Articles | Volume 26, issue 9
https://doi.org/10.5194/hess-26-2365-2022
© Author(s) 2022. 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-26-2365-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
06 May 2022
Research article |
| 06 May 2022
| 06 May 2022
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
Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA
NASA Goddard Earth and Sciences Technology and Research (GESTAR), Greenbelt, Maryland, USA
Sujay V. Kumar
Hydrological Science Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Kristi R. Arsenault
Hydrological Science Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Science Applications International Corporation, McLean, Virginia, USA
Christa D. Peters-Lidard
Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Iliana E. Mladenova
USDA Foreign Agricultural Service, Washington, DC, USA
Karim Bergaoui
ACQUATEC Solutions, Dubai Technology Entrepreneur Campus (DTEC), Dubai Silicon Oasis, Dubai, UAE
Abheera Hazra
Hydrological Science Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
Benjamin F. Zaitchik
Department of Earth and Planetary Sciences, Johns Hopkins University, Baltimore, Maryland, USA
Sarith P. Mahanama
Science Systems and Applications Inc., Lanham, Maryland, USA
Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Rachael McDonnell
Water, Climate Change and Resilience Program, International Water Management Institute – Rome office, Rome, Italy
David M. Mocko
Hydrological Science Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Science Applications International Corporation, McLean, Virginia, USA
Mahdi Navari
Hydrological Science Laboratory, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland, USA
Related authors
No articles found.
Peyman Abbaszadeh, Fadji Zaouna Maina, Chen Yang, Dan Rosen, Sujay Kumar, Matthew Rodell, and Reed Maxwell
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-280, https://doi.org/10.5194/hess-2024-280, 2024
Preprint under review for HESS
Short summary
Short summary
To manage Earth's water resources effectively amid climate change, it's crucial to understand both surface and groundwater processes. We developed a new modeling system that combines two advanced tools, ParFlow and LIS/Noah-MP, to better simulate both land surface and groundwater interactions. By testing this integrated model in the Upper Colorado River Basin, we found it improves predictions of hydrologic processes, especially in complex terrains.
Louise Busschaert, Michel Bechtold, Sara Modanesi, Christian Massari, Dirk Raes, Sujay V. Kumar, and Gabrielle J. M. De Lannoy
EGUsphere, https://doi.org/10.2139/ssrn.4974019, https://doi.org/10.2139/ssrn.4974019, 2024
Short summary
Short summary
This study estimates irrigation in the Po Valley using AquaCrop and Noah-MP models with sprinkler irrigation. Noah-MP shows higher annual rates than AquaCrop due to more water losses. After adjusting, both align with reported irrigation ranges (500–600 mm/yr). Soil moisture estimates from both models match satellite data, though both have limitations in vegetation and evapotranspiration modeling. The study emphasizes the need for observations to improve irrigation estimates.
Pengfei Xue, Chenfu Huang, Yafang Zhong, Michael Notaro, Miraj B. Kayastha, Xing Zhou, Chuyan Zhao, Christa Peters-Lidard, Carlos Cruz, and Eric Kemp
Geosci. Model Dev. Discuss., https://doi.org/10.5194/gmd-2024-146, https://doi.org/10.5194/gmd-2024-146, 2024
Preprint under review for GMD
Short summary
Short summary
This study introduces a new lake-ice-atmosphere coupled model that significantly improves winter climate simulation for the Great Lakes compared to traditional one-dimensional (1D) lake models. It better simulates both lake conditions and over-lake atmospheric conditions. More importantly, the study highlights three critical 3D lake processes—ice movement, heat transport, and turbulent mixing—as essential for accurately simulating lake-atmosphere interactions and the Great Lakes’ winter climate.
Min Huang, Gregory R. Carmichael, James H. Crawford, Kevin W. Bowman, Isabelle De Smedt, Andreas Colliander, Michael H. Cosh, Sujay V. Kumar, Alex B. Guenther, Scott J. Janz, Ryan M. Stauffer, Anne M. Thompson, Niko M. Fedkin, Robert J. Swap, John D. Bolten, and Alicia T. Joseph
EGUsphere, https://doi.org/10.5194/egusphere-2024-484, https://doi.org/10.5194/egusphere-2024-484, 2024
Short summary
Short summary
This study uses model simulations along with multiplatform, multidisciplinary observations and a range of analysis methods to estimate and understand the distributions, temporal changes, and impacts of reactive nitrogen and ozone over the most populous US region that has undergone significant environmental changes. Deposition, biogenic emissions, and extra-regional sources have been playing increasingly important roles in controlling pollutants’ budgets in this area as local emissions go down.
Justin M. Pflug, Melissa L. Wrzesien, Sujay V. Kumar, Eunsang Cho, Kristi R. Arsenault, Paul R. Houser, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 28, 631–648, https://doi.org/10.5194/hess-28-631-2024, https://doi.org/10.5194/hess-28-631-2024, 2024
Short summary
Short summary
Estimates of 250 m of snow water equivalent in the western USA and Canada are improved by assimilating observations representative of a snow-focused satellite mission with a land surface model. Here, by including a gap-filling strategy, snow estimates could be improved in forested regions where remote sensing is challenging. This approach improved estimates of winter maximum snow water volume to within 4 %, on average, with persistent improvements to both spring snow and runoff in many regions.
Eunsang Cho, Yonghwan Kwon, Sujay V. Kumar, and Carrie M. Vuyovich
Hydrol. Earth Syst. Sci., 27, 4039–4056, https://doi.org/10.5194/hess-27-4039-2023, https://doi.org/10.5194/hess-27-4039-2023, 2023
Short summary
Short summary
An airborne gamma-ray remote-sensing technique provides reliable snow water equivalent (SWE) in a forested area where remote-sensing techniques (e.g., passive microwave) typically have large uncertainties. Here, we explore the utility of assimilating the gamma snow data into a land surface model to improve the modeled SWE estimates in the northeastern US. Results provide new insights into utilizing the gamma SWE data for enhanced land surface model simulations in forested environments.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere, 17, 3915–3931, https://doi.org/10.5194/tc-17-3915-2023, https://doi.org/10.5194/tc-17-3915-2023, 2023
Short summary
Short summary
As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (a so-called observing system simulation experiment) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, Rhae Sung Kim, and Jennifer M. Jacobs
Hydrol. Earth Syst. Sci., 26, 5721–5735, https://doi.org/10.5194/hess-26-5721-2022, https://doi.org/10.5194/hess-26-5721-2022, 2022
Short summary
Short summary
While land surface models are a common approach for estimating macroscale snow water equivalent (SWE), the SWE accuracy is often limited by uncertainties in model physics and forcing inputs. In this study, we found large underestimations of modeled SWE compared to observations. Precipitation forcings and melting physics limitations dominantly contribute to the SWE underestimations. Results provide insights into prioritizing strategies to improve the SWE simulations for hydrologic applications.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
Short summary
Short summary
Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Amy McNally, Jossy Jacob, Kristi Arsenault, Kimberly Slinski, Daniel P. Sarmiento, Andrew Hoell, Shahriar Pervez, James Rowland, Mike Budde, Sujay Kumar, Christa Peters-Lidard, and James P. Verdin
Earth Syst. Sci. Data, 14, 3115–3135, https://doi.org/10.5194/essd-14-3115-2022, https://doi.org/10.5194/essd-14-3115-2022, 2022
Short summary
Short summary
The Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) global and Central Asia data streams described here generate routine estimates of snow, soil moisture, runoff, and other variables useful for tracking water availability. These data are hosted by NASA and USGS data portals for public use.
Min Huang, James H. Crawford, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Colm Sweeney
Atmos. Chem. Phys., 22, 7461–7487, https://doi.org/10.5194/acp-22-7461-2022, https://doi.org/10.5194/acp-22-7461-2022, 2022
Short summary
Short summary
This study demonstrates that ozone dry-deposition modeling can be improved by revising the model's dry-deposition parameterizations to better represent the effects of environmental conditions including the soil moisture fields. Applying satellite soil moisture data assimilation is shown to also have added value. Such advancements in coupled modeling and data assimilation can benefit the assessments of ozone impacts on human and vegetation health.
Jawairia A. Ahmad, Barton A. Forman, and Sujay V. Kumar
Hydrol. Earth Syst. Sci., 26, 2221–2243, https://doi.org/10.5194/hess-26-2221-2022, https://doi.org/10.5194/hess-26-2221-2022, 2022
Short summary
Short summary
Assimilation of remotely sensed data into a land surface model to improve the spatiotemporal estimation of soil moisture across South Asia exhibits potential. Satellite retrieval assimilation corrects biases that are generated due to an unmodeled hydrologic phenomenon, i.e., irrigation. The improvements in fine-scale, modeled soil moisture estimates by assimilating coarse-scale retrievals indicates the utility of the described methodology for data-scarce regions.
Justin Schulte, Frederick Policelli, and Benjamin Zaitchik
Nonlin. Processes Geophys., 29, 1–15, https://doi.org/10.5194/npg-29-1-2022, https://doi.org/10.5194/npg-29-1-2022, 2022
Short summary
Short summary
The skewness of a time series is commonly used to quantify the extent to which positive (negative) deviations from the mean are larger than negative (positive) ones. However, in some cases, traditional skewness may not provide reliable information about time series skewness, motivating the development of a waveform skewness index in this paper. The waveform skewness index is used to show that changes in the relationship strength between climate time series could arise from changes in skewness.
Huisheng Bian, Eunjee Lee, Randal D. Koster, Donifan Barahona, Mian Chin, Peter R. Colarco, Anton Darmenov, Sarith Mahanama, Michael Manyin, Peter Norris, John Shilling, Hongbin Yu, and Fanwei Zeng
Atmos. Chem. Phys., 21, 14177–14197, https://doi.org/10.5194/acp-21-14177-2021, https://doi.org/10.5194/acp-21-14177-2021, 2021
Short summary
Short summary
The study using the NASA Earth system model shows ~2.6 % increase in burning season gross primary production and ~1.5 % increase in annual net primary production across the Amazon Basin during 2010–2016 due to the change in surface downward direct and diffuse photosynthetically active radiation by biomass burning aerosols. Such an aerosol effect is strongly dependent on the presence of clouds. The cloud fraction at which aerosols switch from stimulating to inhibiting plant growth occurs at ~0.8.
Min Huang, James H. Crawford, Joshua P. DiGangi, Gregory R. Carmichael, Kevin W. Bowman, Sujay V. Kumar, and Xiwu Zhan
Atmos. Chem. Phys., 21, 11013–11040, https://doi.org/10.5194/acp-21-11013-2021, https://doi.org/10.5194/acp-21-11013-2021, 2021
Short summary
Short summary
This study evaluates the impact of satellite soil moisture data assimilation on modeled weather and ozone fields at various altitudes above the southeastern US during the summer. It emphasizes the importance of soil moisture in the understanding of surface ozone pollution and upper tropospheric chemistry, as well as air pollutants’ source–receptor relationships between the US and its downwind areas.
Michiel Maertens, Gabriëlle J. M. De Lannoy, Sebastian Apers, Sujay V. Kumar, and Sarith P. P. Mahanama
Hydrol. Earth Syst. Sci., 25, 4099–4125, https://doi.org/10.5194/hess-25-4099-2021, https://doi.org/10.5194/hess-25-4099-2021, 2021
Short summary
Short summary
In this study, we simulated the water balance over the South American Dry Chaco and assessed the impact of land cover changes thereon using three different land surface models. Our simulations indicated that different models result in a different partitioning of the total water budget, but all showed an increase in soil moisture and percolation over the deforested areas. We also found that, relative to independent data, no specific land surface model is significantly better than another.
