Articles | Volume 25, issue 9
https://doi.org/10.5194/hess-25-4995-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-25-4995-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
16 Sep 2021
Research article |
| 16 Sep 2021
| 16 Sep 2021
Sequential data assimilation for real-time probabilistic flood inundation mapping
Keighobad Jafarzadegan
CORRESPONDING AUTHOR
Center for Complex Hydrosystems Research, Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA
Peyman Abbaszadeh
Center for Complex Hydrosystems Research, Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA
Hamid Moradkhani
Center for Complex Hydrosystems Research, Department of Civil, Construction, and Environmental Engineering, University of Alabama, Tuscaloosa, AL, USA
Related authors
Francisco Javier Gomez, Keighobad Jafarzadegan, Hamed Moftakhari, and Hamid Moradkhani
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-26, https://doi.org/10.5194/nhess-2024-26, 2024
Preprint under review for NHESS
Short summary
Short summary
This study utilizes the Global Copula Bayesian Model Averaging technique for accurate and reliable flood modeling, especially in coastal regions. By integrating multiple precipitation datasets within this framework, we can effectively address sources of error in each dataset, leading to the generation of probabilistic flood maps. The creation of these probabilistic maps is essential for disaster preparedness and mitigation in densely populated areas susceptible to extreme weather events.
Keighobad Jafarzadegan, David F. Muñoz, Hamed Moftakhari, Joseph L. Gutenson, Gaurav Savant, and Hamid Moradkhani
Nat. Hazards Earth Syst. Sci., 22, 1419–1435, https://doi.org/10.5194/nhess-22-1419-2022, https://doi.org/10.5194/nhess-22-1419-2022, 2022
Short summary
Short summary
The high population settled in coastal regions and the potential damage imposed by coastal floods highlight the need for improving coastal flood hazard assessment techniques. This study introduces a topography-based approach for rapid estimation of flood hazard areas in the Savannah River delta. Our validation results demonstrate that, besides the high efficiency of the proposed approach, the estimated areas accurately overlap with reference flood maps.
Francisco Javier Gomez, Keighobad Jafarzadegan, Hamed Moftakhari, and Hamid Moradkhani
Nat. Hazards Earth Syst. Sci. Discuss., https://doi.org/10.5194/nhess-2024-26, https://doi.org/10.5194/nhess-2024-26, 2024
Preprint under review for NHESS
Short summary
Short summary
This study utilizes the Global Copula Bayesian Model Averaging technique for accurate and reliable flood modeling, especially in coastal regions. By integrating multiple precipitation datasets within this framework, we can effectively address sources of error in each dataset, leading to the generation of probabilistic flood maps. The creation of these probabilistic maps is essential for disaster preparedness and mitigation in densely populated areas susceptible to extreme weather events.
David F. Muñoz, Hamed Moftakhari, and Hamid Moradkhani
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-9, https://doi.org/10.5194/hess-2024-9, 2024
Revised manuscript under review for HESS
Short summary
Short summary
The advantage of linking hydrodynamic and machine learning models for compound flood modeling is that they enable a robust characterization of nonlinear interactions among the sources of uncertainty. Such an approach enables the quantification of cascading uncertainty and relative contributions to total uncertainty while also tracking its evolution during compound flooding. The proposed approach is a feasible alternative to conventional statistical approaches designed for uncertainty analysis.
Keighobad Jafarzadegan, David F. Muñoz, Hamed Moftakhari, Joseph L. Gutenson, Gaurav Savant, and Hamid Moradkhani
Nat. Hazards Earth Syst. Sci., 22, 1419–1435, https://doi.org/10.5194/nhess-22-1419-2022, https://doi.org/10.5194/nhess-22-1419-2022, 2022
Short summary
Short summary
The high population settled in coastal regions and the potential damage imposed by coastal floods highlight the need for improving coastal flood hazard assessment techniques. This study introduces a topography-based approach for rapid estimation of flood hazard areas in the Savannah River delta. Our validation results demonstrate that, besides the high efficiency of the proposed approach, the estimated areas accurately overlap with reference flood maps.
Sahani Pathiraja, Daniela Anghileri, Paolo Burlando, Ashish Sharma, Lucy Marshall, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 22, 2903–2919, https://doi.org/10.5194/hess-22-2903-2018, https://doi.org/10.5194/hess-22-2903-2018, 2018
Short summary
Short summary
Hydrologic modeling methodologies must be developed that are capable of predicting runoff in catchments with changing land cover conditions. This article investigates the efficacy of a recently developed approach that allows for runoff prediction in catchments with unknown land cover changes, through experimentation in a deforested catchment in Vietnam. The importance of key elements of the method in ensuring its success, such as the chosen hydrologic model, is investigated.
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Uncertainty analysis
Technical note: Complexity–uncertainty curve (c-u-curve) – a method to analyse, classify and compare dynamical systems
Technical note: The CREDIBLE Uncertainty Estimation (CURE) toolbox: facilitating the communication of epistemic uncertainty
Why do our rainfall–runoff models keep underestimating the peak flows?
Use of expert elicitation to assign weights to climate and hydrological models in climate impact studies
Pitfalls and a feasible solution for using KGE as an informal likelihood function in MCMC methods: DREAM(ZS) as an example
Benchmarking global hydrological and land surface models against GRACE in a medium-sized tropical basin
Guidance on evaluating parametric model uncertainty at decision-relevant scales
Quantifying input uncertainty in the calibration of water quality models: reordering errors via the secant method
Key challenges facing the application of the conductivity mass balance method: a case study of the Mississippi River basin
Coupled machine learning and the limits of acceptability approach applied in parameter identification for a distributed hydrological model
A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda
Technical note: Uncertainty in multi-source partitioning using large tracer data sets
Assessment of climate change impact and difference on the river runoff in four basins in China under 1.5 and 2.0 °C global warming
A likelihood framework for deterministic hydrological models and the importance of non-stationary autocorrelation
Technical note: Analytical sensitivity analysis and uncertainty estimation of baseflow index calculated by a two-component hydrograph separation method with conductivity as a tracer
Understanding the water cycle over the upper Tarim Basin: retrospecting the estimated discharge bias to atmospheric variables and model structure
The effect of input data resolution and complexity on the uncertainty of hydrological predictions in a humid vegetated watershed
Parameter uncertainty analysis for an operational hydrological model using residual-based and limits of acceptability approaches
Technical note: Pitfalls in using log-transformed flows within the KGE criterion
Improvement of model evaluation by incorporating prediction and measurement uncertainty
Transferability of climate simulation uncertainty to hydrological impacts
Intercomparison of different uncertainty sources in hydrological climate change projections for an alpine catchment (upper Clutha River, New Zealand)
Mapping (dis)agreement in hydrologic projections
Consistency assessment of rating curve data in various locations using Bidirectional Reach (BReach)
The critical role of uncertainty in projections of hydrological extremes
Residual uncertainty estimation using instance-based learning with applications to hydrologic forecasting
Characterizing and reducing equifinality by constraining a distributed catchment model with regional signatures, local observations, and process understanding
Effects of uncertainty in soil properties on simulated hydrological states and fluxes at different spatio-temporal scales
Extending flood forecasting lead time in a large watershed by coupling WRF QPF with a distributed hydrological model
Quantifying uncertainty on sediment loads using bootstrap confidence intervals
Event-scale power law recession analysis: quantifying methodological uncertainty
Disentangling timing and amplitude errors in streamflow simulations
Reliability of lumped hydrological modeling in a semi-arid mountainous catchment facing water-use changes
Using dry and wet year hydroclimatic extremes to guide future hydrologic projections
Uncertainty contributions to low-flow projections in Austria
Accounting for dependencies in regionalized signatures for predictions in ungauged catchments
Climate change and its impacts on river discharge in two climate regions in China
Uncertainty in hydrological signatures
Climate model uncertainty versus conceptual geological uncertainty in hydrological modeling
Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments
Transferring global uncertainty estimates from gauged to ungauged catchments
Spatial sensitivity analysis of snow cover data in a distributed rainfall-runoff model
Uncertainty reduction and parameter estimation of a distributed hydrological model with ground and remote-sensing data
The skill of seasonal ensemble low-flow forecasts in the Moselle River for three different hydrological models
Flow pathways and nutrient transport mechanisms drive hydrochemical sensitivity to climate change across catchments with different geology and topography
The importance of hydrological uncertainty assessment methods in climate change impact studies
Regional water balance modelling using flow-duration curves with observational uncertainties
Climate change impacts on the hydrologic regime of a Canadian river: comparing uncertainties arising from climate natural variability and lumped hydrological model structures
From maps to movies: high-resolution time-varying sensitivity analysis for spatially distributed watershed models
Bridging the gap between GLUE and formal statistical approaches: approximate Bayesian computation
Uwe Ehret and Pankaj Dey
Hydrol. Earth Syst. Sci., 27, 2591–2605, https://doi.org/10.5194/hess-27-2591-2023, https://doi.org/10.5194/hess-27-2591-2023, 2023
Short summary
Short summary
We propose the
c-u-curvemethod to characterize dynamical (time-variable) systems of all kinds.
