Large Spatial Domains Research Articles

Incorporating Network Scale River Bathymetry to Improve Characterization of Fluvial Processes in Flood Modeling

AbstractSeveral studies have focused on the importance of river bathymetry (channel geometry) in hydrodynamic routing along individual reaches. However, its effect on other watershed processes such as infiltration and surface water (SW)‐groundwater (GW) interactions has not been explored across large river networks. Surface and sbsurface processes are interdependent, therefore, errors due to inaccurate representation of one watershed process can cascade across other hydraulic or hydrologic processes. This study hypothesizes that accurate bathymetric representation is not only essential for simulating channel hydrodynamics but also affects subsurface processes by impacting SW‐GW interactions. Moreover, quantifying the effect of bathymetry on surface and subsurface hydrological processes across a river network can facilitate an improved understanding of how bathymetric characteristics affect these processes across large spatial domains. The study tests this hypothesis by developing physically based distributed models capable of bidirectional coupling (SW‐GW) with four configurations with progressively reduced levels of bathymetric representation. A comparison of hydrologic and hydrodynamic outputs shows that changes in channel geometry across the four configurations has a considerable effect on infiltration, lateral seepage, and location of water table across the entire river network. For example, when using bathymetry with inaccurate channel conveyance capacity but accurate channel depth, peak lateral seepage rate exhibited 58% error. The results from this study provide insights into the level of bathymetric detail required for accurately simulating flooding‐related physical processes while also highlighting potential issues with ignoring bathymetry across lower order streams such as spurious backwater flow, inaccurate water table elevations, and incorrect inundation extents.

Toward More Actionable Flood‐Risk Information

AbstractThe increasingly urgent need to develop knowledge and practices to manage flood risks drives innovative information design. However, experts often disagree about design practices. As a result, flood‐risk estimates can diverge, leading to different conclusions for decision‐making. Using examples of household‐scale fluvial (riverine) flood‐risk information in the United States, we assess design features and risk communication approaches that may lead to more actionable information for decision‐making. We argue that increased attention to uncertainty characterization and model diagnostics is a critical intermediate step for developing simpler approaches for designing flood‐risk information. Simpler frameworks are desirable because flood risks change over time, and simpler frameworks are less costly to update. Developing frameworks for large spatial domains require collaboration grounded in principles of open science. Finally, systematically evaluating how decision‐makers access and use information can provide new insights to guide risk communication and information design.

A Novel Workflow for Crop Type Mapping with a Time Series of Synthetic Aperture Radar and Optical Images in the Google Earth Engine

High-resolution crop type mapping is of importance for site-specific agricultural management and food security in smallholder farming regions, but is challenging due to limited data availability and the need for image-based algorithms. In this paper, we developed an efficient object- and pixel-based mapping algorithm to generate a 10 m resolution crop type map over large spatial domains by integrating time series optical images (Sentinel-2) and synthetic aperture radar (SAR) images (Sentinel-1) using the Google Earth Engine (GEE) platform. The results showed that the proposed method was reliable for crop type mapping in the study area with an overall accuracy (OA) of 93.22% and a kappa coefficient (KC) of 0.89. Through experiments, we also found that the monthly median values of the vertical transmit/vertical receive (VV) and vertical transmit/horizontal receive (VH) bands were insensitive to crop type mapping itself, but adding this information to supplement the optical images improved the classification accuracy, with an OA increase of 0.09–2.98%. Adding the slope of vegetation index change (VIslope) at the critical period to crop type classification was obviously better than that of relative change ratio of vegetation index (VIratio), both of which could make an OA improvement of 2.58%. These findings not only highlighted the potential of the VIslope and VIratio indices during the critical period for crop type mapping in small plots, but suggested that SAR images could be included to supplement optical images for crop type classification.

Improving computational efficiency of numerical modelling of horizontal ground source heat pump systems for accommodating complex and realistic atmospheric processes

