Surface Process Model Research Articles

Quantifying Earth's Topography: Steeper and Larger Than Projected in Digital Terrain Models

AbstractGrid‐ or pixel‐based models, used across various scientific disciplines from microscopic to planetary scales, contain an unquantified error that bias our interpretation of the data. The error is produced by projecting 3D data onto a 2D grid. For Digital Terrain Models (DTMs) the projection error affects all slope‐dependent topographic metrics, like surface area or slope angle. Due to the proportionality of the error to the cosine of the slope, we can correct for it. We quantify the error and test the correction using synthetic landscapes for which we have analytical solutions of their metrics. Application to real‐world landscapes in California, reveal the systematic underestimation of surface area by up to a third, and mean slope angles by up to 10° in steep topography in current DTMs. Correcting projection errors allow for true estimates of surface areas and slope distributions enabling physics‐based models of surface processes at any spatial scale.

Coupled Surface Process and Orographic Precipitation Model for the Landscape Evolution of the Himalayas

Coupled Surface Process and Orographic Precipitation Model for the Landscape Evolution of the Himalayas

Estimating components of the hydrological cycle in the Atlantic Forest Biome using the INLAND surface model

ABSTRACT The Atlantic Forest biome has lost a significant portion of its natural habitat and little research has studied how deforestation impacts the biome’s local water balance. The objective of this research is to understand how the hydrological cycle of the region is affected by the change in land use. For this, the INLAND (Integrated Model of Surface Processes) surface model was calibrated and validated for the biome. Subsequently, changes in precipitation in the biome and the influence of deforestation on the surface components of the hydrological cycle were analyzed. To determine the influence of deforestation on the surface components of the hydrological cycle, two scenarios were simulated and compared, a deforestation scenario and a pasture scenario. The results showed that the total runoff in the deforestation scenario between 1951–1960 was, on average, 16.82% lower than the values found from 1971 to 1980. The use of modeling also made it possible to estimate the behavior of the difference between evapotranspiration and total runoff simulated with the occurrence of deforestation and with the area preserved in its entirety. It was observed that the biome has a certain resilience to deforestation up to a particular point, after which evapotranspiration decreases and the total runoff increases.

Evaluation of Ten Fresh Snow Density Parameterization Schemes for Simulating Snow Depth and Surface Energy Fluxes on the Eastern Tibetan Plateau

Snow cover on the Tibetan Plateau has a shallow depth, plaque distribution, and repeated ablation. The applicability of the snow parameterization scheme in the current land surface process model on the TP needs to be further tested using observational data. In this paper, using the land surface process model CLM4.5 and ten fresh snow density parameterization schemes characterized by temperature, wind speed, and relative humidity, three discontinuous snow processes in Maqu, Madoi, and Yakou and two continuous snow processes in Madoi and Yakou were simulated. By comparing the simulated snow depth with the observed, it was found that this model can clearly describe repeated snow accumulation and ablation processes for the discontinuous snow cover process. The KW scheme, compared with the original Anderson scheme, performed the best regarding snow depth simulation. However, all schemes overestimated the melting rate of snow, and were not able to simulate continuous snow accumulation. The simulation effect of the Schmucki scheme on radiation and energy flux under discontinuous snow cover was significantly improved compared with other scheme. None of schemes performed perfectly, so future studies that focus on the simulations of snow depth, radiation flux, and energy flux under continuous snow cover for accurate and wide applications are recommended.

Sensitivity Analysis of the Land Surface Characteristic Parameters in Different Climatic Regions of the Loess Plateau

Land surface parameters are crucial in land surface process model simulations. Considering the complex land surface characteristics of the Loess Plateau, a parametric sensitivity analysis was conducted to determine the key parameters of its Noah Multi-Parameterization (Noah-MP) land surface model. Sensitivity analysis can better elucidate the influence of different parameters on the model simulation results and evaluate the rationality of each model parameter. The extended Fourier amplitude sensitivity test (EFAST) method is a classical global sensitivity analysis method, whose theory is derived from the analysis of variance and Fourier transform. In this study, the EFAST method was used to perform sensitivity analyses on the land surface characteristic parameters in different climatic regions of the Loess Plateau. The results showed that the Noah-MP model can represent the land surface characteristics of the Loess Plateau well. With sensible and latent heat fluxes as criteria, the main sensitivity parameters were the vegetation roughness length (Z0), the soil quartz content (QUARTZ), the maximum volumetric soil moisture (MAXSMC), and the soil parameter “b”. The coupling effect between parameters has a greater impact on the sensitivity analysis. The probability densities of the three most sensitive parameters were evenly distributed in each interval, whereas those of the other parameters were distributed within 0–0.2 of the standardized value. Moreover, almost half of the land surface parameters accounted for 80% of the total sensitivity. Based on the seasonal sensitivity distribution of the land surface parameters, Z0 dominated throughout all four seasons, QUARTZ sensitivity was high in spring, and both MAXSMC and QUARTZ showed high sensitivities in winter.

