Amplified signals of soil moisture and evaporative stresses across Poland in the twenty-first century

Somorowska, Urszula

doi:10.1016/j.scitotenv.2021.151465

Cited by 22 publications

(20 citation statements)

References 75 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The country‐averaged PSMD amounts to 42 and 61 mm for RCP4.5 and 8.5, respectively. Such a spatial variation of water deficits closely corresponds with patterns reported in other studies published in recent years for Poland (Piniewski et al, 2020; Somorowska, 2022). In future scenarios, a decrease in PSMD is projected by up to 30 and 35 mm by the end of century for RCP4.5 and 8.5, respectively.…”

Section: Resultssupporting

confidence: 90%

Assessment of projected climate change impact on agro‐climatic indicators in Poland

Marcinkowski

Piniewski

Jefimow

2023

Intl Journal of Climatology

View full text Add to dashboard Cite

This study presents a long‐term assessment of the impact of projected climate change on selected agro‐climatic indicators in Poland. The analysis was based on an ensemble of six bias‐corrected EURO‐CORDEX projections covering the period from 2006 to 2100 under Representative Concentration Pathways: RCP4.5 and 8.5, and partly on simulations of an agro‐hydrological Soil and Water Assessment Tool model. The indicators included growing season (length, beginning, and ending date), growing degree days, frost days, accumulated frost, and planting and harvest timing. The study focused on four major crops cultivated in Poland, namely spring barley, rapeseed, maize silage, and potato. The analysis revealed a significant future increase in precipitation and mean temperature in Poland reaching +10.3% and 3.4°C by the end of century, respectively. Warmer conditions will significantly accelerate the beginning of the growing season (by up to 24 days) and delay its ending (by up to 18 days), consequently significantly extending its length throughout the country (by up to 42 days). A decrease is projected in potential soil moisture deficit by up to 26 mm by the end of century in the high‐end emission scenario. A significant advancement (by up to 30 days) in planting timing is projected for the future for all crops under consideration. A significant increase in growing degree days (by up to 760°C‐days) will also advance the harvest timing (by up to 54 days). The projected temperature increase over the winter season will result in a significant decrease in frost days (by 56 days) and accumulated frost (by 375°C‐days), which may lead to an increased presence of pests. This study can provide a scientific basis for assessing the future risks to the local agriculture from climate change and will be beneficial in planning adaption and mitigation actions for food security at the national and local scale.

show abstract

Section: Resultssupporting

confidence: 90%

Assessment of projected climate change impact on agro‐climatic indicators in Poland

Marcinkowski

Piniewski

Jefimow

2023

Intl Journal of Climatology

View full text Add to dashboard Cite

show abstract

“…A significant increase in mean daily ET o values (0.12 mm/10 year, p < 0.01) during the summer (June–August) in the years 1971–2010 is indicated by Bogawski and Bednorz (2016). This is also confirmed by results obtained by Somorowska (2022), who concludes that, in the period 1980–2020, in most of Poland, the evapotranspiration trend was increasing, and evaporation was exceptionally high in dry and warm years, such as 1982, 2015 and 2018.…”

Section: Discussionsupporting

confidence: 87%

“…At the turn of the 21st century, the amount of reference evapotranspiration in Poland increased (e.g., Bogawski & Bednorz, 2016; Łabędzki et al, 2011, 2012, 2014; Somorowska, 2022). It is mainly attributed to the warming being observed worldwide, including in this part of Europe (e.g., Climate of Poland 2020, 2021; Kejna & Rudzki, 2021; Twardosz et al, 2021; Ustrnul et al, 2021) and increased sunshine duration (Bartoszek et al, 2021; Bartoszek & Matuszko, 2021; Kożuchowski & Degirmendžić, 2005; Matuszko et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Reference evapotranspiration during the growing season in Poland (central Europe) in response to ongoing climate changes (1966–2020)

