Articles | Volume 16, issue 10
https://doi.org/10.5194/hess-16-3863-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Special issue:
https://doi.org/10.5194/hess-16-3863-2012
© Author(s) 2012. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
29 Oct 2012
Research article |
| 29 Oct 2012
Advancing data assimilation in operational hydrologic forecasting: progresses, challenges, and emerging opportunities
Y. Liu
Earth System Science Interdisciplinary Center, the University of Maryland, College Park, MD, USA
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
A. H. Weerts
Deltares, Delft, The Netherlands
M. Clark
Research Applications Laboratory, National Center for Atmospheric Research, Boulder, CO, USA
H.-J. Hendricks Franssen
Agrosphere (IBG 3), Forschungszentrum Jülich, Jülich, Germany
S. Kumar
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Science Applications International Corporation, Beltsville, MD, USA
H. Moradkhani
Department of Civil and Environmental Engineering, Portland State University, Portland, OR, USA
D.-J. Seo
Department of Civil Engineering, University of Texas at Arlington, Arlington, TX, USA
D. Schwanenberg
Deltares, Delft, The Netherlands
Institute of Hydraulic Engineering and Water Resources Management, University of Duisburg-Essen, Duisburg, Germany
P. Smith
Lancaster Environment Centre, Lancaster University, Lancaster, UK
A. I. J. M. van Dijk
Fenner School for Environment and Society, Australian National University/CSIRO Land and Water, Canberra, Australia
N. van Velzen
Delft Technology University, Delft, The Netherlands
VORtech, Delft, The Netherlands
M. He
Office of Hydrologic Development, National Weather Service, Silver Spring, MD, USA
Riverside Technology, Inc., Fort Collins, CO, USA
H. Lee
Office of Hydrologic Development, National Weather Service, Silver Spring, MD, USA
University Corporation for Atmospheric Research, Boulder, CO, USA
S. J. Noh
Department of Urban and Environmental Engineering, Kyoto University, Kyoto, Japan
Water Resources and Environment Research Department, Korea Institute of Construction Technology, Goyang Si Gyeonggi Do, Korea
O. Rakovec
Hydrology and Quantitative Water Management Group, Wageningen University, Wageningen, The Netherlands
P. Restrepo
North Central River Forecast Center, National Weather Service, Chanhassen, MN, USA
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Mathematical applications
Hydrological objective functions and ensemble averaging with the Wasserstein distance
Spatial variability in Alpine reservoir regulation: deriving reservoir operations from streamflow using generalized additive models
Regional significance of historical trends and step changes in Australian streamflow
River flooding mechanisms and their changes in Europe revealed by explainable machine learning
Changes in nonlinearity and stability of streamflow recession characteristics under climate warming in a large glaciated basin of the Tibetan Plateau
A data-driven method for estimating the composition of end-members from stream water chemistry time series
Evaporation loss estimation of the river-lake continuum of arid inland river: Evidence from stable isotopes
Technical note: PMR – a proxy metric to assess hydrological model robustness in a changing climate
Causal effects of dams and land cover changes on flood changes in mainland China
Can the two-parameter recursive digital filter baseflow separation method really be calibrated by the conductivity mass balance method?
Simultaneously determining global sensitivities of model parameters and model structure
Technical note: Calculation scripts for ensemble hydrograph separation
Specific climate classification for Mediterranean hydrology and future evolution under Med-CORDEX regional climate model scenarios
A line-integral-based method to partition climate and catchment effects on runoff
Technical note: A two-sided affine power scaling relationship to represent the concentration–discharge relationship
On the flood peak distributions over China
New water fractions and transit time distributions at Plynlimon, Wales, estimated from stable water isotopes in precipitation and streamflow
Does the weighting of climate simulations result in a better quantification of hydrological impacts?
A 50-year analysis of hydrological trends and processes in a Mediterranean catchment
Technical Note: On the puzzling similarity of two water balance formulas – Turc–Mezentsev vs. Tixeront–Fu
Climate or land cover variations: what is driving observed changes in river peak flows? A data-based attribution study
Quantifying new water fractions and transit time distributions using ensemble hydrograph separation: theory and benchmark tests
Land cover effects on hydrologic services under a precipitation gradient
Technical note: Long-term persistence loss of urban streams as a metric for catchment classification
Responses of runoff to historical and future climate variability over China
Characterization and evaluation of controls on post-fire streamflow response across western US watersheds
Analysis and modelling of a 9.3 kyr palaeoflood record: correlations, clustering, and cycles
Climate change impacts on Yangtze River discharge at the Three Gorges Dam
Can assimilation of crowdsourced data in hydrological modelling improve flood prediction?
Delineation of homogenous regions using hydrological variables predicted by projection pursuit regression
Multivariate hydrological data assimilation of soil moisture and groundwater head
On the propagation of diel signals in river networks using analytic solutions of flow equations
Dominant climatic factors driving annual runoff changes at the catchment scale across China
Data assimilation in integrated hydrological modelling in the presence of observation bias
Recent changes in climate, hydrology and sediment load in the Wadi Abd, Algeria (1970–2010)
Technical Note: Testing an improved index for analysing storm discharge–concentration hysteresis
Estimating spatially distributed soil water content at small watershed scales based on decomposition of temporal anomaly and time stability analysis
Improving flood forecasting capability of physically based distributed hydrological models by parameter optimization
Time series analysis of the long-term hydrologic impacts of afforestation in the Águeda watershed of north-central Portugal
Data assimilation in integrated hydrological modeling using ensemble Kalman filtering: evaluating the effect of ensemble size and localization on filter performance
Attribution of high resolution streamflow trends in Western Austria – an approach based on climate and discharge station data
A constraint-based search algorithm for parameter identification of environmental models
Hydrologic landscape classification evaluates streamflow vulnerability to climate change in Oregon, USA
Teleconnection analysis of runoff and soil moisture over the Pearl River basin in southern China
Assessing the predictive capability of randomized tree-based ensembles in streamflow modelling
Streamflow input to Lake Athabasca, Canada
Flood-initiating catchment conditions: a spatio-temporal analysis of large-scale soil moisture patterns in the Elbe River basin
Multivariate return periods in hydrology: a critical and practical review focusing on synthetic design hydrograph estimation
Impact of climate change and anthropogenic activities on stream flow and sediment discharge in the Wei River basin, China
An effective depression filling algorithm for DEM-based 2-D surface flow modelling
Jared C. Magyar and Malcolm Sambridge
Hydrol. Earth Syst. Sci., 27, 991–1010, https://doi.org/10.5194/hess-27-991-2023, https://doi.org/10.5194/hess-27-991-2023, 2023
Short summary
Short summary
Measuring the similarity of distributions of water is a useful tool for model calibration and assessment. We provide a new way of measuring this similarity for streamflow time series. It is derived from the concept of the amount of
workrequired to rearrange one mass distribution into the other. We also use similar mathematical techniques for defining a type of
averagebetween water distributions.
Manuela Irene Brunner and Philippe Naveau
Hydrol. Earth Syst. Sci., 27, 673–687, https://doi.org/10.5194/hess-27-673-2023, https://doi.org/10.5194/hess-27-673-2023, 2023
Short summary
Short summary
Reservoir regulation affects various streamflow characteristics. Still, information on when water is stored in and released from reservoirs is hardly available. We develop a statistical model to reconstruct reservoir operation signals from observed streamflow time series. By applying this approach to 74 catchments in the Alps, we find that reservoir management varies by catchment elevation and that seasonal redistribution from summer to winter is strongest in high-elevation catchments.
Gnanathikkam Emmanuel Amirthanathan, Mohammed Abdul Bari, Fitsum Markos Woldemeskel, Narendra Kumar Tuteja, and Paul Martinus Feikema
Hydrol. Earth Syst. Sci., 27, 229–254, https://doi.org/10.5194/hess-27-229-2023, https://doi.org/10.5194/hess-27-229-2023, 2023
Short summary
Short summary
We used statistical tests to detect annual and seasonal streamflow trends and step changes across Australia. The Murray–Darling Basin and other rivers in the southern and north-eastern areas showed decreasing trends. Only rivers in the Timor Sea region in northern Australia showed significant increasing trends. Our results assist with infrastructure planning and management of water resources. This study was undertaken by the Bureau of Meteorology with its responsibility under the Water Act 2007.
Shijie Jiang, Emanuele Bevacqua, and Jakob Zscheischler
Hydrol. Earth Syst. Sci., 26, 6339–6359, https://doi.org/10.5194/hess-26-6339-2022, https://doi.org/10.5194/hess-26-6339-2022, 2022
Short summary
Short summary
Using a novel explainable machine learning approach, we investigated the contributions of precipitation, temperature, and day length to different peak discharges, thereby uncovering three primary flooding mechanisms widespread in European catchments. The results indicate that flooding mechanisms have changed in numerous catchments over the past 70 years. The study highlights the potential of artificial intelligence in revealing complex changes in extreme events related to climate change.
Jiarong Wang, Xi Chen, Man Gao, Qi Hu, and Jintao Liu
Hydrol. Earth Syst. Sci., 26, 3901–3920, https://doi.org/10.5194/hess-26-3901-2022, https://doi.org/10.5194/hess-26-3901-2022, 2022
Short summary
Short summary
The accelerated climate warming in the Tibetan Plateau after 1997 has strong consequences for hydrology, geography, and social wellbeing. In hydrology, the change in streamflow as a result of changes in dynamic water storage originating from glacier melt and permafrost thawing in a warming climate directly affects the available water resources for societies of some of the most populated nations in the world.
Esther Xu Fei and Ciaran Joseph Harman
Hydrol. Earth Syst. Sci., 26, 1977–1991, https://doi.org/10.5194/hess-26-1977-2022, https://doi.org/10.5194/hess-26-1977-2022, 2022
Short summary
Short summary
Water in streams is a mixture of water from many sources. It is sometimes possible to identify the chemical fingerprint of each source and track the time-varying contribution of that source to the total flow rate. But what if you do not know the chemical fingerprint of each source? Can you simultaneously identify the sources (called end-members), and separate the water into contributions from each, using only samples of water from the stream? Here we suggest a method for doing just that.
Guofeng Zhu, Zhigang Sun, Yuanxiao Xu, Yuwei Liu, Zhuanxia Zhang, Liyuan Sang, and Lei Wang
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-75, https://doi.org/10.5194/hess-2022-75, 2022
Revised manuscript not accepted
Short summary
Short summary
We analyzed the stable isotopic composition of surface water and estimated its evaporative loss in the Shiyang River Basin. The characteristics of stable isotopes in surface water show a gradual enrichment from mountainous areas to deserts, and the evaporation loss of surface water also shows a gradually increasing trend from upstream to downstream. The study of evaporative losses in the river-lake continuum contributes to the sustainable use of water resources.
Paul Royer-Gaspard, Vazken Andréassian, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 25, 5703–5716, https://doi.org/10.5194/hess-25-5703-2021, https://doi.org/10.5194/hess-25-5703-2021, 2021
Short summary
Short summary
Most evaluation studies based on the differential split-sample test (DSST) endorse the consensus that rainfall–runoff models lack climatic robustness. In this technical note, we propose a new performance metric to evaluate model robustness without applying the DSST and which can be used with a single hydrological model calibration. Our work makes it possible to evaluate the temporal transferability of any hydrological model, including uncalibrated models, at a very low computational cost.
Wencong Yang, Hanbo Yang, Dawen Yang, and Aizhong Hou
Hydrol. Earth Syst. Sci., 25, 2705–2720, https://doi.org/10.5194/hess-25-2705-2021, https://doi.org/10.5194/hess-25-2705-2021, 2021
Short summary
Short summary
This study quantified the causal effects of land cover changes and dams on the changes in annual maximum discharges (Q) in 757 catchments of China using panel regressions. We found that a 1 % point increase in urban areas causes a 3.9 % increase in Q, and a 1 unit increase in reservoir index causes a 21.4 % decrease in Q for catchments with no dam before. This study takes the first step to explain the human-caused flood changes on a national scale in China.
