Intermittency as an indicator of drought in streamflow and groundwater

Yildirim, I Ilhan; Aksoy, Hafzullah

doi:10.1002/hyp.14615

Cited by 4 publications

(4 citation statements)

References 44 publications

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“…Here, it is worth to mention that the intermittent flow regime of the river basins might cause the lack of resilience to hydrological drought triggered by meteorological drought for all river basins. This reminds the important role of intermittency, which likely amplifies drought propagation (Yildirim & Aksoy, 2022).…”

Section: Resultsmentioning

confidence: 73%

“…Apurv et al (2017) used numerical experiments in order to understand the roles of climate characteristics by identifying three different drought propagation mechanisms; seasonality, aridity and timing, among which the timing of precipitation was found to have the largest impact. Intermittency in streamflow, that is, the ratio of time with no flow to the total length of observation, was also found to be a driving indicator of the drought propagation (Yildirim & Aksoy, 2022). In a slightly different approach, Roodari et al (2021) studied human interventions to show how they contribute to moderating or amplifying drought propagation in the downstream and upstream parts of a river basin over time.…”

Section: Introductionmentioning

confidence: 99%

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Formative drought rate to quantify propagation from meteorological to hydrological drought

Yildirim,

Aksoy,

Hrachowitz

2024

Hydrological Processes

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In this study, we propose a probabilistic metric, the formative drought rate (FDR), to quantify drought propagation. It is the probability that a meteorological drought in precipitation forms a hydrological drought in streamflow. Drought events were identified based on the standardized precipitation index and streamflow drought index, respectively, at 1‐, 3‐, 6‐ and 12‐month timescales. The method was tested in three river basins in Turkey (Kucuk Menderes, Gediz and Ergene). In each river basin, meteorological stations were coupled with streamflow gauging stations to form pairs of stations depending on their distance from each other and the length of their common record periods. The FDR was calculated across all timescales for each pair of stations. It was found capable to describe the river basin‐specific spatial and temporal variability of drought propagation. As the FDR is defined in the form of probability, it is expected to be a useful metric for quantifying propagation from meteorological to hydrological drought. Thus, it carries a potential for scientific research and practice in water resources management.

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Section: Resultsmentioning

confidence: 73%

Section: Introductionmentioning

confidence: 99%

Formative drought rate to quantify propagation from meteorological to hydrological drought

Yildirim,

Aksoy,

Hrachowitz

2024

Hydrological Processes

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“…These synergies between connectivity changes and drought imply furthermore, that research on IRES and on drought should not be independent, as elaborated e.g. by (Yildirim and Aksoy, 2022).…”

Section: Sensitivity Of Leakage Flow To Stress Testsmentioning

confidence: 99%

An investigation of anthropogenic influences on hydrologic connectivity using stress tests

Herzog,

Hellwig,

Stahl

2023

Preprint

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Abstract. Worldwide, human influences directly and indirectly threaten environmental flows through groundwater (GW) abstraction. This highlights the need to consider GW withdrawals together with climatic changes in future water management plans to maintain water availability in river networks. In alluvial geological contexts, such as the Dreisam River in SouthGermany, contributions from GW often sustain streamflow in the summer months. In the specific case of the Dreisam however, several hydrological drought events between 2015 and 2022 lead to interruptions of longitudinal connectivity in the stream network. This raises the question on where and when the stream network is gaining or losing water from the GW and how these vertical connectivity changes influence streambed drying. This study therefore aims to analyse both, how changes in longitudinal and vertical connectivity in the Dreisam valley respond to stresses from climatic variations of recharge and to anthropogenic water abstractions from the hydrological system. As GW-SW interaction is difficult to measure, numerical groundwater modeling was used to obtain a spatial distribution of the exchange flow between GW and SW. The results show in which stream reaches, the connection between GW and SW ceases during dry conditions. Changes of vertical connectivity due to GW abstraction were found to be stronger than due to recharge stress on short timescales. A combined analysis of vertical and longitudinal connectivity depicts local points along the stream network, where the effect of GW abstraction on temporal drying dynamics is likely particularly strong. These results have to be interpreted within the limits of model reality and uncertainty. Simulated zero water levels were in good agreement with measured zero water levels at 50 % of measurement locations, whereof the majority was in the valley bottom. Future work needs to improve coupling to upstream contributions and bedrock aquifers along the hillslopes of the valley. Overall, our findings highlight the value of a combined analysis of different dimensions of hydrologic connectivity for the evaluation of model results. Approaches that better distinguish locations affected by natural and anthropogenic drivers of hydrologic drought and streamflow intermittency deserve further development and are needed for application on different spatial and temporal scales.

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“…Understanding the relationship between groundwater levels and streamflow is critical for effective water resource management and proper preparation for drought conditions [6][7][8]. The fluctuations in groundwater levels play a pivotal role in influencing soil moisture availability, directly impacting the occurrence and duration of soil drought conditions [9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods

Zhang,

Zhou,

Deng

et al. 2024

Water

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Considering the increased risk of urban flooding and drought due to global climate change and rapid urbanization, the imperative for more accurate methods for streamflow forecasting has intensified. This study introduces a pioneering approach leveraging the available network of real-time monitoring stations and advanced machine learning algorithms that can accurately simulate spatial–temporal problems. The Spatio-Temporal Attention Gated Recurrent Unit (STA-GRU) model is renowned for its computational efficacy in forecasting streamflow events with a forecast horizon of 7 days. The novel integration of the groundwater level, precipitation, and river discharge as predictive variables offers a holistic view of the hydrological cycle, enhancing the model’s accuracy. Our findings reveal that for a 7-day forecasting period, the STA-GRU model demonstrates superior performance, with a notable improvement in mean absolute percentage error (MAPE) values and R-square (R2) alongside reductions in the root mean squared error (RMSE) and mean absolute error (MAE) metrics, underscoring the model’s generalizability and reliability. Comparative analysis with seven conventional deep learning models, including the Long Short-Term Memory (LSTM), the Convolutional Neural Network LSTM (CNNLSTM), the Convolutional LSTM (ConvLSTM), the Spatio-Temporal Attention LSTM (STA-LSTM), the Gated Recurrent Unit (GRU), the Convolutional Neural Network GRU (CNNGRU), and the STA-GRU, confirms the superior predictive power of the STA-LSTM and STA-GRU models when faced with long-term prediction. This research marks a significant shift towards an integrated network of real-time monitoring stations with advanced deep-learning algorithms for streamflow forecasting, emphasizing the importance of spatially and temporally encompassing streamflow variability within an urban watershed’s stream network.

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Intermittency as an indicator of drought in streamflow and groundwater

Cited by 4 publications

References 44 publications

Formative drought rate to quantify propagation from meteorological to hydrological drought

Formative drought rate to quantify propagation from meteorological to hydrological drought

An investigation of anthropogenic influences on hydrologic connectivity using stress tests

Daily Streamflow Forecasting Using Networks of Real-Time Monitoring Stations and Hybrid Machine Learning Methods

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