Articles | Volume 24, issue 6
https://doi.org/10.5194/hess-24-2873-2020
© 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-24-2873-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| Highlight paper
| 
02 Jun 2020
Research article | Highlight paper |
| 02 Jun 2020
| 02 Jun 2020
Linking economic and social factors to peak flows in an agricultural watershed using socio-hydrologic modeling
David Dziubanski
CORRESPONDING AUTHOR
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA, USA
Kristie J. Franz
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA, USA
William Gutowski
Department of Geological and Atmospheric Sciences, Iowa State University, Ames, IA, USA
Related authors
No articles found.
Koichi Sakaguchi, L. Ruby Leung, Colin M. Zarzycki, Jihyeon Jang, Seth McGinnis, Bryce E. Harrop, William C. Skamarock, Andrew Gettelman, Chun Zhao, William J. Gutowski, Stephen Leak, and Linda Mearns
Geosci. Model Dev., 16, 3029–3081, https://doi.org/10.5194/gmd-16-3029-2023, https://doi.org/10.5194/gmd-16-3029-2023, 2023
Short summary
Short summary
We document details of the regional climate downscaling dataset produced by a global variable-resolution model. The experiment is unique in that it follows a standard protocol designed for coordinated experiments of regional models. We found negligible influence of post-processing on statistical analysis, importance of simulation quality outside of the target region, and computational challenges that our model code faced due to rapidly changing super computer systems.
Michael A. Brunke, John J. Cassano, Nicholas Dawson, Alice K. DuVivier, William J. Gutowski Jr., Joseph Hamman, Wieslaw Maslowski, Bart Nijssen, J. E. Jack Reeves Eyre, José C. Renteria, Andrew Roberts, and Xubin Zeng
Geosci. Model Dev., 11, 4817–4841, https://doi.org/10.5194/gmd-11-4817-2018, https://doi.org/10.5194/gmd-11-4817-2018, 2018
Short summary
Short summary
The Regional Arctic System Model version 1 (RASM1) was recently developed for high-resolution simulation of the coupled atmosphere–ocean–sea ice–land system in the Arctic. Its simulation of the atmosphere–land–ocean–sea ice interface is evaluated by using the spread in recent reanalyses and a global Earth system model as baselines. Such comparisons reveal that RASM1 simulates precipitation well and improves the simulation of surface fluxes over sea ice.
William J. Gutowski Jr., Filippo Giorgi, Bertrand Timbal, Anne Frigon, Daniela Jacob, Hyun-Suk Kang, Krishnan Raghavan, Boram Lee, Christopher Lennard, Grigory Nikulin, Eleanor O'Rourke, Michel Rixen, Silvina Solman, Tannecia Stephenson, and Fredolin Tangang
Geosci. Model Dev., 9, 4087–4095, https://doi.org/10.5194/gmd-9-4087-2016, https://doi.org/10.5194/gmd-9-4087-2016, 2016
Short summary
Short summary
The Coordinated Regional Downscaling Experiment (CORDEX) is a diagnostic MIP in CMIP6. CORDEX builds on a foundation of previous downscaling intercomparison projects to provide a common framework for downscaling activities around the world. The CORDEX Regional Challenges provide a focus for downscaling research and a basis for making use of CMIP6 global output to produce downscaled projected changes in regional climates, and assess sources of uncertainties in the projections.
J. Y. Wu, J. R. Thompson, R. K. Kolka, K. J. Franz, and T. W. Stewart
Hydrol. Earth Syst. Sci., 17, 4743–4758, https://doi.org/10.5194/hess-17-4743-2013, https://doi.org/10.5194/hess-17-4743-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
When best is the enemy of good – critical evaluation of performance criteria in hydrological models
The suitability of differentiable, physics-informed machine learning hydrologic models for ungauged regions and climate change impact assessment
Producing reliable hydrologic scenarios from raw climate model outputs without resorting to meteorological observations
Using normalised difference infrared index patterns to constrain semi-distributed rainfall–runoff models in tropical nested catchments
Revisiting the hydrological basis of the Budyko framework with the principle of hydrologically similar groups
Reconstructing five decades of sediment export from two glacierized high-alpine catchments in Tyrol, Austria, using nonparametric regression
Water and energy budgets over hydrological basins on short and long timescales
Hydrological response to climate change and human activities in the Three-River Source Region
Incorporating experimentally derived streamflow contributions into model parameterization to improve discharge prediction
Machine-learning- and deep-learning-based streamflow prediction in a hilly catchment for future scenarios using CMIP6 GCM data
River hydraulic modeling with ICESat-2 land and water surface elevation
Hydrological modeling using the Soil and Water Assessment Tool in urban and peri-urban environments: the case of Kifisos experimental subbasin (Athens, Greece)
Technical note: How physically based is hydrograph separation by recursive digital filtering?
A comprehensive open-source course for teaching applied hydrological modelling in Central Asia
Impact of distributed meteorological forcing on simulated snow cover and hydrological fluxes over a mid-elevation alpine micro-scale catchment
Technical note: Extending the SWAT model to transport chemicals through tile and groundwater flow
Long-term reconstruction of satellite-based precipitation, soil moisture, and snow water equivalent in China
Disentangling scatter in long-term concentration–discharge relationships: the role of event types
Can the combining of wetlands with reservoir operation largely reduce the risk of future flood and droughts?
Simulating the hydrological impacts of land use conversion from annual crop to perennial forage in the Canadian Prairies using the Cold Regions Hydrological Modelling platform
How can we benefit from regime information to make more effective use of long short-term memory (LSTM) runoff models?
On the value of satellite remote sensing to reduce uncertainties of regional simulations of the Colorado River
Assessing runoff sensitivity of North American Prairie Pothole Region basins to wetland drainage using a basin classification-based virtual modelling approach
A large-sample investigation into uncertain climate change impacts on high flows across Great Britain
Effects of passive-storage conceptualization on modeling hydrological function and isotope dynamics in the flow system of a cockpit karst landscape
Technical note: Data assimilation and autoregression for using near-real-time streamflow observations in long short-term memory networks
Attribution of climate change and human activities to streamflow variations with a posterior distribution of hydrological simulations
A time-varying distributed unit hydrograph method considering soil moisture
Flood patterns in a catchment with mixed bedrock geology and a hilly landscape: identification of flashy runoff contributions during storm events
A graph neural network (GNN) approach to basin-scale river network learning: the role of physics-based connectivity and data fusion
Improving hydrologic models for predictions and process understanding using neural ODEs
Response of active catchment water storage capacity to a prolonged meteorological drought and asymptotic climate variation
HESS Opinions: Participatory Digital eARth Twin Hydrology systems (DARTHs) for everyone – a blueprint for hydrologists
Development of a national 7-day ensemble streamflow forecasting service for Australia
Future snow changes and their impact on the upstream runoff in Salween
Technical note: Do different projections matter for the Budyko framework?
Representation of seasonal land use dynamics in SWAT+ for improved assessment of blue and green water consumption
Large-sample assessment of varying spatial resolution on the streamflow estimates of the wflow_sbm hydrological model
An algorithm for deriving the topology of belowground urban stormwater networks
Assessing the influence of water sampling strategy on the performance of tracer-aided hydrological modeling in a mountainous basin on the Tibetan Plateau
Flood forecasting with machine learning models in an operational framework
Precipitation fate and transport in a Mediterranean catchment through models calibrated on plant and stream water isotope data
Knowledge-Informed Deep Learning for Hydrological Model Calibration: An Application to Coal Creek Watershed in Colorado
High-resolution satellite products improve hydrological modeling in northern Italy
Uncertainty in water transit time estimation with StorAge Selection functions and tracer data interpolation
Analysis of high streamflow extremes in climate change studies: how do we calibrate hydrological models?
A conceptual-model-based sediment connectivity assessment for patchy agricultural catchments
The Great Lakes Runoff Intercomparison Project Phase 4: the Great Lakes (GRIP-GL)
Spatial extrapolation of stream thermal peaks using heterogeneous time series at a national scale
Revisiting parameter sensitivities in the variable infiltration capacity model across a hydroclimatic gradient
Guillaume Cinkus, Naomi Mazzilli, Hervé Jourde, Andreas Wunsch, Tanja Liesch, Nataša Ravbar, Zhao Chen, and Nico Goldscheider
Hydrol. Earth Syst. Sci., 27, 2397–2411, https://doi.org/10.5194/hess-27-2397-2023, https://doi.org/10.5194/hess-27-2397-2023, 2023
Short summary
Short summary
The Kling–Gupta Efficiency (KGE) is a performance criterion extensively used to evaluate hydrological models. We conduct a critical study on the KGE and its variant to examine counterbalancing errors. Results show that, when assessing a simulation, concurrent over- and underestimation of discharge can lead to an overall higher criterion score without an associated increase in model relevance. We suggest that one carefully choose performance criteria and use scaling factors.
Dapeng Feng, Hylke Beck, Kathryn Lawson, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 27, 2357–2373, https://doi.org/10.5194/hess-27-2357-2023, https://doi.org/10.5194/hess-27-2357-2023, 2023
Short summary
Short summary
Powerful hybrid models (called δ or delta models) embrace the fundamental learning capability of AI and can also explain the physical processes. Here we test their performance when applied to regions not in the training data. δ models rivaled the accuracy of state-of-the-art AI models under the data-dense scenario and even surpassed them for the data-sparse one. They generalize well due to the physical structure included. δ models could be ideal candidates for global hydrologic assessment.