Weifei Yang, Changlai Xiao, Zhihao Zhang, and Xiujuan Liang
Hydrol. Earth Syst. Sci., 25, 1747–1760, https://doi.org/10.5194/hess-25-1747-2021, https://doi.org/10.5194/hess-25-1747-2021, 2021
Short summary
Short summary
This study analyzed the effectiveness of the conductivity mass balance (CMB) method for correcting the Eckhardt method. The results showed that the approach of calibrating the Eckhardt method against the CMB method provides a
falsecalibration of total baseflow by offsetting the inherent biases in the baseflow sequences generated by the two methods. The reason for this phenomenon is the baseflow series generated by the two methods containing different transient water sources.
Juliane Mai, James R. Craig, and Bryan A. Tolson
Hydrol. Earth Syst. Sci., 24, 5835–5858, https://doi.org/10.5194/hess-24-5835-2020, https://doi.org/10.5194/hess-24-5835-2020, 2020
James W. Kirchner and Julia L. A. Knapp
Hydrol. Earth Syst. Sci., 24, 5539–5558, https://doi.org/10.5194/hess-24-5539-2020, https://doi.org/10.5194/hess-24-5539-2020, 2020
Short summary
Short summary
Ensemble hydrograph separation is a powerful new tool for measuring the age distribution of streamwater. However, the calculations are complex and may be difficult for researchers to implement on their own. Here we present scripts that perform these calculations in either MATLAB or R so that researchers do not need to write their own codes. We explain how these scripts work and how to use them. We demonstrate several potential applications using a synthetic catchment data set.
Antoine Allam, Roger Moussa, Wajdi Najem, and Claude Bocquillon
Hydrol. Earth Syst. Sci., 24, 4503–4521, https://doi.org/10.5194/hess-24-4503-2020, https://doi.org/10.5194/hess-24-4503-2020, 2020
Short summary
Short summary
With serious concerns about global change rising in the Mediterranean, we established a new climatic classification to follow hydrological and ecohydrological activities. The classification coincided with a geographical distribution ranging from the most seasonal and driest class in the south to the least seasonal and most humid in the north. RCM scenarios showed that northern classes evolve to southern ones with shorter humid seasons and earlier snowmelt which might affect hydrologic regimes.
Mingguo Zheng
Hydrol. Earth Syst. Sci., 24, 2365–2378, https://doi.org/10.5194/hess-24-2365-2020, https://doi.org/10.5194/hess-24-2365-2020, 2020
Short summary
Short summary
This paper developed a mathematically precise method to partition climate and catchment effects on streamflow. The method reveals that both the change magnitude and pathway (timing of change), not the magnitude alone, dictate the partition unless for a linear system. The method has wide relevance. For example, it suggests that the global warming effect of carbon emission is path dependent, and an optimal pathway would facilitate a higher global budget of carbon emission.
José Manuel Tunqui Neira, Vazken Andréassian, Gaëlle Tallec, and Jean-Marie Mouchel
Hydrol. Earth Syst. Sci., 24, 1823–1830, https://doi.org/10.5194/hess-24-1823-2020, https://doi.org/10.5194/hess-24-1823-2020, 2020
Short summary
Short summary
This paper deals with the mathematical representation of concentration–discharge relationships. We propose a two-sided affine power scaling relationship (2S-APS) as an alternative to the classic one-sided power scaling relationship (commonly known as
power law). We also discuss the identification of the parameters of the proposed relationship, using an appropriate numerical criterion, based on high-frequency chemical time series of the Orgeval-ORACLE observatory.
Long Yang, Lachun Wang, Xiang Li, and Jie Gao
Hydrol. Earth Syst. Sci., 23, 5133–5149, https://doi.org/10.5194/hess-23-5133-2019, https://doi.org/10.5194/hess-23-5133-2019, 2019
Julia L. A. Knapp, Colin Neal, Alessandro Schlumpf, Margaret Neal, and James W. Kirchner
Hydrol. Earth Syst. Sci., 23, 4367–4388, https://doi.org/10.5194/hess-23-4367-2019, https://doi.org/10.5194/hess-23-4367-2019, 2019
Short summary
Short summary
We describe, present, and make publicly available two extensive data sets of stable water isotopes in streamwater and precipitation at Plynlimon, Wales, consisting of measurements at 7-hourly intervals for 17 months and at weekly intervals for 4.25 years. We use these data to calculate new water fractions and transit time distributions for different discharge rates and seasons, thus quantifying the contribution of recent precipitation to streamflow under different conditions.
Hui-Min Wang, Jie Chen, Chong-Yu Xu, Hua Chen, Shenglian Guo, Ping Xie, and Xiangquan Li
Hydrol. Earth Syst. Sci., 23, 4033–4050, https://doi.org/10.5194/hess-23-4033-2019, https://doi.org/10.5194/hess-23-4033-2019, 2019
Short summary
Short summary
When using large ensembles of global climate models in hydrological impact studies, there are pragmatic questions on whether it is necessary to weight climate models and how to weight them. We use eight methods to weight climate models straightforwardly, based on their performances in hydrological simulations, and investigate the influences of the assigned weights. This study concludes that using bias correction and equal weighting is likely viable and sufficient for hydrological impact studies.
Nathalie Folton, Eric Martin, Patrick Arnaud, Pierre L'Hermite, and Mathieu Tolsa
Hydrol. Earth Syst. Sci., 23, 2699–2714, https://doi.org/10.5194/hess-23-2699-2019, https://doi.org/10.5194/hess-23-2699-2019, 2019
Short summary
Short summary
The long-term study of precipitation, flows, flood or drought mechanisms, in the Réal Collobrier research Watershed, located in South-East France, in the Mediterranean forest, improves knowledge of the water cycle and is unique tool for understanding of how catchments function. This study shows a small decrease in rainfall and a marked tendency towards a decrease in the water resources of the catchment in response to climate trends, with a consistent increase in drought severity and duration.
Vazken Andréassian and Tewfik Sari
Hydrol. Earth Syst. Sci., 23, 2339–2350, https://doi.org/10.5194/hess-23-2339-2019, https://doi.org/10.5194/hess-23-2339-2019, 2019
Short summary
Short summary
In this Technical Note, we present two water balance formulas: the Turc–Mezentsev and Tixeront–Fu formulas. These formulas have a puzzling numerical similarity, which we discuss in detail and try to interpret mathematically and hydrologically.
Jan De Niel and Patrick Willems
Hydrol. Earth Syst. Sci., 23, 871–882, https://doi.org/10.5194/hess-23-871-2019, https://doi.org/10.5194/hess-23-871-2019, 2019
James W. Kirchner
Hydrol. Earth Syst. Sci., 23, 303–349, https://doi.org/10.5194/hess-23-303-2019, https://doi.org/10.5194/hess-23-303-2019, 2019
Short summary
Short summary
How long does it take for raindrops to become streamflow? Here I propose a new approach to this old problem. I show how we can use time series of isotope data to measure the average fraction of same-day rainfall appearing in streamflow, even if this fraction varies greatly from rainstorm to rainstorm. I show that we can quantify how this fraction changes from small rainstorms to big ones, and from high flows to low flows, and how it changes with the lag time between rainfall and streamflow.
Ane Zabaleta, Eneko Garmendia, Petr Mariel, Ibon Tamayo, and Iñaki Antigüedad
Hydrol. Earth Syst. Sci., 22, 5227–5241, https://doi.org/10.5194/hess-22-5227-2018, https://doi.org/10.5194/hess-22-5227-2018, 2018
Short summary
Short summary
This study establishes relationships between land cover and river discharge. Using discharge data from 20 catchments of the Bay of Biscay findings showed the influence of land cover on discharge changes with the amount of precipitation, with lower annual water resources associated with the greater presence of forests. Results obtained illustrate the relevance of land planning to the management of water resources and the opportunity to consider it in future climate-change adaptation strategies.
Dusan Jovanovic, Tijana Jovanovic, Alfonso Mejía, Jon Hathaway, and Edoardo Daly
Hydrol. Earth Syst. Sci., 22, 3551–3559, https://doi.org/10.5194/hess-22-3551-2018, https://doi.org/10.5194/hess-22-3551-2018, 2018
Short summary
Short summary
A relationship between the Hurst (H) exponent (a long-term correlation coefficient) within a flow time series and various catchment characteristics for a number of catchments in the USA and Australia was investigated. A negative relationship with imperviousness was identified, which allowed for an efficient catchment classification, thus making the H exponent a useful metric to quantitatively assess the impact of catchment imperviousness on streamflow regime.
Chuanhao Wu, Bill X. Hu, Guoru Huang, Peng Wang, and Kai Xu
Hydrol. Earth Syst. Sci., 22, 1971–1991, https://doi.org/10.5194/hess-22-1971-2018, https://doi.org/10.5194/hess-22-1971-2018, 2018
Short summary
Short summary
China has suffered some of the effects of global warming, and one of the potential implications of climate warming is the alteration of the temporal–spatial patterns of water resources. In this paper, the Budyko-based elasticity method was used to investigate the responses of runoff to historical and future climate variability over China at both grid and catchment scales. The results help to better understand the hydrological effects of climate change and adapt to a changing environment.
Samuel Saxe, Terri S. Hogue, and Lauren Hay
Hydrol. Earth Syst. Sci., 22, 1221–1237, https://doi.org/10.5194/hess-22-1221-2018, https://doi.org/10.5194/hess-22-1221-2018, 2018
Short summary
Short summary
We investigate the impact of wildfire on watershed flow regimes, examining responses across the western United States. On a national scale, our results confirm the work of prior studies: that low, high, and peak flows typically increase following a wildfire. Regionally, results are more variable and sometimes contradictory. Our results may be significant in justifying the calibration of watershed models and in contributing to the overall observational analysis of post-fire streamflow response.
Annette Witt, Bruce D. Malamud, Clara Mangili, and Achim Brauer
Hydrol. Earth Syst. Sci., 21, 5547–5581, https://doi.org/10.5194/hess-21-5547-2017, https://doi.org/10.5194/hess-21-5547-2017, 2017
Short summary
Short summary
Here we present a unique 9.5 m palaeo-lacustrine record of 771 palaeofloods which occurred over a period of 10 000 years in the Piànico–Sèllere basin (southern Alps) during an interglacial period in the Pleistocene (sometime between 400 000 and 800 000 years ago). We analyse the palaeoflood series correlation, clustering, and cyclicity properties, finding a long-range cyclicity with a period of about 2030 years superimposed onto a fractional noise.
Steve J. Birkinshaw, Selma B. Guerreiro, Alex Nicholson, Qiuhua Liang, Paul Quinn, Lili Zhang, Bin He, Junxian Yin, and Hayley J. Fowler
Hydrol. Earth Syst. Sci., 21, 1911–1927, https://doi.org/10.5194/hess-21-1911-2017, https://doi.org/10.5194/hess-21-1911-2017, 2017
Short summary
Short summary
The Yangtze River basin in China is home to more than 400 million people and susceptible to major floods. We used projections of future precipitation and temperature from 35 of the most recent global climate models and applied this to a hydrological model of the Yangtze. Changes in the annual discharge varied between a 29.8 % decrease and a 16.0 % increase. The main reason for the difference between the models was the predicted expansion of the summer monsoon north and and west into the basin.
