Statistical atmospheric downscaling of short-term extreme rainfall by neutral networks

Statistical atmospheric rainfall downscaling, that is, statistical estimation of local or regional rainfall on the basis of large-scale atmospheric circulation, has been advocated to make the output from global and regional climate models more accurate for a particular location or basin. Neural networks (NNs) have been used for such downscaling, but their application has proved problematic, mainly due to the numerous zero-values present in short-term rainfall time series. In the present study, using serially coupled NNs was tested as a way to improve performance. Mean 12-hour rainfall in the Chikugo River basin, Kyushu Island, Southern Japan, was downscaled from observations of precipitable water and zonal and meridional wind speed at 850 hPa, averaged over areas within which the temporal variation was found to be significantly correlated with basin rainfall. Basin rainfall was ranked into four categories: No-rain (0) and low (1), high (2) and extreme (3) intensity. A series of NN experiments showed that the best overall performance in terms of hit rates was achieved by a two-stage approach in which a first NN distinguished between no-rain (0) and rain (1-3), and a second NN distinguished between low, high, and extreme rainfalls. Using either a single NN to distinguish between all four categories or three NNs to successively detect extreme values proved inferior. The results demonstrate the need for an elaborate configuration when using NNs for short-term downscaling, and the importance of including physical considerations in the NN application

Technical Note : Initial assessment of a multi-method approach to spring-flood forecasting in Sweden

Hydropower is a major energy source in Sweden, and proper reservoir management prior to the spring-flood onset is crucial for optimal production. This requires accurate forecasts of the accumulated discharge in the spring-flood period (i.e. the spring-flood volume, SFV). Today's SFV forecasts are generated using a model-based climatological ensemble approach, where time series of precipitation and temperature from historical years are used to force a calibrated and initialized set-up of the HBV model. In this study, a number of new approaches to spring-flood forecasting that reflect the latest developments with respect to analysis and modelling on seasonal timescales are presented and evaluated. Three main approaches, represented by specific methods, are evaluated in SFV hindcasts for the Swedish river Vindelalven over a 10-year period with lead times between 0 and 4 months. In the first approach, historically analogue years with respect to the climate in the period preceding the spring flood are identified and used to compose a reduced ensemble. In the second, seasonal meteorological ensemble forecasts are used to drive the HBV model over the spring-flood period. In the third approach, statistical relationships between SFV and the large-sale atmospheric circulation are used to build forecast models. None of the new approaches consistently outperform the climatological ensemble approach, but for early forecasts improvements of up to 25% are found. This potential is reasonably well realized in a multi-method system, which over all forecast dates reduced the error in SFV by similar to 4 %. This improvement is limited but potentially significant for e.g. energy trading

Initial assessment of a multi-model approach to spring flood forecasting in Sweden

Hydropower is a major energy source in Sweden and proper reservoir management prior to the spring flood onset is crucial for optimal production. This requires useful forecasts of the accumulated discharge in the spring flood period (i.e. the spring-flood volume, SFV). Today's SFV forecasts are generated using a model-based climatological ensemble approach, where time series of precipitation and temperature from historical years are used to force a calibrated and initialised set-up of the HBV model. In this study, a number of new approaches to spring flood forecasting, that reflect the latest developments with respect to analysis and modelling on seasonal time scales, are presented and evaluated. Three main approaches, represented by specific methods, are evaluated in SFV hindcasts for three main Swedish rivers over a 10-year period with lead times between 0 and 4 months. In the first approach, historically analogue years with respect to the climate in the period preceding the spring flood are identified and used to compose a reduced ensemble. In the second, seasonal meteorological ensemble forecasts are used to drive the HBV model over the spring flood period. In the third approach, statistical relationships between SFV and the large-sale atmospheric circulation are used to build forecast models. None of the new approaches consistently outperform the climatological ensemble approach, but for specific locations and lead times improvements of 20-30 % are found. When combining all forecasts in a weighted multi-model approach, a mean improvement over all locations and lead times of nearly 10 % was indicated. This demonstrates the potential of the approach and further development and optimisation into an operational system is ongoing

Drought Assessment in São Francisco River Basin, Brazil : Characterization through SPI and Associated Anomalous Climate Patterns

The São Francisco River Basin (SFRB) is one of the main watersheds in Brazil, standing out for generating energy and consumption, among other ecosystem services. Hence, it is important to identify hydrological drought events and the anomalous climate patterns associated with dry conditions. The Standard Precipitation Index (SPI) for 12 months was used to identify hydrological drought episodes over SFRB 1979 and 2020. For these episodes, the severity, duration, intensity, and peak were obtained, and SPI-1 was applied for the longest and most severe episode to identify months with wet and dry conditions within the rainy season (Nov–Mar). Anomalous atmospheric and oceanic patterns associated with this episode were also analyzed. The results revealed the longest and most severe hydrological drought episode over the basin occurred between 2012 and 2020. The episode over the Upper portion of the basin lasted 103 months. The results showed a deficit of monthly precipitation up to 250 mm in the southeast and northeast regions of the country during the anomalous dry months identified through SPI-1. The dry conditions observed during the rainy season of this episode were associated with an anomalous high-pressure system acting close to the coast of Southeast Brazil, hindering the formation of precipitating systems