Multi-objective Calibration Research Articles

Multi-objective calibration and evaluation of the ORCHIDEE land surface model over France at high resolution

Abstract. Here we present a strategy to obtain a reliable hydrological simulation over France with the ORCHIDEE land surface model. The model is forced by the SAFRAN atmospheric reanalysis at 8 km resolution and hourly time steps from 1959 to 2020 and by a high-resolution DEM (around 1.3 km in France). Each SAFRAN grid cell is decomposed into a graph of hydrological transfer units (HTUs) based on the higher-resolution DEM to better describe lateral water movements. In particular, it is possible to accurately locate 3507 stations among the 4081 stations collected from the national hydrometric network HydroPortail (filtered to drain an upstream area larger than 64 km2). A simple trial-and-error calibration is conducted by modifying selected parameters of ORCHIDEE to reduce the biases of the simulated water budget compared to the evapotranspiration products (the GLEAM and FLUXCOM datasets) and the HydroPortail observations of river discharge. The simulation that is eventually preferred is extensively assessed with classic goodness-of-fit indicators complemented by trend analysis at 1785 stations (filtered to have records for at least 8 entire years) across France. For example, the median bias of evapotranspiration is −0.5 % against GLEAM (−4.3 % against FLUXCOM), the median bias of river discharge is 6.3 %, and the median Kling–Gupta efficiency (KGE) of square-rooted river discharge is 0.59. These indicators, however, exhibit a large spatial variability, with poor performance in the Alps and the Seine sedimentary basin. The spatial contrasts and temporal trends of river discharge across France are well represented with an accuracy of 76.4 % for the trend sign and an accuracy of 62.7 % for the trend significance. Although it does not yet integrate human impacts on river basins, the selected parameterization of ORCHIDEE offers a reliable historical overview of water resources and a robust configuration for climate change impact analysis at the nationwide scale of France.

A multi-variable calibration framework at the grid scale for integrating streamflow with evapotranspiration data to improve the simulation of distributed hydrological model

Study regionThe Ganjiang River Basin, China Study focusParameter calibration is crucial for the accurate and reliable operation of hydrological models. Traditional methods face challenges in calibrating spatially heterogeneous parameters of distributed hydrological models, and existing multi-variable calibration strategies often fall short in comprehensively improving model performance, particularly in streamflow simulations. To address these challenges, this study proposes a multi-objective calibration framework that integrates observed streamflow data and satellite-based evapotranspiration (ET) data. The spatiotemporal information of the merged ET is utilized to calibrate six hydrological parameters of the Variable Infiltration Capacity (VIC) model at the grid scale, enhancing hydrological simulations for the Ganjiang River basin. New hydrological insightsCompared to the benchmark scheme based solely on streamflow, the proposed calibration framework improves simulations of area-average ET at the sub-basin scale and soil moisture content in the Ganjiang River basin, without compromising the accuracy of daily streamflow simulations. Additionally, notable enhancements are observed in monthly streamflow simulations. This study provides a promising and comprehensive calibration framework using satellite-based data to constrain parameters and enhance the performance of distributed hydrological models.

Regional multi-objective calibration for distributed hydrological modelling: a decision tree based approach

Regional multi-objective calibration for distributed hydrological modelling: a decision tree based approach

Multi-objective calibration and uncertainty analysis for the event-based modelling of flash floods

ABSTRACT This study investigates the best approach to calibrate an event-based conceptual Hydrologiska Byråns Vattenbalansavdelning (HBV) model, comparing different trials of single-objective, single-event multi-objective (SEMO), and multi-event-multi-objective (MEMO) model calibrations using root mean squared error (RMSE), Nash-Sutcliffe model efficiency coefficient (NSE), and Bias as objective functions. Model performance was validated for several peak events via 90% confidence interval (CI)-based output uncertainty quantification of relative error of discharges. Multi-objective optimization yielded more accurate and robust solutions compared to single-objective calibrations. Ensembles of Pareto solutions from the multi-objective calibrations better characterized the flood peaks within the uncertainty intervals. MEMO calibration exhibited lower uncertainties and better prediction of peak events versus SEMO calibration. Moreover, the MEMO_6D (six-dimensional) approach outperformed the SEMO_3D and MEMO_3D in capturing the larger peak events. This study suggests that the MEMO_6D is the best approach for predicting large flood events with lower model output uncertainties when the calibration is performed with a better combination of peak events.