Maurizio Mazzoleni, Martin Verlaan, Leonardo Alfonso, Martina Monego, Daniele Norbiato, Miche Ferri, and Dimitri P. Solomatine
Hydrol. Earth Syst. Sci., 21, 839–861, https://doi.org/10.5194/hess-21-839-2017, https://doi.org/10.5194/hess-21-839-2017, 2017
Short summary
Short summary
This study assesses the potential use of crowdsourced data in hydrological modeling, which are characterized by irregular availability and variable accuracy. We show that even data with these characteristics can improve flood prediction if properly integrated into hydrological models. This study provides technological support to citizen observatories of water, in which citizens can play an active role in capturing information, leading to improved model forecasts and better flood management.
Martin Durocher, Fateh Chebana, and Taha B. M. J. Ouarda
Hydrol. Earth Syst. Sci., 20, 4717–4729, https://doi.org/10.5194/hess-20-4717-2016, https://doi.org/10.5194/hess-20-4717-2016, 2016
Short summary
Short summary
For regional flood frequency, it is challenging to identify regions with similar hydrological properties. Therefore, previous works have mainly proposed to use regions with similar physiographical properties. This research proposes instead to nonlinearly predict the desired hydrological properties before using them for delineation. The presented method is applied to a case study in Québec, Canada, and leads to hydrologically relevant regions, while enhancing predictions made inside them.
Donghua Zhang, Henrik Madsen, Marc E. Ridler, Jacob Kidmose, Karsten H. Jensen, and Jens C. Refsgaard
Hydrol. Earth Syst. Sci., 20, 4341–4357, https://doi.org/10.5194/hess-20-4341-2016, https://doi.org/10.5194/hess-20-4341-2016, 2016
Short summary
Short summary
We present a method to assimilate observed groundwater head and soil moisture profiles into an integrated hydrological model. The study uses the ensemble transform Kalman filter method and the MIKE SHE hydrological model code. The proposed method is shown to be more robust and provide better results for two cases in Denmark, and is also validated using real data. The hydrological model with assimilation overall improved performance compared to the model without assimilation.
Morgan Fonley, Ricardo Mantilla, Scott J. Small, and Rodica Curtu
Hydrol. Earth Syst. Sci., 20, 2899–2912, https://doi.org/10.5194/hess-20-2899-2016, https://doi.org/10.5194/hess-20-2899-2016, 2016
Short summary
Short summary
We design and implement a theoretical experiment to show that, under low-flow conditions, observed streamflow discrepancies between early and late summer can be attributed to different flow velocities in the river network. By developing an analytic solution to represent flow along a given river network, we emphasize the dependence of streamflow amplitude and time delay on the geomorphology of the network. We also simulate using a realistic river network to highlight the effects of scale.
Zhongwei Huang, Hanbo Yang, and Dawen Yang
Hydrol. Earth Syst. Sci., 20, 2573–2587, https://doi.org/10.5194/hess-20-2573-2016, https://doi.org/10.5194/hess-20-2573-2016, 2016
Short summary
Short summary
The hydrologic processes have been influenced by different climatic factors. However, the dominant climatic factor driving annual runoff change is still unknown in many catchments in China. By using the climate elasticity method proposed by Yang and Yang (2011), the elasticity of runoff to climatic factors was estimated, and the dominant climatic factors driving annual runoff change were detected at catchment scale over China.
Jørn Rasmussen, Henrik Madsen, Karsten Høgh Jensen, and Jens Christian Refsgaard
Hydrol. Earth Syst. Sci., 20, 2103–2118, https://doi.org/10.5194/hess-20-2103-2016, https://doi.org/10.5194/hess-20-2103-2016, 2016
Short summary
Short summary
In the paper, observations are assimilated into a hydrological model in order to improve the model performance. Two methods for detecting and correcting systematic errors (bias) in groundwater head observations are used leading to improved results compared to standard assimilation methods which ignores any bias. This is demonstrated using both synthetic (user generated) observations and real-world observations.
Mohammed Achite and Sylvain Ouillon
Hydrol. Earth Syst. Sci., 20, 1355–1372, https://doi.org/10.5194/hess-20-1355-2016, https://doi.org/10.5194/hess-20-1355-2016, 2016
Short summary
Short summary
Changes of T, P, Q and sediment fluxes in a semi-arid basin little affected by human activities are analyzed from 40 years of measurements. T increased, P decreased, an earlier onset of first summer rains occurred. The flow regime shifted from perennial to intermittent. Sediment flux almost doubled every decade. The sediment regime shifted from two equivalent seasons of sediment delivery to a single major season regime. The C–Q rating curve ability declined due to enhanced hysteresis effects.
C. E. M. Lloyd, J. E. Freer, P. J. Johnes, and A. L. Collins
Hydrol. Earth Syst. Sci., 20, 625–632, https://doi.org/10.5194/hess-20-625-2016, https://doi.org/10.5194/hess-20-625-2016, 2016
Short summary
Short summary
This paper examines the current methodologies for quantifying storm behaviour through hysteresis analysis, and explores a new method. Each method is systematically tested and the impact on the results is examined. Recommendations are made regarding the most effective method of calculating a hysteresis index. This new method allows storm hysteresis behaviour to be directly compared between storms, parameters, and catchments, meaning it has wide application potential in water quality research.
W. Hu and B. C. Si
Hydrol. Earth Syst. Sci., 20, 571–587, https://doi.org/10.5194/hess-20-571-2016, https://doi.org/10.5194/hess-20-571-2016, 2016
Short summary
Short summary
Spatiotemporal SWC was decomposed into into three terms (spatial forcing, temporal forcing, and interactions between spatial and temporal forcing) for near surface and root zone; Empirical orthogonal function indicated that underlying patterns exist in the interaction term at small watershed scales; Estimation of spatially distributed SWC benefits from decomposition of the interaction term; The suggested decomposition of SWC with time stability analysis has potential in SWC downscaling.
Y. Chen, J. Li, and H. Xu
Hydrol. Earth Syst. Sci., 20, 375–392, https://doi.org/10.5194/hess-20-375-2016, https://doi.org/10.5194/hess-20-375-2016, 2016
Short summary
Short summary
Parameter optimization is necessary to improve the flood forecasting capability of physically based distributed hydrological model. A method for parameter optimization with particle swam optimization (PSO) algorithm has been proposed for physically based distributed hydrological model in catchment flood forecasting and validated in southern China. It has found that the appropriate particle number and maximum evolution number of PSO algorithm are 20 and 30 respectively.
D. Hawtree, J. P. Nunes, J. J. Keizer, R. Jacinto, J. Santos, M. E. Rial-Rivas, A.-K. Boulet, F. Tavares-Wahren, and K.-H. Feger
Hydrol. Earth Syst. Sci., 19, 3033–3045, https://doi.org/10.5194/hess-19-3033-2015, https://doi.org/10.5194/hess-19-3033-2015, 2015
J. Rasmussen, H. Madsen, K. H. Jensen, and J. C. Refsgaard
Hydrol. Earth Syst. Sci., 19, 2999–3013, https://doi.org/10.5194/hess-19-2999-2015, https://doi.org/10.5194/hess-19-2999-2015, 2015
C. Kormann, T. Francke, M. Renner, and A. Bronstert
Hydrol. Earth Syst. Sci., 19, 1225–1245, https://doi.org/10.5194/hess-19-1225-2015, https://doi.org/10.5194/hess-19-1225-2015, 2015
S. Gharari, M. Shafiei, M. Hrachowitz, R. Kumar, F. Fenicia, H. V. Gupta, and H. H. G. Savenije
Hydrol. Earth Syst. Sci., 18, 4861–4870, https://doi.org/10.5194/hess-18-4861-2014, https://doi.org/10.5194/hess-18-4861-2014, 2014
S. G. Leibowitz, R. L. Comeleo, P. J. Wigington Jr., C. P. Weaver, P. E. Morefield, E. A. Sproles, and J. L. Ebersole
Hydrol. Earth Syst. Sci., 18, 3367–3392, https://doi.org/10.5194/hess-18-3367-2014, https://doi.org/10.5194/hess-18-3367-2014, 2014
J. Niu, J. Chen, and B. Sivakumar
Hydrol. Earth Syst. Sci., 18, 1475–1492, https://doi.org/10.5194/hess-18-1475-2014, https://doi.org/10.5194/hess-18-1475-2014, 2014
S. Galelli and A. Castelletti
Hydrol. Earth Syst. Sci., 17, 2669–2684, https://doi.org/10.5194/hess-17-2669-2013, https://doi.org/10.5194/hess-17-2669-2013, 2013
K. Rasouli, M. A. Hernández-Henríquez, and S. J. Déry
Hydrol. Earth Syst. Sci., 17, 1681–1691, https://doi.org/10.5194/hess-17-1681-2013, https://doi.org/10.5194/hess-17-1681-2013, 2013
M. Nied, Y. Hundecha, and B. Merz
Hydrol. Earth Syst. Sci., 17, 1401–1414, https://doi.org/10.5194/hess-17-1401-2013, https://doi.org/10.5194/hess-17-1401-2013, 2013
B. Gräler, M. J. van den Berg, S. Vandenberghe, A. Petroselli, S. Grimaldi, B. De Baets, and N. E. C. Verhoest
Hydrol. Earth Syst. Sci., 17, 1281–1296, https://doi.org/10.5194/hess-17-1281-2013, https://doi.org/10.5194/hess-17-1281-2013, 2013
P. Gao, V. Geissen, C. J. Ritsema, X.-M. Mu, and F. Wang
Hydrol. Earth Syst. Sci., 17, 961–972, https://doi.org/10.5194/hess-17-961-2013, https://doi.org/10.5194/hess-17-961-2013, 2013
D. Zhu, Q. Ren, Y. Xuan, Y. Chen, and I. D. Cluckie
Hydrol. Earth Syst. Sci., 17, 495–505, https://doi.org/10.5194/hess-17-495-2013, https://doi.org/10.5194/hess-17-495-2013, 2013
Cited articles
Abramowitz, G. and Gupta, H.: Toward a model space and model independencemetric, Geophys. Res. Lett., 35, L05705, https://doi.org/10.1029/2007GL032834, 2008.
Ackermann, T., Loucks, D. P., Schwanenberg, D. and Detering, M.: Real-time modeling for navigation and hydropower in the River Mosel, J. Water Res. Pl.-ASCE, 126, 298–303, https://doi.org/10.1061/(ASCE)0733-9496(2000)126:5(298), 2000.
Ajami, N. K., Duan, Q., and Sorooshian, S.: An integrated hydrologic Bayesian multimodel combination framework: Confronting input, parameter, and model structural uncertainty in hydrologic prediction, Water Resour. Res., 43, W01403, https://doi.org/200710.1029/2005WR004745, 2007.
Albergel, C., Rüdiger, C., Pellarin, T., Calvet, J.-C., Fritz, N., Froissard, F., Suquia, D., Petitpa, A., Piguet, B., and Martin, E.: From near-surface to root-zone soil moisture using an exponential filter: an assessment of the method based on in-situ observations and model simulations, Hydrol. Earth Syst. Sci., 12, 1323–1337, https://doi.org/10.5194/hess-12-1323-2008, 2008.
Alsdorf, D. E., Rodríguez, E., and Lettenmaier, D. P.: Measuring surface water from space, Rev. Geophys., 45, RG2002, https://doi.org/10.1029/2006RG000197, 2007.
Anderson, J., Hoar, T., Raeder, K., Liu, H., Collins, N., Torn, R., and Arellano, A.: The Data Assimilation Research Testbed: a community facility, B. Am. Meteorol. Soc., 90, 1283–1296, https://doi.org/10.1175/2009BAMS2618.1, 2009.
Andreadis, K. M. and Lettenmaier, D. P.: Assimilating remotely sensed snow observations into a macroscale hydrology model, Adv. Water Resour., 29, 872–886, 2006.