Rhae Sung Kim, Sujay Kumar, Carrie Vuyovich, Paul Houser, Jessica Lundquist, Lawrence Mudryk, Michael Durand, Ana Barros, Edward J. Kim, Barton A. Forman, Ethan D. Gutmann, Melissa L. Wrzesien, Camille Garnaud, Melody Sandells, Hans-Peter Marshall, Nicoleta Cristea, Justin M. Pflug, Jeremy Johnston, Yueqian Cao, David Mocko, and Shugong Wang
The Cryosphere, 15, 771–791, https://doi.org/10.5194/tc-15-771-2021, https://doi.org/10.5194/tc-15-771-2021, 2021
Short summary
Short summary
High SWE uncertainty is observed in mountainous and forested regions, highlighting the need for high-resolution snow observations in these regions. Substantial uncertainty in snow water storage in Tundra regions and the dominance of water storage in these regions points to the need for high-accuracy snow estimation. Finally, snow measurements during the melt season are most needed at high latitudes, whereas observations at near peak snow accumulations are most beneficial over the midlatitudes.
Mahmoud Osman, Benjamin F. Zaitchik, Hamada S. Badr, Jordan I. Christian, Tsegaye Tadesse, Jason A. Otkin, and Martha C. Anderson
Hydrol. Earth Syst. Sci., 25, 565–581, https://doi.org/10.5194/hess-25-565-2021, https://doi.org/10.5194/hess-25-565-2021, 2021
Short summary
Short summary
Our study of flash droughts' definitions over the United States shows that published definitions yield markedly different inventories of flash drought geography and frequency. Results suggest there are several pathways that can lead to events that are characterized as flash droughts. Lack of consensus across definitions helps to explain apparent contradictions in the literature on trends and indicates the selection of a definition is important for accurate monitoring of different mechanisms.
Yifan Zhou, Benjamin F. Zaitchik, Sujay V. Kumar, Kristi R. Arsenault, Mir A. Matin, Faisal M. Qamer, Ryan A. Zamora, and Kiran Shakya
Hydrol. Earth Syst. Sci., 25, 41–61, https://doi.org/10.5194/hess-25-41-2021, https://doi.org/10.5194/hess-25-41-2021, 2021
Short summary
Short summary
South and Southeast Asia face significant food insecurity and hydrological hazards. Here we introduce a South and Southeast Asia hydrological monitoring and sub-seasonal to seasonal forecasting system (SAHFS-S2S) to help local governments and decision-makers prepare for extreme hydroclimatic events. The monitoring system captures soil moisture variability well in most regions, and the forecasting system offers skillful prediction of soil moisture variability 2–3 months in advance, on average.
Justin Schulte, Frederick Policielli, and Benjamin Zaitchik
Hydrol. Earth Syst. Sci., 24, 5473–5489, https://doi.org/10.5194/hess-24-5473-2020, https://doi.org/10.5194/hess-24-5473-2020, 2020
Short summary
Short summary
Wavelet coherence is now a commonly used method for detecting scale-dependent relationships between time series. In this study, the concept of wavelet coherence is generalized to higher-order wavelet coherence methods that quantify the relationship between higher-order statistical moments associated with two time series. The methods are applied to the El Niño–Southern Oscillation (ENSO) and the Indian monsoon to show that the ENSO–Indian monsoon relationship is impacted by ENSO nonlinearity.
Xinxuan Zhang, Viviana Maggioni, Azbina Rahman, Paul Houser, Yuan Xue, Timothy Sauer, Sujay Kumar, and David Mocko
Hydrol. Earth Syst. Sci., 24, 3775–3788, https://doi.org/10.5194/hess-24-3775-2020, https://doi.org/10.5194/hess-24-3775-2020, 2020
Short summary
Short summary
This study assesses the extent to which a land surface model can be optimized via the assimilation of leaf area index (LAI) observations at the global scale. The model performance is evaluated by the model-estimated LAI and five water flux/storage variables. Results show the LAI assimilation reduces errors in the model-estimated LAI. The LAI assimilation also improves the five water variables under wet conditions, but some of the model-estimated variables tend to be worse under dry conditions.
Sujay V. Kumar, Thomas R. Holmes, Rajat Bindlish, Richard de Jeu, and Christa Peters-Lidard
Hydrol. Earth Syst. Sci., 24, 3431–3450, https://doi.org/10.5194/hess-24-3431-2020, https://doi.org/10.5194/hess-24-3431-2020, 2020
Short summary
Short summary
Vegetation optical depth (VOD) is a byproduct of the soil moisture retrieval from passive microwave instruments. This study demonstrates that VOD information can be utilized for improving land surface water budget and carbon conditions through data assimilation.
Shraddhanand Shukla, Kristi R. Arsenault, Abheera Hazra, Christa Peters-Lidard, Randal D. Koster, Frank Davenport, Tamuka Magadzire, Chris Funk, Sujay Kumar, Amy McNally, Augusto Getirana, Greg Husak, Ben Zaitchik, Jim Verdin, Faka Dieudonne Nsadisa, and Inbal Becker-Reshef
Nat. Hazards Earth Syst. Sci., 20, 1187–1201, https://doi.org/10.5194/nhess-20-1187-2020, https://doi.org/10.5194/nhess-20-1187-2020, 2020
Short summary
Short summary
The region of southern Africa is prone to climate-driven food insecurity events, as demonstrated by the major drought event in 2015–2016. This study demonstrates that recently developed NASA Hydrological Forecasting and Analysis System-based root-zone soil moisture monitoring and forecasting products are well correlated with interannual regional crop yield, can identify below-normal crop yield events and provide skillful crop yield forecasts, and hence support early warning of food insecurity.
Nevil Quinn, Günter Blöschl, András Bárdossy, Attilio Castellarin, Martyn Clark, Christophe Cudennec, Demetris Koutsoyiannis, Upmanu Lall, Lubomir Lichner, Juraj Parajka, Christa D. Peters-Lidard, Graham Sander, Hubert Savenije, Keith Smettem, Harry Vereecken, Alberto Viglione, Patrick Willems, Andy Wood, Ross Woods, Chong-Yu Xu, and Erwin Zehe
Proc. IAHS, 380, 3–8, https://doi.org/10.5194/piahs-380-3-2018, https://doi.org/10.5194/piahs-380-3-2018, 2018
Nevil Quinn, Günter Blöschl, András Bárdossy, Attilio Castellarin, Martyn Clark, Christophe Cudennec, Demetris Koutsoyiannis, Upmanu Lall, Lubomir Lichner, Juraj Parajka, Christa D. Peters-Lidard, Graham Sander, Hubert Savenije, Keith Smettem, Harry Vereecken, Alberto Viglione, Patrick Willems, Andy Wood, Ross Woods, Chong-Yu Xu, and Erwin Zehe
Hydrol. Earth Syst. Sci., 22, 5735–5739, https://doi.org/10.5194/hess-22-5735-2018, https://doi.org/10.5194/hess-22-5735-2018, 2018
Kristi R. Arsenault, Sujay V. Kumar, James V. Geiger, Shugong Wang, Eric Kemp, David M. Mocko, Hiroko Kato Beaudoing, Augusto Getirana, Mahdi Navari, Bailing Li, Jossy Jacob, Jerry Wegiel, and Christa D. Peters-Lidard
Geosci. Model Dev., 11, 3605–3621, https://doi.org/10.5194/gmd-11-3605-2018, https://doi.org/10.5194/gmd-11-3605-2018, 2018
Short summary
Short summary
The Earth’s land surface hydrology and physics can be represented in highly sophisticated models known as land surface models. The Land surface Data Toolkit (LDT) software was developed to meet these models’ input processing needs. LDT supports a variety of land surface and hydrology models and prepares the inputs (e.g., meteorological data, satellite observations to be assimilated into a model), which can be used for inter-model studies and to initialize weather and climate forecasts.
Christa D. Peters-Lidard, Martyn Clark, Luis Samaniego, Niko E. C. Verhoest, Tim van Emmerik, Remko Uijlenhoet, Kevin Achieng, Trenton E. Franz, and Ross Woods
Hydrol. Earth Syst. Sci., 21, 3701–3713, https://doi.org/10.5194/hess-21-3701-2017, https://doi.org/10.5194/hess-21-3701-2017, 2017
Short summary
Short summary
In this synthesis of hydrologic scaling and similarity, we assert that it is time for hydrology to embrace a fourth paradigm of data-intensive science. Advances in information-based hydrologic science, coupled with an explosion of hydrologic data and advances in parameter estimation and modeling, have laid the foundation for a data-driven framework for scrutinizing hydrological hypotheses. We call upon the community to develop a focused effort towards a fourth paradigm for hydrology.
Martyn P. Clark, Marc F. P. Bierkens, Luis Samaniego, Ross A. Woods, Remko Uijlenhoet, Katrina E. Bennett, Valentijn R. N. Pauwels, Xitian Cai, Andrew W. Wood, and Christa D. Peters-Lidard
Hydrol. Earth Syst. Sci., 21, 3427–3440, https://doi.org/10.5194/hess-21-3427-2017, https://doi.org/10.5194/hess-21-3427-2017, 2017
Short summary
Short summary
The diversity in hydrologic models has led to controversy surrounding the “correct” approach to hydrologic modeling. In this paper we revisit key modeling challenges on requirements to (1) define suitable model equations, (2) define adequate model parameters, and (3) cope with limitations in computing power. We outline the historical modeling challenges, summarize modeling advances that address these challenges, and define outstanding research needs.
Sujay V. Kumar, Jiarui Dong, Christa D. Peters-Lidard, David Mocko, and Breogán Gómez
Hydrol. Earth Syst. Sci., 21, 2637–2647, https://doi.org/10.5194/hess-21-2637-2017, https://doi.org/10.5194/hess-21-2637-2017, 2017
Short summary
Short summary
Data assimilation deals with the blending of model forecasts and observations based on their relative errors. This paper addresses the importance of accurately representing the errors in the model forecasts for skillful data assimilation performance.
Julie E. Shortridge, Seth D. Guikema, and Benjamin F. Zaitchik
Hydrol. Earth Syst. Sci., 20, 2611–2628, https://doi.org/10.5194/hess-20-2611-2016, https://doi.org/10.5194/hess-20-2611-2016, 2016
Short summary
Short summary
This paper compares six methods for data-driven rainfall–runoff simulation in terms of predictive accuracy, error structure, interpretability, and uncertainty. We demonstrate that autocorrelation in model errors can result in biased estimates of important values and show how certain model structures can be more easily interpreted to yield insights on physical watershed function. Finally, we explore how model structure can impact uncertainty in climate change sensitivity estimates.
