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Kutschera, Ellynne, John B. Kim, G. Stephen Pitts, and Ray Drapek. "“What’s Past Is Prologue”: Vegetation Model Calibration with and without Future Climate." Land 12, no. 6 (May 24, 2023): 1121. http://dx.doi.org/10.3390/land12061121.
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Many models are designed to generate future predictions under climate-change scenarios. Such models are typically calibrated for a study area with climate data that represent historical conditions. However, future projections of the model may include outputs for which the model has not been calibrated. Ideally, a climate-change-impacts model would be calibrated for recent conditions and also for possible future climate conditions. We demonstrate an approach, where a vegetation model is subjected to two calibrations: conventionally to the study area and separately to the study area plus additional areas representing analogues of potential future climate. We apply the dynamic vegetation model MC2 to a mountainous ecosystem in the Pacific Northwest, USA. We compare the conventional model calibration with the extra-study-area calibration and future projections. The two calibrations produce different outputs in key ecosystem variables, where some differences vary with time. Some model output trends for net primary productivity and plant functional type are more influenced by climatic input, while for others, the calibration area has greater consequence. Excluding areas representing potential future climate may be an important omission in model calibration, making the inclusion of such areas a decisive consideration in climate-change-impact simulations.
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Lee, Jeonghoon, Jeonghyeon Choi, Jiyu Seo, Jeongeun Won, and Sangdan Kim. "Exploring Climate Sensitivity in Hydrological Model Calibration." Water 15, no. 23 (November 25, 2023): 4094. http://dx.doi.org/10.3390/w15234094.
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In the context of hydrological model calibration, observational data play a central role in refining and evaluating model performance and uncertainty. Among the critical factors, the length of the data records and the associated climatic conditions are paramount. While there is ample research on data record length selection, the same cannot be said for the selection of data types, particularly when it comes to choosing the climatic conditions for calibration. Conceptual hydrological models inherently simplify the representation of hydrological processes, which can lead to structural limitations, which is particularly evident under specific climatic conditions. In this study, we explore the impact of climatic conditions during the calibration period on model predictive performance and uncertainty. We categorize the inflow data from AnDong Dam and HapCheon Dam in southeastern South Korea from 2001 to 2021 into four climatic conditions (dry years, normal years, wet years, and mixed years) based on the Budyko dryness index. We then use data from periods within the same climatic category to calibrate the hydrological model. Subsequently, we analyze the model’s performance and posterior distribution under various climatic conditions during validation periods. Our findings underscore the substantial influence of the climatic conditions during the calibration period on model performance and uncertainty. We discover that when calibrating the hydrological model using data from periods with wet climatic conditions, achieving comparable predictive performance in validation periods with different climatic conditions remains challenging, even when the calibration period exhibits excellent model performance. Furthermore, when considering model parameters and predicted streamflow uncertainty, it is advantageous to calibrate the hydrological model under dry climatic conditions to achieve more robust results.
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Tett, Simon F. B., Jonathan M. Gregory, Nicolas Freychet, Coralia Cartis, Michael J. Mineter, and Lindon Roberts. "Does Model Calibration Reduce Uncertainty in Climate Projections?" Journal of Climate 35, no. 8 (April 15, 2022): 2585–602. http://dx.doi.org/10.1175/jcli-d-21-0434.1.
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Abstract Uncertainty in climate projections is large as shown by the likely uncertainty ranges in equilibrium climate sensitivity (ECS) of 2.5–4 K and in the transient climate response (TCR) of 1.4–2.2 K. Uncertainty in model projections could arise from the way in which unresolved processes are represented, the parameter values used, or the targets for model calibration. We show that, in two climate model ensembles that were objectively calibrated to minimize differences from observed large-scale atmospheric climatology, uncertainties in ECS and TCR are about 2–6 times smaller than in the CMIP5 or CMIP6 multimodel ensemble. We also find that projected uncertainties in surface temperature, precipitation, and annual extremes are relatively small. Residual uncertainty largely arises from unconstrained sea ice feedbacks. The more than 20-year-old HadAM3 standard model configuration simulates observed hemispheric-scale observations and preindustrial surface temperatures about as well as the median CMIP5 and CMIP6 ensembles while the optimized configurations simulate these better than almost all the CMIP5 and CMIP6 models. Hemispheric-scale observations and preindustrial temperatures are not systematically better simulated in CMIP6 than in CMIP5 although the CMIP6 ensemble seems to better simulate patterns of large-scale observations than the CMIP5 ensemble and the optimized HadAM3 configurations. Our results suggest that most CMIP models could be improved in their simulation of large-scale observations by systematic calibration. However, the uncertainty in climate projections (for a given scenario) likely largely arises from the choice of parameterization schemes for unresolved processes (“structural uncertainty”), with different tuning targets another possible contributor. Significance Statement Climate models represent unresolved phenomena controlled by uncertain parameters. Changes in these parameters impact how well a climate model simulates current climate and its climate projections. Multiple calibrations of a single climate model, using an objective method, to large-scale atmospheric observations are performed. These models produce very similar climate projections at both global and regional scales. An analysis that combines uncertainties in observations with simulated sensitivity to observations and climate response also has small uncertainty showing that, for this model, current observations constrain climate projections. Recently developed climate models have a broad range of abilities to simulate large-scale climate with only some improvement in their ability to simulate this despite a decade of model development.
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O'Reilly, Christopher H., Daniel J. Befort, and Antje Weisheimer. "Calibrating large-ensemble European climate projections using observational data." Earth System Dynamics 11, no. 4 (November 19, 2020): 1033–49. http://dx.doi.org/10.5194/esd-11-1033-2020.
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Abstract. This study examines methods of calibrating projections of future regional climate for the next 40–50 years using large single-model ensembles (the Community Earth System Model (CESM) Large Ensemble and Max Planck Institute (MPI) Grand Ensemble), applied over Europe. The three calibration methods tested here are more commonly used for initialised forecasts from weeks up to seasonal timescales. The calibration techniques are applied to ensemble climate projections, fitting seasonal ensemble data to observations over a reference period (1920–2016). The calibration methods were tested and verified using an “imperfect model” approach using the historical/representative concentration pathway 8.5 (RCP8.5) simulations from the Coupled Model Intercomparison Project 5 (CMIP5) archive. All the calibration methods exhibit a similar performance, generally improving the out-of-sample projections in comparison to the uncalibrated (bias-corrected) ensemble. The calibration methods give results that are largely indistinguishable from one another, so the simplest of these methods, namely homogeneous Gaussian regression (HGR), is used for the subsequent analysis. As an extension to the HGR calibration method it is applied to dynamically decomposed data, in which the underlying data are separated into dynamical and residual components (HGR-decomp). Based on the verification results obtained using the imperfect model approach, the HGR-decomp method is found to produce more reliable and accurate projections than the uncalibrated ensemble for future climate over Europe. The calibrated projections for temperature demonstrate a particular improvement, whereas the projections for changes in precipitation generally remain fairly unreliable. When the two large ensembles are calibrated using observational data, the climate projections for Europe are far more consistent between the two ensembles, with both projecting a reduction in warming but a general increase in the uncertainty of the projected changes.
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Beltran, Cesar, N. R. Edwards, A. B. Haurie, J. P. Vial, and D. S. Zachary. "Oracle-based optimization applied to climate model calibration." Environmental Modeling & Assessment 11, no. 1 (October 20, 2005): 31–43. http://dx.doi.org/10.1007/s10666-005-9024-4.
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Meinshausen, M., S. C. B. Raper, and T. M. L. Wigley. "Emulating coupled atmosphere-ocean and carbon cycle models with a simpler model, MAGICC6 – Part 1: Model description and calibration." Atmospheric Chemistry and Physics 11, no. 4 (February 16, 2011): 1417–56. http://dx.doi.org/10.5194/acp-11-1417-2011.
