Articles | Volume 24, issue 9
https://doi.org/10.5194/hess-24-4463-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-24-4463-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
15 Sep 2020
Research article |
| 15 Sep 2020
| 15 Sep 2020
A systematic assessment of uncertainties in large-scale soil loss estimation from different representations of USLE input factors – a case study for Kenya and Uganda
Christoph Schürz
CORRESPONDING AUTHOR
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna (BOKU), Vienna, Austria
Bano Mehdi
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna (BOKU), Vienna, Austria
Institute of Agronomy, University of Natural
Resources and Life Sciences, Vienna (BOKU), Tulln, Austria
Jens Kiesel
Leibniz Institute of Freshwater Ecology and Inland Fisheries (IGB), Berlin, Germany
Institute of Natural Resource Conservation, Department of Hydrology and Water Resources Management, Christian Albrechts University of Kiel, Kiel, Germany
Karsten Schulz
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna (BOKU), Vienna, Austria
Mathew Herrnegger
CORRESPONDING AUTHOR
Institute for Hydrology and Water Management, University of Natural Resources and Life Sciences, Vienna (BOKU), Vienna, Austria
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Björn Guse, Matthias Pfannerstill, Abror Gafurov, Jens Kiesel, Christian Lehr, and Nicola Fohrer
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Performance measures are used to evaluate the representation of hydrological processes in parameters of hydrological models. In this study, we investigated how strongly model parameters and performance measures are connected. It was found that relationships are different for varying flow conditions, indicating that precise parameter identification requires multiple performance measures. The suggested approach contributes to a better handling of parameters in hydrological modelling.
Benjamin Müller, Matthias Bernhardt, Conrad Jackisch, and Karsten Schulz
Hydrol. Earth Syst. Sci., 20, 3765–3775, https://doi.org/10.5194/hess-20-3765-2016, https://doi.org/10.5194/hess-20-3765-2016, 2016
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S. Härer, M. Bernhardt, and K. Schulz
Geosci. Model Dev., 9, 307–321, https://doi.org/10.5194/gmd-9-307-2016, https://doi.org/10.5194/gmd-9-307-2016, 2016
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M. Herrnegger, H. P. Nachtnebel, and K. Schulz
Hydrol. Earth Syst. Sci., 19, 4619–4639, https://doi.org/10.5194/hess-19-4619-2015, https://doi.org/10.5194/hess-19-4619-2015, 2015
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Especially in alpine catchments, areal rainfall estimates often exhibit large errors. Runoff measurements are, on the other hand, one of the most robust observations within the hydrological cycle. We therefore calculate mean catchment rainfall by inverting an HBV-type rainfall-runoff model, using runoff observations as input. The inverse model may e.g. be used to analyse rainfall conditions of extreme flood events or estimation of snowmelt contribution.
B. Müller, M. Bernhardt, and K. Schulz
Hydrol. Earth Syst. Sci., 18, 5345–5359, https://doi.org/10.5194/hess-18-5345-2014, https://doi.org/10.5194/hess-18-5345-2014, 2014
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We present a method to define hydrological landscape units by a time series of thermal infrared satellite data. Land surface temperature is calculated for 28 images in 12 years for a catchment in Luxembourg. Pattern measures show spatio-temporal persistency; principle component analysis extracts relevant patterns. Functional units represent similar behaving entities based on a representative set of images. Resulting classification and patterns are discussed regarding potential applications.
