Articles | Volume 26, issue 21
Hydrol. Earth Syst. Sci., 26, 5627–5646, 2022
https://doi.org/10.5194/hess-26-5627-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Hydrol. Earth Syst. Sci., 26, 5627–5646, 2022
https://doi.org/10.5194/hess-26-5627-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
10 Nov 2022
Research article
| 10 Nov 2022
On the value of satellite remote sensing to reduce uncertainties of regional simulations of the Colorado River
Mu Xiao et al.
Related authors
No articles found.
Naika Meili, Gabriele Manoli, Paolo Burlando, Elie Bou-Zeid, Winston T. L. Chow, Andrew M. Coutts, Edoardo Daly, Kerry A. Nice, Matthias Roth, Nigel J. Tapper, Erik Velasco, Enrique R. Vivoni, and Simone Fatichi
Geosci. Model Dev., 13, 335–362, https://doi.org/10.5194/gmd-13-335-2020, https://doi.org/10.5194/gmd-13-335-2020, 2020
Short summary
Short summary
We developed a novel urban ecohydrological model (UT&C v1.0) that is able to account for the effects of different plant types on the urban climate and hydrology, as well as the effects of the urban environment on plant well-being and performance. UT&C performs well when compared against energy flux measurements in three cities in different climates (Singapore, Melbourne, Phoenix) and can be used to assess urban climate mitigation strategies that aim at increasing or changing urban green cover.
Enrica Perra, Monica Piras, Roberto Deidda, Claudio Paniconi, Giuseppe Mascaro, Enrique R. Vivoni, Pierluigi Cau, Pier Andrea Marras, Ralf Ludwig, and Swen Meyer
Hydrol. Earth Syst. Sci., 22, 4125–4143, https://doi.org/10.5194/hess-22-4125-2018, https://doi.org/10.5194/hess-22-4125-2018, 2018
Katrina E. Bennett, Theodore J. Bohn, Kurt Solander, Nathan G. McDowell, Chonggang Xu, Enrique Vivoni, and Richard S. Middleton
Hydrol. Earth Syst. Sci., 22, 709–725, https://doi.org/10.5194/hess-22-709-2018, https://doi.org/10.5194/hess-22-709-2018, 2018
Short summary
Short summary
We applied the Variable Infiltration Capacity hydrologic model to examine scenarios of change under climate and landscape disturbances in the San Juan River basin, a major sub-watershed of the Colorado River basin. Climate change coupled with landscape disturbance leads to reduced streamflow in the San Juan River basin. Disturbances are expected to be widespread in this region. Therefore, accounting for these changes within the context of climate change is imperative for water resource planning.
A. P. Schreiner-McGraw, E. R. Vivoni, G. Mascaro, and T. E. Franz
Hydrol. Earth Syst. Sci., 20, 329–345, https://doi.org/10.5194/hess-20-329-2016, https://doi.org/10.5194/hess-20-329-2016, 2016
Short summary
Short summary
Soil moisture estimates from a novel method were evaluated in two semiarid watersheds. We found good agreements between the technique and estimates derived from watershed instruments designed to close the water balance. We then investigated local hydrologic processes and link between evapotranspiration and soil moisture obtained from the novel measurements.
M. Piras, G. Mascaro, R. Deidda, and E. R. Vivoni
Hydrol. Earth Syst. Sci., 18, 5201–5217, https://doi.org/10.5194/hess-18-5201-2014, https://doi.org/10.5194/hess-18-5201-2014, 2014
Short summary
Short summary
We quantified the hydrologic impacts of climate change in the Rio Mannu basin (472.5 km2), Sardinia, Italy.
We created high-resolution climate forcings for a physically based distributed hydrologic model by combining four climate models with two statistical downscaling tools of precipitation and potential evapotranspiration. A significant diminution of mean annual runoff at the basin outlet (mean of -32%), and a reduction of soil water content and actual evapotranspiration are expected.
G. Mascaro, M. Piras, R. Deidda, and E. R. Vivoni
Hydrol. Earth Syst. Sci., 17, 4143–4158, https://doi.org/10.5194/hess-17-4143-2013, https://doi.org/10.5194/hess-17-4143-2013, 2013
G. Mascaro, R. Deidda, and M. Hellies
Hydrol. Earth Syst. Sci., 17, 355–369, https://doi.org/10.5194/hess-17-355-2013, https://doi.org/10.5194/hess-17-355-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
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
High-resolution satellite products improve hydrological modeling in northern Italy
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
Deep learning rainfall–runoff predictions of extreme events
Diel streamflow cycles suggest more sensitive snowmelt-driven streamflow to climate change than land surface modeling does
Teaching hydrological modelling: illustrating model structure uncertainty with a ready-to-use computational exercise
Effects of spatial and temporal variability in surface water inputs on streamflow generation and cessation in the rain–snow transition zone
Quantifying multi-year hydrological memory with Catchment Forgetting Curves
On constraining a lumped hydrological model with both piezometry and streamflow: results of a large sample evaluation
Influences of land use changes on the dynamics of water quantity and quality in the German lowland catchment of the Stör
Impact of spatial distribution information of rainfall in runoff simulation using deep learning method
Towards effective drought monitoring in the Middle East and North Africa (MENA) region: implications from assimilating leaf area index and soil moisture into the Noah-MP land surface model for Morocco
The effects of spatial and temporal resolution of gridded meteorological forcing on watershed hydrological responses
Hydrological response of a peri-urban catchment exploiting conventional and unconventional rainfall observations: the case study of Lambro Catchment
Disentangling Scatter in Long-Term Concentration-Discharge Relationships: the Role of Event Types
Simulating the hydrological impacts of land use conversion from annual crop to perennial forages in the Canadian Prairies using the Cold Regions Hydrological Model
Assessing hydrological sensitivity of grassland basins in the Canadian Prairies to climate using a basin classification-based virtual modelling approach
The value of satellite soil moisture and snow cover data for the transfer of hydrological model parameters to ungauged sites
Storylines of UK drought based on the 2010–2012 event
Uncertainty estimation with deep learning for rainfall–runoff modeling
Applying non-parametric Bayesian networks to estimate maximum daily river discharge: potential and challenges
Contrasting changes in hydrological processes of the Volta River basin under global warming
A retrospective on hydrological catchment modelling based on half a century with the HBV model
Ecosystem adaptation to climate change: the sensitivity of hydrological predictions to time-dynamic model parameters
Rainfall–runoff relationships at event scale in western Mediterranean ephemeral streams
Combined impacts of uncertainty in precipitation and air temperature on simulated mountain system recharge from an integrated hydrologic model
Simultaneous assimilation of water levels from river gauges and satellite flood maps for near-real-time flood mapping
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.
