Coupled Land Surface Research Articles

Hydrometeorological response to afforestation in the UK: findings from a kilometer-scale climate model

Afforestation is of international interest for its positive benefits on carbon storage, ecology, and society, but its impacts on terrestrial and atmospheric processes are still poorly understood. This study presents the first use of a coupled land surface and convection permitting atmospheric model (CPM) to quantify hydrometeorological effects of afforestation across the United Kingdom, focusing on atmospheric processes often missing in hydrological models. Generating a scenario of 93 000 km2 (40%) additional woodland across the UK, the periods of 2042–2052 and 2062–2072 are analysed. Simulated afforestation alters seasonal and regional UK hydrometeorology. Countrywide runoff increases in all seasons (between 5.4–11 mm and 4.3–8.6% per season) due to elevated subsurface flows from greater soil moisture. Evaporation decreases in summer (−20.6 mm, −10%) but increases in winter (8.1 mm, 15%) whereas rainfall increases throughout all seasons (between 2.2–6.86 mm and 0.9%–2.2% per season). Greater winter rainfall is detected along Great Britain’s west coastline as increased surface roughness produces prolonged and heavier rainfall. In the summer, lower albedo increases potential evapotranspiration and reduces near surface specific humidity: water is locked in deeper soil layers as transpiration diminishes and the topsoil dries out. However, the magnitude of hydrometeorological change due to altered land cover is smaller than the uncertainty in local climate change projections. This work sets a precedent in illustrating the impacts of afforestation on hydrology using a high-resolution CPM and highlights the importance of coupled hydrometeorological processes when investigating land cover impacts on hydrological processes.

Towards an Indian land data assimilation system (ILDAS): A coupled hydrologic-hydraulic system for water balance assessments

Effective management of water resources requires reliable estimates of land surface states and fluxes, including water balance components. But most land surface models run in uncoupled mode and do not produce river discharge at catchment scales to be useful for water resources management applications. Such integrated systems are also rare over India where hydrometeorological extremes have wreaked havoc on the economy and people. So, an Indian Land Data Assimilation System (ILDAS) with a coupled land surface and a hydrodynamic model has been developed and driven by multiple meteorological forcings (0.1°, daily) to estimate land surface states, channel discharge, and floodplain inundation. ILDAS benefits from an integrated framework as well as the largest suite of observation records collected over India and has been used to produce a reanalysis product for 1981–2021 using four forcing datasets, namely, Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS), ECMWF’s ERA-5, and Indian Meteorological Department (IMD) gridded precipitation. We assessed the uncertainty and bias in these precipitation datasets and validated all major components of the terrestrial water balance, i.e., surface runoff, soil moisture, terrestrial water storage anomalies, evapotranspiration, and streamflow, against a combination of satellite and in situ observation datasets. Our assessment shows that ILDAS can represent the hydrological processes reasonably well over the Indian landmass with IMD precipitation showing the best relative performance. Evaluation against ESA-CCI soil moisture shows that MERRA-2 based estimates outperform the others, whereas ERA-5 performs best in simulating evapotranspiration when evaluated against MODIS ET. Evaluations against observed records show that CHIRPS-based estimates have the highest performance in reconstructing surface runoff and streamflow. Once operational, this system will be useful for supporting transboundary water management decision making in the region.

Design principles for engineering wetlands to improve resilience of coupled built and natural water infrastructure

Intensifying climate extremes and the ageing of built infrastructure have prompted the idea of replacing the ageing built infrastructure with natural infrastructure. In this paper, we discuss how a distributed portfolio of smaller wetlands performs compared to a flood control reservoir in terms of flood mitigation. Using a framework of a loosely coupled land surface model with a hydrodynamic model, in the Brazos basin (Texas), we find that (i) two smaller wetlands have more impact on flood mitigation than one double sized wetland, and (ii) creating multiple wetlands (prioritized based on storage) increases flood mitigation. Further, we find that a portfolio of wetlands equivalent to the size of the submergence area of the biggest flood control reservoir (Whitney) in the basin, can create additional flood control storage, equivalent to ∼10% of the total storage of Lake Whitney. Creating a portfolio of wetlands can increase the overall resiliency of the basin.