E. Zehe, U. Ehret, L. Pfister, T. Blume, B. Schröder, M. Westhoff, C. Jackisch, S. J. Schymanski, M. Weiler, K. Schulz, N. Allroggen, J. Tronicke, L. van Schaik, P. Dietrich, U. Scherer, J. Eccard, V. Wulfmeyer, and A. Kleidon
Hydrol. Earth Syst. Sci., 18, 4635–4655, https://doi.org/10.5194/hess-18-4635-2014, https://doi.org/10.5194/hess-18-4635-2014, 2014
S. Härer, M. Bernhardt, J. G. Corripio, and K. Schulz
Geosci. Model Dev., 6, 837–848, https://doi.org/10.5194/gmd-6-837-2013, https://doi.org/10.5194/gmd-6-837-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Uncertainty analysis
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Hydrol. Earth Syst. Sci., 28, 4099–4126, https://doi.org/10.5194/hess-28-4099-2024, https://doi.org/10.5194/hess-28-4099-2024, 2024
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Tongtiegang Zhao, Zexin Chen, Yu Tian, Bingyao Zhang, Yu Li, and Xiaohong Chen
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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Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-55, https://doi.org/10.5194/hess-2024-55, 2024
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Uwe Ehret and Pankaj Dey
Hydrol. Earth Syst. Sci., 27, 2591–2605, https://doi.org/10.5194/hess-27-2591-2023, https://doi.org/10.5194/hess-27-2591-2023, 2023
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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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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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We adapt the informal Kling–Gupta efficiency (KGE) with a gamma distribution to apply it as an informal likelihood function in the DiffeRential Evolution Adaptive Metropolis DREAM(ZS) method. Our adapted approach performs as well as the formal likelihood function for exploring posterior distributions of model parameters. The adapted KGE is superior to the formal likelihood function for calibrations combining multiple observations with different lengths, frequencies and units.
Silvana Bolaños Chavarría, Micha Werner, Juan Fernando Salazar, and Teresita Betancur Vargas
Hydrol. Earth Syst. Sci., 26, 4323–4344, https://doi.org/10.5194/hess-26-4323-2022, https://doi.org/10.5194/hess-26-4323-2022, 2022
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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
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.
David N. Dralle, Nathaniel J. Karst, Kyriakos Charalampous, Andrew Veenstra, and Sally E. Thompson
Hydrol. Earth Syst. Sci., 21, 65–81, https://doi.org/10.5194/hess-21-65-2017, https://doi.org/10.5194/hess-21-65-2017, 2017
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The streamflow recession is the period following rainfall during which flow declines. This paper examines a common method of recession analysis and identifies sensitivity of the technique's results to necessary, yet subjective, methodological choices. The results have implications for hydrology, sediment and solute transport, and geomorphology, as well as for testing numerous hydrologic theories which predict the mathematical form of the recession.
Simon Paul Seibert, Uwe Ehret, and Erwin Zehe
Hydrol. Earth Syst. Sci., 20, 3745–3763, https://doi.org/10.5194/hess-20-3745-2016, https://doi.org/10.5194/hess-20-3745-2016, 2016
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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
Cited articles
Alewell, C., Borrelli, P., Meusburger, K., and Panagos, P.: Using the USLE:
Chances, challenges and limitations of soil erosion modelling, International
Soil and Water Conservation Research, 7, 203–225,
https://doi.org/10.1016/j.iswcr.2019.05.004, 2019. a, b, c, d
Angima, S. D., Stott, D. E., O'Neill, M. K., Ong, C. K., and Weesies, G. A.:
Soil erosion prediction using RUSLE for central Kenyan highland conditions,
Agriculture, Ecosystems and Environment, 97, 295–308,
https://doi.org/10.1016/S0167-8809(03)00011-2, 2003. a, b
Arnoldus, H. M. J.: An approximation of the rainfall factor in the USLE, in:
Assessment of Erosion, edited by: DeBoodt, M. and Gabriels, D., 127–132,
John Wiley & Sons, Chichester, 1980. a
Bai, Z. G., Dent, D. L., Olsson, L., and Schaepman, M. E.: Proxy global
assessment of land degradation, Soil Use Manage., 24, 223–234,
https://doi.org/10.1111/j.1475-2743.2008.00169.x, 2008. a
Bamutaze, Y.: Revisiting socio-ecological resilience and sustainability in the
coupled mountain landscapes in Eastern Africa, Curr. Opin.
Env. Sust., 14, 257–265,
https://doi.org/10.1016/j.cosust.2015.06.010, 2015. a
Benavidez, R., Jackson, B., Maxwell, D., and Norton, K.: A review of the (Revised) Universal Soil Loss Equation ((R)USLE): with a view to increasing its global applicability and improving soil loss estimates, Hydrol. Earth Syst. Sci., 22, 6059–6086, https://doi.org/10.5194/hess-22-6059-2018, 2018. a, b
Beven, K. and Young, P.: A guide to good practice in modeling semantics for
authors and referees, Water Resour. Res., 49, 5092–5098,
https://doi.org/10.1002/wrcr.20393, 2013. a, b
Bivand, R., Keitt, T., and Rowlingson, B.: rgdal: Bindings for the “Geospatial” Data Abstraction Library, r package version 1.4-3,
available at: https://CRAN.R-project.org/package=rgdal, last access: 11 March 2019. a
Boardman, J.: Soil erosion by water: problems and prospects for research, in:
Advances in Hillslope Processes, edited by: Anderson, M. G. and Brooks, S. M.,
Wiley, Chichester, UK, 489–505, 1996. a
Borrelli, P., Robinson, D. A., Fleischer, L. R., Lugato, E., Ballabio, C.,
Alewell, C., Meusburger, K., Modugno, S., Schütt, B., Ferro, V.,
Bagarello, V., Oost, K. V., Montanarella, L., and Panagos, P.: An assessment
of the global impact of 21st century land use change on soil erosion, Nat.