Andreadis, K. M., Clark, E. A., Lettenmaier, D. P., and Alsdorf, D. E.: Prospects for river discharge and depth estimation through assimilation of swath-altimetry into a raster-based hydrodynamics model, Geophys. Res. Lett., 34, 10403, https://doi.org/10.1029/2007GL029721, 2007.
Arulampalam, M. S.: A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking, IEEE T. Signal Proces., 50, 174–188, https://doi.org/10.1109/78.978374, 2002.
Aubert, D., Loumagne, C., and Oudin, L.: Sequential assimilation of soil moisture and streamflow data in a conceptual rainfallrunoff model, J. Hydrol., 280, 145–161, 2003.
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Kautul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W., Paw, K. T., Schmid, H. P., Valentini, R., Verma, S., Vesala, T., Wilson, K., and Wofsy, S.: FLUXNET: A New Tool to Study the Temporal and Spatial Variability of Ecosystem–Scale Carbon Dioxide, Water Vapor, and Energy Flux Densities, B. Am. Meteorol. Soc., 82, 2415–2434, 2001.
Balsamo, G., Mahfouf, J.-F., Bélair, S., and Deblonde, G.: A land data assimilation system for soil moisture and temperature: an information content study, J. Hydrometeor, 8, 1225–1242, https://doi.org/10.1175/2007JHM819.1, 2007.
Bárdossy, A. and Das, T.: Influence of rainfall observation network on model calibration and application, Hydrol. Earth Syst. Sci., 12, 77–89, https://doi.org/10.5194/hess-12-77-2008, 2008.
Bauser, G., Hendricks Franssen, H. J., Kaiser, H. P., Kuhlmann, U., Stauffer, F., and Kinzelbach, W.: Real-time management of an urban groundwater well field threatened by pollution, Environ. Sci. Technol., 44, 6802–6807, https://doi.org/10.1021/es100648j, 2010.
Beven, K.: A manifesto for the equifinality thesis, J. Hydrol., 320, 18–36, 2006.
Beven, K. and Binley, A.: The future of distributed models: calibration and uncertainty prediction, Hydrol. Process., 6, 279–298, 1992.
Biancamaria, S., Durand, M., Andreadis, K. M., Bates, P. D., Boone, A., Mongard, N. M., Rodr\'iguez, E., Alsdorf, D. E., Lettenmaier, D. P., and Clark, E. A.: Assimilation of virtual wide swath altimetry to improve Arctic river modeling, Remote Sens. Environ., 115, 373–381, 2011.
Birkett, C. M.: The contribution of TOPEX/Poseidon to the global monitoring of climatically sensitive lakes, J. Geophys. Res., 100, 25179–25204, 1995.
Blanco, T. B., Willems, P., Chiang, P. K., Haverbeke, N., Berlamont, J., and De Moor, B.: Flood regulation using nonlinear model predictive control, Control Eng. Pract., 18, 1147–1157, 2010.
Breckpot, M., Blanco, T. B., and De Moor, B.: Flood control of rivers with nonlinear model predictive control and moving horizon estimation, 49th IEEE Conference on Decision and Control, 6107–6112, 2010.
Brocca, L., Melone, F., Moramarco, T., Wagner, W., Naeimi, V., Bartalis, Z., and Hasenauer, S.: Improving runoff prediction through the assimilation of the ASCAT soil moisture product, Hydrol. Earth Syst. Sci., 14, 1881–1893, https://doi.org/10.5194/hess-14-1881-2010, 2010.
Brocca, L., Moramarco, T., Melone, F., Wagner, W., Hasenauer, S., and Hahn, S.: Assimilation of surface- and root-zone ASCAT soilmoisture products into rainfall-runoff modeling, IEEE T. Geosci. Remote, 50, 2534–2555, https://doi.org/10.1109/TGRS.2011.2177468, 2012.
Broersen, P. M. and Weerts, A. H.: Automatic error correction of rainfall-runoff models in flood forecasting systems, in Instrumentation and Measurement Technology Conference, IMTC 2005, Proceedings of the IEEE, 2, 963–968, 2005.
Brown, J. D.: Prospects for the open treatment of uncertainty in environmental research, Prog. Phys. Geog., 34, 75–100, https://doi.org/10.1177/0309133309357000, 2010.
Bulygina, N. and Gupta, H.: How Bayesian data assimilation can be used to estimate the mathematical structure of a model, Stoch. Env. Res. Risk A., 24, 925–937, 2010.
Camporese, M., Paniconi, C., Putti, M., and Salandin, P.: Ensemble Kalman filter data assimilation for a process-based catchment scale model of surface and subsurface flow, Water Resour. Res., 45, W10421, https://doi.org/10.1029/2008WR007031, 2009.
Castaings, W., Dartus, D., Le Dimet, F.-X., and Saulnier, G.-M.: Sensitivity analysis and parameter estimation for distributed hydrological modeling: potential of variational methods, Hydrol. Earth Syst. Sci., 13, 503–517, https://doi.org/10.5194/hess-13-503-2009, 2009.
Castelletti, A., Pianosi, F., and Soncini-Sessa, R.: Water reservoir control under economic, social and environmental constraints, Automatica, 44, 1595–1607, 2008.
Chen, J., Hubbard, S. S., Williams, K. H., Pride, S., Li, L., Steefel, C., and Slater, L.: A state-space Bayesian framework for estimating biogeochemical transformations using time-lapse geophysical data, Water Resour. Res., 45, W08420, https://doi.org/10.1029/2008WR007698, 2009.
Clark, M. P. and Kavetski, D.: Ancient numerical daemons of conceptual hydrological modeling: 1. Fidelity and efficiency of time stepping schemes, Water Resour. Res., 46, W10510, https://doi.org/10.1029/2009WR008894, 2010.
Clark, M. P. and Slater, A. G.: Probabilistic quantitative precipitation estimation in complex terrain, J. Hydrometeorol., 7, 3–22, 2006.
Clark, M. P. and Vrugt, J. A.: Unraveling uncertainties in hydrologic model calibration: Addressing the problem of compensatory parameters, Geophys. Res. Lett., 33, L06406, https://doi.org/10.1029/2005GL025604, 2006.
Clark, M. P., Gangopadhyay, S., Hay, L. E., Rajagopalan, B., and Wilby, R. L.: The Schaake Shuffle: A method to reconstruct the space-time variability of forecasated precipitation and temperature fields, J. Hydrometeo., 5, 243–262, 2004a.
Clark, M. P., Gangopadhyay, S., Brandon, D., Werner, K., Hay, L. E., Rajagopalan, B., and Yates, D.: A resampling procedure for generating conditioned daily weather sequences, Water Resour. Res., 40, W04304, https://doi.org/10.1029/2003WR002747, 2004b.
Clark, M. P., Rupp, D. E., Woods, R. A., Zheng, X., Ibbitt, R. P., Slater, A. G., Schmidt, J., and Uddstrom, M. J.: Hydrological data assimilation with the ensemble Kalman filter: Use of streamflow observations to update states in a distributed hydrological model, Adv. Water Resour., 31, 1309–1324, https://doi.org/10.1016/j.advwatres.2008.06.005, 2008a.
Clark, M. P., Slater, A. G., Rupp, D. E., Woods, R. A., Vrugt, J. A., Gupta, H. V., Wagener, T. and Hay, L. E.: Framework for Understanding Structural Errors (FUSE): A modular framework to diagnose differences between hydrological models, Water Resour. Res., 44, W00B02, https://doi.org/10.1029/2007WR006735, 2008b.
Coccia, G. and Todini, E.: Recent developments in predictive uncertainty assessment based on the model conditional processor approach, Hydrol. Earth Syst. Sci., 15, 3253–3274, https://doi.org/10.5194/hess-15-3253-2011, 2011.
Cole, S. J., Robson, A. J., Bell, V. A., and Moore, R. J.: Model initialisation, data assimilation and probabilistic flood forecasting for distributed hydrological models, in: Geophysical Research Abstracts, 11, EGU2009-8048-3, 2009.
Crow, W. T. and Ryu, D.: A new data assimilation approach for improving runoff prediction using remotely-sensed soil moisture retrievals, Hydrol. Earth Syst. Sci., 13, 1–16, https://doi.org/10.5194/hess-13-1-2009, 2009.
Crow, W. T. and Van Loon, E.: Impact of Incorrect Model Error Assumptions on the Sequential Assimilation of Remotely Sensed Surface Soil Moisture, J. Hydrometeorol., 7, 421–432, https://doi.org/10.1175/JHM499.1, 2006.
Da Ros, D. and Borga, M.: Adaptive use of a conceptual model for real time flood forecasting, Nord. Hydrol., 28, 169–188, 1997.
DeChant, C. M. and Moradkhani, H.: Radiance data assimilation for operational snow and streamflow forecasting, Adv. Water Resour. Res., 34, 351–364, 2011a.
DeChant, C. M. and Moradkhani, H.: Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation, Hydrol. Earth Syst. Sci., 15, 3399–3410, https://doi.org/10.5194/hess-15-3399-2011, 2011b.
DeChant, C. M. and Moradkhani, H.: Examining the effectiveness and robustness of data assimilation methods for calibration and quantification of uncertainty in hydrologic forecasting, Water Resour. Res., 48, W04518, https://doi.org/10.1029/2011WR011011, 2012.
Dee, D. P.: Bias and data assimilation, Q. J. Roy. Meteor. Soc., 131, 3323–3343, https://doi.org/10.1256/qj.05.137, 2005.
De Lannoy, G. J. M., Reichle, R. H., Houser, P. R., Pauwels, V. R., and Verhoest, N. E.: Correcting for forecast bias in soil moisture assimilation with the ensemble Kalman filter, Water Resour. Res., 43, W09410, https://doi.org/10.1029/2006WR005449, 2007.
De Lannoy, G. J. M., Reichle, R. H., Arsenault, K. R., Houser, P. R., Kumar, S., Verhoest, N. E. C., and Pauwels, V. R. N.: Multiscale assimilation of Advanced Microwave Scanning Radiometer.EOS snow water equivalent and Moderate Resolution Imaging Spectroradiometer snow cover fraction observations in northern Colorado, Water Resour. Res., 48, W01522, https://doi.org/10.1029/2011WR010588, 2012.
Demargne, J., Brown, J., Liu, Y., Seo, D. J., Wu, L., Toth, Z., and Zhu, Y.: Diagnostic verification of hydrometeorological and hydrologic ensembles, Atmos. Sci. Lett., 11, 114–122, 2010.
Dharssi, I., Bovis, K. J., Macpherson, B., and Jones, C. P.: Operational assimilation of ASCAT surface soil wetness at the Met Office, Hydrol. Earth Syst. Sci., 15, 2729–2746, https://doi.org/10.5194/hess-15-2729-2011, 2011.
Diehl, M., Ferreau, H. J., and Haverbeke, N.: Efficient numerical methods for nonlinear MPC and moving horizon estimation, in: Lecture Notes in Control and Information Sciences 384, Nonlinear Model Predictive Control, edited by: Magni, L., Raimondo, D. M., and Allgöwer, F., 391–417, Springer-Verlag Berlin Heidelberg, 2009.
Dorigo, W. A., Scipal, K., Parinussa, R. M., Liu, Y. Y., Wagner, W., de Jeu, R. A. M., and Naeimi, V.: Error characterisation of global active and passive microwave soil moisture datasets, Hydrol. Earth Syst. Sci., 14, 2605–2616, https://doi.org/10.5194/hess-14-2605-2010, 2010.
Drécourt, J.-P., Madsen, H., and Rosbjerg, D.: Calibration framework for a Kalman filter applied to a groundwater model, Adv. Water Resour., 29, 719–734, https://doi.org/16/j.advwatres.2005.07.007, 2006.