Continental-scale evaluation of a fully distributed coupled land surface and groundwater model, ParFlow-CLM (v3.6.0), over Europe

Abstract. High-resolution large-scale predictions of hydrologic states and fluxes are important for many multi-scale applications, including water resource management. However, many of the existing global- to continental-scale hydrological models are applied at coarse resolution and neglect more complex processes such as lateral surface and groundwater flow, thereby not capturing smaller-scale hydrologic processes. Applications of high-resolution and physically based integrated hydrological models are often limited to watershed scales, neglecting the mesoscale climate effects on the water cycle. We implemented an integrated, physically based coupled land surface groundwater model, ParFlow-CLM version 3.6.0, over a pan-European model domain at 0.0275∘ (∼3 km) resolution. The model simulates a three-dimensional variably saturated groundwater-flow-solving Richards equation and overland flow with a two-dimensional kinematic wave approximation, which is fully integrated with land surface exchange processes. A comprehensive evaluation of multiple hydrologic variables including discharge, surface soil moisture (SM), evapotranspiration (ET), snow water equivalent (SWE), total water storage (TWS), and water table depth (WTD) resulting from a 10-year (1997–2006) model simulation was performed using in situ and remote sensing (RS) observations. Overall, the uncalibrated ParFlow-CLM model showed good agreement in simulating river discharge for 176 gauging stations across Europe (average Spearman's rank correlation (R) of 0.77). At the local scale, ParFlow-CLM model performed well for ET (R>0.94) against eddy covariance observations but showed relatively large differences for SM and WTD (median R values of 0.7 and 0.50, respectively) when compared with soil moisture networks and groundwater-monitoring-well data. However, model performance varied between hydroclimate regions, with the best agreement to RS datasets being shown in semi-arid and arid regions for most variables. Conversely, the largest differences between modeled and RS datasets (e.g., for SM, SWE, and TWS) are shown in humid and cold regions. Our findings highlight the importance of including multiple variables using both local-scale and large-scale RS datasets in model evaluations for a better understanding of physically based fully distributed hydrologic model performance and uncertainties in water and energy fluxes over continental scales and across different hydroclimate regions. The large-scale, high-resolution setup also forms a basis for future studies and provides an evaluation reference for climate change impact projections and a climatology for hydrological forecasting considering the effects of lateral surface and groundwater flows.

Assessing the Future Water and Energy Security of a Regulated River Basin with a Coupled Land Surface and Hydrologic Model

To address the water-related issues faced by humans, the planning and construction of dams, water diversion projects, and other water infrastructures have been continuously adopted by decision makers worldwide. This is especially the case for the Yalong River Basin (YRB) in China, which is expected to be one of the most regulated rivers due to reservoir construction and the planned South-to-North Water Diversion project. To understand the potential impact of these water infrastructures on the water resources and hydropower production of the basin and downstream areas, we employ a land surface–hydrologic model with explicit representations of dam operation and water diversions in order to quantify the impact of reservoir operation and water diversion on the future water and energy security of the YRB. In particular, a conceptual reservoir operation scheme and a hydropower-optimized reservoir operation scheme are employed to predict the future release, storage and hydropower generation of the YRB, respectively. Results indicate that reservoirs can have noticeable, cumulative effects in enhancing the water security by reducing the wet season streamflow by 19% and increasing the dry season streamflow by 66%. The water diversion can result in an overall decrease in the streamflow, while the downstream reservoirs are expected to fully mitigate the decline in the dry season streamflow. The hydropower production is likely to decrease by 16% and 10% with conventional and optimized operation schemes, respectively, which suggests that the adaptation of operation rules alone cannot reverse the decline in the electricity production. Our findings can provide implications for sustainable water resource management.