Commun., 8, 2013, https://doi.org/10.1038/s41467-017-02142-7, 2017. a, b, c, d, e, f, g, h, i, j
Bosco, C., Rigo, D. D., and Dewitte, O.: Visual Validation of the e-RUSLE Model
Applied at the Pan-European Scale, Scientific Topics Focus 1,
MRI-11a13. Notes on Transdisciplinary
Modelling for Environment, Maieutike Research Initiative, Figshare, https://doi.org/10.6084/m9.figshare.844627.v5, 2014. a, b
Browning, G. M., Parish, C. L., and Glass, J. A.: A method for determining the
use of limitations of rotation and conservation practices in the control of
soil erosion in Iowa, J. Am. Soc. Agron., 39,
65–73, 1947. a
Channan, S., Collins, K., and Emanuel, W. R.: Global mosaics of the standard
MODIS land cover type data, University of Maryland and the Pacific Northwest National Laboratory, College Park, Maryland, USA, available at: http://glcf.umd.edu/data/lc/ (last access: 25 June 2018), 2014. a
Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., Wehberg, J., Wichmann, V., and Böhner, J.: System for Automated Geoscientific Analyses (SAGA) v. 2.1.4, Geosci. Model Dev., 8, 1991–2007, https://doi.org/10.5194/gmd-8-1991-2015, 2015. a, b
Didan, K.: MOD13Q1 MODIS/Terra vegetation indices 16-day L3 global 250m SIN
grid V006, NASA EOSDIS Land Processes DAAC, https://doi.org/10.5067/MODIS/MOD13Q1.006,
2015. a, b
Dissmeyer, G. and Foster, G.: A guide for predicting sheet and rill erosion on
forest land, Technical Publication SA-TP-11, USDA Forest Service-State and
Private Forestry Southeastern Area, 1980. a
ESA: ESA Land Cover Climate Change Initiative (ESA LC CCI) data:
ESACCI-LC-L4-LCCS-Map-300m-P1Y-1992_2015‐v2.0.7.tif via Centre for
Environmental Data Analysis, available at: http://maps.elie.ucl.ac.be/CCI (last access: 25 June 2018), 2017. a
Estrada-Carmona, N., Harper, E. B., DeClerck, F., and Fremier, A. K.:
Quantifying model uncertainty to improve watershed-level ecosystem service
quantification: a global sensitivity analysis of the RUSLE, Int. Ecosyst. Serv. Manage., 13, 40–50, https://doi.org/10.1080/21513732.2016.1237383, 2017. a, b
Evans, R.: Some methods of directly assessing water erosion of cultivated land
– a comparison of measurements made on plots and in fields, Prog.
Phys. Geogr., 19, 115–129, https://doi.org/10.1177/030913339501900106, 1995. a, b, c
Evans, R.: An alternative way to assess water erosion of cultivated land
– field-based measurements: and analysis of some results, Appl.
Geogr., 22, 187–207, https://doi.org/10.1016/s0143-6228(02)00004-8, 2002. a
Evans, R.: Assessment and monitoring of accelerated water erosion of cultivated
land – when will reality be acknowledged?, Soil Use Manage., 29,
105–118, https://doi.org/10.1111/sum.12010, 2013. a, b, c
Evans, R. and Boardman, J.: The new assessment of soil loss by water erosion in
Europe. Panagos P. et al., 2015 Environmental Science & Policy 54,
438–447 – A response, Environ. Sci.
