Tree-based threshold modeling for short-term forecast of daily maximum ozone level

This paper proposes a simple class of threshold autoregressive model for purpose of forecasting daily maximum ozone concentrations in Southern California. Linear time series model has been widely considered in environmental modeling. However, this class of models fails to capture the nonlinearity in ozone process and the complexity of meteorological interactions with ozone. In this article, we used the threshold autoregressive models with two classes of regimes; periodic and meteorological regimes. Days in week were used for the periodic regimes and the regression tree method was used to define the regimes as a function of meteorological variables. As the reference model we used the autoregressive model with lagged ozone and various lagged meteorological variables as the covariates. The proposed models were applied to a 3-year dataset of daily maximum ozone concentrations obtained from five monitoring stations in San Bernardino County, CA and their forecast performances were evaluated using an independent year-long dataset from the same stations. The results showed that the threshold models well capture the nonlinearity in ozone process and remove the nonstationarity in model residuals. The threshold models outperformed the non-threshold autoregressive models in day-ahead forecasts. The tree-based model showed slightly better performance than the periodic threshold model.     

Selection of the data time interval for the prediction of maximum ozone concentrations

This paper highlights the problem of step-length selection for the one-step-ahead prediction of ozone called the data time interval. This is done using a case study-based comparison of two approaches for predicting the maximum daily values of tropospheric ozone. The first approach is the 1-day-ahead prediction and the second is the prediction of the maximum values based on a multi-step-ahead iteration of 1-h predictions. Gaussian process modelling is utilised for this comparison. In particular, evolving Gaussian-process models are used that update on-line with the incoming measurement data. These sorts of models have been successfully used in the past for the prediction of ozone pollution. This paper contributes an assessment of the way that the maximum ozone values are predicted. A comparison of the daily maximum ozone values forecasted by a model based on 1-day-ahead predictions with those obtained by iterated 1-h-ahead predictions of the ozone with predictions at predetermined hours of the day is given. The forecast results are in favour of the on-line model based on hourly predictions when approaching closer to the real maximum values of ozone, and in favour of the daily predictions when they are made on a daily basis.     

Extreme value modelling of daily areal rainfall over Mediterranean catchments in a changing climate

Heavy rainfall events during the fall season are causing extended damages in Mediterranean catchments. A peaks‐over‐threshold model is developed for the extreme daily areal rainfall occurrence and magnitude in fall over six catchments in Southern France. The main driver of the heavy rainfall events observed in this region is the humidity flux (FHUM) from the Mediterranean Sea. Reanalysis data are used to compute the daily FHUM during the period 1958–2008, to be included as a covariate in the model parameters. Results indicate that the introduction of FHUM as a covariate can improve the modelling of extreme areal precipitation. The seasonal average of FHUM can improve the modelling of the seasonal occurrences of heavy rainfall events, whereas daily FHUM values can improve the modelling of the events magnitudes. In addition, an ensemble of simulations produced by five different general circulation models are considered to compute FHUM in future climate with the emission scenario A1B and hence to evaluate the effect of climate change on the heavy rainfall distribution in the selected catchments. This ensemble of climate models allows the evaluation of the uncertainties in climate projections. By comparison to the reference period 1960–1990, all models project an amplification of the mean seasonal FHUM from the Mediterranean Sea for the projection period 2070–2099, on average by +22%. This increase in FHUM leads to an increase in the number of heavy rainfall events, from an average of 2.55 events during the fall season in present climate to 3.57 events projected for the period 2070–2099. However, the projected changes have limited effects on the magnitude of extreme events, with only a 5% increase in the median of the 100‐year quantiles. Copyright © 2011 John Wiley & Sons, Ltd.     

