODIS images well, while providing more detailed patterns of the surface urban heat island. However, the simulated surface temperatures capture the warmer urban land cover and are 10.3°C warmer on average than those derived from the coarser MODIS data. For other land cover types values are more similar. Downscaled, higher temporal and spatial resolution air temperatures are compared to observations at 110 Automatic Weather Stations across Shanghai. After downscaling with the TsT2m model, the average forecast accuracy of near surface air temperature is improved by about 20%. The scheme developed has considerable potential for prediction and mitigation of urban climate conditions, particularly for weather and climate services related to heat stres

    Uncertainty Modeling in The Assessment of Climate Change Impacts on Water Resources Management

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    Climate change has significant impacts on water resource systems. The objective of this study is to assess climate change impacts on water resource management. The methodology includes (a) the assessment of uncertainty introduced by choice of precipitation downscaling methods; (b) uncertainty assessment and quantification of the impact of climate change on projected streamflow; and (c) uncertainty in and impact of climate change on the management of reservoirs used for hydropower production. The assessment is conducted for two future time periods (2036 to 2065 and 2066 to 2095). The study area, Campbell River basin, British Columbia, Canada, consists of three reservoirs (Strathcona, Ladore and John Hart). A new multisite statistical downscaling method based on beta regression (BR) is developed for generating synthetic precipitation series, which can preserve temporal and spatial dependence along with other historical statistics (e.g. mean, standard deviation). To account for different uncertainty sources, four global climate models (GCMs), three greenhouse gas emission scenarios (RCPs), six downscaling models (DSMs), are considered, and the differences in projected variables of interest are analyzed. For streamflow generation a hydrologic model is used. The results show that the downscaling models contribute the highest amount of uncertainty to future streamflow predictions when compared to the contributions by GCMs or RCPs. It is also observed that the summer (June, July & August) and fall (September, October & December) flows into Strathcona dam (British Columbia) will decrease, while winter (December, January & February) flows will increase in both future time periods. In addition, the flow magnitude becomes more uncertain for higher return period flooding events in the Campbell River system under climate change than the low return period flooding events. To assess the climate change impacts on reservoir operation, in this study a system dynamics model is used for reservoir flow simulation. Results from system dynamics model show that as the inflow decreases in summer and fall, it also affects reservoir release and power production. It is projected that power production from downstream reservoirs (LDR & JHT) will decrease more drastically than the upstream reservoir (SCA) in both future time periods considered in this study

    SimulaciĂłn secular de la potencia eĂłlica generada mediante el empleo e algoritmos soft-computing

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    Tesis inédita de la Universidad Complutense de Madrid, Facultad de Ciencias Físicas, Departamento de Astrofísica y Ciencias de la Atmósfera, leída el 08/04/2014Depto. de Física de la Tierra y AstrofísicaFac. de Ciencias FísicasTRUEunpu

    Transfer function models for statistical downscaling of monthly precipitation

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    Three transfer function based statistical downscaling namely, linear regression model (LM), generalized linear model (GLM), generalized additive model (GAM) have been developed to assess their performance in downscaling monthly rainfall. Previous studies reported that performance of downscaling model depends on climate region and characteristics of climatic variable being downscaled. This has motivated to assess the performance of these three statistical downscaling models to identify most suitable model for downscaling monthly rainfall in the East coast of Peninsular Malaysia. Assessment of model performance using standard statistical measures revealed that LM model performs best in downscaling monthly precipitation in the study area. The Nash–Sutcliffe efficiency (NSE) for LM was found always greater than 0.9 and 0.7 with predictor set selected using stepwise multiple regression method during model calibration and validation, respectively. The finding opposes the general conception of better performance of non-linear models compared to linear models in downscaling rainfall. The near normal distribution of monthly rainfall in the tropical region has made the LM model much stronger compared to other models which assume that distribution of dependent variable is not norma

    Comparative study on the reservoir operation planning with the climate change adaptation

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    The management planning of Pedu–Muda reservoir, Kedah, was investigated in the context of the climate change evolution. The aim of this study was to evaluate the impact of the climate change to the reservoir operating management system and its sustainability. The study was divided into two sections; Analysis 1 refers to the reservoir optimization adapted with the climate assessment. The statistical downscaling model reacted as the climate model to generate the long-term pattern of the local climates affected by the greenhouse gases. Analysis 2 refers to the reservoir optimization but excluded the climate changes assessment in the analyses. The non-dominated sorting genetic algorithm version II (NSGA-II) was applied in both analyses to optimize the water use due to the multi-objectives demand, maximizing water release, minimizing water shortage and maximizing reservoir storage. The formation of Pareto optimal solutions from both analyses was measured and compared. The results showed the Analysis 1 potential to produce consistence monthly flow with lesser error and higher correlation values. It also produced better Pareto optimal solution set and considered all the objectives demands. The NSGA-II also successfully improves and re-manages the reservoir storage efficiently and reduce the dependency of these reservoirs

    Statistical downscaling in climatology

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