Simon Ricard, Philippe Lucas-Picher, Antoine Thiboult, and François Anctil
Hydrol. Earth Syst. Sci., 27, 2375–2395, https://doi.org/10.5194/hess-27-2375-2023, https://doi.org/10.5194/hess-27-2375-2023, 2023
Short summary
Short summary
A simplified hydroclimatic modelling workflow is proposed to quantify the impact of climate change on water discharge without resorting to meteorological observations. Results confirm that the proposed workflow produces equivalent projections of the seasonal mean flows in comparison to a conventional hydroclimatic modelling approach. The proposed approach supports the participation of end-users in interpreting the impact of climate change on water resources.
Nutchanart Sriwongsitanon, Wasana Jandang, James Williams, Thienchart Suwawong, Ekkarin Maekan, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 27, 2149–2171, https://doi.org/10.5194/hess-27-2149-2023, https://doi.org/10.5194/hess-27-2149-2023, 2023
Short summary
Short summary
We developed predictive semi-distributed rainfall–runoff models for nested sub-catchments in the upper Ping basin, which yielded better or similar performance compared to calibrated lumped models. The normalised difference infrared index proves to be an effective proxy for distributed root zone moisture capacity over sub-catchments and is well correlated with the percentage of evergreen forest. In validation, soil moisture simulations appeared to be highly correlated with the soil wetness index.
Yuchan Chen, Xiuzhi Chen, Meimei Xue, Chuanxun Yang, Wei Zheng, Jun Cao, Wenting Yan, and Wenping Yuan
Hydrol. Earth Syst. Sci., 27, 1929–1943, https://doi.org/10.5194/hess-27-1929-2023, https://doi.org/10.5194/hess-27-1929-2023, 2023
Short summary
Short summary
This study addresses the quantification and estimation of the watershed-characteristic-related parameter (Pw) in the Budyko framework with the principle of hydrologically similar groups. The results show that Pw is closely related to soil moisture and fractional vegetation cover, and the relationship varies across specific hydrologic similarity groups. The overall satisfactory performance of the Pw estimation model improves the applicability of the Budyko framework for global runoff estimation.
Lena Katharina Schmidt, Till Francke, Peter Martin Grosse, Christoph Mayer, and Axel Bronstert
Hydrol. Earth Syst. Sci., 27, 1841–1863, https://doi.org/10.5194/hess-27-1841-2023, https://doi.org/10.5194/hess-27-1841-2023, 2023
Short summary
Short summary
We present a suitable method to reconstruct sediment export from decadal records of hydroclimatic predictors (discharge, precipitation, temperature) and shorter suspended sediment measurements. This lets us fill the knowledge gap on how sediment export from glacierized high-alpine areas has responded to climate change. We find positive trends in sediment export from the two investigated nested catchments with step-like increases around 1981 which are linked to crucial changes in glacier melt.
Samantha Petch, Bo Dong, Tristan Quaife, Robert P. King, and Keith Haines
Hydrol. Earth Syst. Sci., 27, 1723–1744, https://doi.org/10.5194/hess-27-1723-2023, https://doi.org/10.5194/hess-27-1723-2023, 2023
Short summary
Short summary
Gravitational measurements of water storage from GRACE (Gravity Recovery and Climate Experiment) can improve understanding of the water budget. We produce flux estimates over large river catchments based on observations that close the monthly water budget and ensure consistency with GRACE on short and long timescales. We use energy data to provide additional constraints and balance the long-term energy budget. These flux estimates are important for evaluating climate models.
Ting Su, Chiyuan Miao, Qingyun Duan, Jiaojiao Gou, Xiaoying Guo, and Xi Zhao
Hydrol. Earth Syst. Sci., 27, 1477–1492, https://doi.org/10.5194/hess-27-1477-2023, https://doi.org/10.5194/hess-27-1477-2023, 2023
Short summary
Short summary
The Three-River Source Region (TRSR) plays an extremely important role in water resources security and ecological and environmental protection in China and even all of Southeast Asia. This study used the variable infiltration capacity (VIC) land surface hydrologic model linked with the degree-day factor algorithm to simulate the runoff change in the TRSR. These results will help to guide current and future regulation and management of water resources in the TRSR.
Andreas Hartmann, Jean-Lionel Payeur-Poirier, and Luisa Hopp
Hydrol. Earth Syst. Sci., 27, 1325–1341, https://doi.org/10.5194/hess-27-1325-2023, https://doi.org/10.5194/hess-27-1325-2023, 2023
Short summary
Short summary
We advance our understanding of including information derived from environmental tracers into hydrological modeling. We present a simple approach that integrates streamflow observations and tracer-derived streamflow contributions for model parameter estimation. We consider multiple observed streamflow components and their variation over time to quantify the impact of their inclusion for streamflow prediction at the catchment scale.
Dharmaveer Singh, Manu Vardhan, Rakesh Sahu, Debrupa Chatterjee, Pankaj Chauhan, and Shiyin Liu
Hydrol. Earth Syst. Sci., 27, 1047–1075, https://doi.org/10.5194/hess-27-1047-2023, https://doi.org/10.5194/hess-27-1047-2023, 2023
Short summary
Short summary
This study examines, for the first time, the potential of various machine learning models in streamflow prediction over the Sutlej River basin (rainfall-dominated zone) in western Himalaya during the period 2041–2070 (2050s) and 2071–2100 (2080s) and its relationship to climate variability. The mean ensemble of the model results shows that the mean annual streamflow of the Sutlej River is expected to rise between the 2050s and 2080s by 0.79 to 1.43 % for SSP585 and by 0.87 to 1.10 % for SSP245.
Monica Coppo Frias, Suxia Liu, Xingguo Mo, Karina Nielsen, Heidi Ranndal, Liguang Jiang, Jun Ma, and Peter Bauer-Gottwein
Hydrol. Earth Syst. Sci., 27, 1011–1032, https://doi.org/10.5194/hess-27-1011-2023, https://doi.org/10.5194/hess-27-1011-2023, 2023
Short summary
Short summary
This paper uses remote sensing data from ICESat-2 to calibrate a 1D hydraulic model. With the model, we can make estimations of discharge and water surface elevation, which are important indicators in flooding risk assessment. ICESat-2 data give an added value, thanks to the 0.7 m resolution, which allows the measurement of narrow river streams. In addition, ICESat-2 provides measurements on the river dry portion geometry that can be included in the model.
Evgenia Koltsida, Nikos Mamassis, and Andreas Kallioras
Hydrol. Earth Syst. Sci., 27, 917–931, https://doi.org/10.5194/hess-27-917-2023, https://doi.org/10.5194/hess-27-917-2023, 2023
Short summary
Short summary
Daily and hourly rainfall observations were inputted to a Soil and Water Assessment Tool (SWAT) hydrological model to investigate the impacts of rainfall temporal resolution on a discharge simulation. Results indicated that groundwater flow parameters were more sensitive to daily time intervals, and channel routing parameters were more influential for hourly time intervals. This study suggests that the SWAT model appears to be a reliable tool to predict discharge in a mixed-land-use basin.
Klaus Eckhardt
Hydrol. Earth Syst. Sci., 27, 495–499, https://doi.org/10.5194/hess-27-495-2023, https://doi.org/10.5194/hess-27-495-2023, 2023
Short summary
Short summary
An important hydrological issue is to identify components of streamflow that react to precipitation with different degrees of attenuation and delay. From the multitude of methods that have been developed for this so-called hydrograph separation, a specific, frequently used one is singled out here. It is shown to be derived from plausible physical principles. This increases confidence in its results.
Beatrice Sabine Marti, Aidar Zhumabaev, and Tobias Siegfried
Hydrol. Earth Syst. Sci., 27, 319–330, https://doi.org/10.5194/hess-27-319-2023, https://doi.org/10.5194/hess-27-319-2023, 2023
Short summary
Short summary
Numerical modelling is often used for climate impact studies in water resources management. It is, however, not yet highly accessible to many students of hydrology in Central Asia. One big hurdle for new learners is the preparation of relevant data prior to the actual modelling. We present a robust, open-source workflow and comprehensive teaching material that can be used by teachers and by students for self study.
Aniket Gupta, Alix Reverdy, Jean-Martial Cohard, Basile Hector, Marc Descloitres, Jean-Pierre Vandervaere, Catherine Coulaud, Romain Biron, Lucie Liger, Reed Maxwell, Jean-Gabriel Valay, and Didier Voisin
Hydrol. Earth Syst. Sci., 27, 191–212, https://doi.org/10.5194/hess-27-191-2023, https://doi.org/10.5194/hess-27-191-2023, 2023
Short summary
Short summary
Patchy snow cover during spring impacts mountainous ecosystems on a large range of spatio-temporal scales. A hydrological model simulated such snow patchiness at 10 m resolution. Slope and orientation controls precipitation, radiation, and wind generate differences in snowmelt, subsurface storage, streamflow, and evapotranspiration. The snow patchiness increases the duration of the snowmelt to stream and subsurface storage, which sustains the plants and streamflow later in the summer.
