| 1. |
- Rafee, Sameh A.Abou, et al.
(författare)
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Spatial trends of extreme precipitation events in the Paraná river basin
- 2020
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Ingår i: Journal of Applied Meteorology and Climatology. - 1558-8424. ; 59:3, s. 443-454
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Tidskriftsartikel (refereegranskat)abstract
- This work presents an analysis of the observed trends in extreme precipitation events in the Paraná River basin (PRB) from 1977 to 2016 (40 yr) based on daily records from 853 stations. The Mann–Kendall test and inverse-distance-weighted interpolation were applied to annual and seasonal precipitation and also for four extreme precipitation indices. The results show that the negative trends (significance at 95% confidence level) in annual and seasonal series are mainly located in the northern and northeastern parts of the basin. In contrast, except in the autumn season, positive trends were concentrated in the southern and southeastern regions of the basin, most notably for annual and summer precipitation. The spatial distributions of the indices of annual maximum 5-day precipitation and number of rainstorms indicate that significant positive trends are mostly located in the south-southeast part of the basin and that significant negative trends are mostly located in the north-northeast part. The index of the annual number of dry days shows that 88% of significant trends are positive and that most of these are located in the northern region of the PRB, which is a region with a high number of consecutive dry days (>90). The simple daily intensity index showed the highest number of stations (263) with mostly positive significant trends.
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| 2. |
- Freitas, Aline A., et al.
(författare)
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Drought Assessment in São Francisco River Basin, Brazil : Characterization through SPI and Associated Anomalous Climate Patterns
- 2022
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Ingår i: Atmosphere. - : MDPI AG. - 2073-4433. ; 13:1
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 3. |
- Rudke, A. P., et al.
(författare)
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Landscape changes over 30 years of intense economic activity in the upper Paraná River basin
- 2022
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Ingår i: Ecological Informatics. - : Elsevier BV. - 1574-9541. ; 72
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Tidskriftsartikel (refereegranskat)abstract
- In this study, we show the complexity associated with the recent land cover changes by elucidating the paths of 30 years of changes in the Upper Paraná River Basin (UPRB), a region severely impacted by agricultural activity, one of the areas with the highest density in the production of hydroelectricity, biofuels and food in the world. In this sense, a post-classification comparison approach based on Landsat images was used to identify detailed ‘from-to’ paths behind those land cover changes. The most expressive changes were the expansion of Cropland and Forest areas and the reduction in savannas, with a net change of 17.9%, 4.1%, and −16.9% of the UPRB area, respectively. Cropland areas showed an expressive increase between 1985 and 2015, rising from 249,439 km2 (27.7%) to 412,909 km2 (45.9%). Forest areas increased from 149,389 km2 to 185,839 km2 in the period. Notably, for this class, an intense spatial dynamic of losses (7.5%) and gains (11.6%) took place between 1985 and 2015. This behavior is related to the disappearance of native vegetation fragments in some sub-basins, as well as to afforestation, reforestation, and/or forest restoration in others. The Cerrado (a typical tropical savanna in South America), the most impacted natural biome of the Basin, decreased from 21.9% of the UPRB in 1985 (196,746 km2) to only about 5% of the whole UPRB area in 2015. Grassland areas, mostly used for livestock, decreased from 271,827 km2 (30.2%) to 229,007 km2 (25.5%). This net decrease was associated with a reduction of 160,830 km2 (17.8%) and the appearance of 118,010 km2 (13.2%) in new areas, previously occupied by tropical savannas in 1985. In conclusion, economic factors were the main drivers for land cover changes, especially agriculture and livestock activities, besides forestry and hydroelectric energy production. In addition, Grassland areas that predominated on the left banks of the UPRB in 1985 retreated with the advance of Cropland areas, mainly due to the expansion of sugarcane for ethanol production, a biofuel widely used in Brazil. In turn, pasture areas migrated to the right bank and occupied a significant part of the Cerrado. Finally, our results demonstrate that the transition dynamics among land cover classes can involve complex political-economical mechanisms that are not always captured by remote sensing.
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| 4. |
- Sörensen, Johanna Lykke, et al.
(författare)
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Decision Support Indicators (DSIs) and their role in hydrological planning
- 2024
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Ingår i: Environmental Science and Policy. - 1462-9011. ; 157
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Tidskriftsartikel (refereegranskat)abstract
- Decision Support Indicators (DSIs) are metrics designed to inform local and regional stakeholders about the characteristics of a predicted (or ongoing) event to facilitate decision-making. In this paper, the DSI concept was developed to clarify the different aims of different kinds of indicators by naming them, and a framework was developed to describe and support the usage of such DSIs. The framework includes three kinds of DSI: hydroclimatic DSIs which are easy to calculate but hard to understand by non-experts; impact-based DSIs which are often difficult to calculate but easy to understand by non-experts; and event-based DSIs, which compare a current or projected state to a locally well-known historical event, where hydroclimatic and impact-based DSIs are currently mainly used. Tables and figures were developed to support the DSI development in collaboration with stakeholders. To develop and test the framework, seven case studies, representing different hydrological pressures on three continents (South America, Asia, and Europe), were carried out. The case studies span several temporal and spatial scales (hours-decades; 70–6,000 km2) as well as hydrological pressures (pluvial and riverine floods, drought, and water scarcity), representing different climate zones. Based on stakeholder workshops, DSIs were developed for these cases, which are used as examples of the conceptual framework. The adaptability of the DSI framework to this wide range of cases shows that the framework and related concepts are useful in many contexts.
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| 5. |
- Olsson, J., et al.
(författare)
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Initial assessment of a multi-model approach to spring flood forecasting in Sweden
- 2016
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Ingår i: Hydrology and Earth System Sciences Discussions. - : Copernicus GmbH. - 1812-2108. ; 12:6, s. 6077-6113
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 6. |
- Olsson, J., et al.
(författare)
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Statistical atmospheric downscaling of short-term extreme rainfall by neutral networks
- 2001
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Ingår i: Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere. - 1464-1909. ; 26:9, s. 695-700
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Tidskriftsartikel (refereegranskat)abstract
- 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.
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| 7. |
- Olsson, J., et al.
(författare)
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Technical Note : Initial assessment of a multi-method approach to spring-flood forecasting in Sweden
- 2016
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Ingår i: Hydrology and Earth System Sciences. - : Copernicus GmbH. - 1027-5606 .- 1607-7938. ; 20:2, s. 659-667
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Tidskriftsartikel (refereegranskat)abstract
- 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 Vindelälven 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 ∼4%. This improvement is limited but potentially significant for e.g. energy trading.
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