Uis for uncertainty and expresses how well a system can be predicted in a given period of time.
Cis for complexity and expresses how predictability differs between different periods, i.e. how well predictability itself can be predicted. The method helps to better classify and compare dynamical systems across a wide range of disciplines, thus facilitating scientific collaboration.
Trevor Page, Paul Smith, Keith Beven, Francesca Pianosi, Fanny Sarrazin, Susana Almeida, Liz Holcombe, Jim Freer, Nick Chappell, and Thorsten Wagener
Hydrol. Earth Syst. Sci., 27, 2523–2534, https://doi.org/10.5194/hess-27-2523-2023, https://doi.org/10.5194/hess-27-2523-2023, 2023
Short summary
Short summary
This publication provides an introduction to the CREDIBLE Uncertainty Estimation (CURE) toolbox. CURE offers workflows for a variety of uncertainty estimation methods. One of its most important features is the requirement that all of the assumptions on which a workflow analysis depends be defined. This facilitates communication with potential users of an analysis. An audit trail log is produced automatically from a workflow for future reference.
András Bárdossy and Faizan Anwar
Hydrol. Earth Syst. Sci., 27, 1987–2000, https://doi.org/10.5194/hess-27-1987-2023, https://doi.org/10.5194/hess-27-1987-2023, 2023
Short summary
Short summary
This study demonstrates the fact that the large river flows forecasted by the models show an underestimation that is inversely related to the number of locations where precipitation is recorded, which is independent of the model. The higher the number of points where the amount of precipitation is recorded, the better the estimate of the river flows.
Eva Sebok, Hans Jørgen Henriksen, Ernesto Pastén-Zapata, Peter Berg, Guillaume Thirel, Anthony Lemoine, Andrea Lira-Loarca, Christiana Photiadou, Rafael Pimentel, Paul Royer-Gaspard, Erik Kjellström, Jens Hesselbjerg Christensen, Jean Philippe Vidal, Philippe Lucas-Picher, Markus G. Donat, Giovanni Besio, María José Polo, Simon Stisen, Yvan Caballero, Ilias G. Pechlivanidis, Lars Troldborg, and Jens Christian Refsgaard
Hydrol. Earth Syst. Sci., 26, 5605–5625, https://doi.org/10.5194/hess-26-5605-2022, https://doi.org/10.5194/hess-26-5605-2022, 2022
Short summary
Short summary
Hydrological models projecting the impact of changing climate carry a lot of uncertainty. Thus, these models usually have a multitude of simulations using different future climate data. This study used the subjective opinion of experts to assess which climate and hydrological models are the most likely to correctly predict climate impacts, thereby easing the computational burden. The experts could select more likely hydrological models, while the climate models were deemed equally probable.
Yan Liu, Jaime Fernández-Ortega, Matías Mudarra, and Andreas Hartmann
Hydrol. Earth Syst. Sci., 26, 5341–5355, https://doi.org/10.5194/hess-26-5341-2022, https://doi.org/10.5194/hess-26-5341-2022, 2022
Short summary
Short summary
We adapt the informal Kling–Gupta efficiency (KGE) with a gamma distribution to apply it as an informal likelihood function in the DiffeRential Evolution Adaptive Metropolis DREAM(ZS) method. Our adapted approach performs as well as the formal likelihood function for exploring posterior distributions of model parameters. The adapted KGE is superior to the formal likelihood function for calibrations combining multiple observations with different lengths, frequencies and units.
Silvana Bolaños Chavarría, Micha Werner, Juan Fernando Salazar, and Teresita Betancur Vargas
Hydrol. Earth Syst. Sci., 26, 4323–4344, https://doi.org/10.5194/hess-26-4323-2022, https://doi.org/10.5194/hess-26-4323-2022, 2022
Short summary
Short summary
Using total water storage (TWS) from GRACE satellites, we assess the reliability of global hydrological and land surface models over a medium-sized tropical basin with a well-developed gauging network. We find the models poorly represent TWS for the monthly series, but they improve in representing seasonality and long-term trends. We conclude that GRACE provides a valuable dataset to benchmark global simulations of TWS change, offering a useful tool to improve global models in tropical basins.
Jared D. Smith, Laurence Lin, Julianne D. Quinn, and Lawrence E. Band
Hydrol. Earth Syst. Sci., 26, 2519–2539, https://doi.org/10.5194/hess-26-2519-2022, https://doi.org/10.5194/hess-26-2519-2022, 2022
Short summary
Short summary
Watershed models are used to simulate streamflow and water quality, and to inform siting and sizing decisions for runoff and nutrient control projects. Data are limited for many watershed processes that are represented in such models, which requires selecting the most important processes to be calibrated. We show that this selection should be based on decision-relevant metrics at the spatial scales of interest for the control projects. This should enable more robust project designs.
Xia Wu, Lucy Marshall, and Ashish Sharma
Hydrol. Earth Syst. Sci., 26, 1203–1221, https://doi.org/10.5194/hess-26-1203-2022, https://doi.org/10.5194/hess-26-1203-2022, 2022
Short summary
Short summary
Decomposing parameter and input errors in model calibration is a considerable challenge. This study transfers the direct estimation of an input error series to their rank estimation and develops a new algorithm, i.e., Bayesian error analysis with reordering (BEAR). In the context of a total suspended solids simulation, two synthetic studies and a real study demonstrate that the BEAR method is effective for improving the input error estimation and water quality model calibration.
