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Global warming intensifies heat stress, posing substantial challenges to cultivated plants and agricultural yield production. The high solar absorptance of soil results in elevated temperatures, pushing plants beyond their ideal growth range. Additionally, this rise in soil temperature accelerates soil moisture evaporation, further aggravating existing water scarcity issues. Common cooling solutions tends to consume significant amounts of water or offer limited cooling capacities. In response, a radiative cooling and moisturizing film composed of biodegradable ethyl-cellulose was developed. With a solar reflectance of 97% and a thermal emissivity of 0.93, this film provides efficient zero-energy cooling for soil surfaces. Field tests have demonstrated that compared to commercial cooling mulch, the radiative cooling film significantly reduces soil temperature and moisture evaporation by 50% and 60%, respectively. Furthermore, it boosts plant growth by 30% by moderating leaf temperatures and augmenting the exposure to reflected sunlight, crucial for photosynthesis on hot days. Global heat-water simulations reveal that the film increases soil moisture preservation by over 80% and alleviates agricultural water scarcity by over 60% in arid regions during hot seasons. This work offers a practical and sustainable solution to mitigate heat stress and promote resilient cultivation practices in the context of global warming.

Global water models (GWMs) simulate the terrestrial water cycle on the global scale and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modelling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how 16 state-of-the-art GWMs are designed. We analyse water storage compartments, water flows, and human water use sectors included in models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to enhance model intercomparison, improvement, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Six models used six compartments, while four models (DBH, JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water for the irrigation sector. We conclude that, even though hydrological processes are often based on similar equations for various processes, in the end these equations have been adjusted or models have used different values for specific parameters or specific variables. The similarities and differences found among the models analysed in this study are expected to enable us to reduce the uncertainty in multi-model ensembles, imp

Global water models (GWMs) simulate the terrestrial water cycle, on the global scale, and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modeling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how state-of-the-art GWMs are designed. We analyze water storage compartments, water flows, and human water use sectors included in 16 GWMs that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to further enhance model improvement, intercomparison, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Seven models used six compartments, while three models (JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPIHM) simulate only water used by humans for the irrigation sector. We conclude that even though hydrologic processes are often based on similar equations, in the end, these equations have been adjusted or have used different values for specific parameters or specific variables. Our results highlight that the predictive uncertainty of GWMs can be reduced through improvements of the existing hydrologic processes, implementation of new processes in the

Global water models (GWMs) simulate the terrestrial water cycle on the global scale and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modelling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how 16 state-of-the-art GWMs are designed. We analyse water storage compartments, water flows, and human water use sectors included in models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to enhance model intercomparison, improvement, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Six models used six compartments, while four models (DBH, JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water for the irrigation sector. We conclude that, even though hydrological processes are often based on similar equations for various processes, in the end these equations have been adjusted or models have used different values for specific parameters or specific variables. The similarities and differences found among the models analysed in this study are expected to enable us to reduce the uncertainty in multi-model ensembles, improv

Precipitation on the Tibetan Plateau (TP) showed different spatial changes during 1979-2016, with an increasing trend over the northern Tibetan Plateau (NTP) and a slightly negative trend over the southern Tibetan Plateau (STP). The changes in precipitation moisture sources over the NTP and STP are investigated using the improved Water Accounting Model with an atmospheric reanalysis as well as observational precipitation and evaporation data. The results show the region in the northwest (region NW), ranging from the TP to Europe dominated by the westerlies, provides 38.9% of precipitation moisture for the NTP, and the region in the southeast (region SE), ranging from the TP to the Indian Ocean and Indochina dominated by the Asian monsoons, provides 51.4% of precipitation moisture for the STP. For the precipitation increase over the NTP, the SE and TP are the main contributors, contributing around 35.8% and 51.7% of the increase, respectively. The contributions from the SE and TP to the STP are, however, minor and insignificant. Meanwhile, the NW shows a negative trend of -4.2 ± 2.9mmyr-1 decade-1 (significant at the 0.01 level), which contributes to the negative precipitation trend over the STP. Results during the wet season indicate that moisture sources from the areas dominated by the Asian monsoons have contributed more precipitated moisture for the NTP, but not for the STP. Further analysis reveals that precipitated moisture originating from the Indian subcontinent has increased for the NTP while it has decreased for the STP during 1979-2016.

