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Open research questions in Hydrology and Drought Analysis

31 unresolved questions extracted from the limitations and future-work sections of 512 Hydrology and Drought Analysis papers in our library. Each links back to the study that raised it.

What the literature leaves open

  • This persis- tence demands a proactive, science-informed, and locally grounded approach to adapta- tion planning, where both climatic and human systems are jointly addressed to build resilience in the face of accelerating climate change; an issue that needs to be further investigated. These need to be investigated further. Despite a general increase in precipitation across the region, the persistence of negative SPAI values in several municipalities indicates that increased precipitation alone is insufficient to offset long-term moisture deficits.

    Drought projections in a high-latitude continental region of Quebec · 2026 · DOI

  • Drought remains one of the most complex and multifaceted natural hazards, shaped by nonlinear interactions among climatic, hydrological, and ecological systems. Over the past decades, drought research has evolved from simple statistical indices to advanced hybrid and deep learning frameworks, reflecting a concerted effort to improve accu- racy, reliability, and interpretability under changing climatic conditions. Traditional drought indices—such as SPI, SPEI, and PDSI—have long served as foundational tools for his- torical monitoring and trend assessment. However, their reliance on stationary climate assumptions and limited flex- ibility constrains their performance under evolving environ- mental conditions. Conversely, machine learning (ML) and deep learning (DL) techniques—including Random Forests, SVM, ANN, CNN, and LSTM—offer superior capabili- ties in capturing nonlinear patterns and temporal depen- dencies. By leveraging multi-source datasets from remote sensing, ground observations, and climate projections, these models enable more accurate and spatially resolved 1 3Machine and Deep Learning Approaches for Drought Characterization and Prediction: a Comprehensive… drought forecasting. Recent developments emphasize the rise of hybrid and ensemble frameworks, which integrate the strengths of process-based and data-driven methods. These models combine the physical realism of hydrological simulations with the adaptive learning power of AI, yield- ing improved predictive accuracy and interpretability. The integration of CMIP6-based climate projections with deep learning architectures represents a promising direction for future drought scenario modeling and adaptive water-man- agement planning. Recent developments have also focused on improving the operational accessibility of drought analysis through dedicated software platforms, which address limitations of existing tools such as restricted functionality, limited distri- butional flexibility, and dependence on advanced program- ming expertise (Terzi and Üçüncü) [256]. The emergence of cloud computing, IoT-enabled observation systems, and explainable AI has further transformed the drought moni- toring landscape. Platforms such as Google Earth Engine facilitate near-real-time drought mapping, while physics- informed and interpretable AI models promote transparent, trustworthy predictions. Integrating community feedback and participatory data systems enhances the social relevance of drought forecasts, bridging scientific insights and practi- cal decision-making. Looking forward, the future of drought research lies in interdisciplinary collaboration, adaptive modeling, and scalable AI systems that couple hydrologi- cal knowledge with advanced analytics. By uniting physical understanding, data-driven innovation, and policy integra- tion, researchers and decision-makers can transition from reactive drought response toward proactive, predictive, and sustainable management. Such integrated approaches will be critical for ensuring water security, supporting agricul- tural resilience, and building climate-adaptive systems for future generations. Acknowledgements The authors wish to thank the anonymous reviewers for their constructive comments to enhance the quality of the manuscript. Author Contributions C.N.: Conceptualization, Methodology, Data curation, Formal analysis, Investigation, Validation, Visualization, Writing – original draft editing. A.M: Methodology, Data curation, Formal analysis, Writing – review & editing, Supervision. Funding There was no funding for this project. Data Availability Data will be made available on request.

    Machine and Deep Learning Approaches for Drought Characterization and Prediction: a Comprehensive Review · 2026 · DOI

  • All governments and other relevant stakeholders around the world are encouraged to manage drought risks in an integrated, proactive and prospective manner, shifting from the current reactive crisis-oriented approach, develop and strengthen drought policies and turn them into action by considering inter alia the recommendations of the DR+10 workstreams: 1. Drought resilience and global alignment: Strengthen international collaboration and dialogue on drivers of globally networked risks (e.g. trade and food security impacts from droughts in different parts of the world), on monitoring, and on vertical and horizontal coordination across regions, nations, sectors, and communities. Use the opportunity provided by drought events and lessons from experience to increase coherence, align financing, and prioritize resilience building across the Paris Agreement, Sendai Framework, Kunming- Montreal Biodiversity Framework and Sustainable Development Goals, and build forward smarter at appropriate scales. 2. Drought risk governance: Extend proactive drought risk management towards integrated drought resilience management by implementing a whole-of-society, systemic approach. Ensure that drought management is further integrated into Sustainable Land Management (SLM), Integrated Water Resources Management (IWRM), and climate change adaptation strategies while mainstreaming responsive approaches. Indigenous and Local Knowledge (ILK) and gender- 3. Monitoring, assessing, and forecasting of droughts and their impacts: Tailor drought monitoring, assessment, impact-based forecasting, and early warning systems for national governments, NGOs, relevant sectors, and vulnerable groups to prevent or reduce impacts. Make local knowledge central to the full climate services value chain. Prioritize better monitoring, prediction, and systematic collection of impact data, including for cascading and compounding impacts, as well as fast-moving flash droughts, to address key challenges.

