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We analyzed the influence of hillslope flow on projections of climate change by comparing two transient climate simulations with the IPSL climate model between 1980 and 2100. Hillslope flow induces a reorganization and increment of soil moisture (+10%), which increases evapotranspiration (+4%) and precipitation (+1%) and decreases total runoff (−3%) and air temperature (−0.1 °C) on an annual average over land for 1980–2010 when compared to simulation not representing hillslope flow. These changes in land/atmosphere fluxes are not homogenous and depend on regional climate and surface conditions. Hillslope flow also influences climate change projections. On average over land, it amplifies the positive trend of soil moisture (+23%), evapotranspiration (+50%), and precipitation (+7%) and slightly attenuates global warming (−1%), especially for daily maximum air temperature. The role of hillslope flow in supporting surface/atmosphere fluxes is more evident at a regional scale. Where precipitation is projected to decrease, hillslope flow is shown to attenuate the related declines in evapotranspiration, precipitation, and total runoff, regardless of aridity conditions and mean air temperature. Where precipitation is projected to increase, hillslope flow amplifies evapotranspiration enhancement but attenuates the increase in precipitation and total runoff. Warming is generally attenuated, especially in semiarid and cold areas, and humid and warm/temperate regions, but the signal is weak. These results demonstrate the role of hillslope flow in enhancing water and energy fluxes between the surface and the atmosphere. They also suggest that including hillslope flow in climate models would weaken the projected intensification of hydrological extreme events. Plain Language Summary: We analyze how the flows caused by topography, called hillslope flow, affect the evolution of climate using simulations from a climate model. Results show that hillslope flow increases soil moisture in the valleys. More soil moisture enhances evapotranspiration and precipitation and decreases total runoff and air temperature for the period 1980–2010. But the increase in water exchanges between land and atmosphere is not homogenous. In the future, hillslope flow amplifies positive trends of climate change for soil moisture, evapotranspiration and precipitation, while global warming is minimally slowed. The role of hillslope flow in sustaining exchanges of water and energy in the future is most evident in the regions. Where precipitation decreases in the future, evapotranspiration and precipitation declines are less intense when hillslope flow is included, and that is the case for the decline in total runoff as well. In regions where precipitation increases in the future, evapotranspiration increases faster, but precipitation and runoff increases slower. Effect of hillslope flow on warming is weak, but in general, the air warms up more slowly, especially in both semiarid/cold regions, and humid and warm/temperate regions. The results highlight the role of hillslope flows in increasing water exchange between the surface and atmosphere Key Points: Hillslope flow sustains higher soil moisture, enhancing evapotranspiration and precipitation rates and cooling down the air temperatureHillslope flow slightly attenuates global warmingAt regional scale, hillslope flow attenuates climate change trends of all hydrological variables except for evapotranspiration increases [ABSTRACT FROM AUTHOR]

The Sahara was significantly wetter and greener than today during the mid‐Holocene (∼6,000 years before present), and those conditions were likely maintained by feedbacks from evaporating wetlands and riparian zones. A lack of spatially continuous wetland reconstruction is the major obstacle to investigating their impacts on climate and vegetation during that epoch. Here, we estimate high‐resolution gridded wetland distribution up to 15″ in the mid‐Holocene North Africa obtained with three statistical and hydrological modeling approaches forced by enhanced and calibrated precipitation from climate models. These wetland models have good performance for present‐day conditions and reproduce mid‐Holocene hydrological elements evaluated by 297 paleo‐records. Simulation results show that 18.9 ± 4.0% of land surface in North Africa was covered by wetlands during the mid‐Holocene. Our results highlight the impact of natural climate change on wetland areas and provide a data set for modeling studies to include wetland feedbacks. Plain Language Summary: Several lines of evidence show that northern Africa was considerably wetter and greener than today at ∼6,000 years ago, which is known as the mid‐Holocene Green Sahara (GS). However, most current models could not reproduce climate in the GS. The importance of wetland feedbacks on sustaining a wetter climate has partially been recognized while large uncertainties in wetland coverage make it difficult to examine wetland feedbacks in climate models. We trained several wetland models under present climate and applied them to wetland reconstructions of GS. The produced wetland maps could capture dense wetlands indicated by 297 paleo‐records. The total wetland fraction in the mid‐Holocene North Africa is 18.9 ± 4.0%, which is more than five times the area today (∼2.8%). The relationship between wetland fraction and precipitation is examined in our models. This work improves our understanding of the GS enigma, and has implications for potential greening and wetting of Sahel and Sahara in the future. Key Points: We present high‐resolution reconstructions of wetland distribution up to 15″ for the mid‐Holocene North AfricaDuring the mid‐Holocene, 18.9 ± 4.0% of land surface in North Africa was covered by wetlandsThe relationship between precipitation and wetland fraction in the mid‐Holocene Sahara is explored [ABSTRACT FROM AUTHOR]