Policy, 58, 11–15, https://doi.org/10.1016/j.envsci.2015.12.013, 2016a. a, b
Evans, R. and Boardman, J.: A reply to panagos et al., 2016 (Environmental
science & policy 59 (2016) 53–57, Environ. Sci. Policy, 60,
63–68, https://doi.org/10.1016/j.envsci.2016.03.004, 2016b. a
Falk, M. G., Denham, R. J., and Mengersen, K. L.: Estimating Uncertainty in the
Revised Universal Soil Loss Equation via Bayesian Melding, J.
Agric. Biol. Env. S., 15, 20–37,
https://doi.org/10.1007/s13253-009-0005-y, 2010. a, b, c, d
FAO-PNUMA-UNESCO: Metodología provisional para la evaluación de la
degradación de los suelos, Tech. rep., Organización de las Naciones Unidas
para el Desarrollo de la Agricultura y la Alimentación (FAO), Programa de
las Naciones Unidas para el Medio Ambiente (PNUMA), Organización de las
Naciones para el Medio Ambiente (UNESCO), Roma, Italia, 1980 (in Spanish). a, b, c
Favis-Mortlock, D.: Validation of Field-Scale Soil Erosion Models Using Common
Datasets, in: Modelling Soil Erosion by Water, NATO ASI Series book series,
vol. 55, edited by: Boardman, J. and Favis-Mortlock, D., chap. 9,
Springer Berlin Heidelberg, 89–127, https://doi.org/10.1007/978-3-642-58913-3_9, 1998. a, b, c
Fenta, A. A., Tsunekawa, A., Haregeweyn, N., Poesen, J., Tsubo, M., Borrelli,
P., Panagos, P., Vanmaercke, M., Broeckx, J., Yasuda, H., Kawai, T., and
Kurosaki, Y.: Land susceptibility to water and wind erosion risks in the East
Africa region, Sci. Total Environ., 703, 135016,
https://doi.org/10.1016/j.scitotenv.2019.135016, 2020. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v, w, x, y, z
Ferro, V. and Minacapilli, M.: Sediment delivery processes at basin scale,
Hydrolog. Sci. J., 40, 703–717, https://doi.org/10.1080/02626669509491460,
1995. a
Fick, S. E. and Hijmans, R. J.: WorldClim 2: new 1-km spatial resolution
climate surfaces for global land areas, Int. J.
Climatol., 37, 4302–4315, https://doi.org/10.1002/joc.5086, 2017. a, b
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N.,
Sibley, A., and Huang, X.: MODIS Collection 5 global land cover: Algorithm
refinements and characterization of new datasets, 2001–2012, Collection 5.1
IGBP Land Cover, available at: http://glcf.umd.edu/data/lc/ (last access: 25 June 2018), 2010. a
García-Ruiz, J. M., Beguería, S., Nadal-Romero, E., González-Hidalgo, J. C.,
Lana-Renault, N., and Sanjuán, Y.: A meta-analysis of soil erosion rates
across the world, Geomorphology, 239, 160–173,
https://doi.org/10.1016/j.geomorph.2015.03.008, 2015. a, b, c
Graham, W. R.: Use of Erosion Equations and Sediment-Delivery Ratios for
Predicting Sediment Yield, in: Present and Prospective Technology for
Predicting Sediment Yields and Sources, U.S. Department of
Agriculture, Agricultural Research Service, ARS-S-40, 33–45, 1975. a
Grolemund, G. and Wickham, H.: Dates and Times Made Easy with lubridate,
J. Stat. Softw., 40, 1–25, https://doi.org/10.18637/jss.v040.i03,
2011. a
Hengl, T., De Jesus, J. M., Heuvelink, G. B., Gonzalez, M. R., Kilibarda, M.,
Blagotić, A., Shangguan, W., Wright, M. N., Geng, X.,
Bauer-Marschallinger, B., Guevara, M. A., Vargas, R., MacMillan, R. A.,
Batjes, N. H., Leenaars, J. G., Ribeiro, E., Wheeler, I., Mantel, S., and
Kempen, B.: SoilGrids250m: Global gridded soil information based on machine
learning, PLoS ONE, 12, 1–40, https://doi.org/10.1371/journal.pone.0169748, 2017. a
Henry, L. and Wickham, H.: purrr: Functional Programming Tools, r package version 0.3.2, available at: https://CRAN.R-project.org/package=purrr, last access: 15 March 2019,. a