Araźny

Kępińska-Kasprzak

Łaszyca

et al. 2023

Intl Journal of Climatology

View full text Add to dashboard Cite

Evapotranspiration is an important part of the water cycle and influences, for example, water management and agricultural production. The article presents temporal and spatial changes in reference evapotranspiration (ETo) in the growing season (April–September) against the background of climatic conditions in Poland in the years 1966–2020. ETo was calculated using the Penman–Monteith method using data from 37 meteorological stations in the network of the Institute of Meteorology and Water Management‐National Research Institute. For the growing season of April–September, the average sum of ETo for Poland was 529 mm. In terms of spatial distribution, the values were lowest in northern Poland (Kołobrzeg, 480 mm; Lębork, 488 mm) and highest in the central lowlands (Warsaw, 570 mm; Poznań, 566 mm). For individual months, the average sum of ETo ranged from 53 to 63 mm in April and September to 108–112 mm in June and July. In Poland, in the years 1966–2020, ETo showed a statistically significant upward trend of 16.4 mm/10 year. This is the result of, inter alia, statistically significant increases in sunshine duration and air temperature and a decrease in relative humidity (respectively: 62 h/10 year, 0.40°C/10 year and −1%/10 year) during the growing season in Poland. Within the multiyear study period, the maximum ETo values occurred in 2018, reaching values of >700 mm. At that time, Poland was under the influence of very intense anticyclonic circulation. This is confirmed by the relationship between ETo and the anticyclonic circulation index for central Poland, which is statistically significant for the 55 study years.

show abstract

“…Considering unfavourable forecasts of climatic changes (Piccolroaz et al, 2021; Tomczyk et al, 2022), the near future may bring a situation involving hierarchisation of demand for water. The occurrence of broadly defined drought is already a frequent phenomenon in Poland (Jaagus et al, 2022; Karamuz & Romanowicz, 2021; Somorowska, 2022). Some regions face the problem of serious water deficit (Nowak & Ptak, 2019).…”

Section: Discussionmentioning

confidence: 99%

Daily water‐level forecasting for multiple polish lakes using multiple data‐driven models

Ptak

et al. 2022

Geographical Journal

View full text Add to dashboard Cite

Water level in lakes fluctuates frequently due to the impact of natural and anthropogenic forcing. Frequent fluctuations of water level will impact lake ecosystems, and it is thus of great significance to have a good knowledge of water-level dynamics in lakes. However, forecasting daily water-level fluctuation in lake systems remains a tough task due to its non-linearity and complexity. In this study, two deep data-driven models, including gated recurrent unit (GRU) and long short-term memory (LSTM), were coupled with attention mechanism for the forecasting of daily water level in lakes for the first time. Daily water-level times series in five lowland lakes in Poland were used to evaluate the models. Root mean squared error (RMSE) and mean average error (MAE) were used for the evaluation of model performance. The modelling results were compared with the traditional feed-forward neural networks (FFNN), GRU, LSTM, and zero-order forecast. The modelling results showed that sequential deep learning models do not outperform feed-forward models in all cases. In most cases, LSTM with attention mechanism (average RMSE = 0.88 cm, average MAE = 0.69 cm) outperforms GRU with attention mechanism (average RMSE = 1.00 cm, average MAE = 0.81 cm). However, attention mechanism did not help to improve the accuracy of the GRU and LSTM for most cases. Based on the average performance in different lakes, GRU performs the best among the deep learning models (average RMSE = 0.84 cm, average MAE = 0.66 cm).Zero-order forecast models perform better than deep learning models for predicting tomorrow (average RMSE = 0.71 cm, average MAE = 0.39 cm), while deep learning models perform better as the horizon of prediction increases.

show abstract

Amplified signals of soil moisture and evaporative stresses across Poland in the twenty-first century

Cited by 22 publications

References 75 publications

Assessment of projected climate change impact on agro‐climatic indicators in Poland

Assessment of projected climate change impact on agro‐climatic indicators in Poland

Reference evapotranspiration during the growing season in Poland (central Europe) in response to ongoing climate changes (1966–2020)

Daily water‐level forecasting for multiple polish lakes using multiple data‐driven models

Contact Info

Product

Resources

About