Evaluating gross primary productivity over 9 ChinaFlux sites based on random forest regression models, remote sensing, and eddy covariance data

Accurate modeling of Gross Primary Productivity (GPP) in terrestrial ecosystems is a major challenge in quantifying the carbon cycle. Many light use efficiency (LUE) models have been developed, but the variables and algorithms used for environmental constraints in different models vary importantly. It is still unclear whether the models can be further improved by machine learning methods and the combination of different variables. Here, we have developed a series of RFR-LUE models, which used the random forest regression (RFR) algorithm based on variables of LUE models, to explore the potential of estimating site-level GPP. Based on remote sensing indices, eddy covariance and meteorological data, we applied RFR-LUE models to evaluate the effects of different variables combined on GPP on daily, 8-day, 16-day and monthly scales, respectively. Cross-validation analyses revealed performances of RFR-LUE models varied significantly among sites with R2 of 0.52–0.97. Slopes of the regression relationship between simulated and observed GPP ranged from 0.59 to 0.95. Most models performed better in capturing the temporal changes and magnitude of GPP in mixed forests and evergreen needle-leaf forests than in evergreen broadleaf forests and grasslands. Performances were improved at the longer temporal scale, with the average R2 for four-time resolutions of 0.81, 0.87, 0.88, and 0.90, respectively. Additionally, the importance of the variables showed that temperature and vegetation indices were critical variables for RFR-LUE models, followed by radiation and moisture variables. The importance of moisture variables was higher in non-forests than in forests. A comparison with four GPP products indicated that RFR-LUE model predicted GPP better matcher observed GPP across sites. The study provided an approach to deriving GPP fluxes and evaluating the extent to which variables affect GPP estimation. It may be used for predicting vegetation GPP at the regional scales and for calibration and evaluation of land surface process models.

Evaluation of CLM5.0 for simulating surface energy budget and soil hydrothermal regime in permafrost regions of the Qinghai-Tibet Plateau

Surface energy budget and soil hydrothermal regime are crucial for understanding the interactions between the atmosphere and land surface. However, large uncertainties in current land surface process models exist, especially for the permafrost regions in the Qinghai-Tibet Plateau. In this study, observed soil temperature, moisture, and surface energy fluxes at four sites in permafrost regions are chosen to evaluate the performance of CLM5.0. Furthermore, the soil property data, different thermal roughness length schemes, and dry surface layer (DSL) scheme are investigated. The results show that the soil property data is important for CLM5.0. The default scheme in CLM5.0 yields large errors for surface energy fluxes. The combination of the thermal roughness length and DSL scheme significantly improved the simulation of surface energy fluxes, especially for latent heat flux. The optimization of DSL scheme significantly improved soil temperature simulation and decreased the RMSE from 1.95 °C, 2.07 °C, 2.02 °C, and 2.95 °C to 1.34 °C, 1.35 °C, 1.35 °C and 2.29 °C in TGL site, respectively. The combination of the thermal roughness length and DSL scheme performed the best in shallow soil moisture, decreasing the RMSE from 0.136 m3 m−3 to 0.049 m3 m−3 in the XDT site but slightly enhancing the errors in middle soil. The interactions between surface energy and soil hydrothermal regime also discussed. However, the thermal roughness length and the DSL schemes are highly dependent on the condition of the underlying surface. Different schemes should be selected for different regions.