Modelling horizontal ground loops for a horizontal ground source heat pump (HGSHP) system is complex and computationally expensive. The computation precision is highly reliant on the prescription of an undisturbed ground temperature in the unsaturated ground as well as realistic and accurate atmospheric processes at the ground surface boundary. Conventionally, modelling of such a system would include direct application of the atmospheric processes at the soil-atmosphere boundary and solve it in a single-stage approach. However, low efficiency is found for large spatial domain and long-term transient problems as the boundary processes need to be solved and expressed in terms of primary model variables at each simulation time-step. This paper proposes an equivalent two-stage modelling approach, for the first time, based on an advanced coupled thermal-hydraulic (TH) model to improve computation efficiency while maintaining adequate accuracy. In this approach, firstly, the model is solved for an intact ground that is imposed by complex atmospheric processes, e.g., rainfall, solar radiation, humidity, evaporation, etc. at the soil-atmosphere boundary, and the spatial and temporal variations of the primary model variables are recorded. Afterwards, the recorded data are incorporated in the simulator, as model inputs, for the same ground including a HGSHP system. Predicted results from both 2D and 3D simulations show that the ground temperatures calculated by the proposed two-stage approach are in good agreement with that of the traditional single-stage approach. However, the two-stage approach is computationally robust. For the presented 2D and 3D simulations, it required only 32% and 37% of the time of the single-stage approach, respectively, while maintaining great accuracy. This demonstrates the utility of the proposed two-stage approach for modelling complex scenarios of realistic HGSHP systems installed in a large spatial domain and for long-term operation.

Observation-based assessment of secondary water effects on seasonal vegetation decay across Africa.

Local studies and modeling experiments suggest that shallow groundwater and lateral redistribution of soil moisture, together with soil properties, can be highly important secondary water sources for vegetation in water-limited ecosystems. However, there is a lack of observation-based studies of these terrain-associated secondary water effects on vegetation over large spatial domains. Here, we quantify the role of terrain properties on the spatial variations of dry season vegetation decay rate across Africa obtained from geostationary satellite acquisitions to assess the large-scale relevance of secondary water effects. We use machine learning based attribution to identify where and under which conditions terrain properties related to topography, water table depth, and soil hydraulic properties influence the rate of vegetation decay. Over the study domain, the machine learning model attributes about one-third of the spatial variations of vegetation decay rates to terrain properties, which is roughly equally split between direct terrain effects and interaction effects with climate and vegetation variables. The importance of secondary water effects increases with increasing topographic variability, shallower groundwater levels, and the propensity to capillary rise given by soil properties. In regions with favorable terrain properties, more than 60% of the variations in the decay rate of vegetation are attributed to terrain properties, highlighting the importance of secondary water effects on vegetation in Africa. Our findings provide an empirical assessment of the importance of local-scale secondary water effects on vegetation over Africa and help to improve hydrological and vegetation models for the challenge of bridging processes across spatial scales.

High return level estimates of daily ERA-5 precipitation in Europe estimated using regionalized extreme value distributions

Accurate estimation of daily rainfall return levels associated with large return periods is needed for a number of hydrological planning purposes, including protective infrastructure, dams, and retention basins. This is especially relevant at small spatial scales. The ERA-5 reanalysis product provides seasonal daily precipitation over Europe on a 0.25∘×0.25∘ grid (about 27 × 27 [km]). This translates more than 20,000 land grid points and leads to models with a large number of parameters when estimating return levels. To bypass this abundance of parameters, we build on the regional frequency analysis (RFA), a well-known strategy in statistical hydrology. This approach consists in identifying homogeneous regions, by gathering locations with similar distributions of extremes up to a normalizing factor and developing sparse regional models. In particular, we propose a step-by-step blueprint that leverages a recently developed and fast clustering algorithm to infer return level estimates over large spatial domains. This enables us to produce maps of return level estimates of ERA-5 reanalysis daily precipitation over continental Europe for various return periods and seasons. We discuss limitations and practical challenges and also provide a git hub repository. We show that a relatively parsimonious model with only a spatially varying scale parameter can compete well against statistical models of higher complexity.

Spatial organisation plasticity reduces disease infection risk in rock–paper–scissors models

We study a three-species cyclic game system where organisms face a contagious disease whose virulence may change by a pathogen mutation. As a responsive defence strategy, organisms’ mobility is restricted to reduce disease dissemination in the system. The impact of the collective self-preservation strategy on the disease infection risk is investigated by performing stochastic simulations of the spatial version of the rock–paper–scissors game. Our outcomes show that the mobility control strategy induces plasticity in the spatial patterns with groups of organisms of the same species inhabiting spatial domains whose characteristic length scales depend on the level of dispersal restrictions. The spatial organisation plasticity allows the ecosystems to adapt to minimise the individuals’ disease contamination risk if an eventual pathogen alters the disease virulence. We discover that if a pathogen mutation makes the disease more transmissible or less lethal, the organisms benefit more if the mobility is not strongly restricted, thus forming large spatial domains. Conversely, the benefits of protecting against a pathogen causing a less contagious or deadlier disease are maximised if the average size of groups of individuals of the same species is significantly limited, reducing the dimensions of groups of organisms significantly. Our findings may help biologists understand the effects of dispersal control as a conservation strategy in ecosystems affected by epidemic outbreaks.

Large-scale snow data assimilation using a spatialized particle filter: recovering the spatial structure of the particles

Abstract. Data assimilation is an essential component of any hydrological forecasting system. Its purpose is to incorporate some observations from the field when they become available in order to correct the state variables of the model prior to the forecasting phase. The goal is to ensure that the forecasts are initialized from state variables that are as representative of reality as possible, and also to estimate the uncertainty of the state variables. There are several data assimilation methods, and particle filters are increasingly popular because of their minimal assumptions. The baseline idea is to produce an ensemble of scenarios (i.e. the particles) using perturbations of the forcing variables and/or state variables of the model. The different particles are weighted using the observations when they become available. However, implementing a particle filter over a domain with large spatial dimensions remains challenging, as the number of required particles rises exponentially as the domain size increases. Such a situation is referred to as the “curse of dimensionality”, or a “dimensionality limit”. A common solution to overcome this curse is to localize the particle filter. This consists in dividing the large spatial domain into smaller portions, or “blocks”, and applying the particle filter separately for each block. This can solve the above-mentioned dimensionality problem because it reduces the spatial scale at which each particle filter must be applied. However, it can also cause spatial discontinuities when the blocks are reassembled to form the whole domain. This issue can become even more problematic when additional data are assimilated. The purpose of this study is to test the possibility of remedying the spatial discontinuities of the particles by locally reordering them. We implement a spatialized particle filter to estimate the snow water equivalent (SWE) over a large territory in eastern Canada by assimilating local SWE observations from manual snow surveys. We apply two reordering strategies based on (1) a simple ascending-order sorting and (2) the Schaake shuffle and evaluate their ability to maintain the spatial structure of the particles. To increase the amount of assimilated data, we investigate the inclusion of a second data set (SR50), in which the SWE is indirectly estimated from automatic measurements of snow depth using sonic sensors. The two reordering solutions maintain the spatial structure of the individual particles throughout the winter season, which significantly reduces the spatial random noise in the distribution of the particles and decreases the uncertainty associated with the estimation. The Schaake shuffle proves to be a better tool for maintaining a realistic spatial structure for all particles, although we also found that sorting provides a simpler and satisfactory solution. The assimilation of the secondary data set improved SWE estimates in ungauged sites when compared with the deterministic model, but we noted no significant improvement when both snow courses and the SR50 data were assimilated.

An efficient operator-splitting radial basis function-generated finite difference (RBF-FD) scheme for image noise removal based on nonlinear total variation models

Image noise removal is one of the most important parts of image processing that can dramatically improve other parts of image processing performance by enhancing the quality of images in databases. Total variation models are second-order partial differential equations for image denoising. These models have some complexities, such as being multidimensional problems, non-linearity, and having large spatial and temporal domains making them challenging problems to be solved numerically. Thus, in this work, we propose the radial basis function generated finite differences (RBF-FD) method in conjunction with a suitable operator splitting technique to overcome these difficulties. This approach has some significant advantages, such as high accuracy, low computational complexity, and the sparsity of the coefficients matrices derived from it. The peak signal-to-noise ratio, structure similarity index measure, and mean-square error metrics are considered to evaluate the proposed approach’s effectiveness and accuracy compared to the other common denoising approaches.

Geometric Mapping Evaluation for Real-Time Local Sensor Simulation.

Medical augmented reality and simulated test environments struggle in accurately simulating local sensor measurements across large spatial domains while maintaining the proper resolution of information required and real time capability. Here, a simple method for real-time simulation of intraoperative sensors is presented to aid with medical sensor development and professional training. During a surgical intervention, the interaction between medical sensor systems and tissue leads to mechanical deformation of the tissue. Through the inclusion of detailed finite element simulations in a real-time augmented reality system the method presented will allow for more accurate simulation of intraoperative sensor measurements that are independent of the mechanical state of the tissue. This concept uses a coarse, macro-level deformation mesh to maintain both computational speed and the illusion of reality and a simple geometric point mapping method to include detailed fine mesh information. The resulting system allows for flexible simulation of different types of localized sensor measurement techniques. Preliminary simulation results are provided using a real-time capable simulation environment and prove the feasibility of the method.

