Sensitivity analysis and uncertainty assessment in water budgets simulated by the variable infiltration capacity model for Canadian subarctic watersheds

In this study, we evaluate uncertainties propagated through different climate data sets in seasonal and annual hydrological simulations over 10 subarctic watersheds of northern Manitoba, Canada, using the variable infiltration capacity (VIC) model. Further, we perform a comprehensive sensitivity and uncertainty analysis of the VIC model using a robust and state-of-the-art approach. The VIC model simulations utilize the recently developed variogram analysis of response surfaces (VARS) technique that requires in this application more than 6,000 model simulations for a 30-year (1981–2010) study period. The method seeks parameter sensitivity, identifies influential parameters, and showcases streamflow sensitivity to parameter uncertainty at seasonal and annual timescales. Results suggest that the Ensemble VIC simulations match observed streamflow closest, whereas global reanalysis products yield high flows (0.5–3.0 mm day⁻¹) against observations and an overestimation (10–60%) in seasonal and annual water balance terms. VIC parameters exhibit seasonal importance in VARS, and the choice of input data and performance metrics substantially affect sensitivity analysis. Uncertainty propagation due to input forcing selection in each water balance term (i.e., total runoff, soil moisture, and evapotranspiration) is examined separately to show both time and space dimensionality in available forcing data at seasonal and annual timescales. Reliable input forcing, the most influential model parameters, and the uncertainty envelope in streamflow prediction are presented for the VIC model. These results, along with some specific recommendations, are expected to assist the broader VIC modelling community and other users of VARS and land surface schemes, to enhance their modelling applications.     

Coupling basin- and site-scale inverse models of the Española aquifer

Large-scale models are frequently used to estimate fluxes to small-scale models. The uncertainty associated with these flux estimates, however, is rarely addressed. We present a case study from the Espa?ola Basin, northern New Mexico, where we use a basin-scale model coupled with a high-resolution, nested site-scale model. Both models are three-dimensional and are analyzed by codes FEHM and PEST. Using constrained nonlinear optimization, we examine the effect of parameter uncertainty in the basin-scale model on the nonlinear confidence limits of predicted fluxes to the site-scale model. We find that some of the fluxes are very well constrained, while for others there is fairly large uncertainty. Site-scale transport simulation results, however, are relatively insensitive to the estimated uncertainty in the fluxes. We also compare parameter estimates obtained by the basin- and site-scale inverse models. Differences in the model grid resolution (scale of parameter estimation) result in differing delineation of hydrostratigraphic units, so the two models produce different estimates for some units. The effect is similar to the observed scale effect in medium properties owing to differences in tested volume. More important, estimation uncertainty of model parameters is quite different at the two scales. Overall, the basin inverse model resulted in significantly lower estimates of uncertainty, because of the larger calibration dataset available. This suggests that the basin-scale model contributes not only important boundary condition information but also improved parameter identification for some units. Our results demonstrate that caution is warranted when applying parameter estimates inferred from a large-scale model to small-scale simulations, and vice versa.     

Numerical investigation of the impact of uncertainties in satellite rainfall estimation and land surface model parameters on simulation of soil moisture

This study aims at evaluating the uncertainty in the prediction of soil moisture (1D, vertical column) from an offline land surface model (LSM) forced by hydro-meteorological and radiation data. We focus on two types of uncertainty: an input error due to satellite rainfall retrieval uncertainty, and, LSM soil-parametric error. The study is facilitated by in situ and remotely sensed data-driven (precipitation, radiation, soil moisture) simulation experiments comprising a LSM and stochastic models for error characterization. The parametric uncertainty is represented by the generalized likelihood uncertainty estimation (GLUE) technique, which models the parameter non-uniqueness against direct observations. Half-hourly infra-red (IR) sensor retrievals were used as satellite rainfall estimates. The IR rain retrieval uncertainty is characterized on the basis of a satellite rainfall error model (SREM). The combined uncertainty (i.e., SREM + GLUE) is compared with the partial assessment of uncertainty. It is found that precipitation (IR) error alone may explain moderate to low proportion of the soil moisture simulation uncertainty, depending on the level of model accuracy—50–60% for high model accuracy, and 20–30% for low model accuracy. Comparisons on the basis of two different sites also yielded an increase (50–100%) in soil moisture prediction uncertainty for the more vegetated site. This study exemplified the need for detailed investigations of the rainfall retrieval-modeling parameter error interaction within a comprehensive space-time stochastic framework for achieving optimal integration of satellite rain retrievals in land data assimilation systems.     