Groundwater provides critical freshwater supply, particularly in dry regions where surface water availability is limited. Climate change impacts on GWS (groundwater storage) could affect the sustainability of freshwater resources. Here, we used a fully-coupled climate model to investigate GWS changes over seven critical aquifers identified as significantly distressed by satellite observations. We assessed the potential climate-driven impacts on GWS changes throughout the 21st century under the business-as-usual scenario (RCP8.5). Results show that the climate-driven impacts on GWS changes do not necessarily reflect the long-term trend in precipitation; instead, the trend may result from enhancement of evapotranspiration, and reduction in snowmelt, which collectively lead to divergent responses of GWS changes across different aquifers. Finally, we compare the climate-driven and anthropogenic pumping impacts. The reduction in GWS is mainly due to the combined impacts of over-pumping and climate effects; however, the contribution of pumping could easily far exceed the natural replenishment. Climate change may impact groundwater storage and thus the availability of freshwater resources. Here the authors use climate models to examine seven aquifers and find that storage changes are primarily the result of enhancement of evapotranspiration, reduction in snowmelt, and over-pumping rather than long-term precipitation changes. [ABSTRACT FROM AUTHOR]

The importance of northern peatlands in the global carbon cycle has recently been recognized, especially for long-term changes. Yet, the complex interactions between climate and peatland hydrology, carbon storage and area dynamics make it challenging to represent these systems in land surface models. This study describes how peatland are included as an independent sub-grid hydrological soil unit (HSU) into the ORCHIDEE-MICT land surface model. The peatland soil column in this tile is characterized by multi-layered vertical water and carbon transport, and peat-specific hydrological properties. A cost-efficient TOPMODEL approach is implemented to simulate the dynamics of peatland area, calibrated by present-day wetland areas that are regularly inundated or subject to shallow water tables. The model is tested across a range of northern peatland sites and for gridded simulations over the Northern Hemisphere (> 30° N). Simulated northern peatland area (3.9 million km²), peat carbon stock (463 PgC) and peat depth are generally consistent with observed estimates of peatland area (3.4-4.0 million km²), peat carbon (270-540 PgC) and data compilations of peat core depths. Our results show that both net primary production (NPP) and heterotrophic respiration (HR) of northern peatlands increased over the past century in response to CO2 and climate change. NPP increased more rapidly than HR, and thus net ecosystem production (NEP) exhibited a positive trend, contributing a cumulative carbon storage of 11.13 Pg C since 1901, most of it being realized after the 1950s. [ABSTRACT FROM AUTHOR]

The river routing scheme (RRS) in the Organising Carbon and Hydrology in Dynamic Ecosystems (ORCHIDEE) land surface model is a valuable tool for closing the water cycle in a coupled environment and for validating the model performance. This study presents a revision of the RRS of the ORCHIDEE model that aims to benefit from the high-resolution topography provided by the Hydrological data and maps based on SHuttle Elevation Derivatives at multiple Scales (HydroSHEDS), which is processed to a resolution of approximately 1 km. Adapting a new algorithm to construct river networks, the new RRS in ORCHIDEE allows for the preservation of as much of the hydrological information from HydroSHEDS as the user requires. The evaluation focuses on 12 rivers of contrasting size and climate which contribute freshwater to the Mediterranean Sea. First, the numerical aspect of the new RRS is investigated, in order to identify the practical configuration offering the best trade-off between computational cost and simulation quality for ensuing validations. Second, the performance of the new scheme is evaluated against observations at both monthly and daily timescales. The new RRS satisfactorily captures the seasonal variability of river discharge, although important biases stem from the water budget simulated by the ORCHIDEE model. The results highlight that realistic streamflow simulations require accurate precipitation forcing data and a precise river catchment description over a wide range of scales, as permitted by the new RRS. Detailed analyses at the daily timescale show the promising performance of this high-resolution RRS with respect to replicating river flow variation at various frequencies. Furthermore, this RRS may also eventually be well adapted for further developments in the ORCHIDEE land surface model to assess anthropogenic impacts on river processes (e.g. damming for irrigation operation). [ABSTRACT FROM AUTHOR]