Hang Lyu, Chenxi Xia, Jinghan Zhang, and Bo Li
Hydrol. Earth Syst. Sci., 24, 6075–6090, https://doi.org/10.5194/hess-24-6075-2020, https://doi.org/10.5194/hess-24-6075-2020, 2020
Short summary
Short summary
Baseflow separation plays a critical role in science-based management of water resources. This study addressed key challenges hindering the application of the generally accepted conductivity mass balance (CMB). Monitoring data for over 200 stream sites of the Mississippi River basin were collected to answer the following questions. What are the characteristics of a watershed that determine the method suitability? What length of monitoring data is needed? How can the parameters be more accurate?
Aynom T. Teweldebrhan, Thomas V. Schuler, John F. Burkhart, and Morten Hjorth-Jensen
Hydrol. Earth Syst. Sci., 24, 4641–4658, https://doi.org/10.5194/hess-24-4641-2020, https://doi.org/10.5194/hess-24-4641-2020, 2020
Christoph Schürz, Bano Mehdi, Jens Kiesel, Karsten Schulz, and Mathew Herrnegger
Hydrol. Earth Syst. Sci., 24, 4463–4489, https://doi.org/10.5194/hess-24-4463-2020, https://doi.org/10.5194/hess-24-4463-2020, 2020
Short summary
Short summary
The USLE is a commonly used model to estimate soil erosion by water. It quantifies soil loss as a product of six inputs representing rainfall erosivity, soil erodibility, slope length and steepness, plant cover, and support practices. Many methods exist to derive these inputs, which can, however, lead to substantial differences in the estimated soil loss. Here, we analyze the effect of different input representations on the estimated soil loss in a large-scale study in Kenya and Uganda.
Alicia Correa, Diego Ochoa-Tocachi, and Christian Birkel
Hydrol. Earth Syst. Sci., 23, 5059–5068, https://doi.org/10.5194/hess-23-5059-2019, https://doi.org/10.5194/hess-23-5059-2019, 2019
Short summary
Short summary
The applications and availability of large tracer data sets have vastly increased in recent years leading to research into the contributions of multiple sources to a mixture. We introduce a method based on Taylor series approximation to estimate the uncertainties of such sources' contributions. The method is illustrated with examples of hydrology (14 tracers) and a MATLAB code is provided for reproducibility. This method can be generalized to any number of tracers across a range of disciplines.
Hongmei Xu, Lüliu Liu, Yong Wang, Sheng Wang, Ying Hao, Jingjin Ma, and Tong Jiang
Hydrol. Earth Syst. Sci., 23, 4219–4231, https://doi.org/10.5194/hess-23-4219-2019, https://doi.org/10.5194/hess-23-4219-2019, 2019
Short summary
Short summary
1.5 and 2 °C have become targets in the discussion of climate change impacts. However, climate research is also challenged to provide more robust information on the impact of climate change at local and regional scales to assist the development of sound scientific adaptation and mitigation measures. This study assessed the impacts and differences of 1.5 and 2.0 °C global warming on basin-scale river runoff by examining four river basins covering a wide hydroclimatic setting in China.
Lorenz Ammann, Fabrizio Fenicia, and Peter Reichert
Hydrol. Earth Syst. Sci., 23, 2147–2172, https://doi.org/10.5194/hess-23-2147-2019, https://doi.org/10.5194/hess-23-2147-2019, 2019
Short summary
Short summary
The uncertainty of hydrological models can be substantial, and its quantification and realistic description are often difficult. We propose a new flexible probabilistic framework to describe and quantify this uncertainty. It is show that the correlation of the errors can be non-stationary, and that accounting for temporal changes in correlation can lead to strongly improved probabilistic predictions. This is a promising avenue for improving uncertainty estimation in hydrological modelling.
Weifei Yang, Changlai Xiao, and Xiujuan Liang
Hydrol. Earth Syst. Sci., 23, 1103–1112, https://doi.org/10.5194/hess-23-1103-2019, https://doi.org/10.5194/hess-23-1103-2019, 2019
Short summary
Short summary
This paper analyzed the sensitivity of the baseflow index to the parameters of the conductivity two-component hydrograph separation method. The results indicated that the baseflow index is more sensitive to the conductivity of baseflow and the separation method may be more suitable for the long time series in a small watershed. After considering the mutual offset of the measurement errors of conductivity and streamflow, the uncertainty in baseflow index was reduced by half.
Xudong Zhou, Jan Polcher, Tao Yang, Yukiko Hirabayashi, and Trung Nguyen-Quang
Hydrol. Earth Syst. Sci., 22, 6087–6108, https://doi.org/10.5194/hess-22-6087-2018, https://doi.org/10.5194/hess-22-6087-2018, 2018
Short summary
Short summary
Model bias is commonly seen in discharge simulation by hydrological or land surface models. This study tested an approach with the Budyko hypothesis to retrospect the estimated discharge bias to different bias sources including the atmospheric variables and model structure. Results indicate that the bias is most likely caused by the forcing variables, and the forcing bias should firstly be assessed and reduced in order to perform pertinent analysis of the regional water cycle.
Linh Hoang, Rajith Mukundan, Karen E. B. Moore, Emmet M. Owens, and Tammo S. Steenhuis
Hydrol. Earth Syst. Sci., 22, 5947–5965, https://doi.org/10.5194/hess-22-5947-2018, https://doi.org/10.5194/hess-22-5947-2018, 2018
Short summary
Short summary
The paper analyzes the effect of two input data (DEMs and the combination of soil and land use data) with different resolution and complexity on the uncertainty of model outputs (the predictions of streamflow and saturated areas) and parameter uncertainty using SWAT-HS. Results showed that DEM resolution has significant effect on the spatial pattern of saturated areas and using complex soil and land use data may not necessarily improve model performance or reduce model uncertainty.
Aynom T. Teweldebrhan, John F. Burkhart, and Thomas V. Schuler
Hydrol. Earth Syst. Sci., 22, 5021–5039, https://doi.org/10.5194/hess-22-5021-2018, https://doi.org/10.5194/hess-22-5021-2018, 2018
Léonard Santos, Guillaume Thirel, and Charles Perrin
Hydrol. Earth Syst. Sci., 22, 4583–4591, https://doi.org/10.5194/hess-22-4583-2018, https://doi.org/10.5194/hess-22-4583-2018, 2018
Short summary
Short summary
The Kling and Gupta efficiency (KGE) is a score used in hydrology to evaluate flow simulation compared to observations. In order to force the evaluation on the low flows, some authors used the log-transformed flow to calculate the KGE. In this technical note, we show that this transformation should be avoided because it produced numerical flaws that lead to difficulties in the score value interpretation.
Lei Chen, Shuang Li, Yucen Zhong, and Zhenyao Shen
Hydrol. Earth Syst. Sci., 22, 4145–4154, https://doi.org/10.5194/hess-22-4145-2018, https://doi.org/10.5194/hess-22-4145-2018, 2018
Short summary
Short summary
In this study, the cumulative distribution function approach (CDFA) and the Monte Carlo approach (MCA) were used to develop two new approaches for model evaluation within an uncertainty framework. These proposed methods could be extended to watershed models to provide a substitution for traditional model evaluations within an uncertainty framework.
Hui-Min Wang, Jie Chen, Alex J. Cannon, Chong-Yu Xu, and Hua Chen
Hydrol. Earth Syst. Sci., 22, 3739–3759, https://doi.org/10.5194/hess-22-3739-2018, https://doi.org/10.5194/hess-22-3739-2018, 2018
Short summary
Short summary
Facing a growing number of climate models, many selection methods were proposed to select subsets in the field of climate simulation, but the transferability of their performances to hydrological impacts remains doubtful. We investigate the transferability of climate simulation uncertainty to hydrological impacts using two selection methods, and conclude that envelope-based selection of about 10 climate simulations based on properly chosen climate variables is suggested for impact studies.
