The Surface Water and Ocean Topography (SWOT) mission provides the first global measurements of river water surface elevation at reach scale, captured through the vector-based SWORD database. While these observations offer unprecedented spatial detail, most large-scale hydrological models represent rivers on gridded routing networks, creating a structural mismatch that limits the direct use of SWOT data in global analyses. A robust and scalable translation between SWORD reaches and model grid cells is therefore essential for enabling SWOT-based hydrology. Here we present a global, confidence-oriented strategy for aligning SWORD reaches with the 1/12° river network of the CTRIP routing model. The method evaluates candidate associations using several hydrologically meaningful criteria, including geographic proximity, upstream area and basin delineation coherence inherited from MERIT-Hydro, reach morphology, and alignment with D8 flow directions. Each pixel receives a confidence category that distinguishes unambiguous single-reach matches from robust or uncertain multi-reach configurations. This classification provides transparent information on mapping quality and identifies locations where model–observation alignment is intrinsically ambiguous. We demonstrate the performance of the method through a global application at 1/12° resolution. The resulting reach-to-grid associations produce spatially coherent river corridors, consistent basin topology, and near-complete coverage across observable rivers. Diagnostics across continents show that the framework performs reliably in challenging systems such as deltas, braided rivers, and multi-thread channels where simpler geometric approaches commonly fail. The final outputs include confidence-tier maps, reach–pixel match tables, and gridded river masks that translate SWOT’s vector observations into hydrologically meaningful model space. These products provide the community with a ready-to-use, reproducible translation layer that supports a wide range of SWOT-based research activities, including large-scale river characterization, network comparison, uncertainty assessment, and future assimilation experiments. The approach enables consistent use of SWOT observations in global hydrology and opens new avenues for connecting reach-scale satellite measurements with continental-scale hydrological understanding.

This article presents a comprehensive description of the 3.0.2 stable release of the Crocus snowpack model in the SURFEX modelling platform. It synthesizes and harmonizes a number of equations disseminated in various previous publications, introduces a number of unpublished parameterizations and includes new developments implemented since 2012. Among the novelties, an explicit representation of the evolution of impurity mass in snow (e.g. black carbon, mineral dust) allows representing their impact on solar radiation absorption in the snowpack at different wavelengths and their feedback on all snowpack properties. The model also allows the formation of surface ice layers due to freezing rain. In addition, Crocus is coupled to the MEB “big-leaf” vegetation scheme and can therefore be applied in forested areas. A module for snow management can also be optionally activated to simulate the snowpack on ski slopes in ski resorts. The model can be coupled with various blowing snow schemes. The MEPRA expert system which analyses the mechanical stability of the simulated snowpack has been implemented directly within SURFEX. For each physical process represented by empirical parameterizations, several new parameterizations from the literature were implemented. The different combinations of these parameterizations constitute the ESCROC multiphysics ensemble model. It allows the quantification of simulations uncertainty for various applications. Finally, a technical solution was proposed for externalized applications allowing the use of the scheme in other Land Surface Models. The paper also reviews the available scientific evaluations and applications of the model. It describes its numerical efficiency and the main scientific and technical challenges providing guidance for the future of snow modelling.