Sensitivities of hydrological processes under climate warming and Landuse/landcover change in the Lhasa basin, Tibetan plateau

Study Region: The Lhasa River Basin (LRB) on the Tibetan Plateau.Study Focus: This study assessed the influence of climate change and landuse/landcover (LULC) change on hydrological processes using the water balancing simulation model (WaSiM). We used multiple data sources in a multi-objective calibration and validation approach, such as streamflow, snow cover, and glacier inventory, to minimize the uncertainties in the simulation results. Heat transfer in the soil was implemented in this study, which is crucial for permafrost regions.New Hydrological Insights for the Region: The results revealed spatiotemporal variation of precipitation change induced by climate warming in the last few decades. Runoff, actual evapotranspiration, and glacier melt are experiencing an increased trend. Meanwhile, decreased trends are signaled for snowmelt and water storage changes. Snowmelt showed an earlier peak that changed from June to May, which, combined with increased rainfall, resulted in an earlier peak of runoff in the basin. Rainfall is the main factor affecting runoff generation in LRB, while snowmelt and storage change are driven by the amount of snowfall, and actual evapotranspiration and glacier melt are most influenced by temperature. The annual discharge in LRB is projected to decrease, ranging from 20% to 39%, in response to the total permafrost degradation. Finally, the apparent variations in LULC exhibited in this study reveal a variety of water cycle and hydrological implications.

On optimization of calibrations of a distributed hydrological model with spatially distributed information on snow

Abstract. In northern cold-temperate countries, a large portion of annual streamflow is produced by spring snowmelt, which often triggers floods. It is important to have spatial information about snow variables such as snow water equivalent (SWE), which can be incorporated into hydrological models, making them more efficient tools for improved decision-making. The present research implements a unique spatial pattern metric in a multi-objective framework for calibration of hydrological models and attempts to determine whether raw SNODAS (SNOw Data Assimilation System) data can be utilized for hydrological model calibration. The spatial efficiency (SPAEF) metric is explored for spatially calibrating SWE. Different calibration experiments are performed combining Nash–Sutcliffe efficiency (NSE) for streamflow and root-mean-square error (RMSE) and SPAEF for SWE, using the Dynamically Dimensioned Search (DDS) and Pareto Archived Dynamically Dimensioned Search multi-objective optimization (PADDS) algorithms. Results of the study demonstrate that multi-objective calibration outperforms sequential calibration in terms of model performance (SWE and discharge simulations). Traditional model calibration involving only streamflow produced slightly higher NSE values; however, the spatial distribution of SWE could not be adequately maintained. This study indicates that utilizing SPAEF for spatial calibration of snow parameters improved streamflow prediction compared to the conventional practice of using RMSE for calibration. SPAEF is further implied to be a more effective metric than RMSE for both sequential and multi-objective calibration. During validation, the calibration experiment incorporating multi-objective SPAEF exhibits enhanced performance in terms of NSE and Kling–Gupta efficiency (KGE) compared to calibration experiment solely based on NSE. This observation supports the notion that incorporating SPAEF computed on raw SNODAS data within the calibration framework results in a more robust hydrological model. The novelty of this study is the implementation of SPAEF with respect to spatially distributed SWE for calibrating a distributed hydrological model.

Multi-objective calibration of vertical-axis wind turbine controllers: balancing aero-servo-elastic performance and noise

Abstract. Vertical-axis wind turbines (VAWTs) are considered promising solutions for urban wind energy generation due to their design, low maintenance costs, and reduced noise and visual impact compared to horizontal-axis wind turbines (HAWTs). However, deploying these turbines close to densely populated urban areas often triggers considerable local opposition to wind energy projects. Among the primary concerns raised by communities is the issue of noise emissions. Noise annoyance should be considered in the design and decision-making process to foster the social acceptance of VAWTs in urban environments. At the same time, maximising the operational efficiency of VAWTs in terms of power generation and actuation effort is equally important. This paper balances noise and aero-servo-elastic performance by formulating and solving a multi-objective optimisation problem from a controller calibration perspective. Psychoacoustic annoyance is taken as a novel indicator for the noise objective by providing a more reliable estimate of the human perception of wind turbine noise than conventional sound metrics. The computation of the psychoacoustic annoyance metric is made feasible by integrating it with an accurate and computationally efficient low-fidelity noise prediction model. For optimisation, an advanced partial-load control scheme – often used in industrial turbines – is considered, with the Kω2 controller as a baseline for comparison. Optimal solutions balancing the defined objectives are identified using a multi-criteria decision-making method (MCDM) and are subsequently assessed using a frequency-domain controller analysis framework and mid-fidelity time-domain aero-servo-elastic simulations. The MCDM results indicate the potential application of this controller in small-scale urban VAWTs to attain power gains of up to 39 % on one side and to trade off a reduction in actuation effort of up to 25 % at the cost of only a 2 % power decrease and a 6 % increase in psychoacoustic annoyance on the other side compared to the baseline. These findings confirm the flexible structure of the optimally calibrated wind speed estimator and tip-speed ratio (WSE–TSR) tracking controller, effectively balancing aero-servo-elastic performance with noise emissions and marking the first instance of integrating residential concerns into the decision-making process.