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.
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.
Jonathan M. Frame, Frederik Kratzert, Daniel Klotz, Martin Gauch, Guy Shalev, Oren Gilon, Logan M. Qualls, Hoshin V. Gupta, and Grey S. Nearing
Hydrol. Earth Syst. Sci., 26, 3377–3392, https://doi.org/10.5194/hess-26-3377-2022, https://doi.org/10.5194/hess-26-3377-2022, 2022
Short summary
Short summary
The most accurate rainfall–runoff predictions are currently based on deep learning. There is a concern among hydrologists that deep learning models may not be reliable in extrapolation or for predicting extreme events. This study tests that hypothesis. The deep learning models remained relatively accurate in predicting extreme events compared with traditional models, even when extreme events were not included in the training set.
Sebastian A. Krogh, Lucia Scaff, James W. Kirchner, Beatrice Gordon, Gary Sterle, and Adrian Harpold
Hydrol. Earth Syst. Sci., 26, 3393–3417, https://doi.org/10.5194/hess-26-3393-2022, https://doi.org/10.5194/hess-26-3393-2022, 2022
Short summary
Short summary
We present a new way to detect snowmelt using daily cycles in streamflow driven by solar radiation. Results show that warmer sites have earlier and more intermittent snowmelt than colder sites, and the timing of early snowmelt events is strongly correlated with the timing of streamflow volume. A space-for-time substitution shows greater sensitivity of streamflow timing to climate change in colder rather than in warmer places, which is then contrasted with land surface simulations.
Wouter J. M. Knoben and Diana Spieler
Hydrol. Earth Syst. Sci., 26, 3299–3314, https://doi.org/10.5194/hess-26-3299-2022, https://doi.org/10.5194/hess-26-3299-2022, 2022
Short summary
Short summary
This paper introduces educational materials that can be used to teach students about model structure uncertainty in hydrological modelling. There are many different hydrological models and differences between these models impact their usefulness in different places. Such models are often used to support decision making about water resources and to perform hydrological science, and it is thus important for students to understand that model choice matters.
Leonie Kiewiet, Ernesto Trujillo, Andrew Hedrick, Scott Havens, Katherine Hale, Mark Seyfried, Stephanie Kampf, and Sarah E. Godsey
Hydrol. Earth Syst. Sci., 26, 2779–2796, https://doi.org/10.5194/hess-26-2779-2022, https://doi.org/10.5194/hess-26-2779-2022, 2022
Short summary
Short summary
Climate change affects precipitation phase, which can propagate into changes in streamflow timing and magnitude. This study examines how variations in rainfall and snowmelt affect discharge. We found that annual discharge and stream cessation depended on the magnitude and timing of rainfall and snowmelt and on the snowpack melt-out date. This highlights the importance of precipitation timing and emphasizes the need for spatiotemporally distributed simulations of snowpack and rainfall dynamics.
Alban de Lavenne, Vazken Andréassian, Louise Crochemore, Göran Lindström, and Berit Arheimer
Hydrol. Earth Syst. Sci., 26, 2715–2732, https://doi.org/10.5194/hess-26-2715-2022, https://doi.org/10.5194/hess-26-2715-2022, 2022
Short summary
Short summary
A watershed remembers the past to some extent, and this memory influences its behavior. This memory is defined by the ability to store past rainfall for several years. By releasing this water into the river or the atmosphere, it tends to forget. We describe how this memory fades over time in France and Sweden. A few watersheds show a multi-year memory. It increases with the influence of groundwater or dry conditions. After 3 or 4 years, they behave independently of the past.
Antoine Pelletier and Vazken Andréassian
Hydrol. Earth Syst. Sci., 26, 2733–2758, https://doi.org/10.5194/hess-26-2733-2022, https://doi.org/10.5194/hess-26-2733-2022, 2022
Short summary
Short summary
A large part of the water cycle takes place underground. In many places, the soil stores water during the wet periods and can release it all year long, which is particularly visible when the river level is low. Modelling tools that are used to simulate and forecast the behaviour of the river struggle to represent this. We improved an existing model to take underground water into account using measurements of the soil water content. Results allow us make recommendations for model users.
Chaogui Lei, Paul D. Wagner, and Nicola Fohrer
Hydrol. Earth Syst. Sci., 26, 2561–2582, https://doi.org/10.5194/hess-26-2561-2022, https://doi.org/10.5194/hess-26-2561-2022, 2022
Short summary
Short summary
We presented an integrated approach to hydrologic modeling and partial least squares regression quantifying land use change impacts on water and nutrient balance over 3 decades. Results highlight that most variations (70 %–80 %) in water quantity and quality variables are explained by changes in land use class-specific areas and landscape metrics. Arable land influences water quantity and quality the most. The study provides insights on water resources management in rural lowland catchments.
Yang Wang and Hassan A. Karimi
Hydrol. Earth Syst. Sci., 26, 2387–2403, https://doi.org/10.5194/hess-26-2387-2022, https://doi.org/10.5194/hess-26-2387-2022, 2022
Short summary
Short summary
We found that rainfall data with spatial information can improve the model's performance, especially when simulating the future multi-day discharges. We did not observe that regional LSTM as a regional model achieved better results than LSTM as individual model. This conclusion applies to both one-day and multi-day simulations. However, we found that using spatially distributed rainfall data can reduce the difference between individual LSTM and regional LSTM.
Wanshu Nie, Sujay V. Kumar, Kristi R. Arsenault, Christa D. Peters-Lidard, Iliana E. Mladenova, Karim Bergaoui, Abheera Hazra, Benjamin F. Zaitchik, Sarith P. Mahanama, Rachael McDonnell, David M. Mocko, and Mahdi Navari
Hydrol. Earth Syst. Sci., 26, 2365–2386, https://doi.org/10.5194/hess-26-2365-2022, https://doi.org/10.5194/hess-26-2365-2022, 2022
Short summary
Short summary
The MENA (Middle East and North Africa) region faces significant food and water insecurity and hydrological hazards. Here we investigate the value of assimilating remote sensing data sets into an Earth system model to help build an effective drought monitoring system and support risk mitigation and management by countries in the region. We highlight incorporating satellite-informed vegetation conditions into the model as being one of the key processes for a successful application for the region.