Implementation and sensitivity analysis of the Dam-Reservoir OPeration model (DROP v1.0) over Spain

Abstract. The prediction of water resource evolution is considered to be a major challenge for the coming century, particularly in the context of climate change and increasing demographic pressure. Water resources are directly linked to the continental water cycle, and the main processes modulating changes can be represented by global hydrological models. However, anthropogenic impacts on water resources, and in particular the effects of dams-reservoirs on river flows, are still poorly known and generally neglected in coupled land surface–river routing models. This paper presents a parameterized reservoir model, DROP (Dam-Reservoir OPeration), based on Hanasaki's scheme to compute monthly releases given inflows, water demands and the management purpose. With its significantly anthropized river basins, Spain has been chosen as a study case for which simulated outflows and water storage variations are evaluated against in situ observations over the period 1979–2014. Using a default configuration of the reservoir model, results reveal its positive contribution in representing the seasonal cycle of discharge and storage variation, specifically for large-storage capacity irrigation reservoirs. Based on a bounded version of the Nash–Sutcliffe efficiency (NSE) index, called C2M, the overall outflow representation is improved by 43 % in the median. For irrigation reservoirs, the improvement rate reaches 80 %. A comprehensive sensitivity analysis of DROP model parameters was conducted based on the performance of C2M on outflows and volumes using the Sobol method. The results show that the most influential parameter is the threshold coefficient describing the demand-controlled release level. The analysis also reveals the parameters that need to be focused on in order to improve river flow or reservoir water storage modeling by highlighting the difference in the individual effects of the parameters and their interactions depending on whether one focuses on outflows or volume mean seasonal patterns. The results of this generic reservoir scheme show promise for modeling present and future reservoir impacts on the continental hydrology within global land surface–river routing models.

Contribution of summer net precipitation to winter river discharge in permafrost zone of the Lena River basin

Winter discharge of the Lena River (Russia) has increased over the previous several decades. However, the impact of permafrost thawing and of changing hydrological processes induced by climate change on the river’s winter discharge is not well-quantified. Here, using a coupled land surface model and a distributed discharge model, we conducted trend analyses to examine the sensitivity of winter discharge to permafrost thawing and water budget change in the Lena River basin during 1979–2016. An increasing trend of winter baseflow was found in upper parts of both the Lena River basin and the Aldan River basin, where summer net precipitation showed a statistically significant increase. The increased summer net precipitation resulted in higher soil moisture in the deepened active layer in late summer and early autumn, which was linked to autumn and winter baseflow. These implications were examined from the perspective of interrelations among the trends of active layer thickness, soil moisture, and baseflow in the cold season by identifying regions in which all the variables exhibited positive trends. The identified source regions were primarily in the lower Lena River basin and upper basins of the Lena and Aldan rivers, although winter baseflow was more dominant in the latter regions owing to the freezing effect of the active layer. Thinning of river ice induced by warming temperatures also contributed to the increase of winter river discharge. These results suggest that the increased winter discharge was strongly associated with climate-change-related enhancement of permafrost thawing and increase in net precipitation that affected soil hydrological processes, which will be strengthened further in the context of global warming.

Assessing Climate Change Impact on Water Resources in Water Demand Scenarios Using SWAT-MODFLOW-WEAP

In this article, we present the use of the coupled land surface model and groundwater flow model SWAT-MODFLOW with the decision support tool WEAP (Water Evaluation and Planning software) to predict future surface-water abstraction scenarios in a complex river basin under conditions of climate change. The modelling framework is applied to the Dee River catchment in Wales, United Kingdom. Regarding hydrology, the coupled model improves overall water balance and low-streamflow conditions compared with a stand-alone SWAT model. The calibrated SWAT-MODFLOW is employed with high-resolution climate model data from the UKCP18 project with the future scenario of RCP85 from 2020 to 2040. Then, water supply results from SWAT-MODFLOW are fed into WEAP as input for the river reach in the downstream region of the river basin. This system is utilized to create various future scenarios of the surface-water abstraction of public water supply in the downstream region—maximum licensed withdraw, 50% authorized abstractions, monthly time series with 1% increases in water use, and maximum water withdraw per year based on historical records repeated every year with 1% increases in water use—to estimate the unmet demands and streamflow requirement. This modelling approach can be used in other river basins to manage scenarios of supply and demand.