    Ten key insights and gaps to inform drought risk research, policy and practice · 2026 · DOI

  • Future research should explore the coupling between biophysical indicators and socio- economic vulnerabilities, integrate additional ecological parameters, such as soil moisture and evapotranspiration, and incorporate lagged correlation analyses at monthly or seasonal scales to better capture the temporal dynamics of vegetation response to hydrometeorological drought.

    Integrated drought assessment in Morocco: a combined approach using meteorological, hydrological, and remote sensing observations (1975–2023) · 2026 · DOI

  • Limited discussion of how the EKE distribution performs with small sample sizes, which is common in hydrological applications where extreme events are rare.

    On the Extreme Kumaraswamy-Epsilon Distribution and Its Application to Exceedances of Flood Peaks · 2026 · DOI

  • The paper lacks explicit discussion of limitations regarding the applicability of the EKE distribution to different types of hydrological data beyond flood peaks.

    On the Extreme Kumaraswamy-Epsilon Distribution and Its Application to Exceedances of Flood Peaks · 2026 · DOI

  • This research will help local water managers identify related precipitation areas within the region, compare Navajo Nation precipitation with climate indices to ascertain larger‐scale atmospheric contributors to precipitation in the Four Corners region, and support future water planning in this understudied region.

    Navajo Nation, USA, Precipitation Variability from 2002 to 2015 · 2018 · DOI

  • Some of the limitations of this study include lack of equipment for automatic and continu- ous groundwater level measurements as our measurements have focused on two seasons (end of the dry season and end of the rainy seasons).

    Drought resilient groundwater systems: evidence from semi-arid areas of northern Ethiopia · 2026 · DOI

  • Despite increasing concerns over climate variability, limited research has examined the spatiotemporal dynamics and long-term trends of extreme rainfall events in southeastern Ethiopia at the watershed scale.

    Spatio-temporal analysis of rainfall variability, extremes and trends in Southeastern Ethiopia · 2026 · DOI

  • These results highlight the necessity of adopting a multivariate, spatiotemporally integrated perspective to accurately assess extreme droughts, even where observational data remain limited.

    Spatiotemporal variations in hydrometeorological drought across North Africa and indications of anthropogenic amplification · 2026 · DOI

  • The spatiotemporal variability of moisture conditions in the Lake Baikal basin, a key region of Inner Asia, remains insufficiently understood due to the short duration of instrumental observations and the complex interplay of climatic factors.

    Spatiotemporal variability of palmer drought severity index inferred from long tree-ring chronologies in the Russian and Mongolian parts of the lake Baikal basin · 2026 · DOI

  • It is suggested that the short duration (1–2 years) of most droughts in Jordan prevents mild and moderate severity levels from escalating to extreme levels, as there is insufficient time for their intensity to increase.

    Drought in Jordan: Analysing Severity and Spatiotemporal Patterns · 2026 · DOI

  • Future research should examine whether similar qualitative dynamics arise in other climatic set- tings and explore how external climate drivers, land-surface processes, and anthropogenic influences may alter these regime structures over longer time scales.

    Regime-based drought dynamics: a data-driven differential equation framework for state transitions, weak seasonality, and irregular variability · 2026 · DOI

  • Coupling rainfall variability with hydrological or land–atmosphere models would help clarify how fluctuations translate into runoff responses across different catchments in Chiang Rai Province.

    Rainfall Variability Analysis Using Rolling Statistics in Chiang Rai Province · 2026 · DOI

  • This part underscores the complementary role of satellite data in climatic studies, especially valuable in regions like West Africa where ground data are sparse.

    Rainfall variability and drought in West Africa: challenges and implications for rainfed agriculture · 2024 · DOI

  • To date, the results from these experiments have varied widely, and thus, patterns of drought sensitivities and the underlying mechanisms have been difficult to discern.

    Experimental droughts with rainout shelters: a methodological review · 2018 · DOI

  • The findings suggest that comparative studies of biotic responses to rainfall variability between arid Australia (particular southern regions) and other deserts are warranted before paradigms and models based on the uniqueness of Australia's arid environment are accepted.

    Inter-annual Rainfall Variability of Arid Australia: greater than elsewhere? · 2009 · DOI

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31 open questions have been extracted from the limitations and future-work passages of 512 Hydrology and Drought Analysis papers in our library. Each one below links back to the study that raised it, so you can read the original claim in context.

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