Pin Shuai, Xingyuan Chen, Utkarsh Mital, Ethan T. Coon, and Dipankar Dwivedi
Hydrol. Earth Syst. Sci., 26, 2245–2276, https://doi.org/10.5194/hess-26-2245-2022, https://doi.org/10.5194/hess-26-2245-2022, 2022
Short summary
Short summary
Using an integrated watershed model, we compared simulated watershed hydrologic variables driven by three publicly available gridded meteorological forcings (GMFs) at various spatial and temporal resolutions. Our results demonstrated that spatially distributed variables are sensitive to the spatial resolution of the GMF. The temporal resolution of the GMF impacts the dynamics of watershed responses. The choice of GMF depends on the quantity of interest and its spatial and temporal scales.
Greta Cazzaniga, Carlo De Michele, Michele D'Amico, Cristina Deidda, Antonio Ghezzi, and Roberto Nebuloni
Hydrol. Earth Syst. Sci., 26, 2093–2111, https://doi.org/10.5194/hess-26-2093-2022, https://doi.org/10.5194/hess-26-2093-2022, 2022
Short summary
Short summary
Rainfall estimates are usually obtained from rain gauges, weather radars, or satellites. An alternative is the measurement of the signal loss induced by rainfall on commercial microwave links (CMLs). In this work, we assess the hydrologic response of Lambro Basin when CML-retrieved rainfall is used as model input. CML estimates agree with rain gauge data. CML-driven discharge simulations show performance comparable to that from rain gauges if a CML-based calibration of the model is undertaken.
Felipe Saavedra, Andreas Musolff, Jana von Freyberg, Ralf Merz, Stefano Basso, and Larisa Tarasova
EGUsphere, https://doi.org/10.5194/egusphere-2022-205, https://doi.org/10.5194/egusphere-2022-205, 2022
Short summary
Short summary
Nitrate contamination of rivers from agricultural sources is a challenging problem for water quality management. During runoff events of different generation processes, different transport paths within the catchment might be activated, generating a variety of responses in nitrate concentration in stream water. Using data for 184 German catchments we demonstrate that wetness conditions during runoff events at the time of sampling are a key control of nitrate transport from catchments to streams.
Marcos R. C. Cordeiro, Kang Liang, Henry F. Wilson, Jason Vanrobaeys, David A. Lobb, Xing Fang, and John W. Pomeroy
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-128, https://doi.org/10.5194/hess-2022-128, 2022
Revised manuscript accepted for HESS
Short summary
Short summary
This study addresses an issue of increasing interest about 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) modeling study is expected to provide stakeholders with science-based information regarding the hydrological impacts of land use conversion from annual crop to perennial forages in the Canadian Prairies.
Christopher Spence, Zhihua He, Kevin R. Shook, Balew A. Mekonnen, John W. Pomeroy, Colin J. Whitfield, and Jared D. Wolfe
Hydrol. Earth Syst. Sci., 26, 1801–1819, https://doi.org/10.5194/hess-26-1801-2022, https://doi.org/10.5194/hess-26-1801-2022, 2022
Short summary
Short summary
We determined how snow and flow in small creeks change with temperature and precipitation in the Canadian Prairie, a region where water resources are often under stress. We tried something new. Every watershed in the region was placed in one of seven groups based on their landscape traits. We selected one of these groups and used its traits to build a model of snow and streamflow. It worked well, and by the 2040s there may be 20 %–40 % less snow and 30 % less streamflow than the 1980s.
Rui Tong, Juraj Parajka, Borbála Széles, Isabella Greimeister-Pfeil, Mariette Vreugdenhil, Jürgen Komma, Peter Valent, and Günter Blöschl
Hydrol. Earth Syst. Sci., 26, 1779–1799, https://doi.org/10.5194/hess-26-1779-2022, https://doi.org/10.5194/hess-26-1779-2022, 2022
Short summary
Short summary
The role and impact of using additional data (other than runoff) for the prediction of daily hydrographs in ungauged basins are not well understood. In this study, we assessed the model performance in terms of runoff, soil moisture, and snow cover predictions with the existing regionalization approaches. Results show that the best transfer methods are the similarity and the kriging approaches. The performance of the transfer methods differs between lowland and alpine catchments.
Wilson C. H. Chan, Theodore G. Shepherd, Katie Facer-Childs, Geoff Darch, and Nigel W. Arnell
Hydrol. Earth Syst. Sci., 26, 1755–1777, https://doi.org/10.5194/hess-26-1755-2022, https://doi.org/10.5194/hess-26-1755-2022, 2022
Short summary
Short summary
We select the 2010–2012 UK drought and investigate an alternative unfolding of the drought from changes to its attributes. We created storylines of drier preconditions, alternative seasonal contributions, a third dry winter, and climate change. Storylines of the 2010–2012 drought show alternative situations that could have resulted in worse conditions than observed. Event-based storylines exploring plausible situations are used that may lead to high impacts and help stress test existing systems.
Daniel Klotz, Frederik Kratzert, Martin Gauch, Alden Keefe Sampson, Johannes Brandstetter, Günter Klambauer, Sepp Hochreiter, and Grey Nearing
Hydrol. Earth Syst. Sci., 26, 1673–1693, https://doi.org/10.5194/hess-26-1673-2022, https://doi.org/10.5194/hess-26-1673-2022, 2022
Short summary
Short summary
This contribution evaluates distributional runoff predictions from deep-learning-based approaches. We propose a benchmarking setup and establish four strong baselines. The results show that accurate, precise, and reliable uncertainty estimation can be achieved with deep learning.
Elisa Ragno, Markus Hrachowitz, and Oswaldo Morales-Nápoles
Hydrol. Earth Syst. Sci., 26, 1695–1711, https://doi.org/10.5194/hess-26-1695-2022, https://doi.org/10.5194/hess-26-1695-2022, 2022
Short summary
Short summary
We explore the ability of non-parametric Bayesian networks to reproduce maximum daily discharge in a given month in a catchment when the remaining hydro-meteorological and catchment attributes are known. We show that a saturated network evaluated in an individual catchment can reproduce statistical characteristics of discharge in about ~ 40 % of the cases, while challenges remain when a saturated network considering all the catchments together is evaluated.