Hernando, D. and Romana, M. G.: Development of a Soil Erosion Classification
System for Cut and Fill Slopes, Transportation Infrastructure Geotechnology,
2, 155–166, https://doi.org/10.1007/s40515-015-0024-9, 2015. a
Hijmans, R. J.: raster: Geographic Data Analysis and Modeling, r package version 2.9-5, available at: https://CRAN.R-project.org/package=raster, last access: 14 May 2019. a
Huffman, G. J., Bolvin, D. T., Nelkin, E. J., Wolff, D. B., Adler, R. F., Gu,
G., Hong, Y., Bowman, K. P., and Stocker, E. F.: The TRMM Multisatellite
Precipitation Analysis (TMPA): Quasi-Global, Multiyear, Combined-Sensor
Precipitation Estimates at Fine Scales, J. Hydrometeorol., 8,
38–55, https://doi.org/10.1175/jhm560.1, 2007. a
Jagger, P. and Kittner, N.: Deforestation and biomass fuel dynamics in Uganda,
Biomass Bioenerg., 105, 1–9, https://doi.org/10.1016/j.biombioe.2017.06.005, 2017. a
Jetten, V. and Favis-Mortlock, D.: Modelling Soil Erosion in Europe, in: Soil
Erosion in Europe, edited by: Boardman, J. and Poesen, J., chap. 50,
John Wiley & Sons, Ltd, 695–716, https://doi.org/10.1002/0470859202.ch50, 2006. a, b
Jones, A., Breuning-Madsen, H., Brossard, M., Dampha, A., Deckers, J.,
Dewitte, O., Gallali, T., Hallett, S., Jones, R., Kilasara, M., Le Roux,
P., Micheli, E., Spaargaren, O., Thombiano, L., Van Ranst, E., Yemefack, M.,
and Zougmoré, R., (Eds.): Soil Atlas of Africa, European Union Joint
Research Centre, Luxembourg, 176 pp., ISBN 978-92-79-26715-4, 2013. a
Kinnell, P.: Event soil loss, runoff and the Universal Soil Loss Equation
family of models: A review, J. Hydrol., 385, 384–397,
https://doi.org/10.1016/j.jhydrol.2010.01.024, 2010. a, b, c, d
Kithiia, S. M.: Land use changes and their effects on sediment transport and
soil erosion within the Athi drainage basin, Kenya, in: Human Impact on
Erosion and Sedimentation (Proceedings of Rabat Symposium 56, April 1997),
IAHS Publ. no. 245, edited by: Walling, D. E. and Probst, J.-L.,
International Association of Hydrological Sciences, 145–150, 1997. a, b, c, d, e
KNBS: Section agriculture, in: County Statistical Abstracts, Kenya National
Bureau of Statistics, Nairobi, Kenya, 2015. a
LUCAS: Land Use/Cover Area Frame Statistical Survey Database,
available at: http://epp.eurostat.ec.europa.eu/portal/page/portal/lucas/data/LUCAS_primary_data/2012 (last access: 1 April 2019), 2012. a
Lufafa, A., Tenywa, M., Isabirye, M., Majaliwa, M., and Woomer, P.: Prediction
of soil erosion in a Lake Victoria basin catchment using a GIS-based
Universal Soil Loss model, Agr. Syst., 76, 883–894,
https://doi.org/10.1016/s0308-521x(02)00012-4, 2003. a
Maetens, W., Vanmaercke, M., Poesen, J., Jankauskas, B., Jankauskiene, G., and
Ionita, I.: Effects of land use on annual runoff and soil loss in Europe and
the Mediterranean: A meta-analysis of plot data, Prog. Phys.
Geogr.- Earth and Environment, 36, 599–653,
https://doi.org/10.1177/0309133312451303, 2012. a, b
Microsoft Corporation and Weston, S.: doSNOW: Foreach Parallel Adaptor for
the “snow” Package, r package version 1.0.16,
available at: https://CRAN.R-project.org/package=doSNOW (last access: 23 October 2018), 2017a. a
Microsoft Corporation and Weston, S.: foreach: Provides Foreach Looping
Construct for R, r package version 1.4.4, available at: https://CRAN.R-project.org/package=foreach (last access: 23 October 2018), 2017b. a
Montgomery, D. R.: Soil erosion and agricultural sustainability, P.
