Articles | Volume 21, issue 1
https://doi.org/10.5194/hess-21-65-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-65-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
04 Jan 2017
Research article |
| 04 Jan 2017
Event-scale power law recession analysis: quantifying methodological uncertainty
University of California Berkeley, Department of Civil and Environmental Engineering, Berkeley, CA, USA
Nathaniel J. Karst
Babson College, Department of Mathematics, Wellesley, MA, USA
Kyriakos Charalampous
University of California Berkeley, Department of Civil and Environmental Engineering, Berkeley, CA, USA
University of Bristol, Department of Civil Engineering, Bristol, UK
Andrew Veenstra
University of California Berkeley, Department of Civil and Environmental Engineering, Berkeley, CA, USA
Sally E. Thompson
University of California Berkeley, Department of Civil and Environmental Engineering, Berkeley, CA, USA
Related authors
Dana A. Lapides, W. Jesse Hahm, Matthew Forrest, Daniella M. Rempe, Thomas Hickler, and David N. Dralle
Biogeosciences, 21, 1801–1826, https://doi.org/10.5194/bg-21-1801-2024, https://doi.org/10.5194/bg-21-1801-2024, 2024
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Water stored in weathered bedrock is rarely incorporated into vegetation and Earth system models despite increasing recognition of its importance. Here, we add a weathered bedrock component to a widely used vegetation model. Using a case study of two sites in California and model runs across the United States, we show that more accurately representing subsurface water storage and hydrology increases summer plant water use so that it better matches patterns in distributed data products.
David N. Dralle, W. Jesse Hahm, K. Dana Chadwick, Erica McCormick, and Daniella M. Rempe
Hydrol. Earth Syst. Sci., 25, 2861–2867, https://doi.org/10.5194/hess-25-2861-2021, https://doi.org/10.5194/hess-25-2861-2021, 2021
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Root zone water storage capacity determines how much water can be stored belowground to support plants during periods without precipitation. Here, we develop a satellite remote sensing method to estimate this key variable at large scales that matter for management. Importantly, our method builds on previous approaches by accounting for snowpack, which may bias estimates from existing approaches. Ultimately, our method will improve large-scale understanding of plant access to subsurface water.
Dana A. Lapides, W. Jesse Hahm, Matthew Forrest, Daniella M. Rempe, Thomas Hickler, and David N. Dralle
Biogeosciences, 21, 1801–1826, https://doi.org/10.5194/bg-21-1801-2024, https://doi.org/10.5194/bg-21-1801-2024, 2024
Short summary
Short summary
Water stored in weathered bedrock is rarely incorporated into vegetation and Earth system models despite increasing recognition of its importance. Here, we add a weathered bedrock component to a widely used vegetation model. Using a case study of two sites in California and model runs across the United States, we show that more accurately representing subsurface water storage and hydrology increases summer plant water use so that it better matches patterns in distributed data products.
David N. Dralle, W. Jesse Hahm, K. Dana Chadwick, Erica McCormick, and Daniella M. Rempe
Hydrol. Earth Syst. Sci., 25, 2861–2867, https://doi.org/10.5194/hess-25-2861-2021, https://doi.org/10.5194/hess-25-2861-2021, 2021
Short summary
Short summary
Root zone water storage capacity determines how much water can be stored belowground to support plants during periods without precipitation. Here, we develop a satellite remote sensing method to estimate this key variable at large scales that matter for management. Importantly, our method builds on previous approaches by accounting for snowpack, which may bias estimates from existing approaches. Ultimately, our method will improve large-scale understanding of plant access to subsurface water.