S. V. Kumar, C. D. Peters-Lidard, J. A. Santanello, R. H. Reichle, C. S. Draper, R. D. Koster, G. Nearing, and M. F. Jasinski
Hydrol. Earth Syst. Sci., 19, 4463–4478, https://doi.org/10.5194/hess-19-4463-2015, https://doi.org/10.5194/hess-19-4463-2015, 2015
S. Satti, B. Zaitchik, and S. Siddiqui
Hydrol. Earth Syst. Sci., 19, 2275–2293, https://doi.org/10.5194/hess-19-2275-2015, https://doi.org/10.5194/hess-19-2275-2015, 2015
Z. Tao, J. A. Santanello, M. Chin, S. Zhou, Q. Tan, E. M. Kemp, and C. D. Peters-Lidard
Atmos. Chem. Phys., 13, 6207–6226, https://doi.org/10.5194/acp-13-6207-2013, https://doi.org/10.5194/acp-13-6207-2013, 2013
A. C. V. Getirana and C. Peters-Lidard
Hydrol. Earth Syst. Sci., 17, 923–933, https://doi.org/10.5194/hess-17-923-2013, https://doi.org/10.5194/hess-17-923-2013, 2013
Related subject area
Subject: Catchment hydrology | Techniques and Approaches: Modelling approaches
Improved representation of soil moisture processes through incorporation of cosmic-ray neutron count measurements in a large-scale hydrologic model
Spatio-temporal patterns and trends of streamflow in water-scarce Mediterranean basins
A large-sample modelling approach towards integrating streamflow and evaporation data for the Spanish catchments
Seasonal variation in land cover estimates reveals sensitivities and opportunities for environmental models
Estimating response times, flow velocities, and roughness coefficients of Canadian Prairie basins
Learning landscape features from streamflow with autoencoders
On the use of streamflow transformations for hydrological model calibration
Simulation-based inference for parameter estimation of complex watershed simulators
Multi-scale soil moisture data and process-based modeling reveal the importance of lateral groundwater flow in a subarctic catchment
Catchment response to climatic variability: implications for root zone storage and streamflow predictions
Hybrid hydrological modeling for large alpine basins: a semi-distributed approach
Karst aquifer discharge response to rainfall interpreted as anomalous transport
HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin
Large-sample hydrology – a few camels or a whole caravan?
Comment on “Are soils overrated in hydrology?” by Gao et al. (2023)
Projections of streamflow intermittence under climate change in European drying river networks
Multi-decadal fluctuations in root zone storage capacity through vegetation adaptation to hydro-climatic variability have minor effects on the hydrological response in the Neckar River basin, Germany
Projected future changes in the cryosphere and hydrology of a mountainous catchment in the upper Heihe River, China
On the importance of plant phenology in the evaporative process of a semi-arid woodland: could it be why satellite-based evaporation estimates in the miombo differ?
Achieving water budget closure through physical hydrological processes modelling: insights from a large-sample study
Regionalization of GR4J model parameters for river flow prediction in Paraná, Brazil
Heavy-tailed flood peak distributions: What is the effect of the spatial variability of rainfall and runoff generation?
Evolution of river regimes in the Mekong River basin over 8 decades and the role of dams in recent hydrological extremes
Skill of seasonal flow forecasts at catchment scale: an assessment across South Korea
To what extent do flood-inducing storm events change future flood hazards?
State updating in the Xin'anjiang Model: Joint assimilating streamflow and multi-source soil moisture data via Asynchronous Ensemble Kalman Filter with enhanced Error Models
When ancient numerical demons meet physics-informed machine learning: adjoint-based gradients for implicit differentiable modeling
Assessing the impact of climate change on high return levels of peak flows in Bavaria applying the CRCM5 large ensemble
Impacts of climate and land surface change on catchment evapotranspiration and runoff from 1951 to 2020 in Saxony, Germany
Quantifying and reducing flood forecast uncertainty by the CHUP-BMA method
Developing a tile drainage module for the Cold Regions Hydrological Model: lessons from a farm in southern Ontario, Canada
To bucket or not to bucket? Analyzing the performance and interpretability of hybrid hydrological models with dynamic parameterization
Widespread flooding dynamics under climate change: characterising floods using grid-based hydrological modelling and regional climate projections
HESS Opinions: The sword of Damocles of the impossible flood
A diversity centric strategy for the selection of spatio-temporal training data for LSTM-based streamflow forecasting
Metamorphic testing of machine learning and conceptual hydrologic models
The influence of human activities on streamflow reductions during the megadrought in central Chile
Elevational control of isotopic composition and application in understanding hydrologic processes in the mid Merced River catchment, Sierra Nevada, California, USA
Lack of robustness of hydrological models: A large-sample diagnosis and an attempt to identify the hydrological and climatic drivers
Exploring the Potential Processes Controls for Changes of Precipitation-Runoff Relationships in Non-stationary Environments
The Significance of the Leaf-Area-Index on the Evapotranspiration Estimation in SWAT-T for Characteristic Land Cover Types of Western Africa
Enhancing long short-term memory (LSTM)-based streamflow prediction with a spatially distributed approach
Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain
CH-RUN: A data-driven spatially contiguous runoff monitoring product for Switzerland
Simulating the Tone River Eastward Diversion Project in Japan Carried Out Four Centuries Ago
Impacts of spatiotemporal resolutions of precipitation on flood event simulation based on multimodel structures – a case study over the Xiang River basin in China
A network approach for multiscale catchment classification using traits
Multi-model approach in a variable spatial framework for streamflow simulation
Advancing understanding of lake–watershed hydrology: a fully coupled numerical model illustrated by Qinghai Lake
Technical note: Testing the connection between hillslope-scale runoff fluctuations and streamflow hydrographs at the outlet of large river basins
Eshrat Fatima, Rohini Kumar, Sabine Attinger, Maren Kaluza, Oldrich Rakovec, Corinna Rebmann, Rafael Rosolem, Sascha E. Oswald, Luis Samaniego, Steffen Zacharias, and Martin Schrön
Hydrol. Earth Syst. Sci., 28, 5419–5441, https://doi.org/10.5194/hess-28-5419-2024, https://doi.org/10.5194/hess-28-5419-2024, 2024
Short summary
Short summary
This study establishes a framework to incorporate cosmic-ray neutron measurements into the mesoscale Hydrological Model (mHM). We evaluate different approaches to estimate neutron counts within the mHM using the Desilets equation, with uniformly and non-uniformly weighted average soil moisture, and the physically based code COSMIC. The data improved not only soil moisture simulations but also the parameterisation of evapotranspiration in the model.
Laia Estrada, Xavier Garcia, Joan Saló-Grau, Rafael Marcé, Antoni Munné, and Vicenç Acuña
Hydrol. Earth Syst. Sci., 28, 5353–5373, https://doi.org/10.5194/hess-28-5353-2024, https://doi.org/10.5194/hess-28-5353-2024, 2024
Short summary
Short summary
Hydrological modelling is a powerful tool to support decision-making. We assessed spatio-temporal patterns and trends of streamflow for 2001–2022 with a hydrological model, integrating stakeholder expert knowledge on management operations. The results provide insight into how climate change and anthropogenic pressures affect water resources availability in regions vulnerable to water scarcity, thus raising the need for sustainable management practices and integrated hydrological modelling.
Patricio Yeste, Matilde García-Valdecasas Ojeda, Sonia R. Gámiz-Fortis, Yolanda Castro-Díez, Axel Bronstert, and María Jesús Esteban-Parra
Hydrol. Earth Syst. Sci., 28, 5331–5352, https://doi.org/10.5194/hess-28-5331-2024, https://doi.org/10.5194/hess-28-5331-2024, 2024
Short summary
Short summary
Integrating streamflow and evaporation data can help improve the physical realism of hydrologic models. We investigate the capabilities of the Variable Infiltration Capacity (VIC) to reproduce both hydrologic variables for 189 headwater located in Spain. Results from sensitivity analyses indicate that adding two vegetation parameters is enough to improve the representation of evaporation and that the performance of VIC exceeded that of the largest modelling effort currently available in Spain.
Daniel T. Myers, David Jones, Diana Oviedo-Vargas, John Paul Schmit, Darren L. Ficklin, and Xuesong Zhang
Hydrol. Earth Syst. Sci., 28, 5295–5310, https://doi.org/10.5194/hess-28-5295-2024, https://doi.org/10.5194/hess-28-5295-2024, 2024
Short summary
Short summary
We studied how streamflow and water quality models respond to land cover data collected by satellites during the growing season versus the non-growing season. The land cover data showed more trees during the growing season and more built areas during the non-growing season. We next found that the use of non-growing season data resulted in a higher modeled nutrient export to streams. Knowledge of these sensitivities would be particularly important when models inform water resource management.
Kevin R. Shook, Paul H. Whitfield, Christopher Spence, and John W. Pomeroy
Hydrol. Earth Syst. Sci., 28, 5173–5192, https://doi.org/10.5194/hess-28-5173-2024, https://doi.org/10.5194/hess-28-5173-2024, 2024
Short summary
Short summary
Recent studies suggest that the velocities of water running off landscapes in the Canadian Prairies may be much smaller than generally assumed. Analyses of historical flows for 23 basins in central Alberta show that many of the rivers responded more slowly and that the flows are much slower than would be estimated from equations developed elsewhere. The effects of slow flow velocities on the development of hydrological models of the region are discussed, as are the possible causes.
Alberto Bassi, Marvin Höge, Antonietta Mira, Fabrizio Fenicia, and Carlo Albert
Hydrol. Earth Syst. Sci., 28, 4971–4988, https://doi.org/10.5194/hess-28-4971-2024, https://doi.org/10.5194/hess-28-4971-2024, 2024
Short summary
Short summary
The goal is to remove the impact of meteorological drivers in order to uncover the unique landscape fingerprints of a catchment from streamflow data. Our results reveal an optimal two-feature summary for most catchments, with a third feature associated with aridity and intermittent flow that is needed for challenging cases. Baseflow index, aridity, and soil or vegetation attributes strongly correlate with learnt features, indicating their importance for streamflow prediction.
Guillaume Thirel, Léonard Santos, Olivier Delaigue, and Charles Perrin
Hydrol. Earth Syst. Sci., 28, 4837–4860, https://doi.org/10.5194/hess-28-4837-2024, https://doi.org/10.5194/hess-28-4837-2024, 2024
Short summary
Short summary
We discuss how mathematical transformations impact calibrated hydrological model simulations. We assess how 11 transformations behave over the complete range of streamflows. Extreme transformations lead to models that are specialized for extreme streamflows but show poor performance outside the range of targeted streamflows and are less robust. We show that no a priori assumption about transformations can be taken as warranted.
Robert Hull, Elena Leonarduzzi, Luis De La Fuente, Hoang Viet Tran, Andrew Bennett, Peter Melchior, Reed M. Maxwell, and Laura E. Condon
Hydrol. Earth Syst. Sci., 28, 4685–4713, https://doi.org/10.5194/hess-28-4685-2024, https://doi.org/10.5194/hess-28-4685-2024, 2024
Short summary
Short summary
Large-scale hydrologic simulators are a needed tool to explore complex watershed processes and how they may evolve with a changing climate. However, calibrating them can be difficult because they are costly to run and have many unknown parameters. We implement a state-of-the-art approach to model calibration using neural networks with a set of experiments based on streamflow in the upper Colorado River basin.
Jari-Pekka Nousu, Kersti Leppä, Hannu Marttila, Pertti Ala-aho, Giulia Mazzotti, Terhikki Manninen, Mika Korkiakoski, Mika Aurela, Annalea Lohila, and Samuli Launiainen
Hydrol. Earth Syst. Sci., 28, 4643–4666, https://doi.org/10.5194/hess-28-4643-2024, https://doi.org/10.5194/hess-28-4643-2024, 2024
Short summary
Short summary
We used hydrological models, field measurements, and satellite-based data to study the soil moisture dynamics in a subarctic catchment. The role of groundwater was studied with different ways to model the groundwater dynamics and via comparisons to the observational data. The choice of groundwater model was shown to have a strong impact, and representation of lateral flow was important to capture wet soil conditions. Our results provide insights for ecohydrological studies in boreal regions.
