Twenty-three unsolved problems in hydrology (UPH) - a community perspective
The Growth of Hydrological Understanding: Technologies, Ideas, and Societal Needs Shape the Field
The essence of research lies in the people who carry it out. Breakthroughs in research do not come from instrumentation, computers, and satellites; they grow in the minds of individual men and women.
—Freeze,
…. inspired guesses are not enough. Progress comes primarily from the introduction of new observational and theoretical tools.
—Harwit,
| Era | Societal needs | Technological opportunities | Euphoria | Typical discoveries of phenomena | Progress in prediction methods | Disenchantment |
|---|---|---|---|---|---|---|
| 1910–1930 Empirical Era | Flood design | National instrumented networks | Predictability, clear benefit for technical progress | Correlations between water levels exist | Regressions, envelope curves | Lack of transferablity to other places |
| 1950–1970 Systems Era | Economic efficiency | Operations Research, first digital applications | Objectivization by Systems Approach (to overcome subjectivity) | Linearity of hydrological response | Unit hydrograph estimation, time series models | Inability to extrapolate to other conditions |
| 1970–1990 Process Era | Water quality (chemical) | Fast computers, new data collection methods | Solve hydrology as a physical problem (to overcome inability to extrapolate) | Variable source area runoff generation; Event water stems from pre-event rainfall | Physically-based spatially distributed models, stochastic hydrogeology | Scale problems, it is not just a physical but also a biological problem (transpiration, roots) |
| 1990–2010 Geosciences Era | Climate change, ecosystem health | Remote sensing, internet | Interdisciplinarity allows more accurate representation of complex processes | Controls on spatial patterns of soil moisture | Coupled process models, model chains, climate scenarios, data assimilation | Quasi-stationary coupling misses long term dynamics |
| 2010–2030 Coevolution Era | Sustainable development given dominant human footprint | Big data, faster computers, finer resolution remote sensing | Including feedbacks explicitly promises predictability over decades/centuries | Root adaptation to climate, levee effect of people moving into floodplains | Models representing catchments as complex systems (linking time scales) | Parameters of complex systems cannot be measured, spatial feedbacks missed |
“Panta Rhei—Everything Flows”: Change in hydrology and society—The IAHS Scientific Decade 2013–2022
Authors again emphysized the underpinning importance of co-evolving modeling of water-human instead of one-way feedback.
“The future of hydrology is closely related to improving our understanding of changing behaviours of hydrological systems through the study of their two-way interaction with connected processes (Baresel and Destouni 2005, 2007, Wagener et al. 2010, Schaefli et al. 2011).”
“While exploitation is relatively straightforward to estimate, the feedbacks—such as those triggered by reservoir impacts, or those considered in flood risk analysis, by Di Baldassarre et al. (2013a)—the climatic changes, the degradation of water bodies and many others, are poorly understood.”
Examples of related activities |Item|Activity| |—-|——–| |1.|developing new measurement techniques to constrain uncertainty in closure of the mass balance that is fundamental to improved process understanding| |2.|theoretical and experimental analysis of hydrological processes and their links with and feedbacks to connected systems, assessment of their behavioural determinants, intrinsic dynamics and indeterminacy| |3.|climate and land-use impact studies conducted with a bottom-up approach, namely, by focusing on the resilience of hydrological systems to change, either natural or human-induced| |4.|theoretical and experimental comprehensive analyses of the impacts and feedbacks of human activity on the dynamics of connected hydrological systems| |5.|analysis of the co-evolution of hydrological processes and catchment signatures, ecosystems and social systems| |6.|analysis of the scaling properties of hydrological processes and their long-term patterns| |7.|reconstruction of past conditions and climate (historical hydrology)| |8.|new modelling philosophies and approaches for hydrological systems in close connection with human activities| |9.|entropy modelling and evolution of natural systems| |10.|coupled modelling of environmental and human systems (socio-hydrology interactions and feedback processes)| |11.|integrated water resources management and economics| |12.|comparative analysis of hydrological systems to better understand the reaction of hydrological processes to different perturbations in different environments, and to learn from the similarities and differences of different places| |13.|development of theoretical schemes for the integrated modelling of hydrological knowledge and hydrological uncertainty (Beven 2008)| |14.|setting up strategies for estimating and communicating uncertainty, and solutions for reducing decision-making and operational uncertainty| |15.|use of advanced monitoring techniques for reducing data errors| |16.|development of advanced prediction methods in the presence of indeterminacy| |17.|proactive research on opportunities conveyed by advanced monitoring methods| |18.|enhanced use of remote sensing for water resources estimation and management| |19.|integration of advanced information into hydro-logical models, through development of increasingly sophisticated data assimilation approaches| |20.|development of advanced monitoring techniques for deciphering the interaction between hydrological processes and human settlements and activities| |21.|linking new observations and techniques with historical data sets| |22.|identification of hot-spots of human vulnerabilities under on-going hydrological changes| |23.|estimation of thresholds of hydrological loading capacities where overtopping would affect the societies, as well as nature, in an unbearable way| |24.|raising public awareness of human-induced changes in hydrological conditions| |25.|transboundary water resources management, and water conflicts| |26.|impact of large-scale water structures and large-scale water transfer|
Where should hydrology go? An early-career perspective on the next IAHS Scientific Decade: 2023-2032
Three main themes are discussed among Early Career Scientists in Europe regarding next decadal focal point of Hydrology:
- The tipping point of hydrologic science
This involves three questions: How can hydrological tipping points and thresholds be identified?
At what scales are the identified tipping points and thresholds relevant and how do these scales interact?
Which non-hydrological tipping points affect hydrological systems?
What needs to be included in hydrological models to simulate and predict tipping points and thresholds? How reliable are modelled tipping points and thresholds?
How can we use our knowledge of tipping points and complex systems to mitigate the impacts of environmental and climate change?
- Intensification of water cycle
What is the impact of an intensified hydrological cycle on the environment, ecosystem services and society?
What areas are most at risk of the intensification of the hydrological cycle?
How reliable are extreme event predictions that are based on extrapolating relatively short data series and how can this reliability be improved?
How can early-warning systems be improved so that extreme events can be accurately predicted?
Which mitigation strategies are suitable in the context of ongoing intensification of the hydrological cycle?
- Water services under pressure
How can we assess quantitative and qualitative water availability for sustainable water services?
What hydrological knowledge is missing to provide solutions to support water services?
How can the development of pressures on water services be identified, monitored and predicted?
What are the scales and spatiotemporal distributions of pressures on water services?
Barriers to progress in distributed hydrological modelling
Authors mention the main barriers to hydrological modeling, which is that no one seems to care about whether the model is capable of representing a wide-range of hydrological processes, and instead, they often assume that models can be justified through calibration.
Another barrier is data which hinders model validation.