Abstract. Water use efficiency (WUE), a key ecohydrological indicator linking carbon assimilation and vegetation water loss, is critical for understanding ecosystem responses under changing hydro-climatic conditions. Process-based land surface models (LSMs) are widely used to represent carbon-water interactions; however, their ability to simulate ecosystem-scale WUE across contrasting climates remains limited. This study evaluates the performance of the Interactions between Soil-Biosphere-Atmosphere model with A-gs photosynthesis scheme (ISBA-A-gs) implemented within the SURFEX land surface modelling platform in simulating gross primary productivity (GPP), evapotranspiration (ET), and WUE (GPP/ET) for maize grown under temperate (France, FR-Lam) and tropical semi-arid (India, Ind-IITH) climates. The model was driven by site-specific meteorological and vegetation variables across six growing seasons under sprinkler irrigation at FR-Lam, and two seasons (monsoon and winter) under alternate furrow irrigation (AFI) at Ind-IITH. Model calibration revealed that FR-Lam is characterized by relatively higher cuticular conductance and pronounced atmospheric control on stomatal behaviour, whereas at Ind-IITH, AFI-induced adjustments in mesophyll conductance and soil moisture stress thresholds. At FR-Lam, ISBA-A-gs simulated the seasonal mean cumulative GPP, ET, and WUE of 1039 ± 20 gC m-2, 610 ± 31 kg H2O m-2, and 1.70 ± 0.10 gC kg-1 H2O, respectively, as compared to measured values of 1026 ± 30 gC m-2, 562 ± 42 kg H2O m-2, and 1.82 ± 0.11 gC kg-1 H2O correspondingly. At Ind-IITH, the model simulated the seasonal mean cumulative GPP, ET, and WUE of 766 ± 15 gC m-2, 567 ± 30 kg H2O m-2, and 1.35 ± 0.11 gC kg-1 H2O, respectively, as compared to measured values of 793 ± 11 gC m-2, 522 ± 20 kg H2O m-2, and 1.51 ± 0.12 gC kg-1 H2O correspondingly. Further, the diagnostic analysis using the GPP·VPD0.5-ET relationship revealed that ISBA-A-gs realistically captures the coupling between carbon assimilation and transpiration-driven water loss. Overall, ISBA-A-gs demonstrates strong capability in simulating carbon and water fluxes of maize, particularly in representing WUE dynamics under contrasting climate regimes.

Estimating latent heat fluxes in semi-arid environments remains challenging due to the strong spatial heterogeneity of soils and plants, land management practices, and limited observational data. In particular, accurately predicting the partition of evapotranspiration into evaporation and transpiration from observations remains very challenging. Land surface models (LSMs) can be used as a tool in this regard, when their validation is possible, but recent studies have indicated that LSMs generally overestimate soil evaporation.This study evaluates the performance of the land surface model ISBA within the SURFEX platform using data from two contrasting sites during the Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) field experiment: an alfalfa field subjected to flood irrigation, and a natural grassland which is nearly senescent during the study period. It was found that the ISBA model tended to overestimate the evapotranspiration. Therefore, a dry surface layer (DSL) resistance was implemented in the ISBA model to improve the simulation of evaporation, which has proved successful in other models. The implementation of a DSL resistance led to an improvement in the simulated latent heat flux by reducing bare soil evaporation compared to simulations without a soil resistance. This approach reduced the daily RMSE of the latent heat flux by 29 % and 32 % at the alfalfa and natural grass sites respectively, while marginally increasing the correlation at both sites. Sensible heat flux and net radiation have improved on the order of 10 W m-2, whereas the ground heat flux has deteriorated within the same order. The resulting DSL simulations reduced the overall global error compared to a simulation without a DSL resistance. A sensitivity test of the parameters that drive a DSL resistance in ISBA further improved the simulations, reducing excessive diminution of LE after rain events. The new DSL parameterization helps overcome current problems of ET modeling by reducing bare soil evaporation within LSMs.

Abstract. Land surface models (LSMs) are simulating land–atmosphere exchanges and are widely used in hydrology, operational weather prediction, research meteorology, and to assess land surface responses to future climate change. LSMs exhibit distinct differences in simulated water fluxes due to varying physical process representations and input land characteristics. We challenged seven state-of-the-art LSMs by altering soil hydraulic parameters from representing sand or silt to disentangle the responses of the water fluxes. The LSMs reacted differently due to complex, sometimes counter-intuitive interactions of infiltration, soil evaporation, and plant transpiration. We identified the representations of surface runoff and soil evaporation as the two main reasons behind model differences. We show how subgrid parameterization of a saturated fraction led to diverging sensitivities of runoff to soil parameters. Soil evaporation was the largest and most sensitive share of evapotranspiration in almost all models. Process parameterizations at the soil surface are identified as critical and should be improved to lead to more consistent flux partitioning. We demonstrate here that it is possible and worthwhile in model intercomparison studies to relate model results to specific process descriptions, helping users to understand model results of LSMs and helping modelling groups to identify weaknesses and move forward.