Andreas M. Jobst, Daniel G. Kingston, Nicolas J. Cullen, and Josef Schmid
Hydrol. Earth Syst. Sci., 22, 3125–3142, https://doi.org/10.5194/hess-22-3125-2018, https://doi.org/10.5194/hess-22-3125-2018, 2018
Lieke A. Melsen, Nans Addor, Naoki Mizukami, Andrew J. Newman, Paul J. J. F. Torfs, Martyn P. Clark, Remko Uijlenhoet, and Adriaan J. Teuling
Hydrol. Earth Syst. Sci., 22, 1775–1791, https://doi.org/10.5194/hess-22-1775-2018, https://doi.org/10.5194/hess-22-1775-2018, 2018
Short summary
Short summary
Long-term hydrological predictions are important for water management planning, but are also prone to uncertainty. This study investigates three sources of uncertainty for long-term hydrological predictions in the US: climate models, hydrological models, and hydrological model parameters. Mapping the results revealed spatial patterns in the three sources of uncertainty: different sources of uncertainty dominate in different regions.
Katrien Van Eerdenbrugh, Stijn Van Hoey, Gemma Coxon, Jim Freer, and Niko E. C. Verhoest
Hydrol. Earth Syst. Sci., 21, 5315–5337, https://doi.org/10.5194/hess-21-5315-2017, https://doi.org/10.5194/hess-21-5315-2017, 2017
Short summary
Short summary
Consistency in stage–discharge data is investigated using a methodology called Bidirectional Reach (BReach). Various measurement stations in the UK, New Zealand and Belgium are selected based on their historical ratings information and their characteristics related to data consistency. When applying a BReach analysis on them, the methodology provides results that appear consistent with the available knowledge and thus facilitates a reliable assessment of (in)consistency in stage–discharge data.
Hadush K. Meresa and Renata J. Romanowicz
Hydrol. Earth Syst. Sci., 21, 4245–4258, https://doi.org/10.5194/hess-21-4245-2017, https://doi.org/10.5194/hess-21-4245-2017, 2017
Short summary
Short summary
Evaluation of the uncertainty in projections of future hydrological extremes in the mountainous catchment was performed. The uncertainty of the estimate of 1-in-100-year return maximum flow based on the 1971–2100 time series exceeds 200 % of its median value with the largest influence of the climate model uncertainty, while the uncertainty of the 1-in-100-year return minimum flow is of the same order (i.e. exceeds 200 %) but it is mainly influenced by the hydrological model parameter uncertainty.
Omar Wani, Joost V. L. Beckers, Albrecht H. Weerts, and Dimitri P. Solomatine
Hydrol. Earth Syst. Sci., 21, 4021–4036, https://doi.org/10.5194/hess-21-4021-2017, https://doi.org/10.5194/hess-21-4021-2017, 2017
Short summary
Short summary
We generate uncertainty intervals for hydrologic model predictions using a simple instance-based learning scheme. Errors made by the model in some specific hydrometeorological conditions in the past are used to predict the probability distribution of its errors during forecasting. We test it for two different case studies in England. We find that this technique, even though conceptually simple and easy to implement, performs as well as some other sophisticated uncertainty estimation methods.
Christa Kelleher, Brian McGlynn, and Thorsten Wagener
Hydrol. Earth Syst. Sci., 21, 3325–3352, https://doi.org/10.5194/hess-21-3325-2017, https://doi.org/10.5194/hess-21-3325-2017, 2017
Short summary
Short summary
Models are tools for understanding how watersheds function and may respond to land cover and climate change. Before we can use models towards these purposes, we need to ensure that a model adequately represents watershed-wide observations. In this paper, we propose a new way to evaluate whether model simulations match observations, using a variety of information sources. We show how this information can reduce uncertainty in inputs to models, reducing uncertainty in hydrologic predictions.
Gabriele Baroni, Matthias Zink, Rohini Kumar, Luis Samaniego, and Sabine Attinger
Hydrol. Earth Syst. Sci., 21, 2301–2320, https://doi.org/10.5194/hess-21-2301-2017, https://doi.org/10.5194/hess-21-2301-2017, 2017
Short summary
Short summary
Three methods are used to characterize the uncertainty in soil properties. The effect on simulated states and fluxes is quantified using a distributed hydrological model. Different impacts are identified as function of the perturbation method, of the model outputs and of the spatio-temporal resolution. The study underlines the importance of a proper characterization of the uncertainty in soil properties for a correct assessment of their role and further improvements in the model application.
Ji Li, Yangbo Chen, Huanyu Wang, Jianming Qin, Jie Li, and Sen Chiao
Hydrol. Earth Syst. Sci., 21, 1279–1294, https://doi.org/10.5194/hess-21-1279-2017, https://doi.org/10.5194/hess-21-1279-2017, 2017
Short summary
Short summary
Quantitative precipitation forecast produced by the WRF model has a similar pattern to that estimated by rain gauges in a southern China large watershed, hydrological model parameters should be optimized with QPF produced by WRF, and simulating floods by coupling the WRF QPF with a distributed hydrological model provides a good reference for large watershed flood warning and could benefit the flood management communities due to its longer lead time.
Johanna I. F. Slaets, Hans-Peter Piepho, Petra Schmitter, Thomas Hilger, and Georg Cadisch
Hydrol. Earth Syst. Sci., 21, 571–588, https://doi.org/10.5194/hess-21-571-2017, https://doi.org/10.5194/hess-21-571-2017, 2017
Short summary
Short summary
Determining measures of uncertainty on loads is not trivial, as a load is a product of concentration and discharge per time point, summed up over time. A bootstrap approach enables the calculation of confidence intervals on constituent loads. Ignoring the uncertainty on the discharge will typically underestimate the width of 95 % confidence intervals by around 10 %. Furthermore, confidence intervals are asymmetric, with the largest uncertainty on the upper limit.
David N. Dralle, Nathaniel J. Karst, Kyriakos Charalampous, Andrew Veenstra, and Sally E. Thompson
Hydrol. Earth Syst. Sci., 21, 65–81, https://doi.org/10.5194/hess-21-65-2017, https://doi.org/10.5194/hess-21-65-2017, 2017
Short summary
Short summary
The streamflow recession is the period following rainfall during which flow declines. This paper examines a common method of recession analysis and identifies sensitivity of the technique's results to necessary, yet subjective, methodological choices. The results have implications for hydrology, sediment and solute transport, and geomorphology, as well as for testing numerous hydrologic theories which predict the mathematical form of the recession.
Simon Paul Seibert, Uwe Ehret, and Erwin Zehe
Hydrol. Earth Syst. Sci., 20, 3745–3763, https://doi.org/10.5194/hess-20-3745-2016, https://doi.org/10.5194/hess-20-3745-2016, 2016
Short summary
Short summary
While the assessment of "vertical" (magnitude) errors of streamflow simulations is standard practice, "horizontal" (timing) errors are rarely considered. To assess their role, we propose a method to quantify both errors simultaneously which closely resembles visual hydrograph comparison. Our results reveal differences in time–magnitude error statistics for different flow conditions. The proposed method thus offers novel perspectives for model diagnostics and evaluation.
