Great Lakes Runoff Intercomparison Project Phase 3: Lake Erie (GRIP-E)

Hydrologic model intercomparison studies help to evaluate the agility of models to simulate variables such as streamflow, evaporation, and soil moisture. This study is the third in a sequence of the Great Lakes Runoff Intercomparison Projects. The densely populated Lake Erie watershed studied here is an important international lake that has experienced recent flooding and shoreline erosion alongside excessive nutrient loads that have contributed to lake eutrophication. Understanding the sources and pathways of flows is critical to solve the complex issues facing this watershed. Seventeen hydrologic and land-surface models of different complexity are set up over this domain using the same meteorological forcings, and their simulated streamflows at 46 calibration and seven independent validation stations are compared. Results show that: (1) the good performance of Machine Learning models during calibration decreases significantly in validation due to the limited amount of training data; (2) models calibrated at individual stations perform equally well in validation; and (3) most distributed models calibrated over the entire domain have problems in simulating urban areas but outperform the other models in validation

Twenty-three unsolved problems in hydrology (UPH) – a community perspective

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through on-line media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focussed on process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come

ESCOMP/mizuRoute: cesm-coupling.n02_v2.1.2

<h1>What's Changed</h1> <h2>Model physics</h2> <ul> <li>capacity to scale or offset the inputs (runoff, evaporation, and precipitation) by @ShervanGharari in https://github.com/ESCOMP/mizuRoute/pull/412</li> <li>connect lake module to the other routing methods by @nmizukami in https://github.com/ESCOMP/mizuRoute/pull/421</li> <li>connect lake module to kwt routing routine by @nmizukami in https://github.com/ESCOMP/mizuRoute/pull/427</li> <li>Adding a few channel properties and water take option for other routing by @nmizukami in https://github.com/ESCOMP/mizuRoute/pull/428</li> <li>Enable to run at user specified routing time step rather than using coupling_frequency by @nmizukami in https://github.com/ESCOMP/mizuRoute/pull/429</li> <li>IRF water take by @nmizukami in https://github.com/ESCOMP/mizuRoute/pull/430</li> </ul> <h2>Bugfixes</h2> <ul> <li>Fix a restart issue for a single core by @nmizukami in https://github.com/ESCOMP/mizuRoute/pull/415</li> <li>set runoff depth unit correctly in meta by @nmizukami in https://github.com/ESCOMP/mizuRoute/pull/422</li> <li>Fix error in writing history_file variable for gauge-only history file in restart file by @nmizukami in https://github.com/ESCOMP/mizuRoute/pull/423</li> <li>Fixed upstream reach detection for cesm-coupling branch by @nmizukami in https://github.com/ESCOMP/mizuRoute/pull/441</li> </ul> <h2>Miscellaneous improvement</h2> <ul> <li>Refactoring related to PIO decomposition initialization by @nmizukami in https://github.com/ESCOMP/mizuRoute/pull/431</li> </ul> <h2>Documentation</h2> <ul> <li>Fixing readthedoc setup by @nmizukami in https://github.com/ESCOMP/mizuRoute/pull/435</li> <li>new readthedoc requirement by @nmizukami in https://github.com/ESCOMP/mizuRoute/pull/433</li> <li>conf.py path fixed in .readthedoc.yaml by @nmizukami in https://github.com/ESCOMP/mizuRoute/pull/434</li> <li>cheyenne build readme update by @nmizukami in https://github.com/ESCOMP/mizuRoute/pull/442</li> <li>Remove testmod fixes for ccs_config by @ekluzek in https://github.com/ESCOMP/mizuRoute/pull/416</li> </ul> <h2>Library updates</h2> <ul> <li>Externals update by @nmizukami in https://github.com/ESCOMP/mizuRoute/pull/432</li> <li>update manage_externals version by @nmizukami in https://github.com/ESCOMP/mizuRoute/pull/440</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/ESCOMP/mizuRoute/compare/cesm-coupling.n01_v2.1.0...cesm-coupling.n02_v2.1.2</p&gt

Advancing Process Representation in Hydrological Models : Integrating New Concepts, Knowledge, and Data