Modeling large‐scale landform evolution with a stream power law for glacial erosion (OpenLEM v37): benchmarking experiments against a more process-based description of ice flow (iSOSIA v3.4.3)

Abstract. Following the tradition of modeling fluvial landscape evolution, a novel approach describing glacial erosion based on an empirical stream power law was proposed. This approach differs substantially from well-established process-based models applied to describe glacial erosion in mountain landscapes. Outstanding computational performance but a number of potential limitations compared to process-based models requires extensive testing to evaluate the applicability of this novel approach. In this study, we test the validity of the glacial stream power law and its implementation into a 2-D landform evolution model (OpenLEM) by benchmarking it against a state of the art surface process model based on the integrated second-order shallow-ice approximation (iSOSIA). Despite completely different approaches, OpenLEM and iSOSIA predict similar ice flow patterns and erosion rates for a wide range of climatic conditions without re-adjusting a set of calibrated parameters. This parameter set is valid for full glacial conditions where the entire precipitation is converted to ice but also for an altitude-dependent glacier mass balance as characteristic for most glaciated mountain ranges on Earth. In both models characteristic glacial features, such as overdeepenings, hanging valleys and steps at confluences emerge roughly at the same locations, resulting in a consistent altitude-dependent adjustment of channel slope and relief. Compared to iSOSIA, however, distinctly higher erosion rates occur in OpenLEM at valley flanks during the initial phase of the fluvial to glacial transition. This is mainly due to the simplified description of glacier width and ice surface in OpenLEM. In this respect, we found that the glacial stream power approach cannot replace process-based models such as iSOSIA but is complementary to them by addressing research questions that could not previously be answered due to a lack of computational efficiency. The implementation of the glacial stream power law is primarily suitable for large-scale simulations investigating the evolution of mountain topography in the interplay of tectonics and climate. As coupling glacial and fluvial erosion with sediment transport shows nearly the same computational efficiency as its purely fluvial counterpart, mountain-range-scale simulations at high spatial resolution are not exclusively restricted to the fluvial domain anymore, and a series of exciting research questions can be addressed by this novel approach.

New measurement of the diffusion of carbon dioxide on non-porous amorphous solid water

The diffusion of molecules on interstellar grain surfaces is one of the most important driving forces for the molecular complexity in the interstellar medium. Due to the lack of laboratory measurements, astrochemical modeling of grain surface processes usually assumes a constant ratio between the diffusion energy barrier and the desorption energy. This over-simplification inevitably causes large uncertainty in model predictions. We present a new measurement of the diffusion of CO 2 molecules on the surface of non-porous amorphous solid water (np-ASW), an analog of the ice mantle that covers cosmic dust grains. A small coverage of CO 2 was deposited onto an np-ASW surface at 40 K, the subsequent warming of the ice activated the diffusion of CO 2 molecules, and a transition from isolated CO 2 to CO 2 clusters was seen in the infrared spectra. To obtain the diffusion energy barrier and pre-exponential factor simultaneously, a set of isothermal experiments were carried out. The values for the diffusion energy barrier and pre-exponential factor were found to be 1300 ± 110 K and 10 7.6 ± 0.8 s − 1 . A comparison with prior laboratory measurements on diffusion is discussed.

Exact Voronoi diagram for topographic spatial analysis

ABSTRACT The Voronoi diagram (VD) is a fundamental geo-computing structure that has crucial applications. Computing this structure on a topographic surface requires having every point clustered on the geodesic distances, and thus the same challenging task as the geodesic distance mapping. This article proposes a new algorithm for the geodesic VD (GVD) by breaking up the highly complicated task into regular routines on the exact computation. The exact approach is due to the irregular rough nature of the Earth surface, where the discrete computation is more appropriate. The key operation involves a direct window growth devised to avoid the overloaded facet splitting and realized in a conic arrangement. Conventional clustering and GVD structure post-extraction are built on top of the window growth. The fundamental role of GVD in geo-computing is then demonstrated. The experimental results showed that the dual structure of GVD is useful in justifying the potential wrong triangulations from the popularly used 2D Delaunay, and the geometric exactness of GVD is more reliable in guaranteeing the surface process model efficiency and convergence, under the harsh checking of centroidal Voronoi tessellation optimization.

Soil Moisture Data Assimilation in MISDc for Improved Hydrological Simulation in Upper Huai River Basin, China

In recent years, flash floods have become increasingly serious. Improving the runoff simulation and forecasting ability of hydrological models is urgent. Therefore, data assimilation (DA) methods have become an important tool. Many studies have shown that the assimilation of remotely sensed soil moisture (SM) data could help improve the simulation and forecasting capability of hydrological models. Still, very few studies have attempted to assimilate SM data from land surface process models into hydrological models to improve model simulation and forecasting accuracy. Therefore, in this study, we used the ensemble Kalman filter (EnKF) to assimilate the China Land Data Assimilation System (CLDAS) SM product into the MISDc model. We also corrected the CLDAS SM and assimilated the corrected SM data into the hydrological model. In addition, the effects of the 5th and 95th percentiles of flow were evaluated to see how SM DA affected low and high flows, respectively. Additionally, we tried to find an appropriate size for the number of ensemble members of the EnKF for this study. The results showed that the EnKF SM DA improved the runoff simulation ability of the hydrological model, especially for the high flows of the model; however, the simulation for the low flows deteriorated. In general, SM DA positively affected the ability of the MISDc model runoff simulation.

FRESF model: An ArcGIS toolbox for rapid assessment of the supply, demand, and flow of flood regulation ecosystem services

• An ArcGIS toolbox and FRESF model can measures flood related indices. • It is a practical tool for ecosystem services conservation planning. • The study provided here show the usability and credibility of proposed toolbox. River floods are globally the most damaging and costliest natural disasters. The functions of nonstructural flood mitigation measures (e.g., water retention by vegetation and soil) provide critical regulation ecosystem services to human beings. For example, the Sponge Cities are national watershed management projects in China to increase the nonstructural flood regulation services, aiming to enable the urban ecosystem to absorb, infiltrate, and retain storm water to mitigate pluvial flooding. Decision-makers need to acquire critical areas for the supply, demand, and flow of flood regulation ecosystem services, which would affect the delivery of services and the distribution of any benefits among stakeholders. However, relatively few empirical studies present this information. One reason is the lack of appropriate methods and tools for a rapid assessment of flood regulation ecosystem services that do not require substantial resources or rely heavily on hydrological process data. This article presents the Flood Regulation Ecosystem Service Flow (FRESF) model to address this gap. FRESF is an open-access and open-source ArcGIS script toolbox for an integrated evaluation of flood regulation ecosystem services' supply, demand, and flow. Based on a land surface process model and a generic framework quantifying ecosystem services flow, FRESF provides a rapid and serviceable assessment framework for the relevant ecosystem services at a relatively low cost and effort. The validation results suggest that the outputs are moderately consistent with the actual flood conditions, compared with the flood disaster of the Yangtze River in 2020. Its open codes and flexible inputs imply that ecologists, planners, decision-makers, and developers involved in ecosystem services would have the opportunity to test and apply the FRESF model in several contexts and even contribute further improvements.

Estimation of Vegetation Leaf-Area-Index Dynamics from Multiple Satellite Products through Deep-Learning Method

A high-quality leaf-area index (LAI) is important for land surface process modeling and vegetation growth monitoring. Although multiple satellite LAI products have been generated, they usually show spatio-temporal discontinuities and are sometimes inconsistent with vegetation growth patterns. A deep-learning model was proposed to retrieve time-series LAIs from multiple satellite data in this paper. The fusion of three global LAI products (i.e., VIIRS, GLASS, and MODIS LAI) was first carried out through a double logistic function (DLF). Then, the DLF LAI, together with MODIS reflectance (MOD09A1) data, served as the training samples of the deep-learning long short-term memory (LSTM) model for the sequential LAI estimations. In addition, the LSTM models trained by a single LAI product were considered as indirect references for the further evaluation of our proposed approach. The validation results showed that our proposed LSTMfusion LAI provided the best performance (R2 = 0.83, RMSE = 0.82) when compared to LSTMGLASS (R2 = 0.79, RMSE = 0.93), LSTMMODIS (R2 = 0.78, RMSE = 1.25), LSTMVIIRS (R2 = 0.70, RMSE = 0.94), GLASS (R2 = 0.68, RMSE = 1.05), MODIS (R2 = 0.26, RMSE = 1.75), VIIRS (R2 = 0.44, RMSE = 1.37) and DLF LAI (R2 = 0.67, RMSE = 0.98). A temporal comparison among LSTMfusion and three LAI products demonstrated that the LSTMfusion model efficiently generated a time-series LAI that was smoother and more continuous than the VIIRS and MODIS LAIs. At the crop peak growth stage, the LSTMfusion LAI values were closer to the reference maps than the GLASS LAI. Furthermore, our proposed method was proved to be effective and robust in maintaining the spatio-temporal continuity of the LAI when noisy reflectance data were used as the LSTM input. These findings highlighted that the DLF method helped to enhance the quality of the original satellite products, and the LSTM model trained by the coupled satellite products can provide reliable and robust estimations of the time-series LAI.

Generating 5 km resolution 1981–2018 daily global land surface longwave radiation products from AVHRR shortwave and longwave observations using densely connected convolutional neural networks

Surface longwave radiation (SLWR) components, including downward longwave radiation (DLR), upward longwave radiation (ULR), and net longwave radiation (NLR), are major contributors to the Earth's surface radiation budget and play important roles in ecological, hydrological, and atmospheric processes. Previous SLWR products have different drawbacks, such as being temporally short (after 2000), spatially coarse (≥ 25 km), and instantaneous values, which hinder their in-depth applications in land surface process modeling and climate trends analysis. Here, we reported the Advanced Very High-Resolution Radiometer (AVHRR)-based Global LAnd Surface Satellites (GLASS-AVHRR) SLWR products over the global land surface at a 5 km spatial resolution and 1 day temporal resolution between 1981 and 2018. These products were generated using multiple densely connected convolutional neural networks (DesCNNs) from the AVHRR top-of-atmosphere (TOA) reflected and emitted observations and European Centre for Medium-Range Weather Forecasts (ECMWF) fifth generation reanalysis (ERA5) near-surface meteorological data. DesCNNs were trained using integrated SLWR samples derived from the Moderate Resolution Imaging Spectroradiometer (MODIS)-based GLASS, Clouds and the Earth's Radiant Energy System Synoptic (CERES-SYN), and ERA5 SLWR products. In situ measurements from 231 globally distributed sites were used to evaluate the GLASS-AVHRR SLWR estimates. The results illustrated the overall high accuracies of GLASS-AVHRR SLWR products with root-mean-square-errors (RMSEs) of 18.66, 14.92, and 16.29 Wm−2, and mean bias errors (MBEs) of −2.69, −3.77, and 0.49 Wm−2 for all-sky DLR, ULR, and NLR, respectively. We found good correlation and consistency between GLASS-AVHRR and both CERES-SYN and ERA5 in terms of spatial patterns, latitudinal gradient, and temporal evolution. Our results revealed the significant contribution of shortwave observations to SLWR estimation owing to the high amounts of clouds over polar regions and water vapor and clouds in tropical areas, which was not previously widely recognized by the remote sensing community. GLASS-AVHRR SLWR products were updated, documented, and made available to the public at www.glass.umd.edu and www.geodata.cn.

Microwave radiometry experiment for snow in Altay, China: time series of in situ data for electromagnetic and physical features of snowpack

Abstract. In this paper, we present a comprehensive experiment, namely, an Integrated Microwave Radiometry Campaign for snow (IMCS), in Xinjiang, China, during the snow season of 2015–2016. The campaign hosted a dual-polarized microwave radiometer operating at L, K, and Ka bands to provide minutely passive microwave observations of snow cover at a fixed site, along with daily manual snow pit observations of snow physical parameters, automatic observations of 10 min four-component radiation and layered snow temperatures, and meteorological observations of hourly weather data and soil data. To the best of our knowledge, our dataset is unique in providing continuous daily snow pit data and coincident microwave brightness temperatures, radiation and meteorological data, at a fixed site over a full season, which can be straightforwardly used for evaluation and development of microwave radiative transfer models and snow process models, along with land surface process and hydrology models. The consolidated data are available at (https://doi.org/10.11888/Snow.tpdc.270886) (Dai, 2020).

Climate Change Impacts on Stream Water Temperatures in a Snowy Cold Region According to Geological Conditions

This study clarifies how climate change affects stream temperatures in snowy cold regions, where groundwater impacts vary with geological conditions. We developed a physics-based water circulation model that incorporates an atmospheric and land surface process model considering snow processes, a runoff model, and a water temperature estimation model. Small watersheds in the mountainous area of Hokkaido formed the study area, and the runoff model was assigned different parameters depending on the geological characteristics. Using these parameters, changes in water temperature were calculated with respect to changes in the meteorological data in historical and future simulations. Current water temperatures were effectively reproduced by the model, and following the IPCC RCP 8.5 scenario, future water temperatures in the distribution area for new pyroclastic flows were predicted to remain lower in summer than in the distribution area of older formations. The findings of this study will be useful in informing conservation measures for river ecosystems, including the prioritization of streams where cold-water fish need to be conserved.

Ecological Sustainability Assessment of Water Distribution for the Maintenance of Ecosystems, their Services and Biodiversity

Water provision and distribution are subject to conflicts between users worldwide, with agriculture as a major driver of discords. Water sensitive ecosystems and their services are often impaired by man-made water shortage. Nevertheless, they are not sufficiently included in sustainability or risk assessments and neglected when it comes to distribution of available water resources. The herein presented contribution to the Sustainable Development Goals Clean Water and Sanitation (SDG 6) and Life on Land (SDG 15) is the Ecological Sustainability Assessment of Water distribution (ESAW-tool). The ESAW-tool introduces a watershed sustainability assessment that evaluates the sustainability of the water supply-demand ratio on basin level, where domestic water use and the water requirements of ecosystems are considered as most important water users. An ecological risk assessment estimates potential impacts of agricultural depletion of renewable water resources on (ground)water-dependent ecosystems. The ESAW-tool works in standard GIS applications and is applicable in basins worldwide with a set of broadly available input data. The ESAW-tool is tested in the Danube river basin through combination of high-resolution hydro-agroecological model data (hydrological land surface process model PROMET and groundwater model OpenGeoSys) and further freely available data (water use, biodiversity and wetlands maps). Based on the results, measures for more sustainable water management can be deduced, such as increase of rainfed agriculture near vulnerable ecosystems or change of certain crops. The tool can support decision making of authorities from local to national level as well as private enterprises who want to improve the sustainability of their supply chains.

Incorporating Snowmelt into Daily Estimates of Recharge Using a State-Space Model of Infiltration.

A state-space model (SSM) of infiltration estimates daily groundwater recharge using time-series of groundwater-level altitude and meteorological inputs (liquid precipitation, snowmelt, and evapotranspiration). The model includes diffuse and preferential flow through the unsaturated zone, where preferential flow is a function of liquid precipitation and snowmelt rates and a threshold rate, above which there is direct recharge to the water table. Model parameters are estimated over seasonal periods and the SSM is coupled with the Kalman Filter (KF) to assimilate recent observations (hydraulic head) and meteorological inputs into recharge estimates. The approach can take advantage of real-time hydrologic and meteorological data to deliver real-time recharge estimates. The model is demonstrated on daily observations from two bedrock wells in carbonate aquifers of northwestern New York (USA) between 2013 and 2018. Meteorological inputs for liquid precipitation and snowmelt are compiled from SNODAS (2021). Results for recharge during winter and spring seasons show preferential flow events to the water table from liquid precipitation, snowmelt, or a combination of the two. Recharge estimates summed annually are consistent with previous estimates of recharge reported from groundwater flow and surface-process models. Results from the SSM and KF point to errors in meteorological inputs, such as the snowmelt rate, that are not compatible with hydraulic head observations. Whereas liquid and solid precipitation are measured at discrete stations and extrapolated to 1-km2 grid cells, snowmelt is a meteorological modeled outcome that may not represent conditions in the vicinity of monitoring well locations.

Contrasting influence of sediments vs surface processes on retreating subduction zones dynamics

Surface processes and sediments have been suggested to play a crucial role in subduction initiation and to speed up subduction and plate tectonics. The exact roles of sediments and surface processes and the way they influence slab retreat dynamics has however not been constrained yet. Here, we analyse these roles by using 3D numerical thermo-mechanical modelling code coupled to surface processes model in order to investigate large scale dynamics of retreating subduction zones. We focus on two regional-scale geodynamic scenarios: a slab retreating in a homogeneous oceanic domain and a slab retreating along a continental margin before entering an oceanic domain. We study the influence of the thickness of the sediment cover on the oceanic crust and the intensity of surface processes. We show that the sediment thickness and the surface processes both affect the retreat dynamics in a distinct manner, and, when the slab retreats along a continental margin, also influence its trajectory. A thick sediment cover leads to the building of a large accretionary prism which acts as a lock on the system and slows down the slab retreat. When the slab retreats along a continental margin, the thicker the sediment cover, the stronger the slab deviation from that margin. Surface processes have a more complex role which depends on the amount of sediments available. In systems where little sediments are available, a higher efficiency of surface processes leads to a slow down of slab retreat by accumulating sediments in the accretionary wedge. In systems where a lot of sediments are available, a higher efficiency of surface processes slightly speeds up the retreat, by eroding the overriding plate and the subducting plate respectively at the front and the back of the accretionary wedge, as well as the prism itself when reaching a high enough elevation, thereby decreasing the mechanical locking caused by the accumulation of sediments.

Editorial: Advances in Quantitative Geomorphology: From DEM Analysis to Modeling of Surface Processes

Editorial: Advances in Quantitative Geomorphology: From DEM Analysis to Modeling of Surface Processes