Precipitation on the Tibetan Plateau (TP) showed different spatial changes during 1979-2016, with an increasing trend over the northern Tibetan Plateau (NTP) and a slightly negative trend over the southern Tibetan Plateau (STP). The changes in precipitation moisture sources over the NTP and STP are investigated using the improved Water Accounting Model with an atmospheric reanalysis as well as observational precipitation and evaporation data. The results show the region in the northwest (region NW), ranging from the TP to Europe dominated by the westerlies, provides 38.9% of precipitation moisture for the NTP, and the region in the southeast (region SE), ranging from the TP to the Indian Ocean and Indochina dominated by the Asian monsoons, provides 51.4% of precipitation moisture for the STP. For the precipitation increase over the NTP, the SE and TP are the main contributors, contributing around 35.8% and 51.7% of the increase, respectively. The contributions from the SE and TP to the STP are, however, minor and insignificant. Meanwhile, the NW shows a negative trend of -4.2 ± 2.9mmyr -1 decade -1 (significant at the 0.01 level), which contributes to the negative precipitation trend over the STP. Results during the wet season indicate that moisture sources from the areas dominated by the Asian monsoons have contributed more precipitated moisture for the NTP, but not for the STP. Further analysis reveals that precipitated moisture originating from the Indian subcontinent has increased for the NTP while it has decreased for the STP during 1979-2016., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Water Resources

Precipitation on the Tibetan Plateau (TP) showed different spatial changes during 1979-2016, with an increasing trend over the northern Tibetan Plateau (NTP) and a slightly negative trend over the southern Tibetan Plateau (STP). The changes in precipitation moisture sources over the NTP and STP are investigated using the improved Water Accounting Model with an atmospheric reanalysis as well as observational precipitation and evaporation data. The results show the region in the northwest (region NW), ranging from the TP to Europe dominated by the westerlies, provides 38.9% of precipitation moisture for the NTP, and the region in the southeast (region SE), ranging from the TP to the Indian Ocean and Indochina dominated by the Asian monsoons, provides 51.4% of precipitation moisture for the STP. For the precipitation increase over the NTP, the SE and TP are the main contributors, contributing around 35.8% and 51.7% of the increase, respectively. The contributions from the SE and TP to the STP are, however, minor and insignificant. Meanwhile, the NW shows a negative trend of -4.2 ± 2.9mmyr -1 decade -1 (significant at the 0.01 level), which contributes to the negative precipitation trend over the STP. Results during the wet season indicate that moisture sources from the areas dominated by the Asian monsoons have contributed more precipitated moisture for the NTP, but not for the STP. Further analysis reveals that precipitated moisture originating from the Indian subcontinent has increased for the NTP while it has decreased for the STP during 1979-2016., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Water Resources

Precipitation on the Tibetan Plateau (TP) showed different spatial changes during 1979-2016, with an increasing trend over the northern Tibetan Plateau (NTP) and a slightly negative trend over the southern Tibetan Plateau (STP). The changes in precipitation moisture sources over the NTP and STP are investigated using the improved Water Accounting Model with an atmospheric reanalysis as well as observational precipitation and evaporation data. The results show the region in the northwest (region NW), ranging from the TP to Europe dominated by the westerlies, provides 38.9% of precipitation moisture for the NTP, and the region in the southeast (region SE), ranging from the TP to the Indian Ocean and Indochina dominated by the Asian monsoons, provides 51.4% of precipitation moisture for the STP. For the precipitation increase over the NTP, the SE and TP are the main contributors, contributing around 35.8% and 51.7% of the increase, respectively. The contributions from the SE and TP to the STP are, however, minor and insignificant. Meanwhile, the NW shows a negative trend of -4.2 ± 2.9mmyr -1 decade -1 (significant at the 0.01 level), which contributes to the negative precipitation trend over the STP. Results during the wet season indicate that moisture sources from the areas dominated by the Asian monsoons have contributed more precipitated moisture for the NTP, but not for the STP. Further analysis reveals that precipitated moisture originating from the Indian subcontinent has increased for the NTP while it has decreased for the STP during 1979-2016., Green Open Access added to TU Delft Institutional Repository ‘You share, we take care!’ – Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Water Resources

Actual land evapotranspiration (ET) is a key component of the global hydrological cycle and an essential variable determining the evolution of hydrological extreme events under different climate change scenarios. However, recently available ET products show persistent uncertainties that are impeding a precise attribution of human-induced climate change. Here, we aim at comparing a range of independent global monthly land ET estimates with historical model simulations from the global water, agriculture, and biomes sectors participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). Among the independent estimates, we use the EartH2Observe Tier-1 dataset (E2O), two commonly used reanalyses, a pre-compiled ensemble product (LandFlux-EVAL), and an updated collection of recently published datasets that algorithmically derive ET from observations or observations-based estimates (diagnostic datasets). A cluster analysis is applied in order to identify spatio-temporal differences among all datasets and to thus identify factors that dominate overall uncertainties. The clustering is controlled by several factors including the model choice, the meteorological forcing used to drive the assessed models, the data category (models participating in the different sectors of ISIMIP2a, E2O models, diagnostic estimates, reanalysis-based estimates or composite products), the ET scheme, and the number of soil layers in the models. By using these factors to explain spatial and spatio-temporal variabilities in ET, we find that the model choice mostly dominates (24%-40% of variance explained), except for spatio-temporal patterns of total ET, where the forcing explains the largest fraction of the variance (29%). The most dominant clusters of datasets are further compared with individual diagnostic and reanalysis-based estimates to assess their representation of selected heat waves and droughts in the Great Plains, Central Europe and western Russia.

