Hydrological and meteorological records from the Vernagtferner Basin - Vernagtbach station, for the years 2002 to 2012

In November 2001, two separate Campbell loggers ("Meteologger" and "Hydrologger", both type CR23X) were installed at the Vernagtbach site in the Oetztal Alps, Austria (Latitude: 46.85; Longitude: 10.82; Elevation: 2640 m). On these loggers, 10-minutes centred averages for the meteorological data and 5-minutes centred averages for the hydrological data are recorded. The meteorological parameters comprise air temperature, humidity of the air, air pressure, four radiation components, wind direction and speed, precipitation and snow height. For air temperature, two records are published, recorded with a ventilated and an unventilated Pt-100 in a Stevenson screen; for precipitation, three time series are available: (I) the cumulative record of a weighing gauge for the whole year, (II) single events derived from (I), and (III) single events from a tipping bucket; (II) and (III) are only provided for the period 1, May to 31, October of each year. Wind records are also given with a time step of one hour, as only these records include several statistics of speed and direction. Hydrological parameters are recorded on the "Hydrologger", they comprise water stage, discharge, water temperature and electrolytic conductivity of the water. An identifying number gives the kind of instrument used in the water stage time series. Daily photographs of the glacier are provided and analysed with respect to precipitation type.

Results of hydrological and geological investigations in Benin

Focus of this study is the analysis of a local hydrogeological system in the subhumid outer tropics in the western African country of Benin. The aim was to characterize, qualify and quantify the hydrogeological and hydrological properties of the approx. 30 km2 big study area and to develop a conceptual hydrogeological model. This model should provide the basis for further studies on a regional scale. The main goal was to obtain the process knowledge of the hydrogeological system and to determine the process and the quantity of the groundwater recharge in the working area. According to the objectives, a broad hydrogeological approach was chosen. In a spacious network on the local scale TDR probes, suction cups and groundwater observation bores were installed. Also in a multidisciplinary cooperation with hydrology, geography, soil science, biology, meteorology and plant nutrition sciences, instruments like discharge gauging stations, tensiometers, lysimeter, climate stations, runoff plots and erosion pins were installed in the test site for the investigation of the relevant parameters of the hydrological cycle.

Forecasting inbound tourists in Cambodia

Forecasting tourism demand is crucial for management decisions in the tourism sector. Estimating a vector autoregressive (VAR) model for monthly visitor arrivals disaggregated by three entry points in Cambodia for the years 2006–2015, I forecast the number of arrivals for years 2016 and 2017. The results show that the VAR model fits well with the data on visitor arrivals for each entry point. Ex post forecasting shows that the forecasts closely match the observed data for visitor arrivals, thereby supporting the forecasting accuracy of the VAR model. Visitor arrivals to Siem Reap and Phnom Penh airports are forecast to increase steadily in future periods, with varying fluctuations across months and origin countries of foreign tourists.

Seasonal Dynamic Factor Analysis and Bootstrap Inference: Application to Electricity Market Forecasting

In this work, we propose the Seasonal Dynamic Factor Analysis (SeaDFA), an extension of Nonstationary Dynamic Factor Analysis, through which one can deal with dimensionality reduction in vectors of time series in such a way that both common and specific components are extracted. Furthermore, common factors are able to capture not only regular dynamics (stationary or not) but also seasonal ones, by means of the common factors following a multiplicative seasonal VARIMA(p, d, q) × (P, D, Q)s model. Additionally, a bootstrap procedure that does not need a backward representation of the model is proposed to be able to make inference for all the parameters in the model. A bootstrap scheme developed for forecasting includes uncertainty due to parameter estimation, allowing enhanced coverage of forecasting intervals. A challenging application is provided. The new proposed model and a bootstrap scheme are applied to an innovative subject in electricity markets: the computation of long-term point forecasts and prediction intervals of electricity prices. Several appendices with technical details, an illustrative example, and an additional table are available online as Supplementary Materials.

Forecasting Electricity Prices and their volatilities using Unobserved Components.

The liberalization of electricity markets more than ten years ago in the vast majority of developed countries has introduced the need of modelling and forecasting electricity prices and volatilities, both in the short and long term. Thus, there is a need of providing methodology that is able to deal with the most important features of electricity price series, which are well known for presenting not only structure in conditional mean but also time-varying conditional variances. In this work we propose a new model, which allows to extract conditionally heteroskedastic common factors from the vector of electricity prices. These common factors are jointly estimated as well as their relationship with the original vector of series, and the dynamics affecting both their conditional mean and variance. The estimation of the model is carried out under the state-space formulation. The new model proposed is applied to extract seasonal common dynamic factors as well as common volatility factors for electricity prices and the estimation results are used to forecast electricity prices and their volatilities in the Spanish zone of the Iberian Market. Several simplified/alternative models are also considered as benchmarks to illustrate that the proposed approach is superior to all of them in terms of explanatory and predictive power.