High-resolution Land Surface Model Research Articles

Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain

Abstract. Widespread afforestation has been proposed internationally to reduce atmospheric carbon dioxide; however, the specific hydrological consequences and benefits of such large-scale afforestation (e.g. natural flood management) are poorly understood. We use a high-resolution land surface model, the Joint UK Land Environment Simulator (JULES), with realistic potential afforestation scenarios to quantify possible hydrological change across Great Britain in both present and projected climate. We assess whether proposed afforestation produces significantly different regional responses across regions; whether hydrological fluxes, stores and events are significantly altered by afforestation relative to climate; and how future hydrological processes may be altered up to 2050. Additionally, this enables determination of the relative sensitivity of land surface process representation in JULES compared to climate changes. For these three aims we run simulations using (i) past climate with proposed land cover changes and known floods and drought events; (ii) past climate with independent changes in precipitation, temperature, and CO2; and (iii) a potential future climate (2020–2050). We find the proposed scale of afforestation is unlikely to significantly alter regional hydrology; however, it can noticeably decrease low flows whilst not reducing high flows. The afforestation levels minimally impact hydrological processes compared to changes in precipitation, temperature, and CO2. Warming average temperatures (+3 °C) decreases streamflow, while rising precipitation (130 %) and CO2 (600 ppm) increase streamflow. Changes in high flow are generated because of evaporative parameterizations, whereas low flows are controlled by runoff model parameterizations. In this study, land surface parameters within a land surface model do not substantially alter hydrological processes when compared to climate.

High-resolution land surface modeling of the irrigation effects on evapotranspiration over the Yellow River basin

Accurate estimation of irrigation-induced evapotranspiration (ET) is critical for understanding the terrestrial water cycle. However, current estimation has large uncertainties due to the model limitations and the lack of accurate water use data. In this study, a water-energy balanced irrigation scheme is incorporated into the Conjunctive Surface-Subsurface Process version 2 (CSSPv2) model to improve the hydrological simulations over the Yellow River basin (YRB) where irrigation is extensive. The coupled model (CSSPv2-IRR) is calibrated against long-term water use data from provincial survey, and it is used to perform a 6-km resolution simulation over the YRB during 1991–2017. The results show that the coupled model can well reproduce hydrological processes within the basin, and the simulated irrigation amount, ET and surface soil moisture largely agree with the validation datasets. Specifically, the annual irrigation amount and irrigation-induced ET changes from the CSSPv2-IRR are more reasonable than ISIMIP2a and reanalysis products, with a reduction in bias by 79.5–97.3 % and 81.2–98.8 % respectively. The CSSPv2-IRR model simulation suggests that irrigation-induced annual ET change is 26.4 mm/year during 1991–2017, contributing to an 8.9 % increase in annual ET over the YRB, and the increase can reach 44.3 % over major irrigation districts. In addition, the contribution of irrigation to long-term increasing trend in ET over the irrigation districts in the upper reach is greater than the contribution of climate change. This study suggests a growing control of irrigation on the ET over the YRB, which has implications for regional water cycle and water resources management.

A Long-Term Simulation of Land Surface Conditions at High Resolution over Continental China

Abstract With the improvement of meteorological forcings and surface parameters, high-resolution land surface modeling is expected to provide locally relevant information. Yet, its added value over the state-of-the-art global reanalysis products requires long-term evaluations over large areas, given uneven climate warming and significant land cover change. Here, the Conjunctive Surface–Subsurface Process version 2 (CSSPv2) model, with a reasonable representation of runoff generation, subgrid soil moisture variability and urban dynamics, is calibrated and used to perform a 6-km resolution simulation over China during 1979–2017. Evaluations against observations at thousands of stations and several satellite-based products show that the CSSPv2 has 67%, 29%, and 15% lower simulation errors for snow depth, evapotranspiration (ET), and surface and root-zone soil moisture, respectively, than nine global products. The median Kling–Gupta efficiency of the streamflow for 83 river basins is 0.66 after bulk calibrations, which is 0.38 higher than that of global datasets. The CSSPv2 also accurately simulates urban heat islands (UHIs) and the patterns and magnitudes of long-term snow depth, ET, and soil moisture trends. However, the global products do not detect UHIs and overestimate the trends (or show opposite trends) of snow depth and ET. Sensitivity experiments with coarse-resolution forcings and surface parameters reveal that advanced model physics and high-resolution surface parameters are vital for improved simulations of snow depth, ET, soil moisture, and UHIs, whereas high-resolution meteorological forcings are critical for modeling long-term trends. Our research emphasizes the substantial added value of long-term high-resolution land surface modeling to present global products at continental scales. Significance Statement Highly heterogeneous changes of terrestrial water and energy require kilometer-scale land surface information for the adaptation. High-resolution land surface modeling has been regarded as a promising approach to provide locally relevant information, but most applications are limited to a small region or a short period. By performing sets of 6-km resolution simulations over China during 1979–2017 with the Conjunctive Surface–Subsurface Process version 2 land model, here we show that high-resolution modeling has 15%–67% lower simulation errors of snow depth, streamflow, evapotranspiration, and soil moisture than nine global products, and the improvement is mainly attributed to the advances in model physical parameterizations and high-resolution surface parameters. Our results emphasize the great added value of kilometer-scale land surface modeling at continental scales.