The BRISA project focuses on sea breezes developed in three Spanish semi-arid areas: the coast of the Gulf of Cadiz, the island of Mallorca and the eastern part of the Ebro Valley. The breezes in these regions interact with processes of different scales, such as the mesoscale thermal-low pressure systems formed over the Iberian Peninsula in summer, the secondary circulations generated in inhomogeneous surfaces characterised by wetlands and irrigated/non-irrigated agricultural patches, or the thermally-driven flows favoured in complex-terrain regions. These interactions affect the formation and the characteristics of the sea breezes, with numerous impacts that affect society, highlighting their thermoregulatory role during extreme temperatures, or their importance for inland and offshore wind energy resources, among others.The methodology of the BRISA project (PID2024-159841OA-I00) combines the use of long-term in situ observations and experimental field campaigns, as well as the use of numerical weather prediction models. In this work, we present the BRISA-Cádiz field campaign (July 2026), which will bring together national and international boundary-layer researchers and oceanographers to characterise the horizontal and vertical extension of the breezes. Among the planned activities are the installation of a WindCube LIDAR just at the shoreline, the launching of frequent radiosondes inland and at sea, the profiling of the atmosphere with tethered balloons, the use of drones with instrumentation to characterise the horizontal boundary-layer meteorology and turbulence contrasts between the land and the sea, the use of marine instrumentation to monitor the sea surface conditions, and the use of Distributed Temperature Sensing (DTS) systems together with surface energy balance stations to monitor the near-surface temperature and turbulent fluxes.In this work, we will present the instrumentation deployed during the campaign as well as some first analyses performed after it. The high amount of data expected to be gathered during the intensive observational periods will allow us to study how breezes form, evolve, and impact the meteorological conditions in coastal sites. The next step following the observational characterisation of the breezes will be the evaluation of high-resolution numerical models, to ultimately improve how these processes should be represented within them.

In the companion paper, Decharme (2025) developed a process-based framework using soil mixture theory to represent the effects of soil organic matter on soil physical properties in land surface models. The present study extends this work by testing the framework in global land surface simulations with the ISBA-CTRIP land surface modeling system. The approach derives the volumetric organic matter fraction and phase-specific densities from soil organic carbon and bulk density using mass volume relationships, and computes hydraulic and thermal parameters using mixing rules consistent with the model physics. We also introduce an optional mineral soil compactness adjustment, under the assumption that texture-based pedotransfer functions define a mineral reference state that is not explicitly constrained by bulk density, whereas gridded bulk density products mostly reflect in situ compactness states. We examine the effects of both developments in multidecadal global offline simulations forced by a standard meteorological dataset and driven by SoilGrids soil inputs. Four configurations are compared, a mineral-only control, a previous empirical scheme, the new process-based scheme, and its variant including the mineral soil compactness adjustment. The evaluation combines site-scale constraints on porosity and hydraulic behavior with large-scale benchmarks of the terrestrial water and energy cycles, including terrestrial water storage variations, river discharge, evapotranspiration, soil temperature, and active layer thickness. Overall, the global experiments suggest that the new process-based scheme produces more consistent large-scale hydrothermal responses than the previous empirical scheme, whereas the mineral soil compactness adjustment plays a secondary role and mainly acts as a local modulator.

Abstract. Land evaporation (E) links the water, energy, and carbon cycles and plays a central role in agriculture, water management, and land–climate interactions. However, estimating E at high spatial and temporal resolution remains challenging, especially in irrigated regions. This study presents a novel framework to generate daily 1 km E estimates for 2018–2022 over the Iberian Peninsula by explicitly representing irrigation in the recently released Global Land Evaporation Amsterdam Model version 4 (GLEAM4). To this end, high-resolution (1 km) meteorological forcing is combined with Sentinel-1 soil moisture and ancillary information on irrigated extent and satellite-based crop phenology. Our method constrains E below potential evaporation (Ep) even in irrigated land, leveraging observational data of vapour pressure deficit, air temperature, vegetation optical depth, leaf area index, wind speed, and shortwave radiation, which allows irrigated crops to respond realistically to diverse sources of vegetation stress, rather than assuming Ep rates. Results reveal increases in E over irrigated areas of up to 450 mm yr-1 when irrigation is explicitly considered, with spatial patterns consistent with independent irrigation estimates. Evaluation against eddy-covariance measurements demonstrates marked improvements at two irrigated sites in the Iberian Peninsula, with increases in daily Kling-Gupta Efficiency (KGE) compared to simulations without irrigation of 0.40 and 0.70, respectively. The approach is also relatively robust to false positives in the irrigation mask owing to the fractional vegetation structure of GLEAM4. Overall, the resulting high-resolution E dataset provides a realistic representation of irrigation practices and supports applications in both agricultural management and regional water-resource assessments. The approach will be extended to global scales through integration into future GLEAM releases.

