The use of predefined drought indices for the assessment of groundwater drought episodes in the Baltic States over the period 1989–2018

Babre, Alise; Kalvāns, Andis; Avotniece, Zanita; Retiķe, Inga; Bikše, Jānis; Jemeljanova, Konrāds Popovs Marta; Zelenkevičs, Artjoms; Děliņa, Aija

doi:10.1016/j.ejrh.2022.101049

Cited by 15 publications

(14 citation statements)

References 58 publications

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“…The region is characterized by distinct seasonality and negative temperatures with snow accumulation in the cold season. During the springtime, the first groundwater recharge maximum is driven by the snowmelt water infiltration, while in the period from September to December the second recharge maximum results from increased precipitation and low evapotranspiration (Babre et al, 2022). Generally, groundwater recharge takes place in the upland areas and discharges towards the Baltic Sea (Virbulis et al, 2013).…”

Section: Study Regionmentioning

confidence: 99%

Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns

Bikše¹,

Retiķe²,

Haaf³

et al. 2023

Preprint

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Large groundwater level (GWL) data sets are often patchy with hydrographs containing continuous gaps and irregular measurement frequencies. However, most statistical time series analyses require regular observations, thus hydrographs with larger gaps are routinely excluded from further analysis despite the loss of coverage and representativity of an initially large data set. Missing values can be filled in with different imputation methods, yet the challenge is to assess the imputation performance of automated methods. Assessment of such methods tends to be carried out on randomly introduced missing values. However, large GWL data sets are commonly dominated by more complex patterns of missing values with longer contiguous gaps. This study presents a new artificial gap introduction approach (TGP- typical gap patterns) that improves our understanding of automated imputation performance by mimicking typical gap patterns found in regional scale groundwater hydrographs. Imputation performance of machine learning algorithm missForest and imputePCA is then compared with commonly applied linear interpolation to prepare a gapless daily GWL data set for the Baltic states (Estonia, Latvia, Lithuania). We observed that imputation performance varies among different gap patterns, and performance for all imputation algorithms declined when infilling previously unseen extremes and hydrographs influenced by groundwater abstraction. Further, missForest algorithm substantially outperformed other methods when infilling contiguous gaps (up to 2.5 years), while linear interpolation performs similarly for short random gaps. The value of the developed gap introduction approach lies in aiding the selection of imputation methods, a task not limited to groundwater level time series alone. The results further provide insights into region-specific data peculiarities that can assist groundwater analysis and modelling.

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Section: Study Regionmentioning

confidence: 99%

Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns

Bikše¹,

Retiķe²,

Haaf³

et al. 2023

Preprint

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show abstract

“…By comparing the SPI and SPEI indices during a 46-year period in Ankara Province, Turkey, (Danandeh Mehr & Vaheddoost, 2020) determined that whereas the SPEI index has a declining trend, the SPI index does not exhibit a similar pattern. (Babre et al, 2022) assessed groundwater drought periods in the Baltic States using established drought indices and found that meteorological drought indices (SPI, SPEI, and RDI) were substantially linked with groundwater drought conditions in shallow groundwater wells. (Kubicz & Bąk, 2019) investigated the response of groundwater to a multi-month meteorological drought in Poland.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of meteorological drought effects on underground water level fluctuations using data mining methods (case study: semi-deep wells of Golestan province)

Roshan,

Ghorbani,

Salarijazi

et al. 2024

Environ Monit Assess

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In most arid and semiarid environments, groundwater is one of the precious resources threatened by water table decline and desiccation, thus it must be constantly monitored. Identifying the causes in uencing the variations of the subsurface water level, such as meteorological drought, is one approach for monitoring these uctuations. In the present study, the effect of two meteorological drought indices SPI and SPEI on the uctuations of the underground water level was evaluated, as was their relationship with the drought index of the subsurface water level (SWI) using multivariate linear regression and M5 decision tree regression. After calculating climatic and hydrological drought indicators in a 6-month time window for a long-term statistical period , the semi-deep aquifers of Golestan province, which is located in northern Iran, were considered as a research location for this purpose. The results demonstrated that the effect of meteorological drought does not immeddergiately manifest in the changes of the subsurface water table and the hydrological drought index. By adding the meteorological drought index with a 6-month lag step, the average air temperature, and the total rainfall from the previous 6 months as new variables, the correlation with the SWI index increases, so that in the best-case scenario, the M5 decision tree model provides the best result in predicting the SWI index. The second half of the year yielded a coe cient of determination of 0.92 and an error value of RMSE = 0.27 for the SPEI index. Among the meteorological drought indicators, the SPEI index, which is based on precipitation and evapotranspiration, created a stronger link with the SWI index, which highlights the signi cance of potential evapotranspiration. It is a warning that, as a result of global warming, subsurface water tables in this region may fall in the future.

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“…It is worth mentioning that among all drought indexes, the "standardized precipitation index (SPI)", proposed by [ 19 ], is broadly used to quantify drought across the world and receives considerable attention from researchers [ 5 , 20 , 21 ]. Based on SPI principles, several hydrological parameters have been derived and widely used to assess and monitor droughts, such as standardized streamflow index (SSI) [ 22 , 23 ], standardized hydrological drought index (SHDI) [ 24 ], standardized groundwater level index (SGI) [ 25 ], and standardized runoff index (SRI) [ 26 , 27 ]. These indices’ popularity and widespread use can be attributed to their ability to effectively evaluate and track drought conditions across diverse timeframes and geographic locations while requiring minimal data inputs and maintaining strong comparability in time and space [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America

Hameed,

Razali,

Mohtar

et al. 2023

PLoS ONE

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The Great Lakes are critical freshwater sources, supporting millions of people, agriculture, and ecosystems. However, climate change has worsened droughts, leading to significant economic and social consequences. Accurate multi-month drought forecasting is, therefore, essential for effective water management and mitigating these impacts. This study introduces the Multivariate Standardized Lake Water Level Index (MSWI), a modified drought index that utilizes water level data collected from 1920 to 2020. Four hybrid models are developed: Support Vector Regression with Beluga whale optimization (SVR-BWO), Random Forest with Beluga whale optimization (RF-BWO), Extreme Learning Machine with Beluga whale optimization (ELM-BWO), and Regularized ELM with Beluga whale optimization (RELM-BWO). The models forecast droughts up to six months ahead for Lake Superior and Lake Michigan-Huron. The best-performing model is then selected to forecast droughts for the remaining three lakes, which have not experienced severe droughts in the past 50 years. The results show that incorporating the BWO improves the accuracy of all classical models, particularly in forecasting drought turning and critical points. Among the hybrid models, the RELM-BWO model achieves the highest level of accuracy, surpassing both classical and hybrid models by a significant margin (7.21 to 76.74%). Furthermore, Monte-Carlo simulation is employed to analyze uncertainties and ensure the reliability of the forecasts. Accordingly, the RELM-BWO model reliably forecasts droughts for all lakes, with a lead time ranging from 2 to 6 months. The study’s findings offer valuable insights for policymakers, water managers, and other stakeholders to better prepare drought mitigation strategies.

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The use of predefined drought indices for the assessment of groundwater drought episodes in the Baltic States over the period 1989–2018

Cited by 15 publications

References 58 publications

Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns

Assessing automated gap imputation of regional scale groundwater level data sets with typical gap patterns

Evaluation of meteorological drought effects on underground water level fluctuations using data mining methods (case study: semi-deep wells of Golestan province)

Machine learning models development for accurate multi-months ahead drought forecasting: Case study of the Great Lakes, North America

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