Moctar Dembélé, Mathieu Vrac, Natalie Ceperley, Sander J. Zwart, Josh Larsen, Simon J. Dadson, Grégoire Mariéthoz, and Bettina Schaefli
Hydrol. Earth Syst. Sci., 26, 1481–1506, https://doi.org/10.5194/hess-26-1481-2022, https://doi.org/10.5194/hess-26-1481-2022, 2022
Short summary
Short summary
Climate change impacts on water resources in the Volta River basin are investigated under various global warming scenarios. Results reveal contrasting changes in future hydrological processes and water availability, depending on greenhouse gas emission scenarios, with implications for floods and drought occurrence over the 21st century. These findings provide insights for the elaboration of regional adaptation and mitigation strategies for climate change.
Jan Seibert and Sten Bergström
Hydrol. Earth Syst. Sci., 26, 1371–1388, https://doi.org/10.5194/hess-26-1371-2022, https://doi.org/10.5194/hess-26-1371-2022, 2022
Short summary
Short summary
Hydrological catchment models are commonly used as the basis for water resource management planning. The HBV model, which is a typical example of such a model, was first applied about 50 years ago in Sweden. We describe and reflect on the model development and applications. The aim is to provide an understanding of the background of model development and a basis for addressing the balance between model complexity and data availability that will continue to face hydrologists in the future.
Laurène J. E. Bouaziz, Emma E. Aalbers, Albrecht H. Weerts, Mark Hegnauer, Hendrik Buiteveld, Rita Lammersen, Jasper Stam, Eric Sprokkereef, Hubert H. G. Savenije, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 26, 1295–1318, https://doi.org/10.5194/hess-26-1295-2022, https://doi.org/10.5194/hess-26-1295-2022, 2022
Short summary
Short summary
Assuming stationarity of hydrological systems is no longer appropriate when considering land use and climate change. We tested the sensitivity of hydrological predictions to changes in model parameters that reflect ecosystem adaptation to climate and potential land use change. We estimated a 34 % increase in the root zone storage parameter under +2 K global warming, resulting in up to 15 % less streamflow in autumn, due to 14 % higher summer evaporation, compared to a stationary system.
Roberto Serrano-Notivoli, Alberto Martínez-Salvador, Rafael García-Lorenzo, David Espín-Sánchez, and Carmelo Conesa-García
Hydrol. Earth Syst. Sci., 26, 1243–1260, https://doi.org/10.5194/hess-26-1243-2022, https://doi.org/10.5194/hess-26-1243-2022, 2022
Short summary
Short summary
Ephemeral streams in the western Mediterranean area are driven by the duration, magnitude, and intensity of rainfall events (REs). A detailed statistical analysis showed that the average RE (1.2 d and 1.5 mm) is not enough to generate new flow, which is only guaranteed by events occurring in return periods from 2 to > 50 years. REs explain near to 75 % of new flow, meaning that terrain and lithological characteristics play a fundamental role.
Adam P. Schreiner-McGraw and Hoori Ajami
Hydrol. Earth Syst. Sci., 26, 1145–1164, https://doi.org/10.5194/hess-26-1145-2022, https://doi.org/10.5194/hess-26-1145-2022, 2022
Short summary
Short summary
We assess the impact of uncertainty in measurements of precipitation and air temperature on simulated groundwater processes in a mountainous watershed. We illustrate the role of topography in controlling how uncertainty in the input datasets propagates through the soil and into the groundwater. While the focus of previous investigations has been on the impact of precipitation uncertainty, we show that air temperature uncertainty is equally important in controlling the groundwater recharge.
Antonio Annis, Fernando Nardi, and Fabio Castelli
Hydrol. Earth Syst. Sci., 26, 1019–1041, https://doi.org/10.5194/hess-26-1019-2022, https://doi.org/10.5194/hess-26-1019-2022, 2022
Short summary
Short summary
In this work, we proposed a multi-source data assimilation framework for near-real-time flood mapping. We used a quasi-2D hydraulic model to update model states by injecting both stage gauge observations and satellite-derived flood extents. Results showed improvements in terms of water level prediction and reduction of flood extent uncertainty when assimilating both stage gauges and satellite images with respect to the disjoint assimilation of both observations.
Cited articles
Ajami, N. K., Gupta, H., Wagener, T., and Sorooshian, S.: Calibration of a
semi-distributed hydrologic model for streamflow estimation along a river
system, J. Hydrol., 298, 112–135, https://doi.org/10.1016/j.jhydrol.2004.03.033, 2004.
Alder, J. R. and Hostetler, S. W.: The Dependence of Hydroclimate Projections in Snow-Dominated Regions of the Western United States on the Choice of Statistically Downscaled Climate Data, Water Resour. Res., 55, 2279–2300, https://doi.org/10.1029/2018WR023458, 2019.
Andreadis, K. M., Storck, P., and Lettenmaier, D. P.: Modeling snow accumulation and ablation processes in forested environments, Water Resour.
Res., 45, 1–13, https://doi.org/10.1029/2008WR007042, 2009.
Baker, I. T., Sellers, P. J., Denning, A. S., Medina, I., Kraus, P., Haynes,
K. D., and Biraud, S. C.: Closing the scale gap between land surface
parameterizations and GCMs with a new scheme, SiB3-Bins, J. Adv. Model. Earth Syst., 9, 691–711, https://doi.org/10.1002/2016MS000764, 2017.
Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein,
A., Katul, G., Law, B., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel,
W., Paw, U. K. T., Pilegaard, K., Schmid, H. P., Valentini, R., Verma, S.,
Vesala, T., Wilson, K., and Wofsy, S.: FLUXNET: A New Tool to Study the
Temporal and Spatial Variability of Ecosystem-Scale Carbon Dioxide, Water
Vapor, and Energy Flux Densities, B. Am. Meteorol. Soc., 82, 2415–2434, https://doi.org/10.1175/1520-0477(2001)082<2415:FANTTS>2.3.CO;2, 2001.
Becker, R., Koppa, A., Schulz, S., Usman, M., aus der Beek, T., and Schüth, C.: Spatially distributed model calibration of a highly managed
hydrological system using remote sensing-derived ET data, J. Hydrol., 577, 123944, https://doi.org/10.1016/j.jhydrol.2019.123944, 2019.