Assessing the importance of mesoscale sea‐surface temperature variations for surface turbulent cooling of the Kuroshio Extension in wintertime

Western boundary currents are well known to exhibit a strong eddy field and meandering on subseasonal timescales and wavelengths of a few hundreds of kilometers. However, the extent to which the ocean mesoscale activity associated with these motions can enhance the turbulent air–sea heat flux averaged over a large spatial domain has not been fully characterized. This study aims to do so during the cold season using reanalysis data and simplified air–sea interaction models. The correlation between sea-surface height anomaly and the wintertime mean heat fluxes does not indicate any significant relations between eddy activity and large-scale surface heat flux in the Kuroshio Extension during winter over the 2003–2018 period. The results from simple models designed to isolate the contributions from the eddies suggest that the eddy enhancement of the heat flux via a rectified effect (a bit more anomalous cooling over a warm mesoscale feature than anomalous heating over a cold one) is small compared with the long-time, large-spatial scale mean in the reanalysis data, especially along the Kuroshio Extension. It is found larger north of it, where it can reach 5 W · $$ \cdotp $$ m − 2 $$ {}^{-2} $$ . Although different datasets might disagree about the level of energy of the sea-surface temperature field at the mesoscale, a simple scaling analysis gives confidence in the relatively small values of the rectified effect found here.

Automated soybean mapping based on canopy water content and chlorophyll content using Sentinel-2 images

Accurate and timely spatiotemporal distribution information of soybean is vital for sustainable agriculture development. However, it is challenging to establish a phenology-based automated crops mapping algorithm at large spatial domains by simply applying vegetation index temporal profile. This study developed a Phenology-based automatic Soybean mapping algorithm through combined Canopy water and Chlorophyll variations (PSCC). Three phenology-based indices were designed: the ratio of change magnitudes of vegetation index to water stress index during the late growth stage (T1), the mean concentration of chlorophyll during the whole growth period (T2), and the accumulated variations of chlorophyll before and after heading date (T3). Soybean was distinguished by lower T1 and T3 and higher T1 due to higher senescence water loss and chlorophyll content. The PSCC method was validated in Northeast China from 2017 to 2021 and in four states (Missouri, Illinois, Indiana, and Ohio) of the United States (US) in 2020 using Sentinel-2 datasets. Soybean planting areas obtained by PSCC were consistent with the corresponding agricultural statistical area (R2 > 0.83). The soybean maps were evaluated using 5702 reference data, and the overall accuracy and kappa coefficient were 91.99% and 0.8338, respectively. The overall accuracy for soybean mapping was improved by 16.07% compared with using only canopy water variation. The result showed that our method could be applied to large spatial domains and multi-years without retraining. The soybean planting area in Northeast China expanded substantially 25,867 km2 (by 89.10%) during the period 2015–2020 and decreased slightly 7,535 km2 (by 13.73%) from 2020 to 2021. Soybean expansion occurred mainly in ever-planted regions. Northeast China contributed about 60% to the national soybean revitalization goal in 2020. This study provided information on the soybean spatiotemporal changes in Northeast China, which was significant for agricultural policymakers to formulate soybean production plans to achieve national soybean revitalization.

Multifidelity Computer Model Emulation with High-Dimensional Output: An Application to Storm Surge

AbstractHurricane-driven storm surge is one of the most deadly and costly natural disasters, making precise quantification of the surge hazard of great importance. Surge hazard quantification is often performed through physics-based computer models of storm surges. Such computer models can be implemented with a wide range of fidelity levels, with computational burdens varying by several orders of magnitude due to the nature of the system. The threat posed by surge makes greater fidelity highly desirable, however, such models and their high-volume output tend to come at great computational cost, which can make detailed study of coastal flood hazards prohibitive. These needs make the development of an emulator combining high-dimensional output from multiple complex computer models with different fidelity levels important. We propose a parallel partial autoregressive cokriging model to predict highly accurate storm surges in a computationally efficient way over a large spatial domain. This emulator has the capability of predicting storm surges as accurately as a high-fidelity computer model given any storm characteristics over a large spatial domain.