Climate projections at a convection-permitting scale of extreme temperature indices for an archipelago with a complex microclimate structure

In island systems with complex orography (e.g. Canary Islands), obtaining projections of climate extremes throughout the 21st century is necessary to evaluate the possible adverse effects of climate change. In this work, a dynamic downscaling methodology was applied to obtain the projections of temperature extremes indices. The WRF modeling system was properly configured with a spatial resolution of 3 km, for the periods: 2030–2059 (MID) and 2070–2099 (END), and for the RCPs 4.5 and 8.5 scenarios. This spatial-temporal resolution allows better modeling of the land-surface coupling processes (e.g., latent and sensible heat fluxes), which are one of the main sources of uncertainties in temperature extremes modeling. The initial and boundary conditions were defined by three CMIP5 Earth Systems Models: GFDL-ESM2M, MIROC-ESM, and IPSL-CM5. The future changes were calculated against the modeled reference period was 1980–2009 (HIS). The selected extremes indices were those defined by the Team of Experts on Climate Change Detection and Indices (ETCCDI) and were: monthly absolute maximum and minimum temperature respectively (TX and TN), monthly maximum of the diurnal temperature range (DTR), tropical nights (TR), warm days (TX90P), cold nights (TN10P), warm-spell duration index (WSDI) and cold-spell duration index (CSDI). Also, the return levels and return periods for annual maximum temperature were analyzed using the Generalized Extreme Value distribution (GEV).The modeled indices were compared with those obtained from observations at nine ground-based stations for the HIS period. Despite the high spatial and temporal resolution of the models a bias is still observed between the modeled and observational values for the absolute indices, even when the simulations are driven by reanalysis data. However, the comparison of these indices around their previously unbiased means yields values on average of 0.85 with a standard deviation of 0.06 on a Perkins-based skill score. Regarding the 20-year return levels for maximum temperature, differences between the average of the models and observations are below 2 °C for all sites, except for the highest stations IZO and TFN, which reach 2.9 and 4.2 °C, respectively.The analyses of the results indicate that the future projections of the indices obtained using any of the models remain constant from the mid-century to the end of the century for the RCP45 whereas they continue to increase if the RCP85 is considered. This finding shows that all models closely follow the variation in the CO2-equivalent concentrations used as input. Thus, TX and TN are expected to increase, with an average change for the END period and RCP8.5 of 4.0 ± 0.5 °C and 4.4 ± 0.4 °C for TN. TX90p increases considerably (30 percentage points), and the TN10p index will be close to zero. The increase in temperatures is mainly due to, in addition to the modification of the synoptic patterns, a decrease in total cloud cover and soil moisture.This decrease in soil moisture has a direct effect on the decrease in latent heat flux and an increase in sensible heat flux, associated with a projected increase of DTR. Also, the 20-year return levels for maximum temperature obtained for the HIS period will correspond to return periods between 1 and 6 years at the END period and RCP8.5.

Lateral terrestrial water flow contribution to summer precipitation at continental scale – A comparison between Europe and West Africa with WRF‐Hydro‐tag ensembles