Bennett, A., Hamman, J., and Nijssen, B.: MetSim: A Python package for estimation and disaggregation of meteorological data, J. Open Source Softw.,
5, 2042, https://doi.org/10.21105/joss.02042, 2020.
Bennett, K. E., Cherry, J. E., Balk, B., and Lindsey, S.: Using MODIS estimates of fractional snow cover area to improve streamflow forecasts in
interior Alaska, Hydrol. Earth Syst. Sci., 23, 2439–2459,
https://doi.org/10.5194/hess-23-2439-2019, 2019.
Bergeron, J., Royer, A., Turcotte, R., and Roy, A.: Snow cover estimation
using blended MODIS and AMSR-E data for improved watershed-scale spring
streamflow simulation in Quebec, Canada, Hydrol. Process., 28, 4626–4639, https://doi.org/10.1002/hyp.10123, 2014.
Beven, K. and Binley, A.: The future of distributed models: Model calibration and uncertainty prediction, Hydrol. Process., 6, 279–298,
https://doi.org/10.1002/hyp.3360060305, 1992.
Bohn, T. J. and Vivoni, E. R.: Process-based characterization of evapotranspiration sources over the North American monsoon region, Water
Resour. Res., 52, 358–384, https://doi.org/10.1002/2015WR017934, 2016.
Bohn, T. J. and Vivoni, E. R.: MOD-LSP, MODIS-based parameters for hydrologic modeling of North American land cover change, Sci. Data, 6, 1–13, https://doi.org/10.1038/s41597-019-0150-2, 2019.
Bohn, T. J., Livneh, B., Oyler, J. W., Running, S. W., Nijssen, B., and
Lettenmaier, D. P.: Global evaluation of MTCLIM and related algorithms for
forcing of ecological and hydrological models, Agr. Forest Meteorol., 176,
38–49, https://doi.org/10.1016/j.agrformet.2013.03.003, 2013.
Boschetti, L., Roy, D. P., Giglio, L., Huang, H., Zubkova, M., and Humber, M.
L.: Global validation of the collection 6 MODIS burned area product, Remote
Sens. Environ., 235, 111490, https://doi.org/10.1016/j.rse.2019.111490, 2019.
CAP: CAP water deliveries, Central Arizona Projec, https://www.cap-az.com/departments/water-operations/deliveries, last access: 4 December 2021.
Cherkauer, K. A. and Lettenmaier, D. P.: Hydrologic effects of frozen soils
in the upper Mississippi River basin, J. Geophys. Res.-Atmos., 104, 19599–19610, https://doi.org/10.1029/1999JD900337, 1999.
Cherkauer, K. A. and Lettenmaier, D. P.: Simulation of spatial variability
in snow and frozen soil, J. Geophys. Res.-Atmos., 108, 1–14,
https://doi.org/10.1029/2003jd003575, 2003.
Cherkauer, K. A., Bowling, L. C., and Lettenmaier, D. P.: Variable infiltration capacity cold land process model updates, Global Planet. Change,
38, 151–159, https://doi.org/10.1016/S0921-8181(03)00025-0, 2003.
Christensen, N. S., Wood, A. W., Voisin, N., Lettenmaier, D. P., and Palmer,
R. N.: The effects of climate change on the hydrology and water resources of
the Colorado River basin, Climatic Change, 62, 337–363,
https://doi.org/10.1023/B:CLIM.0000013684.13621.1f, 2004.
Clark, M. P., Fan, Y., Lawrence, D. M., Adam, J. C., Bolster, D., Gochis, D.
J., Hooper, R. P., Kumar, M., Leung, L. R., Mackay, D. S., and Maxwell, R. M.: Hydrological partitioning in the critical zone: Recent advances and opportunities for developing transferable understanding of water cycle
dynamics, Water Resour. Res., 51, 5929–5956, https://doi.org/10.1002/2015WR017096, 2015.
Corbari, C. and Mancini, M.: Calibration and validation of a distributed
energy-water balance model using satellite data of land surface temperature
and ground discharge measurements, J. Hydrometeorol., 15, 376–392,
https://doi.org/10.1175/JHM-D-12-0173.1, 2014.
Crow, W. T., Wood, E. F., and Pan, M.: Multiobjective calibration of land
surface model evapotranspiration predictions using streamflow observations
and spaceborne surface radiometric temperature retrievals, J. Geophys. Res.-Atmos., 108, 1–12, https://doi.org/10.1029/2002jd003292, 2003.
Di Luzio, M., Johnson, G. L., Daly, C., Eischeid, J. K., and Arnold, J. G.:
Constructing retrospective gridded daily precipitation and temperature
datasets for the conterminous United States, J. Appl. Meteorol. Clim., 47, 475–497, https://doi.org/10.1175/2007JAMC1356.1, 2008.
Entekhabi, D., Njoku, E. G., O'Neill, P. E., Kellogg, K. H., Crow, W. T.,
Edelstein, W. N., Entin, J. K., Goodman, S. D., Jackson, T. J., Johnson, J.,
Kimball, J., Piepmeier, J. R., Koster, R. D., Martin, N., McDonald, K. C.,
Moghaddam, M., Moran, S., Reichle, R., Shi, J. C., Spencer, M. W., Thurman,
S. W., Tsang, L., and Van Zyl, J.: The Soil Moisture Active Passive (SMAP)
Mission, Proc. IEEE, 98, 704–716, https://doi.org/10.1109/JPROC.2010.2043918, 2010.
Fatichi, S., Vivoni, E. R., Ogden, F. L., Ivanov, V. Y., Mirus, B., Gochis,
D., Downer, C. W., Camporese, M., Davison, J. H., Ebel, B., Jones, N., Kim,
J., Mascaro, G., Niswonger, R., Restrepo, P., Rigon, R., Shen, C., Sulis, M.,
and Tarboton, D.: An overview of current applications, challenges, and
future trends in distributed process-based models in hydrology, J. Hydrol.,
537, 45–60, https://doi.org/10.1016/j.jhydrol.2016.03.026, 2016.