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Abstract. Current scientific knowledge on the future response of the climate system to human-induced perturbations is comprehensively captured by various model intercomparison efforts. In the preparation of the Fourth Assessment Report (AR4) of the Intergovernmental Panel on Climate Change (IPCC), intercomparisons were organized for atmosphere-ocean general circulation models (AOGCMs) and carbon cycle models, named "CMIP3" and "C4MIP", respectively. Despite their tremendous value for the scientific community and policy makers alike, there are some difficulties in interpreting the results. For example, radiative forcings were not standardized across the various AOGCM integrations and carbon cycle runs, and, in some models, key forcings were omitted. Furthermore, the AOGCM analysis of plausible emissions pathways was restricted to only three SRES scenarios. This study attempts to address these issues. We present an updated version of MAGICC, the simple carbon cycle-climate model used in past IPCC Assessment Reports with enhanced representation of time-varying climate sensitivities, carbon cycle feedbacks, aerosol forcings and ocean heat uptake characteristics. This new version, MAGICC6, is successfully calibrated against the higher complexity AOGCMs and carbon cycle models. Parameterizations of MAGICC6 are provided. The mean of the emulations presented here using MAGICC6 deviates from the mean AOGCM responses by only 2.2% on average for the SRES scenarios. This enhanced emulation skill in comparison to previous calibrations is primarily due to: making a "like-with-like comparison" using AOGCM-specific subsets of forcings; employing a new calibration procedure; as well as the fact that the updated simple climate model can now successfully emulate some of the climate-state dependent effective climate sensitivities of AOGCMs. The diagnosed effective climate sensitivity at the time of CO2 doubling for the AOGCMs is on average 2.88 °C, about 0.33 °C cooler than the mean of the reported slab ocean climate sensitivities. In the companion paper (Part 2) of this study, we examine the combined climate system and carbon cycle emulations for the complete range of IPCC SRES emissions scenarios and the new RCP pathways.
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Koch, Julian, Mehmet Cüneyd Demirel, and Simon Stisen. "Climate Normalized Spatial Patterns of Evapotranspiration Enhance the Calibration of a Hydrological Model." Remote Sensing 14, no. 2 (January 11, 2022): 315. http://dx.doi.org/10.3390/rs14020315.
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Spatial pattern-oriented evaluations of distributed hydrological models have contributed towards an improved realism of hydrological simulations. This advancement has been supported by the broad range of readily available satellite-based datasets of key hydrological variables, such as evapotranspiration (ET). At larger scale, spatial patterns of ET are often driven by underlying climate gradients, and with this study, we argue that gradient dominated patterns may hamper the potential of spatial pattern-oriented evaluation frameworks. We hypothesize that the climate control of spatial patterns of ET overshadows the effect model parameters have on the simulated patterns. To address this, we propose a climate normalization strategy. This is demonstrated for the Senegal River basin as a modeling case study, where the dominant north-south precipitation gradient is the main driver of the observed hydrological variability. We apply the mesoscale Hydrological Model (mHM) to model the hydrological cycle of the Senegal River basin. Two multi-objective calibration experiments investigate the effect of climate normalization. Both calibrations utilize observed discharge (Q) in combination with remote sensing ET data, where one is based on the original ET pattern and the other utilizes the normalized ET pattern. As objective functions we applied the Kling-Gupta-Efficiency (KGE) for Q and the Spatial Efficiency (SPAEF) for ET. We identify parameter sets that balance the tradeoffs between the two independent observations and find that the calibration using the normalized ET pattern does not compromise the spatial pattern performance of the original pattern. However, vice versa, this is not necessarily the case, since the calibration using the original ET pattern showed a poorer performance for the normalized pattern, i.e., a 30% decrease in SPAEF. Both calibrations reached comparable performance of Q, i.e., KGE around 0.7. With this study, we identified a general shortcoming of spatial pattern-oriented model evaluations using ET in basins dominated by a climate gradient, but we argue that this also applies to other variables such as, soil moisture or land surface temperature.
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Guzmán-Cruz, R., R. Castañeda-Miranda, J. J. García-Escalante, A. Lara-Herrera, I. Serroukh, and L. O. Solis-Sánchez. "GENETIC ALGORITHMS FOR CALIBRATION OF A GREENHOUSE CLIMATE MODEL." Revista Chapingo Serie Horticultura XVI, no. 1 (January 2010): 23–30. http://dx.doi.org/10.5154/r.rchsh.2010.16.003.
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Kim, Daeha, Il Won Jung, and Jong Ahn Chun. "A comparative assessment of rainfall–runoff modelling against regional flow duration curves for ungauged catchments." Hydrology and Earth System Sciences 21, no. 11 (November 15, 2017): 5647–61. http://dx.doi.org/10.5194/hess-21-5647-2017.
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Abstract. Rainfall–runoff modelling has long been a special subject in hydrological sciences, but identifying behavioural parameters in ungauged catchments is still challenging. In this study, we comparatively evaluated the performance of the local calibration of a rainfall–runoff model against regional flow duration curves (FDCs), which is a seemingly alternative method of classical parameter regionalisation for ungauged catchments. We used a parsimonious rainfall–runoff model over 45 South Korean catchments under semi-humid climate. The calibration against regional FDCs was compared with the simple proximity-based parameter regionalisation. Results show that transferring behavioural parameters from gauged to ungauged catchments significantly outperformed the local calibration against regional FDCs due to the absence of flow timing information in the regional FDCs. The behavioural parameters gained from observed hydrographs were likely to contain intangible flow timing information affecting predictability in ungauged catchments. Additional constraining with the rising limb density appreciably improved the FDC calibrations, implying that flow signatures in temporal dimensions would supplement the FDCs. As an alternative approach in data-rich regions, we suggest calibrating a rainfall–runoff model against regionalised hydrographs to preserve flow timing information. We also suggest use of flow signatures that can supplement hydrographs for calibrating rainfall–runoff models in gauged and ungauged catchments.
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Steele, Katie, and Charlotte Werndl. "Climate Models, Calibration, and Confirmation." British Journal for the Philosophy of Science 64, no. 3 (September 1, 2013): 609–35. http://dx.doi.org/10.1093/bjps/axs036.
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Nguyen-Ky, Sy, and Katariina Penttilä. "Indoor Climate and Energy Model Calibration with Monitored Data of a Naturally Ventilated Dairy Barn in a Cold Climate." Applied Engineering in Agriculture 37, no. 5 (2021): 851–59. http://dx.doi.org/10.13031/aea.14280.
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HighlightsIndoor climate and energy model of a dairy barn is constructed and calibrated with collected data.Long-term monitoring of indoor conditions and electricity consumption greatly facilitates the model calibration process.Statistical benchmarks given by guidelines confirm the usability and reliability of the model.Abstract. This study demonstrates an application of ICE model calibration by using sensor building metrics in a naturally ventilated dairy house in a cold climate. The barn, at the time of the study, had 70 lactating cows and 30 calves with a total animal area of 1922 m2 and other auxiliary areas of 268 m2. Indoor condition data were collected by four integrated sensors inside the barn for six months, from March to August 2019. IDA ICE 4.8 SP1 simulation software was used to build and simulate the model, with calibration steps conducted first manually, then statistically. Actual weather and indoor condition data during the monitored period were used for calibration; statistical indices of the calibrated model were confirmed by the benchmarks given from ASHRAE Guideline 14-2014, IPMVP version 2016, and FEMP version 4.0 2015. The yielded result was a baseline ICE model, which can be further utilized in the study of energy conservation measures (ECMs), retrofitting feasibility, and ammonia and other contaminant gas emission mitigation. The abovementioned calibration practice and the proposals built on it open a pathway to achieve a higher level of energy efficiency for this type of livestock building. Keywords: Cold weather, Dairy farms, Model calibration, Natural ventilation.
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Urabe, Tomoyuki, Xiaoxiong Xiong, Taichiro Hashiguchi, Shigemasa Ando, Yoshihiko Okamura, and Kazuhiro Tanaka. "Radiometric Model and Inter-Comparison Results of the SGLI-VNR On-Board Calibration." Remote Sensing 12, no. 1 (December 23, 2019): 69. http://dx.doi.org/10.3390/rs12010069.