Nienke Tempel, Laurène Bouaziz, Riccardo Taormina, Ellis van Noppen, Jasper Stam, Eric Sprokkereef, and Markus Hrachowitz
Hydrol. Earth Syst. Sci., 28, 4577–4597, https://doi.org/10.5194/hess-28-4577-2024, https://doi.org/10.5194/hess-28-4577-2024, 2024
Short summary
Short summary
This study explores the impact of climatic variability on root zone water storage capacities and, thus, on hydrological predictions. Analysing data from 286 areas in Europe and the US, we found that, despite some variations in root zone storage capacity due to changing climatic conditions over multiple decades, these changes are generally minor and have a limited effect on water storage and river flow predictions.
Bu Li, Ting Sun, Fuqiang Tian, Mahmut Tudaji, Li Qin, and Guangheng Ni
Hydrol. Earth Syst. Sci., 28, 4521–4538, https://doi.org/10.5194/hess-28-4521-2024, https://doi.org/10.5194/hess-28-4521-2024, 2024
Short summary
Short summary
This paper developed hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing deep learning to parameterize and replace internal modules. The main contribution is to provide a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and to improve understanding about the hydrological sensitivities to climate change in large alpine basins.
Dan Elhanati, Nadine Goeppert, and Brian Berkowitz
Hydrol. Earth Syst. Sci., 28, 4239–4249, https://doi.org/10.5194/hess-28-4239-2024, https://doi.org/10.5194/hess-28-4239-2024, 2024
Short summary
Short summary
A continuous time random walk framework was developed to allow modeling of a karst aquifer discharge response to measured rainfall. The application of the numerical model yielded robust fits between modeled and measured discharge values, especially for the distinctive long tails found during recession times. The findings shed light on the interplay of slow and fast flow in the karst system and establish the application of the model for simulating flow and transport in such systems.
Frederik Kratzert, Martin Gauch, Daniel Klotz, and Grey Nearing
Hydrol. Earth Syst. Sci., 28, 4187–4201, https://doi.org/10.5194/hess-28-4187-2024, https://doi.org/10.5194/hess-28-4187-2024, 2024
Short summary
Short summary
Recently, a special type of neural-network architecture became increasingly popular in hydrology literature. However, in most applications, this model was applied as a one-to-one replacement for hydrology models without adapting or rethinking the experimental setup. In this opinion paper, we show how this is almost always a bad decision and how using these kinds of models requires the use of large-sample hydrology data sets.
Franziska Clerc-Schwarzenbach, Giovanni Selleri, Mattia Neri, Elena Toth, Ilja van Meerveld, and Jan Seibert
Hydrol. Earth Syst. Sci., 28, 4219–4237, https://doi.org/10.5194/hess-28-4219-2024, https://doi.org/10.5194/hess-28-4219-2024, 2024
Short summary
Short summary
We show that the differences between the forcing data included in three CAMELS datasets (US, BR, GB) and the forcing data included for the same catchments in the Caravan dataset affect model calibration considerably. The model performance dropped when the data from the Caravan dataset were used instead of the original data. Most of the model performance drop could be attributed to the differences in precipitation data. However, differences were largest for the potential evapotranspiration data.
Ying Zhao, Mehdi Rahmati, Harry Vereecken, and Dani Or
Hydrol. Earth Syst. Sci., 28, 4059–4063, https://doi.org/10.5194/hess-28-4059-2024, https://doi.org/10.5194/hess-28-4059-2024, 2024
Short summary
Short summary
Gao et al. (2023) question the importance of soil in hydrology, sparking debate. We acknowledge some valid points but critique their broad, unsubstantiated views on soil's role. Our response highlights three key areas: (1) the false divide between ecosystem-centric and soil-centric approaches, (2) the vital yet varied impact of soil properties, and (3) the call for a scale-aware framework. We aim to unify these perspectives, enhancing hydrology's comprehensive understanding.
Louise Mimeau, Annika Künne, Alexandre Devers, Flora Branger, Sven Kralisch, Claire Lauvernet, Jean-Philippe Vidal, Núria Bonada, Zoltán Csabai, Heikki Mykrä, Petr Pařil, Luka Polović, and Thibault Datry
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-272, https://doi.org/10.5194/hess-2024-272, 2024
Preprint under review for HESS
Short summary
Short summary
Our study projects how climate change will affect drying of river segments and stream networks in Europe, using advanced modeling techniques to assess changes in six river networks across diverse ecoregions. We found that drying events will become more frequent, intense and start earlier or last longer, potentially turning some river sections from perennial to intermittent. The results are valuable for river ecologists in evaluating the ecological health of river ecosystem.
Siyuan Wang, Markus Hrachowitz, and Gerrit Schoups
Hydrol. Earth Syst. Sci., 28, 4011–4033, https://doi.org/10.5194/hess-28-4011-2024, https://doi.org/10.5194/hess-28-4011-2024, 2024
Short summary
Short summary
Root zone storage capacity (Sumax) changes significantly over multiple decades, reflecting vegetation adaptation to climatic variability. However, this temporal evolution of Sumax cannot explain long-term fluctuations in the partitioning of water fluxes as expressed by deviations ΔIE from the parametric Budyko curve over time with different climatic conditions, and it does not have any significant effects on shorter-term hydrological response characteristics of the upper Neckar catchment.
Zehua Chang, Hongkai Gao, Leilei Yong, Kang Wang, Rensheng Chen, Chuntan Han, Otgonbayar Demberel, Batsuren Dorjsuren, Shugui Hou, and Zheng Duan
Hydrol. Earth Syst. Sci., 28, 3897–3917, https://doi.org/10.5194/hess-28-3897-2024, https://doi.org/10.5194/hess-28-3897-2024, 2024
Short summary
Short summary
An integrated cryospheric–hydrologic model, FLEX-Cryo, was developed that considers glaciers, snow cover, and frozen soil and their dynamic impacts on hydrology. We utilized it to simulate future changes in cryosphere and hydrology in the Hulu catchment. Our projections showed the two glaciers will melt completely around 2050, snow cover will reduce, and permafrost will degrade. For hydrology, runoff will decrease after the glacier has melted, and permafrost degradation will increase baseflow.
Henry M. Zimba, Miriam Coenders-Gerrits, Kawawa E. Banda, Petra Hulsman, Nick van de Giesen, Imasiku A. Nyambe, and Hubert H. G. Savenije
Hydrol. Earth Syst. Sci., 28, 3633–3663, https://doi.org/10.5194/hess-28-3633-2024, https://doi.org/10.5194/hess-28-3633-2024, 2024
Short summary
Short summary
The fall and flushing of new leaves in the miombo woodlands co-occur in the dry season before the commencement of seasonal rainfall. The miombo species are also said to have access to soil moisture in deep soils, including groundwater in the dry season. Satellite-based evaporation estimates, temporal trends, and magnitudes differ the most in the dry season, most likely due to inadequate understanding and representation of the highlighted miombo species attributes in simulations.
Xudong Zheng, Dengfeng Liu, Shengzhi Huang, Hao Wang, and Xianmeng Meng
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-230, https://doi.org/10.5194/hess-2024-230, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Water budget non-closure is a widespread phenomenon among multisource datasets, which undermines the robustness of hydrological inferences. This study proposes a Multisource Datasets Correction Framework grounded in Physical Hydrological Processes Modelling to enhance water budget closure, called PHPM-MDCF. We examined the efficiency and robustness of the framework using the CAMELS dataset, and achieved an average reduction of 49 % in total water budget residuals across 475 CONUS basins.
Louise Akemi Kuana, Arlan Scortegagna Almeida, Emílio Graciliano Ferreira Mercuri, and Steffen Manfred Noe
Hydrol. Earth Syst. Sci., 28, 3367–3390, https://doi.org/10.5194/hess-28-3367-2024, https://doi.org/10.5194/hess-28-3367-2024, 2024
Short summary
Short summary
The authors compared regionalization methods for river flow prediction in 126 catchments from the south of Brazil, a region with humid subtropical and hot temperate climate. The regionalization method based on physiographic–climatic similarity had the best performance for predicting daily and Q95 reference flow. We showed that basins without flow monitoring can have a good approximation of streamflow using machine learning and physiographic–climatic information as inputs.
Elena Macdonald, Bruno Merz, Viet Dung Nguyen, and Sergiy Vorogushyn
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-181, https://doi.org/10.5194/hess-2024-181, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Flood peak distributions indicate how likely the occurrence of an extreme flood is at a certain river. If the distribution has a so-called heavy tail, extreme floods are more likely than might be anticipated. We find heavier tails in small compared to large catchments, and that spatially variable rainfall leads to a lower occurrence probability of extreme floods. Spatially variable runoff does not show an effect. The results can improve estimations of occurrence probabilities of extreme floods.
Huy Dang and Yadu Pokhrel
Hydrol. Earth Syst. Sci., 28, 3347–3365, https://doi.org/10.5194/hess-28-3347-2024, https://doi.org/10.5194/hess-28-3347-2024, 2024
Short summary
Short summary
By examining basin-wide simulations of a river regime over 83 years with and without dams, we present evidence that climate variation was a key driver of hydrologic variabilities in the Mekong River basin (MRB) over the long term; however, dams have largely altered the seasonality of the Mekong’s flow regime and annual flooding patterns in major downstream areas in recent years. These findings could help us rethink the planning of future dams and water resource management in the MRB.
Yongshin Lee, Francesca Pianosi, Andres Peñuela, and Miguel Angel Rico-Ramirez
Hydrol. Earth Syst. Sci., 28, 3261–3279, https://doi.org/10.5194/hess-28-3261-2024, https://doi.org/10.5194/hess-28-3261-2024, 2024
Short summary
Short summary
Following recent advancements in weather prediction technology, we explored how seasonal weather forecasts (1 or more months ahead) could benefit practical water management in South Korea. Our findings highlight that using seasonal weather forecasts for predicting flow patterns 1 to 3 months ahead is effective, especially during dry years. This suggest that seasonal weather forecasts can be helpful in improving the management of water resources.
Mariam Khanam, Giulia Sofia, and Emmanouil N. Anagnostou
Hydrol. Earth Syst. Sci., 28, 3161–3190, https://doi.org/10.5194/hess-28-3161-2024, https://doi.org/10.5194/hess-28-3161-2024, 2024
Short summary
Short summary
Flooding worsens due to climate change, with river dynamics being a key in local flood control. Predicting post-storm geomorphic changes is challenging. Using self-organizing maps and machine learning, this study forecasts post-storm alterations in stage–discharge relationships across 3101 US stream gages. The provided framework can aid in updating hazard assessments by identifying rivers prone to change, integrating channel adjustments into flood hazard assessment.
Junfu Gong, Xingwen Liu, Cheng Yao, Zhijia Li, Albrecht Weerts, Qiaoling Li, Satish Bastola, Yingchun Huang, and Junzeng Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-211, https://doi.org/10.5194/hess-2024-211, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Our study introduces a new method to improve flood forecasting by combining soil moisture and streamflow data using an advanced data assimilation technique. By integrating field and reanalysis soil moisture data and assimilating this with streamflow measurements, we aim to enhance the accuracy of flood predictions. This approach reduces the accumulation of past errors in the initial conditions at the start of the forecast, helping better prepare for and respond to floods.