Hendrik Rathjens, Jens Kiesel, Michael Winchell, Jeffrey Arnold, and Robin Sur
Hydrol. Earth Syst. Sci., 27, 159–167, https://doi.org/10.5194/hess-27-159-2023, https://doi.org/10.5194/hess-27-159-2023, 2023
Short summary
Short summary
The SWAT model can simulate the transport of water-soluble chemicals through the landscape but neglects the transport through groundwater or agricultural tile drains. These transport pathways are, however, important to assess the amount of chemicals in streams. We added this capability to the model, which significantly improved the simulation. The representation of all transport pathways in the model enables watershed managers to develop robust strategies for reducing chemicals in streams.
Wencong Yang, Hanbo Yang, Changming Li, Taihua Wang, Ziwei Liu, Qingfang Hu, and Dawen Yang
Hydrol. Earth Syst. Sci., 26, 6427–6441, https://doi.org/10.5194/hess-26-6427-2022, https://doi.org/10.5194/hess-26-6427-2022, 2022
Short summary
Short summary
We produced a daily 0.1° dataset of precipitation, soil moisture, and snow water equivalent in 1981–2017 across China via reconstructions. The dataset used global background data and local on-site data as forcing input and satellite-based data as reconstruction benchmarks. This long-term high-resolution national hydrological dataset is valuable for national investigations of hydrological processes.
Felipe A. Saavedra, Andreas Musolff, Jana von Freyberg, Ralf Merz, Stefano Basso, and Larisa Tarasova
Hydrol. Earth Syst. Sci., 26, 6227–6245, https://doi.org/10.5194/hess-26-6227-2022, https://doi.org/10.5194/hess-26-6227-2022, 2022
Short summary
Short summary
Nitrate contamination of rivers from agricultural sources is a challenge for water quality management. During runoff events, different transport paths within the catchment might be activated, generating a variety of responses in nitrate concentration in stream water. Using nitrate samples from 184 German catchments and a runoff event classification, we show that hydrologic connectivity during runoff events is a key control of nitrate transport from catchments to streams in our study domain.
Yanfeng Wu, Jingxuan Sun, Boting Hu, Y. Jun Xu, Alain N. Rousseau, and Guangxin Zhang
EGUsphere, https://doi.org/10.5194/egusphere-2022-1103, https://doi.org/10.5194/egusphere-2022-1103, 2022
Short summary
Short summary
Reservoirs and wetlands are important regulators of watershed hydrology, which should be considered when projecting floods and droughts. We developed a semi-spatially integration modeling framework that coupling wetland modules and reservoir operation into basin hydrological simulation. We applied the framework in a large river basin and found that the risk of future floods and droughts will overall increase further even under the combined influence of reservoirs and wetlands.
Marcos R. C. Cordeiro, Kang Liang, Henry F. Wilson, Jason Vanrobaeys, David A. Lobb, Xing Fang, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 26, 5917–5931, https://doi.org/10.5194/hess-26-5917-2022, https://doi.org/10.5194/hess-26-5917-2022, 2022
Short summary
Short summary
This study addresses the issue of increasing interest in the hydrological impacts of converting cropland to perennial forage cover in the Canadian Prairies. By developing customized models using the Cold Regions Hydrological Modelling (CRHM) platform, this long-term (1992–2013) modelling study is expected to provide stakeholders with science-based information regarding the hydrological impacts of land use conversion from annual crop to perennial forage cover in the Canadian Prairies.
Reyhaneh Hashemi, Pierre Brigode, Pierre-André Garambois, and Pierre Javelle
Hydrol. Earth Syst. Sci., 26, 5793–5816, https://doi.org/10.5194/hess-26-5793-2022, https://doi.org/10.5194/hess-26-5793-2022, 2022
Short summary
Short summary
Hydrologists have long dreamed of a tool that could adequately predict runoff in catchments. Data-driven long short-term memory (LSTM) models appear very promising to the hydrology community in this respect. Here, we have sought to benefit from traditional practices in hydrology to improve the effectiveness of LSTM models. We discovered that one LSTM parameter has a hydrologic interpretation and that there is a need to increase the data and to tune two parameters, thereby improving predictions.
Mu Xiao, Giuseppe Mascaro, Zhaocheng Wang, Kristen M. Whitney, and Enrique R. Vivoni
Hydrol. Earth Syst. Sci., 26, 5627–5646, https://doi.org/10.5194/hess-26-5627-2022, https://doi.org/10.5194/hess-26-5627-2022, 2022
Short summary
Short summary
As the major water resource in the southwestern United States, the Colorado River is experiencing decreases in naturalized streamflow and is predicted to face severe challenges under future climate scenarios. Here, we demonstrate the value of Earth observing satellites to improve and build confidence in the spatiotemporal simulations from regional hydrologic models for assessing the sensitivity of the Colorado River to climate change and supporting regional water managers.
Christopher Spence, Zhihua He, Kevin R. Shook, John W. Pomeroy, Colin J. Whitfield, and Jared D. Wolfe
Hydrol. Earth Syst. Sci., 26, 5555–5575, https://doi.org/10.5194/hess-26-5555-2022, https://doi.org/10.5194/hess-26-5555-2022, 2022
Short summary
Short summary
We learnt how streamflow from small creeks could be altered by wetland removal in the Canadian Prairies, where this practice is pervasive. Every creek basin in the region was placed into one of seven groups. We selected one of these groups and used its traits to simulate streamflow. The model worked well enough so that we could trust the results even if we removed the wetlands. Wetland removal did not change low flow amounts very much, but it doubled high flow and tripled average flow.
Rosanna A. Lane, Gemma Coxon, Jim Freer, Jan Seibert, and Thorsten Wagener
Hydrol. Earth Syst. Sci., 26, 5535–5554, https://doi.org/10.5194/hess-26-5535-2022, https://doi.org/10.5194/hess-26-5535-2022, 2022
Short summary
Short summary
This study modelled the impact of climate change on river high flows across Great Britain (GB). Generally, results indicated an increase in the magnitude and frequency of high flows along the west coast of GB by 2050–2075. In contrast, average flows decreased across GB. All flow projections contained large uncertainties; the climate projections were the largest source of uncertainty overall but hydrological modelling uncertainties were considerable in some regions.
Guangxuan Li, Xi Chen, Zhicai Zhang, Lichun Wang, and Chris Soulsby
Hydrol. Earth Syst. Sci., 26, 5515–5534, https://doi.org/10.5194/hess-26-5515-2022, https://doi.org/10.5194/hess-26-5515-2022, 2022
Short summary
Short summary
We developed a coupled flow–tracer model to understand the effects of passive storage on modeling hydrological function and isotope dynamics in a karst flow system. Models with passive storages show improvement in matching isotope dynamics performance, and the improved performance also strongly depends on the number and location of passive storages. Our results also suggested that the solute transport is primarily controlled by advection and hydrodynamic dispersion in the steep hillslope unit.
Grey S. Nearing, Daniel Klotz, Jonathan M. Frame, Martin Gauch, Oren Gilon, Frederik Kratzert, Alden Keefe Sampson, Guy Shalev, and Sella Nevo
Hydrol. Earth Syst. Sci., 26, 5493–5513, https://doi.org/10.5194/hess-26-5493-2022, https://doi.org/10.5194/hess-26-5493-2022, 2022
Short summary
Short summary
When designing flood forecasting models, it is necessary to use all available data to achieve the most accurate predictions possible. This manuscript explores two basic ways of ingesting near-real-time streamflow data into machine learning streamflow models. The point we want to make is that when working in the context of machine learning (instead of traditional hydrology models that are based on
bio-geophysics), it is not necessary to use complex statistical methods for injecting sparse data.
Xiongpeng Tang, Guobin Fu, Silong Zhang, Chao Gao, Guoqing Wang, Zhenxin Bao, Yanli Liu, Cuishan Liu, and Junliang Jin
Hydrol. Earth Syst. Sci., 26, 5315–5339, https://doi.org/10.5194/hess-26-5315-2022, https://doi.org/10.5194/hess-26-5315-2022, 2022
Short summary
Short summary
In this study, we proposed a new framework that considered the uncertainties of model simulations in quantifying the contribution rate of climate change and human activities to streamflow changes. Then, the Lancang River basin was selected for the case study. The results of quantitative analysis using the new framework showed that the reason for the decrease in the streamflow at Yunjinghong station was mainly human activities.
Bin Yi, Lu Chen, Hansong Zhang, Vijay P. Singh, Ping Jiang, Yizhuo Liu, Hexiang Guo, and Hongya Qiu
Hydrol. Earth Syst. Sci., 26, 5269–5289, https://doi.org/10.5194/hess-26-5269-2022, https://doi.org/10.5194/hess-26-5269-2022, 2022
Short summary
Short summary
An improved GIS-derived distributed unit hydrograph routing method considering time-varying soil moisture was proposed for flow routing. The method considered the changes of time-varying soil moisture and rainfall intensity. The response of underlying surface to the soil moisture content was considered an important factor in this study. The SUH, DUH, TDUH and proposed routing methods (TDUH-MC) were used for flood forecasts, and the simulated results were compared and discussed.
