Winter post-droughts amplify extreme nitrate concentrations in German rivers

Saavedra, Felipe; Musolff, Andreas; Von Freyberg, Jana; Merz, Ralf; Knöller, Kay; Müller, Christin; Brunner, Manuela; Tarasova, Larisa

doi:10.1088/1748-9326/ad19ed

Cited by 4 publications

(2 citation statements)

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“…The impacts of snow drought are often widespread across ecosystems, water resource management, operation of reservoirs, and winter recreation. Effects of winter droughts have been reported by Saavedra et al [113] on nitrate concentrations in the streamflow of German catchments where significantly lower nitrate concentrations were attributed to winter drought or post-winter droughts.…”

Section: Challenges In Hydrological Drought Research-some Future Dire...mentioning

confidence: 79%

Current State of Advances in Quantification and Modeling of Hydrological Droughts

Sharma,

Panu

2024

Water

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Hydrological droughts may be referred to as sustained and regionally extensive water shortages as reflected in streamflows that are noticeable and gauged worldwide. Hydrological droughts are largely analyzed using the truncation level approach to represent the desired flow condition such as the median, mean, or any other flow quantile of an annual, monthly, or weekly flow sequence. The quantification of hydrologic droughts is accomplished through indices, such as the standardized streamflow index (SSI) in tandem with the standardized precipitation index (SPI) commonly used in meteorological droughts. The runs of deficits in the SSI sequence below the truncation level are treated as drought episodes, and thus, the theory of runs forms an essential tool for analysis. The parameters of significance from the modeling perspective of hydrological droughts (or tantamount to streamflow droughts in this paper) are the longest duration and the largest magnitude over a desired return period of T-year (or month or week) of the streamflow sequences. It is to be stressed that the magnitude component of the hydrological drought is of paramount importance for the design and operation of water resource storage systems such as reservoirs. The time scales chosen for the hydrologic drought analysis range from daily to annual, but for most applications, a monthly scale is deemed appropriate. For modeling the aforesaid parameters, several methodologies are in vogue, i.e., the empirical fitting of the historical drought sequences through a known probability density function (pdf), extreme number theorem, Markov chain analysis, log-linear, copulas, entropy-based analyses, and machine learning (ML)-based methods such as artificial neural networks (ANN), wavelet transform (WT), support vector machines (SVM), adaptive neuro-fuzzy inference systems (ANFIS), and hybrid methods involving entropy, copulas, and machine learning-based methods. The forecasting of the hydrologic drought is rigorously conducted through machine learning-based methodologies. However, the traditional stochastic methods such as autoregressive integrated moving average (ARIMA), seasonal autoregressive integrated moving average (SARIMA), copulas, and entropy-based methods are still popular. New techniques for flow simulation are based on copula and entropy-based concepts and machine learning methodologies such as ANN, WT, SVM, etc. The simulated flows could be used for deriving drought parameters in consonance with traditional Monte Carlo methods of data generation. Efforts are underway to use hydrologic drought models for reservoir sizing across rivers. The ML methods whilst combined in the hybrid form hold promise in drought forecasting for better management of existing water resources during the drought periods. Data mining and pre-processing techniques are expected to play a significant role in hydrologic drought modeling and forecasting in future.

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Section: Challenges In Hydrological Drought Research-some Future Dire...mentioning

confidence: 79%

Current State of Advances in Quantification and Modeling of Hydrological Droughts

Sharma,

Panu

2024

Water

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“…The impacts of climate change have been extensively addressed [7][8][9][10][11][12]. Droughts tend to amplify extreme nitrate concentrations, as widely observed in European rivers and groundwater [13,14], and generally, increase ion concentrations with progressive deterioration of water quality [15,16]. Additionally, the increase in groundwater temperatures in the Piedmont Plain [17,18] could influence groundwater chemistry [19].…”

Section: Introductionmentioning

confidence: 99%

Groundwater Chemical Trends Analyses in the Piedmont Po Plain (NW Italy): Comparison with Groundwater Level Variations (2000–2020)

Cocca,

Lasagna,

De Luca

2024

Water

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The concentrations of chemicals in the groundwater chemical values in the Piedmont Po Plain (NW Italy) show significant temporal variability and need to be characterised due to the lack of regional-scale assessments. The aim of this study was to analyse the trends (period 2000–2020) in the main physicochemical parameters and main ions in 227 wells in the shallow aquifer and to identify the potential causes. The identification of change points (points of sudden change) and comparisons with groundwater level variations were also performed. Results highlight general increasing trends for Na, Cl and HCO3, decreasing trends for SO4 and NO3, stationary conditions for pH and heterogeneous behaviours for electrolytic conductivity, Ca and Mg. Change points occurred in at least 50% of the monitoring wells, mainly during the 2008–2011 period. The comparison between groundwater levels and chemistry highlights a direct proportionality. Superimposed processes that induce an absence of proportionality are shown. The comparison of results with those of previous studies conducted under similar conditions revealed similar variations.. In conclusion, the potential responsible factors (e.g., road-salt dissolution and agricultural practices) and the relevant role of groundwater level variation were identified.

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