Remote Sensing, Geophysics, and Modeling to Support Precision Agriculture—Part 2: Irrigation Management

Food and water security are considered the most critical issues globally due to the projected population growth placing pressure on agricultural systems. Because agricultural activity is known to be the largest consumer of freshwater, the unsustainable irrigation water use required by crops to grow might lead to rapid freshwater depletion. Precision agriculture has emerged as a feasible concept to maintain farm productivity while facing future problems such as climate change, freshwater depletion, and environmental degradation. Agriculture is regarded as a complex system due to the variability of soil, crops, topography, and climate, and its interconnection with water availability and scarcity. Therefore, understanding these variables’ spatial and temporal behavior is essential in order to support precision agriculture by implementing optimum irrigation water use. Nowadays, numerous cost- and time-effective methods have been highlighted and implemented in order to optimize on-farm productivity without threatening the quantity and quality of the environmental resources. Remote sensing can provide lateral distribution information for areas of interest from the regional scale to the farm scale, while geophysics can investigate non-invasively the sub-surface soil (vertically and laterally), mapping large spatial and temporal domains. Likewise, agro-hydrological modelling can overcome the insufficient on-farm physicochemical dataset which is spatially and temporally required for precision agriculture in the context of irrigation water scheduling.

Space-time susceptibility modeling of hydro-morphological processes at the Chinese national scale

Hydro-morphological processes (HMP; any process in the spectrum between debris flows and flash floods) threaten human lives and infrastructure; and their effects are only expected to worsen under the influence of climate change. Limiting the potential damage of HMPs by taking preventive or remedial actions requires the probabilistic expectation of where and how frequently these processes may occur. The information on where and how frequently a given earth surface process may manifest can be expressed via susceptibility modeling. For the whole Chinese territory, a susceptibility model for HMP is currently not available. To address this issue, we propose a yearly space-time model built on the basis of a binomial Generalized Linear Model. The target variable of such model is the annual presences/absences of HMP per catchment across China, from 1985 to 2015. This information has been accessed via the Chinese catalogue of HMP, a data repository the Chinese Government has activated in 1950 and which is still currently in use. This binary spatio-temporal information is regressed against a set of time-invariant (catchment shape indices and geomorphic attributes) and time-variant (urban coverage, rainfall, vegetation density and land use) covariates. Furthermore, we include a regression constant for each of the 31 years under consideration and also a three-years aggregated information on previously occurred (and not-occurred) HMP. We consider two versions of our modeling approach, an explanatory benchmark where we fit the whole space-time HMP data, including a multiple intercept per year. Furthermore, we also extend this explanatory model into a predictive one, by considering four temporal cross-validation schemes. As a result, we portrayed the annual susceptibility models into 30 maps, where the south-east of China is shown to exhibit the largest variation in the spatio-temporal probability of HMP occurrence. Also, we compressed the whole spatio-temporal prediction into three summary maps. These report the mean, maximum and 95% confidence interval of the spatio-temporal susceptibility distribution per catchment, per year. The information we present has a dual value. On the one hand, we provide a platform to interpret environmental effects controlling the occurrence of HMP over a very large spatial (the whole Chinese country) and temporal (31 years of records) domain. On the other hand, we provide information on which catchments are more prone to experience a HMP-driven hazard. Hence, a step further would be to select the most susceptible catchments for detailed analysis where physically-based models could be tested to estimate the potentially impacted areas. For transparency, the results generated in this work are shared in the supplementary material as GIS (geopackage) files.

Hybrid domain decomposition filters for advection–diffusion PDEs with mobile sensors

This paper examines one aspect of computational control, namely the reduction of computational load for the real-time estimation of distributed parameter systems over large spatial domains. One way to improve the performance of the state estimator is to employ a small number of mobile devices that are free to move within the spatial domain while collecting process information. Another way is to use numerical methods to reduce the computational load as dictated by the discretization index of the finite dimensional representation of the filter. This is done via the use of domain decomposition methods and the domain decomposition filter is presented here for the first time along with its well-posedness. In the subdomain surrounding the sensor a refined grid is used as it improves the spatial resolution, and in the subdomain excluding the vicinity of the sensing device, a naïve observer with coarse grid is used which results in a significant reduction of the computational cost. However, due to the transmission conditions that couple the information between the inner subdomain surrounding the sensor and the outer subdomain excluding the sensor, the filters in the two subdomains implement an information exchange protocol rendering them consensus filters. The two approaches are combined thus resulting in a state estimation scheme where the mobile sensor residing in the inner subdomain carries its own refined grid. Not only is the sensor mobile, but also the refined grid surrounding the sensor is moving along with the sensor. A further reduction in computational costs is presented in the form of a hybrid DD filter wherein the filter kernel is rendered sparse thus ensuring that no residual measurement signals enter as disturbance signals in the naïve observer defined over the outer subdomain. Extensive simulation studies of a 2D PDE with realistic parameters are included to demonstrate the proposed hybrid DD filter.