California and adjacent regions received record-breaking precipitation in the winters of 2016–2017 and 2022–2023, causing extraordinary damage and severe societal impacts. However, subseasonal to seasonal predictive skill for Californian winter precipitation has remained persistently low. Furthermore, these extreme hydroclimate events occurred during La Niña conditions, which are traditionally associated with dry conditions in California. A fundamental lack of predictability has been suggested. This study shows that anomalous early-winter heating over the Tibetan Plateau (TP) played a key role in driving the extreme precipitation based on observational analyses and Earth system model experiments. In the control simulation, the model failed to reproduce the observed very warm 2-m air temperature anomaly over the TP and the extreme precipitation over California. After improving the temperature initialization with a mask over the TP, the model reproduced most of the observed TP 2-m temperature anomaly and successfully generated about 56% (January 2017) and 38% (March 2023) of the observed extreme precipitation anomalies over California and adjacent regions. The TP heating modulated a Tibetan Plateau–Rocky Mountain wave train, which in turn affected atmospheric rivers and triggered Rossby wave breaking over the northeastern Pacific and the western coast of North America. Both processes are well known as major contributors to extreme precipitation in the western United States. These results suggest that the two catastrophic winter precipitation events were predictable from remote land-surface thermal conditions and identify high-elevation terrestrial temperature anomalies as a previously unidentified source of subseasonal to seasonal predictability for winter extreme hydroclimate events.

The integration of satellite-based observations into hydrological models offers transformation potential for improving discharge predictions globally, especially in regions lacking in situ measurements. This study presents CTRIP-HyDAS, a global-scale hydrological data assimilation framework that merges SWOT-derived discharge observations with the CTRIP river routing model at 1/12 degrees spatial resolution. The framework was applied at the global scale and evaluated using Observing System Simulation Experiments under controlled discharge observation uncertainty scenarios (10%, 20%, and 40%). Performance metrics computed globally show widespread improvements, with Assimilation Index (AI) values exceeding 0.7 in most regions and relative errors reduced to within 5%-10% under low-error conditions. To illustrate the framework's adaptability, six representative river basins, that is, Amazon, Congo, Ganges, Indus, Mississippi, and Reka, were selected to showcase HyDAS performance under diverse hydrological regimes. A physics-based localization method enabled efficient propagation of corrections beyond the observed swath. These findings confirm the scalability and robustness of CTRIP-HyDAS for global SWOT-based assimilation and underline its potential to enhance discharge prediction and water management in data-scarce regions.

Earth-system and weather forecasting models are moving to km-scale resolutions to provide more pertinent information to society on extreme events or the impacts of climate change. As some parametrized processes can be represented explicitly, increasing spatial resolution is expected to be beneficial for the atmosphere and oceanic components. It is not obvious that the same benefits will be achieved for land-surface models (LSMs), as landscape organizing processes start to play a role. To evaluate the consequences of increasing resolution, six LSMs driven by 3-km resolution forcings are compared with their reference simulation at 50-km resolution. These high-resolution atmospheric forcing data are developed over a region covering all catchments flowing off the Pyrenees. It is shown that these forcings capture the contrasts in atmospheric conditions between mountainous areas and valleys absent at coarser resolutions. At finer resolution, the LSMs display reduced evaporation over semi-arid catchments, which cannot be explained by differences in the atmospheric forcings. The cause has to be sought in the lack of spatial redistribution of water within the catchments. The observed diurnal amplitude of land-surface temperature shows that the models do not reproduce the local minima along rivers and in irrigated areas caused by increased evaporation. We conclude that, at km-scale resolution, lateral transfers of water that organize landscapes play an important role in predicting evaporation correctly. At resolutions of a few deca-kilometres, the contribution of grid-cell lateral flows to evaporation can be neglected. However, at higher resolutions, groundwater, riparian recharge, and human water management for irrigation need to be simulated to represent realistic spatial contrasts in the surface fluxes that drive the atmosphere. We call upon the community to invest in the development of representation of these processes in LSMs, so that they are ready for higher resolution applications.