Paul Hublart, Denis Ruelland, Inaki García de Cortázar-Atauri, Simon Gascoin, Stef Lhermitte, and Antonio Ibacache
Hydrol. Earth Syst. Sci., 20, 3691–3717, https://doi.org/10.5194/hess-20-3691-2016, https://doi.org/10.5194/hess-20-3691-2016, 2016
Short summary
Short summary
Our paper explores the reliability of conceptual catchment models in the dry Andes. First, we show that explicitly accounting for irrigation water use improves streamflow predictions during dry years. Second, we show that sublimation losses can be easily incorporated into temperature-based melt models without increasing model complexity too much. Our work also highlights areas requiring additional research, including the need for a better conceptualization of runoff generation processes.
Stephen Oni, Martyn Futter, Jose Ledesma, Claudia Teutschbein, Jim Buttle, and Hjalmar Laudon
Hydrol. Earth Syst. Sci., 20, 2811–2825, https://doi.org/10.5194/hess-20-2811-2016, https://doi.org/10.5194/hess-20-2811-2016, 2016
Short summary
Short summary
This paper presents an important framework to improve hydrologic projections in cold regions. Hydrologic modelling/projections are often based on model calibration to long-term data. Here we used dry and wet years as a proxy to quantify uncertainty in projecting hydrologic extremes. We showed that projections based on long-term data could underestimate runoff by up to 35% in boreal regions. We believe the hydrologic modelling community will benefit from new insights derived from this study.
Juraj Parajka, Alfred Paul Blaschke, Günter Blöschl, Klaus Haslinger, Gerold Hepp, Gregor Laaha, Wolfgang Schöner, Helene Trautvetter, Alberto Viglione, and Matthias Zessner
Hydrol. Earth Syst. Sci., 20, 2085–2101, https://doi.org/10.5194/hess-20-2085-2016, https://doi.org/10.5194/hess-20-2085-2016, 2016
Short summary
Short summary
Streamflow estimation during low-flow conditions is important for estimation of environmental flows, effluent water quality, hydropower operations, etc. However, it is not clear how the uncertainties in assumptions used in the projections translate into uncertainty of estimated future low flows. The objective of the study is to explore the relative role of hydrologic model calibration and climate scenarios in the uncertainty of low-flow projections in Austria.
Susana Almeida, Nataliya Le Vine, Neil McIntyre, Thorsten Wagener, and Wouter Buytaert
Hydrol. Earth Syst. Sci., 20, 887–901, https://doi.org/10.5194/hess-20-887-2016, https://doi.org/10.5194/hess-20-887-2016, 2016
Short summary
Short summary
The absence of flow data to calibrate hydrologic models may reduce the ability of such models to reliably inform water resources management. To address this limitation, it is common to condition hydrological model parameters on regionalized signatures. In this study, we justify the inclusion of larger sets of signatures in the regionalization procedure if their error correlations are formally accounted for and thus enable a more complete use of all available information.
H. Xu and Y. Luo
Hydrol. Earth Syst. Sci., 19, 4609–4618, https://doi.org/10.5194/hess-19-4609-2015, https://doi.org/10.5194/hess-19-4609-2015, 2015
Short summary
Short summary
This study quantified the climate impact on river discharge in the River Huangfuchuan in semi-arid northern China and the River Xiangxi in humid southern China. Climate projections showed trends toward warmer and wetter conditions, particularly for the River Huangfuchuan. The main projected hydrologic impact was a more pronounced increase in annual discharge in both catchments. Peak flows are projected to appear earlier than usual in the River Huangfuchuan and later than usual in River Xiangxi.
I. K. Westerberg and H. K. McMillan
Hydrol. Earth Syst. Sci., 19, 3951–3968, https://doi.org/10.5194/hess-19-3951-2015, https://doi.org/10.5194/hess-19-3951-2015, 2015
Short summary
Short summary
This study investigated the effect of uncertainties in data and calculation methods on hydrological signatures. We present a widely applicable method to evaluate signature uncertainty and show results for two example catchments. The uncertainties were often large (i.e. typical intervals of ±10–40% relative uncertainty) and highly variable between signatures. It is therefore important to consider uncertainty when signatures are used for hydrological and ecohydrological analyses and modelling.
T. O. Sonnenborg, D. Seifert, and J. C. Refsgaard
Hydrol. Earth Syst. Sci., 19, 3891–3901, https://doi.org/10.5194/hess-19-3891-2015, https://doi.org/10.5194/hess-19-3891-2015, 2015
Short summary
Short summary
The impacts of climate model uncertainty and geological model uncertainty on hydraulic head, stream flow, travel time and capture zones are evaluated. Six versions of a physically based and distributed hydrological model, each containing a unique interpretation of the geological structure of the model area, are forced by 11 climate model projections. Geology is the dominating uncertainty source for travel time and capture zones, while climate dominates for hydraulic heads and steam flow.
N. Dogulu, P. López López, D. P. Solomatine, A. H. Weerts, and D. L. Shrestha
Hydrol. Earth Syst. Sci., 19, 3181–3201, https://doi.org/10.5194/hess-19-3181-2015, https://doi.org/10.5194/hess-19-3181-2015, 2015
F. Bourgin, V. Andréassian, C. Perrin, and L. Oudin
Hydrol. Earth Syst. Sci., 19, 2535–2546, https://doi.org/10.5194/hess-19-2535-2015, https://doi.org/10.5194/hess-19-2535-2015, 2015
T. Berezowski, J. Nossent, J. Chormański, and O. Batelaan
Hydrol. Earth Syst. Sci., 19, 1887–1904, https://doi.org/10.5194/hess-19-1887-2015, https://doi.org/10.5194/hess-19-1887-2015, 2015
F. Silvestro, S. Gabellani, R. Rudari, F. Delogu, P. Laiolo, and G. Boni
Hydrol. Earth Syst. Sci., 19, 1727–1751, https://doi.org/10.5194/hess-19-1727-2015, https://doi.org/10.5194/hess-19-1727-2015, 2015
M. C. Demirel, M. J. Booij, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 19, 275–291, https://doi.org/10.5194/hess-19-275-2015, https://doi.org/10.5194/hess-19-275-2015, 2015
Short summary
Short summary
This paper investigates the skill of 90-day low-flow forecasts using three models. From the results, it appears that all models are prone to over-predict runoff during low-flow periods using ensemble seasonal meteorological forcing. The largest range for 90-day low-flow forecasts is found for the GR4J model. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low-flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.
J. Crossman, M. N. Futter, P. G. Whitehead, E. Stainsby, H. M. Baulch, L. Jin, S. K. Oni, R. L. Wilby, and P. J. Dillon
Hydrol. Earth Syst. Sci., 18, 5125–5148, https://doi.org/10.5194/hess-18-5125-2014, https://doi.org/10.5194/hess-18-5125-2014, 2014
Short summary
Short summary
We projected potential hydrochemical responses in four neighbouring catchments to a range of future climates. The highly variable responses in streamflow and total phosphorus (TP) were governed by geology and flow pathways, where larger catchment responses were proportional to greater soil clay content. This suggests clay content might be used as an indicator of catchment sensitivity to climate change, and highlights the need for catchment-specific management plans.