Enhancing flood event predictions: Multi-objective calibration using gauge and satellite data

Rainfall-runoff models are widely used for predicting watershed runoff. The accuracy of the predictions heavily relies on effective model calibration, which is majorly influenced by the employed objective functions that measure the agreement between predictions and real-world observations. In this study, we propose a novel multi-objective calibration approach with the aim of improving the flood event predictions of a conceptual rainfall-runoff model applied to a catchment in the Austrian Alps. To achieve this, we include a total of five carefully selected objective functions into calibration. Three of these objective functions can be regarded as more classical and are roughly based on comparing absolute deviations, square of the residuals, as well as bias, correlation and variability of the flow. Another objective function relies on remote observations of the snow covered area, for which the MODIS Terra (MOD10A1.061) and Aqua (MYD10A1.061) datasets are used. The final objective function measures the differences in peak magnitude and timing for the largest events in the observation period. To showcase the proposed approach’s effectiveness, we compare the calibration and validation results with those obtained by a model that was only calibrated for two runoff objective functions and to carefully selected benchmark cases. The presented study reveals that our approach enhances the model’s capacity to depict snow cover dynamics, a crucial factor in accurately predicting snow-driven flooding events. Additionally, the results underscore that incorporating an error metric targeting magnitude and timing errors of large flood events can enhance the model’s ability to predict peak runoff. The results of this study further suggest that in general a more robust model can be obtained by incorporating additional error metrics that specifically target physical states and individual parts of the runoff curve into model calibration.

To tame a land: Limiting factors in model performance for the multi-objective calibration of a pan-European, semi-distributed hydrological model for discharge and sediments

Study RegionEurope Study FocusThe semi-distributed Hydrological Predictions for the Environment (HYPE) model for the European domain, E-HYPE4, was calibrated for discharge and sediments within a framework allowing for evaluation of the factors limiting model performance. Calibration was conducted using a multi-phase approach during which an initial multi-objective discharge/sediment calibration was followed by an exhaustive sediment calibration in which combinations of sediment routines were assessed. During each calibration phase, ensembles of parameter sets and model routines were simultaneously evaluated against discharge, evapotranspiration, and sediment observations. In total, 20,000 parameter sets were evaluated during the discharge calibration, and a further 20,000 model setups were assessed during the sediment calibration. New Hydrological Insights for the RegionModel performance was best with a highly regionalized model, and the largest drop in achievable performance occurred when transitioning from an ensemble of candidates to a single model setup. Much of the performance gains from a highly regionalized model could, however, be achieved with a much less regionalized model. Inclusion of sediments as a calibration objective provided more value than that of evapotranspiration in regards to reducing equifinality in the calibration for discharge. Evaluation of the erosion routines indicated that an index-based routine performed equally well as a more complex, process-based routine. Finally, analysis of model performance by subbasin attributes revealed the dominant factors — such as landuse, glaciers, abstractions/regulations, groundwater, and lakes/wetlands — affecting model biases for various regions.