Estimating soil moisture conditions for drought monitoring with random forests and a simple soil moisture accounting scheme

Abstract. Soil moisture is a key variable for drought monitoring, but soil moisture measurements networks are very scarce. Land-surface models can provide a valuable alternative for simulating soil moisture dynamics, but only a few countries have such modelling schemes implemented for monitoring soil moisture at high spatial resolution. In this study, a soil moisture accounting model (SMA) was regionalized over the Iberian Peninsula, taking as a reference the soil moisture simulated by a high-resolution land-surface model. To estimate the soil water holding capacity, the sole parameter required to run the SMA model, two approaches were compared: the direct estimation from European soil maps using pedotransfer functions or an indirect estimation by a machine learning approach, random forests, using as predictors altitude, temperature, precipitation, potential evapotranspiration and land use. Results showed that the random forest model estimates are more robust, especially for estimating low soil moisture levels. Consequently, the proposed approach can provide an efficient way to simulate daily soil moisture and therefore monitor soil moisture droughts, in contexts where high-resolution soil maps are not available, as it relies on a set of covariates that can be reliably estimated from global databases.

Emulation of high-resolution land surface models using sparse Gaussian processes with application to JULES

Abstract. Land surface models are typically integrated into global climate projections, but as their spatial resolution increases the prospect of using them to aid in local policy decisions becomes more appealing. If these complex models are to be used to make local decisions, then a full quantification of uncertainty is necessary, but the computational cost of running just one full simulation at high resolution can hinder proper analysis. Statistical emulation is an increasingly common technique for developing fast approximate models in a way that maintains accuracy but also provides comprehensive uncertainty bounds for the approximation. In this work, we developed a statistical emulation framework for land surface models, enabling fast predictions at a high resolution. To do so, our emulation framework acknowledges, and makes use of, the multitude of contextual data that are often fed into land surface models (sometimes called forcing data, or driving data), such as air temperature or various soil properties. We use The Joint UK Land Environment Simulator (JULES) as a case study for this methodology, and perform initial sensitivity analysis and parameter tuning to showcase its capabilities. The JULES is perhaps one of the most complex land surface models and so our success here suggests incredible gains can be made for all types of land surface model.

Hydrological impact of widespread afforestation in Great Britain using a large ensemble of modelled scenarios

Ambitious afforestation proposals in the last decade target potential flood mitigation and carbon storage benefits but without a systematic, large-scale (>1000 km2) quantitative evaluation of their impacts on streamflow. Here, we assess the impact of afforestation on streamflow across twelve diverse catchments (c.500-10,000 km2) using a high-resolution land-surface model with a large ensemble of afforestation scenarios. Afforestation consistently decreases median and low streamflow. Median modelled flow is reduced by 2.8% ± 1.0 (1 s.d.), or 10 mm yr−1 ± 2.1 (1 s.d.), for a ten-percentage point increase in catchment broadleaf woodland. We find no nationally-consistent reduction of extreme floods. In larger catchments, planting extent is a stronger control on streamflow than location. Our results suggest that despite its potential environmental and societal benefits, widespread afforestation may inadvertently reduce water availability, particularly in drier areas, whilst only providing a modest reduction in extreme flood flows.