Aim of study: To understand throughfall (TF) sensitivity to variability in rainfall amount (Pg) for typical forest sites across the main climate types of Iran.Area of study: Nine forest stands of several common native and introduced tree species situated in all common Iranian climate types, but located primarily in northern Iran.Material and methods: A nondimensional relative sensitivity coefficient was employed to predict responses of TF to Pg changes. Projected Pg changes over the measurement sites for the period 2020-50 were estimated using one of the Coupled Model Intercomparison Project phase 5 (CMIP5) known as HadGEM2-ES under low and high emission scenarios (RCP 2.6 and 8.5).Main results: TF displayed strong positive linear relationships with Pg at all sites [TF=0.66 Pg -0.16; R2=0.91]. The sensitivity coefficient ranged from 0.96-2.35 across the nine forest sites and large sensitivity coefficientdifferences were found between small (< mean annual Pg) and large (> mean annual Pg) storms for arid and Mediterranean plantations. Shifts in Pg and increased small storm frequency are predicted for these regions (2020-50) under low and high emission scenarios.Research highlights: TF sensitivity may be a useful variable when selecting tree species for afforestation to buffer expected shifts in Pg due to climate change.Keywords: climate change; forest ecosystems; precipitation projection; throughfall sensitivity.

Aim of study: To understand throughfall (TF) sensitivity to variability in rainfall amount (Pg) for typical forest sites across the main climate types of Iran.Area of study: Nine forest stands of several common native and introduced tree species situated in all common Iranian climate types, but located primarily in northern Iran.Material and methods: A nondimensional relative sensitivity coefficient was employed to predict responses of TF to Pg changes. Projected Pg changes over the measurement sites for the period 2020-50 were estimated using one of the Coupled Model Intercomparison Project phase 5 (CMIP5) known as HadGEM2-ES under low and high emission scenarios (RCP 2.6 and 8.5).Main results: TF displayed strong positive linear relationships with Pg at all sites [TF=0.66 Pg -0.16; R2=0.91]. The sensitivity coefficient ranged from 0.96-2.35 across the nine forest sites and large sensitivity coefficientdifferences were found between small (< mean annual Pg) and large (> mean annual Pg) storms for arid and Mediterranean plantations. Shifts in Pg and increased small storm frequency are predicted for these regions (2020-50) under low and high emission scenarios.Research highlights: TF sensitivity may be a useful variable when selecting tree species for afforestation to buffer expected shifts in Pg due to climate change.Keywords: climate change; forest ecosystems; precipitation projection; throughfall sensitivity.

Human impacts increasingly affect the global hydrological cycle and indeed dominate hydrological changes in some regions. Hydrologists have sought to identify the human-impact-induced hydrological variations via parameterizing anthropogenic water uses in global hydrological models (GHMs). The consequently increased model complexity is likely to introduce additional uncertainty among GHMs. Here, using four GHMs, between-model uncertainties are quantified in terms of the ratio of signal to noise (SNR) for average river flow during 1971–2000 simulated in two experiments, with representation of human impacts (VARSOC) and without (NOSOC). It is the first quantitative investigation of between-model uncertainty resulted from the inclusion of human impact parameterizations. Results show that the between-model uncertainties in terms of SNRs in the VARSOC annual flow are larger (about 2% for global and varied magnitude for different basins) than those in the NOSOC, which are particularly significant in most areas of Asia and northern areas to the Mediterranean Sea. The SNR differences are mostly negative (−20% to 5%, indicating higher uncertainty) for basin-averaged annual flow. The VARSOC high flow shows slightly lower uncertainties than NOSOC simulations, with SNR differences mostly ranging from −20% to 20%. The uncertainty differences between the two experiments are significantly related to the fraction of irrigation areas of basins. The large additional uncertainties in VARSOC simulations introduced by the inclusion of parameterizations of human impacts raise the urgent need of GHMs development regarding a better understanding of human impacts. Differences in the parameterizations of irrigation, reservoir regulation and water withdrawals are discussed towards potential directions of improvements for future GHM development. We also discuss the advantages of statistical approaches to reduce the between-model uncertainties, and the importance of calibration of GHMs for not o, National Youth Top-notch Talent Support Program in China, Environment Research and Technology Development Fund (S-10) of the Ministry of the Environment, Key Research Program of the Chinese Academy of Sciences, National Natural Science Foundation of China 10.13039/501100001809, Peer Reviewed