Durand, D., 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.
Durand, M., Kim, E. J., and Margulis, S. A.: Radiance assimilation shows promise for snowpack characterization, Geophys. Res. Lett., 36, L02503, https://doi.org/10.1029/2008GL035214, 2009.
Ehret, U., Zehe, E., Wulfmeyer, V., Warrach-Sagi, K., and Liebert, J.: HESS Opinions "Should we apply bias correction to global and regional climate model data?", Hydrol. Earth Syst. Sci., 16, 3391–3404, https://doi.org/10.5194/hess-16-3391-2012, 2012.
El Serafy, G. Y., Gerritsen, H., Hummel, S., Weerts, A. H., Mynett, A. E., and Tanaka, M.: Application of data assimilation in portable operational forecasting systems, the DATools assimilation environment, Ocean Dynam., 57, 485–499, 2007.
Entekhabi, D., Njoku, E., O.Neill, P., Kellogg, K., Crow, W., Edelstein, W., Entin, J., Goodman, S., Jackson, T., Johnson, J., Kimball, J., Piepmeier, J., Koster, R., McDonald, K., Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer, M., Thurman, S., Tsang, L., and Van Zyl, J.: The Soil Moisture Active and Passive (SMAP) mission, P. IEEE, 98, 704–716, 2010.
Evensen, G.: Sequential data assimilation with a non-linear quasi-geostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 97, 905–924, 1994.
Famiglietti, J., Murdoch, L., Lakshmi, V., and Hooper, R.: Community modeling in hydrologic science, Eos Trans. AGU, 89, 292, https://doi.org/10.1029/2008EO320005, 2008.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Wernerm M., Oskin, M., Burbank, D., and Alsdorf, D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007.
Fearnhead, P. and Clifford, P.: On-line inference for hidden Markov models via particle filters, J. R. Stat. Soc. B Met., 65, 887–899, 2003.
Fischer, C., Montmerle, T., Berre, L., Auger, L., and TEFNESCU, S. E.: An overview of the variational assimilation in the ALADIN/France numerical weather-prediction system, Q. J. Roy. Meteor. Soc., 131, 3477–3492, 2005.
Forman, B. A., Reichle, R. H., and Rodell, M.: Assimilation of terrestrial water storage from GRACE in a snow dominated basin, Water Resour. Res., 48, W01507, https://doi.org/10.1029/2011WR011239, 2012.
Foster, J. L., Hall, D. K., Eylander, J. B., Riggs, G. A., Nghiem, S. V., Tedesco, M., Kim, E., Montesano, P. M., Kelly, R. E. J., Casey, K. A., and Choudhury, B.: A blended global snow product using visible, passive microwave and scatterometer satellite data, Int. J. Remote Sens., 32, 1371–1395, 2011.
Franssen, H. J. and Kinzelbach, W.: Real-time groundwater flow modeling with the Ensemble Kalman Filter: Joint estimation of states and parameters and the filter inbreeding problem, Water Resour. Res., 44, W09408, https://doi.org/10.1029/2007WR006505, 2008.
Franssen, H. J., Gómez-Hernández, J., and Sahuquillo, A.: Coupled inverse modelling of groundwater flow and mass transport and the worth of concentration data, J. Hydrol., 281, 281–295, 2003.
Franssen, H. J. H., Brunner, P., Makobo, P., and Kinzelbach, W.: Equally likely inverse solutions to a groundwater flow problem including pattern information from remote sensing images, Water Resour. Res., 44, W01419, https://doi.org/10.1029/2007WR006097, 2008.
Franssen, H. J., Kaiser, H. P., Kuhlmann, U., Bauser, G., Stauffer, F., Müller, R., and Kinzelbach, W.: Operational real-time modeling with ensemble Kalman filter of variably saturated subsurface flow including stream-aquifer interaction and parameter updating, Water Resour. Res., 47, W02532, https://doi.org/10.1029/2010WR009480, 2011.
Frappart, F., Calmant, S., Cauhope, M., Seyler, F., and Cazenave, A.: Preliminary results of ENVISAT RA-2–derived water levels validation over the Amazon Basin, Remote Sens. Environ., 100, 252–264, 2006.
Gallagher, M. and Doherty, J.: Parameter estimation and uncertainty analysis for a watershed model, Environ. Model. Softw., 22, 1000–1020, 2007.
Georgakakos, K. P.: A generalized stochastic hydrometeorological model for flood and flash flood forecasting. 1 Formulation, Water Resour.Res., 22, 2083–2095, 1986a.
Georgakakos, K. P.: A generalized stochastic hydrometeorological model for flood and flash-flood forecasting: 2. Case studies, Water Resour. Res., 22, 2096–2106, 1986b.
Georgakakos, K. P., Seo, D. J., Gupta, H., Schaake, J., and Butts, M. B.: Towards the characterization of streamflow simulation uncertainty through multimodel ensembles, J. Hydrol., 298, 222–241, 2004.
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.
Glanzmann, G., von Siebenthal, M., Geyer, T., Papafotiou, G., and Morari, M.: Supervisory water level control for cascaded river power plants, in Proceedings of the Hydropower Conference, Stavanger, Norway, 2005.
Götzinger, J. and Bárdossy, A.: Generic error model for calibration and uncertainty estimation of hydrological models, Water Resour. Res., 44, W00B0, https://doi.org/200810.1029/2007WR006691, 2008.
Gregersen, J., Gijsbers, P., and Westen, S.: OpenMI: Open modelling interface, J. Hydroinform., 9, 175–191, 2007.
Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Toward improved calibration of hydrologic models: Multiple and noncommensurable measures of information, Water Resour. Res., 34, 751–763, 1998.
Gupta, H. V., Wagener, T., and Liu, Y.: Reconciling theory with observations: elements of a diagnostic approach to model evaluation, Hydrol. Process., 22, 3802–3813, 2008.
Hagedorn, R., Doblas-Reyes, F. J., and Palmer, T. N.: The rationale behind the success of multi-model ensembles in seasonal forecasting .I. Basic concept, Tellus A, 57, 219–233, 2005.
Hall, D. K., Riggs, G. A., Salomonson, V. V., DiGirolamo, N. E., and Bayr, K. J.: MODIS snow-cover products, Remote Sens. Environ., 83, 181–194, 2002.
Hall, D. K., Riggs, G. A., Foster, J. L., and Kumar, S. V.: Development and evaluation of a cloud-gap-filled MODIS daily snow-cover product, Remote Sens. Environ., 114, 496–503, 2010.
Hong, Y., Hsu, K., Moradkhani, H., and Sorooshian, S.: Uncertainty Quantification of Satellite Precipitation Estimation and Monte Carlo Assessment of the Error Propagation into Hydrologic Response, Water Resour. Res., 42, W08421, https://doi.org/10.1029/2005WR004398, 2006.
Hossain, F. and Huffman, G. J.: Investigating error metrics for satellite rainfall data at hydrologically relevant scales, J. Hydrometeorol., 9, 563–575, 2008.
Houser, P. R., Shuttleworth, W. J., Famiglietti, J. S., Gupta, H. V., Syed, K. H., and Goodrich, D. C.: Integration of soil moisture remote sensing and hydrologic modeling using data assimilation, Water Resour. Res., 34, 3405–3420, 1998.
Hsu, K.-L., Moradkhani, H., and Sorooshian, S.: A sequential Bayesian approach for hydrologic model selection and prediction, Water Resour. Res., 45, W00B12, https://doi.org/10.1029/2008WR006824, 2009.
Huard, D. and Mailhot, A.: A Bayesian perspective on input uncertainty in model calibration: Application to hydrological model "abc", Water Resour. Res., 42, W07416, https://doi.org/10.1029/2005WR004661, 2006.
Jeu, R. A.: Retrieval of land surface parameters using passive microwave remote sensing, Ph.D. Thesis, Vrije Universiteit Amsterdam, The Netherlands, 144 pp., 2003.
Julier, S. J. and Uhlmann, J. K.: A new extension of the Kalman filter to nonlinear systems, P. SPIE, 3068, 182–193, https://doi.org/10.1117/12.280797, 1997.
Kalman, R. E.: A new approach to linear filtering and prediction problems, Transactions of the ASME-Journal of Basic Engineering, 82, 35–45, 1960.
Kavetski, D., Kuczera, G., and Franks, S. W.: Bayesian analysis of input uncertainty in hydrological modeling: 1. Theory, Water Resour. Res., 42, W03407, https://doi.org/10.1029/2005WR004368, 2006a.
Kavetski, D., Kuczera, G., and Franks, S. W.: Bayesian analysis of input uncertainty in hydrological modeling: 2. Application, Water Resour. Res., 42, W03408, https://doi.org/10.1029/2005WR004376, 2006b.
Kearney, M., Cantoni, M., and Dower, P. M.: Model predictive control for systems with scheduled load and its application to automated irrigation channels, in IEEE International Conference on Networking, Sensing and Control (ICNSC), 186–191, IEEE, 2011.
Kelly, R.: The AMSR-E snow depth algorithm: description and initial results, Journal of the Remote Sensing Society of Japan, 29, 307–317, 2009.
Kerr, Y. H. and Levine, D. M.: Guest editorial foreword to the special issue on the Soil Moisture and Ocean Salinity (SMOS) mission, IEEE T. Geosci. Remote, 46, 583–584, 2008.
Kirk, D. E.: Optimal control theory: an introduction, Dover Pubs., 2004.
Kitanidis, P. K. and Bras, R.: Real-time forecasting with a conceptual hydrological model. 1. Analysis of uncertainty, Water Resour. Res., 16, 1025–1033, 1980.
Kolberg, S. and Gottschalk, L.: Interannual stability of grid cell snow depletion curves as estimated from MODIS images, Water Resour. Res., 46, W11555, https://doi.org/10.1029/2008WR007617, 2010.
Komma, J., Blöschl, G., and Reszler, C.: Soil moisture updating by Ensemble Kalman Filtering in real-time flood forecasting, J. Hydrol., 357, 228–242, 2008.
Krishnamurti, T., Kishtawal, C., LaRow, T. E., Bachiochi, D. R., Zhang, Z., Williford, C. E., Gadgil, S., and Surendran, S.: Improved weather and seasonal climate forecasts from multimodel superensemble, Science, 285, 1548–1550, https://doi.org/10.1126/science.285.5433.1548, 1999.
Krzysztofowicz, R. and Maranzano, C. J.: Hydrologic uncertainty processor for probabilistic stage transition forecasting, J. Hydrol., 293, 57–73, https://doi.org/10.1016/j.jhydrol.2004.01.003, 2004.
Kuchment, L. S., Romanov, P., Gelfan, A. N., and Demidov, V. N.: Use of satellite-derived data for characterization of snow cover and simulation of snowmelt runoff through a distributed physically based model of runoff generation, Hydrol. Earth Syst. Sci., 14, 339–350, https://doi.org/10.5194/hess-14-339-2010, 2010.
Kuczera, G. and Franks, S. W.: On the testing of hydrological models: falsification or fortification, in Mathematical models of watershed hydrology, Littleton, CO: Water Resource Publications, 141–186, 2002.
Kuczera, G., Kavetski, D., Franks, S., and Thyer, M.: Towards a Bayesian total error analysis of conceptual rainfall-runoff models: Characterising model error using storm-dependent parameters, J. Hydrol., 331, 161–177, 2006.
Kumar, S. V., Peters-Lidard, C. D., Tian, Y., Houser, P. R., Geiger, J., Olden, S., Lighty, L., Eastman, J. L., Doty, B., Dirmeyer, P., Adams, J., Mitchell, K., Wood, E. F., and Sheffield, J.: Land information system: An interoperable framework for high resolution land surface modeling, Environ. Model. Softw., 21, 1402–1415, 2006.