Global estimates of 500 m daily aerodynamic roughness length from MODIS data

Aerodynamic roughness length (z0m) is a key parameter in the characterization of land surface turbulent heat fluxes and widely used in many surface and climate-related process models. The global products of time series of z0m at finer spatio-temporal resolution, however, have never been publicly available. Here we presented a practical method for global estimates of 500 m daily z0m with a combination of machine learning techniques, wind profile equation, observations from 273 sites and MODIS remote sensing data. Results showed that the random forest (RF) model outperformed the deep neural network (DNN) and convolutional neural network (CNN) models, and it could well reproduce the magnitude and temporal variability of daily z0m at almost all sites for all land cover types. In the validation of the RF-estimated daily z0m with the in-situ observations, the root mean square error (RMSE) varied between 0.02 m and 0.09 m, the mean absolute error (MAE) varied between 0.01 m and 0.05 m and the coefficient of determination (R2) was 1 for medium-to-high canopy shrublands, savannas and forests; for short-canopy croplands, grasslands and wetlands, the RMSE and MAE were 0.02 m and 0.01 m, respectively, and the R2 varied between 0.94 and 1. Compared to the Climate Forecast System Version 2 (CFSv2, 0.3°/monthly) and ECMWF Reanalysis v5 (ERA5, 0.25°/monthly) products in 2019, the RF-estimated z0m was found to have the similar global spatial pattern but significantly larger temporal variability, and it also showed a higher and lower global mean of z0m over forests and non-forests, respectively. The RF-estimated z0m displayed a higher temporal variability but a similar global spatial pattern of this variability compared to the CFSv2, whereas the ERA5 z0m product exhibited almost no temporal variability except for grasslands and croplands. This study is beneficial for improving the simulation of the momentum, water and energy transfer between land and atmosphere and helping boost the development of high-resolution land surface models and Earth system models.

SMAP-HydroBlocks, a 30-m satellite-based soil moisture dataset for the conterminous US

Soil moisture plays a key role in controlling land-atmosphere interactions, with implications for water resources, agriculture, climate, and ecosystem dynamics. Although soil moisture varies strongly across the landscape, current monitoring capabilities are limited to coarse-scale satellite retrievals and a few regional in-situ networks. Here, we introduce SMAP-HydroBlocks (SMAP-HB), a high-resolution satellite-based surface soil moisture dataset at an unprecedented 30-m resolution (2015–2019) across the conterminous United States. SMAP-HB was produced by using a scalable cluster-based merging scheme that combines high-resolution land surface modeling, radiative transfer modeling, machine learning, SMAP satellite microwave data, and in-situ observations. We evaluated the resulting dataset over 1,192 observational sites. SMAP-HB performed substantially better than the current state-of-the-art SMAP products, showing a median temporal correlation of 0.73 ± 0.13 and a median Kling-Gupta Efficiency of 0.52 ± 0.20. The largest benefit of SMAP-HB is, however, the high spatial detail and improved representation of the soil moisture spatial variability and spatial accuracy with respect to SMAP products. The SMAP-HB dataset is available via zenodo and at https://waterai.earth/smaphb.

Improving soil moisture prediction of a high-resolution land surface model by parameterising pedotransfer functions through assimilation of SMAP satellite data

Abstract. Pedotransfer functions are used to relate gridded databases of soil texture information to the soil hydraulic and thermal parameters of land surface models. The parameters within these pedotransfer functions are uncertain and calibrated through analyses of point soil samples. How these calibrations relate to the soil parameters at the spatial scale of modern land surface models is unclear because gridded databases of soil texture represent an area average. We present a novel approach for calibrating such pedotransfer functions to improve land surface model soil moisture prediction by using observations from the Soil Moisture Active Passive (SMAP) satellite mission within a data assimilation framework. Unlike traditional calibration procedures, data assimilation always takes into account the relative uncertainties given to both model and observed estimates to find a maximum likelihood estimate. After performing the calibration procedure, we find improved estimates of soil moisture and heat flux for the Joint UK Land Environment Simulator (JULES) land surface model (run at a 1 km resolution) when compared to estimates from a cosmic-ray soil moisture monitoring network (COSMOS-UK) and three flux tower sites. The spatial resolution of the COSMOS probes is much more representative of the 1 km model grid than traditional point-based soil moisture sensors. For 11 cosmic-ray neutron soil moisture probes located across the modelled domain, we find an average 22 % reduction in root mean squared error, a 16 % reduction in unbiased root mean squared error and a 16 % increase in correlation after using data assimilation techniques to retrieve new pedotransfer function parameters.