Uncertainty and sensitivity analysis of a travel-time distribution-based stream water electrical conductivity model

In this paper, we analyse the uncertainty and parameter sensitivity of a conceptual water quality model, based on a travel time distribution (TTD) approach, simulating electrical conductivity (EC) in the Duck River, Northwest Tasmania, Australia for a 2-year period. Dynamic TTDs of stream water were estimated using the StorAge Selection (SAS) approach, which was coupled with two alternate methods to model stream water EC: (1) a solute-balance approach and (2) a water age-based approach. Uncertainty analysis using the Differential Evaluation Adoptive Metropolis (DREAM) algorithm showed that: 1. parameter uncertainty was a small contribution to the overall uncertainty; 2. most uncertainty was related to input data uncertainty and model structure; 3. slightly lower total error was obtained in the water age-based model than the solute-balance model; 4. using time-variant SAS functions reduced the model uncertainty markedly, which likely reflects the effect of dynamic hydrological conditions over the year affecting the relative importance of different flow pathways over time. Model parameter sensitivity analysis using the Variogram Analysis of Response Surfaces (VARS-TOOL) framework found that parameters directly related to the EC concentration were most sensitive. In the solute-balance model, the rainfall concentration C_rain and in the age-based model, the parameter controlling the rate of change of EC with age (λ) were the most sensitive parameter. Model parameters controlling the age mixes of both evapotranspiration and streamflow water fluxes (i.e., the SAS function parameters) were influential for the solute-balance model. Little change in parameter sensitivity over time was found for the age-based concentration relationship; however, the parameter sensitivity was quite dynamic over time for the solute-balance approach. The overarching outcomes provide water quality modellers, engineers and managers greater insight into catchment functioning and its dependence on hydrological conditions.     

Constraining water table depth simulations in a land surface model using estimated baseflow

Several recent studies have shown the significance of representing groundwater in land surface hydrologic simulations. However, optimal methods for model parameter calibration in order to realistically simulate baseflow and groundwater depth have received little attention. Most studies still use globally constant groundwater parameters due to the lack of available datasets for calibration. Moreover, when models are calibrated, various parameter combinations are found to exhibit equifinality in simulated total runoff due to model parameter interactions. In this study, a simple lumped groundwater model is incorporated into the Community Land Model (CLM), in which the water table is interactively coupled to soil moisture through the groundwater recharge fluxes. The coupled model (CLMGW) is successfully validated in Illinois using a 22-year (1984–2005) monthly observational dataset. Baseflow estimates from the digital recursive filter technique are used to calibrate the CLMGW parameters. The advantage obtained from incorporating baseflow calibration in addition to traditional calibration based on measured streamflow alone is demonstrated by a Monte Carlo-type simulation analysis. Using the optimal parameter sets identified from baseflow calibration, flow partitioning and water table depth simulations using CLMGW are improved, and the equifinality problem is alleviated. For other regions that lack observations of water table depth, the baseflow calibration approach can be used to enhance parameter estimation and constrain water table depth simulations.     