Kumar, S. V., Reichle, R. H., Peters-Lidard, C. D., Koster, R. D., Zhan, X., Crow, W. T., Eylander, J. B., and Houser, P. R.: A land surface data assimilation framework using the land information system: Description and applications, Adv. Water Resour., 31, 1419–1432, 2008a.
Kumar, S. V., Peters-Lidard, C., Tian, Y., Reichle, R., Geiger, J., Alonge, C., Eylander, J., and Houser, P.: An integrated hydrologic modeling and data assimilation framework, Computer, 41, 52–59, 2008b.
Kumar, S. V., Reichle, R. H., Koster, R. D., Crow, W. T., and Peters-Lidard, C. D.: Role of subsurface physics in the assimilation of surface soil moisture observations, J. Hydrometeorol., 10, 1534–1547, https://doi.org/10.1175/2009JHM1134.1, 2009.
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, 2011.
Lee, H., Seo, D.-J., Liu, Y., Koren, V., McKee, P., and Corby, R.: Variational assimilation of streamflow into operational distributed hydrologic models: effect of spatiotemporal scale of adjustment, Hydrol. Earth Syst. Sci., 16, 2233–251, https://doi.org/10.5194/hess-16-2233-2012, 2012.
Lee, S., Klein, A. G., and Over, T. M.: A comparison of MODIS and NOHRSC snow-cover products for simulating streamflow using the Snowmelt Runoff Model, Hydrol. Process., 19, 2951–2972, 2005.
Lees, M., Young, P. C., Ferguson, S., Beven, K. J., and Burns, J.: An adaptive flood warning scheme for the River Nith at Dumfries, River Flood Hydraulics, Wiley, Chichester, 65–75, 1994.
Leisenring, M. and Moradkhani, H.: Snow water equivalent prediction using Bayesian data assimilation methods, Stoch. Env. Res. Risk A., 25, 1–18, 2011.
Leisenring, M. and Moradkhani, H.: Analyzing the uncertaintyof suspended sediment load prediction using sequential MonteCarlo methods, J. Hydrol., 468–469, 268–282, 2012.
Li, B., Toll, D., Zhan, X., and Cosgrove, B.: Improving estimated soil moisture fields through assimilation of AMSR-E soil moisture retrievals with an ensemble Kalman filter and a mass conservation constraint, Hydrol. Earth Syst. Sci., 16, 105–119, https://doi.org/10.5194/hess-16-105-2012, 2012.
Li, L., Gaiser, P. W., Gao, B. C., Bevilacqua, R. M., Jackson, T. J., Njoku, E. G., Rudiger, C., Calvet, J. C., and Bindlish, R.: WindSat global soil moisture retrieval and validation, IEEE T. Geosci. Remote, 48, 2224–2241, 2010.
Li, Z. and Navon, I. M.: Optimality of variational data assimilation and its relationship with the Kalman filter and smoother, Q. J. Roy. Meteor. Soc., 127, 661–683, 2001.
Linke, H., Karimanzira, D., Rauschenbach, T., and Pfutzenreuter, T.: Flash flood prediction for small rivers, in 2011 IEEE International Conference on Networking, Sensing and Control (ICNSC), 86–91, IEEE, 2011.
Liston, G. E. and Hiemstra, C. A.: A simple data assimilation system for complex snow distributions (SnowAssim), J. Hydrometeorol., 9, 989–1004, 2008.
Liu, J. S. and Chen, R.: Sequential Monte Carlo methods for dynamic systems, J. Am. Stat. Assoc., 93, 1032–1044, 1998.
Liu, Y. and Gupta, H. V.: Uncertainty in hydrologic modeling: Toward an integrated data assimilation framework, Water Resour. Res., 43, W07401, https://doi.org/10.1029/2006WR005756, 2007.
Liu, Y., Brown, J., Demargne, J., and Seo, D.-J.: A wavelet-based approach to assessing timing errors in hydrologic predictions, J. Hydrol., 397, 210–224, 2011.
Liu, Y., Dorigo, W. A., Parinussa, R. M., de Jeu, R. A. M., Wagner, W., McCabe, M. F., Evans, J. P., van Dijk, A. I. J. M.: Trend-preserving blending of passive and active microwave soil moisture retrievals, Remote Sens. Environ., 123, 280–297, 10.1016/j.rse.2012.03.014, 2012.
Lorenc, A. C. and Rawlins, F.: Why does 4D-Var beat 3D-Var?, Q. J. Roy. Meteor. Soc., 131, 3247–3257, 2005.
Lü, H., Yu, Z., Zhu, Y., Drake, S., Hao, Z., and Sudicky, E. A.: Dual state-parameter estimation of root zone soil moisture by optimal parameter estimation and extended Kalman filter data assimilation, Adv. Water Resour., 34, 395–406, https://doi.org/10.1016/j.advwatres.2010.12.005, 2011.
Madsen, H., Rosbjerg, D., Damgard, J., and Hansen, F. S.: Data assimilation into MIKE 11 flood forecasting system using Kalman filtering, Water Resources Systems – Hydrological Risk, Management and Development, IAHS Publ no. 281, 75–81, 2003.
Masahiro, K., Liu, Q., Treadon, R., and Derber, J. C.: Impact study of AMSR-E radiances in the NCEP Global Data Assimilation System, Mon. Weather Rev., 136, 541–559, https://doi.org/10.1175/2007MWR2147.1, 2008.
Mason, S. J. and Mimmack, G. M.: Comparison of some statistical methods of probabilistic forecasting of ENSO, J. Climate, 15, 8–29, 2002.
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.
McEnery, J., Ingram, J., Duan, Q., Adams, T., and Anderson, L.: NOAA.S Advanced Hydrologic Prediction Service: Building Pathways for Better Science in Water Forecasting, B. Am. Meteorol. Soc., 86, 375–385, https://doi.org/10.1175/BAMS-86-3-375, 2005.
McLaughlin, D.: An integrated approach to hydrologic data assimilation: interpolation, smoothing, and filtering, Adv. Water Res., 25, 1275–1286, 2002.
McMillan, H., Jackson, B., Clark, M., Kavetski, D., and Woods, R.: Rainfall uncertainty in hydrological modelling: An evaluation of multiplicative error models, J. Hydrol., 400, 83–94, https://doi.org/10.1016/j.jhydrol.2011.01.026, 2011.
Mo, X., Chen, J. M., Ju, W., and Black, T. A.: Optimization of ecosystem model parameters through assimilating eddy covariance flux data with an Ensemble Kalman filter, Ecol. Model., 217, 157–173, 2008.
Montanari, M., Hostache, R., Matgen, P., Schumann, G., Pfister, L., and Hoffmann, L.: Calibration and sequential updating of a coupled hydrologic-hydraulic model using remote sensing-derived water stages, Hydrol. Earth Syst. Sci., 13, 367–380, https://doi.org/10.5194/hess-13-367-2009, 2009.
Montzka, C., Moradkhani, H., Weihermuller, L., Franssen, H. J. H., Canty, M., and Vereecken, H.: Hydraulic parameter estimation by remotely-sensed top soil moisture observations with the particle filter, J. Hydrol., 399, 410–421, 2011.
Moore, R. J.: The PDM rainfall-runoff model, Hydrol. Earth Syst. Sci., 11, 483–499, https://doi.org/10.5194/hess-11-483-2007, 2007.
Moore, R. V. and Tindall, C. I.: An overview of the open modelling interface and environment (the OpenMI), Environ. Sci. Policy, 8, 279–286, 2005.
Moradkhani, H. and Sorooshian, S.: General review of rainfall-runoff modeling: model calibration, data assimilation, and uncertainty analysis, in Hydrological Modeling and Water Cycle, Coupling of the Atmospheric and Hydrological Models, Springer, Water Science and Technology Library, 63, 1–24, https://doi.org/10.1007/978-3-540-77843-1_1, 2008.
Moradkhani, H., Hsu, K., Gupta, H. V., and Sorooshian, S.: Uncertainty asssessment of hydrologic model states and parameters: sequential data assimilation using particle filter, Water Resour. Res., 41, W05012, https://doi.org/10.1029/2004WR003604, 2005a.
Moradkhani, H., Sorooshian, S., Gupta, H. V., and Houser, P.: Dual state-parameter estimation of hydrological models using Ensemble Kalman filter, Adv. Water Resour., 28, 135–147, 2005b.
Moradkhani, H., Hsu, K., Hong, Y., and Sorooshian, S.: Investigating the impact of remotely sensed precipitation and hydrologic model uncertainties on the ensemble streamflow forecasting, Geophys. Res. Lett., 33, L12401, https://doi.org/10.1029/2006GL026855, 2006.
Moradkhani, H., DeChant C. M., and Sorooshian, S.: Evolution of ensemble data assimilation for uncertainty quantification using the Particle Filter-Markov Chain Monte Carlo method, Water Resour. Res., in review, 2012.
Naevdal, G., Johnsen, L., Aanonsen, S., and Vefring, E.: Reservoir monitoring and continuous model updating using ensemble Kalman filter, SPE Journal, 10, 66–74, 2003.
Nagarajan, K., Judge, J., Graham, W. D., and Monsivais-Huertero, A.: Particle Filter-based assimilation algorithms for improved estimation of root-zone soil moisture under dynamic vegetation conditions, Adv. Water Resour., 34, 433–447, 2010.
National Research Council (US): Completing the Forecast: Characterizing and Communicating Uncertainty for Better Decisions Using Weather and Climate Forecasts, Natl. Academy Pr., 2006.
National Research Council (US): Earth science and applications from space: national imperatives for the next decade and beyond, Natl. Academy Pr., 2007.
Neal, J. C., Atkinson, P. M., and Hutton, C. W.: Flood inundation model updating using an ensemble Kalman filter and spatially distributed measurements, J. Hydrol., 336, 401–415, 2007.
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, 2009.
Nelly, J.-B., Pierre-Olivier, M., Christophe, D., and Jacques, S.: Data assimilation for real-time estimation of hydraulic states and unmeasured perturbations in a 1D hydrodynamic model, Journal of Mathematics and Computers in Simulation, 81, 2201–2214, https://doi.org/10.1016/j.matcom.2010.12.021, 2011.
Negenborn, R. R., van Overloop, P. J., Keviczky, T., and De Schutter, B.: Distributed model predictive control of irrigation canals, Networks and Heterogeneous Media (NHM), 4, 359–380, 2009.
Nerger, L., Hiller, W., and Schröter, J.: PDAF-The Parallel Data Assimilation Framework: Experiences with Kalman Filtering, in: Use of High Performance Computing in Meteorology-Proceedings of the 11, ECMWF Workshop, 63–83, 2005.
Neuman, S. P.: Maximum likelihood Bayesian averaging of uncertain model predictions, Stoch. Env. Res. Risk A., 17, 291–305, 2003.
Nie, S., Zhu, J., and Luo, Y.: Simultaneous estimation of land surface scheme states and parameters using the ensemble Kalman filter: identical twin experiments, Hydrol. Earth Syst. Sci., 15, 2437–2457, https://doi.org/10.5194/hess-15-2437-2011, 2011.
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale measurements, J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Noh, S. J., Tachikawa, Y., Shiiba, M., and Kim, S.: Applying sequential Monte Carlo methods into a distributed hydrologic model: lagged particle filtering approach with regularization, Hydrol. Earth Syst. Sci., 15, 3237–3251, https://doi.org/10.5194/hess-15-3237-2011, 2011.
Ott, E., Hunt, B. R., Szunyogh, I., Zimin, A. V., Kostelich, E. J., Corazza, M., Kalnay, E., Patil, D., and Yorke, J. A.: A local ensemble Kalman filter for atmospheric data assimilation, Tellus A, 56, 415–428, 2004.