Fisher, J. B., Lee, B., Purdy, A. J., Halverson, G. H., Dohlen, M. B.,
Cawse-Nicholson, K., Wang, A., Anderson, R. G., Aragon, B., Arain, M. A.,
Baldocchi, D. D., Baker, J. M., Barral, H., Bernacchi, C. J., Bernhofer, C.,
Biraud, S. C., Bohrer, G., Brunsell, N., Cappelaere, B., Castro-Contreras,
S., Chun, J., Conrad, B. J., Cremonese, E., Demarty, J., Desai, A. R., De
Ligne, A., Foltýnová, L., Goulden, M. L., Griffis, T. J., Grünwald, T., Johnson, M. S., Kang, M., Kelbe, D., Kowalska, N., Lim, J., Maïnassara, I., McCabe, M. F., Missik, J. E. C., Mohanty, B. P., Moore, C. E., Morillas, L., Morrison, R., Munger, J. W., Posse, G., Richardson, A. D., Russell, E. S., Ryu, Y., Sanchez-Azofeifa, A., Schmidt, M., Schwartz, E., Sharp, I., Šigut, L., Tang, Y., Hulley, G., Anderson, M., Hain, C., French, A., Wood, E., and Hook, S.: ECOSTRESS: NASA's Next Generation Mission to Measure Evapotranspiration From the International Space Station, Water Resour. Res., 56, e2019WR026058, https://doi.org/10.1029/2019WR026058, 2020.
Gautam, J. and Mascaro, G.: Evaluation of Coupled Model Intercomparison Project Phase 5 historical simulations in the Colorado River basin, Int. J.
Climatol., 38, 3861–3877, https://doi.org/10.1002/joc.5540, 2018.
Gibson, J., Franz, T. E., Wang, T., Gates, J., Grassini, P., Yang, H., and
Eisenhauer, D.: A case study of field-scale maize irrigation patterns in
western Nebraska: implications for water managers and recommendations for
hyper-resolution land surface modeling, Hydrol. Earth Syst. Sci., 21,
1051–1062, https://doi.org/10.5194/hess-21-1051-2017, 2017.
Gou, J., Miao, C., Wu, J., Guo, X., Samaniego, L., and Xiao, M.: CNRD v1.0: A
High-Quality Natural Runoff Dataset for Hydrological and Climate Studies in
China, B. Am. Meteorol. Soc., 102, E929–E947, https://doi.org/10.1175/BAMS-D-20-0094.1, 2021.
Gupta, A. and Govindaraju, R. S.: Propagation of structural uncertainty in
watershed hydrologic models, J. Hydrol., 575, 66–81,
https://doi.org/10.1016/j.jhydrol.2019.05.026, 2019.
Gutmann, E. D. and Small, E. E.: A method for the determination of the
hydraulic properties of soil from MODIS surface temperature for use in
land-surface models, Water Resour. Res., 46, W06520, https://doi.org/10.1029/2009WR008203, 2010.
Hall, D. K. and Riggs, G. A.: MODIS/Aqua Snow Cover Monthly L3 Global 0.05Deg CMG, Version 61, Boulder, Colorado, USA, NASA National Snow and Ice Data Center Distributed Active Archive Center [data set], https://doi.org/10.5067/MODIS/MYD10CM.061, 2021.
Hamman, J. J., Nijssen, B., Bohn, T. J., Gergel, D. R., and Mao, Y.: The
variable infiltration capacity model version 5 (VIC-5): Infrastructure
improvements for new applications and reproducibility, Geosci. Model Dev.,
11, 3481–3496, https://doi.org/10.5194/gmd-11-3481-2018, 2018.
Hoerling, M., Barsugli, J., Livneh, B., Eischeid, J., Quan, X., and Badger,
A.: Causes for the Century-Long Decline in Colorado River Flow, J. Climate,
32, 8181–8203, https://doi.org/10.1175/jcli-d-19-0207.1, 2019.
Homer, C., Dewitz, J., Jin, S., Xian, G., Costello, C., Danielson, P., Gass,
L., Funk, M., Wickham, J., Stehman, S., Auch, R., and Riitters, K.: Conterminous United States land cover change patterns 2001–2016 from the
2016 National Land Cover Database, ISPRS J. Photogram. Remote Sens., 162, 184–199, https://doi.org/10.1016/j.isprsjprs.2020.02.019, 2020.
Kerr, Y. H., Waldteufel, P., Wigneron, J.-P., Martinuzzi, J., Font, J., and Berger, M.: Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission, IEEE T. Geosci. Remote, 39, 1729–1735, 2001.
Ko, A., Mascaro, G., and Vivoni, E. R.: Strategies to Improve and Evaluate
Physics-Based Hyperresolution Hydrologic Simulations at Regional Basin Scales, Water Resour. Res., 88, 1–24, https://doi.org/10.1029/2018WR023521, 2019.
Koch, J., Siemann, A., Stisen, S., and Sheffield, J.: Spatial validation of
large-scale land surface models against monthly land surface temperature
patterns using innovative performance metrics, J. Geophys. Res.-Atmos., 121, 5430–5452, https://doi.org/10.1002/2015JD024482, 2016.
Koch, J., Demirel, M. C., and Stisen, S.: The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models, Geosci. Model Dev., 11, 1873–1886, https://doi.org/10.5194/gmd-11-1873-2018, 2018.
Lahmers, T. M., Gupta, H., Castro, C. L., Gochis, D. J., Yates, D., Dugger,
A., Goodrich, D., and Hazenberg, P.: Enhancing the structure of the WRF-hydro
hydrologic model for semiarid environments, J. Hydrometeorol., 20, 691–714, https://doi.org/10.1175/JHM-D-18-0064.1, 2019.
Lahmers, T. M., Hazenberg, P., Gupta, H., Castro, C., Gochis, D., Dugger, A., Yates, D., Read, L., Karsten, L., and Wang, Y.-H.: Evaluation of NOAA National Water Model Parameter Calibration in Semi-Arid Environments Prone
to Channel Infiltration, J. Hydrometeorol., 22, 2939–2970, https://doi.org/10.1175/jhm-d-20-0198.1, 2021.
Lawrence, D. M., Oleson, K. W., Flanner, M. G., Thornton, P. E., Swenson, S.
C., Lawrence, P. J., Zeng, X., Yang, Z.-L., Levis, S., Sakaguchi, K., Bonan,
G. B., and Slater, A. G.: Parameterization improvements and functional and
structural advances in Version 4 of the Community Land Model, J. Adv. Model.