M. Honti, A. Scheidegger, and C. Stamm
Hydrol. Earth Syst. Sci., 18, 3301–3317, https://doi.org/10.5194/hess-18-3301-2014, https://doi.org/10.5194/hess-18-3301-2014, 2014
I. K. Westerberg, L. Gong, K. J. Beven, J. Seibert, A. Semedo, C.-Y. Xu, and S. Halldin
Hydrol. Earth Syst. Sci., 18, 2993–3013, https://doi.org/10.5194/hess-18-2993-2014, https://doi.org/10.5194/hess-18-2993-2014, 2014
G. Seiller and F. Anctil
Hydrol. Earth Syst. Sci., 18, 2033–2047, https://doi.org/10.5194/hess-18-2033-2014, https://doi.org/10.5194/hess-18-2033-2014, 2014
J. D. Herman, J. B. Kollat, P. M. Reed, and T. Wagener
Hydrol. Earth Syst. Sci., 17, 5109–5125, https://doi.org/10.5194/hess-17-5109-2013, https://doi.org/10.5194/hess-17-5109-2013, 2013
M. Sadegh and J. A. Vrugt
Hydrol. Earth Syst. Sci., 17, 4831–4850, https://doi.org/10.5194/hess-17-4831-2013, https://doi.org/10.5194/hess-17-4831-2013, 2013
Cited articles
Abbaszadeh, P., Moradkhani, H., and Yan, H.: Enhancing hydrologic data assimilation by evolutionary Particle Filter and Markov Chain Monte Carlo, Adv. Water Resour., 111, 192–204, https://doi.org/10.1016/j.advwatres.2017.11.011, 2018.
Abbaszadeh, P., Moradkhani, H., and Daescu, D. N.: The Quest for Model Uncertainty Quantification: A Hybrid Ensemble and Variational Data Assimilation Framework, Water Resour. Res., 55, 2407–2431, https://doi.org/10.1029/2018WR023629, 2019.
Abbaszadeh, P., Gavahi, K., and Moradkhani, H.: Multivariate remotely sensed and in-situ data assimilation for enhancing community WRF-Hydro model forecasting, Adv. Water Resour., 145, 103721, https://doi.org/10.1016/j.advwatres.2020.103721, 2020.
Ahmadisharaf, E., Kalyanapu, A. J., and Bates, P. D.: A probabilistic framework for floodplain mapping using hydrological modeling and unsteady hydraulic modeling, Hydrolog. Sci. J., 63, 1759–1775, https://doi.org/10.1080/02626667.2018.1525615, 2018.
Alemohammad, S. H., McLaughlin, D. B., and Entekhabi, D.: Quantifying precipitation uncertainty for land data assimilation applications, Mon. Weather Rev., 143, 3276–3299, https://doi.org/10.1175/MWR-D-14-00337.1, 2015.
Alfieri, L., Salamon, P., Bianchi, A., Neal, J., Bates, P., and Feyen, L.: Advances in pan-European flood hazard mapping, Hydrol. Process., 28, 4067–4077, https://doi.org/10.1002/hyp.9947, 2014.
Anderson, J. L. and Anderson, S. L.: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev., 127, 2741–2758, https://doi.org/10.1175/1520-0493(1999)127<2741:AMCIOT>2.0.CO;2, 1999.
Aronica, G., Bates, P. D., and Horritt, M. S.: Assessing the uncertainty in distributed model predictions using observed binary pattern information within GLUE, Hydrol. Process., 16, 2001–2016, https://doi.org/10.1002/hyp.398, 2002.
Aronica, G. T., Franza, F., Bates, P. D., and Neal, J. C.: Probabilistic evaluation of flood hazard in urban areas using Monte Carlo simulation, Hydrol. Process., 26, 3962–3972, https://doi.org/10.1002/hyp.8370, 2012.
Azimi, S., Dariane, A. B., Modanesi, S., Bauer-Marschallinger, B., Bindlish, R., Wagner, W., and Massari, C.: Assimilation of Sentinel 1 and SMAP–based satellite soil moisture retrievals into SWAT hydrological model: The impact of satellite revisit time and product spatial resolution on flood simulations in small basins, J. Hydrol., 581, 124367, https://doi.org/10.1016/j.jhydrol.2019.124367, 2020.
Bales, J. D. and Wagner, C. R.: Sources of uncertainty in flood inundation maps, J. Flood Risk Manag., 2, 139–147, https://doi.org/10.1111/j.1753-318X.2009.01029.x, 2009.
Bates, P. D. and De Roo, A. P. J.: A simple raster-based model for flood inundation simulation, J. Hydrol., 236, 54–77, https://doi.org/10.1016/S0022-1694(00)00278-X, 2000.
Brêda, J. P. L. F., Paiva, R. C. D., Bravo, J. M., Passaia, O. A., and Moreira, D. M.: Assimilation of Satellite Altimetry Data for Effective River Bathymetry, Water Resour. Res., 55, 7441–7463, https://doi.org/10.1029/2018WR024010, 2019.
Clark, M. P. and Hay, L. E.: Use of Medium-Range Numerical Weather Prediction Model Output to Produce Forecasts of Streamflow, J. Hydrometeorol., 5, 15–32, https://doi.org/10.1175/1525-7541(2004)005<0015:UOMNWP>2.0.CO;2, 2004.
Courtier, P., Derber, J., Errico, R. O. N., Louis, J.-F., and VukiĆEviĆ, T.: Important literature on the use of adjoint, variational methods and the Kalman filter in meteorology, Tellus A, 45, 342–357, https://doi.org/10.3402/tellusa.v45i5.14898, 1993.
Cuo, L., Pagano, T. C., and Wang, Q. J.: A Review of Quantitative Precipitation Forecasts and Their Use in Short- to Medium-Range Streamflow Forecasting, J. Hydrometeorol., 12, 713–728, https://doi.org/10.1175/2011JHM1347.1, 2011.
DeChant, C. M. and Moradkhani, H.: Examining the effectiveness and robustness of sequential data assimilation methods for quantification of uncertainty in hydrologic forecasting, Water Resour. Res., 48, W04518, https://doi.org/10.1029/2011WR011011, 2012.
DeChant, C. M. and Moradkhani, H.: Toward a reliable prediction of seasonal forecast uncertainty: Addressing model and initial condition uncertainty with ensemble data assimilation and sequential Bayesian combination, J. Hydrol., 519, 2967–2977, https://doi.org/10.1016/j.jhydrol.2014.05.045, 2014.
Di Baldassarre, G. and Montanari, A.: Uncertainty in river discharge observations: a quantitative analysis, Hydrol. Earth Syst. Sci., 13, 913–921, https://doi.org/10.5194/hess-13-913-2009, 2009.
Di Baldassarre, G., Schumann, G., and Bates, P. D.: A technique for the calibration of hydraulic models using uncertain satellite observations of flood extent, J. Hydrol., 367, 276–282, https://doi.org/10.1016/j.jhydrol.2009.01.020, 2009.
Domeneghetti, A., Vorogushyn, S., Castellarin, A., Merz, B., and Brath, A.: Probabilistic flood hazard mapping: effects of uncertain boundary conditions, Hydrol. Earth Syst. Sci., 17, 3127–3140, https://doi.org/10.5194/hess-17-3127-2013, 2013.
Durand, M., Andreadis, K. M., Alsdorf, D. E., Lettenmaier, D. P., Moller, D., and Wilson, M.: Estimation of bathymetric depth and slope from data assimilation of swath altimetry into a hydrodynamic model, Geophys. Res. Lett., 35, L20401, https://doi.org/10.1029/2008GL034150, 2008.
Fawcett, T.: An introduction to ROC analysis, Pattern Recogn. Lett., 27, 861–874, https://doi.org/10.1016/j.patrec.2005.10.010, 2006.