Gopal Penny, Veena Srinivasan, Iryna Dronova, Sharachchandra Lele, and Sally Thompson
Hydrol. Earth Syst. Sci., 22, 595–610, https://doi.org/10.5194/hess-22-595-2018, https://doi.org/10.5194/hess-22-595-2018, 2018
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Water resources in the Arkavathy watershed in southern India are changing due to human modification of the landscape, including changing agricultural practices and urbanization. We analyze surface water resources in man-made lakes in satellite imagery over a period of 4 decades and find drying in the northern part of the watershed (characterized by heavy agriculture) and wetting downstream of urban areas. Drying in the watershed is associated with groundwater-irrigated agriculture.
Veena Srinivasan, Gopal Penny, Sharachchandra Lele, Bejoy K. Thomas, and Sally Thompson
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2017-543, https://doi.org/10.5194/hess-2017-543, 2017
Revised manuscript has not been submitted
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This case study of the data-scarce, upper Arkavathy watershed, near the city of Bengaluru in southern India, attempts to systematically explain the observed disappearance of surface and groundwater in recent decades using a multi-scale model. The results indicate that the disappearance can only be explained by including human drivers – both social feedbacks to changes in water availability and external drivers such as technology change and urbanization.
M. F. Müller and S. E. Thompson
Hydrol. Earth Syst. Sci., 20, 669–683, https://doi.org/10.5194/hess-20-669-2016, https://doi.org/10.5194/hess-20-669-2016, 2016
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We compare a stochastic (process-based) and statistical (data-based) method to predict flow duration curves in ungauged basins, under stationary and non-stationary conditions, and using Nepal as a case study. Both methods worked well in stationary conditions, with performances driven by the main source of runoff heterogeneity (climate vs. recession). The stochastic model worked better under change, and the performance of the statistical model was determined by the resilience of the flow regime.
T. J. Troy, M. Konar, V. Srinivasan, and S. Thompson
Hydrol. Earth Syst. Sci., 19, 3667–3679, https://doi.org/10.5194/hess-19-3667-2015, https://doi.org/10.5194/hess-19-3667-2015, 2015
M. F. Müller and S. E. Thompson
Hydrol. Earth Syst. Sci., 19, 2925–2942, https://doi.org/10.5194/hess-19-2925-2015, https://doi.org/10.5194/hess-19-2925-2015, 2015
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We introduce TopREML as a method to predict runoff signatures in ungauged basins using linear mixed models with spatially correlated random effects. The nested nature of streamflow networks is accounted for by allowing for stronger correlations between flow-connected basins. The restricted maximum likelihood framework provides best linear unbiased predictions of both the predicted flow variable and its uncertainty as shown in Monte Carlo and cross-validation analyses in Nepal and Austria.
V. Srinivasan, S. Thompson, K. Madhyastha, G. Penny, K. Jeremiah, and S. Lele
Hydrol. Earth Syst. Sci., 19, 1905–1917, https://doi.org/10.5194/hess-19-1905-2015, https://doi.org/10.5194/hess-19-1905-2015, 2015
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The paper asks why the Arkavathy River in southern India is drying. The study results indicate that anthropogenic drivers like groundwater pumping, eucalyptus plantations and channel fragmentation are much more likely to have caused the decline than changing climate. The multiple-hypothesis approach presents a systematic way of quantifying the relative contributions of different drivers, contributing to the policy debate and prioritizing new scientific research.
S. E. Thompson, M. Sivapalan, C. J. Harman, V. Srinivasan, M. R. Hipsey, P. Reed, A. Montanari, and G. Blöschl
Hydrol. Earth Syst. Sci., 17, 5013–5039, https://doi.org/10.5194/hess-17-5013-2013, https://doi.org/10.5194/hess-17-5013-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Uncertainty analysis
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Hydrol. Earth Syst. Sci., 28, 3597–3611, https://doi.org/10.5194/hess-28-3597-2024, https://doi.org/10.5194/hess-28-3597-2024, 2024
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Revised manuscript accepted for HESS
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We propose the
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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
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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.
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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
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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
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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
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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
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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
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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.