Deforestation in Amazon is expected to decrease evapotranspiration (ET) and to increase soil moisture and river discharge under prevailing energy-limited conditions. The magnitude and sign of the response of ET to deforestation depend both on the magnitude and regional patterns of land-cover change (LCC), as well as on climate change and CO2 levels. On the one hand, elevated CO2 decreases leaf-scale transpiration, but this effect could be offset by increased foliar area density. Using three regional LCC scenarios specifically established for the Brazilian and Bolivian Amazon, we investigate the impacts of climate change and deforestation on the surface hydrology of the Amazon Basin for this century, taking 2009 as a reference. For each LCC scenario, three land surface models (LSMs), LPJmL-DGVM, INLAND-DGVM and ORCHIDEE, are forced by bias-corrected climate simulated by three general circulation models (GCMs) of the IPCC 4th Assessment Report (AR4). On average, over the Amazon Basin with no deforestation, the GCM results indicate a temperature increase of 3.3 °C by 2100 which drives up the evaporative demand, whereby precipitation increases by 8.5%, with a large uncertainty across GCMs. In the case of no deforestation, we found that ET and runoff increase by 5.0 and 14 %, respectively. However, in south-east Amazonia, precipitation decreases by 10%at the end of the dry season and the three LSMs produce a 6% decrease of ET, which is less than precipitation, so that runoff decreases by 22%. For instance, the minimum river discharge of the Rio Tapajós is reduced by 31% in 2100. To study the additional effect of deforestation, we prescribed to the LSMs three contrasted LCC scenarios, with a forest decline going from 7 to 34% over this century. All three scenarios partly offset the climate-induced increase of ET, and runoff increases over the entire Amazon. In the southeast, however, deforestation amplifies the decrease of ET at the end of dry season, leading to a large increase of runoff (up to +27% in the extreme deforestation case), offsetting the negative effect of climate change, thus balancing the decrease of low flows in the Rio Tapajós. These projections are associated with large uncertainties, which we attribute separately to the differences in LSMs, GCMs and to the uncertain range of deforestation. At the subcatchment scale, the uncertainty range on ET changes is shown to first depend on GCMs, while the uncertainty of runoff projections is predominantly induced by LSM structural differences. By contrast, we found that the uncertainty in both ET and runoff changes attributable to uncertain future deforestation is low. [ABSTRACT FROM AUTHOR]

The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of land surface, snow and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth system models (ESMs). The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems). The experiments are subdivided in two components, the first addressing systematic land biases in offline mode ("LMIP", building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework ("LFMIP", building upon the GLACE-CMIP blueprint). [ABSTRACT FROM AUTHOR]

The Land Surface, Snow and Soil Moisture Model Intercomparison Project (LS3MIP) is designed to provide a comprehensive assessment of land surface, snow, and soil moisture feedbacks on climate variability and climate change, and to diagnose systematic biases in the land modules of current Earth System Models (ESMs). The solid and liquid water stored at the land surface has a large influence on the regional climate, its variability and predictability, including effects on the energy, water and carbon cycles. Notably, snow and soil moisture affect surface radiation and flux partitioning properties, moisture storage and land surface memory. They both strongly affect atmospheric conditions, in particular surface air temperature and precipitation, but also large-scale circulation patterns. However, models show divergent responses and representations of these feedbacks as well as systematic biases in the underlying processes. LS3MIP will provide the means to quantify the associated uncertainties and better constrain climate change projections, which is of particular interest for highly vulnerable regions (densely populated areas, agricultural regions, the Arctic, semi-arid and other sensitive terrestrial ecosystems). The experiments are subdivided in two components, the first addressing systematic land biases in offline mode ("LMIP", building upon the 3rd phase of Global Soil Wetness Project; GSWP3) and the second addressing land feedbacks attributed to soil moisture and snow in an integrated framework ("LFMIP", building upon the GLACE-CMIP blueprint). [ABSTRACT FROM AUTHOR]

Correction to: I Scientific Data i https://doi.org/10.1038/s41597-022-01460-w, published online 18 June 2022 The citations for the ERA data in this paper were incorrect in the original version at references 30 and 31. Author Correction: Gridded maps of wetlands dynamics over mid-low latitudes for 1980-2020 based on TOPMODEL The ERA-Interim reanalysis: configuration and performance of the data assimilation system. [Extracted from the article]

This paper investigates three categories of models that are derived from the equilibrium temperature concept to estimate water temperatures in the Loire River in France and the sensitivity to changes in hydrology and climate. We test the models' individual performances for simulating water temperatures and assess the variability of the thermal responses under the extreme changing climate scenarios that are projected for 2081-2100. We attempt to identify the most reliable models for studying the impact of climate change on river temperature ( Tw). Six models are based on a linear relationship between air temperatures ( Ta) and equilibrium temperatures ( Te), six depend on a logistic relationship, and six rely on the closure of heat budgets. For each category, three approaches that account for the river's thermal exchange coefficient are tested. In addition to air temperatures, an index of day length is incorporated to compute equilibrium temperatures. Each model is analysed in terms of its ability to simulate the seasonal patterns of river temperatures and heat peaks. We found that including the day length as a covariate in regression-based approaches improves the performance in comparison with classical approaches that use only Ta. Moreover, the regression-based models that rely on the logistic relationship between Te and Ta exhibit root mean square errors comparable (0.90 °C) with those obtained with a classical five-term heat budget model (0.82 °C), despite a small number of required forcing variables. In contrast, the regressive models that are based on a linear relationship Te = f( Ta) fail to simulate the heat peaks and are not advisable for climate change studies. The regression-based approaches that are based on a logistic relationship and the heat balance approaches generate notably similar responses to the projected climate changes scenarios. This similarity suggests that sophisticated thermal models are not preferable to cruder ones, which are less time-consuming and require fewer input data. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