Global hydrological models (GHMs) have been applied to assess global flood hazards, but their capacity to capture the timing and amplitude of peak river discharge - which is crucial in flood simulations - has traditionally not been the focus of examination. Here we evaluate to what degree the choice of river routing scheme affects simulations of peak discharge and may help to provide better agreement with observations. To this end we use runoff and discharge simulations of nine GHMs forced by observational climate data (1971-2010) within the ISIMIP2a project. The runoff simulations were used as input for the global river routing model CaMa-Flood. The simulated daily discharge was compared to the discharge generated by each GHM using its native river routing scheme. For each GHM both versions of simulated discharge were compared to monthly and daily discharge observations from 1701 GRDC stations as a benchmark. CaMa-Flood routing shows a general reduction of peak river discharge and a delay of about two to three weeks in its occurrence, likely induced by the buffering capacity of floodplain reservoirs. For a majority of river basins, discharge produced by CaMa-Flood resulted in a better agreement with observations. In particular, maximum daily discharge was adjusted, with a multi-model averaged reduction in bias over about 2/3 of the analysed basin area. The increase in agreement was obtained in both managed and near-natural basins. Overall, this study demonstrates the importance of routing scheme choice in peak discharge simulation, where CaMa-Flood routing accounts for floodplain storage and backwater effects that are not represented in most GHMs. Our study provides important hints that an explicit parameterisation of these processes may be essential in future impact studies.

Water scarcity severely impairs food security and economic prosperity in many countries today. Expected future population changes will, in many countries as well as globally, increase the pressure on available water resources. On the supply side, renewable water resources will be affected by projected changes in precipitation patterns, temperature, and other climate variables. Here we use a large ensemble of global hydrological models (GHMs) forced by five global climate models and the latest greenhouse-gas concentration scenarios (Representative Concentration Pathways) to synthesize the current knowledge about climate change impacts on water resources. We show that climate change is likely to exacerbate regional and global water scarcity considerably. In particular, the ensemble average projects that a global warming of 2 °C above present (approximately 2.7 °C above preindustrial) will confront an additional approximate 15% of the global population with a severe decrease in water resources and will increase the number of people living under absolute water scarcity (<500 m3 per capita per year) by another 40% (according to some models, more than 100%) compared with the effect of population growth alone. For some indicators of moderate impacts, the steepest increase is seen between the present day and 2 °C, whereas indicators of very severe impacts increase unabated beyond 2 °C. At the same time, the study highlights large uncertainties associated with these estimates, with both global climate models and GHMs contributing to the spread. GHM uncertainty is particularly dominant in many regions affected by declining water resources, suggesting a high potential for improved water resource projections through hydrological model development.

Water scarcity severely impairs food security and economic prosperity in many countries today. Expected future population changes will, in many countries as well as globally, increase the pressure on available water resources. On the supply side, renewable water resources will be affected by projected changes in precipitation patterns, temperature, and other climate variables. Here we use a large ensemble of global hydrological models (GHMs) forced by five global climate models and the latest greenhouse-gas concentration scenarios (Representative Concentration Pathways) to synthesize the current knowledge about climate change impacts on water resources. We show that climate change is likely to exacerbate regional and global water scarcity considerably. In particular, the ensemble average projects that a global warming of 2 °C above present (approximately 2.7 °C above preindustrial) will confront an additional approximate 15% of the global population with a severe decrease in water resources and will increase the number of people living under absolute water scarcity (<500 m3 per capita per year) by another 40% (according to some models, more than 100%) compared with the effect of population growth alone. For some indicators of moderate impacts, the steepest increase is seen between the present day and 2 °C, whereas indicators of very severe impacts increase unabated beyond 2 °C. At the same time, the study highlights large uncertainties associated with these estimates, with both global climate models and GHMs contributing to the spread. GHM uncertainty is particularly dominant in many regions affected by declining water resources, suggesting a high potential for improved water resource projections through hydrological model development.

Future changes in runoff can have important implications for water resources and flooding. In this study, runoff projections from ISI-MIP (Inter-sectoral Impact Model Intercomparison Project) simulations forced with HadGEM2-ES bias-corrected climate data under the Representative Concentration Pathway 8.5 have been analysed for differences between impact models. Projections of change from a baseline period (1981–2010) to the future (2070–2099) from 12 impacts models which contributed to the hydrological and biomes sectors of ISI-MIP were studied. The biome models differed from the hydrological models by the inclusion of CO2 impacts and most also included a dynamic vegetation distribution. The biome and hydrological models agreed on the sign of runoff change for most regions of the world. However, in West Africa, the hydrological models projected drying, and the biome models a moistening. The biome models tended to produce larger increases and smaller decreases in regionally averaged runoff than the hydrological models, although there is large inter-model spread. The timing of runoff change was similar, but there were differences in magnitude, particularly at peak runoff. The impact of vegetation distribution change was much smaller than the projected change over time, while elevated CO2 had an effect as large as the magnitude of change over time projected by some models in some regions. The effect of CO2 on runoff was not consistent across the models, with two models showing increases and two decreases. There was also more spread in projections from the runs with elevated CO2 than with constant CO2. The biome models which gave increased runoff from elevated CO2 were also those which differed most from the hydrological models. Spatially, regions with most difference between model types tended to be projected to have most effect from elevated CO2, and seasonal differences were also similar, so elevated CO2 can partly explain the differences between hydrological and bio