Keighobad Jafarzadegan, Peyman Abbaszadeh, and Hamid Moradkhani
Hydrol. Earth Syst. Sci., 25, 4995–5011, https://doi.org/10.5194/hess-25-4995-2021, https://doi.org/10.5194/hess-25-4995-2021, 2021
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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.
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
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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
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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
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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
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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
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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
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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
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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
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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
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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
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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
Cited articles
Abdi, H.: The Bonferonni and Šidák corrections for multiple comparisons, Encyclop. Measure. Stat., 3, 103–107, 2007.
Bart, R. and Hope, A.: Inter-seasonal variability in baseflow recession rates: The role of aquifer antecedent storage in central California watersheds, J. Hydrol., 519, 205–213, https://doi.org/10.1016/j.jhydrol.2014.07.020, 2014.
Basso, S., Schirmer, M., and Botter, G.: On the emergence of heavy-tailed streamflow distributions, Adv. Water Resour., 82, 98–105, 2015.
Berghuijs, W., Hartmann, A., and Woods, R.: Streamflow sensitivity to water storage changes across Europe, Geophys. Res. Lett., 43, 1980–1987, https://doi.org/10.1002/2016GL067927, 2016.
Biswal, B. and Marani, M.: Geomorphological origin of recession curves, Geophys. Res. Lett., 37, L24403, https://doi.org/10.1029/2010GL045415, 2010.
Biswal, B. and Marani, M.: 'Universal' recession curves and their geomorphological interpretation, Adv. Water Resour., 65, 34–42, https://doi.org/10.1016/j.advwatres.2014.01.004, 2014.
Biswal, B. and Nagesh, K. D.: Study of dynamic behaviour of recession curves, Hydrol. Process., 28, 784–792, https://doi.org/10.1002/hyp.9604, 2014.
Bogaart, P. W., v. d. Velde, Y., Lyon, S. W., and Dekker, S. C.: Streamflow recession patterns can help unravel the role of climate and humans in landscape co-evolution, Hydrol. Earth Syst. Sci., 20, 1413–1432, https://doi.org/10.5194/hess-20-1413-2016, 2016.
Botter, G., Basso, S., Rodriguez-Iturbe, I., and Rinaldo, A.: Resilience of river flow regimes, P. Natl. Acad. Sci. USA, 110, 12925–12930, 2013.
Boussinesq, J.: Essai sur la théorie des eaux courantes, vol. 2, Imprimerie nationale, Paris, France, 1877.
Boussinesq, J.: Recherches théoriques sur l'écoulement des nappes d'eau infiltrées dans le sol et sur le débit des sources, Journal de Mathématiques Pures et Appliquées, 10, 5–78, 1904.
Brutsaert, W.: The unit response of groundwater outflow from a hillslope, Water Resour. Res., 30, 2759–2763, https://doi.org/10.1029/94WR01396, 1994.
Brutsaert, W. and Nieber, J. L.: Regionalized drought flow hydrographs from a mature glaciated plateau, Water Resour. Res., 13, 637–648, https://doi.org/10.1029/WR013i003p00637, 1977.
Chen, B. and Krajewski, W.: Analysing individual recession events: sensitivity of parameter determination to the calculation procedure, Hydrolog. Sci. J., 61, 2887–2901, 2016.
Chen, B. and Krajewski, W. F.: Recession analysis across scales: The impact of both random and nonrandom spatial variability on aggregated hydrologic response, J. Hydrol., 523, 97–106, 2015.
Clark, M. P., Rupp, D. E., Woods, R. A., Tromp-van Meerveld, H. J., Peters, N. E., and Freer, J. E.: Consistency between hydrological models and field observations: linking processes at the hillslope scale to hydrological responses at the watershed scale, Hydrol. Process., 23, 311–319, https://doi.org/10.1002/hyp.7154, 2009.
Dralle, D., Karst, N., and Thompson, S. E.: a, b careful: The challenge of scale invariance for comparative analyses in power law models of the streamflow recession, Geophys. Res. Lett., 42, 9285–9293, https://doi.org/10.1002/2015GL066007, 2015.
