Articles | Volume 26, issue 18
https://doi.org/10.5194/hess-26-4741-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Special issue:
https://doi.org/10.5194/hess-26-4741-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Pan evaporation is increased by submerged macrophytes
Brigitta Simon-Gáspár
CORRESPONDING AUTHOR
Institute of Agronomy, Georgikon Campus, Hungarian University of
Agriculture and Life Sciences, 8360 Keszthely, Hungary
Gábor Soós
Institute of Agronomy, Georgikon Campus, Hungarian University of
Agriculture and Life Sciences, 8360 Keszthely, Hungary
Angela Anda
Institute of Agronomy, Georgikon Campus, Hungarian University of
Agriculture and Life Sciences, 8360 Keszthely, Hungary
Related subject area
Subject: Hydrometeorology | Techniques and Approaches: Modelling approaches
On the combined use of rain gauges and GPM IMERG satellite rainfall products for hydrological modelling: impact assessment of the cellular-automata-based methodology in the Tanaro River basin in Italy
An increase in the spatial extent of European floods over the last 70 years
140-year daily ensemble streamflow reconstructions over 661 catchments in France
The agricultural expansion in South America's Dry Chaco: regional hydroclimate effects
Machine-learning-constrained projection of bivariate hydrological drought magnitudes and socioeconomic risks over China
Improving runoff simulation in the Western United States with Noah-MP and variable infiltration capacity
Spatial variability in the seasonal precipitation lapse rates in complex topographical regions – application in France
Assessing downscaling methods to simulate hydrologically relevant weather scenarios from a global atmospheric reanalysis: case study of the upper Rhône River (1902–2009)
Global total precipitable water variations and trends over the period 1958–2021
Assessing decadal- to centennial-scale nonstationary variability in meteorological drought trends
Identification of compound drought and heatwave events on a daily scale and across four seasons
Potential for historically unprecedented Australian droughts from natural variability and climate change
Multi-objective calibration and evaluation of the ORCHIDEE land surface model over France at high resolution
Flood risk assessment for Indian sub-continental river basins
Key ingredients in regional climate modelling for improving the representation of typhoon tracks and intensities
Divergent future drought projections in UK river flows and groundwater levels
Predicting extreme sub-hourly precipitation intensification based on temperature shifts
Assessing rainfall radar errors with an inverse stochastic modelling framework
Spatiotemporal responses of runoff to climate change on the southern Tibetan Plateau
FROSTBYTE: A reproducible data-driven workflow for probabilistic seasonal streamflow forecasting in snow-fed river basins across North America
Hydroclimatic processes as the primary drivers of the Early Khvalynian transgression of the Caspian Sea: new developments
Accounting for hydroclimatic properties in flood frequency analysis procedures
Understanding the influence of “hot” models in climate impact studies: a hydrological perspective
A semi-parametric hourly space–time weather generator
A principal-component-based strategy for regionalisation of precipitation intensity–duration–frequency (IDF) statistics
Accounting for precipitation asymmetry in a multiplicative random cascade disaggregation model
Mapping soil moisture across the UK: assimilating cosmic-ray neutron sensors, remotely-sensed indices, rainfall radar and catchment water balance data in a Bayesian hierarchical model
Seasonal soil moisture and crop yield prediction with fifth-generation seasonal forecasting system (SEAS5) long-range meteorological forecasts in a land surface modelling approach
A genetic particle filter scheme for univariate snow cover assimilation into Noah-MP model across snow climates
Investigating the response of land–atmosphere interactions and feedbacks to spatial representation of irrigation in a coupled modeling framework
Validation of precipitation reanalysis products for rainfall-runoff modelling in Slovenia
Statistical post-processing of precipitation forecasts using circulation classifications and spatiotemporal deep neural networks
Sensitivity of the pseudo-global warming method under flood conditions: a case study from the northeastern US
Hybrid forecasting: blending climate predictions with AI models
Sensitivities of subgrid-scale physics schemes, meteorological forcing, and topographic radiation in atmosphere-through-bedrock integrated process models: a case study in the Upper Colorado River basin
Local moisture recycling across the globe
How well does a convection-permitting regional climate model represent the reverse orographic effect of extreme hourly precipitation?
Regionalisation of rainfall depth–duration–frequency curves with different data types in Germany
The suitability of a seasonal ensemble hybrid framework including data-driven approaches for hydrological forecasting
Continuous streamflow prediction in ungauged basins: long short-term memory neural networks clearly outperform traditional hydrological models
Daily ensemble river discharge reforecasts and real-time forecasts from the operational Global Flood Awareness System
Spatial distribution of oceanic moisture contributions to precipitation over the Tibetan Plateau
Ensemble streamflow prediction considering the influence of reservoirs in Narmada River Basin, India
Declining water resources in response to global warming and changes in atmospheric circulation patterns over southern Mediterranean France
Linking the complementary evaporation relationship with the Budyko framework for ungauged areas in Australia
Risks of seasonal extreme rainfall events in Bangladesh under 1.5 and 2.0 °C warmer worlds – how anthropogenic aerosols change the story
Evaluation of water flux predictive models developed using eddy-covariance observations and machine learning: a meta-analysis
Characterizing basin-scale precipitation gradients in the Third Pole region using a high-resolution atmospheric simulation-based dataset
A comparison of hydrological models with different level of complexity in Alpine regions in the context of climate change
Modelling evaporation with local, regional and global BROOK90 frameworks: importance of parameterization and forcing
Annalina Lombardi, Barbara Tomassetti, Valentina Colaiuda, Ludovico Di Antonio, Paolo Tuccella, Mario Montopoli, Giovanni Ravazzani, Frank Silvio Marzano, Raffaele Lidori, and Giulia Panegrossi
Hydrol. Earth Syst. Sci., 28, 3777–3797, https://doi.org/10.5194/hess-28-3777-2024, https://doi.org/10.5194/hess-28-3777-2024, 2024
Short summary
Short summary
The accurate estimation of precipitation and its spatial variability within a watershed is crucial for reliable discharge simulations. The study is the first detailed analysis of the potential usage of the cellular automata technique to merge different rainfall data inputs to hydrological models. This work shows an improvement in the performance of hydrological simulations when satellite and rain gauge data are merged.
Beijing Fang, Emanuele Bevacqua, Oldrich Rakovec, and Jakob Zscheischler
Hydrol. Earth Syst. Sci., 28, 3755–3775, https://doi.org/10.5194/hess-28-3755-2024, https://doi.org/10.5194/hess-28-3755-2024, 2024
Short summary
Short summary
We use grid-based runoff from a hydrological model to identify large spatiotemporally connected flood events in Europe, assess extent trends over the last 70 years, and attribute the trends to different drivers. Our findings reveal a general increase in flood extent, with regional variations driven by diverse factors. The study not only enables a thorough examination of flood events across multiple basins but also highlights the potential challenges arising from changing flood extents.
Alexandre Devers, Jean-Philippe Vidal, Claire Lauvernet, Olivier Vannier, and Laurie Caillouet
Hydrol. Earth Syst. Sci., 28, 3457–3474, https://doi.org/10.5194/hess-28-3457-2024, https://doi.org/10.5194/hess-28-3457-2024, 2024
Short summary
Short summary
Daily streamflow series for 661 near-natural French catchments are reconstructed over 1871–2012 using two ensemble datasets: HydRE and HydREM. They include uncertainties coming from climate forcings, streamflow measurement, and hydrological model error (for HydrREM). Comparisons with other hydrological reconstructions and independent/dependent observations show the added value of the two reconstructions in terms of quality, uncertainty estimation, and representation of extremes.
María Agostina Bracalenti, Omar V. Müller, Miguel A. Lovino, and Ernesto Hugo Berbery
Hydrol. Earth Syst. Sci., 28, 3281–3303, https://doi.org/10.5194/hess-28-3281-2024, https://doi.org/10.5194/hess-28-3281-2024, 2024
Short summary
Short summary
The Gran Chaco is a large, dry forest in South America that has been heavily deforested, particularly in the dry Chaco subregion. This deforestation, mainly driven by the expansion of the agricultural frontier, has changed the land's characteristics, affecting the local and regional climate. The study reveals that deforestation has resulted in reduced precipitation, soil moisture, and runoff, and if intensive agriculture continues, it could make summers in this arid region even drier and hotter.