A century of nitrogen dynamics in agricultural watersheds of Denmark

Intensive agriculture has been linked to increased nitrogen loads and adverse effects on downstream aquatic ecosystems. Sustained large net nitrogen surpluses have been shown in several contexts to form legacies in soil or waters, which delay the effects of reduction measures. In this study, detailed land use and agricultural statistics were used to reconstruct the annual nitrogen surpluses in three agriculture-dominated watersheds of Denmark (600–2700 km2) with well-drained loamy soils. These surpluses and long-term hydrological records were used as inputs to the process model ELEMeNT to quantify the nitrogen stores and fluxes for 1920–2020. A multi-objective calibration using timeseries of river nitrate loads, as well as other non-conventional data sources, allowed to explore the potential of these different data to constrain the nitrogen cycling model. We found the flux-weighted nitrate concentrations in the root zone percolate below croplands, a dataset not commonly used in calibrating watershed models, to be critical in reducing parameter uncertainty. Groundwater nitrate legacies built up in all three studied watersheds during 1950–1990 corresponding to ∼2% of the surplus (or ∼1 kg N ha yr−1) before they went down at a similar rate during 1990–2015. Over the same periods active soil nitrogen legacies first accumulated by approximately 10% of the surplus (∼5 kg N ha yr−1), before undergoing a commensurate reduction. Both legacies appear to have been the drivers of hysteresis in the diffuse load at the catchments’ outlet and hindrances to reaching water quality goals. Results indicate that the low cropland surpluses enforced during 2008–2015 had a larger impact on the diffuse river loads than the European Union’s untargeted grass set-aside policy of 1993–2008. Collectively, the measures of 1990–2015 are estimated to have reset the diffuse load regimes of the watersheds back to the situation prevailing in the 1960s.

Point-scale multi-objective calibration of the Community Land Model (version 5.0) using in situ observations of water and energy fluxes and variables

Abstract. This study evaluates water and energy fluxes and variables in combination with parameter optimization of version 5 of the state-of-the-art Community Land Model (CLM5) land surface model, using 6 years of hourly observations of latent heat flux, sensible heat flux, groundwater recharge, soil moisture and soil temperature from an agricultural observatory in Denmark. The results show that multi-objective calibration in combination with truncated singular value decomposition and Tikhonov regularization is a powerful method to improve the current practice of using lookup tables to define parameter values in land surface models. Using measurements of turbulent fluxes as the target variable, parameter optimization is capable of matching simulations and observations of latent heat, especially during the summer period, whereas simulated sensible heat is clearly biased. Of the 30 parameters considered, the soil texture, monthly leaf area index (LAI) in summer, stomatal conductance and root distribution have the highest influence on the local-scale simulation results. The results from this study contribute to improvements of the model characterization of water and energy fluxes. This work highlights the importance of performing parameter calibration using observations of hydrologic and energy fluxes and variables to obtain the optimal parameter values for a land surface model.

A Pareto‐Based Sensitivity Analysis and Multiobjective Calibration Approach for Integrating Streamflow and Evaporation Data

AbstractEvaporation is gaining increasing attention as a calibration and evaluation variable in hydrologic studies that seek to improve the physical realism of hydrologic models and go beyond the long‐established streamflow‐only calibration. However, this trend is not yet reflected in sensitivity analyses aimed at determining the relevant parameters to calibrate, where streamflow has traditionally played a leading role. On the basis of a Pareto optimization approach, we propose a framework to integrate the temporal dynamics of streamflow and evaporation into the sensitivity analysis and calibration stages of the hydrologic modeling exercise, here referred to as “Pareto‐based sensitivity analysis” and “multiobjective calibration.” The framework is successfully applied to a case study using the Variable Infiltration Capacity (VIC) model in three catchments located in Spain as representative of the different hydroclimatic conditions within the Iberian Peninsula. Several VIC vegetation parameters were identified as important to the performance estimates for evaporation during sensitivity analysis, and therefore were suitable candidates to improve the model representation of evaporative fluxes. Sensitivities for the streamflow performance, in turn, were mostly driven by the soil and routing parameters, with little contribution from the vegetation parameters. The multiobjective calibration experiments were carried out for the most parsimonious parameterization after a comparative analysis of the performance gains and losses for streamflow and evaporation, and yielded optimal adjustments for both hydrologic variables simultaneously. Results from this study will help to develop a better understanding of the trade‐offs resulting from the joint integration of streamflow and evaporation data into modeling frameworks.

A multi-objective calibration framework for capturing the behavioral patterns of autonomously-driven vehicles

Calibration of car-following (CF) models is considered a very important task towards reproduction of individual vehicle behaviors and collective traffic phenomena. In most works, calibration is performed on trajectories of leading and following vehicles either on speed or spacing quantities, with the combination of the two plus the acceleration quantity to be considered as the most appropriate according to the literature. With the advent of adaptive cruise control (ACC) technology, there are intrinsic behavioral differences between human- and ACC-driven vehicles that are visible in experimental observations. Some examples include constant headway for ACC-enabled vehicles, string instability, hysteretic behavior, human-like response time and others. Since calibration is performed only on basic vehicle dynamics, i.e., the spacing, speed and acceleration, even after proper parametrization we cannot be certain that CF models will be able to reproduce some or all of the above phenomena and to what extend. The aim of this work is to propose a multi-objective calibration framework validated on empirical observations of car platoons. Furthermore, it investigates if, and to what extent, the traffic dynamics and behavioral patterns of ACC-driven vehicles can be reproduced. Two state-of-the-art CF models are tested with the empirical dataset. The results indicate that the proposed framework leads to acceptable errors and comparable performance, facilitating the models to capture phenomena in a way that is not possible until now, using the calibration on basic vehicle dynamics.

Verifying model performance using validation of Pareto solutions

Calibrating a hydrological model using more than one independent data set (multi-objective calibration) can potentially improve parameter estimation. However, it often identifies multiple parameter sets whose combined performance is generally indistinguishable, as visualized via a Pareto front. The objective of this paper is to identify how Pareto optimal solutions perform during model validation, and to identify how objective function tradeoffs in calibration might shift along the Pareto front during validation. Model calibration was based on an ecohydrological model simulating leaf area index (LAI), evapotranspiration (ET) and streamflow. Focusing on a predominantly forested Australian catchment and using satellite derived LAI and ET with observed streamflow for calibration, gave understanding of differences in model behavior related to observed data. The performance shifts between validation and calibration periods were assessed for combinations of ET and streamflow and LAI and streamflow. For both observed data combinations the performance deteriorated between calibration to validation and the “best” performing validation parameter solutions were scattered along the calibration Pareto front. In general, the streamflow/LAI data pairing showed a greater deterioration in streamflow performance in validation compared to the streamflow/ET data pairing. However, in both cases there was significant parameter equifinality. The results highlight that model structural limitations are a greater issue for model validation performance than in calibration. The identification of one optimal parameter set from a Pareto set remains elusive.

Integration of the generalized complementary relationship into a lumped hydrological model for improving water balance partitioning: A case study with the Xinanjiang model

Lumped hydrological models (LHMs) are useful tools for simulating catchment runoff (Q) owing to their low data requirements, high computational efficiency, and robust performance. However, most LHMs are purposely conceptualized for rainfall-runoff modeling with over-simplified consideration of other water balance components, especially evapotranspiration (E). The generalized complementary relationship (GCR) can reliably estimate the regional E using only routine meteorological observations. Here, a lumped hydrological model (Xinanjiang model, XAJ) was integrated with the GCR (XAJ-GCR) to improve the accuracy of the water balance partitioning of LHMs. Long-term daily observations of Q and E in the Seolmacheon experimental watershed (8.54 km2) in South Korea were collected to test the capability of the XAJ-GCR model to capture Q and E dynamics with three different integration schemes and three different calibration strategies (single or multi-objective). Results show that XAJ-GCR model obtained excellent results in simulating Q (NSEQ = 0.91 ± 0.02) and markedly improves simulating E with NSEE from − 0.35 ± 0.27 (mean ± standard deviation) to 0.56 ± 0.10. The multi-objective calibration strategy achieves better simulation results for Q and E simultaneously with NSEQ = 0.93 and NSEE = 0.69 ± 0.01, in which the built-in structure of the XAJ model is the optimal scheme. This study demonstrated that the integration of GCR into LHMs is feasible and applicable for improving water balance partitioning. The integration maintains all the advantages of LHM and does not increase the model complexity and data requirements.

Ten strategies towards successful calibration of environmental models

Model calibration is the procedure of finding model settings such that simulated model outputs best match the observed data. Model calibration is necessary when the model parameters cannot directly be measured as is the case with a wide range of environmental models where parameters are conceptually describing upscaled and effective physical processes. Model calibration is therefore an important step of environmental modeling as the model might otherwise provide random outputs if never compared to a ground truth. Model calibration itself is often referred to be an art due to its plenitude of intertwined steps and necessary decisions along the way before a calibration can be carried out or can be regarded successful.This work provides a general guide specifying which steps a modeler needs to undertake, how to diagnose the success of each step, and how to identify the right action to revise steps that were not successful. The procedure is formalized into ten iterative steps generally appearing in calibration experiments. Each step of this “calibration life cycle” is either illustrated with an exemplary calibration experiment or providing an explicit checklist the modeler can follow. These ten strategies are: (1) using sensitivity information to guide the calibration, (2) handling of parameters with constraints, (3) handling of data ranging orders of magnitude, (4) choosing the data to base the calibration on, (5) presenting various methods to sample model parameters, (6) finding appropriate parameter ranges, (7) choosing objective functions, (8) selecting a calibration algorithm, (9) determining the success and quality of a multi-objective calibration, and (10) providing a checklist to diagnose calibration performance using ideas introduced in the previous steps. The formal definition of strategies through the calibration process is providing an overview while shedding a light on connections between these main ingredients to calibrate an environmental model and will therefore enable especially novice modelers to succeed.

Considering flood scaling property in multi-objective calibration of the SWAT model: a case study in Zijinguan watershed, Northern China

Considering flood scaling property in multi-objective calibration of the SWAT model: a case study in Zijinguan watershed, Northern China

Spatiotemporal green water dynamics and their responses to variations of climatic and underlying surface factors: A case study in the Sanjiang Plain, China

Study regionThe Sanjiang Plain (SJP), located at the confluence reaches of the Heilong, Songhua, and Wusuli Rivers in Northeast China. Study focusThis study aimed to quantify the effects of varying climate and land-use/land-cover (LULC) dynamics on green water (GW) over the SJP during two distinctive periods (i.e., pre-2000 and post-2000), when synergetic effects of increased precipitation and temperature and rapid development of agriculture occurred. This assessment used the distributed eco-hydrological model ESSI-3. Multivariable and multi-objective calibration approaches (i.e., discharge, evapotranspiration, and terrestrial water storage anomaly) were used to ensure the high accuracies of the model outputs. New hydrological insights for the regionThis research concluded that GW flow and GW storage in the SJP evidently increased after 2000 compared with before. Across the SJP, GW flow and GW storage responded differently to climate changes and LULC dynamics during pre-2000 and post-2000 period. Our results demonstrated that GW storage changes were predominately affected by climatic changes, especially variations in precipitation, whose contribution accounted for more than 56% after 2000. However, GW flow changes were mainly governed by LULC changes, especially the influence of cropland transformation, whose contribution increased over 20% in the post-2000 period. This study posed a deep insight regarding impacts of regional climatic and LULC changes on GW dynamics, which may benefit decision-making for planting structure optimization and adjustment over the region.

Evaluation of overland flow modelling hypotheses with a multi-objective calibration using discharge and sediment data.

Conceptual hydrological models can move towards process-oriented modelling when addressing broader issues than discharge modelling alone. For instance, water quality modelling generally requires understanding of both pathways and travel times which might not be easily identified because observations at the outlet aggregate all processes at the catchment scale. In this study we tested if adding a second kind of observation, specifically sediment data, can help distinguish overland flow from total discharge. We applied a multi-objective calibration on both discharge and suspended sediment concentration simulation performance to the World-Wide Hydrological Predictions for the Environment (HYPE) model for 111 catchments spread over the USA. Results show that in comparison to two calibrations made one after the other, the multi-objective calibration leads to a significant improvement on the simulation performance of suspended sediments without a significant impact on the performance of discharge. New modelling hypotheses for overland flow calculations are proposed and resulted in similar discharge performances as the original one but with fewer parameters, which reduces equifinality and can prevent unwarranted model complexity in data-poor areas.

Hydrodynamic Modeling of Inundation Patterns of a Large African Floodplain Indicates Sensitivity to Waterway Restoration

AbstractLarge‐scale floodplains are important features of the African continent. Regular inundation provides the means to support large populations but can also present problems such as access to health facilities and water body formation that sustain malaria vectors. Modeling of these floodplains is therefore important, but complex. In this research, we develop, calibrate, and validate a hydrodynamic model of the Barotse Floodplain, of the Upper Zambezi, Zambia. The floodplain has seen recent infrastructure developments including the restoration of the canal network and construction of a cross floodplain causeway. In order to create a robust model, a multiobjective calibration uses a time slice approach based on available Landsat satellite image overpasses. An emulator‐based sensitivity analysis indicates the significance of hydrological processes in the model. Model evaluation is undertaken for two events in the gauge record (2009 and 2018), of similar magnitude that occur before and after modifications to the floodplain. Results indicate a complex impact of infrastructure development on the hydrodynamics of the floodplain, with a higher peak flow, but with a redistribution of water throughout the floodplain. Deeper flooding is observed in some areas while others experience lower water levels. The sensitivity results also reflect a change in processes, where floodplain flows dominate the 2009 event, whilst channel process dominate the 2018 event. Overall, we show that relatively modest modifications to the floodplain have impacted flood water levels, which in turn will influence access route availability and alter malaria transmission rates.