Seasonal flooding in Bangladesh – variability and predictability

At present, Bangladesh has a flood forecasting lead time of only 3 days or so. There is demand for a forecasting lead time of a month to a season. The primary objectives of this paper are to study the variability and predictability of seasonal flooding in Bangladesh, as revealed by large‐scale predictors of the climate across the watersheds. To explore the source of predictability, accessible Bangladesh hydrological indicators are related to large‐scale oceanic variability and to large‐scale atmospheric circulation patterns predicted by general circulation models (GCMs). Correlation analyses between the flood‐affected area (FAA) for July–September and tropical sea‐surface temperature (SST) indicate connections to tropical Pacific and Indian Ocean SSTs, at a short lead time of a month or so. These are related to El Niño–southern oscillation (ENSO). Correlations between the SSTs of the preceding October–December and the July–September FAA are weaker but notable. Forecasts of the FAA using the leading principal components (PCs) of SST were made, which provided good skill with a lead time of a month or so. The streamflows and rainfall observed in Bangladesh have been added to these prediction models. Finally, the SST PCs were replaced with PCs of GCM prediction fields (precipitation). The prediction models at short lead time that were constructed for FAA were of generally similar levels of skill to that for SST. This is encouraging, as it suggests that linkages with SST can be successfully recovered in a physical model of the climate system in Bangladesh. This study concludes that seasonal flood prediction in Bangladesh is possible from the unusually warm or cold SST in parts of the tropics. This predictability can be enhanced with the information achievable from monitoring the downstream streamflows (which are generated mainly from upstream rainfall conditions) in advance of the flooding season. Finally, this study recommends formalizing a regional cooperation among the countries in the principal co‐basin areas of the Ganges–Brahmaputra–Meghna to achieve this goal. Copyright © 2006 John Wiley & Sons, Ltd.     

Effects of conditional parameterization on performance of rainfall‐runoff model regarding hydrologic non‐stationarity

Better parameterization of a hydrological model can lead to improved streamflow prediction. This is particularly important for seasonal streamflow forecasting with the use of hydrological modelling. Considering the possible effects of hydrologic non‐stationarity, this paper examined ten parameterization schemes at 12 catchments located in three different climatic zones in east Australia. These schemes are grouped into four categories according to the period when the data are used for model calibration, i.e. calibration using data: (1) from a fixed period in the historical records; (2) from different lengths of historical records prior to prediction year; (3) from different climatic analogue years in the past; and (4) data from the individual months. Parameterization schemes were evaluated according to model efficiency in both the calibration and verification period. The results show that the calibration skill changes with the different historic periods when data are used at all catchments. Comparison of model performance between the calibration schemes indicates that it is worth calibrating the model with the use of data from each individual month for the purpose of seasonal streamflow forecasting. For the catchments in the winter‐dominant rainfall region of south‐east Australia, a more significant shift in rainfall‐runoff relationships at different periods was found. For those catchments, model calibration with the use of 20 years of data prior to the prediction year leads to a more consistent performance. Copyright © 2011 John Wiley & Sons, Ltd.     

One-day-ahead prediction of maximum carbon monoxide concentration in urban environments

The adverse health effects associated with exposure to CO range from the more subtle cardiovascular effects at low concentrations to death after acute or chronic exposure to higher concentrations. The forecasting of the daily CO maximum levels is therefore essential in every attempt for protecting and improving public health in urban areas. The objective of this work is to create a suite of statistical models for predicting the one-day-ahead maximum CO levels based on both the meteorological and the pollutant data recorded in six monitoring sites in the greater area of Athens, Greece. The meteorological variables used as input consist of hourly values of the surface air Temperature, the Relative Humidity, the Wind Speed and the Wind Direction, while the pollutant parameters consist of hourly concentrations of nitrogen oxide, nitric dioxide, ozone and sulfur dioxide, all corresponding to the 7-year-period between 2001 and 2007. The models were developed on a seasonal (warm vs. cold period) and hebdomadal (workdays vs. weekends) basis and revealed that the influence of the air pollution levels recorded one day before (day m?1) on the maximum CO concentrations of day m is quite variable and depends on the site/type of the station, the local meteorology and the emission sources. Additionally, the analysis revealed that the CO concentrations are influenced by both local and/or wider area CO sources, suggesting a strong persistence of the CO levels, while only local meteorology (e.g. in the vicinity of the station and especially during working days) plays a role in the formation of present day’s CO levels. The derived models were validated against an independent yearlong data set (2008) through the use of a classical set of validation parameters known as the Model Validation Kit. Indices assessing the ability of the models to predict the CO exceedances of the EC limit value were also used. On the whole, it was found that the prognostic models introduced here manage to predict the CO maximum daily values in a satisfactory level, with Pearson’s correlation coefficients ranging between 0.62 and 0.76 during the warm period and between 0.51 and 0.80 during the cold period of the year. Similarly the index of agreement ranges between 0.50–0.95 during the warm period and 0.57–0.81 during the cold period of the year, revealing a rather adequate model performance.     