Ottlé, C. and Vidal-Madjar, D.: Assimilation of soil moisture inferred from infrared remote sensing in a hydrological model over the HAPEX-MOBILHY region, J. Hydrol., 158, 241–264, 1994.
Owe, M., de Jeu, R., and Holmes, T.: Multisensor historical climatology of satellite-derived global land surface moisture, J. Geophys. Res., 113, F01002, https://doi.org/10.1029/2007JF000769, 2008.
Palmer, T. N., Branković, Č, and Richardson, D. S.: A probability and decision-model analysis of PROVOST seasonal multi-model ensemble integrations, Q. J. Roy. Meteor. Soc., 126, 2013–2033, 2000.
Pan, M. and Wood, E. F.: A multiscale ensemble filtering system for hydrologic data assimilation. Part II: Application to land surface modeling with satellite rainfall forcing, J. Hydrometeorol., 10, 1493–1506, 2009.
Pappenberger, F. and Beven, K. J.: Ignorance is bliss: Or seven reasons not to use uncertainty analysis, Water Resour. Res., 42, W05302, https://doi.org/10.1029/2005WR004820, 2006.
Pappenberger, F., Bartholmes, J., Thielen, J., Cloke, H. L., Buizza, R., and de Roo, A.: New dimensions in early flood warning across the globe using grand-ensemble weather predictions, Geophys. Res. Lett., 35, L10404, https://doi.org/10.1029/2008GL033837, 2008.
Pappenberger, F., Thielen, J., and Del Medico, M.: The impact of weather forecast improvements on large scale hydrology: analysing a decade of forecasts of the European Flood Alert System, Hydrol. Process., 25, 1091–1113, 2011.
Parajka, J. and Blösch, G.: The value of MODIS snow cover data in validating and calibrating conceptual hydrologic models, J. Hydrol., 358, 240–258, 2008.
Parrish, M., Moradkhani, H., and DeChant, C. M. Towards reduction of model uncertainty: integration of Bayesian model averaging and data assimilation, Water Resour. Res., 48, W03519, https://doi.org/10.1029/2011WR011116, 2012.
Pathe, C., Wagner, W., Sabel, D., Doubkova, M., and Basara, J. B.: Using ENVISAT ASAR global mode data for surface soil moisture retrieval over Oklahoma, USA, IEEE T. Geosci. Remote, 47, 468–480, 2009.
Pauwels, V. R. and De Lannoy, G. J.: Improvement of modeled soil wetness conditions and turbulent fluxes through the assimilation of observed discharge, J. Hydrometeorol., 7, 458–477, 2006.
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, 2001.
Peters-Lidard, C. D., Kumar, S. V., Mocko, D. M., and Tian, Y.: Estimating evapotranspiration with land data assimilation systems, Hydrol. Process., 25, 3979–3992, 2011.
Piani, C., Weedon, G., Best, M., Gomes, S., Viterbo, P., Hagemann, S., and Haerter, J.: Statistical bias correction of global simulated daily precipitation and temperature for the applicationof hydrological models, J. Hydrol., 395, 199–215, https://doi.org/10.1016/j.jhydrol.2010.10.024, 2010.
Plaza, D. A., De Keyser, R., De Lannoy, G. J. M., Giustarini, L., Matgen, P., and Pauwels, V. R. N.: The importance of parameter resampling for soil moisture data assimilation into hydrologic models using the particle filter, Hydrol. Earth Syst. Sci., 16, 375–390, https://doi.org/10.5194/hess-16-375-2012, 2012.
Parajka, J., Naeimi, V., Blöschl, G., Wagner, W., Merz, R., and Scipal, K.: Assimilating scatterometer soil moisture data into conceptual hydrologic models at the regional scale, Hydrol. Earth Syst. Sci., 10, 353–368, https://doi.org/10.5194/hess-10-353-2006, 2006.
Rabier, F.: Overview of global data assimilation developments in numerical weather-prediction centres, Q. J. Roy. Meteor. Soc., 131, 3215–3233, 2005.
Rakovec, O., Weerts, A. H., Hazenberg, P., Torfs, P. J. J. F., and Uijlenhoet, R.: State updating of a distributed hydrological model with Ensemble Kalman Filtering: effects of updating frequency and observation network density on forecast accuracy, Hydrol. Earth Syst. Sci., 16, 3435–3449, https://doi.org/10.5194/hess-16-3435-2012, 2012a.
Rakovec, O., Hazenberg, P., Torfs, P. J. J. F., Weerts, A. H., and Uijlenhoet, R.: Generating spatial precipitation ensembles: impact of temporal correlation structure, Hydrol. Earth Syst. Sci., 16, 3419–3434, https://doi.org/10.5194/hess-16-3419-2012, 2012b.
Ramamoorthi, A. S.: Snow-melt run-off studies using remote sensing data, Sadhana, 6, 279–286, 1983.
Raso, L., Schwanenberg, D., van der Giesen, N., and van Overloop, P. J.: Tree-Scenario Based Model Predictive Control, in Geophysical Research Abstracts, 12, EGU2010-3178-1, 2010
Reggiani, P. and Weerts, A. H.: A Bayesian approach to decision-making under uncertainty: An application to real-time forecasting in the river Rhine, J. Hydrol., 356, 56–69, https://doi.org/10.1016/j.jhydrol.2008.03.027, 2008.
Reggiani, P., Renner, M., Weerts, A. H., and van Gelder, P.: Uncertainty assessment via Bayesian revision of ensemble streamflow predictions in the operational river Rhine forecasting system, Water Resour. Res., 45, W02428, https://doi.org/10.1029/2007WR006758, 2009.
Regonda, S. K., Rajagopalan, B., Clark, M., and Zagona, E.: A multimodel ensemble forecast framework: Application to spring seasonal flows in the Gunnison River Basin, Water Resour. Res., 42, W09404, https://doi.org/10.1029/2005WR004653, 2006.
Reichle, R. H.: Data assimilation methods in the Earth sciences, Adv. Water Resour., 31, 1411–1418, https://doi.org/10.1016/j.advwatres.2008.01.001, 2008.
Reichle, R. H., Walker, J. P., Koster, R. D., and Houser, P. R.: Extended versus Ensemble Kalman Filtering for Land Data Assimilation, J. Hydrometeorol., 3, 728–740, 2002.
Reichle, R. H., Koster, R. D., Dong, J., and Berg, A. A.: Global Soil Moisture from Satellite Observations, Land Surface Models, and Ground Data: Implications for Data Assimilation, J. Hydrometeor, 5, 430–442, 2004.
Reichle, R. H., Crow, W. T., and Keppenne, C. L.: An adaptive ensemble Kalman filter for soil moisture data assimilation, Water Resour. Res., 44, W03423, https://doi.org/200810.1029/2007WR006357, 2008.
Reichle, R. H., Kumar, S. V., Mahanama, S. P. P., Koster, R. D., and Liu, Q.: Assimilation of Satellite-Derived Skin Temperature Observations into Land Surface Models, J. Hydrometeorol., 11, 1103–1122, https://doi.org/10.1175/2010JHM1262.1, 2010.
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.
Ricci, S., Piacentini, A., Thual, O., Le Pape, E., and Jonville, G.: Correction of upstream flow and hydraulic state with data assimilation in the context of flood forecasting, Hydrol. Earth Syst. Sci., 15, 3555–3575, https://doi.org/10.5194/hess-15-3555-2011, 2011.
Rodell, M. and Houser, P. R.: Updating a land surface model with MODIS-derived snow cover, J. Hydrometeorol., 5, 1064–1075, 2004.
Roest, M. and Vollebregt, E.: Parallel Kalman filtering for a shallowwater flow model, in: Parallel Computational Fluid Dynamics-Practice and Theory, edited by: Wilders, P., Ecer, A., Periaux, J., Satofuka, N., and Fox, P., 301–308, Elsevier Science B.V., Amsterdam, The Netherlands, 2002.
Rojas, R., Feyen, L., Dosio, A., and Bavera, D.: Improving pan-European hydrological simulation of extreme events through statistical bias correction of RCM-driven climate simulations, Hydrol. Earth Syst. Sci., 15, 2599–2620, https://doi.org/10.5194/hess-15-2599-2011, 2011.
Rungo, M., Refsgaard, J., and Havno, K.: The updating procedure in the MIKE 11 modelling system for real-time forecasting, in Proceedings of the International Symposium for Hydrological Applications of Weather Radar, University of Salford, 14–17, 1989.
Ryu, D., Crow, W. T., and Zhan, X.: Correcting Unintended Perturbation Biases in Hydrologic Data Assimilation, J. Hydrometeorol., 10, 734–750, 2009.
Sahin, A.: Model predictive control for large scale systems: two industrial applications, PhD thesis, ETH Zurich, https://doi.org/10.3929/ethz-a-006086198, 2009.
Sahuquillo, A., Capilla, J. E., Gómez-Hernández, J. J., and Andreu, J.: Conditional simulation of transmissivity fields honoring piezometric data, Hydraulic engineering software IV, Hyd. Eng. Sof., 2, 201–214, 1992.
Salamon, P. and Feyen, L.: Disentangling uncertainties in distributed hydrological modeling using multiplicative error models and sequential data assimilation, Water Resour. Res., 46, W12501, https://doi.org/10.1029/2009WR009022, 2010.
Schaake, J., Franz, K., Bradley, A., and Buizza, R.: The Hydrologic Ensemble Prediction EXperiment (HEPEX), Hydrol. Earth Syst. Sci. Discuss., 3, 3321–3332, https://doi.org/10.5194/hessd-3-3321-2006, 2006.
Schumann, G., Bates, P. D., Horritt, M. S., Matgen, P., and Pappenberger, F.: Progress in integration of remote sensing derived flood extent and stage data and hydraulic models, Rev. Geophys, 47, RG4001, https://doi.org/10.1029/2008RG000274, 2009.
Schutz, B. E., Zwally, H. J., Shuman, C. A., Hancock, D., and DiMarzio, J. P.: Overview of the ICESat mission, Geophys. Res. Lett., 32, L21S01, https://doi.org/10.1029/2005GL024009, 2005.
Schwanenberg, D., van Breukelen, A., and Hummel, S.: Data assimilation for supporting optimum control in large-scale river networks, in 2011 IEEE International Conference on Networking, Sensing and Control (ICNSC), 98–103, 2011.
Seo, D.-J., Koren, V., and Cajina, N.: Real-time variational assimilation of hydrologic and hydrometeorological data into operational hydrologic forecasting, J. Hydrometeorol., 4, 627–641, 2003.
Seo, D.-J., Herr, H. D., and Schaake, J. C.: A statistical post-processor for accounting of hydrologic uncertainty in short-range ensemble streamflow prediction, Hydrol. Earth Syst. Sci. Discuss., 3, 1987–2035, https://doi.org/10.5194/hessd-3-1987-2006, 2006.
Seo, D.-J., Cajina, L., Corby, R., and Howieson, T.: Automatic state updating for operational streamflow forecasting via variational data assimilation, J. Hydrol., 367, 255–275, https://doi.org/10.1016/j.jhydrol.2009.01.019, 2009.
Setz, C., Heinrich, A., Rostalski, P., Papafotiou, G., and Morari, M.: Application of model predictive control to a cascade of river power plants, in Proceedings of the IFAC World Congress, 2008.
Shamir, E., Lee, B.-J., Bae, D.-H., and Georgakakos, K. P.: Flood Forecasting in Regulated Basins Using the Ensemble Extended Kalman Filter with the Storage Function Method, J. Hydrol. Eng., 15, 1030, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000282, 2010.
Shiiba, M., Laurenson, X., and Tachikawa, Y.: Real-time stage and discharge estimation by a stochastic-dynamic flood routing model, Hydrol. Process., 14, 481–495, 2000.