Natl. Acad. Sci. USA, 104, 13268–13272,
https://doi.org/10.1073/pnas.0611508104, 2007. a, b
Morgan, R. P. C.: Soil erosion and conservation, 3rd Edn., Blackwell Publishing, Oxford, UK, ISBN 1405117818, 2009. a
Musgrave, G. W.: The quantitative evaluation of factors in water erosion, a
first approximation, J. Soil Water Conserv., 2, 133–138,
1947. a
Müller, K. and Wickham, H.: tibble: Simple Data Frames, r package version 2.1.3, available at: https://CRAN.R-project.org/package=tibble (last access: 6 June 2018), 2019. a
Müller, K., Wickham, H., James, D. A., and Falcon, S.: RSQLite: “SQLite”
Interface for R, r package version 2.1.1, available at: https://CRAN.R-project.org/package=RSQLite, last access: 24 April 2018. a
NASA/METI/AIST/Japan Spacesystems, and U.S./Japan ASTER Science Team: ASTER
Global Digital Elevation Model, NASA, https://doi.org/10.5067/ASTER/ASTGTM.002, 2009. a
Nearing, M.: Why soil erosion models over-predict small soil losses and
under-predict large soil losses, CATENA, 32, 15–22,
https://doi.org/10.1016/S0341-8162(97)00052-0, 1998. a
Olivares, R. U., Bulos, A. D. M., and Sombrito, E. Z.: Environmental assessment
of soil erosion in Inabanga watershed (Bohol, Philippines), Energy, Ecology
and Environment, 1, 98–108, https://doi.org/10.1007/s40974-016-0012-0, 2016. a
Panagos, P., Meusburger, K., Ballabio, C., Borrelli, P., and Alewell, C.: Soil
erodibility in Europe: A high-resolution dataset based on LUCAS, Sci.
Total Environ., 479–480, 189–200,
https://doi.org/10.1016/j.scitotenv.2014.02.010, 2014. a, b, c
Panagos, P., Ballabio, C., Borrelli, P., Meusburger, K., Klik, A., Rousseva,
S., Tadić, M. P., Michaelides, S., Hrabalíková, M., Olsen,
P., Aalto, J., Lakatos, M., Rymszewicz, A., Dumitrescu, A., Beguería,
S., and Alewell, C.: Rainfall erosivity in Europe, Sci. Total
Environ., 511, 801–814, https://doi.org/10.1016/j.scitotenv.2015.01.008,
2015a. a, b, c
Panagos, P., Borrelli, P., and Meusburger, K.: A New European Slope Length and
Steepness Factor (LS-Factor) for Modeling Soil Erosion by Water,
Geosciences, 5, 117–126, https://doi.org/10.3390/geosciences5020117,
2015b. a, b, c
Panagos, P., Borrelli, P., Meusburger, K., van der Zanden, E. H., Poesen, J.,
and Alewell, C.: Modelling the effect of support practices (P-factor) on the
reduction of soil erosion by water at European scale, Environ. Sci. Policy, 51, 23–34, https://doi.org/10.1016/j.envsci.2015.03.012,
2015d. a, b
Panagos, P., Borrelli, P., Poesen, J., Meusburger, K., Ballabio, C., Lugato,
E., Montanarella, L., and Alewell, C.: Reply to “The new
assessment of soil loss by water erosion in Europe. Panagos P. et al., 2015
Environ. Sci. Policy 54, 438–447 – A
response” by Evans and Boardman [Environ. Sci. Policy 58,
11–15], Environ. Sci. Policy, 59, 53–57,
https://doi.org/10.1016/j.envsci.2016.02.010, 2016. a
Panagos, P., Borrelli, P., Meusburger, K., Yu, B., Klik, A., Jae Lim, K.,
Yang, J. E., Ni, J., Miao, C., Chattopadhyay, N., Sadeghi, S. H., Hazbavi,
Z., Zabihi, M., Larionov, G. A., Krasnov, S. F., Gorobets, A. V., Levi, Y.,
Erpul, G., Birkel, C., Hoyos, N., Naipal, V., Oliveira, P. T. S., Bonilla,
C. A., Meddi, M., Nel, W., Al Dashti, H., Boni, M., Diodato, N., Van
Oost, K., Nearing, M., and Ballabio, C.: Global rainfall erosivity
assessment based on high-temporal resolution rainfall records, Sci.