Rutong Liu, Jiabo Yin, Louise Slater, Shengyu Kang, Yuanhang Yang, Pan Liu, Jiali Guo, Xihui Gu, Xiang Zhang, and Aliaksandr Volchak
Hydrol. Earth Syst. Sci., 28, 3305–3326, https://doi.org/10.5194/hess-28-3305-2024, https://doi.org/10.5194/hess-28-3305-2024, 2024
Short summary
Short summary
Climate change accelerates the water cycle and alters the spatiotemporal distribution of hydrological variables, thus complicating the projection of future streamflow and hydrological droughts. We develop a cascade modeling chain to project future bivariate hydrological drought characteristics over China, using five bias-corrected global climate model outputs under three shared socioeconomic pathways, five hydrological models, and a deep-learning model.
Lu Su, Dennis P. Lettenmaier, Ming Pan, and Benjamin Bass
Hydrol. Earth Syst. Sci., 28, 3079–3097, https://doi.org/10.5194/hess-28-3079-2024, https://doi.org/10.5194/hess-28-3079-2024, 2024
Short summary
Short summary
We fine-tuned the variable infiltration capacity (VIC) and Noah-MP models across 263 river basins in the Western US. We developed transfer relationships to similar basins and extended the fine-tuned parameters to ungauged basins. Both models performed best in humid areas, and the skills improved post-calibration. VIC outperforms Noah-MP in all but interior dry basins following regionalization. VIC simulates annual mean streamflow and high flow well, while Noah-MP performs better for low flows.
Valentin Dura, Guillaume Evin, Anne-Catherine Favre, and David Penot
Hydrol. Earth Syst. Sci., 28, 2579–2601, https://doi.org/10.5194/hess-28-2579-2024, https://doi.org/10.5194/hess-28-2579-2024, 2024
Short summary
Short summary
The increase in precipitation as a function of elevation is poorly understood in areas with complex topography. In this article, the reproduction of these orographic gradients is assessed with several precipitation products. The best product is a simulation from a convection-permitting regional climate model. The corresponding seasonal gradients vary significantly in space, with higher values for the first topographical barriers exposed to the dominant air mass circulations.
Caroline Legrand, Benoît Hingray, Bruno Wilhelm, and Martin Ménégoz
Hydrol. Earth Syst. Sci., 28, 2139–2166, https://doi.org/10.5194/hess-28-2139-2024, https://doi.org/10.5194/hess-28-2139-2024, 2024
Short summary
Short summary
Climate change is expected to increase flood hazard worldwide. The evolution is typically estimated from multi-model chains, where regional hydrological scenarios are simulated from weather scenarios derived from coarse-resolution atmospheric outputs of climate models. We show that two such chains are able to reproduce, from an atmospheric reanalysis, the 1902–2009 discharge variations and floods of the upper Rhône alpine river, provided that the weather scenarios are bias-corrected.
Nenghan Wan, Xiaomao Lin, Roger A. Pielke Sr., Xubin Zeng, and Amanda M. Nelson
Hydrol. Earth Syst. Sci., 28, 2123–2137, https://doi.org/10.5194/hess-28-2123-2024, https://doi.org/10.5194/hess-28-2123-2024, 2024
Short summary
Short summary
Global warming occurs at a rate of 0.21 K per decade, resulting in about 9.5 % K−1 of water vapor response to temperature from 1993 to 2021. Terrestrial areas experienced greater warming than the ocean, with a ratio of 2 : 1. The total precipitable water change in response to surface temperature changes showed a variation around 6 % K−1–8 % K−1 in the 15–55° N latitude band. Further studies are needed to identify the mechanisms leading to different water vapor responses.
Kyungmin Sung, Max C. A. Torbenson, and James H. Stagge
Hydrol. Earth Syst. Sci., 28, 2047–2063, https://doi.org/10.5194/hess-28-2047-2024, https://doi.org/10.5194/hess-28-2047-2024, 2024
Short summary
Short summary
This study examines centuries of nonstationary trends in meteorological drought and pluvial climatology. A novel approach merges tree-ring proxy data (North American Seasonal Precipitation Atlas – NASPA) with instrumental precipitation datasets by temporally downscaling proxy data, correcting biases, and analyzing shared trends in normal and extreme precipitation anomalies. We identify regions experiencing recent unprecedented shifts towards drier or wetter conditions and shifts in seasonality.
Baoying Shan, Niko E. C. Verhoest, and Bernard De Baets
Hydrol. Earth Syst. Sci., 28, 2065–2080, https://doi.org/10.5194/hess-28-2065-2024, https://doi.org/10.5194/hess-28-2065-2024, 2024
Short summary
Short summary
This study developed a convenient and new method to identify the occurrence of droughts, heatwaves, and co-occurring droughts and heatwaves (CDHW) across four seasons. Using this method, we could establish the start and/or end dates of drought (or heatwave) events. We found an increase in the frequency of heatwaves and CDHW events in Belgium caused by climate change. We also found that different months have different chances of CDHW events.
Georgina M. Falster, Nicky M. Wright, Nerilie J. Abram, Anna M. Ukkola, and Benjamin J. Henley
Hydrol. Earth Syst. Sci., 28, 1383–1401, https://doi.org/10.5194/hess-28-1383-2024, https://doi.org/10.5194/hess-28-1383-2024, 2024
Short summary
Short summary
Multi-year droughts have severe environmental and economic impacts, but the instrumental record is too short to characterise multi-year drought variability. We assessed the nature of Australian multi-year droughts using simulations of the past millennium from 11 climate models. We show that multi-decadal
megadroughtsare a natural feature of the Australian hydroclimate. Human-caused climate change is also driving a tendency towards longer droughts in eastern and southwestern Australia.
Peng Huang, Agnès Ducharne, Lucia Rinchiuso, Jan Polcher, Laure Baratgin, Vladislav Bastrikov, and Eric Sauquet
EGUsphere, https://doi.org/10.5194/egusphere-2024-445, https://doi.org/10.5194/egusphere-2024-445, 2024
Short summary
Short summary
We conducted a high-resolution hydrological simulation from 1959 to 2020 across France. We used a simple trial-and-error calibration to reduce the biases of the simulated water budget compared to observations. The selected simulation satisfactorily reproduces water fluxes, including their spatial contrasts and temporal trends. This work offers a thorough historical overview of water resources and a robust configuration for climate change impact analysis at the nationwide scale of France.
Urmin Vegad, Yadu Pokhrel, and Vimal Mishra
Hydrol. Earth Syst. Sci., 28, 1107–1126, https://doi.org/10.5194/hess-28-1107-2024, https://doi.org/10.5194/hess-28-1107-2024, 2024
Short summary
Short summary
A large population is affected by floods, which leave their footprints through human mortality, migration, and damage to agriculture and infrastructure, during almost every summer monsoon season in India. Despite the massive damage of floods, sub-basin level flood risk assessment is still in its infancy and needs to be improved. Using hydrological and hydrodynamic models, we reconstructed sub-basin level observed floods for the 1901–2020 period.
Qi Sun, Patrick Olschewski, Jianhui Wei, Zhan Tian, Laixiang Sun, Harald Kunstmann, and Patrick Laux
Hydrol. Earth Syst. Sci., 28, 761–780, https://doi.org/10.5194/hess-28-761-2024, https://doi.org/10.5194/hess-28-761-2024, 2024
Short summary
Short summary
Tropical cyclones (TCs) often cause high economic loss due to heavy winds and rainfall, particularly in densely populated regions such as the Pearl River Delta (China). This study provides a reference to set up regional climate models for TC simulations. They contribute to a better TC process understanding and assess the potential changes and risks of TCs in the future. This lays the foundation for hydrodynamical modelling, from which the cities' disaster management and defence could benefit.
Simon Parry, Jonathan D. Mackay, Thomas Chitson, Jamie Hannaford, Eugene Magee, Maliko Tanguy, Victoria A. Bell, Katie Facer-Childs, Alison Kay, Rosanna Lane, Robert J. Moore, Stephen Turner, and John Wallbank
Hydrol. Earth Syst. Sci., 28, 417–440, https://doi.org/10.5194/hess-28-417-2024, https://doi.org/10.5194/hess-28-417-2024, 2024
Short summary
Short summary
We studied drought in a dataset of possible future river flows and groundwater levels in the UK and found different outcomes for these two sources of water. Throughout the UK, river flows are likely to be lower in future, with droughts more prolonged and severe. However, whilst these changes are also found in some boreholes, in others, higher levels and less severe drought are indicated for the future. This has implications for the future balance between surface water and groundwater below.