Earth Syst., 3, 1–27, https://doi.org/10.1029/2011MS00045, 2011.
Li, D., Wrzesien, M. L., Durand, M., Adam, J., and Lettenmaier, D. P.: How
much runoff originates as snow in the western United States, and how will
that change in the future?, Geophys. Res. Lett., 44, 6163–6172,
https://doi.org/10.1002/2017GL073551, 2017.
Li, D., Lettenmaier, D. P., Margulis, S. A., and Andreadis, K.: The Role of
Rain-on-Snow in Flooding Over the Conterminous United States, Water Resour.
Res., 55, 8492–8513, https://doi.org/10.1029/2019WR024950, 2019.
Liang, X. and Lettenmaier, D.: a simple hydrologically based model of land
surface water and energy fluxes for general circulation models, J. Geophys.
Res.-Atmos., 99, 14415–14428, https://doi.org/10.1029/94JD00483, 1994.
Livneh, B., Rosenberg, E. A., Lin, C., Nijssen, B., Mishra, V., Andreadis,
K. M., Maurer, E. P., and Lettenmaier, D. P.: A long-term hydrologically based dataset of land surface fluxes and states for the conterminous States: Update and extensions, J. Climate, 26, 9384–9392,
https://doi.org/10.1175/JCLI-D-12-00508.1, 2013.
Livneh, B., Bohn, T. J., Pierce, D. W., Munoz-Arriola, F., Nijssen, B., Vose, R., Cayan, D. R., and Brekke, L.: A spatially comprehensive, hydrometeorological data set for Mexico, the U.S., and Southern Canada
1950–2013, Sci. Data, 2, 1–12, https://doi.org/10.1038/sdata.2015.42, 2015.
Maidment, D. R.: Conceptual Framework for the National Flood Interoperability Experiment, J. Am. Water Resour. Assoc., 53, 245–257, https://doi.org/10.1111/1752-1688.12474, 2017.
Mitchell, J. P., Shrestha, A., Epstein, L., Dahlberg, J. A., Ghezzehei, T.,
Araya, S., Richter, B., Kaur, S., Henry, P., Munk, D. S., Light, S., Bottens, M., and Zaccaria, D.: No-tillage sorghum and garbanzo yields match or exceed standard tillage yields, Calif. Agric., 75, 112–120, https://doi.org/10.3733/ca.2021a0017, 2022.
MRLC: Multi-Resolution Land Characteristics (MRLC) Consortium, Multi-Resolution Land Characteristics (MRLC) Consortium, https://www.mrlc.gov/, last access: 6 November 2022.
Nijssen, B., Lettenmaier, D. P., Liang, X., Wetzel, S. W., and Wood, E. F.: Streamflow simulation for continental-scale river basins, Water Resour. Res., 33, 711–724, https://doi.org/10.1029/96WR03517, 1997.
Niu, G. Y., Yang, Z. L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options (Noah-MP): 1. Model description and evaluation with local-scale
measurements, J. Geophys. Res.-Atmos., 116, 1–19, https://doi.org/10.1029/2010JD015139, 2011.
Njoku, E. G., Jackson, T. J., Lakshmi, V., Chan, T. K., and Nghiem, S. V.: Soil moisture retrieval from AMSR-E. Geoscience and Remote Sensing, T. IEEE, 41, 215–229, 2003.
Norton, C. L., Dannenberg, M. P., Yan, D., Wallace, C. S. A., Rodriguez, J.
R., Munson, S. M., van Leeuwen, W. J. D., and Smith, W. K.: Climate and
Socioeconomic Factors Drive Irrigated Agriculture Dynamics in the Lower
Colorado River Basin, Remote Sens., 13, 1659, https://doi.org/10.3390/rs13091659, 2021.
Painter, T. H., Rittger, K., McKenzie, C., Slaughter, P., Davis, R. E., and
Dozier, J.: Retrieval of subpixel snow covered area, grain size, and albedo
from MODIS, Remote Sens. Environ., 113, 868–879, https://doi.org/10.1016/j.rse.2009.01.001, 2009.
Rajagopalan, B., Nowak, K., Prairie, J., Hoerling, M., Harding, B., Barsugli, J., Ray, A., and Udall, B.: Water supply risk on the Colorado River: Can management mitigate?, Water Resour. Res., 45, 1–7,
https://doi.org/10.1029/2008WR007652, 2009.
Rajib, A., Evenson, G. R., Golden, H. E., and Lane, C. R.: Hydrologic model
predictability improves with spatially explicit calibration using remotely
sensed evapotranspiration and biophysical parameters, J. Hydrol., 567, 668–683, https://doi.org/10.1016/j.jhydrol.2018.10.024, 2018.
Senatore, A., Mendicino, G., Gochis, D. J., Yu, W., Yates, D. N., and Kunstmann, H.: Fully coupled atmosphere-hydrology simulations for the central Mediterranean: Impact of enhanced hydrological parameterization for short and long time scales, J. Adv. Model. Earth Syst., 7, 1693–1715, https://doi.org/10.1002/2015MS000510, 2015.
Shi, L. and Bates, J. J.: Three decades of intersatellite-calibrated
High-Resolution Infrared Radiation Sounder upper tropospheric water vapor, J. Geophys. Res., 116, D04108, https://doi.org/10.1029/2010JD014847, 2011.
Su, L., Cao, Q., Xiao, M., Mocko, D. M., Barlage, M., Li, D., Peters-Lidard,
C. D., and Lettenmaier, D. P.: Drought Variability over the Conterminous
United States for the Past Century, J. Hydrometeorol., 1153–1168,
https://doi.org/10.1175/jhm-d-20-0158.1, 2021.
Tang, Q. and Lettenmaier, D. P.: Use of satellite snow-cover data for streamflow prediction in the Feather River Basin, California, Int. J. Remote
Sens., 31, 3745–3762, https://doi.org/10.1080/01431161.2010.483493, 2010.
Tapley, B. D., Bettadpur, S., Ries, J. C., Thompson, P. F., and Watkins, M.
M.: GRACE Measurements of Mass Variability in the Earth System, Science, 305, 503–505, https://doi.org/10.1126/science.1099192, 2004.
Tekeli, A. E., Akyürek, Z., Şorman, A., Şensoy, A., and Şorman, Ü. A.: Using MODIS snow cover maps in modeling snowmelt runoff process in the eastern part of Turkey, Remote Sens. Environ., 97, 216–230, https://doi.org/10.1016/j.rse.2005.03.013, 2005.