Projections of future changes in runoff can have important implications for water resources and flooding. In this study, runoff projections from ISI-MIP (Inter-sectoral Impact Model Intercomparison Project) simulations forced with HadGEM2-ES bias-corrected climate data under the Representative Concentration Pathway 8.5 have been analysed. Projections of change from the baseline period (1981–2010) to the future (2070–2099) from a number of different ecosystems and hydrological models were studied. The differences between projections from the two types of model were looked at globally and regionally. Typically, across different regions the ecosystem models tended to project larger increases and smaller decreases in runoff than the hydrological models. However, the differences varied both regionally and seasonally. Sensitivity experiments were also used to investigate the contributions of varying CO2 and allowing vegetation distribution to evolve on projected changes in runoff. In two out of four models which had data available from CO2 sensitivity experiments, allowing CO2 to vary was found to increase runoff more than keeping CO2 constant, while in two models runoff decreased. This suggests more uncertainty in runoff responses to elevated CO2 than previously considered. As CO2 effects on evapotranspiration via stomatal conductance and leaf-area index are more commonly included in ecosystems models than in hydrological models, this may partially explain some of the difference between model types. Keeping the vegetation distribution static in JULES runs had much less effect on runoff projections than varying CO2, but this may be more pronounced if looked at over a longer timescale as vegetation changes may take longer to reach a new state.

Human activities have a profound influence on river discharge, hydrological extremes, and water-related hazards. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simulation of the mean, high, and low flows. The analysis is performed for 471 gauging stations across the globe and for the period 1971-2010. We find that the inclusion of HIP improves the performance of GHMs, both in managed and near-natural catchments. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments. This finding is robust across GHMs, although the level of improvement and reasons for improvement vary greatly by GHM. The inclusion of HIP leads to a significant decrease in the bias of long-term mean monthly discharge in 36-73% of the studied catchments, and an improvement in modelled hydrological variability in 31-74% of the studied catchments. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. Whilst the inclusion of HIP leads to decreases in simulated high-flows, it can lead to either increases or decreases in low-flows. This is due to the relative importance of the timing of return flows and reservoir operations and their associated uncertainties. Even with the inclusion of HIP, we find that model performance still not optimal. This highlights the need for further research linking the human management and hydrological domains, especially in those areas with a dominant human impact. The large variation in performance between GHMs, regions, and performance indicators, calls for a careful selection of GHMs, model components, and evaluation metrics in future model applications.

Global hydrological models (GHMs) have been applied to assess global flood hazards, but their capacity to capture the timing and amplitude of peak river discharge—which is crucial in flood simulations—has traditionally not been the focus of examination. Here we evaluate to what degree the choice of river routing scheme affects simulations of peak discharge and may help to provide better agreement with observations. To this end we use runoff and discharge simulations of nine GHMs forced by observational climate data (1971–2010) within the ISIMIP2a project. The runoff simulations were used as input for the global river routing model CaMa-Flood. The simulated daily discharge was compared to the discharge generated by each GHM using its native river routing scheme. For each GHM both versions of simulated discharge were compared to monthly and daily discharge observations from 1701 GRDC stations as a benchmark. CaMa-Flood routing shows a general reduction of peak river discharge and a delay of about two to three weeks in its occurrence, likely induced by the buffering capacity of floodplain reservoirs. For a majority of river basins, discharge produced by CaMa-Flood resulted in a better agreement with observations. In particular, maximum daily discharge was adjusted, with a multi-model averaged reduction in bias over about 2/3 of the analysed basin area. The increase in agreement was obtained in both managed and near-natural basins. Overall, this study demonstrates the importance of routing scheme choice in peak discharge simulation, where CaMa-Flood routing accounts for floodplain storage and backwater effects that are not represented in most GHMs. Our study provides important hints that an explicit parameterisation of these processes may be essential in future impact studies.

Human impacts increasingly affect the global hydrological cycle and indeed dominate hydrological changes in some regions. Hydrologists have sought to identify the human-impact-induced hydrological variations via parameterizing anthropogenic water uses in global hydrological models (GHMs). The consequently increased model complexity is likely to introduce additional uncertainty among GHMs. Here, using four GHMs, between-model uncertainties are quantified in terms of the ratio of signal to noise (SNR) for average river flow during 1971–2000 simulated in two experiments, with representation of human impacts (VARSOC) and without (NOSOC). It is the first quantitative investigation of between-model uncertainty resulted from the inclusion of human impact parameterizations. Results show that the between-model uncertainties in terms of SNRs in the VARSOC annual flow are larger (about 2% for global and varied magnitude for different basins) than those in the NOSOC, which are particularly significant in most areas of Asia and northern areas to the Mediterranean Sea. The SNR differences are mostly negative (-20% to 5%, indicating higher uncertainty) for basin-averaged annual flow. The VARSOC high flow shows slightly lower uncertainties than NOSOC simulations, with SNR differences mostly ranging from -20% to 20%. The uncertainty differences between the two experiments are significantly related to the fraction of irrigation areas of basins. The large additional uncertainties in VARSOC simulations introduced by the inclusion of parameterizations of human impacts raise the urgent need of GHMs development regarding a better understanding of human impacts. Differences in the parameterizations of irrigation, reservoir regulation and water withdrawals are discussed towards potential directions of improvements for future GHM development. We also discuss the advantages of statistical approaches to reduce the between-model uncertainties, and the importance of calibration of GHMs for not o