Slater, A. G. and Clark, M. P.: Snow data assimilation via an ensemble Kalman filter, J. Hydrometeorol., 7, 478–493, 2006.
Smith, M. B., Laurine, D. P., Koren, V. I., Reed, S. M., and Zhang, Z.: Hydrologic Model Calibration in the National Weather Service, in: Calibration of Watershed Models, Water Science and Application 6, edited by: Duan, Q., Gupta, H., Sorooshian, S., Rousseau, A., and Turcotte, R., AGU Press, Washington, D.C., 133–152, 2003.
Smith, P. J., Beven, K. J., Weerts, A. H., and Leedal, D.: Adaptive correction of deterministic models to produce probabilistic forecasts, Hydrol. Earth Syst. Sci., 16, 2783–2799, https://doi.org/10.5194/hess-16-2783-2012, 2012.
Snyder, C., Bengtsson, T, Bickel, P., and Anderson, J.: Obstacles to high-dimensional particle filtering, Mon. Weather Rev., 136, 4629–4640, 2008.
Sorooshian, S., AghaKouchak, A., Arkin, P., Eylander, J., Foufoula-Georgiou, E., Harmon, R., Hendrickx, J.M.H., Imam, B., Kuligowski, R., Skahill, B., and Skofronock-Jackson, G.: Advanced concepts on remote sensing of precipitation at multiple scales, B. Am. Meteor. Soc., 92, 1353–1357, https://doi.org/10.1175/2011BAMS3158.1, 2011.
Steiner, M.: Uncertainty of estimates of monthly areal rainfall for temporally sparse remote observations, Water Resour. Res., 32, 373–388, 1996.
Stroud, J. R., Lesht, B. M., Schwab, D. J., Beletsky, D., and Stein, M. L.: Assimilation of satellite images into a sediment transport model of Lake Michigan, Water Resour. Res., 45, W02419, https://doi.org/10.1029/2007WR006747, 2009.
Su, H., Yang, Z. L., Dickinson, R. E., Wilson, C. R. and Niu, G. Y.: Multisensor snow data assimilation at the continental scale: The value of Gravity Recovery and Climate Experiment terrestrial water storage information, J. Geophys. Res., 115, D10104, https://doi.org/10.1029/2009JD013035, 2010.
Sun, A. Y., Morris, A. P., and Mohanty, S.: Sequential updating of multimodal hydrogeologic parameter fields using localization and clustering techniques, Water Resour. Res., 45, W07424, https://doi.org/10.1029/2008WR007443, 2009.
Szunyogh, I., Kostelich, E. J., Gyarmati, G., Kalnay, E., Hunt, B. R., Ott, E., Satterfield, E., and Yorke, J. A.: A local ensemble transform Kalman filter data assimilation system for the NCEP global model, Tellus A, 60, 113–130, 2008.
Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F., and Watkins, M. M.: GRACE measurements of mass variability in the Earth system, Science, 305, 503, https://doi.org/10.1126/science.1099192, 2004.
Thielen, J., Bartholmes, J., Ramos, M.-H., and de Roo, A.: The European Flood Alert System – Part 1: Concept and development, Hydrol. Earth Syst. Sci., 13, 125–140, https://doi.org/10.5194/hess-13-125-2009, 2009.
Thirel, G., Martin, E., Mahfouf, J.-F., Massart, S., Ricci, S., and Habets, F.: A past discharges assimilation system for ensemble streamflow forecasts over France – Part 1: Description and validation of the assimilation system, Hydrol. Earth Syst. Sci., 14, 1623–1637, https://doi.org/10.5194/hess-14-1623-2010, 2010a.
Thirel, G., Martin, E., Mahfouf, J.-F., Massart, S., Ricci, S., Regimbeau, F., and Habets, F.: A past discharge assimilation system for ensemble streamflow forecasts over France – Part 2: Impact on the ensemble streamflow forecasts, Hydrol. Earth Syst. Sci., 14, 1639–1653, https://doi.org/10.5194/hess-14-1639-2010, 2010b.
Thyer, M., Renard, B., Kavetski, D., Kuczera, G., Franks, S., and Srikanthan, S.: Critical evaluation of parameter consistency and predictive uncertainty in hydrological modeling: A case study using Bayesian total error analysis, Water Resour. Res., 45, 22, https://doi.org/10.1029/2008WR006825, 2009.
Tian, Y. and Peters-Lidard, C. D.: A global map of uncertainties in satellite.based precipitation measurements, Geophys. Res. Lett., 37, L24407, https://doi.org/10.1029/2010GL046008, 2010.
Todini, E.: A model conditional processor to assess predictive uncertainty in flood forecasting, Int. J. River Basin Manag., 6, 123–137, 2008.
Tracton, M. S. and McPherson, R. D.: On the impact of radiometric sounding data upon operational numerical weather prediction at NMC, Am. Meteorol. Soc., Bulletin, 58, 1201–1209, 1977.
Turner, M., Walker, J., and Oke, P.: Ensemble member generation for sequential data assimilation, Remote Sens. Environ., 112, 1421–1433, 2008.
Valstar, J. R., McLaughlin, D. B., Stroet, C. B. M. te, and Geer, F. C. van: A representer-based inverse method for groundwater flow and transport applications, Water Resour. Res., 40, W05116, https://doi.org/200410.1029/2003WR002922, 2004.
van Dijk, A. I. J. M. and Renzullo, L. J.: Water resource monitoring systems and the role of satellite observations, Hydrol. Earth Syst. Sci., 15, 39–55, https://doi.org/10.5194/hess-15-39-2011, 2011.
van Velzen, N. and Segers, A.: A problem-solving environment for data assimilation in air quality modelling, Environ. Model. Softw., 25, 277–288, 2010.
van Velzen, N. and Verlaan, M.: COSTA a problem solving environment for data assimilation applied for hydrodynamical modelling, Meteorologische Z., 16, 777–793, https://doi.org/10.1127/0941-2948/2007/0241, 2007.
Villarini, G. and Krajewski, W. F.: Empirically-based modeling of spatial sampling uncertainties associated with rainfall measurements by rain gauges, Adv. Water Resour., 31, 1015–1023, 2008.
Volkmann, T. H. M., Lyon, S. W., Gupta, H. V., and Troch, P. A.: Multicriteria design of rain gauge networks for flash flood prediction in semiarid catchments with complex terrain, Water Resour. Res., 46, W11554, https://doi.org/10.1029/2010WR009145, 2010.
van Overloop, P. J., Weijs, S., and Dijkstra, S.: Multiple model predictive control on a drainage canal system, Control Eng. Pract., 16, 531–540, 2008.
Vrugt, J. A., Gupta, H. V., Bouten, W., and Sorooshian, S.: A Shuffled Complex Evolution Metropolis algorithm for optimization and uncertainty assessment of hydrologic model parameters, Water Resour. Res., 39, 1201, https://doi.org/10.1029/2002WR001642, 2003.
Vrugt, J. A., Diks, C. G., Gupta, H. V., Bouten, W., and Verstraten, J. M.: Improved treatment of uncertainty in hydrologic modeling: Combining the strengths of global optimization and data assimilation, Water Resour. Res, 41, W01017, https://doi.org/10.1029/2004WR003059, 2005.
Vrugt, J. A., Gupta, H. V., Nualláin, B., and Bouten, W.: Real-Time Data Assimilation for Operational Ensemble Streamflow Forecasting, J. Hydrometeorol., 7, 548–565, https://doi.org/10.1175/JHM504.1, 2006.
Wächter, A. and Biegler, L. T.: On the implementation of an interior-point filter line-search algorithm for large-scale nonlinear programming, Math. Program., 106, 25–57, 2006.
Wang, D. and Cai, X.: Robust data assimilation in hydrological modeling-A comparison of Kalman and H-infinity filters, Adv. Water Resour., 31, 455–472, 2008.
Weerts, A. H. and El Serafy, G. Y. H.: Particle filtering and ensemble Kalman filtering for state updating with hydrological conceptual rainfall-runoff models, Water Resour. Res., 42, W09403, https://doi.org/10.1029/2005WR004093, 2006.
Weerts, A. H. and Liu, Y.: Advances in Data Assimilation for Operational Hydrologic Forecasting, Eos Trans. AGU, 92(7), 2011.
Weerts, A. H., El Serafy, G. Y., Hummel, S., Dhondia, J., and Gerritsen, H.: Application of generic data assimilation tools (DATools) for flood forecasting purposes, Comput. Geosci., 36, 453–463, https://doi.org/10.1016/j.cageo.2009.07.009, 2010.
Weerts, A. H., Winsemius, H. C., and Verkade, J. S.: Estimation of predictive hydrological uncertainty using quantile regression: examples from the National Flood Forecasting System (England and Wales), Hydrol. Earth Syst. Sci., 15, 255–265, https://doi.org/10.5194/hess-15-255-2011, 2011.
Welles, E., Sorooshian, S., Carter, G., and Olsen, B.: Hydrologic verification: A call for action and collaboration, B. Am. Meteor. Soc., 88, 503, https://doi.org/10.1175/BAMS-88-4-503, 2007.
Werner, M., Cranston, M., Harrison, T., Whitfield, D., and Schellekens, J.: Understanding the value of radar rainfall nowcasts in flood forecasting and warning in flashy catchments, Meteorol. Appl., 16, 41–55, 2009.
Werner, M., Schellekens, J., Gijsbers, P., van Dijk, M., and van den Akker, O., Heynert, K.: The Delft-FEWS flow forecasting system, Environ. Model. Softw., in press, 2012.
Willems, P.: Stochastic description of the rainfall input errors in lumped hydrological models, Stoch. Env. Res. Risk A., 15, 132–152, 2002.
Xiong, X., Navon, I. M., and Uzunoglu, B.: A note on the particle filter with posterior Gaussian resampling, Tellus A, 58, 456–460, 2006.
Yirdaw, S. Z., Snelgrove, K. R., and Agboma, C. O.: GRACE satellite observations of terrestrial moisture changes for drought characterization in the Canadian Prairie, J. Hydrol., 356, 84–92, 2008.
Young, P. C.: Advances in real-time flood forecasting, Philos. T. Roy. Soc. Lond., 360, 1433–1450, 2002.
Zafra-Cabeza, A., Maestre, J. M., Ridao, M. A., Camacho, E. F., and Sanchez, L.: A hierarchical distributed model predictive control approach to irrigation canals: A risk mitigation perspective, J. Process Contr., 21, 787–799, https://doi.org/10.1016/j.jprocont.2010.12.012, 2011.
Zaitchik, B. F., Rodell, M., and Reichle, R. H.: Assimilation of GRACE terrestrial water storage data into a land surface model: results for the Mississippi river basin, J. Hydrometeorol., 9, 535–548, https://doi.org/10.1175/2007JHM951.1, 2008.
Zhang, F., Zhang, M., and Hansen, J. A.: Coupling ensemble Kalman filter with four-dimensional variational data assimilation, Adv. Atmos. Sci., 26, 1–8, https://doi.org/10.1007/s00376-009-0001-8, 2009.
Zhou, H., Gómez-Hernández, J. J., Franssen, H. J. H., and Li, L.: An approach to handling non-Gaussianity of parameters and state variables in ensemble Kalman filtering, Adv. Water Resour., 34, 844–864, https://doi.org/10.1016/j.advwatres.2011.04.014, 2011.
Zreda, M., Desilets, D., Ferre, T. P. A., and Scott, R. L.: Measuringsoil moisture content non-invasively at intermediate spatial scale using cosmic-ray neutrons, Geophys. Res. Lett., 35, L21402, https://doi.org/10.1029/2008GL035655, 2008.