Francesco Marra, Marika Koukoula, Antonio Canale, and Nadav Peleg
Hydrol. Earth Syst. Sci., 28, 375–389, https://doi.org/10.5194/hess-28-375-2024, https://doi.org/10.5194/hess-28-375-2024, 2024
Short summary
Short summary
We present a new physical-based method for estimating extreme sub-hourly precipitation return levels (i.e., intensity–duration–frequency, IDF, curves), which are critical for the estimation of future floods. The proposed model, named TENAX, incorporates temperature as a covariate in a physically consistent manner. It has only a few parameters and can be easily set for any climate station given sub-hourly precipitation and temperature data are available.
Amy Charlotte Green, Chris G. Kilsby, and András Bárdossy
EGUsphere, https://doi.org/10.5194/egusphere-2024-26, https://doi.org/10.5194/egusphere-2024-26, 2024
Short summary
Short summary
Weather radar is a crucial tool in rainfall estimation, but radar rainfall estimates are subject to many error sources, with the true rainfall field unknown. A flexible model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard processing methods. This flexible and efficient model performs well at generating realistic weather radar images visually, for a large range of event types.
He Sun, Tandong Yao, Fengge Su, Wei Yang, and Deliang Chen
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-11, https://doi.org/10.5194/hess-2024-11, 2024
Revised manuscript accepted for HESS
Short summary
Short summary
Our findings revealed runoff generation is dominated by rainfall runoff in the YZ, and the largest glacier runoff contribution is in the downstream sub-basin. Annual runoff trends indicate an increase in the NX but a decrease in the NX-BXK for 1971–2020, due to contrasting precipitation changes. Total runoff across the sub-basins will consistently increase through the 21st century, mostly resulting from increased rainfall runoff.
Louise Arnal, Martyn P. Clark, Alain Pietroniro, Vincent Vionnet, David R. Casson, Paul H. Whitfield, Vincent Fortin, Andrew W. Wood, Wouter J. M. Knoben, Brandi W. Newton, and Colleen Walford
EGUsphere, https://doi.org/10.5194/egusphere-2023-3040, https://doi.org/10.5194/egusphere-2023-3040, 2024
Short summary
Short summary
Forecasting river flows months in advance is crucial for many water sectors and society. In N. America, snowmelt is a key driver of river flow. This study presents a statistical workflow using snow data to forecast flows months ahead in N. American snow-fed rivers. Variations in predictability across the continent are evident, raising concerns about future river flow predictability amid a changing (snow) climate. The reproducible workflow hosted on GitHub supports collaborative and open science.
Alexander Gelfan, Andrey Panin, Andrey Kalugin, Polina Morozova, Vladimir Semenov, Alexey Sidorchuk, Vadim Ukraintsev, and Konstantin Ushakov
Hydrol. Earth Syst. Sci., 28, 241–259, https://doi.org/10.5194/hess-28-241-2024, https://doi.org/10.5194/hess-28-241-2024, 2024
Short summary
Short summary
Paleogeographical data show that 17–13 ka BP, the Caspian Sea level was 80 m above the current level. There are large disagreements on the genesis of this “Great” Khvalynian transgression of the sea, and we tried to shed light on this issue. Using climate and hydrological models as well as the paleo-reconstructions, we proved that the transgression could be initiated solely by hydroclimatic factors within the deglaciation period in the absence of the glacial meltwater effect.
Joeri B. Reinders and Samuel E. Munoz
Hydrol. Earth Syst. Sci., 28, 217–227, https://doi.org/10.5194/hess-28-217-2024, https://doi.org/10.5194/hess-28-217-2024, 2024
Short summary
Short summary
Flooding presents a major hazard for people and infrastructure along waterways; however, it is challenging to study the likelihood of a flood magnitude occurring regionally due to a lack of long discharge records. We show that hydroclimatic variables like Köppen climate regions and precipitation intensity explain part of the variance in flood frequency distributions and thus reduce the uncertainty of flood probability estimates. This gives water managers a tool to locally improve flood analysis.
Mehrad Rahimpour Asenjan, Francois Brissette, Jean-Luc Martel, and Richard Arsenault
Hydrol. Earth Syst. Sci., 27, 4355–4367, https://doi.org/10.5194/hess-27-4355-2023, https://doi.org/10.5194/hess-27-4355-2023, 2023
Short summary
Short summary
Climate models are central to climate change impact studies. Some models project a future deemed too hot by many. We looked at how including hot models may skew the result of impact studies. Applied to hydrology, this study shows that hot models do not systematically produce hydrological outliers.
Ross Pidoto and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 3957–3975, https://doi.org/10.5194/hess-27-3957-2023, https://doi.org/10.5194/hess-27-3957-2023, 2023
Short summary
Short summary
Long continuous time series of meteorological variables (i.e. rainfall, temperature) are required for the modelling of floods. Observed time series are generally too short or not available. Weather generators are models that reproduce observed weather time series. This study extends an existing station-based rainfall model into space by enforcing observed spatial rainfall characteristics. To model other variables (i.e. temperature) the model is then coupled to a simple resampling approach.
Kajsa Maria Parding, Rasmus Emil Benestad, Anita Verpe Dyrrdal, and Julia Lutz
Hydrol. Earth Syst. Sci., 27, 3719–3732, https://doi.org/10.5194/hess-27-3719-2023, https://doi.org/10.5194/hess-27-3719-2023, 2023
Short summary
Short summary
Intensity–duration–frequency (IDF) curves describe the likelihood of extreme rainfall and are used in hydrology and engineering, for example, for flood forecasting and water management. We develop a model to estimate IDF curves from daily meteorological observations, which are more widely available than the observations on finer timescales (minutes to hours) that are needed for IDF calculations. The method is applied to all data at once, making it efficient and robust to individual errors.
Kaltrina Maloku, Benoit Hingray, and Guillaume Evin
Hydrol. Earth Syst. Sci., 27, 3643–3661, https://doi.org/10.5194/hess-27-3643-2023, https://doi.org/10.5194/hess-27-3643-2023, 2023
Short summary
Short summary
High-resolution precipitation data, needed for many applications in hydrology, are typically rare. Such data can be simulated from daily precipitation with stochastic disaggregation. In this work, multiplicative random cascades are used to disaggregate time series of 40 min precipitation from daily precipitation for 81 Swiss stations. We show that very relevant statistics of precipitation are obtained when precipitation asymmetry is accounted for in a continuous way in the cascade generator.
Peter E. Levy and the COSMOS-UK team
EGUsphere, https://doi.org/10.5194/egusphere-2023-2041, https://doi.org/10.5194/egusphere-2023-2041, 2023
Short summary
Short summary
Having accurate up-to-date maps of soil moisture is important for many purposes. However, current modelled and remotely-sensed maps are rather coarse and not very accurate. Here, we demonstrate a simple but accurate approach which is closely linked to direct measurements of soil moisture at a network sites across the UK, and to the water balance (precipitation minus drainage and evaporation) measured at a large number of catchments (1212), as well as to remotely-sensed satellite estimates.
Theresa Boas, Heye Reemt Bogena, Dongryeol Ryu, Harry Vereecken, Andrew Western, and Harrie-Jan Hendricks Franssen
Hydrol. Earth Syst. Sci., 27, 3143–3167, https://doi.org/10.5194/hess-27-3143-2023, https://doi.org/10.5194/hess-27-3143-2023, 2023
Short summary
Short summary
In our study, we tested the utility and skill of a state-of-the-art forecasting product for the prediction of regional crop productivity using a land surface model. Our results illustrate the potential value and skill of combining seasonal forecasts with modelling applications to generate variables of interest for stakeholders, such as annual crop yield for specific cash crops and regions. In addition, this study provides useful insights for future technical model evaluations and improvements.
Yuanhong You, Chunlin Huang, Zuo Wang, Jinliang Hou, Ying Zhang, and Peipei Xu
Hydrol. Earth Syst. Sci., 27, 2919–2933, https://doi.org/10.5194/hess-27-2919-2023, https://doi.org/10.5194/hess-27-2919-2023, 2023
Short summary
Short summary
This study aims to investigate the performance of a genetic particle filter which was used as a snow data assimilation scheme across different snow climates. The results demonstrated that the genetic algorithm can effectively solve the problem of particle degeneration and impoverishment in a particle filter algorithm. The system has revealed a low sensitivity to the particle number in point-scale application of the ground snow depth measurement.