AbstractIt is well accepted that summer precipitation can be altered by soil moisture condition. Coupled land surface – atmospheric models have been routinely used to quantify soil moisture – precipitation feedback processes. However, most of the land surface models (LSMs) assume a vertical soil water transport and neglect lateral terrestrial water flow at the surface and in the subsurface, which potentially reduces the realism of the simulated soil moisture – precipitation feedback. In this study, the contribution of lateral terrestrial water flow to summer precipitation is assessed in two different climatic regions, Europe and West Africa, for the period June–September 2008. A version of the coupled atmospheric‐hydrological model WRF‐Hydro with an option to tag and trace land surface evaporation in the modelled atmosphere, named WRF‐Hydro‐tag, is employed. An ensemble of 30 simulations with terrestrial routing and 30 simulations without terrestrial routing is generated with random realizations of turbulent energy with the stochastic kinetic energy backscatter scheme, for both Europe and West Africa. The ensemble size allows to extract random noise from continental‐scale averaged modelled precipitation. It is found that lateral terrestrial water flow increases the relative contribution of land surface evaporation to precipitation by 3.6% in Europe and 5.6% in West Africa, which enhances a positive soil moisture – precipitation feedback and generates more uncertainty in modelled precipitation, as diagnosed by a slight increase in normalized ensemble spread. This study demonstrates the small but non‐negligible contribution of lateral terrestrial water flow to precipitation at continental scale.

Temporal Changes in Land Surface Coupling Strength: An Example in a Semi-Arid Region of Australia

AbstractOne of the critical components in understanding the climate system is the interaction between the land and the atmosphere. Whereas previous studies on land–atmosphere coupling mostly focus on its spatial hotspots, we explore the temporal evolution of land surface coupling strength (LCS) during a large-scale flood event in a semiarid region in northern Australia. The LCS indicates the relationship between soil moisture and latent heat flux, and the spatiotemporal variability in precipitation and soil water strongly affects the variability of LCS. The LCS is usually positive in the semiarid climate, where evapotranspiration (ET) occurs under the soil moisture–limited regime and thus increases with soil moisture. However, our analyses of combined land surface modeling and observational datasets show high temporal variability of LCS in the course of the extreme flood event followed by a drying period. The wet regions transferred the ET regime from the soil moisture–limited to the transition section, weakening the linear growth of ET with soil moisture, which resulted in the decline of LCS. The LCS remained weak until the flood retreated and the soil water approached the prestorm average state. Such temporal variation of the LCS has important implications for realistic parameterization of the land–atmosphere coupling and consequently improving subseasonal to seasonal climate forecast.

Global Irrigation Characteristics and Effects Simulated by Fully Coupled Land Surface, River, and Water Management Models in E3SM

AbstractIrrigation supports agricultural production, but widespread use of irrigation can perturb the regional and global water cycle. The one‐way coupled irrigation scheme used in some land surface models and Earth system models assumes that surface water demand is always met and ignores the surface water constraints, leading to overestimation of surface water usage, underestimation of groundwater pumping, and unrealistic simulation of their seasonal variability. To better represent the irrigation processes, a two‐way coupled irrigation scheme is developed within the Energy Exascale Earth System Model (E3SM). The new irrigation scheme simulates irrigation water demand and applies irrigation water in E3SM Land Model (ELM), which is coupled to a river routing model and a water management model (MOSART‐WM) that simulate streamflow, reservoir operations, and irrigation water supply. With two‐way coupling, surface water irrigation is constrained by the available runoff, streamflow, and reservoir storage. Simulations were performed for 1979–2008 at 0.5° spatial resolution to estimate irrigation surface water and groundwater use and their seasonality in global and large river basin scales. Compared to one‐way coupling, the two‐way coupling scheme (1) estimates less surface water withdrawal and less return flow due to the surface water constraint; (2) better represents groundwater recharge and groundwater level decline at global scale; and (3) is able to capture the seasonal dynamics of irrigation water allocations which reflect the local water conditions. The new development is an important step to more realistically account for the interactions between human water use and the terrestrial water cycle in an Earth system model.