Offline Land Surface Models (LSMs) are essential for a wide range of applications, including water resource management and agricultural planning. A critical variable in these models is evapotranspiration, but its value is easily biased in irrigated areas. In fact, irrigation fundamentally alters local atmospheric conditions – cooling and humidifying the air and reducing wind speeds – factors that contribute to reducing evapotranspiration rates. This phenomenon is called “atmospheric feedback”, but is often missing or poorly represented in offline LSMs because most of the atmospheric forcings used, such as reanalyses and climate model outputs, overlook the atmospheric effect of irrigation. This leads to a tendency for offline LSMs to overestimate evapotranspiration rates over irrigated areas. In this study, the atmospheric effects of irrigation are quantified using data from the Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) project field campaign. The various surface processes that influence the dynamics of evapotranspiration in response to the atmospheric feedback are then systematically investigated. The results confirm the importance of considering the atmospheric feedback in the Interactions Soil Biosphere Atmosphere (ISBA) LSM over irrigated areas in many configurations. For well irrigated crops, the average overestimation of evapotranspiration is about 25 %. Conversely, for water-stressed crops, this overestimation is negligible because of the delay in stomatal closure caused by the atmospheric feedback mechanisms, providing a compensatory effect which mitigates the overestimation. These findings highlight the need for improved representation of irrigation-related atmospheric feedback in the atmospheric forcings used as upper boundary conditions in LSMs to improve the accuracy of evapotranspiration estimates in agricultural or hydrological contexts.

Surface soil moisture (SSM) products at high spatial resolution are increasingly available, either from the disaggregation of coarse-resolution products such as SMAP and SMOS, or from high-resolution radar data such as Sentinel-1. In contrast to coarse resolution products, there is a lack of intercomparison studies of high spatial resolution products, which are more relevant for applications requiring the plot scale. In this context, the objective of this work is the evaluation and intercomparison of three high spatial resolution SSM products on a large database of in situ SSM measurements collected on two different sites in the Urgell region (Catalonia, Spain) in 2021. The satellite SSM products are: i) SSMTheia product at the plot scale derived from a synergy of Sentinel-1 and Sentinel-2 using a machine learning algorithm; ii) SSMρ product at 14 m resolution derived from the Sentinel-1 backscattering coefficient and interferometric coherence using a brute-force algorithm; and iii) SSMSMAP20m product at 20 m resolution obtained from the disaggregation of SMAP using Sentinel-3 and Sentinel-2 data. Evaluation of the three products over the entire database showed that SSMTheia and SSMρ yielded a better estimate than SSMSMAP20m, and SSMρ is slightly better than SSMTheia. In particular, the correlation coefficient is higher than 0.4 for 72%, 40% and 27% of the fields using SSMρ, SSMTheia and SSMSMAP20m, respectively. The lower performance of SSMTheia compared to SSMρ is due to the saturation of SSMTheia at 0.3 m3/m3. The time series analysis shows that SSMSMAP20m is able to detect rainfall events occurring at large scale while irrigation at the plot scale are not caught. This is explained by the use of Sentinel-2 reflectances, which are not linked to surface water status, for the disaggregation of Sentinel-3 land surface temperature. The approach can therefore be improved by using high spatial and temporal resolution thermal data in the perspective of new missions such as TRISHNA and LSTM. Finally, the results show that although reasonable estimates are obtained for annual crops using SSMTheia and SSMρ, poor performance is observed for trees, suggesting the need for better representation of canopy components for tree crops in SSM inversion approaches.

Root zone soil moisture (RZSM) is a key variable controlling the soil-vegetation-atmosphere exchanges. Its estimation is vital for monitoring hydrological, meteorological and agricultural processes. A number of large-scale products exist but with a coarse resolution (>1 km), which is not suitable for plot-scale studies. The aim of this work is to map RZSM, for the first time, at very high spatial resolution using a very high spatial resolution surface soil moisture (SSM) product and a recursive exponential filter. SSM is estimated from Sentinel-1 data using the water cloud model at a resolution of approximately 50 m. The approach was evaluated on a database consisting of 12 fields, including 7 winter wheat and 5 summer maize fields, irrigated using different techniques. The results show that the approach performs reasonably well using Sentinel-1 SSM product with correlation coefficient (R) between 0.3 and 0.82, root-mean-square error (RMSE) between 0.05 and 0.12 m(3)/m(3) and a bias in the range -0.1-0.07 m(3)/m(3), at 15-20 cm depth. This is equivalent to R = 0.6, RMSE = 0.12 m(3)/m(3) and bias = 0.07 m(3)/m(3) using the entire database, which is quite low compared to the use of in situ SSM measurements (R = 0.81, RMSE = 0.07 m(3)/m(3) and bias = 0.03 m(3)/m(3)). This is related to inaccuracies in the SSM product, where fields with good SSM estimation also resulted in good RZSM estimation and conversely. In addition to SSM, the approach is also sensitive to its time constant T. Analysis of RZSM sensitivity to T shows that the optimum T value depends on soil texture, climate and measurement depth. In particular, low optimum T values (1 day) are obtained for loamy and sandy loam soils, while higher values (5-10 days) are optimal for soils with a high clay fraction, at 15-20 cm depth. These values increase with soil depth and are influenced by seasonal atmospheric demand. Combined to reasonable statistical metrics, the spatial variability depicted by the RZSM maps opens up prospects for high-resolution RZSM mapping from Sentinel-1 SSM data using a simple approach over annual crops. This is of prime relevance for agricultural applications requiring very high-resolution estimation at plot scale, such as crop yield, irrigation and fertilizer management, as well as for the assessment of inter-plot variability.