Ozone concentration studies and ozone flux measurements near the ground at Poona

Using a modified Brewer bubbler ozone sensor, continuous measurements of the ozone concentration near the ground were made at Poona (18°N, 73°E) for one year 1969–1970. The surface ozone concentration shows a pronounced seasonal variation, with a minimum during the monsoon months and a maximum during the pre-monsoon summer months. There is also a marked diurnal variation in surface ozone concentration which clearly follows the diurnal variation of temperature and is again a maximum during the summer months and a minimum during the monsoon. A secondary maximum in ozone concentration occurs in the forenoon during the winter months, associated with the temperature inversions that occur near the ground in this season.Both ozone and radioactive tracers, such as Cs-137 both in air and in precipitation show variations indicating that they have identical source regions and sinks. The latitudinal anomaly of surface ozone and Cs-137 observed in the low latitudes over India is explained as arising from the reduction in the rate of transfer of these tracers from the stratosphere to the troposphere, as a result of the reversed circulation at the upper levels in this season.From continuous measurements of surface ozone made with three electrochemical sensors exposed at three levels, 0, 15 and 35 m above the ground, the ozone flux has been directly calculated for the first time in the tropics. The ozone flux was calculated using both the rate of decay method used by Kroening and Ney and Regener's profile method. The profile method gives values of the order of 1.71 to 7.04×10¹¹ mol/cm²/sec and that obtained by the rate of decay method is found to be 4.2 to 5.6×10¹¹ mol/cm²/sec and are in good agreement with the flux values reported by other investigators.     

Methods used for quantifying the prediction uncertainty of artificial neural network based hydrologic models

Application of artificial neural network (ANN) models has been reported to solve variety of water resources and environmental related problems including prediction, forecasting and classification, over the last two decades. Though numerous research studies have witnessed the improved estimate of ANN models, the practical applications are sometimes limited. The black box nature of ANN models and their parameters hardly convey the physical meaning of catchment characteristics, which result in lack of transparency. In addition, it is perceived that the point prediction provided by ANN models does not explain any information about the prediction uncertainty, which reduce the reliability. Thus, there is an increasing consensus among researchers for developing methods to quantify the uncertainty of ANN models, and a comprehensive evaluation of uncertainty methods applied in ANN models is an emerging field that calls for further improvements. In this paper, methods used for quantifying the prediction uncertainty of ANN based hydrologic models are reviewed based on the research articles published from the year 2002 to 2015, which focused on modeling streamflow forecast/prediction. While the flood forecasting along with uncertainty quantification has been frequently reported in applications other than ANN in the literature, the uncertainty quantification in ANN model is a recent progress in the field, emerged from the year 2002. Based on the review, it is found that methods for best way of incorporating various aspects of uncertainty in ANN modeling require further investigation. Though model inputs, parameters and structure uncertainty are mainly considered as the source of uncertainty, information of their mutual interaction is still lacking while estimating the total prediction uncertainty. The network topology including number of layers, nodes, activation function and training algorithm has often been optimized for the model accuracy, however not in terms of model uncertainty. Finally, the effective use of various uncertainty evaluation indices should be encouraged for the meaningful quantification of uncertainty. This review article also discusses the effectiveness and drawbacks of each method and suggests recommendations for further improvement.     

Six years of regular ozone soundings over Switzerland

Summary The mean vertical ozone distribution as a function of season is computed from almost 6 years of regular soundings (three times per week) over Switzerland. By comparing the concurrent mean values of the total amount with the 35-year average at Arosa, and by using the correlation between ozone concentration at different levels with the total amount, adjusted values for the seasonal variation of the vertical ozone distribution are obtained which are thought to give a better representation of the long-term climatological mean. The data show a prominent biennial variation of the ozone content around the level of the maximum concentration which does not, however, show up in the total amount because it is missing in the lower stratosphere.     