This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the EM. The performance gain offered by MMC suggests that future multimodel applications consider reporting MMCs, alongside the EM and intermodal range, to provide endusers of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained.

Human activities have a profound influence on river discharge, hydrological extremes, and water-related hazards. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simulation of the mean, high, and low flows. The analysis is performed for 471 gauging stations across the globe and for the period 1971-2010. We find that the inclusion of HIP improves the performance of GHMs, both in managed and near-natural catchments. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments. This finding is robust across GHMs, although the level of improvement and reasons for improvement vary greatly by GHM. The inclusion of HIP leads to a significant decrease in the bias of long-term mean monthly discharge in 36-73% of the studied catchments, and an improvement in modelled hydrological variability in 31-74% of the studied catchments. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. Whilst the inclusion of HIP leads to decreases in simulated high-flows, it can lead to either increases or decreases in low-flows. This is due to the relative importance of the timing of return flows and reservoir operations and their associated uncertainties. Even with the inclusion of HIP, we find that model performance still not optimal. This highlights the need for further research linking the human management and hydrological domains, especially in those areas with a dominant human impact. The large variation in performance between GHMs, regions, and performance indicators, calls for a careful selection of GHMs, model components, and evaluation metrics in future model applications.

Human activities have a profound influence on river discharge, hydrological extremes, and water-related hazards. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simulation of the mean, high, and low flows. The analysis is performed for 471 gauging stations across the globe and for the period 1971-2010. We find that the inclusion of HIP improves the performance of GHMs, both in managed and near-natural catchments. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments. This finding is robust across GHMs, although the level of improvement and reasons for improvement vary greatly by GHM. The inclusion of HIP leads to a significant decrease in the bias of long-term mean monthly discharge in 36-73% of the studied catchments, and an improvement in modelled hydrological variability in 31-74% of the studied catchments. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. Whilst the inclusion of HIP leads to decreases in simulated high-flows, it can lead to either increases or decreases in low-flows. This is due to the relative importance of the timing of return flows and reservoir operations and their associated uncertainties. Even with the inclusion of HIP, we find that model performance still not optimal. This highlights the need for further research linking the human management and hydrological domains, especially in those areas with a dominant human impact. The large variation in performance between GHMs, regions, and performance indicators, calls for a careful selection of GHMs, model components, and evaluation metrics in future model applications.

Global hydrological models (GHMs) have been applied to assess global flood hazards, but their capacity to capture the timing and amplitude of peak river discharge—which is crucial in flood simulations—has traditionally not been the focus of examination. Here we evaluate to what degree the choice of river routing scheme affects simulations of peak discharge and may help to provide better agreement with observations. To this end we use runoff and discharge simulations of nine GHMs forced by observational climate data (1971–2010) within the ISIMIP2a project. The runoff simulations were used as input for the global river routing model CaMa-Flood. The simulated daily discharge was compared to the discharge generated by each GHM using its native river routing scheme. For each GHM both versions of simulated discharge were compared to monthly and daily discharge observations from 1701 GRDC stations as a benchmark. CaMa-Flood routing shows a general reduction of peak river discharge and a delay of about two to three weeks in its occurrence, likely induced by the buffering capacity of floodplain reservoirs. For a majority of river basins, discharge produced by CaMa-Flood resulted in a better agreement with observations. In particular, maximum daily discharge was adjusted, with a multi-model averaged reduction in bias over about 2/3 of the analysed basin area. The increase in agreement was obtained in both managed and near-natural basins. Overall, this study demonstrates the importance of routing scheme choice in peak discharge simulation, where CaMa-Flood routing accounts for floodplain storage and backwater effects that are not represented in most GHMs. Our study provides important hints that an explicit parameterisation of these processes may be essential in future impact studies.