Yalan Song, Wouter J. M. Knoben, Martyn P. Clark, Dapeng Feng, Kathryn Lawson, Kamlesh Sawadekar, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 3051–3077, https://doi.org/10.5194/hess-28-3051-2024, https://doi.org/10.5194/hess-28-3051-2024, 2024
Short summary
Short summary
Differentiable models (DMs) integrate neural networks and physical equations for accuracy, interpretability, and knowledge discovery. We developed an adjoint-based DM for ordinary differential equations (ODEs) for hydrological modeling, reducing distorted fluxes and physical parameters from errors in models that use explicit and operation-splitting schemes. With a better numerical scheme and improved structure, the adjoint-based DM matches or surpasses long short-term memory (LSTM) performance.
Florian Willkofer, Raul R. Wood, and Ralf Ludwig
Hydrol. Earth Syst. Sci., 28, 2969–2989, https://doi.org/10.5194/hess-28-2969-2024, https://doi.org/10.5194/hess-28-2969-2024, 2024
Short summary
Short summary
Severe flood events pose a threat to riverine areas, yet robust estimates of the dynamics of these events in the future due to climate change are rarely available. Hence, this study uses data from a regional climate model, SMILE, to drive a high-resolution hydrological model for 98 catchments of hydrological Bavaria and exploits the large database to derive robust values for the 100-year flood events. Results indicate an increase in frequency and intensity for most catchments in the future.
Maik Renner and Corina Hauffe
Hydrol. Earth Syst. Sci., 28, 2849–2869, https://doi.org/10.5194/hess-28-2849-2024, https://doi.org/10.5194/hess-28-2849-2024, 2024
Short summary
Short summary
Climate and land surface changes influence the partitioning of water balance components decisively. Their impact is quantified for 71 catchments in Saxony. Germany. Distinct signatures in the joint water and energy budgets are found: (i) past forest dieback caused a decrease in and subsequent recovery of evapotranspiration in the affected regions, and (ii) the recent shift towards higher aridity imposed a large decline in runoff that has not been seen in the observation records before.
Zhen Cui, Shenglian Guo, Hua Chen, Dedi Liu, Yanlai Zhou, and Chong-Yu Xu
Hydrol. Earth Syst. Sci., 28, 2809–2829, https://doi.org/10.5194/hess-28-2809-2024, https://doi.org/10.5194/hess-28-2809-2024, 2024
Short summary
Short summary
Ensemble forecasting facilitates reliable flood forecasting and warning. This study couples the copula-based hydrologic uncertainty processor (CHUP) with Bayesian model averaging (BMA) and proposes the novel CHUP-BMA method of reducing inflow forecasting uncertainty of the Three Gorges Reservoir. The CHUP-BMA avoids the normal distribution assumption in the HUP-BMA and considers the constraint of initial conditions, which can improve the deterministic and probabilistic forecast performance.
Mazda Kompanizare, Diogo Costa, Merrin L. Macrae, John W. Pomeroy, and Richard M. Petrone
Hydrol. Earth Syst. Sci., 28, 2785–2807, https://doi.org/10.5194/hess-28-2785-2024, https://doi.org/10.5194/hess-28-2785-2024, 2024
Short summary
Short summary
A new agricultural tile drainage module was developed in the Cold Region Hydrological Model platform. Tile flow and water levels are simulated by considering the effect of capillary fringe thickness, drainable water and seasonal regional groundwater dynamics. The model was applied to a small well-instrumented farm in southern Ontario, Canada, where there are concerns about the impacts of agricultural drainage into Lake Erie.
Eduardo Acuña Espinoza, Ralf Loritz, Manuel Álvarez Chaves, Nicole Bäuerle, and Uwe Ehret
Hydrol. Earth Syst. Sci., 28, 2705–2719, https://doi.org/10.5194/hess-28-2705-2024, https://doi.org/10.5194/hess-28-2705-2024, 2024
Short summary
Short summary
Hydrological hybrid models promise to merge the performance of deep learning methods with the interpretability of process-based models. One hybrid approach is the dynamic parameterization of conceptual models using long short-term memory (LSTM) networks. We explored this method to evaluate the effect of the flexibility given by LSTMs on the process-based part.
Adam Griffin, Alison L. Kay, Paul Sayers, Victoria Bell, Elizabeth Stewart, and Sam Carr
Hydrol. Earth Syst. Sci., 28, 2635–2650, https://doi.org/10.5194/hess-28-2635-2024, https://doi.org/10.5194/hess-28-2635-2024, 2024
Short summary
Short summary
Widespread flooding is a major problem in the UK and is greatly affected by climate change and land-use change. To look at how widespread flooding changes in the future, climate model data (UKCP18) were used with a hydrological model (Grid-to-Grid) across the UK, and 14 400 events were identified between two time slices: 1980–2010 and 2050–2080. There was a strong increase in the number of winter events in the future time slice and in the peak return periods.
Alberto Montanari, Bruno Merz, and Günter Blöschl
Hydrol. Earth Syst. Sci., 28, 2603–2615, https://doi.org/10.5194/hess-28-2603-2024, https://doi.org/10.5194/hess-28-2603-2024, 2024
Short summary
Short summary
Floods often take communities by surprise, as they are often considered virtually
impossibleyet are an ever-present threat similar to the sword suspended over the head of Damocles in the classical Greek anecdote. We discuss four reasons why extremely large floods carry a risk that is often larger than expected. We provide suggestions for managing the risk of megafloods by calling for a creative exploration of hazard scenarios and communicating the unknown corners of the reality of floods.
Everett Snieder and Usman T. Khan
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-169, https://doi.org/10.5194/hess-2024-169, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Improving the accuracy of flood forecasts is paramount to minimising flood damage. Machine-learning models are increasingly being applied for flood forecasting. Such models are typically trained to large historic hydrometeorological datasets. In this work, we evaluate methods for selecting training datasets, that maximise the spatiotemproal diversity of the represented hydrological processes. Empirical results showcase the importance of hydrological diversity in training ML models.
Peter Reichert, Kai Ma, Marvin Höge, Fabrizio Fenicia, Marco Baity-Jesi, Dapeng Feng, and Chaopeng Shen
Hydrol. Earth Syst. Sci., 28, 2505–2529, https://doi.org/10.5194/hess-28-2505-2024, https://doi.org/10.5194/hess-28-2505-2024, 2024
Short summary
Short summary
We compared the predicted change in catchment outlet discharge to precipitation and temperature change for conceptual and machine learning hydrological models. We found that machine learning models, despite providing excellent fit and prediction capabilities, can be unreliable regarding the prediction of the effect of temperature change for low-elevation catchments. This indicates the need for caution when applying them for the prediction of the effect of climate change.
Nicolás Álamos, Camila Alvarez-Garreton, Ariel Muñoz, and Álvaro González-Reyes
Hydrol. Earth Syst. Sci., 28, 2483–2503, https://doi.org/10.5194/hess-28-2483-2024, https://doi.org/10.5194/hess-28-2483-2024, 2024
Short summary
Short summary
In this study, we assess the effects of climate and water use on streamflow reductions and drought intensification during the last 3 decades in central Chile. We address this by contrasting streamflow observations with near-natural streamflow simulations. We conclude that while the lack of precipitation dominates streamflow reductions in the megadrought, water uses have not diminished during this time, causing a worsening of the hydrological drought conditions and maladaptation conditions.
Fengjing Liu, Martha H. Conklin, and Glenn D. Shaw
Hydrol. Earth Syst. Sci., 28, 2239–2258, https://doi.org/10.5194/hess-28-2239-2024, https://doi.org/10.5194/hess-28-2239-2024, 2024
Short summary
Short summary
Mountain snowpack has been declining and more precipitation falls as rain than snow. Using stable isotopes, we found flows and flow duration in Yosemite Creek are most sensitive to climate warming due to strong evaporation of waterfalls, potentially lengthening the dry-up period of waterfalls in summer and negatively affecting tourism. Groundwater recharge in Yosemite Valley is primarily from the upper snow–rain transition (2000–2500 m) and very vulnerable to a reduction in the snow–rain ratio.
Léonard Santos, Vazken Andréassian, Torben O. Sonnenborg, Göran Lindström, Alban de Lavenne, Charles Perrin, Lila Collet, and Guillaume Thirel
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-80, https://doi.org/10.5194/hess-2024-80, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
This work aims at investigating how hydrological models can be transferred to a period in which climatic conditions are different to the ones of the period in which it was set up. The RAT method, built to detect dependencies between model error and climatic drivers, was applied to 3 different hydrological models on 352 catchments in Denmark, France and Sweden. Potential issues are detected for a significant number of catchments for the 3 models even though these catchments differ for each model.
Tian Lan, Tongfang Li, Hongbo Zhang, Jiefeng Wu, Yongqin David Chen, and Chong-Yu Xu
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-118, https://doi.org/10.5194/hess-2024-118, 2024
Preprint under review for HESS
Short summary
Short summary
This study develops an integrated framework based on the novel Driving index for changes in Precipitation-Runoff Relationships (DPRR) to explore the controls for changes in precipitation-runoff relationships in non-stationary environments. According to the quantitative results of the candidate driving factors, the possible process explanations for changes in the precipitation-runoff relationships are deduced. The main contribution offers a comprehensive understanding of hydrological processes.
Fabian Merk, Timo Schaffhauser, Faizan Anwar, Ye Tuo, Jean-Martial Cohard, and Markus Disse
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-131, https://doi.org/10.5194/hess-2024-131, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
ET is computed from vegetation (plant transpiration) and soil (soil evaporation). In Western Africa, plant transpiration correlates with vegetation growth. Vegetation is often represented with the leaf-area-index (LAI). In this study, we evaluate the importance of LAI for the ET calculation. We take a close look at the LAI-ET interaction and show the relevance to consider both, LAI and ET. Our work contributes to the understanding of the processes of the terrestrial water cycle.
Qiutong Yu, Bryan A. Tolson, Hongren Shen, Ming Han, Juliane Mai, and Jimmy Lin
Hydrol. Earth Syst. Sci., 28, 2107–2122, https://doi.org/10.5194/hess-28-2107-2024, https://doi.org/10.5194/hess-28-2107-2024, 2024
Short summary
Short summary
It is challenging to incorporate input variables' spatial distribution information when implementing long short-term memory (LSTM) models for streamflow prediction. This work presents a novel hybrid modelling approach to predict streamflow while accounting for spatial variability. We evaluated the performance against lumped LSTM predictions in 224 basins across the Great Lakes region in North America. This approach shows promise for predicting streamflow in large, ungauged basin.
Marcus Buechel, Louise Slater, and Simon Dadson
Hydrol. Earth Syst. Sci., 28, 2081–2105, https://doi.org/10.5194/hess-28-2081-2024, https://doi.org/10.5194/hess-28-2081-2024, 2024
Short summary
Short summary
Afforestation has been proposed internationally, but the hydrological implications of such large increases in the spatial extent of woodland are not fully understood. In this study, we use a land surface model to simulate hydrology across Great Britain with realistic afforestation scenarios and potential climate changes. Countrywide afforestation minimally influences hydrology, when compared to climate change, and reduces low streamflow whilst not lowering the highest flows.