Patricia Lawston-Parker, Joseph A. Santanello Jr., and Nathaniel W. Chaney
Hydrol. Earth Syst. Sci., 27, 2787–2805, https://doi.org/10.5194/hess-27-2787-2023, https://doi.org/10.5194/hess-27-2787-2023, 2023
Short summary
Short summary
Irrigation has been shown to impact weather and climate, but it has only recently been considered in prediction models. Prescribing where (globally) irrigation takes place is important to accurately simulate its impacts on temperature, humidity, and precipitation. Here, we evaluated three different irrigation maps in a weather model and found that the extent and intensity of irrigated areas and their boundaries are important drivers of weather impacts resulting from human practices.
Marcos Julien Alexopoulos, Hannes Müller-Thomy, Patrick Nistahl, Mojca Šraj, and Nejc Bezak
Hydrol. Earth Syst. Sci., 27, 2559–2578, https://doi.org/10.5194/hess-27-2559-2023, https://doi.org/10.5194/hess-27-2559-2023, 2023
Short summary
Short summary
For rainfall-runoff simulation of a certain area, hydrological models are used, which requires precipitation data and temperature data as input. Since these are often not available as observations, we have tested simulation results from atmospheric models. ERA5-Land and COSMO-REA6 were tested for Slovenian catchments. Both lead to good simulations results. Their usage enables the use of rainfall-runoff simulation in unobserved catchments as a requisite for, e.g., flood protection measures.
Tuantuan Zhang, Zhongmin Liang, Wentao Li, Jun Wang, Yiming Hu, and Binquan Li
Hydrol. Earth Syst. Sci., 27, 1945–1960, https://doi.org/10.5194/hess-27-1945-2023, https://doi.org/10.5194/hess-27-1945-2023, 2023
Short summary
Short summary
We use circulation classifications and spatiotemporal deep neural networks to correct raw daily forecast precipitation by combining large-scale circulation patterns with local spatiotemporal information. We find that the method not only captures the westward and northward movement of the western Pacific subtropical high but also shows substantially higher bias-correction capabilities than existing standard methods in terms of spatial scale, timescale, and intensity.
Zeyu Xue, Paul Ullrich, and Lai-Yung Ruby Leung
Hydrol. Earth Syst. Sci., 27, 1909–1927, https://doi.org/10.5194/hess-27-1909-2023, https://doi.org/10.5194/hess-27-1909-2023, 2023
Short summary
Short summary
We examine the sensitivity and robustness of conclusions drawn from the PGW method over the NEUS by conducting multiple PGW experiments and varying the perturbation spatial scales and choice of perturbed meteorological variables to provide a guideline for this increasingly popular regional modeling method. Overall, we recommend PGW experiments be performed with perturbations to temperature or the combination of temperature and wind at the gridpoint scale, depending on the research question.
Louise J. Slater, Louise Arnal, Marie-Amélie Boucher, Annie Y.-Y. Chang, Simon Moulds, Conor Murphy, Grey Nearing, Guy Shalev, Chaopeng Shen, Linda Speight, Gabriele Villarini, Robert L. Wilby, Andrew Wood, and Massimiliano Zappa
Hydrol. Earth Syst. Sci., 27, 1865–1889, https://doi.org/10.5194/hess-27-1865-2023, https://doi.org/10.5194/hess-27-1865-2023, 2023
Short summary
Short summary
Hybrid forecasting systems combine data-driven methods with physics-based weather and climate models to improve the accuracy of predictions for meteorological and hydroclimatic events such as rainfall, temperature, streamflow, floods, droughts, tropical cyclones, or atmospheric rivers. We review recent developments in hybrid forecasting and outline key challenges and opportunities in the field.
Zexuan Xu, Erica R. Siirila-Woodburn, Alan M. Rhoades, and Daniel Feldman
Hydrol. Earth Syst. Sci., 27, 1771–1789, https://doi.org/10.5194/hess-27-1771-2023, https://doi.org/10.5194/hess-27-1771-2023, 2023
Short summary
Short summary
The goal of this study is to understand the uncertainties of different modeling configurations for simulating hydroclimate responses in the mountainous watershed. We run a group of climate models with various configurations and evaluate them against various reference datasets. This paper integrates a climate model and a hydrology model to have a full understanding of the atmospheric-through-bedrock hydrological processes.
Jolanda J. E. Theeuwen, Arie Staal, Obbe A. Tuinenburg, Bert V. M. Hamelers, and Stefan C. Dekker
Hydrol. Earth Syst. Sci., 27, 1457–1476, https://doi.org/10.5194/hess-27-1457-2023, https://doi.org/10.5194/hess-27-1457-2023, 2023
Short summary
Short summary
Evaporation changes over land affect rainfall over land via moisture recycling. We calculated the local moisture recycling ratio globally, which describes the fraction of evaporated moisture that rains out within approx. 50 km of its source location. This recycling peaks in summer as well as over wet and elevated regions. Local moisture recycling provides insight into the local impacts of evaporation changes and can be used to study the influence of regreening on local rainfall.
Eleonora Dallan, Francesco Marra, Giorgia Fosser, Marco Marani, Giuseppe Formetta, Christoph Schär, and Marco Borga
Hydrol. Earth Syst. Sci., 27, 1133–1149, https://doi.org/10.5194/hess-27-1133-2023, https://doi.org/10.5194/hess-27-1133-2023, 2023
Short summary
Short summary
Convection-permitting climate models could represent future changes in extreme short-duration precipitation, which is critical for risk management. We use a non-asymptotic statistical method to estimate extremes from 10 years of simulations in an orographically complex area. Despite overall good agreement with rain gauges, the observed decrease of hourly extremes with elevation is not fully represented by the model. Climate model adjustment methods should consider the role of orography.
Bora Shehu, Winfried Willems, Henrike Stockel, Luisa-Bianca Thiele, and Uwe Haberlandt
Hydrol. Earth Syst. Sci., 27, 1109–1132, https://doi.org/10.5194/hess-27-1109-2023, https://doi.org/10.5194/hess-27-1109-2023, 2023
Short summary
Short summary
Rainfall volumes at varying duration and frequencies are required for many engineering water works. These design volumes have been provided by KOSTRA-DWD in Germany. However, a revision of the KOSTRA-DWD is required, in order to consider the recent state-of-the-art and additional data. For this purpose, in our study, we investigate different methods and data available to achieve the best procedure that will serve as a basis for the development of the new KOSTRA-DWD product.
Sandra M. Hauswirth, Marc F. P. Bierkens, Vincent Beijk, and Niko Wanders
Hydrol. Earth Syst. Sci., 27, 501–517, https://doi.org/10.5194/hess-27-501-2023, https://doi.org/10.5194/hess-27-501-2023, 2023
Short summary
Short summary
Forecasts on water availability are important for water managers. We test a hybrid framework based on machine learning models and global input data for generating seasonal forecasts. Our evaluation shows that our discharge and surface water level predictions are able to create reliable forecasts up to 2 months ahead. We show that a hybrid framework, developed for local purposes and combined and rerun with global data, can create valuable information similar to large-scale forecasting models.
Richard Arsenault, Jean-Luc Martel, Frédéric Brunet, François Brissette, and Juliane Mai
Hydrol. Earth Syst. Sci., 27, 139–157, https://doi.org/10.5194/hess-27-139-2023, https://doi.org/10.5194/hess-27-139-2023, 2023
Short summary
Short summary
Predicting flow in rivers where no observation records are available is a daunting task. For decades, hydrological models were set up on these gauges, and their parameters were estimated based on the hydrological response of similar or nearby catchments where records exist. New developments in machine learning have now made it possible to estimate flows at ungauged locations more precisely than with hydrological models. This study confirms the performance superiority of machine learning models.
Shaun Harrigan, Ervin Zsoter, Hannah Cloke, Peter Salamon, and Christel Prudhomme
Hydrol. Earth Syst. Sci., 27, 1–19, https://doi.org/10.5194/hess-27-1-2023, https://doi.org/10.5194/hess-27-1-2023, 2023
Short summary
Short summary
Real-time river discharge forecasts and reforecasts from the Global Flood Awareness System (GloFAS) have been made publicly available, together with an evaluation of forecast skill at the global scale. Results show that GloFAS is skillful in over 93 % of catchments in the short (1–3 d) and medium range (5–15 d) and skillful in over 80 % of catchments in the extended lead time (16–30 d). Skill is summarised in a new layer on the GloFAS Web Map Viewer to aid decision-making.