Gavahi, K., Abbaszadeh, P., Moradkhani, H., Zhan, X., and Hain, C.: Multivariate Assimilation of Remotely Sensed Soil Moisture and Evapotranspiration for Drought Monitoring, J. Hydrometeorol., 21, 2293–2308, https://doi.org/10.1175/JHM-D-20-0057.1, 2020.
Giustarini, L., Matgen, P., Hostache, R., Montanari, M., Plaza, D., Pauwels, V. R. N., De Lannoy, G. J. M., De Keyser, R., Pfister, L., Hoffmann, L., and Savenije, H. H. G.: Assimilating SAR-derived water level data into a hydraulic model: a case study, Hydrol. Earth Syst. Sci., 15, 2349–2365, https://doi.org/10.5194/hess-15-2349-2011, 2011.
Habets, F., LeMoigne, P., and Noilhan, J.: On the utility of operational precipitation forecasts to served as input for streamflow forecasting, J. Hydrol., 293, 270–288, https://doi.org/10.1016/j.jhydrol.2004.02.004, 2004.
Hall, J. W., Tarantola, S., Bates, P. D., and Horritt, M. S.: Distributed Sensitivity Analysis of Flood Inundation Model Calibration, J. Hydraul. Eng., 131, 117–126, https://doi.org/10.1061/(ASCE)0733-9429(2005)131:2(117), 2005.
Horritt, M. S.: A methodology for the validation of uncertain flood inundation models, J. Hydrol., 326, 153–165, https://doi.org/10.1016/j.jhydrol.2005.10.027, 2006.
Horritt, M. S. and Bates, P. D.: Evaluation of 1D and 2D numerical models for predicting river flood inundation, J. Hydrol., 268, 87–99, https://doi.org/10.1016/S0022-1694(02)00121-X, 2002.
Hostache, R., Lai, X., Monnier, J., and Puech, C.: Assimilation of spatially distributed water levels into a shallow-water flood model. Part II: Use of a remote sensing image of Mosel River, J. Hydrol., 390, 257–268, https://doi.org/10.1016/j.jhydrol.2010.07.003, 2010.
Hostache, R., Chini, M., Giustarini, L., Neal, J., Kavetski, D., Wood, M., Corato, G., Pelich, R.-M., and Matgen, P.: Near-Real-Time Assimilation of SAR-Derived Flood Maps for Improving Flood Forecasts, Water Resour. Res., 54, 5516–5535, https://doi.org/10.1029/2017WR022205, 2018.
Jafarzadegan, K. and Merwade, V.: A DEM-based approach for large-scale floodplain mapping in ungauged watersheds, J. Hydrol., 550, 650–662, https://doi.org/10.1016/j.jhydrol.2017.04.053, 2017.
Jafarzadegan, K., Merwade, V., and Saksena, S.: A geomorphic approach to 100 year floodplain mapping for the Conterminous United States, J. Hydrol., 561, 43–58, https://doi.org/10.1016/j.jhydrol.2018.03.061, 2018.
Jafarzadegan, K., Alipour, A., Gavahi, K., Moftakhari, H., and Moradkhani, H.: Toward improved river boundary conditioning for simulation of extreme floods, Adv. Water Resour., in review, 2021.
Kumar, S. V., Dong, J., Peters-Lidard, C. D., Mocko, D., and Gómez, B.: Role of forcing uncertainty and background model error characterization in snow data assimilation, Hydrol. Earth Syst. Sci., 21, 2637–2647, https://doi.org/10.5194/hess-21-2637-2017, 2017.
Leach, J. M., Kornelsen, K. C., and Coulibaly, P.: Assimilation of near-real time data products into models of an urban basin, J. Hydrol., 563, 51–64, https://doi.org/10.1016/j.jhydrol.2018.05.064, 2018.
Lee, H., Seo, D.-J., and Koren, V.: Assimilation of streamflow and in situ soil moisture data into operational distributed hydrologic models: Effects of uncertainties in the data and initial model soil moisture states, Adv. Water Resour., 34, 1597–1615, https://doi.org/10.1016/j.advwatres.2011.08.012, 2011.
Lievens, H., Reichle, R. H., Liu, Q., De Lannoy, G. J., Dunbar, R. S., Kim, S. B., Das, N. N., Cosh, M., Walker, J. P., and Wagner, W.: Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates, Geophys. Res. Lett., 44, 6145–6153, https://doi.org/10.1002/2017GL073904, 2017.
Maidment, D. R.: Conceptual Framework for the National Flood Interoperability Experiment, J. Am. Water Resour. As., 53, 245–257, https://doi.org/10.1111/1752-1688.12474, 2017.
Matgen, P., Montanari, M., Hostache, R., Pfister, L., Hoffmann, L., Plaza, D., Pauwels, V. R. N., De Lannoy, G. J. M., De Keyser, R., and Savenije, H. H. G.: Towards the sequential assimilation of SAR-derived water stages into hydraulic models using the Particle Filter: proof of concept, Hydrol. Earth Syst. Sci., 14, 1773–1785, https://doi.org/10.5194/hess-14-1773-2010, 2010.
Merwade, V., Cook, A., and Coonrod, J.: GIS techniques for creating river terrain models for hydrodynamic modeling and flood inundation mapping, Environ. Modell. Softw., 23, 1300–1311, https://doi.org/10.1016/j.envsoft.2008.03.005, 2008.
Moradkhani, H., Sorooshian, S., Gupta, H. V., and Houser, P. R.: Dual state–parameter estimation of hydrological models using ensemble Kalman filter, Adv. Water Resour., 28, 135–147, https://doi.org/10.1016/j.advwatres.2004.09.002, 2005a.
Moradkhani, H., Hsu, K.-L., Gupta, H., and Sorooshian, S.: Uncertainty assessment of hydrologic model states and parameters: Sequential data assimilation using the particle filter, Water Resour. Res., 41, W05012, https://doi.org/10.1029/2004WR003604, 2005b.
Moradkhani, H., Nearing, G. S., Abbaszadeh, P., and Pathiraja, S.: Fundamentals of data assimilation and theoretical advances, Handb. Hydrometeorol. Ensemble Forecast, Springer Berlin Heidelberg, Berlin, Heidelberg, 675–699, https://doi.org/10.1007/978-3-642-39925-1_30, 2019.
Munier, S., Polebistki, A., Brown, C., Belaud, G., and Lettenmaier, D. P.: SWOT data assimilation for operational reservoir management on the upper Niger River Basin, Water Resour. Res., 51, 554–575, https://doi.org/10.1002/2014WR016157, 2015.
Neal, J., Schumann, G., Bates, P., Buytaert, W., Matgen, P., and Pappenberger, F.: A data assimilation approach to discharge estimation from space, Hydrol. Process., 23, 3641–3649, https://doi.org/10.1002/hyp.7518, 2009.
Neal, J., Schumann, G., and Bates, P.: A subgrid channel model for simulating river hydraulics and floodplain inundation over large and data sparse areas, Water Resour. Res., 48, W11506, https://doi.org/10.1029/2012WR012514, 2012.
Neal, J., Keef, C., Bates, P., Beven, K., and Leedal, D.: Probabilistic flood risk mapping including spatial dependence, Hydrol. Process., 27, 1349–1363, https://doi.org/10.1002/hyp.9572, 2013.