Rep.-UK, 7, 4175, https://doi.org/10.1038/s41598-017-04282-8, 2017. a, b, c, d, e
Pebesma, E.: Simple Features for R: Standardized Support for Spatial Vector
Data, The R Journal, 10, 439–446, https://doi.org/10.32614/RJ-2018-009, 2018. a
Petursson, J. G., Vedeld, P., and Sassen, M.: An institutional analysis of
deforestation processes in protected areas: The case of the transboundary Mt.
Elgon, Uganda and Kenya, Forest Policy and Econ., 26, 22–33,
https://doi.org/10.1016/j.forpol.2012.09.012, 2013. a
Prasuhn, V., Liniger, H., Gisler, S., Herweg, K., Candinas, A., and
Clément, J.-P.: A high-resolution soil erosion risk map of Switzerland
as strategic policy support system, Land Use Policy, 32, 281–291,
https://doi.org/10.1016/j.landusepol.2012.11.006, 2013. a
Rajbanshi, J. and Bhattacharya, S.: Assessment of soil erosion, sediment yield
and basin specific controlling factors using RUSLE-SDR and PLSR approach in
Konar river basin, India, J. Hydrol., 587, 124935,
https://doi.org/10.1016/j.jhydrol.2020.124935, 2020. a
Renard, K. G. and Freimund, J. R.: Using monthly precipitation data to
estimate the R-factor in the revised USLE, J. Hydrol., 157,
287–306, https://doi.org/10.1016/0022-1694(94)90110-4, 1994. a, b
Risse, L. M., Nearing, M. A., Laflen, J. M., and Nicks, A. D.: Error Assessment in the Universal Soil Loss Equation, Soil Sci. Soc. Am. J., 57, 825–833, https://doi.org/10.2136/sssaj1993.03615995005700030032x, 1993. a, b, c
Ross, N.: fasterize: Fast Polygon to Raster Conversion, r package version 1.0.0, available at: https://CRAN.R-project.org/package=fasterize (last access: 18 May 2019), 2018. a
Shangguan, W., Dai, Y., Duan, Q., Liu, B., and Yuan, H.: A global soil data
set for earth system modeling, J. Adv. Model. Earth
Sy., 6, 249–263, https://doi.org/10.1002/2013MS000293, 2014. a
Shin, G. J.: The analysis of soil erosion analysis in watershed using GIS,
Ph.D. thesis, Doctoral Dissertation, Department of Civil Engineering,
Gang-won National University, 1999. a
Smith, D. D.: Interpretation of soil conservation data for field use,
Agr. Eng., 22, 173–175, 1941. a
Sonneveld, B. and Nearing, M.: A nonparametric/parametric analysis of the
Universal Soil Loss Equation, CATENA, 52, 9–21,
https://doi.org/10.1016/S0341-8162(02)00150-9, 2003. a, b, c
Spaeth, K. E., Pierson, F. B., Weltz, M. A., and Blackburn, W. H.: Evaluation
of USLE and RUSLE Estimated Soil Loss on Rangeland, J. Range
Manage., 56, 234–246, https://doi.org/10.2307/4003812, 2003. a
Tamene, L. and Le, Q. B.: Estimating soil erosion in sub-Saharan Africa based
on landscape similarity mapping and using the revised universal soil loss
equation (RUSLE), Nutr. Cycl. Agroecosys., 102, 17–31,
https://doi.org/10.1007/s10705-015-9674-9, 2015. a, b, c
Tiwari, A. K., Risse, L. M., and Nearing, M. A.: Evaluation of WEPP and its
comparison with USLE and RUSLE, T. ASAE, 43,
1129–1135, https://doi.org/10.13031/2013.3005, 2000. a
UBOS: Volume IV: Crop Area and Production Report, in: Uganda Census of
Agriculture 2008/2009, Uganda Bureau of Statistics, Kampala, Uganda, p. 178,
2010. a
Van-Camp, L., Bujarrabal, B., Gentile, A. R., Jones, R. J., Montanarella, L.,
Olazabal, C., and Selvaradjou, S.-K.: Reports of the Technical Working Groups
Established under the Thematic Strategy for Soil Protection, EUR 21319 EN/6,
Tech. rep., Office for Official Publications of the European
Communities, Luxembourg, 872 pp., 2004. a
Vanmaercke, M., Poesen, J., Broeckx, J., and Nyssen, J.: Sediment yield in
Africa, Earth-Sci. Rev., 136, 350–368,