Real-time flood forecasting with the use of inadequate data

Abstract

A modelling scheme is developed for real-time flood forecasting. It is composed of (a) a rainfall forecasting model, (b) a conceptual rainfall-runoff model, and (c) a stochastic error model of the ARMA family for forecast error correction. Initialization of the rainfall-runoff model is based on running this model on a daily basis for a certain period prior to the flood onset while parameters of the error model are updated through the Recursive Least Squares algorithm. The scheme is suitable for the early stages of operation of flood forecasting systems in the presence of inadequate historical data. A validation framework is set up which simulates real-time flood forecasting conditions. Thus, the effects of the procedures for rainfall-runoff model initialization, forecast error correction and rainfall forecasting are assessed. Two well-known conceptual rainfall-runoff models (the Soil Moisture Accounting model of the US National Weather Service River Forecast Service—SMA-NWSRFS and TANK) together with data from a Greek basin are used for illustration purposes.     

Ensemble prediction of regional droughts using climate inputs and the SVM–copula approach

In this study, the climate teleconnections with meteorological droughts are analysed and used to develop ensemble drought prediction models using a support vector machine (SVM)–copula approach over Western Rajasthan (India). The meteorological droughts are identified using the Standardized Precipitation Index (SPI). In the analysis of large‐scale climate forcing represented by climate indices such as El Niño Southern Oscillation, Indian Ocean Dipole Mode and Atlantic Multidecadal Oscillation on regional droughts, it is found that regional droughts exhibits interannual as well as interdecadal variability. On the basis of potential teleconnections between regional droughts and climate indices, SPI‐based drought forecasting models are developed with up to 3 months' lead time. As traditional statistical forecast models are unable to capture nonlinearity and nonstationarity associated with drought forecasts, a machine learning technique, namely, support vector regression (SVR), is adopted to forecast the drought index, and the copula method is used to model the joint distribution of observed and predicted drought index. The copula‐based conditional distribution of an observed drought index conditioned on predicted drought index is utilized to simulate ensembles of drought forecasts. Two variants of drought forecast models are developed, namely a single model for all the periods in a year and separate models for each of the four seasons in a year. The performance of developed models is validated for predicting drought time series for 10 years' data. Improvement in ensemble prediction of drought indices is observed for combined seasonal model over the single model without seasonal partitions. The results show that the proposed SVM–copula approach improves the drought prediction capability and provides estimation of uncertainty associated with drought predictions. Copyright © 2013 John Wiley & Sons, Ltd.     

Long-term changes (1980–2003) in total ozone time series over Northern Hemisphere midlatitudes

Long-term changes in total ozone time series for Arosa, Belsk, Boulder and Sapporo stations are examined. For each station we analyze time series of the following statistical characteristics of the distribution of daily ozone data: seasonal mean, standard deviation, maximum and minimum of total daily ozone values for all seasons. The iterative statistical model is proposed to estimate trends and long-term changes in the statistical distribution of the daily total ozone data. The trends are calculated for the period 1980–2003. We observe lessening of negative trends in the seasonal means as compared to those calculated by WMO for 1980–2000. We discuss a possibility of a change of the distribution shape of ozone daily data using the Kolmogorov-Smirnov test and comparing trend values in the seasonal mean, standard deviation, maximum and minimum time series for the selected stations and seasons. The distribution shift toward lower values without a change in the distribution shape is suggested with the following exceptions: the spreading of the distribution toward lower values for Belsk during winter and no decisive result for Sapporo and Boulder in summer.     

River flow forecasting using wavelet and cross‐wavelet transform models

In this study, short‐term river flood forecasting models based on wavelet and cross‐wavelet constituent components were developed and evaluated for forecasting daily stream flows with lead times equal to 1, 3, and 7 days. These wavelet and cross‐wavelet models were compared with artificial neural network models and simple perseverance models. This was done using data from the Skrwa Prawa River watershed in Poland. Numerical analysis was performed on daily maximum stream flow data from the Parzen station and on meteorological data from the Plock weather station in Poland. Data from 1951 to 1979 was used to train the models while data from 1980 to 1983 was used to test the models. The study showed that forecasting models based on wavelet and cross‐wavelet constituent components can be used with great accuracy as a stand‐alone forecasting method for 1 and 3 days lead time river flood forecasting, assuming that there are no significant trends in the amplitude for the same Julian day year‐to‐year, and that there is a relatively stable phase shift between the flow and meteorological time series. It was also shown that forecasting models based on wavelet and cross‐wavelet constituent components for forecasting river floods are not accurate for longer lead time forecasting such as 7 days, with the artificial neural network models providing more accurate results. Copyright © 2008 John Wiley & Sons, Ltd.     