Papaioannou, G., Vasiliades, L., Loukas, A., and Aronica, G. T.: Probabilistic flood inundation mapping at ungauged streams due to roughness coefficient uncertainty in hydraulic modelling, Adv. Geosci., 44, 23–34, https://doi.org/10.5194/adgeo-44-23-2017, 2017.
Pappenberger, F., Beven, K. J., Ratto, M., and Matgen, P.: Multi-method global sensitivity analysis of flood inundation models, Adv. Water Resour., 31, 1–14, https://doi.org/10.1016/j.advwatres.2007.04.009, 2008.
Pathiraja, S., Moradkhani, H., Marshall, L., Sharma, A., and Geenens, G.: Data-driven model uncertainty estimation in hydrologic data assimilation, Water Resour. Res., 54, 1252–1280, https://doi.org/10.1002/2018WR022627, 2018.
Pauwels, V. R., Hoeben, R., Verhoest, N. E., and De Troch, F. P.: The importance of the spatial patterns of remotely sensed soil moisture in the improvement of discharge predictions for small-scale basins through data assimilation, J. Hydrol., 251, 88–102, https://doi.org/10.1016/S0022-1694(01)00440-1, 2001.
Pedinotti, V., Boone, A., Ricci, S., Biancamaria, S., and Mognard, N.: Assimilation of satellite data to optimize large-scale hydrological model parameters: a case study for the SWOT mission, Hydrol. Earth Syst. Sci., 18, 4485–4507, https://doi.org/10.5194/hess-18-4485-2014, 2014.
Pedrozo-Acuña, A., Rodríguez-Rincón, J. P., Arganis-Juárez, M., Domínguez-Mora, R., and Villareal, F. J. G.: Estimation of probabilistic flood inundation maps for an extreme event: Pánuco River, México, J. Flood Risk Manag., 8, 177–192, https://doi.org/10.1111/jfr3.12067, 2015.
Pelletier, P. M.: Uncertainties in the single determination of river discharge: a literature review, Can. J. Civil Eng., 15, 834–850, https://doi.org/10.1139/l88-109, 1988.
Pinter, N., Santos, N., and Hui, R.: Preliminary analysis of Hurricane Harvey flooding in Harris County, Texas, Retrieved from UC Davis Center for Watershed Sciences, California WaterBlog, https://californiawaterblog.com/2017/09/01/preliminary-analysis-of-hurricane-harvey-flooding-in-harris-county-texas/ (last access: 1 September 2021), 2017.
Purvis, M. J., Bates, P. D., and Hayes, C. M.: A probabilistic methodology to estimate future coastal flood risk due to sea level rise, Coast. Eng., 55, 1062–1073, https://doi.org/10.1016/j.coastaleng.2008.04.008, 2008.
Reichle, R. H., McLaughlin, D. B., and Entekhabi, D.: Hydrologic data assimilation with the ensemble Kalman filter, Mon. Weather Rev., 130, 103–114, https://doi.org/10.1175/1520-0493(2002)130<0103:HDAWTE>2.0.CO;2, 2002.
Renard, B., Kavetski, D., Kuczera, G., Thyer, M., and Franks, S. W.: Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors, Water Resour. Res., 46, W05521, https://doi.org/10.1029/2009WR008328, 2010.
Romanowicz, R. and Beven, K.: Estimation of flood inundation probabilities as conditioned on event inundation maps, Water Resour. Res., 39, 1073, https://doi.org/10.1029/2001WR001056, 2003.
Sangwan, N. and Merwade, V.: A Faster and Economical Approach to Floodplain Mapping Using Soil Information, J. Am. Water Resour. As., 51, 1286–1304, https://doi.org/10.1111/1752-1688.12306, 2015.
Savage, J. T. S., Bates, P., Freer, J., Neal, J., and Aronica, G.: When does spatial resolution become spurious in probabilistic flood inundation predictions?, Hydrol. Process., 30, 2014–2032, https://doi.org/10.1002/hyp.10749, 2016.
Sheffield, J., Pan, M., Wood, E. F., Mitchell, K. E., Houser, P. R., Schaake, J. C., Robock, A., Lohmann, D., Cosgrove, B., Duan, Q., Luo, L., Higgins, R. W., Pinker, R. T., Tarpley, J. D., and Ramsay, B. H.: Snow process modeling in the North American Land Data Assimilation System (NLDAS): 1. Evaluation of model-simulated snow cover extent, J. Geophys. Res.-Atmos., 108, 8849, https://doi.org/10.1029/2002JD003274, 2003.
Slater, A. G. and Clark, M. P.: Snow data assimilation via an ensemble Kalman filter, J. Hydrometeorol., 7, 478–493, https://doi.org/10.1175/JHM505.1, 2006.
Tayefi, V., Lane, S. N., Hardy, R. J., and Yu, D.: A comparison of one- and two-dimensional approaches to modelling flood inundation over complex upland floodplains, Hydrol. Process., 21, 3190–3202, https://doi.org/10.1002/hyp.6523, 2007.
Teng, J., Jakeman, A. J., Vaze, J., Croke, B. F. W., Dutta, D., and Kim, S.: Flood inundation modelling: A review of methods, recent advances and uncertainty analysis, Environ. Modell. Softw., 90, 201–216, https://doi.org/10.1016/j.envsoft.2017.01.006, 2017.
Watson, K. M., Harwell, G. R., Wallace, D. S., Welborn, T. L., Stengel, V. G., and McDowell, J. S.: Characterization of peak streamflows and flood inundation of selected areas in southeastern Texas and southwestern Louisiana from the August and September 2017 flood resulting from Hurricane Harvey: U.S. Geological Survey Scientific Investigations Report 2018–5070, 44 pp., https://doi.org/10.3133/sir20185070, 2018.
Xu, X., Zhang, X., Fang, H., Lai, R., Zhang, Y., Huang, L., and Liu, X.: A real-time probabilistic channel flood-forecasting model based on the Bayesian particle filter approach, Environ. Modell. Softw., 88, 151–167, https://doi.org/10.1016/j.envsoft.2016.11.010, 2017.
Xu, L., Abbaszadeh, P., Moradkhani, H., Chen, N., and Zhang, X.: Continental drought monitoring using satellite soil moisture, data assimilation and an integrated drought index, Remote Sens. Environ., 250, 112028, https://doi.org/10.1016/j.rse.2020.112028, 2020.
Yoon, Y., Durand, M., Merry, C. J., Clark, E. A., Andreadis, K. M., and Alsdorf, D. E.: Estimating river bathymetry from data assimilation of synthetic SWOT measurements, J. Hydrol., 464, 363–375, https://doi.org/10.1016/j.jhydrol.2012.07.028, 2012.
Zhang, Q., Shi, L., Holzman, M., Ye, M., Wang, Y., Carmona, F., and Zha, Y.: A dynamic data-driven method for dealing with model structural error in soil moisture data assimilation, Adv. Water Resour., 132, 103407, https://doi.org/10.1016/j.advwatres.2019.103407, 2019.
Short summary
In this study, daily observations are assimilated into a hydrodynamic model to update the performance of modeling and improve the flood inundation mapping skill. Results demonstrate that integrating data assimilation with a hydrodynamic model improves the performance of flood simulation and provides more reliable inundation maps. A flowchart provides the overall steps for applying this framework in practice and forecasting probabilistic flood maps before the onset of upcoming floods.
In this study, daily observations are assimilated into a hydrodynamic model to update the...