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The Second Generation Global Imager (SGLI) on Global Change Observation Mission–Climate (GCOM-C) satellite empowers surface and atmospheric measurements related to the carbon cycle and radiation budget, with two radiometers of Visible and Near Infrared Radiometer (SGLI-VNR) and Infrared Scanning Radiometer (SGLI-IRS) that perform a wide-band (380 nm–12 µm) optical observation not only with as wide as a 1150–1400 km field of view (FOV), but also with as high as 0.25–0.5 km resolution. Additionally, polarization and along-track slant view observations are quite characteristic of SGLI. It is important to calibrate radiometers to provide the sensor data records for more than 28 standard products and 23 research products including clouds, aerosols, ocean color, vegetation, snow and ice, and other applications. In this paper, the radiometric model and the first results of on-board calibrations on the SGLI-VNR, which include weekly solar and light-emitting diode (LED) calibration and monthly lunar calibration, will be described. Each calibration data was obtained with corrections, where beta angle correction and avoidance of reflection from multilayer insulation (MLI) were applied for solar calibration; LED temperature correction was performed for LED calibration; and the GIRO (GSICS (Global Space-based Inter-Calibration System) Implementation of the ROLO (RObotic Lunar Observatory) model) model was used for lunar calibration. Results show that the inter-comparison of the relative degradation amount between these three calibrations agreed to within 1% or less.
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Sleziak, Patrik, Ladislav Holko, Michal Danko, and Juraj Parajka. "Uncertainty in the Number of Calibration Repetitions of a Hydrologic Model in Varying Climatic Conditions." Water 12, no. 9 (August 23, 2020): 2362. http://dx.doi.org/10.3390/w12092362.
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The objective of this study is to examine the impact of the number of calibration repetitions on hydrologic model performance and parameter uncertainty in varying climatic conditions. The study is performed in a pristine alpine catchment in the Western Tatra Mountains (the Jalovecký Creek catchment, Slovakia) using daily data from the period 1989–2018. The entire data set has been divided into five 6-years long periods; the division was based on the wavelet analysis of precipitation, air temperature and runoff data. A lumped conceptual hydrologic model TUW (“Technische Universität Wien”) was calibrated by an automatic optimisation using the differential evolution algorithm approach. To test the effect of the number of calibrations in the optimisation procedure, we have conducted 10, 50, 100, 300, 500 repetitions of calibrations in each period and validated them against selected runoff and snow-related model efficiency criteria. The results showed that while the medians of different groups of calibration repetitions were similar, the ranges (max–min) of model efficiency criteria and parameter values differed. An increasing number of calibration repetitions tend to increase the ranges of model efficiency criteria during model validation, particularly for the runoff volume error and snow error, which were not directly used in model calibration. Comparison of model efficiencies in climate conditions that varied among the five periods documented changes in model performance in different periods but the difference between 10 and 500 calibration repetitions did not change much between the selected time periods. The results suggest that ten repetitions of model calibrations provided the same median of model efficiency criteria as a greater number of calibration repetitions and model parameter variability and uncertainty were smaller.
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Boulard, T., B. Draoui, and F. Neirac. "CALIBRATION AND VALIDATION OF A GREENHOUSE CLIMATE CONTROL MODEL." Acta Horticulturae, no. 406 (April 1996): 49–62. http://dx.doi.org/10.17660/actahortic.1996.406.4.
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Guzmán-Cruz, R., R. Castañeda-Miranda, J. J. García-Escalante, I. L. López-Cruz, A. Lara-Herrera, and J. I. de la Rosa. "Calibration of a greenhouse climate model using evolutionary algorithms." Biosystems Engineering 104, no. 1 (September 2009): 135–42. http://dx.doi.org/10.1016/j.biosystemseng.2009.06.006.
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Schepen, Andrew, Yvette Everingham, and Quan J. Wang. "On the Joint Calibration of Multivariate Seasonal Climate Forecasts from GCMs." Monthly Weather Review 148, no. 1 (January 1, 2020): 437–56. http://dx.doi.org/10.1175/mwr-d-19-0046.1.
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Abstract Multivariate seasonal climate forecasts are increasingly required for quantitative modeling in support of natural resources management and agriculture. GCM forecasts typically require postprocessing to reduce biases and improve reliability; however, current seasonal postprocessing methods often ignore multivariate dependence. In low-dimensional settings, fully parametric methods may sufficiently model intervariable covariance. On the other hand, empirical ensemble reordering techniques can inject desired multivariate dependence in ensembles from template data after univariate postprocessing. To investigate the best approach for seasonal forecasting, this study develops and tests several strategies for calibrating seasonal GCM forecasts of rainfall, minimum temperature, and maximum temperature with intervariable dependence: 1) simultaneous calibration of multiple climate variables using the Bayesian joint probability modeling approach; 2) univariate BJP calibration coupled with an ensemble reordering method (the Schaake shuffle); and 3) transformation-based quantile mapping, which borrows intervariable dependence from the raw forecasts. Applied to Australian seasonal forecasts from the ECMWF System4 model, univariate calibration paired with empirical ensemble reordering performs best in terms of univariate and multivariate forecast verification metrics, including the energy and variogram scores. However, the performance of empirical ensemble reordering using the Schaake shuffle is influenced by the selection of historical data in constructing a dependence template. Direct multivariate calibration is the second-best method, with its far superior performance in in-sample testing vanishing in cross validation, likely because of insufficient data relative to the number of parameters. The continued development of multivariate forecast calibration methods will support the uptake of seasonal climate forecasts in complex application domains such as agriculture and hydrology.
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Cakir, Roxelane, Mélanie Raimonet, Sabine Sauvage, Javier Paredes-Arquiola, Youen Grusson, Laure Roset, Maite Meaurio, et al. "Hydrological Alteration Index as an Indicator of the Calibration Complexity of Water Quantity and Quality Modeling in the Context of Global Change." Water 12, no. 1 (December 30, 2019): 115. http://dx.doi.org/10.3390/w12010115.
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Modeling is a useful way to understand human and climate change impacts on the water resources of agricultural watersheds. Calibration and validation methodologies are crucial in forecasting assessments. This study explores the best calibration methodology depending on the level of hydrological alteration due to human-derived stressors. The Soil and Water Assessment Tool (SWAT) model is used to evaluate hydrology in South-West Europe in a context of intensive agriculture and water scarcity. The Index of Hydrological Alteration (IHA) is calculated using discharge observation data. A comparison of two SWAT calibration methodologies are done; a conventional calibration (CC) based on recorded in-stream water quality and quantity and an additional calibration (AC) adding crop managements practices. Even if the water quality and quantity trends are similar between CC and AC, water balance, irrigation and crop yields are different. In the context of rainfall decrease, water yield decreases in both CC and AC, while crop productions present opposite trends (+33% in CC and −31% in AC). Hydrological performance between CC and AC is correlated to IHA: When the level of IHA is under 80%, AC methodology is necessary. The combination of both calibrations appears essential to better constrain the model and to forecast the impact of climate change or anthropogenic influences on water resources.
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Chang, Kai‐Lan, and Serge Guillas. "Computer model calibration with large non‐stationary spatial outputs: application to the calibration of a climate model." Journal of the Royal Statistical Society: Series C (Applied Statistics) 68, no. 1 (September 8, 2018): 51–78. http://dx.doi.org/10.1111/rssc.12309.
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Harp, D. R., A. L. Atchley, S. L. Painter, E. T. Coon, C. J. Wilson, V. E. Romanovsky, and J. C. Rowland. "Effect of soil property uncertainties on permafrost thaw projections: a calibration-constrained analysis." Cryosphere Discussions 9, no. 3 (June 29, 2015): 3351–404. http://dx.doi.org/10.5194/tcd-9-3351-2015.
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Abstract. The effect of soil property uncertainties on permafrost thaw projections are studied using a three-phase subsurface thermal hydrology model and calibration-constrained uncertainty analysis. The Null-Space Monte Carlo method is used to identify soil hydrothermal parameter combinations that are consistent with borehole temperature measurements at the study site, the Barrow Environmental Observatory. Each parameter combination is then used in a forward projection of permafrost conditions for the 21st century (from calendar year 2006 to 2100) using atmospheric forcings from the Community Earth System Model (CESM) in the Representative Concentration Pathway (RCP) 8.5 greenhouse gas concentration trajectory. A 100-year projection allows for the evaluation of intra-annual uncertainty due to soil properties and the inter-annual variability due to year to year differences in CESM climate forcings. After calibrating to borehole temperature data at this well-characterized site, soil property uncertainties are still significant and result in significant intra-annual uncertainties in projected active layer thickness and annual thaw depth-duration even with a specified future climate. Intra-annual uncertainties in projected soil moisture content and Stefan number are small. A volume and time integrated Stefan number decreases significantly in the future climate, indicating that latent heat of phase change becomes more important than heat conduction in future climates. Out of 10 soil parameters, ALT, annual thaw depth-duration, and Stefan number are highly dependent on mineral soil porosity, while annual mean liquid saturation of the active layer is highly dependent on the mineral soil residual saturation and moderately dependent on peat residual saturation. By comparing the ensemble statistics to the spread of projected permafrost metrics using different climate models, we show that the effect of calibration-constrained uncertainty in soil properties, although significant, is less than that produced by structural climate model uncertainty for this location.
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Melsen, Lieke Anna, and Björn Guse. "Climate change impacts model parameter sensitivity – implications for calibration strategy and model diagnostic evaluation." Hydrology and Earth System Sciences 25, no. 3 (March 18, 2021): 1307–32. http://dx.doi.org/10.5194/hess-25-1307-2021.
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Abstract. Hydrological models are useful tools for exploring the impact of climate change. To prioritize parameters for calibration and to evaluate hydrological model functioning, sensitivity analysis can be conducted. Parameter sensitivity, however, varies over climate, and therefore climate change could influence parameter sensitivity. In this study we explore the change in parameter sensitivity for the mean discharge and the timing of the discharge, within a plausible climate change rate. We investigate whether changes in sensitivity propagate into the calibration strategy and diagnostically compare three hydrological models based on the sensitivity results. We employed three frequently used hydrological models (SAC, VIC, and HBV) and explored parameter sensitivity changes across 605 catchments in the United States by comparing GCM(RCP8.5)-forced historical and future periods. Consistent among all hydrological models and both for the mean discharge and the timing of the discharge is that the sensitivity of snow parameters decreases in the future. Which other parameters increase in sensitivity is less consistent among the hydrological models. In 45 % to 55 % of the catchments, dependent on the hydrological model, at least one parameter changes in the future in the top-5 most sensitive parameters for mean discharge. For the timing, this varies between 40 % and 88 %. This requires an adapted calibration strategy for long-term projections, for which we provide several suggestions. The disagreement among the models on the processes that become more relevant in future projections also calls for a strict evaluation of the adequacy of the model structure for long-term simulations.
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Khaki, Mehdi. "Land Surface Model Calibration Using Satellite Remote Sensing Data." Sensors 23, no. 4 (February 7, 2023): 1848. http://dx.doi.org/10.3390/s23041848.
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Satellite remote sensing provides a unique opportunity for calibrating land surface models due to their direct measurements of various hydrological variables as well as extensive spatial and temporal coverage. This study aims to apply terrestrial water storage (TWS) estimated from the gravity recovery and climate experiment (GRACE) mission as well as soil moisture products from advanced microwave scanning radiometer–earth observing system (AMSR-E) to calibrate a land surface model using multi-objective evolutionary algorithms. For this purpose, the non-dominated sorting genetic algorithm (NSGA) is used to improve the model’s parameters. The calibration is carried out for the period of two years 2003 and 2010 (calibration period) in Australia, and the impact is further monitored over 2011 (forecasting period). A new combined objective function based on the observations’ uncertainty is developed to efficiently improve the model parameters for a consistent and reliable forecasting skill. According to the evaluation of the results against independent measurements, it is found that the calibrated model parameters lead to better model simulations both in the calibration and forecasting period.
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Mendoza, Pablo A., Martyn P. Clark, Naoki Mizukami, Andrew J. Newman, Michael Barlage, Ethan D. Gutmann, Roy M. Rasmussen, Balaji Rajagopalan, Levi D. Brekke, and Jeffrey R. Arnold. "Effects of Hydrologic Model Choice and Calibration on the Portrayal of Climate Change Impacts." Journal of Hydrometeorology 16, no. 2 (April 1, 2015): 762–80. http://dx.doi.org/10.1175/jhm-d-14-0104.1.
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Abstract The assessment of climate change impacts on water resources involves several methodological decisions, including choices of global climate models (GCMs), emission scenarios, downscaling techniques, and hydrologic modeling approaches. Among these, hydrologic model structure selection and parameter calibration are particularly relevant and usually have a strong subjective component. The goal of this research is to improve understanding of the role of these decisions on the assessment of the effects of climate change on hydrologic processes. The study is conducted in three basins located in the Colorado headwaters region, using four different hydrologic model structures [PRMS, VIC, Noah LSM, and Noah LSM with multiparameterization options (Noah-MP)]. To better understand the role of parameter estimation, model performance and projected hydrologic changes (i.e., changes in the hydrology obtained from hydrologic models due to climate change) are compared before and after calibration with the University of Arizona shuffled complex evolution (SCE-UA) algorithm. Hydrologic changes are examined via a climate change scenario where the Community Climate System Model (CCSM) change signal is used to perturb the boundary conditions of the Weather Research and Forecasting (WRF) Model configured at 4-km resolution. Substantial intermodel differences (i.e., discrepancies between hydrologic models) in the portrayal of climate change impacts on water resources are demonstrated. Specifically, intermodel differences are larger than the mean signal from the CCSM–WRF climate scenario examined, even after the calibration process. Importantly, traditional single-objective calibration techniques aimed to reduce errors in runoff simulations do not necessarily improve intermodel agreement (i.e., same outputs from different hydrologic models) in projected changes of some hydrological processes such as evapotranspiration or snowpack.
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Bellprat, O., S. Kotlarski, D. Lüthi, and C. Schär. "Objective calibration of regional climate models." Journal of Geophysical Research: Atmospheres 117, no. D23 (December 13, 2012): n/a. http://dx.doi.org/10.1029/2012jd018262.
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Huang, Shaochun, Harsh Shah, Bibi S. Naz, Narayan Shrestha, Vimal Mishra, Prasad Daggupati, Uttam Ghimire, and Tobias Vetter. "Impacts of hydrological model calibration on projected hydrological changes under climate change—a multi-model assessment in three large river basins." Climatic Change 163, no. 3 (September 25, 2020): 1143–64. http://dx.doi.org/10.1007/s10584-020-02872-6.
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AbstractThis study aimed to investigate the influence of hydrological model calibration/validation on discharge projections for three large river basins (the Rhine, Upper Mississippi and Upper Yellow). Three hydrological models (HMs), which have been firstly calibrated against the monthly discharge at the outlet of each basin (simple calibration), were re-calibrated against the daily discharge at the outlet and intermediate gauges under contrast climate conditions simultaneously (enhanced calibration). In addition, the models were validated in terms of hydrological indicators of interest (median, low and high flows) as well as actual evapotranspiration in the historical period. The models calibrated using both calibration methods were then driven by the same bias corrected climate projections from five global circulation models (GCMs) under four Representative Concentration Pathway scenarios (RCPs). The hydrological changes of the indicators were represented by the ensemble median, ensemble mean and ensemble weighted means of all combinations of HMs and GCMs under each RCP. The results showed moderate (5–10%) to strong influence (> 10%) of the calibration methods on the ensemble medians/means for the Mississippi, minor to moderate (up to 10%) influence for the Yellow and minor (< 5%) influence for the Rhine. In addition, the enhanced calibration/validation method reduced the shares of uncertainty related to HMs for three indicators in all basins when the strict weighting method was used. It also showed that the successful enhanced calibration had the potential to reduce the uncertainty of hydrological projections, especially when the HM uncertainty was significant after the simple calibration.
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Jajarmizadeh, Milad, Lariyah Mohd Sidek, Sobri Harun, and Mohsen Salarpour. "Optimal Calibration and Uncertainty Analysis of SWAT for an Arid Climate." Air, Soil and Water Research 10 (January 1, 2017): 117862211773179. http://dx.doi.org/10.1177/1178622117731792.
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One of the major issues for semidistributed models is calibration of sensitive parameters. This study compared 3 scenarios for Soil and Water Assessment Tool (SWAT) model for calibration and uncertainty. Roodan watershed has been selected for simulation of daily flow in southern part of Iran with an area of 10 570 km2. After preparation of required data and implementation of the SWAT model, sensitivity analysis has been performed by Latin Hypercube One-factor-At-a-Time method on those parameters which are effective for flow simulation. Then, SWAT Calibration and Uncertainty Program (SWAT-CUP) has been used for calibration and uncertainty analysis. Three schemes for calibration were followed for the Roodan watershed modeling in calibration analysis as evolution. These include the following: the global method (scheme 1), this is a method that takes in all globally adjusted sensitive parameters for the whole watershed; the discretization method (scheme 2), this method considered the dominant features in calibration such as land use and soil type; the optimum parameters method (scheme 3), this method only adjusted those sensitive parameters by considering the effectiveness of their features. The results show that scheme 3 has better performance criteria for calibration and uncertainty analysis. Nash-Sutcliffe (NS) coefficient has been obtained 0.75 for scheme 3. However, schemes 1 and 2 resulted in NS 0.71 and 0.74, respectively, between predicted and observed daily flows. Moreover, percentage bias (P-bias) obtained was 6.7, 5.2, and 1.5 for schemes 1, 2, and 3, respectively. The result also shows that condition of parameters (parameter set) during calibration in SWAT-CUP program model has an important role to increase the performance of the model.
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Wi, S., Y. C. E. Yang, S. Steinschneider, A. Khalil, and C. M. Brown. "Calibration approaches for distributed hydrologic models using high performance computing: implication for streamflow projections under climate change." Hydrology and Earth System Sciences Discussions 11, no. 9 (September 17, 2014): 10273–317. http://dx.doi.org/10.5194/hessd-11-10273-2014.
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Abstract. This study utilizes high performance computing to test the performance and uncertainty of calibration strategies for a spatially distributed hydrologic model in order to improve model simulation accuracy and understand prediction uncertainty at interior ungaged sites of a sparsely-gaged watershed. The study is conducted using a distributed version of the HYMOD hydrologic model (HYMOD_DS) applied to the Kabul River basin. Several calibration experiments are conducted to understand the benefits and costs associated with different calibration choices, including (1) whether multisite gaged data should be used simultaneously or in a step-wise manner during model fitting, (2) the effects of increasing parameter complexity, and (3) the potential to estimate interior watershed flows using only gaged data at the basin outlet. The implications of the different calibration strategies are considered in the context of hydrologic projections under climate change. Several interesting results emerge from the study. The simultaneous use of multisite data is shown to improve the calibration over a step-wise approach, and both multisite approaches far exceed a calibration based on only the basin outlet. The basin outlet calibration can lead to projections of mid-21st century streamflow that deviate substantially from projections under multisite calibration strategies, supporting the use of caution when using distributed models in data-scarce regions for climate change impact assessments. Surprisingly, increased parameter complexity does not substantially increase the uncertainty in streamflow projections, even though parameter equifinality does emerge. The results suggest that increased (excessive) parameter complexity does not always lead to increased predictive uncertainty if structural uncertainties are present. The largest uncertainty in future streamflow results from variations in projected climate between climate models, which substantially outweighs the calibration uncertainty.
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Zeng, Qiang, Hua Chen, Chong-Yu Xu, Meng-Xuan Jie, and Yu-Kun Hou. "Feasibility and uncertainty of using conceptual rainfall-runoff models in design flood estimation." Hydrology Research 47, no. 4 (October 22, 2015): 701–17. http://dx.doi.org/10.2166/nh.2015.069.
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Hydrological models are developed for different purposes including flood forecasting, design flood estimation, water resources assessment, and impact study of climate change and land use change, etc. In this study, applicability and uncertainty of two deterministic lumped models, the Xinanjiang (XAJ) model and the Hydrologiska Byråns Vattenbalansavdelning (HBV) model, in design flood estimation are evaluated in a data rich catchment in southern China. Uncertainties of the estimated design flood caused by model equifinality and calibration data period are then assessed using the generalized likelihood uncertainty estimation (GLUE) framework. The results show that: (1) the XAJ model is likely to overestimate the design flood while HBV model underestimates the design flood; (2) the model parameter equifinality has significant impact on the design flood estimation results; (3) with the same length of calibration period, the results of design flood estimation are significantly influenced by which period of the data is used for model calibration; and (4) 15–20 years of calibration data are suggested to be necessary and sufficient for calibrating the two models in the study area.
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Yang, Qichun, Quan J. Wang, Andrew W. Western, Wenyan Wu, Yawen Shao, and Kirsti Hakala. "Reconstructing climate trends adds skills to seasonal reference crop evapotranspiration forecasting." Hydrology and Earth System Sciences 26, no. 4 (February 18, 2022): 941–54. http://dx.doi.org/10.5194/hess-26-941-2022.
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Abstract. Evapotranspiration plays an important role in the terrestrial water cycle. Reference crop evapotranspiration (ETo) has been widely used to estimate water transfer from vegetation surface to the atmosphere. Seasonal ETo forecasting provides valuable information for effective water resource management and planning. Climate forecasts from general circulation models (GCMs) have been increasingly used to produce seasonal ETo forecasts. Statistical calibration plays a critical role in correcting bias and dispersion errors in GCM-based ETo forecasts. However, time-dependent errors resulting from GCM misrepresentations of climate trends have not been explicitly corrected in ETo forecast calibrations. We hypothesize that reconstructing climate trends through statistical calibration will add extra skills to seasonal ETo forecasts. To test this hypothesis, we calibrate raw seasonal ETo forecasts constructed with climate forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) SEAS5 model across Australia, using the recently developed Bayesian joint probability trend-aware (BJP-ti) model. Raw ETo forecasts demonstrate significant inconsistencies with observations in both magnitudes and spatial patterns of temporal trends, particularly at long lead times. The BJP-ti model effectively corrects misrepresented trends and reconstructs the observed trends in calibrated forecasts. Improving trends through statistical calibration increases the correlation coefficient between calibrated forecasts and observations (r) by up to 0.25 and improves the continuous ranked probability score (CRPS) skill score by up to 15 (%) in regions where climate trends are misrepresented by raw forecasts. Skillful ETo forecasts produced in this study could be used for streamflow forecasting, modeling of soil moisture dynamics, and irrigation water management. This investigation confirms the necessity of reconstructing climate trends in GCM-based seasonal ETo forecasting and provides an effective tool for addressing this need. We anticipate that future GCM-based seasonal ETo forecasting will benefit from correcting time-dependent errors through trend reconstruction.
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Coopersmith, E. J., B. S. Minsker, and M. Sivapalan. "Using similarity of soil texture and hydroclimate to enhance soil moisture estimation." Hydrology and Earth System Sciences 18, no. 8 (August 20, 2014): 3095–107. http://dx.doi.org/10.5194/hess-18-3095-2014.
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Abstract. Estimating soil moisture typically involves calibrating models to sparse networks of in situ sensors, which introduces considerable error in locations where sensors are not available. We address this issue by calibrating parameters of a parsimonious soil moisture model, which requires only antecedent precipitation information, at gauged locations and then extrapolating these values to ungauged locations via a hydroclimatic classification system. Fifteen sites within the Soil Climate Analysis Network (SCAN) containing multiyear time series data for precipitation and soil moisture are used to calibrate the model. By calibrating at 1 of these 15 sites and validating at another, we observe that the best results are obtained where calibration and validation occur within the same hydroclimatic class. Additionally, soil texture data are tested for their importance in improving predictions between calibration and validation sites. Results have the largest errors when calibration–validation pairs differ hydroclimatically and edaphically, improve when one of these two characteristics are aligned, and are strongest when the calibration and validation sites are hydroclimatically and edaphically similar. These findings indicate considerable promise for improving soil moisture estimation in ungauged locations by considering these similarities.
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Coopersmith, E. J., B. S. Minsker, and M. Sivapalan. "Using hydro-climatic and edaphic similarity to enhance soil moisture prediction." Hydrology and Earth System Sciences Discussions 11, no. 2 (February 25, 2014): 2321–53. http://dx.doi.org/10.5194/hessd-11-2321-2014.
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Abstract. Estimating soil moisture typically involves calibrating models to sparse networks of in~situ sensors, which introduces considerable error in locations where sensors are not available. We address this issue by calibrating parameters of a parsimonious soil moisture model, which requires only antecedent precipitation information, at gauged locations and then extrapolating these values to ungauged locations via a hydro-climatic classification system. Fifteen sites within the soil climate analysis network (SCAN) containing multi-year time series data for precipitation and soil moisture are used to calibrate the model. By calibrating at one of these fifteen sites and validating at another, we observe that the best results are obtained where calibration and validation occur within the same hydro-climatic class. Additionally, soil texture data are tested for their importance in improving predictions between calibration and validation sites. Results have the largest errors when calibration/validation pairs differ hydro-climatically and edaphically, improve when one of these two characteristics are aligned, and are strongest when the calibration and validation sites are hydro-climatically and edaphically similar. These findings indicate considerable promise for improving soil moisture estimation in ungauged locations by considering these similarities.
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Wi, S., Y. C. E. Yang, S. Steinschneider, A. Khalil, and C. M. Brown. "Calibration approaches for distributed hydrologic models in poorly gaged basins: implication for streamflow projections under climate change." Hydrology and Earth System Sciences 19, no. 2 (February 10, 2015): 857–76. http://dx.doi.org/10.5194/hess-19-857-2015.
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Abstract. This study tests the performance and uncertainty of calibration strategies for a spatially distributed hydrologic model in order to improve model simulation accuracy and understand prediction uncertainty at interior ungaged sites of a sparsely gaged watershed. The study is conducted using a distributed version of the HYMOD hydrologic model (HYMOD_DS) applied to the Kabul River basin. Several calibration experiments are conducted to understand the benefits and costs associated with different calibration choices, including (1) whether multisite gaged data should be used simultaneously or in a stepwise manner during model fitting, (2) the effects of increasing parameter complexity, and (3) the potential to estimate interior watershed flows using only gaged data at the basin outlet. The implications of the different calibration strategies are considered in the context of hydrologic projections under climate change. To address the research questions, high-performance computing is utilized to manage the computational burden that results from high-dimensional optimization problems. Several interesting results emerge from the study. The simultaneous use of multisite data is shown to improve the calibration over a stepwise approach, and both multisite approaches far exceed a calibration based on only the basin outlet. The basin outlet calibration can lead to projections of mid-21st century streamflow that deviate substantially from projections under multisite calibration strategies, supporting the use of caution when using distributed models in data-scarce regions for climate change impact assessments. Surprisingly, increased parameter complexity does not substantially increase the uncertainty in streamflow projections, even though parameter equifinality does emerge. The results suggest that increased (excessive) parameter complexity does not always lead to increased predictive uncertainty if structural uncertainties are present. The largest uncertainty in future streamflow results from variations in projected climate between climate models, which substantially outweighs the calibration uncertainty.
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Felikson, Denis, Sophie Nowicki, Isabel Nias, Beata Csatho, Anton Schenk, Michael J. Croteau, and Bryant Loomis. "Choice of observation type affects Bayesian calibration of Greenland Ice Sheet model simulations." Cryosphere 17, no. 11 (November 7, 2023): 4661–73. http://dx.doi.org/10.5194/tc-17-4661-2023.
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Abstract. Determining reliable probability distributions for ice sheet mass change over the coming century is critical to refining uncertainties in sea-level rise projections. Bayesian calibration, a method for constraining projection uncertainty using observations, has been previously applied to ice sheet projections but the impact of the chosen observation type on the calibrated posterior probability distributions has not been quantified. Here, we perform three separate Bayesian calibrations to constrain uncertainty in Greenland Ice Sheet (GrIS) simulations of the committed mass loss in 2100 under the current climate, using observations of velocity change, dynamic ice thickness change, and mass change. Comparing the posterior probability distributions shows that the median ice sheet mass change can differ by 119 % for the particular model ensemble that we used, depending on the observation type used in the calibration. More importantly for risk-averse sea-level planning, posterior probabilities of high-end mass change scenarios are highly sensitive to the observation selected for calibration. Furthermore, we show that using mass change observations alone may result in model simulations that overestimate flow acceleration and underestimate dynamic thinning around the margin of the ice sheet. Finally, we look ahead and present ideas for ways to improve Bayesian calibration of ice sheet projections.
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Erlandsen, Helene Birkelund, Kajsa M. Parding, Rasmus Benestad, Abdelkader Mezghani, and Marie Pontoppidan. "A Hybrid Downscaling Approach for Future Temperature and Precipitation Change." Journal of Applied Meteorology and Climatology 59, no. 11 (November 2020): 1793–807. http://dx.doi.org/10.1175/jamc-d-20-0013.1.
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AbstractWe used empirical–statistical downscaling in a pseudoreality context, in which both large-scale predictors and small-scale predictands were based on climate model results. The large-scale conditions were taken from a global climate model, and the small-scale conditions were taken from dynamical downscaling of the same global model with a convection-permitting regional climate model covering southern Norway. This hybrid downscaling approach, a “perfect model”–type experiment, provided 120 years of data under the CMIP5 high-emission scenario. Ample calibration samples made rigorous testing possible, enabling us to evaluate the effect of empirical–statistical model configurations and predictor choices and to assess the stationarity of the statistical models by investigating their sensitivity to different calibration intervals. The skill of the statistical models was evaluated in terms of their ability to reproduce the interannual correlation and long-term trends in seasonal 2-m temperature T2m, wet-day frequency fw, and wet-day mean precipitation μ. We found that different 30-yr calibration intervals often resulted in differing statistical models, depending on the specific choice of years. The hybrid downscaling approach allowed us to emulate seasonal mean regional climate model output with a high spatial resolution (0.05° latitude and 0.1° longitude grid) for up to 100 GCM runs while circumventing the issue of short calibration time, and it provides a robust set of empirically downscaled GCM runs.
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Ba, Doudou, Jakub Langhammer, Petr Maca, and Ansoumana Bodian. "Testing sensitivity of BILAN and GR2M models to climate conditions in the Gambia River Basin." Journal of Hydrology and Hydromechanics 72, no. 1 (February 8, 2024): 131–47. http://dx.doi.org/10.2478/johh-2023-0044.
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Abstract This study investigates the performance of two lumped hydrological models, BILAN and GR2M, in simulating runoff across six catchments in the Gambia River Basin (Senegal) over a 30-year period employing a 7-year sliding window under different climatic conditions. The results revealed differences in overall performance and variable sensitivity of the models to hydrological conditions and calibration period lengths, stemming from their different structure and complexity. In particular, the BILAN model, which is based on a more complex set of parameters, showed better overall results in simulating dry conditions, while the GR2M model had superior performance in wet conditions. The study emphasized the importance of the length of the calibration period on model performance and on the reduction of uncertainty in the results. Extended calibration periods for both models narrowed the range of the Kling-Gupta Efficiency (KGE) values and reduced the loss of performance during the parameter transfer from calibration to validation. For the BILAN model, a longer calibration period also significantly reduced the variability of performance metric values. Conversely, for the GR2M model, the variability rate did not decrease with the length of the calibration periods. Testing both models under variable conditions underscored the crucial role of comprehending model structure, hydrological sensitivity, and calibration strategy effects on simulation accuracy and uncertainty for reliable results.
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Bolin, David, Arnoldo Frigessi, Peter Guttorp, Ola Haug, Elisabeth Orskaug, Ida Scheel, and Jonas Wallin. "Calibrating regionally downscaled precipitation over Norway through quantile-based approaches." Advances in Statistical Climatology, Meteorology and Oceanography 2, no. 1 (June 9, 2016): 39–47. http://dx.doi.org/10.5194/ascmo-2-39-2016.
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Abstract. Dynamical downscaling of earth system models is intended to produce high-resolution climate information at regional to local scales. Current models, while adequate for describing temperature distributions at relatively small scales, struggle when it comes to describing precipitation distributions. In order to better match the distribution of observed precipitation over Norway, we consider approaches to statistical adjustment of the output from a regional climate model when forced with ERA-40 reanalysis boundary conditions. As a second step, we try to correct downscalings of historical climate model runs using these transformations built from downscaled ERA-40 data. Unless such calibrations are successful, it is difficult to argue that scenario-based downscaled climate projections are realistic and useful for decision makers. We study both full quantile calibrations and several different methods that correct individual quantiles separately using random field models. Results based on cross-validation show that while a full quantile calibration is not very effective in this case, one can correct individual quantiles satisfactorily if the spatial structure in the data are accounted for. Interestingly, different methods are favoured depending on whether ERA-40 data or historical climate model runs are adjusted.
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Ismail, Muhammad Fraz, Bibi S. Naz, Michel Wortmann, Markus Disse, Laura C. Bowling, and Wolfgang Bogacki. "Comparison of two model calibration approaches and their influence on future projections under climate change in the Upper Indus Basin." Climatic Change 163, no. 3 (November 10, 2020): 1227–46. http://dx.doi.org/10.1007/s10584-020-02902-3.
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AbstractThis study performs a comparison of two model calibration/validation approaches and their influence on future hydrological projections under climate change by employing two climate scenarios (RCP2.6 and 8.5) projected by four global climate models. Two hydrological models (HMs), snowmelt runoff model + glaciers and variable infiltration capacity model coupled with a glacier model, were used to simulate streamflow in the highly snow and glacier melt–driven Upper Indus Basin. In the first (conventional) calibration approach, the models were calibrated only at the basin outlet, while in the second (enhanced) approach intermediate gauges, different climate conditions and glacier mass balance were considered. Using the conventional and enhanced calibration approaches, the monthly Nash-Sutcliffe Efficiency (NSE) for both HMs ranged from 0.71 to 0.93 and 0.79 to 0.90 in the calibration, while 0.57–0.92 and 0.54–0.83 in the validation periods, respectively. For the future impact assessment, comparison of differences based on the two calibration/validation methods at the annual scale (i.e. 2011–2099) shows small to moderate differences of up to 10%, whereas differences at the monthly scale reached up to 19% in the cold months (i.e. October–March) for the far future period. Comparison of sources of uncertainty using analysis of variance showed that the contribution of HM parameter uncertainty to the overall uncertainty is becoming very small by the end of the century using the enhanced approach. This indicates that enhanced approach could potentially help to reduce uncertainties in the hydrological projections when compared to the conventional calibration approach.
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Bhattarai, Shreeya, Prem B. Parajuli, and Filip To. "Comparison of Flood Frequency at Different Climatic Scenarios in Forested Coastal Watersheds." Climate 11, no. 2 (February 9, 2023): 41. http://dx.doi.org/10.3390/cli11020041.
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Climate change-induced extreme precipitation causes coastal flooding. A streamflow simulation in coastal watersheds, Wolf River Watershed (WRW) and Jourdan River Watershed (JRW), was conducted using the Soil and Water Assessment Tool (SWAT) to compare variation in flow at different climates and to analyze the flood frequency. Baseline models were auto-calibrated with SWAT calibration and uncertainty programs (SWAT-CUP). Kling–Gupta efficiency (KGE), defined as the objective function in SWAT-CUP, ranged from 0.8 to 0.7 in WRW and from 0.55 to 0.68 in JRW during the calibration–validation process. Results indicated reliability of the model performances. Monthly averaged baseline flow was 1% greater than historical and 8.9% lower than future climate in WRW. In JRW, monthly averaged baseline flow was 11% greater than historical and 5.7% lower than future climate. Flood frequency analysis showed the highest 1% exceedance probability in annual maximum series (AMS) of baseline model in WRW, whereas AMS of projected model was estimated the highest in JRW. This study aids in preparing for future flood management.
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Zhang, Xujie, Martijn J. Booij, and Yue-Ping Xu. "Improved Simulation of Peak Flows under Climate Change: Postprocessing or Composite Objective Calibration?" Journal of Hydrometeorology 16, no. 5 (October 1, 2015): 2187–208. http://dx.doi.org/10.1175/jhm-d-14-0218.1.
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Abstract Climate change is expected to have large impacts on peak flows. However, there may be bias in the simulation of peak flows by hydrological models. This study aims to improve the simulation of peak flows under climate change in Lanjiang catchment, east China, by comparing two approaches: postprocessing of peak flows and composite objective calibration. Two hydrological models [Soil and Water Assessment Tool (SWAT) and modèle du Génie Rural à 4 paramètres Journalier (GR4J)] are employed to simulate the daily flows, and the peaks-over-threshold method is used to extract peak flows from the simulated daily flows. Three postprocessing methods, namely, the quantile mapping method and two generalized linear models, are set up to correct the biases in the simulated raw peak flows. A composite objective calibration of the GR4J model by taking the peak flows into account in the calibration process is also carried out. The regional climate model Providing Regional Climates for Impacts Studies (PRECIS) with boundary forcing from two GCMs (HadCM3 and ECHAM5) under greenhouse gas emission scenario A1B is applied to produce the climate data for the baseline period and the future period 2011–40. The results show that the postprocessing methods, particularly quantile mapping method, can correct the biases in the raw peak flows effectively. The composite objective calibration also resulted in a good simulation performance of peak flows. The final estimated peak flows in the future period show an obvious increase compared with those in the baseline period, indicating there will probably be more frequent floods in Lanjiang catchment in the future.
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Todorovic, Andrijana, and Jasna Plavsic. "The role of conceptual hydrologic model calibration in climate change impact on water resources assessment." Journal of Water and Climate Change 7, no. 1 (June 15, 2015): 16–28. http://dx.doi.org/10.2166/wcc.2015.086.
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Assessment of climate change (CC) impact on hydrologic regime requires a calibrated rainfall-runoff model, defined by its structure and parameters. The parameter values depend, inter alia, on the calibration period. This paper investigates influence of the calibration period on parameter values, model efficiency and streamflow projections under CC. To this end, a conceptual HBV-light model of the Kolubara River catchment in Serbia is calibrated against flows observed within 5 consecutive wettest, driest, warmest and coldest years and in the complete record period. The optimised parameters reveal high sensitivity towards calibration period. Hydrologic projections under climate change are developed by employing (1) five hydrologic models with outputs of one GCM–RCM chain (Global and Regional Climate Models) and (2) one hydrologic model with five GCM–RCM outputs. Sign and magnitude of change in projected variables, compared to the corresponding values simulated over the baseline period, vary with the hydrologic model used. This variability is comparable in magnitude to variability stemming from climate models. Models calibrated over periods with similar precipitation as the projected ones may result in less uncertain projections, while warmer climate is not expected to contribute to the uncertainty in flow projections. Simulations over prolonged dry periods are expected to be uncertain.
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Majone, Bruno, Diego Avesani, Patrick Zulian, Aldo Fiori, and Alberto Bellin. "Analysis of high streamflow extremes in climate change studies: how do we calibrate hydrological models?" Hydrology and Earth System Sciences 26, no. 14 (July 25, 2022): 3863–83. http://dx.doi.org/10.5194/hess-26-3863-2022.
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Abstract. Climate change impact studies on hydrological extremes often rely on hydrological models with parameters inferred through calibration procedures using observed meteorological data as input forcing. We show that this procedure can lead to a biased evaluation of the probability distribution of high streamflow extremes when climate models are used. As an alternative approach, we introduce a methodology, coined “Hydrological Calibration of eXtremes” (HyCoX), in which the calibration of the hydrological model, as driven by climate model output, is carried out by maximizing the probability that the modeled and observed high streamflow extremes belong to the same statistical population. The application to the Adige River catchment (southeastern Alps, Italy) by means of HYPERstreamHS, a distributed hydrological model, showed that this procedure preserves statistical coherence and produces reliable quantiles of the annual maximum streamflow to be used in assessment studies.
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Hundecha, Yeshewatesfa, Berit Arheimer, Peter Berg, René Capell, Jude Musuuza, Ilias Pechlivanidis, and Christiana Photiadou. "Effect of model calibration strategy on climate projections of hydrological indicators at a continental scale." Climatic Change 163, no. 3 (October 2, 2020): 1287–306. http://dx.doi.org/10.1007/s10584-020-02874-4.