Power spectrum analysis of atmospheric ozone content over north India

Summary Atmospheric total ozone contents over three stations in north India have been studied. A power spectrum analysis has been made of daily values recoreded at these stations during the winter season. Three types of periodicities have been observed in the available records, namely, oscillations with a period of (a) 2.5–3.5 days, (b) 4.0–5.3 days and (c) 8.0–9.6 days. The first and third type of oscillations were also observed when the data were extended to cover an entire year, instead of the winter season alone. A possible mechanism for the occurrence of these periodicities is discussed.     

Weather and seasonal climate prediction for flood planning in the Yangtze River Basin

This paper describes the use of numerical weather and climate models for predicting severe rainfall anomalies over the Yangtze River Basin (YRB) from several days to several months in advance. Such predictions are extremely valuable, allowing time for proactive flood protection measures to be taken. Specifically, the dynamical climate prediction system (IAP DCP-II), developed by the Institute of Atmospheric Physics, Chinese Academy of Sciences (IAP), is applied to YRB rainfall prediction and flood planning. IAP DCP-II employs ensemble prediction with dynamically conditioned perturbations to reduce the uncertainty associated with seasonal climate prediction. IAP DCP-II was shown to successfully predict seasonal YRB summer flooding events based on a 15-year (1980–1994) hindcast experiment and the real-time prediction of two summer flooding events (1999 and 2001). Finally, challenges and opportunities for applying seasonal dynamical forecasting to flood management problems in the YRB are discussed.     

Author Index 1991

Abstract

Evapotranspiration (ET) is an important process in the hydrological cycle and needs to be accurately quantified for proper irrigation scheduling and optimal water resources systems operation. The time variant characteristics of ET necessitate the need for forecasting ET. In this paper, two techniques, namely a seasonal ARIMA model and Winter's exponential smoothing model, have been investigated for their applicability for forecasting weekly reference crop ET. A seasonal ARIMA model with one autoregressive and one moving average process and with a seasonality of 52 weeks was found to be an appropriate stochastic model. The ARIMA and Winter's models were compared with a simple ET model to assess their performance in forecasting. The forecast errors produced by these models were very small and the models would be promisingly of great use in real-time irrigation management.     

Simulation of the stratospheric ozone and temperature response to the solar irradiance variability during sun rotation cycle

Despite substantial progress in atmospheric modeling, the agreement of the simulated atmospheric response to decadal scale solar variability with the solar signal in different atmospheric quantities obtained from the statistical analysis of the observations cannot be qualified as successful. An alternative way to validate the simulated solar signal is to compare the sensitivity of the model to the solar irradiance variability on shorter time scales. To study atmospheric response to the 28-day solar rotation cycle, we used the chemistry–climate model SOCOL that represents the main physical–chemical processes in the atmosphere from the ground up to the mesopause. An ensemble simulation has been carried out, which is comprised of nine 1-year long runs, driven by the spectral solar irradiance prescribed on a daily basis using UARS SUSIM measurements for the year 1992. The correlation of zonal mean hydroxyl, ozone and temperature averaged over the tropics with solar irradiance time series have been analyzed. The hydroxyl has robust correlations with solar irradiance in the upper stratosphere and mesosphere, because the hydroxyl concentration is defined mostly by the photolysis. The simulated sensitivity of the hydroxyl to the solar irradiance changes is in good agreement with previous estimations. The ozone and temperature correlations are more complicated because their behavior depends on non-linear dynamics and transport in the atmosphere. The model simulates marginally significant ozone response to the solar irradiance variability during the Sun rotation cycle, but the simulated temperature response is not robust. The physical nature of this is not clear yet. It seems likely that the temperature (and partly the ozone) daily fields possess their own internal variability, which is not stable and can differ from year to year reflecting different dynamical states of the system.     

Season‐based rainfall–runoff modelling using the probability‐distributed model (PDM) for large basins in southeastern Brazil

Southeastern Brazil is characterized by seasonal rainfall variability. This can have a great social, economic, and environmental impact due to both excessive and deficient water availability. During 2014 and 2015, the region experienced one of the most severe droughts since 1960. The resulting water crisis has seriously affected water supply to the metropolitan region of São Paulo and hydroelectric power generation throughout the entire country. This research considered the upstream basins of the southeastern Brazilian reservoirs Cantareira (2,279 km²; water supply) and Emborcação (29,076 km²), Três Marias (51,576 km²), Furnas (52,197 km²), and Mascarenhas (71,649 km²; hydropower) for hydrological modelling. It made the first attempt at configuring a season‐based probability‐distributed model (PDM‐CEMADEN) for simulating different hydrological processes during wet and dry seasons. The model successfully reproduced the intra‐annual and interannual variability of the upstream inflows during 1985–2015. The performance of the model was very satisfactory not only during the wet, dry, and transitional seasons separately but also during the whole period. The best performance was obtained for the upstream basin of Furnas, as it had the highest quality daily precipitation and potential evapotranspiration data. The Nash–Sutcliffe efficiency and logarithmic Nash–Sutcliffe efficiency were 0.92 and 0.93 for the calibration period 1984–2001, 0.87 and 0.88 for the validation period 2001–2010, and 0.93 and 0.90 for the validation period 2010–2015, respectively. Results indicated that during the wet season, the upstream basins have a larger capacity and variation of soil water storage, a larger soil water conductivity, and quicker surface water flow than during the dry season. The added complexity of configuring a season‐based PDM‐CEMADEN relative to the traditional model is well justified by its capacity to better reproduce initial conditions for hydrological forecasting and prediction. The PDM‐CEMADEN is a simple, efficient, and easy‐to‐use model, and it will facilitate early decision making and implement adaptation measures relating to disaster prevention for reservoirs with large‐sized upstream basins.     

Analyses of the warm season rainfall climatology of the northeastern US using regional climate model simulations and radar rainfall fields

We examine the warm season (April-September) rainfall climatology of the northeastern US through analyses of high-resolution radar rainfall fields from the Hydro-NEXRAD system and regional climate model simulations using the weather research and forecasting (WRF) model. Analyses center on the 5-year period from 2003 to 2007 and the study area includes the New York-New Jersey metropolitan region covered by radar rainfall fields from the Fort Dix, NJ WSR-88D. The objective of this study is to develop and test tools for examining rainfall climatology, with a special focus on heavy rainfall. An additional emphasis is on rainfall climatology in regions of complex terrain, like the northeastern US, which is characterized by land-water boundaries, large heterogeneity in land use and cover, and mountainous terrain in the western portion of the region. We develop a 5-year record of warm season radar rainfall fields for the study region using the Hydro-NEXRAD system. We perform regional downscaling simulations for the 5-year study period using the WRF model. Radar rainfall fields are used to characterize the interannual, seasonal and diurnal variation of rainfall over the study region and to examine spatial heterogeneity of rainfall. Regional climate model simulations are characterized by a wet bias in the rainfall fields, with the largest bias in the high-elevation regions of the model domain. We show that model simulations capture broad features of the interannual, seasonal, and diurnal variation of rainfall. Model simulations do not capture spatial gradients in radar rainfall fields around the New York metropolitan region and land-water boundaries to the east. The model climatology of convective available potential energy (CAPE) is used to interpret the regional distribution of warm season rainfall and the seasonal and diurnal variability of rainfall. We use hydrologic and meteorological observations from July 2007 to examine the interactions of land surface processes and rainfall from a regional perspective.     

River suspended sediment modelling using a fuzzy logic approach

Correct estimation of sediment volume carried by a river is very important for many water resources projects. Conventional sediment rating curves, however, are not able to provide sufficiently accurate results. In this paper, a fuzzy logic approach is proposed to estimate suspended sediment concentration from streamflow. This study provides forecasting benchmarks for sediment concentration prediction in the form of a numerical and graphical comparison between fuzzy and rating‐curve models. Benchmarking was based on a 5‐year period of continuous streamflow and sediment concentration data of Quebrada Blanca Station operated by the United States Geological Survey. The benchmark results showed that the fuzzy model was able to produce much better results than rating‐curve models. The fuzzy model proposed in the study is site specific and does not simulate the hysteresis effects. Copyright © 2006 John Wiley & Sons, Ltd.