Land-surface–atmosphere interactions determine atmospheric boundary-layer (ABL) features, and in the case of semi-arid regions the water availability in the root-zone layer is a major factor. To explore this issue, the Land-surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) initiative organized an observational campaign in the Eastern Ebro River sub-basin in summer 2021, focusing on the effect of surface heterogeneities on the ABL in a semi-arid environment enclosing a large irrigated area. With the aim of understanding the performance of numerical models for the region studied, a mesoscale modelling intercomparison was undertaken for a three-day case study in July 2016. The intercomparison consisted of three models, Meso-NH, the Unified Model (UM), and Weather Research and Forecasting (WRF), run with two embedded domains, with corresponding horizontal resolutions of 2 km and 400 m and similar vertical grids. This is a case dominated by well-developed thermally driven circulations in the Ebro Basin, allowing an investigation of the representation of surface features in the models and its impact on the organization of the flow at lower levels. Other effects are also explored, such as using different initial and lateral boundary conditions, changing horizontal resolution, or modifying some surface features. The mesoscale circulations generated by the three models are similar and accurate when compared with the surface observations, variations being found at the ABL scales related to the representation of surface processes in each model, which provide different grid values of the surface fluxes. It is found that the models do not perform as well over the irrigated areas as in the rest of the domain. The challenge at this point is to relate the model biases to the particularities of the parameterizations and physiographic databases used by each model.

In semi-arid areas, irrigation represents the largest human footprint on the water cycle. Representing irrigation in land surface and hydrological models is a challenging task as irrigation decisions depend on environmental, economic and traditional knowledge factors. The objective of this work is twofold: (1) to evaluate the performance of the new irrigation module integrated into the Interactions between Soil, Biosphere, and Atmosphere (ISBA) model and (2) to assess the future trajectories of agricultural water use considering both climate and land use change. The evaluation of the new irrigation module in the ISBA model is done by: (1) comparing the observed and predicted fluxes by the ISBA model, with and without the activation of the irrigation module, and (2) comparing the irrigation water inputs at the level of irrigated perimeters. The evaluation shows that the integration of the new irrigation scheme in ISBA significantly improves the latent heat flux (LE) predictions for the period 2004-2014, compared to the model without this scheme. Considering several flux stations, the LE flux bias was reduced from -60 W/m 2 for the model without irrigation to −15 W/m 2 . The evaluation at the irrigated perimeter scale highlights the ability of the irrigation model to reproduce the overall magnitude and seasonality of observed irrigation water quantities despite a positive bias. The agricultural water use was then projected considering two climate scenarios and two scenarios of land use change based on the actual trend of conversion to tree crops in response to the large subsidy attributed for the conversion to drip irrigation since 2008. It is shown that (1) irrigation water use could almost double for the more extreme scenarios and (2) that most of this drastic increase is attributed to land use change, including irrigation intensification and expansion. Within this context, the results presented in this study highlight the side effect of conversion to drip irrigation largely documented in the literature and open perspectives for making informed decisions for sustainable water management at the watershed level.