Tobin, K. J. and Bennett, M. E.: Constraining SWAT Calibration with Remotely
Sensed Evapotranspiration Data, J. Am. Water Resour. Assoc., 53, 593–604, https://doi.org/10.1111/1752-1688.12516, 2017.
Udall, B. and Overpeck, J.: The twenty-first century Colorado River hot drought and implications for the future, Water Resour. Res., 53, 2404–2418, https://doi.org/10.1002/2016WR019638, 2017.
USBR – US Bureau of Reclamation: Colorado River Basin Water Supply and Demand Study, Washington, DC, https://www.usbr.gov/lc/region/programs/crbstudy/finalreport/index.html
(last access: 7 November 2022), 2012.
US Bureau of Reclamation: Boulder Canyon Operations Office, Lower Colorado Region, Bureau of Reclamation, https://www.usbr.gov/lc/region/g4000/NaturalFlow/documentation.html, last access: 6 November 2022.
UDGS – US Geological Survey: 1 Arc-second Digital Elevation Models (DEMs) – USGS National Map 3DEP Downloadable Data Collection, US Geological Survey https://www.sciencebase.gov/catalog/item/4f70aa71e4b058caae3f8de1, last access: 8 November 2022), 2017.
Vano, J. A., Das, T., and Lettenmaier, D. P.: Hydrologic Sensitivities of
Colorado River Runoff to Changes in Precipitation and Temperature, J.
Hydrometeorol., 13, 932–949, https://doi.org/10.1175/JHM-D-11-069.1, 2012.
Vano, J. A., Udall, B., Cayan, D. R., Overpeck, J. T., Brekke, L. D., Das,
T., Hartmann, H. C., Hidalgo, H. G., Hoerling, M., McCabe, G. J., Morino, K., Webb, R. S., Werner, K., and Lettenmaier, D. P.: Understanding Uncertainties in Future Colorado River Streamflow, B. Am. Meteorol. Soc., 95, 59–78, https://doi.org/10.1175/BAMS-D-12-00228.1, 2014.
Wan, Z. and Dozier, J.: A generalized split-window algorithm for retrieving land-surface temperature from space, IEEE T. Geosci. Remote, 34, 892–905, https://doi.org/10.1109/36.508406, 1996.
Wan, Z., Hook, S., and Hulley, G.: MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1 km SIN Grid V061 NASA EOSDIS Land Processes DAAC, USGS [data set], https://doi.org/10.5067/MODIS/MYD11A1.061, 2021.
Wang, Z. and Bovik, A. C.: A universal image quality index, IEEE Signal Process. Lett., 9, 81–84, https://doi.org/10.1109/97.995823, 2002.
Wang, Z., Vivoni, E. R., Bohn, T. J., and Wang, Z. H.: A Multiyear Assessment
of Irrigation Cooling Capacity in Agricultural and Urban Settings of Central
Arizona, J. Am. Water Resour. Assoc., 57, 771–788, https://doi.org/10.1111/1752-1688.12920, 2021.
Wood, E. F., Roundy, J. K., Troy, T. J., van Beek, L. P. H., Bierkens, M. F.
P., Blyth, E., de Roo, A., Döll, P., Ek, M., Famiglietti, J., Gochis,
D., van de Giesen, N., Houser, P., Jaffé, P. R., Kollet, S., Lehner, B.,
Lettenmaier, D. P., Peters-Lidard, C., Sivapalan, M., Sheffield, J., Wade, A., and Whitehead, P.: Hyperresolution global land surface modeling: Meeting
a grand challenge for monitoring Earth's terrestrial water, Water Resour. Res., 47, W05301, https://doi.org/10.1029/2010WR010090, 2011.
Xiang, T., Vivoni, E. R., and Gochis, D. J.: Seasonal evolution ecohydrological controls on land surface temperature over complex terrain,
Water Resour. Res., 50, 3852–3874, https://doi.org/10.1002/2013WR014787, 2014.
Xiang, T., Vivoni, E. R., Gochis, D. J., and Mascaro, G.: On the diurnal cycle of surface energy fluxes in the North American monsoon region using the WRF-Hydro modeling system, J. Geophys. Res.-Atmos., 122, 9024–9049,
https://doi.org/10.1002/2017JD026472, 2017.
Xiao, M.: VIC5 source code, parameter for the Colorado River Basin and USBR natural flow data records, Zenodo [code], https://doi.org/10.5281/zenodo.7115169, 2022.
Xiao, M., Udall, B., and Lettenmaier, D. P.: On the causes of declining Colorado River streamflows, Water Resour. Res., 2, 1–18,
https://doi.org/10.1029/2018WR023153, 2018.
Yun, X., Tang, Q., Wang, J., Liu, X., Zhang, Y., Lu, H., Wang, Y., Zhang, L.,
and Chen, D.: Impacts of climate change and reservoir operation on streamflow and flood characteristics in the Lancang-Mekong River Basin, J. Hydrol., 590, 125472, https://doi.org/10.1016/j.jhydrol.2020.125472, 2020.
Zhang, Y., You, Q., Chen, C., and Li, X.: Flash droughts in a typical humid
and subtropical basin: A case study in the Gan River Basin, China, J. Hydrol., 551, 162–176, https://doi.org/10.1016/j.jhydrol.2017.05.044, 2017.
Zhao, R. J., Zhang, Y. L., Fang, L. R., Liu, X. R., and Zhang, Q. S.: The Xinanjiang model, IAHS Publ., 129, 351–356, 1980.
Zhou, Q., Yang, S., Zhao, C., Cai, M., Lou, H., Luo, Y., and Hou, L.:
Development and implementation of a spatial unit non-overlapping water
stress index for water scarcity evaluation with a moderate spatial resolution, Ecol. Indic., 69, 422–433, https://doi.org/10.1016/j.ecolind.2016.05.006,
2016.
Zink, M., Mai, J., Cuntz, M., and Samaniego, L.: Conditioning a Hydrologic
Model Using Patterns of Remotely Sensed Land Surface Temperature, Water
Resour. Res., 54, 2976–2998, https://doi.org/10.1002/2017WR021346, 2018.
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.
As the major water resource in the southwestern United States, the Colorado River is...