Basil Kraft, Michael Schirmer, William H. Aeberhard, Massimiliano Zappa, Sonia I. Seneviratne, and Lukas Gudmundsson
EGUsphere, https://doi.org/10.5194/egusphere-2024-993, https://doi.org/10.5194/egusphere-2024-993, 2024
Short summary
Short summary
This study uses deep learning to predict spatially contiguous water runoff in Switzerland from 1962–2023. It outperforms traditional models, requiring less data and computational power. Key findings include increased dry years and summer water scarcity. This method offers significant advancements in water monitoring.
Joško Trošelj and Naota Hanasaki
EGUsphere, https://doi.org/10.5194/egusphere-2024-595, https://doi.org/10.5194/egusphere-2024-595, 2024
Short summary
Short summary
This study presents the first distributed hydrological simulation which confirms the claims raised by historians that the Eastward Diversion Project of the Tone River in Japan was conducted four centuries ago to increase low flows and subsequent travelling possibilities surrounding the Capitol Edo (Tokyo) using inland navigation. We reconstructed six historical river maps and indirectly validated the historical simulations with reachable ancient river ports via increased low-flow water levels.
Qian Zhu, Xiaodong Qin, Dongyang Zhou, Tiantian Yang, and Xinyi Song
Hydrol. Earth Syst. Sci., 28, 1665–1686, https://doi.org/10.5194/hess-28-1665-2024, https://doi.org/10.5194/hess-28-1665-2024, 2024
Short summary
Short summary
Input data, model and calibration strategy can affect the accuracy of flood event simulation and prediction. Satellite-based precipitation with different spatiotemporal resolutions is an important input source. Data-driven models are sometimes proven to be more accurate than hydrological models. Event-based calibration and conventional strategy are two options adopted for flood simulation. This study targets the three concerns for accurate flood event simulation and prediction.
Fabio Ciulla and Charuleka Varadharajan
Hydrol. Earth Syst. Sci., 28, 1617–1651, https://doi.org/10.5194/hess-28-1617-2024, https://doi.org/10.5194/hess-28-1617-2024, 2024
Short summary
Short summary
We present a new method based on network science for unsupervised classification of large datasets and apply it to classify 9067 US catchments and 274 biophysical traits at multiple scales. We find that our trait-based approach produces catchment classes with distinct streamflow behavior and that spatial patterns emerge amongst pristine and human-impacted catchments. This method can be widely used beyond hydrology to identify patterns, reduce trait redundancy, and select representative sites.
Cyril Thébault, Charles Perrin, Vazken Andréassian, Guillaume Thirel, Sébastien Legrand, and Olivier Delaigue
Hydrol. Earth Syst. Sci., 28, 1539–1566, https://doi.org/10.5194/hess-28-1539-2024, https://doi.org/10.5194/hess-28-1539-2024, 2024
Short summary
Short summary
Streamflow forecasting is useful for many applications, ranging from population safety (e.g. floods) to water resource management (e.g. agriculture or hydropower). To this end, hydrological models must be optimized. However, a model is inherently wrong. This study aims to analyse the contribution of a multi-model approach within a variable spatial framework to improve streamflow simulations. The underlying idea is to take advantage of the strength of each modelling framework tested.
Lele Shu, Xiaodong Li, Yan Chang, Xianhong Meng, Hao Chen, Yuan Qi, Hongwei Wang, Zhaoguo Li, and Shihua Lyu
Hydrol. Earth Syst. Sci., 28, 1477–1491, https://doi.org/10.5194/hess-28-1477-2024, https://doi.org/10.5194/hess-28-1477-2024, 2024
Short summary
Short summary
We developed a new model to better understand how water moves in a lake basin. Our model improves upon previous methods by accurately capturing the complexity of water movement, both on the surface and subsurface. Our model, tested using data from China's Qinghai Lake, accurately replicates complex water movements and identifies contributing factors of the lake's water balance. The findings provide a robust tool for predicting hydrological processes, aiding water resource planning.
Ricardo Mantilla, Morgan Fonley, and Nicolás Velásquez
Hydrol. Earth Syst. Sci., 28, 1373–1382, https://doi.org/10.5194/hess-28-1373-2024, https://doi.org/10.5194/hess-28-1373-2024, 2024
Short summary
Short summary
Hydrologists strive to “Be right for the right reasons” when modeling the hydrologic cycle; however, the datasets available to validate hydrological models are sparse, and in many cases, they comprise streamflow observations at the outlets of large catchments. In this work, we show that matching streamflow observations at the outlet of a large basin is not a reliable indicator of a correct description of the small-scale runoff processes.
Cited articles
Albergel, C., Munier, S., Leroux, D. J., Dewaele, H., Fairbairn, D., Barbu, A. L., Gelati, E., Dorigo, W., Faroux, S., Meurey, C., Le Moigne, P., Decharme, B., Mahfouf, J.-F., and Calvet, J.-C.: Sequential assimilation of satellite-derived vegetation and soil moisture products using SURFEX_v8.0: LDAS-Monde assessment over the Euro-Mediterranean area, Geosci. Model Dev., 10, 3889–3912, https://doi.org/10.5194/gmd-10-3889-2017, 2017.
Arsenault, K. R., Kumar, S. V., Geiger, J. V., Wang, S., Kemp, E., Mocko, D. M., Beaudoing, H. K., Getirana, A., Navari, M., Li, B., Jacob, J., Wegiel, J., and Peters-Lidard, C. D.: The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems, Geosci. Model Dev., 11, 3605–3621, https://doi.org/10.5194/gmd-11-3605-2018, 2018.
Ball, J. T., Woodrow, I. E., and Berry, J. A.: A model predicting stomatal
conductance and its contribution to the control of photosynthesis under
different environmental conditions, Prog. Photosynthes. Res., 221–224, 1987.
Barbu, A. L., Calvet, J.-C., Mahfouf, J.-F., and Lafont, S.: Integrating ASCAT surface soil moisture and GEOV1 leaf area index into the SURFEX modelling platform: a land data assimilation application over France, Hydrol. Earth Syst. Sci., 18, 173–192, https://doi.org/10.5194/hess-18-173-2014, 2014.
Bastiaanssen, W., Cheema, M., Immerzeel, W. W., Miltenburg, I. J., and Pelgrum, H.: Surface energy balance and actual evapotranspiration of the
transboundary Indus Basin estimated from satellite measurements and the
ETLook model, Water Resour. Res., 48, W11512, https://doi.org/10.1029/2011WR010482, 2012.
Bergaoui, K., Mitchell, D., Otto, F., Allen, M., Zaaboul, R., and McDonnell,
R.: The contribution of human-induced climate change to the drought of 2014
in the southern Levant region, B. Am. Meteorol. Soc., 96, S66–S70, 2015.
Bhaga, T. D., Dube, T., Shekede, M. D., and Shoko, C.: Impacts of Climate
Variability and Drought on Surface Water Resources in Sub-Saharan Africa
Using Remote Sensing: A Review, Remote Sens., 12, 4184, https://doi.org/10.3390/rs12244184, 2020.
Bijaber, N., El Hadani, D., Saidi, M., Svoboda, M., Wardlow, B., Hain, C.,
Poulsen, C., Yessef, M., and Rochdi, A.: Developing a Remotely Sensed Drought
Monitoring Indicator for Morocco, Geosciences, 8, 55, https://doi.org/10.3390/geosciences8020055, 2018.
Blatchford, M. L., Mannaerts, C. M., Njuki, S. M., Nouri, H., Zeng, Y.,
Pelgrum, H., Wonink, S., and Karimi, P.: Evaluation of WaPOR V2 evapotranspiration products across Africa, Hydrol. Process., 3, 1–22, https://doi.org/10.1002/hyp.13791, 2020.
Bolten, J. D., Crow, W. T., Zhan, X., Jackson, T. J., and Reynolds, C. A.:
Evaluating the utility of remotely sensed soil moisture retrievals for
operational agricultural drought monitoring, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 3, 57–66, 2009.
Bonan, G. B.: Land surface model (LSM version 1.0) for ecological, hydrological, and atmospheric studies: Technical description and users
guide, Technical note No. PB-97-131494/XAB; NCAR/TN-417-STR, National
Center for Atmospheric Research, Climate and Global Dynamics Div., Boulder, CO, USA, http://n2t.net/ark:/85065/d78c9vm7 (last access: 14 July 2020), 1996.
Brocca, L., Crow, W. T., Ciabatta, L., Massari, C., De Rosnay, P., Enenkel,
M., Hahn, S., Amarnath, G., Camici, S., and Tarpanelli, A.: A review of the
applications of ASCAT soil moisture products, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 10, 2285–2306, 2017.
Collatz, G. J., Ball, J. T., Grivet, C., and Berry, J. A.: Physiological and
environmental regulation of stomatal conductance, photosynthesis and
transpiration: a model that includes a laminar boundary layer, Agr. Forest Meteorol., 54, 107–136, 1991.
Cook, B. I., Mankin, J. S., and Anchukaitis, K. J.: Climate change and
drought: From past to future, Curr. Clim. Change Rep., 4, 164–179, 2018.
Crow, W. T., Gomez, C. A., Sabater, J. M., Holmes, T., Hain, C. R., Lei, F.,
Dong, J., Alfieri, J. G., and Anderson, M. C.: Soil Moisture–Evapotranspiration Overcoupling and L-Band Brightness Temperature
Assimilation: Sources and Forecast Implications, J. Hydrometeorol., 21, 2359–2374, https://doi.org/10.1175/JHM-D-20-0088.1, 2020.
Cui, C., Xu, J., Zeng, J., Chen, K.-S., Bai, X., Lu, H., Chen, Q., and Zhao,
T.: Soil moisture mapping from satellites: An intercomparison of SMAP, SMOS,
FY3B, AMSR2, and ESA CCI over two dense network regions at different spatial
scales, Remote Sens., 10, 33, https://doi.org/10.3390/rs10010033, 2018.
Das, N. N., Entekhabi, D., Dunbar, R. S., Chaubell, M. J., Colliander, A.,
Yueh, S., Jagdhuber, T., Chen, F., Crow, W., and O'Neill, P. E.: The SMAP and
Copernicus Sentinel 1A/B microwave active-passive high resolution surface
soil moisture product, Remote Sens. Environ., 233, 111380, https://doi.org/10.1016/j.rse.2019.111380, 2019.
De Lannoy, G. J. M. and Reichle, R. H.: Assimilation of SMOS brightness temperatures or soil moisture retrievals into a land surface model, Hydrol. Earth Syst. Sci., 20, 4895–4911, https://doi.org/10.5194/hess-20-4895-2016, 2016.
Derber, J. C., Parrish, D. F., and Lord, S. J.: The new global operational
analysis system at the National Meteorological Center, Weather Forecast., 6, 538–547, 1991.
Dickinson, R. E., Shaikh, M., and Climate, R. B. O.: Interactive canopies for a climate model, J. Climate, 11, 2823–2836, 1998.
Ek, M. B., Mitchell, K. E., Lin, Y., Rogers, E., Grunmann, P., Koren, V., Gayno, G., and Tarpley, J. D.: Implementation of Noah land surface model
advances in the National Centers for Environmental Prediction operational
mesoscale Eta model, J. Geophys. Res., 108, 8851, https://doi.org/10.1029/2002JD003296, 2003.
