Abstract

Since the beginning of this century, Europe has been experiencing severe drought events (2003, 2007, 2010, 2018, and 2019) which have had adverse impacts on various sectors, such as agriculture, forestry, water management, health, and ecosystems. During the last few decades, projections of the impact of climate change on hydroclimatic extremes were often capable of reproducing changes in the characteristics of these extremes. Recently, the research interest has been extended to include reconstructions of hydro-climatic conditions to provide historical context for present and future extremes. While there are available reconstructions of temperature, precipitation, drought indicators, or the 20th century runoff for Europe, long-term runoff reconstructions are still lacking (e.g, monthly or daily runoff series for short periods are commonly available). Therefore, we considered reconstructed precipitation and temperature fields for the period between 1500 and 2000 together with reconstructed scPDSI, natural proxy data, and observed runoff over 14~European catchments to calibrate and validate the semi-empirical hydrological model GR1A and two data-driven models (Bayesian recurrent and long short-term memory neural network). The validation of input precipitation fields revealed an underestimation of the variance across most of Europe. On the other hand, the data-driven models have been proven to correct this bias in many cases, unlike the semi-empirical hydrological model GR1A. The comparison to observed historical runoff data has shown a good match between the reconstructed and observed runoff and between the runoff characteristics, particularly deficit volumes. The reconstructed runoff is available via figshare, an open source scientific data repository under the DOI https://doi.org/10.6084/m9.figshare.15178107, (Sadaf et al., 2021).

Highlights

  • We focus on 21 specific Central European catchments, corresponding to the available long-term Global Runoff Data Center (GRDC) 120 discharge records

  • We considered two approaches : long short term memory (LSTM) neural networks and Bayesian regularized neural networks (BRNN)

  • We provide a comparison between the Global Historical Climatology Network (GHCN) observed precipitation and temperature, and the corresponding grid cells from Pauling et al (2006) and Luterbacher et al (2004) reconstructions

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Summary

Introduction

Global warming has impacted numerous land surface processes (Reinecke et al, 2021) over the last few decades, resulting in 20 more severe droughts, heat waves, floods, and other extreme weather. The flow of many rivers is greatly hampered by prolonged droughts, which restrain the availability of fresh water for agriculture and domestic use. The 2003 drought significantly reduced European river flows by approximately 60 to 80% relative to the average. The annual flow levels of several river gauges have decreased by 9 to 22% over the last decade (Krysanova et al, 2008; Middelkoop et al, 2001; Uehlinger et al, 2009; Su et al, 2020) due to a lack of 25 rainfall and a warmer climate. For the last 40 years, low river flows have rendered water-power generation impossible in the UK, resulting in a 45£ million loss each year. There has been less focus on the water deficit in streams, rivers and other reservoir’s, the so-called hydrological droughts (Van Loon, 2015). Proxy-based (typically seasonal or annual) reconstructions are alternatively used, considering various proxy data, such as past tree-rings (Cook et al, 2015; Casas-Gómez et al, 2020; Tejedor et al, 2016; Kress et al, 2010; Nicault et al, 2008), speleothem (Vansteenberge et al, 2016), ice cores, sediments (Luoto and Nevalainen, 2017) and documentary and instrumental evidence (Pfister et al, 1999; Dobrovolný et al, 2010; Wetter et al, 35 2011; Brázdil and Dobrovolný, 2009) to pinpoint extreme events and the detection of climate change

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