Human impacts increasingly affect the global hydrological cycle and indeed dominate hydrological changes in some regions. Hydrologists have sought to identify the human-impact-induced hydrological variations via parameterizing anthropogenic water uses in global hydrological models (GHMs). The consequently increased model complexity is likely to introduce additional uncertainty among GHMs. Here, using four GHMs, between-model uncertainties are quantified in terms of the ratio of signal to noise (SNR) for average river flow during 1971–2000 simulated in two experiments, with representation of human impacts (VARSOC) and without (NOSOC). It is the first quantitative investigation of between-model uncertainty resulted from the inclusion of human impact parameterizations. Results show that the between-model uncertainties in terms of SNRs in the VARSOC annual flow are larger (about 2% for global and varied magnitude for different basins) than those in the NOSOC, which are particularly significant in most areas of Asia and northern areas to the Mediterranean Sea. The SNR differences are mostly negative (-20% to 5%, indicating higher uncertainty) for basin-averaged annual flow. The VARSOC high flow shows slightly lower uncertainties than NOSOC simulations, with SNR differences mostly ranging from -20% to 20%. The uncertainty differences between the two experiments are significantly related to the fraction of irrigation areas of basins. The large additional uncertainties in VARSOC simulations introduced by the inclusion of parameterizations of human impacts raise the urgent need of GHMs development regarding a better understanding of human impacts. Differences in the parameterizations of irrigation, reservoir regulation and water withdrawals are discussed towards potential directions of improvements for future GHM development. We also discuss the advantages of statistical approaches to reduce the between-model uncertainties, and the importance of calibration of GHMs for not o

This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the EM. The performance gain offered by MMC suggests that future multimodel applications consider reporting MMCs, alongside the EM and intermodal range, to provide endusers of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained.

Human activities have a profound influence on river discharge, hydrological extremes, and water-related hazards. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simulation of the mean, high, and low flows. The analysis is performed for 471 gauging stations across the globe and for the period 1971-2010. We find that the inclusion of HIP improves the performance of GHMs, both in managed and near-natural catchments. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments. This finding is robust across GHMs, although the level of improvement and reasons for improvement vary greatly by GHM. The inclusion of HIP leads to a significant decrease in the bias of long-term mean monthly discharge in 36-73% of the studied catchments, and an improvement in modelled hydrological variability in 31-74% of the studied catchments. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. Whilst the inclusion of HIP leads to decreases in simulated high-flows, it can lead to either increases or decreases in low-flows. This is due to the relative importance of the timing of return flows and reservoir operations and their associated uncertainties. Even with the inclusion of HIP, we find that model performance still not optimal. This highlights the need for further research linking the human management and hydrological domains, especially in those areas with a dominant human impact. The large variation in performance between GHMs, regions, and performance indicators, calls for a careful selection of GHMs, model components, and evaluation metrics in future model applications.

Global hydrological models (GHMs) have been applied to assess global flood hazards, but their capacity to capture the timing and amplitude of peak river discharge—which is crucial in flood simulations—has traditionally not been the focus of examination. Here we evaluate to what degree the choice of river routing scheme affects simulations of peak discharge and may help to provide better agreement with observations. To this end we use runoff and discharge simulations of nine GHMs forced by observational climate data (1971–2010) within the ISIMIP2a project. The runoff simulations were used as input for the global river routing model CaMa-Flood. The simulated daily discharge was compared to the discharge generated by each GHM using its native river routing scheme. For each GHM both versions of simulated discharge were compared to monthly and daily discharge observations from 1701 GRDC stations as a benchmark. CaMa-Flood routing shows a general reduction of peak river discharge and a delay of about two to three weeks in its occurrence, likely induced by the buffering capacity of floodplain reservoirs. For a majority of river basins, discharge produced by CaMa-Flood resulted in a better agreement with observations. In particular, maximum daily discharge was adjusted, with a multi-model averaged reduction in bias over about 2/3 of the analysed basin area. The increase in agreement was obtained in both managed and near-natural basins. Overall, this study demonstrates the importance of routing scheme choice in peak discharge simulation, where CaMa-Flood routing accounts for floodplain storage and backwater effects that are not represented in most GHMs. Our study provides important hints that an explicit parameterisation of these processes may be essential in future impact studies.

Human impacts increasingly affect the global hydrological cycle and indeed dominate hydrological changes in some regions. Hydrologists have sought to identify the human-impact-induced hydrological variations via parameterizing anthropogenic water uses in global hydrological models (GHMs). The consequently increased model complexity is likely to introduce additional uncertainty among GHMs. Here, using four GHMs, between-model uncertainties are quantified in terms of the ratio of signal to noise (SNR) for average river flow during 1971–2000 simulated in two experiments, with representation of human impacts (VARSOC) and without (NOSOC). It is the first quantitative investigation of between-model uncertainty resulted from the inclusion of human impact parameterizations. Results show that the between-model uncertainties in terms of SNRs in the VARSOC annual flow are larger (about 2% for global and varied magnitude for different basins) than those in the NOSOC, which are particularly significant in most areas of Asia and northern areas to the Mediterranean Sea. The SNR differences are mostly negative (-20% to 5%, indicating higher uncertainty) for basin-averaged annual flow. The VARSOC high flow shows slightly lower uncertainties than NOSOC simulations, with SNR differences mostly ranging from -20% to 20%. The uncertainty differences between the two experiments are significantly related to the fraction of irrigation areas of basins. The large additional uncertainties in VARSOC simulations introduced by the inclusion of parameterizations of human impacts raise the urgent need of GHMs development regarding a better understanding of human impacts. Differences in the parameterizations of irrigation, reservoir regulation and water withdrawals are discussed towards potential directions of improvements for future GHM development. We also discuss the advantages of statistical approaches to reduce the between-model uncertainties, and the importance of calibration of GHMs for not o

This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the EM. The performance gain offered by MMC suggests that future multimodel applications consider reporting MMCs, alongside the EM and intermodal range, to provide endusers of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained.