Accelerated hydrological cycle over the Sanjiangyuan region induces more streamflow extremes at different global warming levels

Abstract. Serving source water for the Yellow, Yangtze and Lancang-Mekong rivers, the Sanjiangyuan region affects 700 million people over its downstream areas. Recent research suggests that the Sanjiangyuan region will become wetter in a warming future, but future changes of streamflow extremes remain unclear due to the complex hydrological processes over high-land areas and limited knowledge of the influences of land cover change and CO2 physiological forcing. Based on high-resolution land surface modeling during 1979–2100 driven by the climate and ecological projections from 11 newly released Coupled Model Intercomparison Project Phase 6 (CMIP6) climate models, we show that different accelerating rates of precipitation and evapotranspiration at 1.5 ∘C global warming level induce 55 % more dry extremes over Yellow River and 138 % more wet extremes over Yangtze River headwaters compared with the reference period (1985–2014). An additional 0.5 ∘C warming leads to a further nonlinear and more significant increase for both dry extremes over Yellow River (22 %) and wet extremes over Yangtze River (64 %). The combined role of CO2 physiological forcing and vegetation greening, which used to be neglected in hydrological projections, is found to alleviate dry extremes at 1.5 and 2.0 ∘C warming levels but to intensify dry extremes at 3.0 ∘C warming level. Moreover, vegetation greening contributes half of the differences between 1.5 and 3.0 ∘C warming levels. This study emphasizes the importance of ecological processes in determining future changes in streamflow extremes and suggests a “dry gets drier, wet gets wetter” condition over the warming headwaters.

Understanding the Impacts of Soil Moisture Initial Conditions on NWP in the Context of Land–Atmosphere Coupling

Abstract The role of soil moisture in NWP has gained more attention in recent years, as studies have demonstrated impacts of land surface states on ambient weather from diurnal to seasonal scales. However, soil moisture initialization approaches in coupled models remain quite diverse in terms of their complexity and observational roots, while assessment using bulk forecast statistics can be simplistic and misleading. In this study, a suite of soil moisture initialization approaches is used to generate short-term coupled forecasts over the U.S. Southern Great Plains using NASA’s Land Information System (LIS) and NASA Unified WRF (NU-WRF) modeling systems. This includes a wide range of currently used initialization approaches, including soil moisture derived from “off the shelf” products such as atmospheric models and land data assimilation systems, high-resolution land surface model spinups, and satellite-based soil moisture products from SMAP. Results indicate that the spread across initialization approaches can be quite large in terms of soil moisture conditions and spatial resolution, and that SMAP performs well in terms of heterogeneity and temporal dynamics when compared against high-resolution land surface model and in situ soil moisture estimates. Case studies are analyzed using the local land–atmosphere coupling (LoCo) framework that relies on integrated assessment of soil moisture, surface flux, boundary layer, and ambient weather, with results highlighting the critical role of inherent model background biases. In addition, simultaneous assessment of land versus atmospheric initial conditions in an integrated, process-level fashion can help address the question of whether improvements in traditional NWP verification statistics are achieved for the right reasons.

Online dynamical downscaling of temperature and precipitation within the <i>i</i>LOVECLIM model (version 1.1)

Abstract. This paper presents the inclusion of an online dynamical downscaling of temperature and precipitation within the model of intermediate complexity iLOVECLIM v1.1. We describe the following methodology to generate temperature and precipitation fields on a 40 km × 40 km Cartesian grid of the Northern Hemisphere from the T21 native atmospheric model grid. Our scheme is not grid specific and conserves energy and moisture in the same way as the original climate model. We show that we are able to generate a high-resolution field which presents a spatial variability in better agreement with the observations compared to the standard model. Although the large-scale model biases are not corrected, for selected model parameters, the downscaling can induce a better overall performance compared to the standard version on both the high-resolution grid and on the native grid. Foreseen applications of this new model feature include the improvement of ice sheet model coupling and high-resolution land surface models.

High Resolution Land Surface Modeling by the Grid-Based Common Land Model

High Resolution Land Surface Modeling by the Grid-Based Common Land Model

Development of a hybrid method for estimating land surface shortwave net radiation from MODIS data

Accurate estimation of shortwave net radiation ( S n ) at a high spatial resolution is critical for regional and global land surface models. Current surface radiation budget products have fine temporal resolutions, but only coarse spatial resolutions that are not suitable for land applications. A hybrid algorithm was developed in this study to estimate S n from MODIS data under both clear and cloudy sky conditions without requiring coarser resolution ancillary data. This algorithm was validated using ground measurements at seven sites of the SURFace RADiation budget observing network (SURFRAD) in the United States. Instantaneous S n estimated by this method was also compared with GEWEX/SRB and ISCCP data, and other methods. The results indicate that our algorithm can produce S n at 1-km resolution with improved accuracy and is easily implemented to generate operational global products. Daily integrated S n is estimated at 1-km resolution using instantaneous S n . These finer spatial resolution datasets capture the specific sequence of the redistribution of the available energy at the Earth's surface. Therefore, they support recent high resolution land surface models.

Satellite-Scale Snow Water Equivalent Assimilation into a High-Resolution Land Surface Model

Abstract Four methods based on the ensemble Kalman filter (EnKF) are tested to assimilate coarse-scale (25 km) snow water equivalent (SWE) observations (typical of passive microwave satellite retrievals) into finescale (1 km) land model simulations. Synthetic coarse-scale observations are assimilated directly using an observation operator for mapping between the coarse and fine scales or, alternatively, after disaggregation (regridding) to the finescale model resolution prior to data assimilation. In either case, observations are assimilated either simultaneously or independently for each location. Results indicate that assimilating disaggregated finescale observations independently (method 1D-F1) is less efficient than assimilating a collection of neighboring disaggregated observations (method 3D-Fm). Direct assimilation of coarse-scale observations is superior to a priori disaggregation. Independent assimilation of individual coarse-scale observations (method 3D-C1) can bring the overall mean analyzed field close to the truth, but does not necessarily improve estimates of the finescale structure. There is a clear benefit to simultaneously assimilating multiple coarse-scale observations (method 3D-Cm) even as the entire domain is observed, indicating that underlying spatial error correlations can be exploited to improve SWE estimates. Method 3D-Cm avoids artificial transitions at the coarse observation pixel boundaries and can reduce the RMSE by 60% when compared to the open loop in this study.

Multidecadal High-Resolution Hydrologic Modeling of the Arkansas–Red River Basin

Abstract Land surface heterogeneity and its effects on surface processes have been a concern to hydrologists and climate scientists for the past several decades. The contrast between the fine spatial scales at which heterogeneity is significant (1 km and finer) and the coarser scales at which most climate simulations with land surface models are generated (hundreds of kilometers) remains a challenge, especially when incorporating land surface and subsurface lateral fluxes of mass. In this study, long-term observational land surface forcings and derived solar radiation were used to force high-resolution land surface model simulations over the Arkansas–Red River basin in the Southern Great Plains region of the United States. The most unique aspect of these simulations is the fine space (1 km2) and time (hourly) resolutions within the model relative to the total simulation period (51 yr) and domain size (575 000 km2). Runoff simulations were validated at the subbasin scale (600–10 000 km2) and were found to be in good agreement with observed discharge from several unregulated subbasins within the system. A hydroclimatological approach was used to assess simulated annual evapotranspiration for all subbasins. Simulated evapotranspiration values at the subbasin scale agree well with predictions from a simple one-parameter empirical model developed in this study according to Budyko’s concept of “geographical zonality.” The empirical model was further extended to predict runoff and evapotranspiration sensitivity to precipitation variability, and good agreement with computed statistics was also found. Both the empirical model and simulation results demonstrate that precipitation variability was amplified in the simulated runoff. The finescale at which the study is performed allows analysis of various aspects of the hydrologic cycle in the system including general trends in precipitation, runoff, and evapotranspiration, their spatial distribution, and the relationship between precipitation anomalies and runoff and soil water storage anomalies at the subbasin scale.

Land information system: An interoperable framework for high resolution land surface modeling

Knowledge of land surface water, energy, and carbon conditions are of critical importance due to their impact on many real world applications such as agricultural production, water resource management, and flood, weather, and climate prediction. Land Information System (LIS) is a software framework that integrates the use of satellite and ground-based observational data along with advanced land surface models and computing tools to accurately characterize land surface states and fluxes. LIS employs the use of scalable, high performance computing and data management technologies to deal with the computational challenges of high resolution land surface modeling. To make the LIS products transparently available to the end users, LIS includes a number of highly interactive visualization components as well. The LIS components are designed using object-oriented principles, with flexible, adaptable interfaces and modular structures for rapid prototyping and development. In addition, the interoperable features in LIS enable the definition, intercomparison, and validation of land surface modeling standards and the reuse of a high quality land surface modeling and computing system.

An unsaturated soil water flow problem and its numerical simulation

A numerical model for the unsaturated flow equation with moisture content as prognostic variable is established in order to simulate liquid moisture flow in an unsaturated zone with homogeneous soil, and different initial and boundary conditions. For an infiltration or evaporation problem, its numerical solution by using a finite difference method is very sensitive to its upper boundary condition and the related soil parameters, and using a traditional finite element method usually yields oscillatory non-physics profiles. However, we obtain a nonoscillatory solution and evade a non-physics solution for the problem by using the mass-lumped finite element method. This kind of boundary conditions is handled very well. Numerical simulations for certain soils show that the numerical scheme can be used in simulation of liquid moisture flow for infiltration, evaporation, re-distribution and their alternate appearances. It can be also applied to a high-resolution land surface model.