Dralle, D. N., Karst, N. J., and Thompson, S. E.: Dry season streamflow persistence in seasonal climates, Water Resour. Res., https://doi.org/10.1002/2015WR017752, in press, 2016.
Fatichi, S., Ivanov, V. Y., and Caporali, E.: Investigating Interannual Variability of Precipitation at the Global Scale: Is There a Connection with Seasonality?, J. Climate, 25, 5512–5523, https://doi.org/10.1175/JCLI-D-11-00356.1, 2012.
Ghosh, D. K., Wang, D., and Zhu, T.: On the transition of base flow recession from early stage to late stage, Adv. Water Resour., 88, 8–13, 2016.
Guzmán, P., Batelaan, O., Huysmans, M., and Wyseure, G.: Comparative analysis of baseflow characteristics of two Andean catchments, Ecuador, Hydrol. Process., 29, 3051–3064, 2015.
Hall, F. R.: Base-flow recessions – a review, Water Resour. Res., 4, 973–983, https://doi.org/10.1029/WR004i005p00973, 1968.
Harman, C. J., Sivapalan, M., and Kumar, P.: Power law catchment-scale recessions arising from heterogeneous linear small-scale dynamics, Water Resour. Res., 45, W09404, https://doi.org/10.1029/2008WR007392, 2009.
Howe, J. W.: Recession characteristics of Iowa streams, Studies in Engineering Bulletin, The University of Iowa, Iowa City, 1–6, 1966.
Huyck, A. A. O., Pauwels, V., and Verhoest, N. E. C.: A base flow separation algorithm based on the linearized Boussinesq equation for complex hillslopes, Water Resour. Res., 41, W08415, https://doi.org/10.1029/2004WR003789, 2005.
Kirchner, J. W.: Catchments as simple dynamical systems: Catchment characterization, rainfall-runoff modeling, and doing hydrology backward, Water Resour. Res., 45, W02409, https://doi.org/10.1029/2008WR006912, 2009.
Kvalseth, T. O.: Cautionary Note about R2, Am. Statist., 39, 279–285, https://doi.org/10.2307/2683704, 1985.
Lamb, R. and Beven, K.: Using interactive recession curve analysis to specify a general catchment storage model, Hydrol. Earth Syst. Sci., 1, 101–113, https://doi.org/10.5194/hess-1-101-1997, 1997.
Mann, H. B. and Whitney, D. R.: On a test of whether one of two random variables is stochastically larger than the other, in: The annals of mathematical statistics, Institute of Mathematical Statistics, Ann Arbor, Michigan, 50–60, 1947.
McMillan, H., Gueguen, M., Grimon, E., Woods, R., Clark, M., and Rupp, D. E.: Spatial variability of hydrological processes and model structure diagnostics in a 50 km2 catchment, Hydrol. Process., 28, 4896–4913, https://doi.org/10.1002/hyp.9988, 2014.
Miller, D. M.: Reducing transformation bias in curve fitting, Am. Stat., 38, 124–126, https://doi.org/10.2307/2683247, 1984.
Motulsky, H. J. and Ransnas, L. A.: Fitting curves to data using nonlinear regression: a practical and nonmathematical review, FASEB J., 1, 365–374, 1987.
Müller, M. F., Dralle, D. N., and Thompson, S. E.: Analytical model for flow duration curves in seasonally dry climates, Water Resour. Res., 50, 5510–5531, https://doi.org/10.1002/2014WR015301, 2014.
Mutzner, R., Bertuzzo, E., Tarolli, P., Weijs, S. V., Nicotina, L., Ceola, S., Tomasic, N., Rodríguez-Iturbe, I., Parlange, M. B., and Rinaldo, A.: Geomorphic signatures on Brutsaert base flow recession analysis, Water Resour. Res., 49, 5462–5472, https://doi.org/10.1002/wrcr.20417, 2013.