Model fidelity and accuracy in process representations have been the crux of scientific hydrological modeling, creating a pressing need for a better linkage between the development of hydrological models and the growing number of data sources and measurement techniques. Improved representation of process dynamics in hydrological models can provide new insights into complex hydrological systems and point out less understood natural phenomena that need further investigation. This special issue includes contributions that offer potential solutions and strategies to improve and test the representation of hydrological processes. We have organized the special issue contributions into four topical categories: (a) Beyond streamflow, which looks into the power of complementary data sources in addition to traditionally used streamflow for process inference. (b) Challenge of subsurface hydrology, that reflects on lesser understood processes under the surface and their impact on the model structure. (c) Evaporation in hydrological modeling, linking ecological aspects to the hydrological functioning of the natural system. Finally, (d) top down vs. bottom up modeling approaches, relied upon for process representation analysis. The special issue and our reflection on the contributions present a snapshot of ongoing efforts for integrating new concepts, knowledge, and data in process representation in hydrological models

The Impact of Meteorological Forcing Uncertainty on Hydrological Modeling: A Global Analysis of Cryosphere Basins

© 2023. The Authors. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.Global Water Futures Natural Sciences and Engineering Research Council of Canada. Grant Number: RGPIN-2019-06894Peer ReviewedMeteorological forcing is a major source of uncertainty in hydrological modeling. The recent development of probabilistic large-domain meteorological data sets enables convenient uncertainty characterization, which however is rarely explored in large-domain research. This study analyzes how uncertainties in meteorological forcing data affect hydrological modeling in 289 representative cryosphere basins by forcing the Structure for Unifying Multiple Modeling Alternatives (SUMMA) and mizuRoute models with precipitation and air temperature ensembles from the Ensemble Meteorological Data set for Planet Earth (EM-Earth). EM-Earth probabilistic estimates are used in ensemble simulation for uncertainty analysis. The results reveal the magnitude, spatial distribution, and scale effect of uncertainties in meteorological, snow, runoff, soil water, and energy variables. There are three main findings. (a) The uncertainties in precipitation and temperature lead to substantial uncertainties in hydrological model outputs, some of which exceed 100% of the magnitude of the output variables themselves. (b) The uncertainties of different variables show distinct scale effects caused by spatial averaging or temporal averaging. (c) Precipitation uncertainties have the dominant impact for most basins and variables, while air temperature uncertainties are also nonnegligible, sometimes contributing more to modeling uncertainties than precipitation uncertainties. We find that three snow-related variables (snow water equivalent, snowfall amount, and snowfall fraction) can be used to estimate the impact of air temperature uncertainties for different model output variables. In summary, this study provides insight into the impact of probabilistic data sets on hydrological modeling and quantifies the uncertainties in cryosphere basin modeling that stem from the meteorological forcing data

Understanding the Information Content in the Hierarchy of Model Development Decisions: Learning From Data

Process-based hydrological models seek to represent the dominant hydrological processes in a catchment. However, due to unavoidable incompleteness of knowledge, the construction of “fidelius” process-based models depends largely on expert judgment. We present a systematic approach that treats models as hierarchical assemblages of hypotheses (conservation principles, system architecture, process parameterization equations, and parameter specification), which enables investigating how the hierarchy of model development decisions impacts model fidelity. Each model development step provides information that progressively changes our uncertainty (increases, decreases, or alters) regarding the input-state-output behavior of the system. Following the principle of maximum entropy, we introduce the concept of “minimally restrictive process parameterization equations—MR-PPEs,” which enables us to enhance the flexibility with which system processes can be represented, and to thereby investigate the important role that the system architectural hypothesis (discretization of the system into subsystem elements) plays in determining model behavior. We illustrate and explore these concepts with synthetic and real-data studies, using models constructed from simple generic buckets as building blocks, thereby paving the way for more-detailed investigations using sophisticated process-based hydrological models. We also discuss how proposed MR-PPEs can bridge the gap between current process-based modeling and machine learning. Finally, we suggest the need for model calibration to evolve from a search over “parameter spaces” to a search over “function spaces.”.Water Resource

Twenty-three unsolved problems in hydrology (UPH)–a community perspective

This paper is the outcome of a community initiative to identify major unsolved scientific problems in hydrology motivated by a need for stronger harmonisation of research efforts. The procedure involved a public consultation through online media, followed by two workshops through which a large number of potential science questions were collated, prioritised, and synthesised. In spite of the diversity of the participants (230 scientists in total), the process revealed much about community priorities and the state of our science: a preference for continuity in research questions rather than radical departures or redirections from past and current work. Questions remain focused on the process-based understanding of hydrological variability and causality at all space and time scales. Increased attention to environmental change drives a new emphasis on understanding how change propagates across interfaces within the hydrological system and across disciplinary boundaries. In particular, the expansion of the human footprint raises a new set of questions related to human interactions with nature and water cycle feedbacks in the context of complex water management problems. We hope that this reflection and synthesis of the 23 unsolved problems in hydrology will help guide research efforts for some years to come