Modelling and numerical synchronization of chaotic system with fractional-order operator

Abstract Numerical solution of nonlinear chaotic fractional in space reaction–diffusion system is considered in this paper on a large but finite spatial domain size x ∈ [0, L] for L ≫ 0, x = x(x, y) and t ∈ [0, T]. The classical order chaotic ordinary differential equation is formulated by introducing the second-order spatial fractional derivative with order β ∈ (1, 2]. This second order spatial derivative is modelled by using the definition of the Riesz fractional derivative. The method of approximation combines the Fourier spectral method with the novel exponential time difference schemes. The proposed technique is known to have gained spectral accuracy over finite difference schemes. Applicability and suitability of the suggested methods are tested on Rössler chaotic system of recurring interests in one and two dimensions.

A coupled discrete element and depth-averaged model for dynamic simulation of flow-like landslides

Flow-like landslides commonly happen in mountainous areas and may threaten people’s lives, damage their properties, and create negative impact on the environment. Computer modelling has become an effective tool to support landslide risk assessment and management. Models based on discrete element method (DEM) can capture micro-mechanical behaviour of soils, simulate large deformation and have been widely used for landslide simulations. However, these models are computationally too demanding for large-scale applications. On the other hand, depth-averaged models (DAM) have been well reported for simulation of flow-like landslides over large spatial domains due to its relatively high computational efficiency. To combine the advantages of both types of modelling approaches, this paper develops a novel landslide model by coupling a DEM model with a DAM for landslide simulation, in which the DEM component is employed to better simulate the complex landslide dynamics in the source area and the DAM is adopted to predict the predominantly convective movement in the runout and deposition zone. Finally, the new coupled landslide model is validated against several test cases, including a field-scale event. Satisfactory results have been obtained, demonstrating that the coupled model is able to reproduce the dynamic process of flow-like landslides.

Numerical Investigation of Hydrodynamics and Cohesive Sediment Transport in Cua Lo and Cua Hoi Estuaries, Vietnam

Two-dimensional models of large spatial domain including Cua Lo and Cua Hoi estuaries in Nghe An province, Vietnam, were established, calibrated, and verified with the observed data of tidal level, wave height, wave period, wave direction, and suspended sediment concentration. The model was then applied to investigate the hydrodynamics, cohesive sediment transport, and the morphodynamics feedbacks between two estuaries. Results reveal opposite patterns of nearshore currents affected by monsoons, which flow from the north to the south during the northeast (NE) monsoon and from the south to the north during the southeast (SE) monsoon. The spectral wave model results indicate that wave climate is the main control of the sediment transport in the study area. In the NE monsoon, sediment from Cua Lo port transported to the south generates the sand bar in the northern bank of the Cua Hoi estuary, while sediment from Cua Hoi cannot be carried to the Cua Lo estuary due to the presence of Hon Ngu Island and Lan Chau headland. As a result, the longshore sediment transport from the Cua Hoi estuary to the Cua Lo estuary is reduced and interrupted. The growth and degradation of the sand bars at the Cua Hoi estuary have a great influence on the stability of the navigation channel to Ben Thuy port as well as flood drainage of Lam River.

Multi‐Objective Adaptive Surrogate Modeling‐Based Optimization for Distributed Environmental Models Based on Grid Sampling

AbstractParameter optimization is needed for reliable simulations and predictions of natural processes by environmental models. The surrogate modeling‐based approach is an efficient way to reduce the number of model evaluations needed for optimization. However, building a surrogate of a distributed environmental model with many output variables over a large spatial domain is computationally intensive as it involves a large number of expensive model simulations on many spatial grid cells. In this study, a novel calibration method called the multi‐objective adaptive surrogate modeling‐based optimization using grid sampling (MO‐ASMOGS) is introduced. This method constructs the response surface surrogate of the original model more efficiently by using both parameter and spatial grid sampling. The spatial grid sampling strategy utilizes the evolutionary elitism and adaptive sampling concepts, thus allowing the surrogate model to be built using a fraction of the total grid cells over a large region. We apply MO‐ASMOGS to calibrating the Noah‐MP model against two surface fluxes: the gross primary production (GPP) and the latent heat flux (LH), over two plant function types (PFTs) across the continental United States. The results demonstrate that the MO‐ASMOGS method can significantly improve the GPP and LH simulations. The new method needs only a small portion of the total grid cells sampled for a given PFT to achieve comparable optimization results obtained by MO‐ASMO using all grid cells. This method can be very valuable in improving model calibration of computationally intensive distributed environmental models.