Accurate soil moisture initial conditions in dynamical subseasonal forecast systems are known to improve the temperature forecast skill regionally, through more realistic water and energy fluxes at the land-atmosphere interface. Recently, results from the GEWEX-GASS LS4P (Impact of initialized land temperature and snowpack on sub-seasonal to seasonal prediction) multi-model coordinated experiment have provided evidence of the primal contribution of the initial surface and subsurface soil temperature over the Tibetan Plateau for capturing a hemispheric scale atmopsheric teleconnection leading to improved subseasonal forecasts. Yet, both the soil temperature and water content are key components of the soil enthalpy and we hypothesize that properly initializing one of them without modifying the other in a consistent manner can alter the soil thermal equilibrium, thereby potentially reducing the benefit of land initial conditions on subsequent atmospheric forecasts. This study builds on the protocol of the above-mentioned multi-model experiment, by testing different land initialization strategies in an Earth system model. Results of this pilot study suggest that a better mass and energy balance in land initial conditions of the Tibetan Plateau triggers a wave train which propagates through the northern hemisphere mid-latitudes, resulting in an improved large scale circulation and temperature anomalies over multiple regions of the globe. While this study is based on a single case, it strongly advocates for enhanced attention towards preserving the soil energy equilibrium at initialization to make the most of land as a driver of atmospheric extended-range predictability.

The utilization of water by various socio-economic sectors has made this resource highly sought after, especially in arid to semi-arid zones where water is already scarce and limited. In this context, effective management of this resource proves to be crucial. Our study aims to: evaluate the performance of the new irrigation module in ISBA, quantify the water balance, and assess the impact of climate change and anthropogenic factors on this resource by the horizon of 2041-2060, utilizing high-resolution futuristic forcings from the study (Moucha et al., 2021). To assess the ISBA model with its new irrigation module, we initially compared observed and predicted fluxes with and without activation of the irrigation module. Subsequently, we compared irrigation water inputs at the ORMVAH-defined irrigated perimeters within the Tensift basin. The results of this evaluation showed that the predictions of latent heat flux (LE) considering all available stations in the basin shifted from -60 W/m² for the model without irrigation to -15 W/m². This indicates that the integration of the new irrigation system into ISBA significantly improves the predictions of latent heat flux (LE) over the period 2004-2014 compared to the regular model. Considering the irrigated perimeters, the study results demonstrated that the model with the integration of the irrigation module was capable of replicating the overall magnitude and seasonality of water quantities provided by ORMVAH despite a positive bias. Exploration of the water balance at the Tensift basin level revealed the ISBA model's ability, equipped with its irrigation module, to describe complex relationships among precipitation, irrigation, evapotranspiration, and drainage. Finally, the assessment of the impact of climate change and vegetation cover for the period 2041-2060, utilizing high-resolution SAFRAN forcings projected to the same horizon (Moucha et al., 2021), revealed an increase in irrigation water needs. These results are of paramount importance in the context of sustainable water resource management in arid and semi-arid regions.

One of the greatest challenges facing environmental science is to better understand the impacts of predicted future changes in the terrestrial hydrological cycle. It has been recognized that human activities play a key role and must therefore be considered in future climate simulations. The representation of anthropization in land surface schemes within global earth system models is at a relatively nascent stage and must be improved for more accurate future projections of water resources. The understanding of the impact of anthropogenic processes has been hampered by the lack of consistent and extensive observations. Here, we present the Land surface Interactions with the Atmosphere over the Iberian Semi-arid Environment (LIAISE) project field campaign which brought together ground-based (surface energy budget estimated at 7 sites, 269 radio soundings made at 2 sites and multiple remote sensing instruments for profiling the lower atmosphere), airborne measurements (3 airplanes and numerous drones measuring surface and atmospheric properties) and satellite data (to derive estimates of irrigation timing, soil moisture, evapotranspiration and surface temperature) to improve our understanding of key natural and anthropogenic land processes and boundary layer feedbacks. The study area is in the Ebro basin of northeastern Spain in a hot, dry Mediterranean climate, with a sharp demarcation between a vast intensively irrigated region and a much drier rainfed zone to the east. Analysis of the observations reveal strong surface heterogeneities of evapotranspiration within the irrigated zone (differences upwards of approximately 7 mm day-1 between fields), linked to the crop type, vegetation phenology and soil moisture, all of which were modulated by irrigation. The significant surface flux differences between the irrigated and rainfed zones were found to result in strongly contrasting atmospheric boundary layer properties (between 2 supersites​ separated by 14 km) extending upwards through the lowest several km of the atmosphere.