Modified Hydrological Drought Risk Assessment Based on Spatial and Temporal Approaches

Hasan, Hasrul Hazman; Razali, Siti Fatin Mohd; Muhammad, Nur Shazwani; Ahmad, Asmadi

doi:10.3390/su14106337

Cited by 8 publications

(2 citation statements)

References 58 publications

(63 reference statements)

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“…This definition includes hazard, exposure, and vulnerability. Hasan et al (2022) presented a risk-based assessment approach specifically for hydrological drought. The presented framework is based on various indices.…”

Section: Introductionmentioning

confidence: 99%

Conceptual approach for a holistic low‐flow risk analysis

Satzinger,

Bachmann

2024

Hydrological Processes

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Low‐flow events, characterized by a significant water deficiency in river systems, have profound impacts on various water users and river ecology. Recent low‐flow events in Europe have had severe economic and ecological consequences such as disruptions to hydropower production, irrigation bans, constraints on navigation and complete river drying. These events highlight the urgent need for effective low‐flow risk management and demand a holistic risk analysis as a basis. The existing approaches to low‐flow analysis often focus on hydrological aspects, utilizing indices such as the Standardized Runoff Index (SRI) or Low‐flow Index. However, these indices lack information regarding consequences and impacts. Other approaches consider parts of a risk approach but often focus on special aspects, such as the economy; in general, no holistic assessment is made. This study introduces a conceptual approach to a holistic low‐flow risk analysis. The approach provides a continuous long‐term simulation to capture the special long‐term behaviour of low‐flow events and therefore avoids the complex definition of scenarios. In this conceptual approach, the low‐flow risk is analysed using a combination of various analyses that cover all aspects from occurrence to consequences. Meteorological analysis is used to generate synthetic long‐term weather data time series, which are transformed into runoff time series in hydrological analysis. Based on these results, hydrodynamic analysis quantifies the water levels, water temperatures, and flow velocities along the river. The consequences are analysed in terms of socio‐economic and ecological consequences. The results represent a long‐term series of damage values. Finally, the damage values are summed in the risk analysis and divided by the number of years considered in the analysis. For testing and demonstration purposes, the presented conceptual risk approach is partly applied to a proof‐of‐concept at the Selke catchment, a small river catchment in Germany. Finally, the results are presented, evaluated, and discussed.

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Section: Introductionmentioning

confidence: 99%

Conceptual approach for a holistic low‐flow risk analysis

Satzinger,

Bachmann

2024

Hydrological Processes

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“…The lack of precipitation for a long time is the direct and leading cause of drought [3]. Since two decades ago, droughts have become increasingly common and severe due to climate change brought on by global warming, causing food and water crises in some regions worldwide [4][5][6]. Moreover, the occurrence of drought leads to various issues, including desertification, water scarcity, and forest fires, which have negative repercussions at both local and global scales [7][8][9][10].…”

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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