Ying Li, Chenghao Wang, Ru Huang, Denghua Yan, Hui Peng, and Shangbin Xiao
Hydrol. Earth Syst. Sci., 26, 6413–6426, https://doi.org/10.5194/hess-26-6413-2022, https://doi.org/10.5194/hess-26-6413-2022, 2022
Short summary
Short summary
Spatial quantification of oceanic moisture contribution to the precipitation over the Tibetan Plateau (TP) contributes to the reliable assessments of regional water resources and the interpretation of paleo archives in the region. Based on atmospheric reanalysis datasets and numerical moisture tracking, this work reveals the previously underestimated oceanic moisture contributions brought by the westerlies in winter and the overestimated moisture contributions from the Indian Ocean in summer.
Urmin Vegad and Vimal Mishra
Hydrol. Earth Syst. Sci., 26, 6361–6378, https://doi.org/10.5194/hess-26-6361-2022, https://doi.org/10.5194/hess-26-6361-2022, 2022
Short summary
Short summary
Floods cause enormous damage to infrastructure and agriculture in India. However, the utility of ensemble meteorological forecast for hydrologic prediction has not been examined. Moreover, Indian river basins have a considerable influence of reservoirs that alter the natural flow variability. We developed a hydrologic modelling-based streamflow prediction considering the influence of reservoirs in India.
Camille Labrousse, Wolfgang Ludwig, Sébastien Pinel, Mahrez Sadaoui, Andrea Toreti, and Guillaume Lacquement
Hydrol. Earth Syst. Sci., 26, 6055–6071, https://doi.org/10.5194/hess-26-6055-2022, https://doi.org/10.5194/hess-26-6055-2022, 2022
Short summary
Short summary
The interest of this study is to demonstrate that we identify two zones in our study area whose hydroclimatic behaviours are uneven. By investigating relationships between the hydroclimatic conditions in both clusters for past observations with the overall atmospheric functioning, we show that the inequalities are mainly driven by a different control of the atmospheric teleconnection patterns over the area.
Daeha Kim, Minha Choi, and Jong Ahn Chun
Hydrol. Earth Syst. Sci., 26, 5955–5969, https://doi.org/10.5194/hess-26-5955-2022, https://doi.org/10.5194/hess-26-5955-2022, 2022
Short summary
Short summary
We proposed a practical method that predicts the evaporation rates on land surfaces (ET) where only atmospheric data are available. Using a traditional equation that describes partitioning of precipitation into ET and streamflow, we could approximately identify the key parameter of the predicting formulation based on land–atmosphere interactions. The simple method conditioned by local climates outperformed sophisticated models in reproducing water-balance estimates across Australia.
Ruksana H. Rimi, Karsten Haustein, Emily J. Barbour, Sarah N. Sparrow, Sihan Li, David C. H. Wallom, and Myles R. Allen
Hydrol. Earth Syst. Sci., 26, 5737–5756, https://doi.org/10.5194/hess-26-5737-2022, https://doi.org/10.5194/hess-26-5737-2022, 2022
Short summary
Short summary
Extreme rainfall events are major concerns in Bangladesh. Heavy downpours can cause flash floods and damage nearly harvestable crops in pre-monsoon season. While in monsoon season, the impacts can range from widespread agricultural loss, huge property damage, to loss of lives and livelihoods. This paper assesses the role of anthropogenic climate change drivers in changing risks of extreme rainfall events during pre-monsoon and monsoon seasons at local sub-regional-scale within Bangladesh.
Haiyang Shi, Geping Luo, Olaf Hellwich, Mingjuan Xie, Chen Zhang, Yu Zhang, Yuangang Wang, Xiuliang Yuan, Xiaofei Ma, Wenqiang Zhang, Alishir Kurban, Philippe De Maeyer, and Tim Van de Voorde
Hydrol. Earth Syst. Sci., 26, 4603–4618, https://doi.org/10.5194/hess-26-4603-2022, https://doi.org/10.5194/hess-26-4603-2022, 2022
Short summary
Short summary
There have been many machine learning simulation studies based on eddy-covariance observations for water flux and evapotranspiration. We performed a meta-analysis of such studies to clarify the impact of different algorithms and predictors, etc., on the reported prediction accuracy. It can, to some extent, guide future global water flux modeling studies and help us better understand the terrestrial ecosystem water cycle.
Yaozhi Jiang, Kun Yang, Hua Yang, Hui Lu, Yingying Chen, Xu Zhou, Jing Sun, Yuan Yang, and Yan Wang
Hydrol. Earth Syst. Sci., 26, 4587–4601, https://doi.org/10.5194/hess-26-4587-2022, https://doi.org/10.5194/hess-26-4587-2022, 2022
Short summary
Short summary
Our study quantified the altitudinal precipitation gradients (PGs) over the Third Pole (TP). Most sub-basins in the TP have positive PGs, and negative PGs are found in the Himalayas, the Hengduan Mountains and the western Kunlun. PGs are positively correlated with wind speed but negatively correlated with relative humidity. In addition, PGs tend to be positive at smaller spatial scales compared to those at larger scales. The findings can assist precipitation interpolation in the data-sparse TP.
Francesca Carletti, Adrien Michel, Francesca Casale, Alice Burri, Daniele Bocchiola, Mathias Bavay, and Michael Lehning
Hydrol. Earth Syst. Sci., 26, 3447–3475, https://doi.org/10.5194/hess-26-3447-2022, https://doi.org/10.5194/hess-26-3447-2022, 2022
Short summary
Short summary
High Alpine catchments are dominated by the melting of seasonal snow cover and glaciers, whose amount and seasonality are expected to be modified by climate change. This paper compares the performances of different types of models in reproducing discharge among two catchments under present conditions and climate change. Despite many advantages, the use of simpler models for climate change applications is controversial as they do not fully represent the physics of the involved processes.
Ivan Vorobevskii, Thi Thanh Luong, Rico Kronenberg, Thomas Grünwald, and Christian Bernhofer
Hydrol. Earth Syst. Sci., 26, 3177–3239, https://doi.org/10.5194/hess-26-3177-2022, https://doi.org/10.5194/hess-26-3177-2022, 2022
Short summary
Short summary
In the study we analysed the uncertainties of the meteorological data and model parameterization for evaporation modelling. We have taken a physically based lumped BROOK90 model and applied it in three different frameworks using global, regional and local datasets. Validating the simulations with eddy-covariance data from five stations in Germany, we found that the accuracy model parameterization plays a bigger role than the quality of the meteorological forcing.
Cited articles
Adeloye, A. J., Rustum, R., and Kariyama, I. D.: Kohonen self-organizing map
estimator for the reference crop evapotranspiration, Water Resour. Res., 47,
W08523, https://doi.org/10.1029/2011WR010690, 2005.
Allen, R. G., Pereira, L. S., Raes, D., and Smith, M.: Crop
Evapotranspiration: Guidelines for Computing Crop Water Requirements,
Irrigation and Drainage Paper 56, Food and Agriculture Organization of the
United Nations: Rome, Italy, http://www.fao.org/3/x0490e/x0490e00.htm (last access: 14 March 2021), 1998.
Allen, R. G., Walter, I. A., Elliott, R., Howell, T., Itenfisu, D., Jensen,
M., and Synder, R. L.: The ASCE Standardized Reference Evapotranspiration
Equation, Final Report (ASCE–EWRI), Task Committee on Standardization of
Reference Evapotranspiration, Environmental and Water Resources Institute of
the American Society of Civil Engineers: Reston, VA, USA,
https://doi.org/10.1061/9780784408056, 2005.
Almedeij, J.: Modeling pan evaporation for Kuwait by multiple linear
regression, Sci. World J., 2012, 574742, https://doi.org/10.1100/2012/574742, 2012.
Alsumaiei, A. A.: Utility of Artificial Neural Networks in Modeling Pan
Evaporation in Hyper-Arid Climates, Water, 12, 1508, https://doi.org/10.3390/w12051508,
2020.
An, N., Wang, K., Zhou, C., and Pinker, R. T.: Observed variability of cloud
frequency and cloud-based height within 3600 m above the surface over the
contiguous United States, J. Climate, 30, 3725–3742,
https://doi.org/10.1175/JCLI-D-16-0559.1, 2017.
Anda, A., Simon, B., Soós, G., Menyhárt, L., Teixeira da Silva, J.
A., and Kucserka, T.: Extending Class A pan evaporation for a shallow lake to
simulate the impact of littoral sediment and submerged macrophytes: a case
study for Keszthely Bay (Lake Balaton, Hungary), Agr. Forest Meteorol.,
250, 277–289, https://doi.org/10.1016/j.agrformet.2018.01.001, 2018
Anda, A., Simon, B., Soos, G., Teixeira da Silva, J. A., and Kucserka, T.:
Effect of submerged, freshwater aquatic macrohytes and littoral sediments on
pan evaporation in the Lake Balaton region, Hungary, J. Hydrol., 542,
615–626, https://doi.org/10.1016/j.jhydrol.2016.09.034, 2016.
Anda, A., Soos, G., Teixeira da Silva, J. A., and Kozma-Bognár, V.:
Regional evapotranspiration from a wetland in Central Europe, in a 16-year
period without human intervention, Agric. Forest Meteor., 205, 60–72, https://doi.org/10.1016/j.agrformet.2015.02.010, 2015.
Andersen, M. R., Sand-Jensen, K., Iestyn Woolway, R., and Jones, I. D.:
Profound daily vertical stratification and mixing in a small, shallow,
wind-exposed lake with submerged macrophytes, Aquat. Sci., 79, 395–406,
https://doi.org/10.1007/s00027-016-0505-0, 2017.
Arunkumar, R. and Jothiprakash, V.: Reservoir evaporation prediction using
data driven techniques, J. Hydrol. Eng., 18, 40–49,
https://doi.org/10.1061/(ASCE)HE.1943-5584.0000597, 2013.
Barko, J. W., Hardin, D. G., and Matthews, M. S.: Growth and morphology of
submersed freshwater macrophytes in relation to light and temperature, Can.
J. Botany, 60, 877–887, https://doi.org/10.1139/b82-113, 1982
Barreto, S. M. A. and Pérez-Uribe, A.: Improving the Correlation Hunting
in a Large Quantity of SOM Component Planes, in: Artificial Neural Networks
– ICANN 2007, edited by: de Sá, J. M.,
Alexandre, L. A., Duch, W., and Mandic, D., Lect. Notes Comput. Sc., 4669, Springer, Berlin, Heidelberg,
379–388, https://doi.org/10.1007/978-3-540-74695-9_39, 2007.
Bedoya, D., Novotny, V., and Manolakos, E. S.: Instream and off stream
environmental conditions and stream biotic integrity importance of scale and
site similarities for learning and prediction, Ecol. Model., 220,
2393–2406, https://doi.org/10.1016/j.ecolmodel.2009.06.017, 2009.
Berkovic, S., Mendelsohn, O. Y., Ilotoviz, E., and Raveh-Rubin, S.:
Self-organizing map classification of the boundary layer profile: A
refinement of Eastern Mediterranean wintersynoptic regimes, Int. J.
Climatol., 41, 3317–3338, https://doi.org/10.1002/joc.7021, 2021.
Brezny, O., Mehta, I., and Sharmas, R. K.: Studies of
evapotranspiration of some aquatic weeds, Weed Sci., 21, 197–204,
https://doi.org/10.1017/S0043174500032112, 1973.
Brutsaert, W. H.: Evaporation into the Atmosphere, Springer, 12–36, https://doi.org/10.1007/978-94-017-1497-6, 1982.
Burman, R. D.: Intercontinental comparison of evaporation estimates, J. Irr.
Drain. Div.-ASCE, 102, 109–118, https://doi.org/10.1061/JRCEA4.0001076, 1976.
Chang, F. J., Chang, L. C., Kao, H. S., and Wu, G. R.: Assessing the effort
of meteorological variables for evaporation estimation by self-organizing
map neural network, J. Hydrol., 384, 118–129,
https://doi.org/10.1016/j.jhydrol.2010.01.016, 2010.
Doan, Q.-V., Kusaka, H., Sato, T., and Chen, F.: S-SOM v1.0: a structural self-organizing map algorithm for weather typing, Geosci. Model Dev., 14, 2097–2111, https://doi.org/10.5194/gmd-14-2097-2021, 2021.
Dong, L., Zeng, W., Wu, L., Lei, G., Chen, H., Srivastava, A. K., and Gaiser,
T.: Estimating the pan evaporation in Northwest China by coupling CatBoost
with Bat algorithm, Water, 13, 256, https://doi.org/10.3390/w13030256, 2021.
Duan, W. Y., Han, Y., Huang, L. M., Zhao, B. B., and Wang, M. H.: A hybrid
EMD-SVR model for the short-term prediction of significant wave height,
Ocean Eng., 124, 54–73, https://doi.org/10.1016/j.oceaneng.2016.05.049, 2016.
Fournier, J., Thiboult, A., Nadeau, D. F., Vercauteren, N., Anctil, F.,
Parent, A.-C., Strachan, I. B., and Tremblay A.: Evaporation from boreal
reservoirs: Acomparison between eddy covariance observations andestimates
relying on limited data, Hydrol. Process., 35, e14335,
https://doi.org/10.1002/hyp.14335, 2021.
Fritz, C., Schneider, T., and Geist, J.: Seasonal Variation in Spectral
Response of Submerged Aquatic Macrophytes: A Case Study at Lake Starnberg
(Germany), Water, 9, 527, https://doi.org/10.1016/10.3390/w9070527, 2017.
Fuentes, van Ogtrop, F., and Vervoot, R. W.: Long term surface water trends
and relationship with open water evaporation losses in the Namoi catchment,
Australia, J. Hydrol., 584, 124714, https://doi.org/10.1016/j.jhydrol.2020.124714,
2020.
Gholami, V., Sahour, H., and Hadian, M. A.: Mapping soil erosion rates using
self-organizing map (SOM) and geographic information system (GIS) on
hillslopes, Earth Sci. Inform., 13, 1175–1185, https://doi.org/10.1007/s12145-020-00499-w, 2020.
Gu, Q., Hu, H., Ma., L., Sheng, L., Yang, S., Zhang, X., Zhang, M., Zheng,
K., and Chen, L.: Characterizing the spatial variations of the relationship
between land use and surface water quality using self-organizing map
approach, Ecol. Indicat., 102, 633–643, https://doi.org/10.1016/j.ecolind.2019.03.017, 2019.
Guntu, R. K., Maheswaran, R., Agarwal, A., and Singh, V. P.: Accounting for
temporal variability for improved precipitation regionalization based on
self-organizing map coupled with information theory, J. Hydrol., 590,
125236, https://doi.org/10.1016/j.jhydrol.2020.125236, 2020.
Hadjisolomou, E., Stefanidis, K., Papatheodorou, G., and Papastergiadou, E.:
Assessment of the eutrophication-related environmental parameters in two
mediterranean lakes by integrating statistical techniques and
self-organizing maps, Int. J. Env. Res. Pub. He., 15, 547,
https://doi.org/10.3390/ijerph15030547, 2018.
Himberg, J.: A SOM Based Cluster Visualization and Its Application for False
Coloring, Proceedings of International Joint Conference on Neural Networks
(IJCNN2000), 3, 587–592, https://doi.org/10.1109/IJCNN.2000.861379, 2000.
Jacobs, A. F. G., Heusinkveld, B. G., and Nieveen, J. P.: Temperature
Behavior of a Natural Shallow Water Body during a Summer Period, Theor.
Appl. Climatol., 59, 121–127, https://doi.org/10.1007/s007040050017, 1998.
Jensen, M. E., Burman, R. D., and Allen, R. G.: Evapotranspiration and
irrigation water requirements, American Society of Civil Engineers 70, New
York, 332 pp., https://doi.org/10.1061/9780784414057, 1990.
Jiménez-Rodríguez, C. D., Esquivel-Vargas, C., Coenders-Gerrits, M.,
and Sasa-Marín, M.: Quantification of the Evaporation Rates from Six
Types of Wetland Cover in Palo Verde National Park, Costa Rica, Water,
11, 674, https://doi.org/10.3390/w11040674, 2019.
Kalteh, A. M., Hjorth, P., and Berndtsson, R.: Review of the
self-organizing map (SOM) approach in water resources: analysis, modelling
and application, Environ. Model. Softw., 23, 835–845, https://doi.org/10.1016/j.envsoft.2007.10.001, 2008.
Keskin, M. E. and Terzi, O.: Artificial neural network models of daily pan
evaporation, J. Irrig. Drain. Eng., 11, 65–70,
https://doi.org/10.1061/(ASCE)1084-0699(2006)11:1(65), 2006.
Khatibi, R., Ghorbani, M. A., Naghshara, S., Aydin, H., and Karimi, V.:
Introducing a framework for “inclusive multiple modelling” with critical
views on modelling practices-Applications to modelling water levels of
Caspian Sea and Lakes Urmia and Van, J. Hydrol., 587, 124923,
https://doi.org/10.1016/j.jhydrol.2020.124923, 2020.
Kim, J. Y. and Nishihiro, J.: Responses of lake macrophyte species and
functional traits to climate and land use changes, Sci. Total Environ., 736,
139628, https://doi.org/10.1016/j.scitotenv.2020.139628, 2020.
Kim, S., Shiri, J., Singh, V. P., Kisi, O., and Landeras, G.: Predicting
daily pan evaporation by soft computing models with limited climatic data,
Hydrolog. Sci. J., 60, 1120–1136, https://doi.org/10.1080/02626667.2014.945937,
2015.
Kimmel, B. L. and Groeger, A. W.: Factors controlling primary production in
lakes and reservoirs: a perspective, Lake Reserv. Manage., 1, 277–281,
https://doi.org/10.1080/07438148409354524, 1984.
Kisi, O., Genc, O., Dinc, S., and Zounemat-Kermani, M.: Daily pan evaporation
modeling using chi-squared automatic interactiondetector, neural networks,
classification and regression tree, Comput. Electron. Agr., 122, 112–117,
https://doi.org/10.1016/j.compag.2016.01.026, 2016.
Kisi, O.: Pan evaporation modeling using least square support vector
machine, multivariate adaptive regression splines and M5 model tree, J.
Hydrol., 528, 312–320, https://doi.org/10.1016/j.jhydrol.2015.06.052, 2015.
Kiviluoto, K.: Topology preservation in self-organizing maps, Proceedings of
International Conference on Neural Networks, 294–299,
https://doi.org/10.1109/ICNN.1996.548907, 1996.
Kohonen, T.: Self-organizing formation of topologically correct feature
maps, Biol. Cybern., 43, 59–69, https://doi.org/10.1007/BF00337288, 1982.
Kohonen, T.: The self-organizing map. Proceedings of the IEEE, 78,
1464–1480, https://doi.org/10.1109/5.58325, 1990.
Kohonen, T.: Self-Organizing Maps, 3rd edition, Berlin, Heildelberg:
Springer-Verlag, p. 501, 2001.
Kohonen, T. and Somervuo, P.: How to make large self-organizing maps for
nonvectorial data. Neural Netw., 15, 945–952, https://doi.org/10.1016/S0893-6080(02)00069-2, 2002.
Kottek, M., Grieser, J., Beck, C., Rudolf, B., and Rubel, F.: WorldMap of the
Köppen-Geiger climate classification updated, Meteorol. Z., 15, 259–263, https://doi.org/10.1127/0941-2948/2006/0130, 2006.
Kumar, M., Raghuwanshi, N. S., Singh, R., Wallender, W. W., and Pruitt, W.
O.: Estimating evapotranspiration using artificial neural network, J. Irrig.
Drai. Eng., 128, 224–233, https://doi.org/10.1061/(ASCE)0733-9437(2002)128:4(224),
2002.
Kumar, N., Rustum, R., Shankar, V., and Adeloye, A. J.: Self-organizing map
estimator for the crop water stress index, Comput. Electron. Agr., 187,
106232, https://doi.org/10.1016/j.compag.2021.106232, 2021a.
Kumar, N., Shankar, V., Rustum, R., and Adeloye, A. J.: Evaluating the
Performance of Self-Organizing Maps to Estimate Well-Watered Canopy
Temperature for Calculating Crop Water Stress Index in Indian Mustard
(Brassica juncea), J. Irrig. Drain. Eng., 147, 04020040, https://doi.org/10.1061/(ASCE)IR.1943-4774.0001526, 2021b.
Lee, E. and Kim, S.: Characterization of soil moisture response patterns and hillslope hydrological processes through a self-organizing map, Hydrol. Earth Syst. Sci., 25, 5733–5748, https://doi.org/10.5194/hess-25-5733-2021, 2021.
Lee, C.-M., Choi, H., Kim, Y., Kim, M, Kim, H., and Hamm, S.-Y.:
Characterizing land use effect on shallow groundwater contamination by using
self-organizing map and buffer zone, Sci. Total Environ., 800, 149632, https://doi.org/10.1016/j.scitotenv.2021.149632, 2021.
Li, M. and Liu, K.: Probabilistic prediction of significant wave height
using dynamic bayesian network and information flow, Water, 12, 2075,
https://doi.org/10.3390/w12082075, 2020.
Li, Y., Wright, A., Liu, H., Wang, J., Wang, G., Wu, Y., and Dai, L.: Land
use pattern, irrigation, and fertilization effects of rice-wheat rotation on
water quality of ponds by using self-organizing map in agricultural
watersheds, Agr. Ecosyst. Environ., 272, 155–164, https://doi.org/10.1016/j.agee.2018.11.021, 2019.
Lin, G. F., Lin, H. Y., and Wu, M. C.: Development of a
support-vector-machine-based model for daily pan evaporation estimation.
Hydrol. Process., 27, 3115–3127, https://doi.org/10.1002/hyp.9428, 2013.
Madsen, T. V. and Cedergreen, N.: Sources of nutrients to rooted submerged
macrophytes growing in a nutrient-rich stream, Freshwater Biol., 47,
283–291, https://doi.org/10.1046/j.1365-2427.2002.00802.x, 2002.
Malik, A., Kumar, A., and Kisi, O.: Monthly pan-evaporation estimation in
Indian central Himalayas using different heuristic approaches and climate
based models, Comput. Electron. Agr., 143, 302–313,
https://doi.org/10.1016/j.compag.2017.11.008, 2017.
Malik, A., Rai, P., Heddam, S., Kisi, O., Sharafati, A., Salih, S. Q.,
Al-Ansari, N., and Yaseen, Z. M.: Pan evaporation estimation in Uttarakhand
and Uttar Pradesh States, India: validity of an integrative data
intelligence model, Atmosphere, 11, 553, https://doi.org/10.3390/atmos11060553, 2020a.
Malik, A., Kumar, A., Kim, S., Kashani, M. H., Karimi, V., Sharafati, A.,
Ghorbani, M. A., Al-Ansari, N., Salih, S. Q., Yaseen, Z. M., and Chau, K.-W.:
Modeling monthly pan evaporation process over the Indian central Himalayas:
application of multiple learning artificial intelligence model, Eng. Appl.
Comp. Fluid., 14, 323–338, https://doi.org/10.1080/19942060.2020.1715845, 2020b.
Mbangiwa, N. C., Savage, M. J., and Mabhaudhi, T.: Modelling and measurement
of water productivity and total evaporation in a dryland soybean crop,
Agric. For. Meteorol., 266, 65–72,
https://doi.org/10.1016/j.agrformet.2018.12.005, 2019.
McVicar ,T. R., Roderick, M. L., Donohue, R. J., Li, L. T., van Niel, T. G.,
Thomas, A., Grieser, J., Jhajharia, D., Himri, Y., Mahowald, N. M.,
Mescherskaya, A. V., Kruger, A. C., Rehman, S., and Dinpashohl, Y.: Global
review and synthesis of trends in observed terrestrial near-surface wind
speeds: implications for evaporation, J. Hydrol., 416, 182–205,
https://doi.org/10.1016/j.jhydrol.2011.10.024, 2012.
Monteith, J. L. and Unsworth, M. H.: Principles of Environmental Physics,
Third Ed. AP, Amsterdam, ISBN: 9780080924793, 2008.
Nada, T., Sahoo, B., and Chatterjee, C.: Enhancing the applicability of
Kohonen Self-Organizing Map (KSOM) estimator for gap-filling in
hydrometeorological timeseries data, J. Hydrol., 549, 133–147, https://doi.org/10.1016/j.jhydrol.2017.03.072, 2017.
Nakagawa, K., Amano, H., Kawamura, A., and Berndtsson, R.: Classification of
groundwater chemistry in Shimabara, using self-organizing maps, Hydrol.
Res., 48, 840–850, https://doi.org/10.2166/nh.2016.072, 2017.
Nakagawa, K., Yu, Z.-Q., Berndtsson, R., and Hosono, R.: Temporal
characteristics of groundwater chemistry affected by the 2016 Kumamoto
earthquake using self-organizing maps, J. Hydrol., 582, 124519, https://doi.org/10.1016/j.jhydrol.2019.124519, 2020.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models part I – a discussion of principles, J. Hydrol., 10, 282–290,
https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Park, Y. S., Lek, S., Scardi, M., Verdonschot, P., and Jorgensen, S. E.:
Patterning exergy of benthic macroinvertebrate communities using
self-organizing maps, Ecol. Model., 195, 105–113,
https://doi.org/10.1016/j.ecolmodel.2005.11.027, 2006.
Patle, G. T., Chettri, M., and Jhajharia, D.: Monthly pan evaporation
modelling using multiple linear regression and artificial neural network
techniques, Water Supply, 20, 800–808, https://doi.org/10.2166/ws.2019.189, 2020.
Pearce, A. R., Rizzo, D. M., and Mouser, P. J.: Subsurface characterization
of groundwater contaminated by landfill leachate using microbial community
profile data and a nonparametric decision-making process, Water Resour.
Res., 47, W06511, https://doi.org/10.1029/2010WR009992, 2011.
Peeters, L., Bação, F., Lobo, V., and Dassargues, A.: Exploratory data analysis and clustering of multivariate spatial hydrogeological data by means of GEO3DSOM, a variant of Kohonen's Self-Organizing Map, Hydrol. Earth Syst. Sci., 11, 1309–1321, https://doi.org/10.5194/hess-11-1309-2007, 2007.
Poikane, S., Birk, S., Böhmer, J., Carvalho, L., de Hoyos, C., Gassner,
H., Hellsten, S., Kelly, M., Lyche Solheim, A., Olin, M., Pall, K.,
Phillips, G., Portielje, P., Ritterbusch, B., Sandin, L., Schartau, A. K.,
Solimini, A. G., van den Berg, M., Wolfram, G., and van de Bund, W.: A
hitchhiker's guide to european lake ecological assessment and
intercalibration, Ecol. Indic., 52, 533–544, https://doi.org/10.1016/j.ecolind.2015.01.005, 2015.
Rahimikhoob, A.: Estimating daily pan evaporation using artificial neural
network in a semi-arid environment, Theor. Appl. Climatol., 98, 101–105,
https://doi.org/10.1007/s00704-008-0096-3, 2009.
Razi, M. A. and Athappilly, K.: A comparative predictive analysis of neural
networks (NNs), nonlinear regression and classification and regression tree
(CART) models, Expert Syst. Appl., 29, 65–74,
https://doi.org/10.1016/j.eswa.2005.01.006, 2005.
Ristić, S., Stamenković, S., Piperac, M. S., Šajn, R.,
Kosanić, M., and Ranković, B.: Searching for lichen indicator
species: the application of self-organizing maps in air quality assessment
– a case study from Balkan area (Serbia), Environ. Monit. Assess, 192, 693,
https://doi.org/10.1007/s10661-020-08633-3, 2020.
Rivas-Tabares, D., de Miguel, Á., Willaarts, B., and Tarquis, A. M.:
Self-organizing map of soil properties in the context of hydrological
modeling, Appl. Math. Model., 88, 175–189, https://doi.org/10.1016/j.apm.2020.06.044,
2020.
Sanikhani, H., Kisi, O., Kiafar, H., and Ghavidel, S. Z. Z.: Comparison of
different data-driven approaches for modeling lake level fluctuations: the
case of Manyas and Tuz Lakes (Turkey), Water Resour. Manag., 29,
1557–1574, https://doi.org/10.1007/s11269-014-0894-6, 2015.
Sattari, M. T., Apaydin, H., and Shamshirband, S.: Performance Evaluation of
Deep Learning-Based Gated Recurrent Units (GRUs) and Tree-Based Models for
Estimating ETo by Using Limited Meteorological Variables, Mathematics, 8,
972, https://doi.org/10.3390/math8060972, 2020.
Sheffield, J., Goteti, G., and Wood, E. F.:. Development of a 50-Year
high-resolution global dataset of meteorological forcings for land surface
modelling, J. Climate, 19, 3088–3111, https://doi.org/10.1175/JCLI3790.1, 2006.
Shiri, J. and Kisi, O.: Application of artificial intelligence to estimate
daily pan evaporation using available and estimated climatic data in the
Khozestan Province (South-Western Iran), J. Irrig. Drain. Eng., 137,
412–425, https://doi.org/10.1061/(ASCE)IR.1943-4774.0000315, 2011.
Soós, G. and Anda, A.: A methodological study on local application of
the FAO-56 Penman-Monteith reference evapotranspiration equation, Georgikon
Agricult., 18, 71–85, 2014.
Sudheer, K. P., Gosain, A. K., and Ramasastri, K. S.: Estimating actual
evapotranspiration from limited climatic data using neural computing
technique, J. Irrig. Drain. Eng., 129, 214–218,
https://doi.org/10.1061/(ASCE)0733-9437(2003)129:3(214), 2003.
Tabari, H., Marofi, S., and Sabziparvar, A. A.: Estimation of daily pan
evaporation using artificial neural network and multivariate non-linear
regression, Irrigation Sci., 28, 399–406, https://doi.org/10.1007/s00271-009-0201-0,
2010.
Tetens, O.: Über einige meteorologische Begriffe, Z. Geophys., 6,
297–309, 1930.
Vilas, M. P., Marti, C. L., Oldham, C. E., and Hipsey, M. R.:
Macrophyte-induced thermal stratification in a shallow urban lake promotes
conditions suitable for nitrogen-fixing cyanobacteria, Hidrobiologica, 806,
411–426, https://doi.org/10.1007/s10750-017-3376-z, 2018.
Vymazal, J.: Emergent plants used in free water surface constructed
wetlands: a re-view, Ecol. Eng., 61, 582–592,
https://doi.org/10.1016/J.ECOLENG.2013.06.023, 2013.
Wang, L., Hang, S., and Tian, F.: Comparison of formulating apparent
potential evaporation with pan measurements and Penman methods. J. Hydrol.,
592, 1258162021, https://doi.org/10.1016/j.jhydrol.2020.125816, 2021.
William, R. H. and Heinz, G. S.: Temperature Stratification nand Mixing
Dynamics in a Shallow Lake With Submersed Macrophytes, Lake Reserv. Manage.,
20, 296–308, https://doi.org/10.1080/07438140409354159, 2004.
WMO Report: Drought and Agriculture, WMO Techn. Note No. 138, 1975.
Wu, L., Huang, G., Fan, J., Ma, X., Zhou, H., and Zeng, W.: Hybrid extreme
learning machine with metaheuristic algorithms for monthly pan evaporation
prediction, Comput. Electron. Agr., 168, 105115,
https://doi.org/10.1016/j.compag.2019.105115, 2020.
Yan, K., Yuan, Z., Goldberg, S., Gao, W., Ostermann, A., Xu, J., Zhang, F.,
and Elser, J.: Phosphorus mitigation remains critical in water protection: A
review andmeta-analysis from one of China's most eutrophicated lakes, Sci.
Total Environ., 689, 1336–1347, https://doi.org/10.1016/j.scitotenv.2019.06.302, 2019.
Yu, Z. Q., Amano, H., Nakagawa, K., and Berndtsson, R.: Hydrogeochemical
evolution of groundwater in a Quaternary sediment and Cretaceous sandstone
unconfined aquifer in Northwestern China, Environ. Earth Sci., 77, 629,
https://doi.org/10.1007/s12665-018-7816-5, 2018.
Zelazny, M., Astel, A., Wolanin, A., and Malek, S.: Spatiotemporal dynamics
of spring and stream water chemistry in a high-mountain area, Environ.
Pollut., 159, 1048–1057, https://doi.org/10.1016/j.envpol.2010.11.021, 2011.
Zhang, Y., Jeppesen, E., Liu, X., Qin, B., Shi, K., Zhou, Y., Thomaz, S. M.,
and Deng, J.: Global loss of aquatic vegetation in lakes, Earth-Sci. Rev.,
173, 259–265, https://doi.org/10.1016/j.earscirev.2017.08.013, 2017.
Short summary
Due to climate change, it is extremely important to determine evaporation as accurately as possible. In nature, there are sediments and macrophytes in the open waters; thus, one of the aims was to investigate their effect on evaporation. The second aim of this paper was to estimate daily evaporation by using different models, which, according to results, have high priority in the evaporation prediction. Water management can obtain useful information from the results of the current research.
Due to climate change, it is extremely important to determine evaporation as accurately as...
Special issue