Audrey Douinot, Jean François Iffly, Cyrille Tailliez, Claude Meisch, and Laurent Pfister
Hydrol. Earth Syst. Sci., 26, 5185–5206, https://doi.org/10.5194/hess-26-5185-2022, https://doi.org/10.5194/hess-26-5185-2022, 2022
Short summary
Short summary
The objective of the paper is to highlight the seasonal and singular shift of the transfer time distributions of two catchments (≅10 km2).
Based on 2 years of rainfall and discharge observations, we compare variations in the properties of TTDs with the physiographic characteristics of catchment areas and the eco-hydrological cycle. The paper eventually aims to deduce several factors conducive to particularly rapid and concentrated water transfers, which leads to flash floods.
Alexander Y. Sun, Peishi Jiang, Zong-Liang Yang, Yangxinyu Xie, and Xingyuan Chen
Hydrol. Earth Syst. Sci., 26, 5163–5184, https://doi.org/10.5194/hess-26-5163-2022, https://doi.org/10.5194/hess-26-5163-2022, 2022
Short summary
Short summary
High-resolution river modeling is of great interest to local governments and stakeholders for flood-hazard mitigation. This work presents a physics-guided, machine learning (ML) framework for combining the strengths of high-resolution process-based river network models with a graph-based ML model capable of modeling spatiotemporal processes. Results show that the ML model can approximate the dynamics of the process model with high fidelity, and data fusion further improves the forecasting skill.
Marvin Höge, Andreas Scheidegger, Marco Baity-Jesi, Carlo Albert, and Fabrizio Fenicia
Hydrol. Earth Syst. Sci., 26, 5085–5102, https://doi.org/10.5194/hess-26-5085-2022, https://doi.org/10.5194/hess-26-5085-2022, 2022
Short summary
Short summary
Neural ODEs fuse physics-based models with deep learning: neural networks substitute terms in differential equations that represent the mechanistic structure of the system. The approach combines the flexibility of machine learning with physical constraints for inter- and extrapolation. We demonstrate that neural ODE models achieve state-of-the-art predictive performance while keeping full interpretability of model states and processes in hydrologic modelling over multiple catchments.
Jing Tian, Zhengke Pan, Shenglian Guo, Jiabo Yin, Yanlai Zhou, and Jun Wang
Hydrol. Earth Syst. Sci., 26, 4853–4874, https://doi.org/10.5194/hess-26-4853-2022, https://doi.org/10.5194/hess-26-4853-2022, 2022
Short summary
Short summary
Most of the literature has focused on the runoff response to climate change, while neglecting the impacts of the potential variation in the active catchment water storage capacity (ACWSC) that plays an essential role in the transfer of climate inputs to the catchment runoff. This study aims to systematically identify the response of the ACWSC to a long-term meteorological drought and asymptotic climate change.
Riccardo Rigon, Giuseppe Formetta, Marialaura Bancheri, Niccolò Tubini, Concetta D'Amato, Olaf David, and Christian Massari
Hydrol. Earth Syst. Sci., 26, 4773–4800, https://doi.org/10.5194/hess-26-4773-2022, https://doi.org/10.5194/hess-26-4773-2022, 2022
Short summary
Short summary
The
Digital Earth(DE) metaphor is very useful for both end users and hydrological modelers. We analyse different categories of models, with the view of making them part of a Digital eARth Twin Hydrology system (called DARTH). We also stress the idea that DARTHs are not models in and of themselves, rather they need to be built on an appropriate information technology infrastructure. It is remarked that DARTHs have to, by construction, support the open-science movement and its ideas.
Hapu Arachchige Prasantha Hapuarachchi, Mohammed Abdul Bari, Aynul Kabir, Mohammad Mahadi Hasan, Fitsum Markos Woldemeskel, Nilantha Gamage, Patrick Daniel Sunter, Xiaoyong Sophie Zhang, David Ewen Robertson, James Clement Bennett, and Paul Martinus Feikema
Hydrol. Earth Syst. Sci., 26, 4801–4821, https://doi.org/10.5194/hess-26-4801-2022, https://doi.org/10.5194/hess-26-4801-2022, 2022
Short summary
Short summary
Methodology for developing an operational 7-day ensemble streamflow forecasting service for Australia is presented. The methodology is tested for 100 catchments to learn the characteristics of different NWP rainfall forecasts, the effect of post-processing, and the optimal ensemble size and bootstrapping parameters. Forecasts are generated using NWP rainfall products post-processed by the CHyPP model, the GR4H hydrologic model, and the ERRIS streamflow post-processor inbuilt in the SWIFT package
Chenhao Chai, Lei Wang, Deliang Chen, Jing Zhou, Hu Liu, Jingtian Zhang, Yuanwei Wang, Tao Chen, and Ruishun Liu
Hydrol. Earth Syst. Sci., 26, 4657–4683, https://doi.org/10.5194/hess-26-4657-2022, https://doi.org/10.5194/hess-26-4657-2022, 2022
Short summary
Short summary
This work quantifies future snow changes and their impacts on hydrology in the upper Salween River (USR) under SSP126 and SSP585 using a cryosphere–hydrology model. Future warm–wet climate is not conducive to the development of snow. The rain–snow-dominated pattern of runoff will shift to a rain-dominated pattern after the 2040s under SSP585 but is unchanged under SSP126. The findings improve our understanding of cryosphere–hydrology processes and can assist water resource management in the USR.
Remko C. Nijzink and Stanislaus J. Schymanski
Hydrol. Earth Syst. Sci., 26, 4575–4585, https://doi.org/10.5194/hess-26-4575-2022, https://doi.org/10.5194/hess-26-4575-2022, 2022
Short summary
Short summary
Most catchments plot close to the empirical Budyko curve, which allows for the estimation of the long-term mean annual evaporation and runoff. The Budyko curve can be defined as a function of a wetness index or a dryness index. We found that differences can occur and that there is an uncertainty due to the different formulations.
Anna Msigwa, Celray James Chawanda, Hans C. Komakech, Albert Nkwasa, and Ann van Griensven
Hydrol. Earth Syst. Sci., 26, 4447–4468, https://doi.org/10.5194/hess-26-4447-2022, https://doi.org/10.5194/hess-26-4447-2022, 2022
Short summary
Short summary
Studies using agro-hydrological models, like the Soil and Water Assessment Tool (SWAT), to map evapotranspiration (ET) do not account for cropping seasons. A comparison between the default SWAT+ set-up (with static land use representation) and a dynamic SWAT+ model set-up (with seasonal land use representation) is made by spatial mapping of the ET. The results show that ET with seasonal representation is closer to remote sensing estimates, giving better performance than ET with static land use.
Jerom P. M. Aerts, Rolf W. Hut, Nick C. van de Giesen, Niels Drost, Willem J. van Verseveld, Albrecht H. Weerts, and Pieter Hazenberg
Hydrol. Earth Syst. Sci., 26, 4407–4430, https://doi.org/10.5194/hess-26-4407-2022, https://doi.org/10.5194/hess-26-4407-2022, 2022
Short summary
Short summary
In recent years gridded hydrological modelling moved into the realm of hyper-resolution modelling (<10 km). In this study, we investigate the effect of varying grid-cell sizes for the wflow_sbm hydrological model. We used a large sample of basins from the CAMELS data set to test the effect that varying grid-cell sizes has on the simulation of streamflow at the basin outlet. Results show that there is no single best grid-cell size for modelling streamflow throughout the domain.
Taher Chegini and Hong-Yi Li
Hydrol. Earth Syst. Sci., 26, 4279–4300, https://doi.org/10.5194/hess-26-4279-2022, https://doi.org/10.5194/hess-26-4279-2022, 2022
Short summary
Short summary
Belowground urban stormwater networks (BUSNs) play a critical and irreplaceable role in preventing or mitigating urban floods. However, they are often not available for urban flood modeling at regional or larger scales. We develop a novel algorithm to estimate existing BUSNs using ubiquitously available aboveground data at large scales based on graph theory. The algorithm has been validated in different urban areas; thus, it is well transferable.
Yi Nan, Zhihua He, Fuqiang Tian, Zhongwang Wei, and Lide Tian
Hydrol. Earth Syst. Sci., 26, 4147–4167, https://doi.org/10.5194/hess-26-4147-2022, https://doi.org/10.5194/hess-26-4147-2022, 2022
Short summary
Short summary
Tracer-aided hydrological models are useful tool to reduce uncertainty of hydrological modeling in cold basins, but there is little guidance on the sampling strategy for isotope analysis, which is important for large mountainous basins. This study evaluated the reliance of the tracer-aided modeling performance on the availability of isotope data in the Yarlung Tsangpo river basin, and provides implications for collecting water isotope data for running tracer-aided hydrological models.