近地层能量闭合度对陆面过程模式影响

大量近地层观测试验表明,利用涡动相关法观测的湍流通量小于近地层可利用能量,即近地层能量是不闭合的,这种不闭合度一般为20%甚至更高.而陆面过程模式是基于地气间能量平衡建立,并且模式中的湍流边界层参数化方案通常根据实际观测的湍流通量来确定,因此能量不闭合必将对陆面过程模式造成一定的影响.本文利用2007年春季SACOL站的近地层观测资料,依据能量守恒将能量不闭合中的残余能量通过波文比分配到观测的湍流通量中,即修正涡动相关法观测的湍流通量使得近地层能量达到平衡;之后分别利用观测和修正的湍流通量,建立了能量不闭合和闭合情形下的湍流参数化方案,借助陆面过程模式SHAW,通过数值模拟和对比分析方法考察近地层能量闭合度对陆面过程模式的影响.研究结果表明近地层能量闭合对陆面过程模式有显著的影响:在陆面过程数值模拟中,当应用近地层能量不闭合的湍流通量形成的湍流参数化方案时,陆面过程模式会明显高估地表长波辐射及土壤温度;但当应用修正湍流通量使得近地层能量达到闭合形成的湍流参数化方案后,在不改变任何地表土壤物理生化属性的情况下,陆面过程模式能较好地模拟地表长波辐射和土壤温度.     

Development of probability distributions for urban hydrologic model parameters and a Monte Carlo analysis of model sensitivity

This paper proposes an approach to estimating the uncertainty related to EPA Storm Water Management Model model parameters, percentage routed (P_R) and saturated hydraulic conductivity (K_sat), which are used to calculate stormwater runoff volumes. The methodology proposed in this paper addresses uncertainty through the development of probability distributions for urban hydrologic parameters through extensive calibration to observed flow data in the Philadelphia collection system. The established probability distributions are then applied to the Philadelphia Southeast district model through a Monte Carlo approach to estimate the uncertainty in prediction of combined sewer overflow volumes as related to hydrologic model parameter estimation. Understanding urban hydrology is critical to defining urban water resource problems. A variety of land use types within Philadelphia coupled with a history of cut and fill have resulted in a patchwork of urban fill and native soils. The complexity of urban hydrology can make model parameter estimation and defining model uncertainty a difficult task. The development of probability distributions for hydrologic parameters applied through Monte Carlo simulations provided a significant improvement in estimating model uncertainty over traditional model sensitivity analysis. Copyright © 2013 John Wiley & Sons, Ltd.     

Bayesian uncertainty analysis in hydrological modeling associated with watershed subdivision level: a case study of SLURP model applied to the Xiangxi River watershed,China

Uncertainty analysis in hydrological modeling would help to better implement decision-making related to water resources management, which relies heavily on hydrologic simulations. However, an important concern will be raised over the uncertainty associated with watershed subdivision broadly applied in distributed/semi-distributed hydrological models since scale issues would significantly affect model performance, and thus, lead to dramatic variations in simulations. To fully understand the uncertainty associated with watershed subdivision level, however, is still a tough work confronting researchers because of complex modeling processes and high computation requirements. In this study, we analyzed this uncertainty within a formal Bayesian framework using a Markov Chain Monte Carlo method based on Metropolis–Hastings algorithm. In a case study using the semi-distributed land use-based runoff processes hydrologic model in the Xiangxi River watershed, results showed that the variation in the simulated discharges due to parameter uncertainty was much smaller than that due to parameter and model uncertainty under different watershed subdivision levels defined using aggregated simulation areas (ASAs). However, the posterior probability distribution of model parameters varied in response to subdivision levels, and four parameters (i.e. maximum infiltration rate, retention constant for slow store, maximum capacity for slow store, and retention constant for fast store) were identified with smaller uncertainty. Although the uncertainty in the simulated discharge due to parameter and model uncertainty varied little across subdivisions, the simulation uncertainty only due to parameter uncertainty was found to be reduced through increasing the subdivisions. In addition, the coarsest subdivision level (7 ASAs) was not sufficient for obtaining satisfying simulations in the Xiangxi River watershed, but inappreciable improvement was achieved through increasing the level among finer subdivisions. Moreover, it was demonstrated that increasing subdivision level would have no advantage of improving the reliability of hydrological simulations beyond the threshold (45 ASAs). The findings of this research may shed light on the design of operational hydrological forecasting in the Three Gorges Reservoir region with profound socio-economic implications.     

Evaluation of the hydrology of the IBIS land surface model in a semi‐arid catchment

This paper evaluates the Integrated BIosphere Simulator (IBIS) land surface model using daily soil moisture data over a 3‐year period (2005–2007) at a semi‐arid site in southeastern Australia, the Stanley catchment, using the Monte Carlo generalized likelihood uncertainty estimation (GLUE) approach. The model was satisfactorily calibrated for both the surface 30 cm and full profile 90 cm. However, full‐profile calibration was not as good as that for the surface, which results from some deficiencies in the evapotranspiration component in IBIS. Relatively small differences in simulated soil moisture were associated with large discrepancies in the predictions of surface runoff, drainage and evapotranspiration. We conclude that while land surface schemes may be effective at simulating heat fluxes, they may be ineffective for prediction of hydrology unless the soil moisture is accurately estimated. Sensitivity analyses indicated that the soil moisture simulations were most sensitive to soil parameters, and the wilting point was the most identifiable parameter. Significant interactions existed between three soils parameters: porosity, saturated hydraulic conductivity and Campbell ‘b’ exponent, so they could not be identified independent of each other. There were no significant differences in parameter sensitivity and interaction for different hydroclimatic years. Even though the data record contained a very dry year and another year with a very large rainfall event, this indicated that the soil model could be calibrated without the data needing to explore the extreme range of dry and wet conditions. IBIS was much less sensitive to vegetation parameters. The leaf area index (LAI) could affect the mean of daily soil moisture time series when LAI < 1, while the variance of the soil moisture time series was sensitive to LAI > 1. IBIS was insensitive to the Jackson rooting parameter, suggesting that the effect of the rooting depth distribution on predictions of hydrology was insignificant. Copyright © 2014 John Wiley & Sons, Ltd.     

Calibration of an integrated land surface process and radiobrightness (LSP/R) model during summertime

In this study, a soil vegetation and atmosphere transfer (SVAT) model was linked with a microwave emission model to simulate microwave signatures for different terrain during summertime, when the energy and moisture fluxes at the land surface are strong. The integrated model, land surface process/radiobrightness (LSP/R), was forced with weather and initial conditions observed during a field experiment. It simulated the fluxes and brightness temperatures for bare soil and brome grass in the Northern Great Plains. The model estimates of soil temperature and moisture profiles and terrain brightness temperatures were compared with the observed values. Overall, the LSP model provides realistic estimates of soil moisture and temperature profiles to be used with a microwave model. The maximum mean differences and standard deviations between the modeled and the observed temperatures (canopy and soil) were 2.6 K and 6.8 K, respectively; those for the volumetric soil moisture were 0.9% and 1.5%, respectively. Brightness temperatures at 19 GHz matched well with the observations for bare soil, when a rough surface model was incorporated indicating reduced dielectric sensitivity to soil moisture by surface roughness. The brightness temperatures of the brome grass matched well with the observations indicating that a simple emission model was sufficient to simulate accurate brightness temperatures for grass typical of that region and surface roughness was not a significant issue for grass-covered soil at 19 GHz. Such integrated SVAT-microwave models allow for direct assimilation of microwave observations and can also be used to understand sensitivity of microwave signatures to changes in weather forcings and soil conditions for different terrain types.     

A Linking Test to reduce the number of hydraulic parameters necessary to simulate groundwater recharge in unsaturated soils

In environmental studies, numerical simulation models are valuable tools for testing hypothesis about systems functioning and to perform sensitivity studies under scenarios of land use or climate changes. The simulations depend upon parameters which are not always measurable quantities and must be calibrated against observations, using for instance inverse modelling. Due to the scarcity of these observations, it has been found that parameter sets allowing a good matching between simulated and measured quantities are often non-unique, leading to the problem of equifinality. This can lead to non-physical values, erroneous fluxes and misleading sensitivity analysis. Therefore, a simple but robust inverse method coined the Linking Test is presented to determine if the parameters are linked. Linked parameters are then sub-divided into classes according to their impact on water fluxes. The Linking Test establishes the causes of non-uniqueness of parameter sets and the feasibility of the inverse modelling.     