https://doi.org/10.1016/j.earscirev.2014.06.004, 2014. a, b
Vrieling, A., Sterk, G., and de Jong, S. M.: Satellite-based estimation of
rainfall erosivity for Africa, J. Hydrol., 395, 235–241,
https://doi.org/10.1016/j.jhydrol.2010.10.035, 2010. a, b, c
Vrieling, A., Hoedjes, J. C. B., and van der Velde, M.: Towards large-scale
monitoring of soil erosion in Africa: Accounting for the dynamics of rainfall
erosivity, Global Planet. Change, 115, 33–43,
https://doi.org/10.1016/j.gloplacha.2014.01.009, 2014. a, b, c
Walsh, R. P. D. and Lawler, D. M.: Rainfall seasonality: description, spatial
patterns and change through time, Weather, 36, 201–208,
https://doi.org/10.1002/j.1477-8696.1981.tb05400.x, 1981. a, b
Warren, S. D., Mitasova, H., Hohmann, M. G., Landsberger, S., Iskander, F. Y.,
Ruzycki, T. S., and Senseman, G. M.: Validation of a 3-D enhancement of the
Universal Soil Loss Equation for prediction of soil erosion and sediment
deposition, CATENA, 64, 281–296, https://doi.org/10.1016/j.catena.2005.08.010, 2005. a
Weltz, M. A., Kidwell, M. R., and Fox, H. D.: Influence of abiotic and biotic
factors in measuring and modeling soil erosion on rangelands: state of
knowledge, Rangeland Ecology & Management/Journal of Range Management
Archives, 51, 482–495, 1998. a
Wickham, H.: forcats: Tools for Working with Categorical Variables (Factors),
r package version 0.4.0, available at: https://CRAN.R-project.org/package=forcats, last access: 17 February 2019. a
Wickham, H. and Henry, L.: tidyr: Easily Tidy Data with “spread()” and
“gather()” Functions, r package version 0.8.3, available at:https://CRAN.R-project.org/package=tidyr, last access: 2 March 2019. a
Wickham, H. and Ruiz, E.: dbplyr: A “dplyr” Back End for Databases,
r package version 1.4.0, available at: https://CRAN.R-project.org/package=dbplyr, last access: 24 April 2019.
a
Wickham, H., Chang, W., Henry, L., Pedersen, T. L., Takahashi, K., Wilke, C.,
Woo, K., and Yutani, H.: ggplot2: Create Elegant Data Visualisations Using
the Grammar of Graphics, r package version 3.1.1, available at: http://ggplot2.tidyverse.org (last access: 11 April 2019), 2019a. a
Wickham, H., François, R., Henry, L., and Müller, K.: dplyr: A Grammar of
Data Manipulation, r package version 0.8.1, available at: https://CRAN.R-project.org/package=dplyr (last access: 17 May 2019), 2019b. a
Young, P. C.: Data-based Mechanistic Modelling and Validation of Rainfall-Flow
Processes, in: Model Validation: Perspectives in Hydrological Science, edited
by: Anderson, M. G. and Bates, P., John Wiley, Chichester, 117–161, 2001. a
Zhang, R., Liu, X., Heathman, G. C., Yao, X., Hu, X., and Zhang, G.: Assessment
of soil erosion sensitivity and analysis of sensitivity factors in the
Tongbai–Dabie mountainous area of China, CATENA, 101, 92–98,
https://doi.org/10.1016/j.catena.2012.10.008, 2013. a
Zingg, A. W.: Degree and length of land slope as it affects soil loss in
run-off., Agr. Eng., 21, 59–64, 1940. a
Short summary
The USLE is a commonly used model to estimate soil erosion by water. It quantifies soil loss as a product of six inputs representing rainfall erosivity, soil erodibility, slope length and steepness, plant cover, and support practices. Many methods exist to derive these inputs, which can, however, lead to substantial differences in the estimated soil loss. Here, we analyze the effect of different input representations on the estimated soil loss in a large-scale study in Kenya and Uganda.
The USLE is a commonly used model to estimate soil erosion by water. It quantifies soil loss as...