Sella Nevo, Efrat Morin, Adi Gerzi Rosenthal, Asher Metzger, Chen Barshai, Dana Weitzner, Dafi Voloshin, Frederik Kratzert, Gal Elidan, Gideon Dror, Gregory Begelman, Grey Nearing, Guy Shalev, Hila Noga, Ira Shavitt, Liora Yuklea, Moriah Royz, Niv Giladi, Nofar Peled Levi, Ofir Reich, Oren Gilon, Ronnie Maor, Shahar Timnat, Tal Shechter, Vladimir Anisimov, Yotam Gigi, Yuval Levin, Zach Moshe, Zvika Ben-Haim, Avinatan Hassidim, and Yossi Matias
Hydrol. Earth Syst. Sci., 26, 4013–4032, https://doi.org/10.5194/hess-26-4013-2022, https://doi.org/10.5194/hess-26-4013-2022, 2022
Short summary
Short summary
Early flood warnings are one of the most effective tools to save lives and goods. Machine learning (ML) models can improve flood prediction accuracy but their use in operational frameworks is limited. The paper presents a flood warning system, operational in India and Bangladesh, that uses ML models for forecasting river stage and flood inundation maps and discusses the models' performances. In 2021, more than 100 million flood alerts were sent to people near rivers over an area of 470 000 km2.
Matthias Sprenger, Pilar Llorens, Francesc Gallart, Paolo Benettin, Scott T. Allen, and Jérôme Latron
Hydrol. Earth Syst. Sci., 26, 4093–4107, https://doi.org/10.5194/hess-26-4093-2022, https://doi.org/10.5194/hess-26-4093-2022, 2022
Short summary
Short summary
Our catchment-scale transit time modeling study shows that including stable isotope data on evapotranspiration in addition to the commonly used stream water isotopes helps constrain the model parametrization and reveals that the water taken up by plants has resided longer in the catchment storage than the water leaving the catchment as stream discharge. This finding is important for our understanding of how water is stored and released, which impacts the water availability for plants and humans.
Peishi Jiang, Pin Shuai, Alexandar Sun, Maruti K. Mudunuru, and Xingyuan Chen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-282, https://doi.org/10.5194/hess-2022-282, 2022
Revised manuscript accepted for HESS
Short summary
Short summary
We developed a novel deep learning approach to estimate the parameters of a computationally expensive hydrological model on only a few hundred realizations. Our approach leverages the knowledge obtained by data-driven analysis to guide the design of the deep learning model used for parameter estimation. We demonstrate this approach by calibrating a state-of-the-art hydrological model against streamflow and evapotranspiration observations at a snow-dominated watershed in Colorado.
Lorenzo Alfieri, Francesco Avanzi, Fabio Delogu, Simone Gabellani, Giulia Bruno, Lorenzo Campo, Andrea Libertino, Christian Massari, Angelica Tarpanelli, Dominik Rains, Diego G. Miralles, Raphael Quast, Mariette Vreugdenhil, Huan Wu, and Luca Brocca
Hydrol. Earth Syst. Sci., 26, 3921–3939, https://doi.org/10.5194/hess-26-3921-2022, https://doi.org/10.5194/hess-26-3921-2022, 2022
Short summary
Short summary
This work shows advances in high-resolution satellite data for hydrology. We performed hydrological simulations for the Po River basin using various satellite products, including precipitation, evaporation, soil moisture, and snow depth. Evaporation and snow depth improved a simulation based on high-quality ground observations. Interestingly, a model calibration relying on satellite data skillfully reproduces observed discharges, paving the way to satellite-driven hydrological applications.
Arianna Borriero, Rohini Kumar, Tam V. Nguyen, Jan H. Fleckenstein, and Stefanie R. Lutz
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-222, https://doi.org/10.5194/hess-2022-222, 2022
Revised manuscript accepted for HESS
Short summary
Short summary
We analyzed the uncertainty of water transit time distribution (TTD) resulting from model input (interpolated tracer data) and structure (StorAge Selection (SAS) functions). We found that uncertainty was mainly associated with tracer data time interpolation, time-variant SAS function and low flow conditions. We convey the importance to characterize the specific uncertainty sources, and their combined effects on predicted TTDs, as it has relevant implications for both water quantity and quality.
Bruno Majone, Diego Avesani, Patrick Zulian, Aldo Fiori, and Alberto Bellin
Hydrol. Earth Syst. Sci., 26, 3863–3883, https://doi.org/10.5194/hess-26-3863-2022, https://doi.org/10.5194/hess-26-3863-2022, 2022
Short summary
Short summary
In this work, we introduce a methodology for devising reliable future high streamflow scenarios from climate change simulations. The calibration of a hydrological model is carried out to maximize the probability that the modeled and observed high flow extremes belong to the same statistical population. Application to the Adige River catchment (southeastern Alps, Italy) showed that this procedure produces reliable quantiles of the annual maximum streamflow for use in assessment studies.
Pedro V. G. Batista, Peter Fiener, Simon Scheper, and Christine Alewell
Hydrol. Earth Syst. Sci., 26, 3753–3770, https://doi.org/10.5194/hess-26-3753-2022, https://doi.org/10.5194/hess-26-3753-2022, 2022
Short summary
Short summary
Patchy agricultural landscapes have a large number of small fields, which are separated by linear features such as roads and field borders. When eroded sediments are transported out of the agricultural fields by surface runoff, these features can influence sediment connectivity. By use of measured data and a simulation model, we demonstrate how a dense road network (and its drainage system) facilitates sediment transport from fields to water courses in a patchy Swiss agricultural catchment.
Juliane Mai, Hongren Shen, Bryan A. Tolson, Étienne Gaborit, Richard Arsenault, James R. Craig, Vincent Fortin, Lauren M. Fry, Martin Gauch, Daniel Klotz, Frederik Kratzert, Nicole O'Brien, Daniel G. Princz, Sinan Rasiya Koya, Tirthankar Roy, Frank Seglenieks, Narayan K. Shrestha, André G. T. Temgoua, Vincent Vionnet, and Jonathan W. Waddell
Hydrol. Earth Syst. Sci., 26, 3537–3572, https://doi.org/10.5194/hess-26-3537-2022, https://doi.org/10.5194/hess-26-3537-2022, 2022
Short summary
Short summary
Model intercomparison studies are carried out to test various models and compare the quality of their outputs over the same domain. In this study, 13 diverse model setups using the same input data are evaluated over the Great Lakes region. Various model outputs – such as streamflow, evaporation, soil moisture, and amount of snow on the ground – are compared using standardized methods and metrics. The basin-wise model outputs and observations are made available through an interactive website.
Aurélien Beaufort, Jacob S. Diamond, Eric Sauquet, and Florentina Moatar
Hydrol. Earth Syst. Sci., 26, 3477–3495, https://doi.org/10.5194/hess-26-3477-2022, https://doi.org/10.5194/hess-26-3477-2022, 2022
Short summary
Short summary
We developed one of the largest stream temperature databases to calculate a simple, ecologically relevant metric – the thermal peak – that captures the magnitude of summer thermal extremes. Using statistical models, we extrapolated the thermal peak to nearly every stream in France, finding the hottest thermal peaks along large rivers without forested riparian zones and groundwater inputs. Air temperature was a poor proxy for the thermal peak, highlighting the need to grow monitoring networks.
Ulises M. Sepúlveda, Pablo A. Mendoza, Naoki Mizukami, and Andrew J. Newman
Hydrol. Earth Syst. Sci., 26, 3419–3445, https://doi.org/10.5194/hess-26-3419-2022, https://doi.org/10.5194/hess-26-3419-2022, 2022
Short summary
Short summary
This paper characterizes parameter sensitivities across more than 5500 grid cells for a commonly used macroscale hydrological model, including a suite of eight performance metrics and 43 soil, vegetation and snow parameters. The results show that the model is highly overparameterized and, more importantly, help to provide guidance on the most relevant parameters for specific target processes across diverse climatic types.
Cited articles
Ahn, K. H. and Merwade, V.: Quantifying the relative impact of climate and
human activities on streamflow, J. Hydrol., 515, 257–266,
https://doi.org/10.1016/j.jhydrol.2014.04.062, 2014.
An, L.: Modeling human decisions in coupled human and natural systems: Review of agent-based models, Ecol. Model., 229, 25–36, https://doi.org/10.1016/j.ecolmodel.2011.07.010, 2012.
An, L., Linderman, M., Qi, J., Shortridge, A., and Liu, J.: Exploring Complexity in a Human–Environment System: An Agent-Based Spatial Model for
Multidisciplinary and Multiscale Integration, Ann. Assoc. Am. Geogr., 95, 54–79, https://doi.org/10.1111/j.1467-8306.2005.00450.x, 2005.
Arbuckle, J. G.: Iowa Farm and Rural Life Poll 2016 Summary Report, Iowa State University Extension and Outreach, Ames, IA, 2017.
Arbuckle, J. G., Morton, L. W., and Hobbs, J.: Understanding farmer perspectives on climate change adaptation and mitigation: the roles of trust
in sources of climate information, climate change beliefs, and perceived
risk, Environ. Behav., 47, 205–234, https://doi.org/10.1177/0013916513503832, 2013.
Asch, M., Boquet, M., and Nodet, M.: Nudging Methods, in Data Assimilation:
Methods, Algorithms, and Applications, SIAM, Philadelphia, Pennsylvania, 120–123, 2017.
Axelrod, R. and Tesfatsion, L.: A Guide for Newcomers to Agent-Based Modeling in the Social Sciences, Handb. Comput. Econ., 2, 1647–1659,
https://doi.org/10.1016/S1574-0021(05)02044-7, 2006.