Palmroth, S., Katul, G. G., Hui, D., McCarthy, H. R., Jackson, R. B., and Oren, R.: Estimation of long-term basin scale evapotranspiration from streamflow time series, Water Resour. Res., 46, W10512, https://doi.org/10.1029/2009WR008838, 2010.
Parlange, J. Y., Stagnitti, F., Heilig, A., Szilagyi, J., Parlange, M. B., Steenhuis, T. S., Hogarth, W. L., Barry, D. A., and Li, L.: Sudden drawdown and drainage of a horizontal aquifer, Water Resour. Res., 37, 2097–2101, 2001.
Patnaik, S., Biswal, B., Kumar, D. N., and Sivakumar, B.: Effect of catchment characteristics on the relationship between past discharge and the power law recession coefficient, J. Hydrol., 528, 321–328, 2015.
Pattyn, F. and Van Huele, W.: Power law or power flaw?, Earth Surf. Proc. Land., 23, 761–767, https://doi.org/10.1002/(SICI)1096-9837(199808)23:8<761::AID-ESP892>3.0.CO;2-K, 1998.
Peel, M. C., Finlayson, B. L., and McMahon, T. A.: Updated world map of the Köppen-Geiger climate classification, Hydrol. Earth Syst. Sci., 11, 1633–1644, https://doi.org/10.5194/hess-11-1633-2007, 2007.
Power, M. E., Bouma-Gregson, K., Higgins, P., and Carlson, S. M.: The Thirsty Eel: Summer and Winter Flow Thresholds that Tilt the Eel River of Northwestern California from Salmon-Supporting to Cyanobacterially Degraded States, Copeia, 2015, 200–211, https://doi.org/10.1643/CE-14-086, 2015.
Rupp, D. E. and Selker, J. S.: On the use of the Boussinesq equation for interpreting recession hydrographs from sloping aquifers, Water Resour. Res., 42, W12421, https://doi.org/10.1029/2006WR005080, 2006a.
Rupp, D. E. and Selker, J. S.: Information, artifacts, and noise in dQ/dt − Q recession analysis, Adv. Water Resour., 29, 154–160, https://doi.org/10.1016/j.advwatres.2005.03.019, 2006b.
Rupp, D. E., Owens, J. M., Warren, K. L., and Selker, J. S.: Analytical methods for estimating saturated hydraulic conductivity in a tile-drained field, J. Hydrol., 289, 111–127, 2004.
Salve, R., Rempe, D. M., and Dietrich, W. E.: Rain, rock moisture dynamics, and the rapid response of perched groundwater in weathered, fractured argillite underlying a steep hillslope, Water Resour. Res., 48, W11528, https://doi.org/10.1029/2012WR012583, 2012.
Sánchez-Murillo, R., Brooks, E. S., Elliot, W. J., Gazel, E., and Boll, J.: Baseflow recession analysis in the inland Pacific Northwest of the United States, Hydrogeol. J., 23, 287–303, 2015.
Sawaske, S. R. and Freyberg, D. L.: An analysis of trends in baseflow recession and low-flows in rain-dominated coastal streams of the pacific coast, J. Hydrol., 519, 599–610, 2014.
Shaw, S. B.: Investigating the linkage between streamflow recession rates and channel network contraction in a mesoscale catchment in New York state, Hydrol. Process., 30, 479–492, https://doi.org/10.1002/hyp.10626, 2016.
Shaw, S. B. and Riha, S. J.: Examining individual recession events instead of a data cloud: Using a modified interpretation of dQ/dt − Q streamflow recession in glaciated watersheds to better inform models of low flow, J. Hydrol., 434–435, 46–54, https://doi.org/10.1016/j.jhydrol.2012.02.034, 2012.
Singh, K. P. and Stall, J. B.: Derivation of base flow recession curves and parameters, Water Resour. Res., 7, 292–303, https://doi.org/10.1029/WR007i002p00292, 1971.