Human activities have a profound influence on river discharge, hydrological extremes, and water-related hazards. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simulation of the mean, high, and low flows. The analysis is performed for 471 gauging stations across the globe and for the period 1971-2010. We find that the inclusion of HIP improves the performance of GHMs, both in managed and near-natural catchments. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments. This finding is robust across GHMs, although the level of improvement and reasons for improvement vary greatly by GHM. The inclusion of HIP leads to a significant decrease in the bias of long-term mean monthly discharge in 36-73% of the studied catchments, and an improvement in modelled hydrological variability in 31-74% of the studied catchments. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. Whilst the inclusion of HIP leads to decreases in simulated high-flows, it can lead to either increases or decreases in low-flows. This is due to the relative importance of the timing of return flows and reservoir operations and their associated uncertainties. Even with the inclusion of HIP, we find that model performance still not optimal. This highlights the need for further research linking the human management and hydrological domains, especially in those areas with a dominant human impact. The large variation in performance between GHMs, regions, and performance indicators, calls for a careful selection of GHMs, model components, and evaluation metrics in future model applications.

Global hydrological models (GHMs) have been applied to assess global flood hazards, but their capacity to capture the timing and amplitude of peak river discharge—which is crucial in flood simulations—has traditionally not been the focus of examination. Here we evaluate to what degree the choice of river routing scheme affects simulations of peak discharge and may help to provide better agreement with observations. To this end we use runoff and discharge simulations of nine GHMs forced by observational climate data (1971–2010) within the ISIMIP2a project. The runoff simulations were used as input for the global river routing model CaMa-Flood. The simulated daily discharge was compared to the discharge generated by each GHM using its native river routing scheme. For each GHM both versions of simulated discharge were compared to monthly and daily discharge observations from 1701 GRDC stations as a benchmark. CaMa-Flood routing shows a general reduction of peak river discharge and a delay of about two to three weeks in its occurrence, likely induced by the buffering capacity of floodplain reservoirs. For a majority of river basins, discharge produced by CaMa-Flood resulted in a better agreement with observations. In particular, maximum daily discharge was adjusted, with a multi-model averaged reduction in bias over about 2/3 of the analysed basin area. The increase in agreement was obtained in both managed and near-natural basins. Overall, this study demonstrates the importance of routing scheme choice in peak discharge simulation, where CaMa-Flood routing accounts for floodplain storage and backwater effects that are not represented in most GHMs. Our study provides important hints that an explicit parameterisation of these processes may be essential in future impact studies.

Human impacts increasingly affect the global hydrological cycle and indeed dominate hydrological changes in some regions. Hydrologists have sought to identify the human-impact-induced hydrological variations via parameterizing anthropogenic water uses in global hydrological models (GHMs). The consequently increased model complexity is likely to introduce additional uncertainty among GHMs. Here, using four GHMs, between-model uncertainties are quantified in terms of the ratio of signal to noise (SNR) for average river flow during 1971–2000 simulated in two experiments, with representation of human impacts (VARSOC) and without (NOSOC). It is the first quantitative investigation of between-model uncertainty resulted from the inclusion of human impact parameterizations. Results show that the between-model uncertainties in terms of SNRs in the VARSOC annual flow are larger (about 2% for global and varied magnitude for different basins) than those in the NOSOC, which are particularly significant in most areas of Asia and northern areas to the Mediterranean Sea. The SNR differences are mostly negative (-20% to 5%, indicating higher uncertainty) for basin-averaged annual flow. The VARSOC high flow shows slightly lower uncertainties than NOSOC simulations, with SNR differences mostly ranging from -20% to 20%. The uncertainty differences between the two experiments are significantly related to the fraction of irrigation areas of basins. The large additional uncertainties in VARSOC simulations introduced by the inclusion of parameterizations of human impacts raise the urgent need of GHMs development regarding a better understanding of human impacts. Differences in the parameterizations of irrigation, reservoir regulation and water withdrawals are discussed towards potential directions of improvements for future GHM development. We also discuss the advantages of statistical approaches to reduce the between-model uncertainties, and the importance of calibration of GHMs for not o

This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the EM. The performance gain offered by MMC suggests that future multimodel applications consider reporting MMCs, alongside the EM and intermodal range, to provide endusers of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained.