435 research outputs found

    Integrating climatic information in water resources modelling and optimisation

    Get PDF

    Atmospheric Downscaling using Multi-Objective Genetic Programming

    Get PDF
    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

    Toward bridging future irrigation deficits utilizing the shark algorithm integrated with a climate change model

    Get PDF
    Climate change is one of the most effectual variables on the dam operations and reservoir water system. This is due to the fact that climate change has a direct effect on the rainfall–runoff process that is influencing the water inflow to the reservoir. This study examines future trends in climate change in terms of temperature and precipitation as an important predictor to minimize the gap between water supply and demand. In this study, temperature and precipitation were predicted for the period between 2046 and 2065, in the context of climate change, based on the A1B scenario and the HAD-CM3 model. Runoff volume was then predicted with the IHACRES model. A new, nature-inspired optimization algorithm, named the shark algorithm, was examined. Climate change model results were utilized by the shark algorithm to generate an optimal operation rule for dam and reservoir water systems to minimize the gap between water supply and demand for irrigation purposes. The proposed model was applied for the Aydoughmoush Dam in Iran. Results showed that, due to the decrease in water runoff to the reservoir and the increase in irrigation demand, serious irrigation deficits could occur downstream of the Aydoughmoush Dam

    Water supply reservoir operation in the framework of climate variability and change

    Get PDF
    The optimal planning and operation of a reservoir system is getting more crucial particularly in view of the recent awareness of potential climate change. In particular, the incorporation of hydrologic uncertainties due to climate change into reservoir operation system requires comprehensive and long-term hydrological database which rarely available in most of the conventional reservoir design. The prime objective of the study is to formulate a multiple approach on the long-term reservoir operation optimization under the scarcity of observed hydrological data and with the influence of climate change. A combined research method using IHACRES for hydrological simulation, HadCM3 for emission scenario and Statistical Downscaling Model were developed along with a Mixed Integer Linear Programming (MILP) for reservoir operation optimization. These approaches were applied to a single purpose Sg Layang Resevoir, that is one of the most prominent water supply reservoir located in Johor State, Malaysia. The climatic variables obtained from general circulation model (GCM) were downscaled corresponding to HadCM3 emission scenario and used in climate change impact analysis. The SDSM was used to produce 100 synthetic climate time-series for 90 years of the participating station, representing the climate change projection and baseline period. With respect to the baseline data, an apparent increase in temperature (1.2 degree Celsius between time periods) and rainfall was observed. The deterministic optimization exercise is performed repetitively for a number of case scenarios based on weekly reservoir’s inflows derived from the projected climate change in a way to determine the optimal operation rule and policy which are based on total pumping volume and pumping cost. Corresponded to the future inflows, the pumping volume has shown an increase trend particularly during southwest monsoon, transition between seasons and autumn. Judged from the decreasing rate of the streamflows, a 34 to 40% increase in the projected monthly pumping volume is anticipated. An opposite scenario is observed during northeast monsoon season which shows a decreasing trend of 28% to 46%. At various degree of statistical reliability, the optimal operational pumping curves of the reservoir were established. These curves provide some basic information on the monthly pumping requirement from various sources of inflow to sustain the reservoir storage and demand. These operation curves are of very useful guidelines for reservoir operators in making decision to follow an optimal pumping operations schedule onsite. Such research findings were expected to generate a general awareness to the public water authorities on the potential long term effect of climate change to the reliability of reservoir operating system

    Construction of the Intensity-Duration-Frequency (IDF) Curves under Climate Change

    Get PDF
    Intensity-Duration-Frequency (IDF) curves are among the standard design tools for various engineering applications, such as storm water management systems. The current practice is to use IDF curves based on historical extreme precipitation quantiles. A warming climate, however, might change the extreme precipitation quantiles represented by the IDF curves, emphasizing the need for updating the IDF curves used for the design of urban storm water management systems in different parts of the world, including Canada. This study attempts to construct the future IDF curves for Saskatoon, Canada, under possible climate change scenarios. For this purpose, LARS-WG, a stochastic weather generator, is used to spatially downscale the daily precipitation projected by Global Climate Models (GCMs) from coarse grid resolution to the local point scale. The stochastically downscaled daily precipitation realizations were further disaggregated into ensemble hourly and sub-hourly (as fine as 5-minute) precipitation series, using a disaggregation scheme developed using the K-nearest neighbor (K-NN) technique. This two-stage modeling framework (downscaling to daily, then disaggregating to finer resolutions) is applied to construct the future IDF curves in the city of Saskatoon. The sensitivity of the K-NN disaggregation model to the number of nearest neighbors (i.e. window size) is evaluated during the baseline period (1961-1990). The optimal window size is assigned based on the performance in reproducing the historical IDF curves by the K-NN disaggregation models. Two optimal window sizes are selected for the K-NN hourly and sub-hourly disaggregation models that would be appropriate for the hydrological system of Saskatoon. By using the simulated hourly and sub-hourly precipitation series and the Generalized Extreme Value (GEV) distribution, future changes in the IDF curves and associated uncertainties are quantified using a large ensemble of projections obtained for the Canadian and British GCMs (CanESM2 and HadGEM2-ES) based on three Representative Concentration Pathways; RCP2.6, RCP4.5, and RCP8.5 available from CMIP5 – the most recent product of the Intergovernmental Panel on Climate Change (IPCC). The constructed IDF curves are then compared with the ones constructed using another method based on a genetic programming technique. The results show that the sign and the magnitude of future variations in extreme precipitation quantiles are sensitive to the selection of GCMs and/or RCPs, and the variations seem to become intensified towards the end of the 21st century. Generally, the relative change in precipitation intensities with respect to the historical intensities for CMIP5 climate models (e.g., CanESM2: RCP4.5) is less than those for CMIP3 climate models (e.g., CGCM3.1: B1), which may be due to the inclusion of climate policies (i.e., adaptation and mitigation) in CMIP5 climate models. The two-stage downscaling-disaggregation method enables quantification of uncertainty due to natural internal variability of precipitation, various GCMs and RCPs, and downscaling methods. In general, uncertainty in the projections of future extreme precipitation quantiles increases for short durations and for long return periods. The two-stage method adopted in this study and the GP method reconstruct the historical IDF curves quite successfully during the baseline period (1961-1990); this suggests that these methods can be applied to efficiently construct IDF curves at the local scale under future climate scenarios. The most notable precipitation intensification in Saskatoon is projected to occur with shorter storm duration, up to one hour, and longer return periods of more than 25 years

    A disposition of interpolation techniques

    Get PDF
    A large collection of interpolation techniques is available for application in environmental research. To help environmental scientists in choosing an appropriate technique a disposition is made, based on 1) applicability in space, time and space-time, 2) quantification of accuracy of interpolated values, 3) incorporation of ancillary information, and 4) incorporation of process knowledge. The described methods include inverse distance weighting, nearest neighbour methods, geostatistical interpolation methods, Kalman filter methods, Bayesian Maximum Entropy methods, etc. The applicability of methods in aggregation (upscaling) and disaggregation (downscaling) is discussed. Software for interpolation is described. The application of interpolation techniques is illustrated in two case studies: temporal interpolation of indicators for ecological water quality, and spatio-temporal interpolation and aggregation of pesticide concentrations in Dutch surface waters. A valuable next step will be to construct a decision tree or decision support system, that guides the environmental scientist to easy-to-use software implementations that are appropriate to solve their interpolation problem. Validation studies are needed to assess the quality of interpolated values, and the quality of information on uncertainty provided by the interpolation method

    Conjunctive Management of Water Resources under Climate Change Projection Uncertainty

    Get PDF
    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

    Avances en la regionalización estadística de escenarios de cambio climático para precipitación basados en técnicas de aprendizaje automático

    Get PDF
    A pesar de ser la principal herramienta para estudiar el cambio climático, los modelos globales de clima (GCM) siguen teniendo una resolución espacial limitada y presentan errores sistemáticos considerables con respecto al clima observado. La regionalización estadística pretende resolver este problema aprendiendo relaciones empíricas entre variables de larga escala, bien reproducidas por los GCM (por ejemplo, los vientos sinópticos o el geopotencial), y observaciones locales de la variable en superficie de interés, como la precipitación, objeto de esta tesis. Proponemos una serie de desarrollos novedosos que permiten mejorar la consistencia de los campos regionalizados y producir escenarios regionales plausibles de cambio climático. Los resultados de esta tesis tienen importantes implicaciones para los diferentes sectores que necesitan información fiable de precipitación para llevar a cabo sus evaluaciones de impactos.Even though they are the main tool to study climate change, global climate models (GCMs) still have a limited spatial resolution and exhibit considerable systematic errors with respect to the observed climate. Statistical downscaling aims to solve this issue by learning empirical relationships between large-scale variables, well reproduced by GCMs (such as synoptic winds or geopotential), and local observations of the target surface variable, such as precipitation, the focus of this thesis. We propose a series of novel developments which allow for improving the consistency of the downscaled fields and producing plausible local-to-regional climate change scenarios. The results of this thesis have important implications for the different sectors in need of reliable precipitation information to undertake their impact assessments

    Convolutional conditional neural processes for local climate downscaling

    Get PDF
    A new model is presented for multisite statistical downscaling of temperature and precipitation using convolutional conditional neural processes (convCNPs). ConvCNPs are a recently developed class of models that allow deep-learning techniques to be applied to off-the-grid spatio-temporal data. In contrast to existing methods that map from low-resolution model output to high-resolution predictions at a discrete set of locations, this model outputs a stochastic process that can be queried at an arbitrary latitude–longitude coordinate. The convCNP model is shown to outperform an ensemble of existing downscaling techniques over Europe for both temperature and precipitation taken from the VALUE intercomparison project. The model also outperforms an approach that uses Gaussian processes to interpolate single-site downscaling models at unseen locations. Importantly, substantial improvement is seen in the representation of extreme precipitation events. These results indicate that the convCNP is a robust downscaling model suitable for generating localised projections for use in climate impact studies
    corecore