Stoelzle, M., Stahl, K., and Weiler, M.: Are streamflow recession characteristics really characteristic?, Hydrol. Earth Syst. Sci., 17, 817–828, https://doi.org/10.5194/hess-17-817-2013, 2013.
Szilagyi, J., Parlange, M. B., and Albertson, J. D.: Recession flow analysis for aquifer parameter determination, Water Resour. Res., 34, 1851–1857, 1998.
Szilagyi, J., Gribovszki, Z., and Kalicz, P.: Estimation of catchment-scale evapotranspiration from baseflow recession data: Numerical model and practical application results, J. Hydrol., 336, 206–217, https://doi.org/10.1016/j.jhydrol.2007.01.004, 2007.
Tague, C. and Grant, G. E.: A geological framework for interpreting the low-flow regimes of Cascade streams, Willamette River Basin, Oregon, Water Resour. Res., 40, W04303, https://doi.org/10.1029/2003WR002629, 2004.
Tallaksen, L. M.: A review of baseflow recession analysis, J. Hydrol., 165, 349–370, 1995.
Thomas, B. F., Vogel, R. M., and Famiglietti, J. S.: Objective hydrograph baseflow recession analysis, J. Hydrol., 525, 102–112, 2015.
Troch, P. A., Berne, A., Bogaart, P., Harman, C., Hilberts, A. G. J., Lyon, S. W., Paniconi, C., Pauwels, V. R., Rupp, D. E., Selker, J. S., Teuling, R., Uijlenhoet, R., and Verhoest, N. E.: The importance of hydraulic groundwater theory in catchment hydrology: The legacy of Wilfried Brutsaert and Jean-Yves Parlange, Water Resour. Res., 49, 1–18, https://doi.org/10.1002/wrcr.20407, 2013.
Vogel, R. M. and Kroll, C. N.: Regional geohydrologic-geomorphic relationships for the estimation of low-flow statistics, Water Resour. Res., 28, 2451–2458, https://doi.org/10.1029/92WR01007, 1992.
Vogel, R. M. and Kroll, C. N.: Estimation of baseflow recession constants, Water Resour. Manage., 10, 303–320, 1996.
Wang, D.: On the base flow recession at the Panola mountain research watershed, Georgia, United States, Water Resour. Res., 47, W03527, https://doi.org/10.1029/2010WR009910, 2011.
Westerberg, I. K. and McMillan, H. K.: Uncertainty in hydrological signatures, Hydrol. Earth Syst. Sci., 19, 3951–3968, https://doi.org/10.5194/hess-19-3951-2015, 2015.
Wittenberg, H.: Nonlinear analysis of flow recession curves, IAHS Publ., 221, 61–68, 1994.
Wittenberg, H.: Baseflow recession and recharge as nonlinear storage processes, Hydrol. Process., 13, 715–726, https://doi.org/10.1002/(SICI)1099-1085(19990415)13:5<715::AID-HYP775>3.0.CO;2-N, 1999.
Wittenberg, H. and Sivapalan, M.: Watershed groundwater balance estimation using streamflow recession analysis and baseflow separation, J. Hydrol., 219, 20–33, 1999.
Ye, S., Li, H., Huang, M., Ali, M., Leng, G., Leung, L. R., Wang, S., and Sivapalan, M.: Regionalization of subsurface stormflow parameters of hydrologic models: Derivation from regional analysis of streamflow recession curves, J. Hydrol., 519, 670–682, 2014.
Yoder, N.: peakfinder, http://www.mathworks.com/matlabcentral/ (last access: October 2016), 2009.
Zecharias, Y. B. and Brutsaert, W.: Recession characteristics of groundwater outflow and base flow from mountainous watersheds, Water Resour. Res., 24, 1651–1658, https://doi.org/10.1029/WR024i010p01651, 1988.
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.
The streamflow recession is the period following rainfall during which flow declines. This paper...