Groundwater influence on soil moisture memory and land–atmosphere fluxes in the Iberian Peninsula

Abstract. Groundwater plays an important role in the terrestrial water cycle, interacting with the land surface via vertical fluxes through the water table and distributing water resources spatially via gravity-driven lateral transport. It is therefore essential to have a correct representation of groundwater processes in land surface models, as land–atmosphere coupling is a key factor in climate research. Here we use the LEAFHYDRO land surface and groundwater model to study the groundwater influence on soil moisture distribution and memory, and evapotranspiration (ET) fluxes in the Iberian Peninsula over a 10-year period. We validate our results with time series of observed water table depth from 623 stations covering different regions of the Iberian Peninsula, showing that the model produces a realistic water table, shallower in valleys and deeper under hilltops. We find patterns of shallow water table and strong groundwater–land surface coupling over extended interior semi-arid regions and river valleys. We show a strong seasonal and interannual persistence of the water table, which induces bimodal memory in the soil moisture fields; soil moisture “remembers” past wet conditions, buffering drought effects, and also past dry conditions, causing a delay in drought recovery. The effects on land–atmosphere fluxes are found to be significant: on average over the region, ET is 17.4 % higher when compared with a baseline simulation with LEAFHYDRO's groundwater scheme deactivated. The maximum ET increase occurs in summer (34.9 %; 0.54 mm d−1). The ET enhancement is larger over the drier southern basins, where ET is water limited (e.g. the Guadalquivir basin and the Mediterranean Segura basin), than in the northern Miño/Minho basin, where ET is more energy limited than water limited. In terms of river flow, we show how dry season baseflow is sustained by groundwater originating from accumulated recharge during the wet season, improving significantly on a free-drain approach, where baseflow comes from water draining through the top soil, resulting in rivers drying out in summer. Convective precipitation enhancement through local moisture recycling over the semi-arid interior regions and summer cooling are potential implications of these groundwater effects on climate over the Iberian Peninsula. Fully coupled land surface and climate model simulations are needed to elucidate this question.

Hydrological impact of a reservoir network in the upper Gan River Basin, China

AbstractA few relatively large reservoirs, hundreds of small reservoirs, and numerous farm dams were built in the upper Gan River Basin, China. The operation of such a reservoir network can serve as a significant source of variability in the local hydrological regime and should be included in research to better understand the interaction between multiple hydrological processes and watershed management. In this study, a reservoir network module that included reservoirs of multiple sizes was developed and fully integrated into the coupled land surface and distributed hydrologic model system (CLHMS), for a detailed description of the hydrological impact of a reservoir network. A generalized release scheme was employed to determine the outflow of both large and small reservoirs. The integrated model was then evaluated against observations and reanalysis data, which indicate that the model can reasonably reconstruct the reservoir operation, streamflow, and other hydrological variables. Results quantitatively demonstrate that a reservoir network can result in an increased streamflow in dry seasons, a decreased streamflow in wet seasons, a generally larger groundwater discharge, higher groundwater level, a slightly damper soil condition, and a larger amount of evapotranspiration at the basin level. With the integrated model, it is feasible to achieve more sustainable watershed planning and management.

Biogenic emissions and land–atmosphere interactions as drivers of the daytime evolution of secondary organic aerosol in the southeastern US

Abstract. The interactions between biogenic volatile organic compounds (BVOCs), like isoprene and monoterpenes, and anthropogenic emissions of nitrogen and sulfur oxides lead to high concentrations of secondary organic aerosol (SOA) in the southeastern United States. To improve our understanding of SOA formation, we study the diurnal evolution of SOA in a land–atmosphere coupling context based on comprehensive surface and upper air observations from a characteristic day during the 2013 Southern Oxidant and Aerosol Study (SOAS) campaign. We use a mixed layer model (MXLCH-SOA) that is updated with new chemical pathways and an interactive land surface scheme that describes both biogeochemical and biogeophysical couplings between the land surface and the atmospheric boundary layer (ABL) to gain insight into the drivers of the daytime evolution of biogenic SOA. MXLCH-SOA reproduces observed BVOC and surface heat fluxes, gas-phase chemistry, and ABL dynamics well, with the exception of isoprene and monoterpene mixing ratios measured close to the land surface. This is likely due to the fact that these species do not have uniform profiles throughout the atmospheric surface layer due to their fast reaction with OH and incomplete mixing near the surface. The flat daytime evolution of the SOA concentration is caused by the dampening of the increase due to locally formed SOA by entrainment of SOA-depleted air from the residual layer. SOA formation from isoprene through the intermediate species isoprene epoxydiols (IEPOXs) and isoprene hydroxyhydroperoxides (ISOPOOHs) is in good agreement with the observations, with a mean isoprene SOA yield of 1.8 %. However, SOA from monoterpenes, oxidised by OH and O3, dominates the locally produced SOA (69 %), with a mean monoterpene SOA yield of 10.7 %. Isoprene SOA is produced primarily through OH oxidation via ISOPOOH and IEPOX (31 %). Entrainment of aged SOA from the residual layer likely contributes to the observed more oxidised oxygenated organic aerosol (MO-OOA) factor. A sensitivity analysis of the coupled land surface–boundary layer–SOA formation system to changing temperatures reveals that SOA concentrations are buffered under increasing temperatures: a rise in BVOC emissions is offset by decreases in OH concentrations and the efficiency with which SVOCs partition into the aerosol phase.