This study presents an analysis of climate-change impacts on the water resources of two basins located in northern France, by integrating four sources of uncertainty: climate modelling, hydrological modelling, downscaling methods, and emission scenarios. The analysis focused on the evolution of the water budget, the river discharges and piezometric heads. Seven hydrological models were used, from lumped rainfall-discharge to distributed hydrogeological models, and led to quite different estimates of the water-balance components. One of the hydrological models, CLSM, was found to be unable to simulate the increased water stress and was, thus, considered as an outlier even though it gave fair results for the present day compared to observations. Although there were large differences in the results between the models, there was a marked tendency towards a decrease of the water resource in the rivers and aquifers (on average in 2050 about −14 % and −2.5 m, respectively), associated with global warming and a reduction in annual precipitation (on average in 2050 +2.1 K and −3 %, respectively). The uncertainty associated to climate models was shown to clearly dominate, while the three others were about the same order of magnitude and 3–4 times lower. In terms of impact, the results found in this work are rather different from those obtained in a previous study, even though two of the hydrological models and one of the climate models were used in both studies. This emphasizes the need for a survey of the climatic-change impact on the water resource. [ABSTRACT FROM AUTHOR]

Summary: The groundwater flow in land surface models (LSMs) is receiving increasing attention, and different groups have recognised the need for an improved representation of the saturated zone. For example, the hydrological model TOPMODEL is now included in several LSMs, which allows the simulation of a shallow water table. In this article, we present an adaptation of the catchment land surface model (CLSM), which is an LSM using the concepts of TOPMODEL to generate runoff and soil moisture patterns, to the Somme River basin located in northern France. This catchment is heavily influenced by the deep groundwater flow in the Chalk aquifer, and groundwater storage exerts a strong buffering effect on the streamflow. However, the TOPMODEL shallow water table is not adapted to store water over long timescales. To account for this process, we propose the implementation of an additional linear storage reservoir (LR). Using 18 years of meteorological and streamflow data, we demonstrate that this parameterisation considerably improves the discharge simulation performance. In particular, it allows the maintanance of low flows and the reduction of overestimated peak flows that were generated by CLSM without this reservoir. Many simulations with different parameter combinations are analysed to investigate the parameter sensitivities. The impact of the LR on the energy budget is assessed using soil temperature data. We conclude that the new LR parameterisation contributes to a better representation of water transfers in an LSM that enables a groundwater-fed catchment to be modelled for impact studies such as those of climate change. [Copyright &y& Elsevier]

Multi-model ensembles have become a standard tool for assessing global climate change impacts. Interpretation of such ensembles is complicated because each model group has a different modeling concept and framework. For example, global scale land surface, water and vegetation models have been widely applied to understand the complex hydrological cycle of the Earth and to assess associated past and future changes. Additionally to this purpose, land surface models assess energy and biogeochemical cycles while vegetation models assess vegetation and carbon cycles. Therefore, all these models differ with respect to the specific processes of the hydrological cycle included in their structure. In this study, we demonstrate how the similarities and differences between models can be better understood and illustrated by using a standard representation of the main model features. We analyze twelve models from the global water sector of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) phase 2b: six land surface models (LSMs), five global hydrological models (GHMs) and one dynamic global vegetation model (DGVM). The majority of the models are run with a daily temporal resolution and with a spatial resolution of 0.5°. Part of these models include a reservoir scheme and water use sectors. The heuristic mappings of the models are designed to ensure the opportunity to choose a model at the initial stage of the analysis, based on the most important qualities, relationships and characteristics, which provide users with significant time saving. Therefore, the review study will provide the basis for: (i) achieving further model (inter)comparison; (ii) selecting the right model(s) output(s) for specific applications; and (iii) assessing the similarities and differences among the models. The models characteristics will be presented in three levels of complexity allowing to reach a large audience. The target audience includes the modeling community, the stakeholder community, and the general public interested in understanding large-scale models, simulating climate change and its impacts. Additionally, stakeholder insights, gathered mostly in Eastern Europe and West Africa, have been considered in the study design. Stakeholders were identified according to their need for climate-impact information provided within the ISIMIP framework and included academics, government officials, employees working in international organizations, NGOs, consultancies, and private companies. In conclusion, the presentation describes the study approach and preliminary results, with particular emphasis on the standard model diagram, differences between the models, and the stakeholder engagement. [ABSTRACT FROM AUTHOR]