Eller, C. B., Rowland, L., Mencuccini, M., Rosas, T., Williams, K., Harper,
A., Medlyn, B. E., Wagner, Y., Klein, T., Teodoro, G. S., Oliveira, R. S.,
Matos, I. S., Rosado, B. H. P., Fuchs, K., Wohlfahrt, G., Montagnani, L.,
Meir, P., Sitch, S., and Cox, P. M.: Stomatal optimization based on xylem
hydraulics (SOX) improves land surface model simulation of vegetation
responses to climate, New Phytol., 226, 1622–1637, https://doi.org/10.1111/nph.16419,
2020.
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., and Johnson,
J.: The soil moisture active passive (SMAP) mission, Proc. IEEE, 98, 704–716, 2010.
FAO: WaPOR Database Methodology: Version 2 release, April 2020, FAO, Rome,
1–91, https://doi.org/10.4060/ca9894en, 2020.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S.,
Kobrick, M., Paller, M., Rodriguez, E., and Roth, L.: The shuttle radar
topography mission, Rev. Geophys., 45, 1–33, https://doi.org/10.1029/2005RG000183, 2007.
Felfelani, F., Pokhrel, Y., Guan, K., and Lawrence, D. M.: Utilizing SMAP
soil moisture data to constrain irrigation in the Community Land Model,
Geophys. Res. Lett., 45, 12–892–12–902, 2018.
Fisher, R. A.: On the Probable Error of a Coefficient of Correlation Deduced
from a Small Sample, Metron, 1, 1–32, 1921.
Fragaszy, S. R., Jedd, T., Wall, N., Knutson, C., Belhaj Fraj, M., Bergaoui,
K., Svoboda, M., Hayes, M., and McDonnell, R.: Drought Monitoring in the
Middle East and North Africa (MENA) Region: Participatory Engagement to
Inform Early Warning Systems, B. Am. Meteorol. Soc., 101, E1148–E1173, https://doi.org/10.1175/BAMS-D-18-0084.1, 2020.
Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N.,
Sibley, A., and Huang, X.: MODIS Collection 5 global land cover: Algorithm
refinements and characterization of new datasets, Remote Sens. Environ., 114, 168–182, 2010.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S.,
Husak, G., Rowland, J., Harrison, L., and Hoell, A.: The climate hazards
infrared precipitation with stations – a new environmental record for monitoring extremes, Scient. Data, 2, 1–21, 2015.
Girotto, M., De Lannoy, G. J., Reichle, R. H., Rodell, M., Draper, C., Bhanja, S. N., and Mukherjee, A.: Benefits and pitfalls of GRACE data
assimilation: A case study of terrestrial water storage depletion in India,
Geophys. Res. Lett., 44, 4107–4115, 2017.
Guanter, L., Zhang, Y., Jung, M., Joiner, J., Voigt, M., Berry, J. A.,
Frankenberg, C., Huete, A. R., Zarco-Tejada, P., and Lee, J.-E.: Global and
time-resolved monitoring of crop photosynthesis with chlorophyll
fluorescence, P. Natl. Acad. Sci. USA, 111, E1327–E1333, 2014.
Hayes, M. J., Svoboda, M. D., Wardlow, B. D., Anderson, M. C., and Kogan, F.:
Drought monitoring: Historical and current perspectives, Digital Commons @ University of Nebraska, Lincoln, https://digitalcommons.unl.edu/droughtfacpub/94 (last access: 14 July 2020), 2012.
Hengl, T., Mendes de Jesus, J., Heuvelink, G. B., Ruiperez Gonzalez, M.,
Kilibarda, M., Blagotić, A., Shangguan, W., Wright, M. N., Geng, X., and
Bauer-Marschallinger, B.: SoilGrids250m: Global gridded soil information
based on machine learning, PLoS ONE, 12, e0169748, https://doi.org/10.1371/journal.pone.0169748, 2017.
Hssaisoune, M., Bouchaou, L., Sifeddine, A., Bouimetarhan, I., and Chehbouni,
A.: Moroccan Groundwater Resources and Evolution with Global Climate Changes, Geosciences, 12, 81, https://doi.org/10.3390/geosciences10020081, 2020.
Huffman, G. J., Bolvin, D. T., Braithwaite, D., Hsu, K., Joyce, R., Xie, P.,
and Yoo, S.-H.: NASA global precipitation measurement (GPM) integrated
multi-satellite retrievals for GPM (IMERG), Algorithm Theoretical Basis
Document (ATBD) Version 4, NASA/GSFC, 26 pp., 2015.
Ines, A. V. M., Das, N. N., Hansen, J. W., and Njoku, E. G.: Assimilation of
remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction, Remote Sens. Environ., 138, 149–164,
https://doi.org/10.1016/j.rse.2013.07.018, 2013.
Jalilvand, E., Abolafia-Rosenzweig, R., Tajrishy, M., and Das, N. N.: Evaluation of SMAP/Sentinel 1 High-resolution soil moisture data to detect
irrigation over agricultural domain, IEEE J. Select. Top. Appl. Earth Obs. Remote Sens., 14, 10733–10747, 2021.
Jedd, T., Fragaszy, S. R., Knutson, C., Hayes, M. J., Fraj, M. B., Wall, N.,
Svoboda, M., and McDonnell, R.: Drought Management Norms: Is the Middle East
and North Africa Region Managing Risks or Crises?, J. Environ. Dev., 116, 107049652096020-38, https://doi.org/10.1177/1070496520960204, 2020.
Joiner, J., Guanter, L., Lindstrot, R., Voigt, M., Vasilkov, A. P., Middleton, E. M., Huemmrich, K. F., Yoshida, Y., and Frankenberg, C.: Global monitoring of terrestrial chlorophyll fluorescence from moderate-spectral-resolution near-infrared satellite measurements: methodology, simulations, and application to GOME-2, Atmos. Meas. Tech., 6, 2803–2823, https://doi.org/10.5194/amt-6-2803-2013, 2013.
Joiner, J., Yoshida, Y., Zhang, Y., Duveiller, G., Jung, M., Lyapustin, A.,
Wang, Y., and Tucker, C.: Estimation of Terrestrial Global Gross Primary
Production (GPP) with Satellite Data-Driven Models and Eddy Covariance Flux
Data, Remote Sens., 10, 1346, https://doi.org/10.3390/rs10091346, 2018.
Kandasamy, S., Baret, F., Verger, A., Neveux, P., and Weiss, M.: A comparison of methods for smoothing and gap filling time series of remote sensing observations – application to MODIS LAI products, Biogeosciences, 10, 4055–4071, https://doi.org/10.5194/bg-10-4055-2013, 2013.
Kerr, Y. H., Wigneron, J. P., Al Bitar, A., Mialon, A., and Srivastava, P. K.: Soil moisture from space: Techniques and limitations, Satellite Soil
Moisture Retrieval, 3–27, https://doi.org/10.1016/B978-0-12-803388-3.00001-2, 2016.
Kharrou, M. H., Er-Raki, S., Chehbouni, A., Duchemin, B., Simonneaux, V.,
LePage, M., Ouzine, L., and Jarlan, L.: Water use efficiency and yield of
winter wheat under different irrigation regimes in a semi-arid region, Agricult. Sci., 2, 273–282, https://doi.org/10.4236/as.2011.23036, 2011.
Kolassa, J., Reichle, R., Liu, Q., Cosh, M., Bosch, D., Caldwell, T., Colliander, A., Holifield Collins, C., Jackson, T., Livingston, S., Moghaddam, M., and Starks, P.: Data Assimilation to Extract Soil Moisture
Information from SMAP Observations, Remote Sens., 9, 1179, https://doi.org/10.3390/rs9111179, 2017.
Kumar, S. V., Peters-Lidard, C. D., Tian, Y., Houser, P. R., Geiger, J., Olden, S., Lighty, L., Eastman, J. L., Doty, B., and Dirmeyer, P.: Land
information system: An interoperable framework for high resolution land
surface modeling, Environ. Model. Softw., 21, 1402–1415, 2006.
Kumar, S. V., Reichle, R. H., Harrison, K. W., Peters-Lidard, C. D.,
Yatheendradas, S., and Santanello, J. A.: A comparison of methods for a priori bias correction in soil moisture data assimilation, Water Resour. Res., 48, W03515, https://doi.org/10.1029/2010WR010261, 2012.
Kumar, S. V., Peters-Lidard, C. D., Mocko, D., Reichle, R., Liu, Y., Arsenault, K. R., Xia, Y., Ek, M., Riggs, G., and Livneh, B.: Assimilation of
remotely sensed soil moisture and snow depth retrievals for drought estimation, J. Hydrometeorol., 15, 2446–2469, 2014.
Kumar, S. V., Zaitchik, B. F., Peters-Lidard, C. D., Rodell, M., Reichle,
R., Li, B., Jasinski, M., Mocko, D., Getirana, A., and De Lannoy, G.:
Assimilation of gridded GRACE terrestrial water storage estimates in the
North American Land Data Assimilation System, J. Hydrometeorol., 17, 1951–1972, 2016.
Kumar, S. V., Dirmeyer, P. A., Peters-Lidard, C. D., Bindlish, R., and
Bolten, J.: Information theoretic evaluation of satellite soil moisture
retrievals, Remote Sens. Environ., 204, 392–400, 2018.
Kumar, S. V., Jasinski, M., Mocko, D. M., Rodell, M., Borak, J., Li, B.,
Beaudoing, H. K., and Peters-Lidard, C. D.: NCA-LDAS land analysis: Development and performance of a multisensor, multivariate land data
assimilation system for the National Climate Assessment, J. Hydrometeorol., 20, 1571–1593, 2019a.
Kumar, S. V., Mocko, D., Wang, S., Peters-Lidard, C. D., and Borak, J.:
Assimilation of Remotely Sensed Leaf Area Index into the Noah-MP Land Surface Model: Impacts on Water and Carbon Fluxes and States over the Continental United States, J. Hydrometeorol., 20, 1359–1377, https://doi.org/10.1175/JHM-D-18-0237.1, 2019b.
Kumar, S. V., Holmes, T. R., Bindlish, R., de Jeu, R., and Peters-Lidard, C.: Assimilation of vegetation optical depth retrievals from passive microwave radiometry, Hydrol. Earth Syst. Sci., 24, 3431–3450, https://doi.org/10.5194/hess-24-3431-2020, 2020.
Lawston, P. M., Santanello Jr., J. A., and Kumar, S. V.: Irrigation signals
detected from SMAP soil moisture retrievals, Geophys. Res. Lett., 44, 11-860–11-867, 2017.
Lei, F., Crow, W. T., Kustas, W. P., Dong, J., Yang, Y., Knipper, K. R.,
Anderson, M. C., Gao, F., Notarnicola, C., Greifeneder, F., McKee, L. M., Alfieri, J. G., Hain, C., and Dokoozlian, N.: Data assimilation of high-resolution thermal and radar remote sensing retrievals for soil moisture monitoring in a drip-irrigated vineyard, Remote Sens. Environ., 239, 111622, https://doi.org/10.1016/j.rse.2019.111622, 2020.
Li, C., Lu, H., Yang, K., Han, M., Wright, J. S., Chen, Y., Yu, L., Xu, S.,
Huang, X., and Gong, W.: The evaluation of SMAP enhanced soil moisture
products using high-resolution model simulations and in-situ observations on
the Tibetan Plateau, Remote Sens., 10, 535, https://doi.org/10.3390/rs10040535, 2018.