A Coupled Land Surface and Radiative Transfer Models Based on Relief Correction for a Reliable Land Data Assimilation Over Mountainous Terrain

This letter presents a new approach to incorporate topographic relief effect in land data assimilation system over mountainous terrain. The conventional radiative transfer model (RTM) used for assimilation of microwave brightness temperature (Tb) is subjected to systematic bias owing to its flat earth model assumption. This is theoretically important for direct Tb assimilation system since a difference between simulated and observed Tb may disarray the assimilation system. Here, we consider three crucial relief effects, namely: 1) change in local incidence angle; 2) rotation of the plane of polarization; and 3) effect of pixels in shadow not visible to radiometer. Results indicate that the RTM formulation with relief effects (Topo) significantly reduce the bias as compared to the conventional RTM (Flat). The simulated Tb for the Topo case shows improved sensitivity toward soil moisture as compared to the Flat case. The proposed land surface model (LSM)–RTM assimilation framework has an immense potential source for initialization of LSM for seasonal climate prediction over mountainous terrain.

Quantitative analysis of the water-energy-climate nexus in Shanxi Province, China

Water and energy security are major challenges for sustainable development in China. This study focuses on the local impact of future climate change and human activities on water resources in Shanxi Province, which contains the largest coal energy base in China. Climate change and water resource variations from 2010‒2050 are investigated using the Coupled Land surface and Hydrology Model System, applying three different IPCC AR5 emission scenarios. The capacity of water resources to support mid-21st century development is discussed with reference to current national policy. The water demand of Shanxi’s energy bases will continue to grow as resource pressure intensifies.

A coupled atmospheric and topographic correction algorithm for remotely sensed satellite imagery over mountainous terrain

Radiometric correction is an important issue in the quantitative remote-sensing community. By integrating dark object subtraction (DOS)-based atmospheric correction with physics-based topographic correction, a coupled land surface reflectance retrieval algorithm (coupled atmospheric and topographic correction algorithm, named the CAT algorithm) for rugged mountainous regions is proposed. Terra MODIS-derived atmospheric characterization data (including aerosol optical depth, integrated precipitable water, surface pressure, and ozone concentration) are employed as inputs for the proposed algorithm. A physics-based path radiance estimation model is proposed and embedded in the CAT algorithm, and band-specific per-pixel path radiance values are calculated. After the CAT algorithm was performed, the correlation between reflectance and terrain was dramatically reduced, with correlation coefficients nearly equal zero, especially for the near infrared and short-wave infrared bands, meanwhile the image information content increased over 20%. To provide a comparison with previous studies, two commonly used methods in the literature (DOS + Cosine and DOS + C) were employed. The results of the comparison show that the proposed algorithm performed better in both atmospheric and topographic corrections without empirical regression.