Barreteau, O., Bousquet, F., Millier, C., and Weber, J.: Suitability of
Multi-Agent Simulations to study irrigated system viability: application to
case studies in the Senegal River Valley, Agric. Syst., 80, 255–275,
https://doi.org/10.1016/j.agsy.2003.07.005, 2004.
Becu, N., Perez, P., Walker, A., Barreteau, O., and Page, C. L.: Agent based
simulation of a small catchment water management in northern Thailand, Ecol.
Model., 170, 319–331, https://doi.org/10.1016/S0304-3800(03)00236-9, 2003.
Berger, T.: Agent-based spatial models applied to agriculture: A simulation
tool for technology diffusion, resource use changes and policy analysis, Agric. Econ., 25, 245–260, https://doi.org/10.1016/S0169-5150(01)00082-2, 2001.
Berger, T. and Troost, C.: Agent-based Modelling of Climate Adaptation and
Mitigation Options in Agriculture, J. Agric. Econ., 65, 323–348,
https://doi.org/10.1111/1477-9552.12045, 2014.
Berger, T., Birner, R., Mccarthy, N., Díaz, J., and Wittmer, H.: Capturing the complexity of water uses and water users within a multi-agent framework, Water Resour. Manage., 21, 129–148, https://doi.org/10.1007/s11269-006-9045-z, 2006.
Berglund, E. Z.: Using agent-based modeling for water resources planning and
management, J. Water Resour. Pl. Manage., 141, 1–17, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000544, 2015.
Bharathy, G. K. and Silverman, B.: Holistically evaluating agent-based social systems models: A case study, Simulation: T. Soc. Model. Simul. Int., 89,
102–135, https://doi.org/10.1177/0037549712446854, 2013.
Bithell, M. and Brasington, J.: Coupling agent-based models of subsistence
farming with individual-based forest models and dynamic models of water
distribution, Environ. Model. Softw., 24, 173–190, https://doi.org/10.1016/j.envsoft.2008.06.016, 2009.
Bonabeau, E.: Agent-based modeling: Methods and techniques for simulating
human systems, P. Natl. Acad. Sci. USA, 99, 7280–7287, https://doi.org/10.1073/pnas.082080899, 2002.
Borrill, P. and Tesfatsion, L.: Agent-based modeling: the right mathematics
for the social sciences?, in: The Elgar Companion to Recent Economic Methodology, Edward Elgar Publishing, New York, 228–258, 2011.
Brown, C. M., Lund, J. R., Cai, X., Reed, P. M., Zagona, E. A., Ostfeld, A.,
Hall, J., Characklis, G. W., Yu, W., and Brekke, L.: Scientific Framework for
Sustainable Water Management, Water Resour. Res., 6110–6124,
https://doi.org/10.1002/2015WR017114, 2015.
Burton, R. J. F.: The influence of farmer demographic characteristics on
environmental behaviour: A review, J. Environ. Manage., 135, 19–26,
https://doi.org/10.1016/j.jenvman.2013.12.005, 2014.
Chu, X. and Steinman, A.: Event and Continuous Hydrologic Modeling with HEC-HMS, J. Irrig. Drain. Eng., 135, 119–124, https://doi.org/10.1061/(ASCE)0733-9437(2009)135:1(119), 2009.
Claassen, R. and Tegene, A.: Agricultural Land Use Choice: A Discrete Choice
Approach, Agric. Resour. Econ. Rev., 28, 26–36, https://doi.org/10.1017/s1068280500000940, 1999.
Cydzik, K. and Hogue, T. S.: Modeling postfire response and recovery using
the hydrologic engineering center hydrologic modeling system (HEC-HMS), J.
Am. Water Resour. Assoc., 45, 702–714, https://doi.org/10.1111/j.1752-1688.2009.00317.x, 2009.
Daloglu, I., Nassauer, J. I., Riolo, R. L., and Scavia, D.: Development of a
farmer typology of agricultural conservation behavior in the american corn
belt, Agric. Syst., 129, 93–102, https://doi.org/10.1016/j.agsy.2014.05.007, 2014.
Davis, C. G. and Gillespie, J. M.: Factors affecting the selection of business arrangements by U.S. hog farmers, Rev. Agric. Econ., 29, 331–348, https://doi.org/10.1111/j.1467-9353.2007.00346.x, 2007.
Dewitz, J.: National Land Cover Database (NLCD) 2016 Products, US Geological Survey data release, https://doi.org/10.5066/P96HHBIE, 2019.
Di Baldassarre, G., Viglione, A., Carr, G., Kuil, L., Salinas, J. L., and
Blöschl, G.: Socio-hydrology: Conceptualising human–flood interactions,
Hydrol. Earth Syst. Sci., 17, 3295–3303, https://doi.org/10.5194/hess-17-3295-2013,
2013.
Du, E., Cai, X., Sun, Z., and Minsker, B.: Exploring the Role of Social Media
and Individual Behaviors in Flood Evacuation Processes: An Agent-Based
Modeling Approach, Water Resour. Res., 53, 9164–9180, https://doi.org/10.1002/2017WR021192, 2017.
Duffy, M.: Conservation Practices for Landlords, Iowa State University Extension and Outreach, Ames, IA, 2015.
Dziubanski, D. J., Franz, K. J., and Helmers, M. J.: Effects of Spatial
Distribution of Prairie Vegetation in an Agricultural Landscape on Curve Number Values, J. Am. Water Resour. Assoc., 53, 365–381,
https://doi.org/10.1111/1752-1688.12510, 2017.
Elshafei, Y., Sivapalan, M., Tonts, M., and Hipsey, M. R.: A prototype framework for models of socio-hydrology: Identification of key feedback loops and parameterisation approach, Hydrol. Earth Syst. Sci., 18, 2141–2166, https://doi.org/10.5194/hess-18-2141-2014, 2014.
Fagiolo, G., Windrum, P., and Moneta, A.: Empirical Validation of Agent Based Models: A Critical Survey. Laboratory of Economics and Management (LEM), in: LEM Papers Series, Sant'Anna School of Advanced Studies, Pisa, Italy, 2006.
Fagiolo, G., Guerini, M., Lamperti, F., Moneta, A., and Roventini, A.: Validation of Agent-Based Models in Economics and Finance BT – Computer
Simulation Validation: Fundamental Concepts, Methodological Frameworks, and
Philosophical Perspectives, edited by: Beisbart, C. and Saam, N. J., Springer International Publishing, Cham, 763–787, 2019.
Frans, C., Istanbulluoglu, E., Mishra, V., Munoz-Arriola, F., and Lettenmaier, D. P.: Are climatic or land cover changes the dominant cause of
runoff trends in the Upper Mississippi River Basin?, Geophys. Res. Lett., 40, 1104–1110, https://doi.org/10.1002/grl.50262, 2013.
Grimm, V., Berger, U., Bastiansen, F., Eliassen, S., Ginot, V., Giske, J., Goss-Custard, J., Grand, T., Heinz, S. K., Huse, G., Huth, A., Jepsen, J.
U., Jørgensen, C., Mooij, W. M., Müller, B., Pe'er, G., Piou, C.,
Railsback, S. F., Robbins, A. M., Robbins, M. M., Rossmanith, E., Rüger,
N., Strand, E., Souissi, S., Stillman, R. A., Vabø, R., Visser, U., and
DeAngelis, D. L.: A standard protocol for describing individual-based and
agent-based models, Ecol. Model., 198, 115–126, https://doi.org/10.1016/j.ecolmodel.2006.04.023, 2006.
Guerini, M. and Moneta, A.: A method for agent-based models validation, J.
Econ. Dyn. Control, 82, 125–141, https://doi.org/10.1016/j.jedc.2017.06.001, 2017.
Gyawali, R. and Watkins, D. W.: Continuous Hydrologic Modeling of Snow-Affected Watersheds in the Great Lakes Basin Using HEC-HMS, J. Hydrol. Eng., 18, 29–39, https://doi.org/10.1061/(ASCE)HE.1943-5584.0000591, 2013.
Hahn, H. A.: The conundrum of verification and validation of social science-based models, Proced. Comput. Sci., 16, 878–887,
https://doi.org/10.1016/j.procs.2013.01.092, 2013.
Halwatura, D. and Najim, M. M. M.: Environmental Modelling & Software Application of the HEC-HMS model for runoff simulation in a tropical
catchment, Environ. Model. Softw., 46, 155–162, https://doi.org/10.1016/j.envsoft.2013.03.006, 2013.
Helmers, M. J., Zhou, X., Asbjornsen, H., Kolka, R., Tomer, M. D., and Cruse,
R. M.: Sediment Removal by Prairie Filter Strips in Row-Cropped Ephemeral
Watersheds, J. Environ. Qual., 41, 1531, https://doi.org/10.2134/jeq2011.0473, 2012.
Hernandez-Santana, V., Zhou, X., Helmers, M. J., Asbjornsen, H., Kolka, R., and Tomer, M.: Native prairie filter strips reduce runoff from hillslopes under annual row-crop systems in Iowa, USA, J. Hydrol., 477, 94–103,
https://doi.org/10.1016/j.jhydrol.2012.11.013, 2013.
Hoag, D., Luloff, A. E., and Osmond, D.: How Farmers and Ranchers Make Decisions on Conservation Practices, North Carolina State University, Raleigh, NC, 2012.
Hofstrand, D.: Tracking the Profitability of Corn Production, Iowa State University Extension and Outreach, Ames, IA, 2018.
Jenkins, K., Surminski, S., Hall, J., and Crick, F.: Assessing surface water
flood risk and management strategies under future climate change: Insights
from an Agent-Based Model, Sci. Total Environ., 595, 159–168,
https://doi.org/10.1016/j.scitotenv.2017.03.242, 2017.
Knebl, M. R., Yang, Z., Hutchison, K., and Maidment, D. R.: Regional scale
flood modeling using NEXRAD rainfall, GIS, and HEC-HMS/RAS: a case study for the San Antonio River Basin Summer 2002 storm event, J. Environ. Manage., 75, 325–336, https://doi.org/10.1016/j.jenvman.2004.11.024, 2005.
Kucharik, C. J.: Evaluation of a process-based agro-ecosystem model (Agro-IBIS) across the U.S. Corn Belt: Simulations of the interannual
variability in maize yield, Earth Interact., 7, 1–33,
https://doi.org/10.1175/1087-3562(2003)007<0001:EOAPAM>2.0.CO;2, 2003.
Kulik, B. and Baker, T.: Putting the organization back into computational
organization theory: a complex Perrowian model of organizational action, Comput. Math. Organ. Theor., 14, 84–119, https://doi.org/10.1007/s10588-008-9022-6, 2008.
Lambert, D. M., Sullivan, P., Claassen, R., and Foreman, L.: Profiles of US farm households adopting conservation-compatible practices, Land Use Policy, 24, 72–88, https://doi.org/10.1016/j.landusepol.2005.12.002, 2007.
Langevin, J., Wen, J., and Gurian, P. L.: Simulating the human-building
interaction: Development and validation of an agent-based model of office
occupant behaviors, Build. Environ., 88, 27–45, https://doi.org/10.1016/j.buildenv.2014.11.037, 2015.
Le, Q., Park, S., and Vlek, P.: Ecological Informatics Land Use Dynamic
Simulator (LUDAS): A multi-agent system model for simulating spatio-temporal
dynamics of coupled human – landscape system 2. Scenario-based application
for impact assessment of land-use policies, Ecol. Inform., 5, 203–221,
https://doi.org/10.1016/j.ecoinf.2010.02.001, 2010.
Macal, C. M. and North, M. J.: Validation of an Agent-based Model of Deregulated Electric Power Markets, in: Proceedings of North American Computational Social and Organization Science (NAACSOS), Notre Dame, IN, 2005.
Marcotty, J.: High crop prices a threat to nature?, StarTribune, Minneapolis, MN, 11 November 2011.
Matthews, R.: The People and Landscape Model (PALM): Towards full integration of human decision-making and biophysical simulation models, Ecol. Model., 194, 329–343, https://doi.org/10.1016/j.ecolmodel.2005.10.032, 2006.
Mays, L.: Water Resources Engineering, 2nd Edn., John Wiler & Songs, Inc.,
Hoboken, NJ, 2011.
McGuire, J., Morton, L. W., and Cast, A. D.: Reconstructing the good farmer
identity: Shifts in farmer identities and farm management practices to improve water quality, Agric. Human Val., 30, 57–69, https://doi.org/10.1007/s10460-012-9381-y, 2013.
McGuire, J. M., Wright, L., Arbuckle, J. G., and Cast, A. D.: Farmer
identities and responses to the social-biophysical environment, J. Rural Stud., 39, 145–155, https://doi.org/10.1016/j.jrurstud.2015.03.011, 2015.
Montanari, A.: Debates-Perspectives on socio-hydrology: Introduction, Water
Resour. Res., 51, 4768–4769, https://doi.org/10.1002/2015WR017430, 2015.
Naik, P. K. and Jay, D. A.: Distinguishing human and climate influences on the Columbia River: Changes in mean flow and sediment transport, J. Hydrol.,
404, 259–277, https://doi.org/10.1016/j.jhydrol.2011.04.035, 2011.
Newton, J.: Change on the Horizon for the Conservation Reserve Program?,
available at: https://www.fb.org/market-intel/change-on-the-horizon-for-the-conservation-reserve-program
(last access: 15 January 2018), 2017.
Ng, T. L., Eheart, J. W., Cai, X., and Braden, J. B.: An agent-based model of
farmer decision-making and water quality impacts at the watershed scale under markets for carbon allowances and a second-generation biofuel crop, Water Resour. Res., 47, W09519, https://doi.org/10.1029/2011WR010399, 2011.
Noel, P. H. and Cai, X.: On the role of individuals in models of coupled human and natural systems?: Lessons from a case study in the Republican River Basin, Environ. Model. Softw., 92, 1–16, https://doi.org/10.1016/j.envsoft.2017.02.010, 2017.
Nowak, P.: Why farmers adopt production technology, Soil Water Conserv., 47, 14–16, 1992.
Ormerod, P. and Rosewell, B.: Validation and Verification of Agent-Based Models in the Social Sciences, Epistemol. Asp. Comput. Simul. Soc. Sci.,
5466, 130–140, https://doi.org/10.1007/978-3-642-01109-2_10, 2009.
Pahl-wostl, C. and Ebenhöh, E.: Heuristics to characterise human behaviour in agent based models, in: International Congress on Environmental Modelling and Software, 2004.
Parker, D. C., Hessl, A., and Davis, S. C.: Complexity, land-use modeling, and the human dimension: Fundamental challenges for mapping unknown outcome spaces, Geoforum, 39, 789–804, https://doi.org/10.1016/j.geoforum.2007.05.005, 2008.
Parunak, H. V. D., Savit, R., and Riolo, R. L.: Multi-agent systems and
agent-based simulation, in: Proceedings of the First International Workshop of Multi-Agent Systems and Agent-Based Simulation, 4–6 July 1998, Paris, France, 10–25, https://doi.org/10.1007/b71639, 1998.
Pfrimmer, J., Gigliotti, L., Stafford, J., and Schumann, D.: Motivations for
Enrollment Into the Conservation Reserve Enhancement Program in the James River Basin of South Dakota, Hum. Dimens. Wildl., 22, 1–8, https://doi.org/10.1080/10871209.2017.1324069, 2017.
Plastina, A.: Estimated Costs of Crop Production in Iowa – 2017, Iowa State University Extension and Outreach, Ames, IA, 2017.
Plastina, A., Zhang, W., and Sawadgo, W.: Iowa Farmland Ownership and Tenure
Survey 1982–2017: A Thirty-five Year Perspective, Iowa State University Extension and Outreach, Ames, IA, 2018.
Prior, J.: Landforms of Iowa, 1st Edn., University of Iowa Press, Iowa City,
Iowa, 1991.
Prokopy, L. S., Floress, K., Arbuckle, J. G., Church, S. P., Eanes, F. R.,
Gao, Y., Gramig, B. M., Ranjan, P., and Singh, A. S.: Adoption of agricultural conservation practices in the United States: Evidence from 35 years of quantitative literature, J. Soil Water Conserv., 74, 520–534,
https://doi.org/10.2489/jswc.74.5.520, 2019.
Reeves, H. W. and Zellner, M. L.: Linking MODFLOW with an agent-based land-use model to support decision making, Ground Water, 48, 649–660, https://doi.org/10.1111/j.1745-6584.2010.00677.x, 2010.
Rogger, M., Agnoletti, M., Alaoui, A., Bathurst, J. C., Bodner, G., Borga, M., Chaplot, V., Gallart, F., Glatzel, G., Hall, J., Holden, J., Holko, L., Horn, R., Kiss, A., Quinton, J. N., Leitinger, G., Lennartz, B., Parajka,
J., Peth, S., Robinson, M., Salinas, J. L., Santoro, A., Szolgay, J., Tron,
S., and Viglione, A.: Land use change impacts on floods at the catchment
scale: Challenges and opportunities for future research, Water Resour. Res., 53, 5209–5219, https://doi.org/10.1002/2017WR020723, 2017.
Rosengrant, M., Cai, X., and Cline, S.: World water and food to 2025, 1st Edn., International Food Policy Research Institute, Washington, D.C., 2002.
Ryan, R. L., Erickson, D. L., and De Young, R.: Farmers' Motivation for Adopting Conservation Practices along Riparian Zones in a Mid-western
Agricultural Watershed, J. Environ. Pl. Manage., 46, 19–37, https://doi.org/10.1080/713676702, 2003.
Saltiel, J., Bauder, J. W., and Palakovich, S.: Adoption of Sustainable
Agricultural Practices: Diffusion, Farm Structure, and Profitability, Rural
Sociol., 59, 333–349, 1994.
Savenije, H. H. G. and Van der Zaag, P.: Integrated water resources management: Concepts and issues, Phys. Chem. Earth, 33, 290–297,
https://doi.org/10.1016/j.pce.2008.02.003, 2008.
Schaible, G. D., Mishra, A. K., Lambert, D. M., and Panterov, G.: Factors
influencing environmental stewardship in U.S. agriculture: Conservation program participants vs. non-participants, Land Use Policy, 46, 125–141,
https://doi.org/10.1016/j.landusepol.2015.01.018, 2015.
Scharffenberg, W. A.: Hydrologic Modeling System User's Manual, US Army Corps of Engineers, Davis, CA, 2013.
Schilling, K. E., Chan, K. S., Liu, H., and Zhang, Y. K.: Quantifying the effect of land use land cover change on increasing discharge in the Upper
Mississippi River, J. Hydrol., 387, 343–345, https://doi.org/10.1016/j.jhydrol.2010.04.019, 2010.
Schlüter, M. and Pahl-wostl, C.: Mechanisms of Resilience in Common-pool Resource Management Systems : an Agent-based Model of Water Use in a River Basin, Ecol. Soc., 12, available at: http://www.ecologyandsociety.org/vol12/iss2/art4/ (last access: 1 September 2019), 2007.
Schmieg, S., Franz, K., Rehmann, C., and van Leeuwen, J. (Hans):
Reparameterization and evaluation of the HEC-HMS modeling application for
the City of Ames, Iowa, Iowa State University, Ames, IA, 2011.
Schreinemachers, P. and Berger, T.: Land use decisions in developing countries and their representation in multi-agent systems, Land Use Sci., 1, 29–44, https://doi.org/10.1080/17474230600605202, 2006.
Schreinemachers, P. and Berger, T.: An agent-based simulation model of
human–environment interactions in agricultural systems, Environ. Model. Softw., 26, 845–859, https://doi.org/10.1016/j.envsoft.2011.02.004, 2011.
Schwarz, N. and Ernst, A.: Agent-based modeling of the diffusion of environmental innovations – An empirical approach, Technol. Forecast. Soc.
Change, 76, 497–511, https://doi.org/10.1016/j.techfore.2008.03.024, 2009.
Secchi, S. and Babcock, B. A.: Impact of High Corn Prices on Conservation
Reserve Program Acreage, Iowa Ag. Rev., 13, 4–7, 2007.
Shreve, C. M. and Kelman, I.: Does mitigation save? Reviewing cost-benefit
analyses of disaster risk reduction, Int. J. Disast. Risk Reduct., 10, 213–235, https://doi.org/10.1016/j.ijdrr.2014.08.004, 2014.
Simon, H.: Models of Man, John Wiley & Sons, New York, 1957.
Sivapalan, M. and Blöschl, G.: Time scale interactions and the coevolution of humans and water, Water Resour. Res., 51, 6988–7022,
https://doi.org/10.1002/2015WR017896, 2015.
Sivapalan, M., Savenije, H. H. G., and Blöschl, G.: Socio-hydrology: A new science of people and water, Hydrol. Process., 26, 1270–1276,
https://doi.org/10.1002/hyp.8426, 2012.
Tannura, M. A., Irwin, S. H., and Good, D. L.: Weather, Technology, and Corn and Soybean Yields in the U.S. Corn Belt, University of Illinois, Champaign, IL, 2008.
Tesfatsion, L., Rehmann, C. R., Cardoso, D. S., Jie, Y., and Gutowski, W. J.:
An agent-based platform for the study of watersheds as coupled natural and
human systems, Environ. Model. Softw., 89, 40–60, https://doi.org/10.1016/j.envsoft.2016.11.021, 2017.
Tigner, R.: Partial Budgeting: A Tool to Analyze Farm Business Changes, Iowa State University Extension and Outreach, Ames, IA, 2006.
Tomer, M. D. and Schilling, K. E.: A simple approach to distinguish land-use
and climate-change effects on watershed hydrology, J. Hydrol., 376, 24–33, https://doi.org/10.1016/j.jhydrol.2009.07.029, 2009.
Troy, T., Pavao-Zuckerman, M., and Evans, T.: Debates – Perspectives on
socio-hydrology: Socio-hydrologic modeling: Tradeoffs, hypothesis testing, and validation, Water Resour. Res., 51, 4806–4814, https://doi.org/10.1002/2015WR017046, 2015.
Tyndall, J. C., Schulte, L. A., Liebman, M., and Helmers, M.: Field-level
financial assessment of contour prairie strips for enhancement of environmental quality, Environ. Manage., 52, 736–747, https://doi.org/10.1007/s00267-013-0106-9, 2013.
USDA: Contour Grass Strips – Practice CP-15A, available at: https://www.fsa.usda.gov/Assets/USDA-FSA-Public/usdafiles/FactSheets/2015/CRPProgramsandInitiatives/Practice_CP15A_Contour_Grass_Strips.pdf (last access: 1 September 2019), 2015.
USDA National Agricultural Statistics Service: 2018 Iowa Agricultural
Statistics, Des Moines, Iowa, 2018.
USDA-NRCS – USDA-Natural Resources Conservation Service: National Engineering Handbook, Part 630, Washington, D.C., 2004.
USDA-NRCS – USDA-Natural Resources Conservation Service: Field Office
Technical Guide, available at:
http://www.nrcs.usda.gov/wps/portal/nrcs/main/national/technical/fotg/
(last access: 9 April 2016), 2015.
van Oel, P. R., Krol, M. S., Hoekstra, A. Y., and Taddei, R. R.: Feedback
mechanisms between water availability and water use in a semi-arid river
basin: A spatially explicit multi-agent simulation approach, Environ. Model. Softw., 25, 433–443, 2010.
Verma, A. K., Jha, M. K., and Mahana, R. K.: Evaluation of HEC-HMS and WEPP
for simulating watershed runoff using remote sensing and geographical information system, Paddy Water Environ., 8, 131–144,
https://doi.org/10.1007/s10333-009-0192-8, 2010.
Viglione, A., Di Baldassarre, G., Brandimarte, L., Kuil, L., Carr, G., Salinas, J. L., Scolobig, A., and Blöschl, G.: Insights from socio-hydrology modelling on dealing with flood risk – Roles of collective memory, risk-taking attitude and trust, J. Hydrol., 518, 71–82,
https://doi.org/10.1016/j.jhydrol.2014.01.018, 2014.
Vorosmarty, C. and Sahagian, D.: Anthropogenic Disturbance of the Terrestrial Water Cycle, Bioscience, 50, 753–765, https://doi.org/10.1641/0006-3568(2000)050[0753:ADOTTW]2.0.CO;2, 2000.
Wainwright, J.: Can modelling enable us to understand the rôle of humans
in landscape evolution?, Geoforum, 39, 659–674, https://doi.org/10.1016/j.geoforum.2006.09.011, 2008.
Wang, D. and Hejazi, M.: Quantifying the relative contribution of the climate and direct human impacts on mean annual streamflow in the contiguous United States, Water Resour. Res., 47, W00J12, https://doi.org/10.1029/2010WR010283, 2011.
Windrum, P., Fagiolo, G., and Moneta, A.: Empirical Validation of Agent-Based Models: Alternatives and Prospects, J. Artif. Soc. Soc. Simul., 10, available at: http://jasss.soc.surrey.ac.uk/10/2/8.html (last access: 1 September 2019), 2007.
Xiang, X., Kennedy, R., and Madey, G.: Verification and Validation of Agent-based Scientific Simulation Models, in: Agent-Directed Simulation Conference, San Diego, CA, 47–55, 2005.
Yang, L. E., Scheffran, J., Süsser, D., Dawson, R., and Chen, Y. D.:
Assessment of Flood Losses with Household Responses: Agent-Based Simulation
in an Urban Catchment Area, Environ. Model. Assess., 23, 369–388,
https://doi.org/10.1007/s10666-018-9597-3, 2018.
Zenobia, B., Weber, C., and Daim, T.: Artificial markets?: A review and
assessment of a new venue for innovation research, Technovation, 29, 338–350, https://doi.org/10.1016/j.technovation.2008.09.002, 2009.
Zhang, H. L., Wang, Y. J., Wang, Y. Q., Li, D. X., and Wang, X. K.: The effect of watershed scale on HEC-HMS calibrated parameters: A case study in the Clear Creek watershed in Iowa, US, Hydrol. Earth Syst. Sci., 17, 2735–2745, https://doi.org/10.5194/hess-17-2735-2013, 2013.
Zhang, W.: Who Owns and Rents Iowa's Farmland?, Ag Decis. Mak., C2-78, 1–7, 2015.
Zhou, X., Helmers, M. J., Asbjornsen, H., Kolka, R., and Tomer, M. D.:
Perennial Filter Strips Reduce Nitrate Levels in Soil and Shallow Groundwater after Grassland-to-Cropland Conversion, J. Environ. Qual., 39, 2006, https://doi.org/10.2134/jeq2010.0151, 2010.
Zhou, X., Helmers, M. J., Asbjornsen, H., Kolka, R., Tomer, M. D., and Cruse,
R. M.: Nutrient removal by prairie filter strips in agricultural landscapes,
J. Soil Water Conserv., 69, 54–64, https://doi.org/10.2489/jswc.69.1.54, 2014.
Short summary
We describe a socio-hydrologic model that couples an agent-based model (ABM) of human decision-making with a hydrologic model. We establish this model for a typical agricultural watershed in Iowa, USA, and simulate the evolution of large discharge events over a 47-year period under changing land use. Using this modeling approach, relationships between seemingly unrelated variables such as crop markets or crop yields and local peak flow trends are quantified.
We describe a socio-hydrologic model that couples an agent-based model (ABM) of human...