A forest floor model for heat and moisture including a litter layer

Forest soils are often covered with a litter that influences the rate of mass and energy transfer between the soil and the air above, thereby modifying the temperature and moisture fields in the soil. The presence of a litter should therefore be accounted for in forest SVAT models, especially when long-term simulations are to be performed. A heat and moisture litter model has been developed by adding two dynamical equations to a force-restore type soil model. The experimental data used for the model validation was collected in a pine forest canopy in the South-West of France, that was part of the Euroflux network. The model is tested and validated over a two-year period. It is shown to provide a fairly good simulation of soil and litter moisture, soil and litter temperature and turbulent fluxes measured above the forest floor. It is also shown that simulations without the litter layer are unable to reproduce all these variables simultaneously. We then perform a sensitivity analysis to the parameters whose values are either uncertain or likely to be variable in time and space, such as the litter thickness, the rainfall fraction intercepted by the litter or the maximum value of the surface resistance. A threshold value of the litter moisture used in the surface resistance parameterisation turns out to be the most critical parameter. Further work is needed to investigate the possible relationships between the various parameters describing the litter, but the present litter model can already be used in combination with other forest SVAT models.     

Terrestrial carbon cycle model-data fusion: Progress and challenges

The terrestrial carbon cycle is an important component of global biogeochemical cycling and is closely related to human well-being and sustainable development. However, large uncertainties exist in carbon cycle simulations and observations.Model-data fusion is a powerful technique that combines models and observational data to minimize the uncertainties in terrestrial carbon cycle estimation. In this paper, we comprehensively overview the sources and characteristics of the uncertainties in terrestrial carbon cycle models and observations. We present the mathematical principles of two model-data fusion methods, i.e., data assimilation and parameter estimation, both of which essentially achieve the optimal fusion of a model with observational data while considering the respective errors in the model and in the observations. Based upon reviewing the progress in carbon cycle models and observation techniques in recent years, we have highlighted the major challenges in terrestrial carbon cycle model-data fusion research, such as the "equifinality" of models, the identifiability of model parameters,the estimation of representativeness errors in surface fluxes and remote sensing observations, the potential role of the posterior probability distribution of parameters obtained from sensitivity analysis in determining the error covariance matrixes of the models, and opportunities that emerge by assimilating new remote sensing observations, such as solar-induced chlorophyll fluorescence. It is also noted that the synthesis of multisource observations into a coherent carbon data assimilation system is by no means an easy task, yet a breakthrough in this bottleneck is a prerequisite for the development of a new generation of global carbon data assimilation systems. This article also highlights the importance of carbon cycle data assimilation systems to generate reliable and physically consistent terrestrial carbon cycle reanalysis data products with high spatial resolution and longterm time series. These products are critical to the accurate estimation of carbon cycles at the global and regional scales and will help future carbon management strategies meet the goals of carbon neutrality.     

Spatial characterization of land surface energy fluxes and uncertainty estimation at the Salar de Atacama,Northern Chile

We use Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) data to estimate spatial energy flux and evaporation distributions at the Salar de Atacama, a playa in Northern Chile. Our approach incorporates ASTER surface kinetic temperature, emissivity, and reflectance data, ground-based meteorological measurements, and empirical parameters. Energy flux distributions are estimated using either spatially constant or spatially distributed values of model parameters, with spatially distributed parameters assigned separately to each land cover category in an image classification. We test the sensitivity of energy budget calculations to state variable and parameter values by conducting Monte Carlo simulations for regions with ground energy budget measurements. Results show that assigning spatially distributed model parameters via land cover classifications yields significant improvements to ground and sensible heat flux predictions. Latent heat fluxes cannot, however, be predicted with sufficient accuracy to allow estimation of area-integrated evaporative moisture loss at this low-evaporation playa.     

Using atmospheric tracers to reduce uncertainty in groundwater recharge areas

A Monte Carlo-based approach to assess uncertainty in recharge areas shows that incorporation of atmospheric tracer observations (in this case, tritium concentration) and prior information on model parameters leads to more precise predictions of recharge areas. Variance-covariance matrices, from model calibration and calculation of sensitivities, were used to generate parameter sets that account for parameter correlation and uncertainty. Constraining parameter sets to those that met acceptance criteria, which included a standard error criterion, did not appear to bias model results. Although the addition of atmospheric tracer observations and prior information produced similar changes in the extent of predicted recharge areas, prior information had the effect of increasing probabilities within the recharge area to a greater extent than atmospheric tracer observations. Uncertainty in the recharge area propagates into predictions that directly affect water quality, such as land cover in the recharge area associated with a well and the residence time associated with the well. Assessments of well vulnerability that depend on these factors should include an assessment of model parameter uncertainty. A formal simulation of parameter uncertainty can be used to delineate probabilistic recharge areas, and the results can be expressed in ways that can be useful to water-resource managers. Although no one model is the correct model, the results of multiple models can be evaluated in terms of the decision being made and the probability of a given outcome from each model.     

Quantifying uncertainty in remotely sensed land cover maps

Remotely sensed land cover maps are increasingly used as inputs into environmental simulation models whose outputs inform decisions and policy-making. Risks associated with these decisions are dependent on model output uncertainty, which is in turn affected by the uncertainty of land cover inputs. This article presents a method of quantifying the uncertainty that results from potential mis-classification in remotely sensed land cover maps. In addition to quantifying uncertainty in the classification of individual pixels in the map, we also address the important case where land cover maps have been upscaled to a coarser grid to suit the users’ needs and are reported as proportions of land cover type. The approach is Bayesian and incorporates several layers of modelling but is straightforward to implement. First, we incorporate data in the confusion matrix derived from an independent field survey, and discuss the appropriate way to model such data. Second, we account for spatial correlation in the true land cover map, using the remotely sensed map as a prior. Third, spatial correlation in the mis-classification characteristics is induced by modelling their variance. The result is that we are able to simulate posterior means and variances for individual sites and the entire map using a simple Monte Carlo algorithm. The method is applied to the Land Cover Map 2000 for the region of England and Wales, a map used as an input into a current dynamic carbon flux model.     

Simultaneous calibration of surface flow and baseflow simulations: a revisit of the SWAT model calibration framework

Accurate analysis of water flow pathways from rainfall to streams is critical for simulating water use, climate change impact, and contaminants transport. In this study, we developed a new scheme to simultaneously calibrate surface flow (SF) and baseflow (BF) simulations of soil and water assessment tool (SWAT) by combing evolutionary multi‐objective optimization (EMO) and BF separation techniques. The application of this scheme demonstrated pronounced trade‐off of SWAT's performance on SF and BF simulations. The simulated major water fluxes and storages variables (e.g. soil moisture, evapotranspiration, and groundwater) using the multiple parameters from EMO span wide ranges. Uncertainty analysis was conducted by Bayesian model averaging of the Pareto optimal solutions. The 90% confidence interval (CI) estimated using all streamflows substantially overestimate the uncertainty of low flows on BF days while underestimating the uncertainty of high flows on SF days. Despite using statistical criteria calculated based on streamflow for model selection, it is important to conduct diagnostic analysis of the agreement of SWAT behaviour and actual watershed dynamics. The new calibration technique can serve as a useful tool to explore the trade‐off between SF and BF simulations and provide candidates for further diagnostic assessment and model identification. Copyright © 2011 John Wiley & Sons, Ltd.     

Uncertainty and multiple objective calibration in regional water balance modelling: case study in 320 Austrian catchments

We examine the value of additional information in multiple objective calibration in terms of model performance and parameter uncertainty. We calibrate and validate a semi‐distributed conceptual catchment model for two 11‐year periods in 320 Austrian catchments and test three approaches of parameter calibration: (a) traditional single objective calibration (SINGLE) on daily runoff; (b) multiple objective calibration (MULTI) using daily runoff and snow cover data; (c) multiple objective calibration (APRIORI) that incorporates an a priori expert guess about the parameter distribution as additional information to runoff and snow cover data. Results indicate that the MULTI approach performs slightly poorer than the SINGLE approach in terms of runoff simulations, but significantly better in terms of snow cover simulations. The APRIORI approach is essentially as good as the SINGLE approach in terms of runoff simulations but is slightly poorer than the MULTI approach in terms of snow cover simulations. An analysis of the parameter uncertainty indicates that the MULTI approach significantly decreases the uncertainty of the model parameters related to snow processes but does not decrease the uncertainty of other model parameters as compared to the SINGLE case. The APRIORI approach tends to decrease the uncertainty of all model parameters as compared to the SINGLE case. Copyright © 2006 John Wiley & Sons, Ltd.     

Modelling the decadal dynamics of reach-scale river channel evolution and floodplain turnover in CAESAR-Lisflood

Sedimentary deposits provide records of environmental change quantifying erosion fluxes conditioned by natural and anthropogenic disturbances. These fluxes are lagged by internal storage, particularly within floodplains, complicating reconstruction of environmental changes. The time sediment remains in storage underpins the interpretation of sedimentary records and accurate monitoring of pollutant fluxes. Turnover time is a measure of the timeframe to erode every floodplain surface. CAESAR-Lisflood is used to simulate fluvial evolution at reach scale, providing a basis for quantifying environmental changes on the timescales of sediment storage. We evaluate the accuracy of CAESAR-Lisflood simulations of channel changes and turnover times for alluvial floodplains using historical channel changes reconstructed for 10 reaches in northern England to quantify model accuracy in replicating mean annual erosion, deposition and channel lateral migration rates, alongside planform morphology. Here, a split-sample testing approach is adopted, whereby five of the reaches were calibrated and the resulting parameter values were applied to the other reaches to evaluate the transferability of parameter settings. The lowest overall integrated error identified the best-fit simulations and showed that modelled process rates were within ~25–50% of rates from historical reconstructions, generally. Calibrated parameters for some reaches are widely transferable, producing accurate geomorphic changes for some uncalibrated sites. However, large errors along some reaches indicate that reach-specific parameterization is recommended. Turnover times are underpinned by the assumption that areas of floodplain previously unvisited by the channel are reworked. This assumption has been challenged by studies that show floodplain (re)occupation rates vary spatially. However, this limitation is less important for the short-duration simulations presented here. The simulations reconstruct floodplain turnover times estimated by mapped rates mostly successfully, demonstrating the potential applicability of calibrated parameters over much longer timescales. Errors in the form of under-predicted erosion rates propagated, resulting in over-predicted turnover times by even greater magnitudes. © 2020 The Authors. Earth Surface Processes and Landforms Published by John Wiley & Sons Ltd.     

Simulating the exchanges of carbon dioxide, water vapor and heat over Changbai Mountains temperate broad-leaved Korean pine mixed forest ecosystem

A process-based ecosystem productivity model BEPS (Boreal Ecosystem Productivity Simulator) was updated to simulate half-hourly exchanges of carbon, water and energy between the atmosphere and terrestrial ecosystem at a temperate broad-leaved Korean pine forest in the Changbai Mountains, China. The BEPSh model is able to capture the diurnal and seasonal variability in carbon dioxide, water vapor and heat fluxes at this site in the growing season of 2003. The model validation showed that the simulated net ecosystem productivity (NEP), latent heat flux (LE), sensible heat flux (Hs) are in good agreement with eddy covariance measurements with an R2 value of 0.68, 0.86 and 0.72 for NEP, LE and Hs, respectively. The simulated annual NEP of this forest in 2003 was 300.5 gC/m2, and was very close to the observed value. Driving this model with different climate scenarios, we found that the NEP in the Changbai Mountains temperate broad-leaved Korean pine mixed forest ecosystem was sensitive to climate variability, and the current carbon sink will be weakened under the condition of global warming. Furthermore, as a process-based model, BEPSh was also sensitive to physiological parameters of plant, such as maximum Rubisco activity (Vcmax) and the maximum stomatal conductance (gmax), and needs to be carefully calibrated for other applications.