Evaluating the performance of coupled snow–soil models in SURFEXv8 to simulate the permafrost thermal regime at a high Arctic site

Abstract. Climate change projections still suffer from a limited representation of the permafrost–carbon feedback. Predicting the response of permafrost temperature to climate change requires accurate simulations of Arctic snow and soil properties. This study assesses the capacity of the coupled land surface and snow models ISBA-Crocus and ISBA-ES to simulate snow and soil properties at Bylot Island, a high Arctic site. Field measurements complemented with ERA-Interim reanalyses were used to drive the models and to evaluate simulation outputs. Snow height, density, temperature, thermal conductivity and thermal insulance are examined to determine the critical variables involved in the soil and snow thermal regime. Simulated soil properties are compared to measurements of thermal conductivity, temperature and water content. The simulated snow density profiles are unrealistic, which is most likely caused by the lack of representation in snow models of the upward water vapor fluxes generated by the strong temperature gradients within the snowpack. The resulting vertical profiles of thermal conductivity are inverted compared to observations, with high simulated values at the bottom of the snowpack. Still, ISBA-Crocus manages to successfully simulate the soil temperature in winter. Results are satisfactory in summer, but the temperature of the top soil could be better reproduced by adequately representing surface organic layers, i.e., mosses and litter, and in particular their water retention capacity. Transition periods (soil freezing and thawing) are the least well reproduced because the high basal snow thermal conductivity induces an excessively rapid heat transfer between the soil and the snow in simulations. Hence, global climate models should carefully consider Arctic snow thermal properties, and especially the thermal conductivity of the basal snow layer, to perform accurate predictions of the permafrost evolution under climate change.

Coupled land surface–subsurface hydrogeophysical inverse modeling to estimate soil organic carbon content and explore associated hydrological and thermal dynamics in the Arctic tundra

Abstract. Quantitative characterization of soil organic carbon (OC) content is essential due to its significant impacts on surface–subsurface hydrological–thermal processes and microbial decomposition of OC, which both in turn are important for predicting carbon–climate feedbacks. While such quantification is particularly important in the vulnerable organic-rich Arctic region, it is challenging to achieve due to the general limitations of conventional core sampling and analysis methods, and to the extremely dynamic nature of hydrological–thermal processes associated with annual freeze–thaw events. In this study, we develop and test an inversion scheme that can flexibly use single or multiple datasets – including soil liquid water content, temperature and electrical resistivity tomography (ERT) data – to estimate the vertical distribution of OC content. Our approach relies on the fact that OC content strongly influences soil hydrological–thermal parameters and, therefore, indirectly controls the spatiotemporal dynamics of soil liquid water content, temperature and their correlated electrical resistivity. We employ the Community Land Model to simulate nonisothermal surface–subsurface hydrological dynamics from the bedrock to the top of canopy, with consideration of land surface processes (e.g., solar radiation balance, evapotranspiration, snow accumulation and melting) and ice–liquid water phase transitions. For inversion, we combine a deterministic and an adaptive Markov chain Monte Carlo (MCMC) optimization algorithm to estimate a posteriori distributions of desired model parameters. For hydrological–thermal-to-geophysical variable transformation, the simulated subsurface temperature, liquid water content and ice content are explicitly linked to soil electrical resistivity via petrophysical and geophysical models. We validate the developed scheme using different numerical experiments and evaluate the influence of measurement errors and benefit of joint inversion on the estimation of OC and other parameters. We also quantify the propagation of uncertainty from the estimated parameters to prediction of hydrological–thermal responses. We find that, compared to inversion of single dataset (temperature, liquid water content or apparent resistivity), joint inversion of these datasets significantly reduces parameter uncertainty. We find that the joint inversion approach is able to estimate OC and sand content within the shallow active layer (top 0.3 m of soil) with high reliability. Due to the small variations of temperature and moisture within the shallow permafrost (here at about 0.6 m depth), the approach is unable to estimate OC with confidence. However, if the soil porosity is functionally related to the OC and mineral content, which is often observed in organic-rich Arctic soil, the uncertainty of OC estimate at this depth remarkably decreases. Our study documents the value of the new surface–subsurface, deterministic–stochastic inversion approach, as well as the benefit of including multiple types of data to estimate OC and associated hydrological–thermal dynamics.