Lievens, H., Reichle, R. H., Liu, Q., De Lannoy, G. J. M., Dunbar, R. S.,
Kim, S. B., Das, N. N., Cosh, M., Walker, J. P., and Wagner, W.: Joint Sentinel-1 and SMAP data assimilation to improve soil moisture estimates,
Geophys. Res. Lett., 44, 6145–6153, https://doi.org/10.1002/2017GL073904, 2017.
Liu, Q., Reichle, R. H., Bindlish, R., Cosh, M. H., Crow, W. T., de Jeu, R.,
De Lannoy, G. J., Huffman, G. J., and Jackson, T. J.: The contributions of
precipitation and soil moisture observations to the skill of soil moisture
estimates in a land data assimilation system, J. Hydrometeorol., 12, 750–765, 2011.
Liu, X., Chen, F., Barlage, M., Zhou, G., and Niyogi, D.: Noah-MP-Crop:
Introducing dynamic crop growth in the Noah-MP land surface model, J. Geophys. Res.-Atmos., 121, 13953–13972, https://doi.org/10.1002/2016JD025597, 2016.
Liu, Y., Kumar, M., Katul, G. G., Feng, X., and Konings, A. G.: Plant
hydraulics accentuates the effect of atmospheric moisture stress on
transpiration, Nat. Clim. Change, 10, 691–695, https://doi.org/10.1038/s41558-020-0781-5, 2020.
Meier, J., Zabel, F., and Mauser, W.: A global approach to estimate irrigated areas – a comparison between different data and statistics, Hydrol. Earth Syst. Sci., 22, 1119–1133, https://doi.org/10.5194/hess-22-1119-2018, 2018.
Mocko, D. M., Kumar, S. V., Peters-Lidard, C. D., and Wang, S.: Assimilation
of vegetation conditions improves the representation of drought over
agricultural areas, J. Hydrometeorol., 22, 1085–1098, https://doi.org/10.1175/JHM-D-20-0065.1, 2021.
MODIS15: MODIS Collectio 6 (C6) LAI/FPAR Product User's Guide, p. 13,
https://lpdaac.usgs.gov/documents/624/MOD15_User_Guide_V6.pdf, last access: 20 July 2020.
Molle, F. and Sanchis-Ibor, C.: Irrigation policies in the Mediterranean:
Trends and challenges, Irrigation in the Mediterranean, Springer, Cham,
279–313, ISBN 13 978-3030036966, 2019.
Müller, M. F., Müller-Itten, M. C., and Gorelick, S. M.: How Jordan and Saudi Arabia are avoiding a tragedy of the commons over shared groundwater, Water Resour. Res., 53, 5451–5468, 2017.
Myneni, R., Knyazikhin, Y., and Park, T.: MCD15A2H MODIS/Terra+Aqua Leaf
Area Index/FPAR 8-day L4 Global 500 m SIN Grid V006, NASA EOSDIS Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MCD15A2H.061, 2015.
Nearing, G., Yatheendradas, S., Crow, W., Zhan, X., Liu, J., and Chen, F.:
The efficiency of data assimilation, Water Resour. Res., 54, 6374–6392, 2018.
Nie, W., Zaitchik, B. F., Rodell, M., Kumar, S. V., Anderson, M. C., and
Hain, C.: Groundwater withdrawals under drought: Reconciling GRACE and land
surface models in the United States High Plains Aquifer, Water Resour. Res.,
54, 5282–5299, 2018.
Nie, W., Kumar, S. V., Arsenault, K. R., Peters-Lidard, C. D., Mladenova, I. E., Bergaoui, K., Hazra, A., Zaitchik, B. F., Mahanama, S. P., McDonnell, R., Mocko, D. M., and Navari, M.: Data associated with the publication: 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, V1, Johns Hopkins University Data Archive [data set], https://doi.org/10.7281/T1/X4MXHC, 2022.
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., 116, 1381–1419,
https://doi.org/10.1029/2010JD015139, 2011.
Niu, G. Y., Fang, Y. H., Chang, L. L., Jin, J., Yuan, H., and Zeng, X.:
Enhancing the Noah-MP Ecosystem Response to Droughts with an Explicit
Representation of Plant Water Storage Supplied by Dynamic Root Water Uptake,
J. Adv. Model. Earth Syst., 12, 1–29, https://doi.org/10.1029/2020MS002062, 2020.
O'Neill, P. E., Chan, S., Njoku, E. G., Jackson, T., Bindlish, R., and
Chaubell, J.: SMAP Enhanced L3 Radiometer Global Daily 9 km EASE-Grid Soil
Moisture, Version 4, NASA National Snow and Ice Data Center Distributed
Active Archive Center, Boulder, Colorado, USA, https://doi.org/10.5067/T90W6VRLCBHI, 2020.
Ozdogan, M., Rodell, M., Beaudoing, H. K., and Toll, D. L.: Simulating the
effects of irrigation over the United States in a land surface model based on satellite-derived agricultural data, J. Hydrometeorol., 11, 171–184, 2010.
Pachauri, R. K., Allen, M. R., Barros, V. R., Broome, J., Cramer, W., Christ, R., Church, J. A., Clarke, L., Dahe, Q., and Dasgupta, P.: Climate change 2014: synthesis report, in: Contribution of Working Groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change, IPCC, 2014.
Pulwarty, R. S. and Sivakumar, M. V. K.: Information systems in a changing
climate_Early warnings and drought risk management, Weather Clim. Extrem., 3, 14–21, https://doi.org/10.1016/j.wace.2014.03.005, 2014.
Rajsekhar, D. and Gorelick, S. M.: Increasing drought in Jordan: Climate
change and cascading Syrian land-use impacts on reducing transboundary flow,
Sci. Adv., 3, e1700581, https://doi.org/10.1126/sciadv.1700581, 2017.
Reichle, R. H.: Data assimilation methods in the Earth sciences, Adv. Water Resour., 31, 1411–1418, 2008.
Reichle, R. H. and Koster, R. D.: Bias reduction in short records of satellite soil moisture, Geophys. Res. Lett., 31, L19501, https://doi.org/10.1029/2004GL020938, 2004.
Reichle, R. H., McLaughlin, D. B., and Entekhabi, D.: Hydrologic data
assimilation with the ensemble Kalman filter, Mon. Weather Rev., 130, 103–114, 2002.
Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J., Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., and Kim, G.-K.:
MERRA: NASA's modern-era retrospective analysis for research and applications, J. Climate, 24, 3624–3648, 2011.
Rossini, M., Nedbal, L., Guanter, L., Ač, A., Alonso, L., Burkart, A.,
Cogliati, S., Colombo, R., Damm, A., and Drusch, M.: Red and far red Sun-induced chlorophyll fluorescence as a measure of plant photosynthesis,
Geophys. Res. Lett., 42, 1632–1639, 2015.
Ryu, D., Crow, W. T., Zhan, X., and Jackson, T. J.: Correcting unintended
perturbation biases in hydrologic data assimilation, J. Hydrometeorol., 10, 734–750, 2009.
Salmon, J. M., Friedl, M. A., Frolking, S., Wisser, D., and Douglas, E. M.:
Global rain-fed, irrigated, and paddy croplands: A new high resolution map
derived from remote sensing, crop inventories and climate data, Int. J. Appl. Earth Obs. Geoinf., 38, 321–334, 2015.
Sheffield, J., Xia, Y., Luo, L., Wood, E. F., Ek, M., and Mitchell, K. E.:
Drought monitoring with the North American Land Data Assimilation System (NLDAS): A framework for merging model and satellite data for improved
drought monitoring, Remote Sensing of Drought: Innovative Monitoring
Approaches, Taylor & Friends Group, 227–259, https://doi.org/10.1201/b11863, 2012.
Stanke, C., Kerac, M., Prudhomme, C., Medlock, J., and Murray, V.: Health
effects of drought: a systematic review of the evidence, PLoS Currents, 5,
https://doi.org/10.1371/currents.dis.7a2cee9e980f91ad7697b570, 2013.
Tavakol, A., Rahmani, V., Quiring, S. M., and Kumar, S. V.: Evaluation
analysis of NASA SMAP L3 and L4 and SPoRT-LIS soil moisture data in the United States, Remote Sen. Environ., 229, 234–246, 2019.
Thenkabail, P. S., Biradar, C. M., Noojipady, P., Dheeravath, V., Li, Y.,
Velpuri, M., Gumma, M., Gangalakunta, O. R. P., Turral, H., and Cai, X.:
Global irrigated area map (GIAM), derived from remote sensing, for the end of the last millennium, Int. J. Remote Sens., 30, 3679–3733, 2009.
Tramontana, G., Jung, M., Schwalm, C. R., Ichii, K., Camps-Valls, G., Ráduly, B., Reichstein, M., Arain, M. A., Cescatti, A., Kiely, G., Merbold, L., Serrano-Ortiz, P., Sickert, S., Wolf, S., and Papale, D.: Predicting carbon dioxide and energy fluxes across global FLUXNET sites with regression algorithms, Biogeosciences, 13, 4291–4313, https://doi.org/10.5194/bg-13-4291-2016, 2016.
Verner, D., Treguer, D., Redwood, J., Christensen, J., McDonnell, R.,
Elbert, C., Konishi, Y., and Belghazi, S.: Climate Variability, Drought, and
Drought Management in Morocco's Agricultural Sector, World Bank, https://doi.org/10.1596/30603, 2018.
Weerasinghe, I., Bastiaanssen, W., Mul, M., Jia, L., and van Griensven, A.:
Can we trust remote sensing evapotranspiration products over Africa?, Hydrol.
Earth Syst. Sci., 24, 1565–1586, https://doi.org/10.5194/hess-24-1565-2020, 2020.
Weinthal, E., Zawahri, N., and Sowers, J.: Securitizing water, climate, and
migration in Israel, Jordan, and Syria, Intern. Environ. Agreements, 15, 293–307, 2015.
Xie, Y., Wang, P., Bai, X., Khan, J., Zhang, S., Li, L., and Wang, L.:
Assimilation of the leaf area index and vegetation temperature condition index for winter wheat yield estimation using Landsat imagery and the
CERES-Wheat model, Agr.d Forest Meteorol., 246, 194–206, 2017.
Yan, K., Park, T., Yan, G., Chen, C., Yang, B., Liu, Z., Nemani, R. R.,
Knyazikhin, Y., and Myneni, R. B.: Evaluation of MODIS LAI/FPAR product
collection 6. Part 1: Consistency and improvements, Remote Sens., 8, 359, https://doi.org/10.3390/rs8050359, 2016a.
Yan, K., Park, T., Yan, G., Liu, Z., Yang, B., Chen, C., Nemani, R. R., Knyazikhin, Y., and Myneni, R. B.: Evaluation of MODIS LAI/FPAR product
collection 6. Part 2: Validation and intercomparison, Remote Sens., 8, 460, https://doi.org/10.3390/rs8060460, 2016b.
Yang, Z. L., Niu, G. Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Longuevergne, L., Manning, K., Niyogi, D., and Tewari, M.: The community Noah
land surface model with multiparameterization options (Noah-MP): 2. Evaluation over global river basins, J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139, 2011.
Zhang, R., Kim, S., and Sharma, A.: A comprehensive validation of the SMAP
Enhanced Level-3 Soil Moisture product using ground measurements over varied
climates and landscapes, Remote Sens. Environ., 223, 82–94, 2019.
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.
The MENA (Middle East and North Africa) region faces significant food and water insecurity and...