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AbstractThe effect of model calibration on the projection of climate change impact on hydrological indicators was assessed by employing variants of a pan-European hydrological model driven by forcing data from an ensemble of climate models. The hydrological model was calibrated using three approaches: calibration at the outlets of major river basins, regionalization through calibration of smaller scale catchments with unique catchment characteristics, and building a model ensemble by sampling model parameters from the regionalized model. The large-scale patterns of the change signals projected by all model variants were found to be similar for the different indicators. Catchment scale differences were observed between the projections of the model calibrated for the major river basins and the other two model variants. The distributions of the median change signals projected by the ensemble model were found to be similar to the distributions of the change signals projected by the regionalized model for all hydrological indicators. The study highlights that the spatial detail to which model calibration is performed can highly influence the catchment scale detail in the projection of climate change impact on hydrological indicators, with an absolute difference in the projections of the locally calibrated model and the model calibrated for the major river basins ranging between 0 and 55% for mean annual discharge, while it has little effect on the large-scale pattern of the projection.
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Giesen, R. H., and J. Oerlemans. "Calibration of a surface mass balance model for global-scale applications." Cryosphere 6, no. 6 (December 7, 2012): 1463–81. http://dx.doi.org/10.5194/tc-6-1463-2012.
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Abstract. Global applications of surface mass balance models have large uncertainties, as a result of poor climate input data and limited availability of mass balance measurements. This study addresses several possible consequences of these limitations for the modelled mass balance. This is done by applying a simple surface mass balance model that only requires air temperature and precipitation as input data, to glaciers in different regions. In contrast to other models used in global applications, this model separately calculates the contributions of net solar radiation and the temperature-dependent fluxes to the energy balance. We derive a relation for these temperature-dependent fluxes using automatic weather station (AWS) measurements from glaciers in different climates. With local, hourly input data, the model is well able to simulate the observed seasonal variations in the surface energy and mass balance at the AWS sites. Replacing the hourly local data by monthly gridded climate data removes summer snowfall and winter melt events and, hence, influences the modelled mass balance most on locations with a small seasonal temperature cycle. Modelled winter mass balance profiles are fitted to observations on 82 glaciers in different regions to determine representative values for the multiplication factor and vertical gradient of precipitation. For 75 of the 82 glaciers, the precipitation provided by the climate dataset has to be multiplied with a factor above unity; the median factor is 2.5. The vertical precipitation gradient ranges from negative to positive values, with more positive values for maritime glaciers and a median value of 1.5 mm a−1 m−1. With calibrated precipitation, the modelled annual mass balance gradient closely resembles the observations on the 82 glaciers, the absolute values are matched by adjusting either the incoming solar radiation, the temperature-dependent flux or the air temperature. The mass balance sensitivity to changes in temperature is particularly sensitive to the chosen calibration method. We additionally calculate the mass balance sensitivity to changes in incoming solar radiation, revealing that widely observed variations in irradiance can affect the mass balance by a magnitude comparable to a 1 °C change in temperature or a 10% change in precipitation.
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Heaven, S., A. M. Salter, and D. Clarke. "Calibration of a simple model for waste stabilisation pond performance in seasonal climates." Water Science and Technology 64, no. 7 (October 1, 2011): 1488–96. http://dx.doi.org/10.2166/wst.2011.550.
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The paper describes the calibration of a model for waste stabilisation pond (WSP) performance in seasonal climates, based on the use of readily available climate data sets. Calibration data were taken from a wide geographical area of Canada and the USA, including coastal and moderately seasonal sites. Good agreement with measured values was shown using a biochemical oxygen demand (BOD) decay constant of 0.3 day−1 for facultative ponds and 0.07–0.1 day−1 for storage/maturation ponds with a temperature-related Arrhenius constant of 1.05, and a fixed BOD decay constant of 0.007 day−1 at water temperatures below 0 °C. The results suggested that such models could potentially be used as the basis for WSP design guidelines tailored to a wide range of climatic conditions.
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Harp, D. R., A. L. Atchley, S. L. Painter, E. T. Coon, C. J. Wilson, V. E. Romanovsky, and J. C. Rowland. "Effect of soil property uncertainties on permafrost thaw projections: a calibration-constrained analysis." Cryosphere 10, no. 1 (February 11, 2016): 341–58. http://dx.doi.org/10.5194/tc-10-341-2016.
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Abstract. The effects of soil property uncertainties on permafrost thaw projections are studied using a three-phase subsurface thermal hydrology model and calibration-constrained uncertainty analysis. The null-space Monte Carlo method is used to identify soil hydrothermal parameter combinations that are consistent with borehole temperature measurements at the study site, the Barrow Environmental Observatory. Each parameter combination is then used in a forward projection of permafrost conditions for the 21st century (from calendar year 2006 to 2100) using atmospheric forcings from the Community Earth System Model (CESM) in the Representative Concentration Pathway (RCP) 8.5 greenhouse gas concentration trajectory. A 100-year projection allows for the evaluation of predictive uncertainty (due to soil property (parametric) uncertainty) and the inter-annual climate variability due to year to year differences in CESM climate forcings. After calibrating to measured borehole temperature data at this well-characterized site, soil property uncertainties are still significant and result in significant predictive uncertainties in projected active layer thickness and annual thaw depth-duration even with a specified future climate. Inter-annual climate variability in projected soil moisture content and Stefan number are small. A volume- and time-integrated Stefan number decreases significantly, indicating a shift in subsurface energy utilization in the future climate (latent heat of phase change becomes more important than heat conduction). Out of 10 soil parameters, ALT, annual thaw depth-duration, and Stefan number are highly dependent on mineral soil porosity, while annual mean liquid saturation of the active layer is highly dependent on the mineral soil residual saturation and moderately dependent on peat residual saturation. By comparing the ensemble statistics to the spread of projected permafrost metrics using different climate models, we quantify the relative magnitude of soil property uncertainty to another source of permafrost uncertainty, structural climate model uncertainty. We show that the effect of calibration-constrained uncertainty in soil properties, although significant, is less than that produced by structural climate model uncertainty for this location.
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Masson, David, and Reto Knutti. "Predictor Screening, Calibration, and Observational Constraints in Climate Model Ensembles: An Illustration Using Climate Sensitivity." Journal of Climate 26, no. 3 (February 1, 2013): 887–98. http://dx.doi.org/10.1175/jcli-d-11-00540.1.
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Abstract Climate projections have been remarkably difficult to constrain by comparing the simulated climatological state from different models with observations, in particular for small ensembles with structurally different models. In this study, the relationship between climate sensitivity and different measures of the present-day climatology is investigated. First, it is shown that 1) a variable proposed earlier that is based on interannual variation of seasonal temperature and 2) the seasonal cycle amplitude are unable to constrain the range of climate sensitivity beyond what was initially covered by the ensemble. Second, it is illustrated how model calibration helps to reveal potentially useful relationships but might also complicate the interpretation of multimodel results. As a consequence, when ensembles are small, when models are neither independently developed nor structurally identical, when observations are likely to have been used in the model development and evaluation process, and when the interpretation of the relationships across models in terms of well-understood physical processes is not obvious, care should be taken when using relationships across models to constrain model projections. This study demonstrates the pitfalls that might occur if emergent statistical relationships are prematurely interpreted as an effective constraint on projected global or regional climate change.
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Voudouri, Antigoni, Euripides Avgoustoglou, Izthak Carmona, Yoav Levi, Edoardo Bucchignani, Pirmin Kaufmann, and Jean-Marie Bettems. "Objective Calibration of Numerical Weather Prediction Model: Application on Fine Resolution COSMO Model over Switzerland." Atmosphere 12, no. 10 (October 18, 2021): 1358. http://dx.doi.org/10.3390/atmos12101358.
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The objective calibration method originally performed on regional climate models is applied to a fine horizontal resolution Numerical Weather Prediction (NWP) model over a mainly continental domain covering the Alpine Arc. The method was implemented on the MeteoSwiss COSMO (consortium for a small-scale modeling) model with a resolution of 0.01° (approximately 1 km). For the model calibration, five tuning parameters of the parameterization schemes affecting turbulence, soil-surface exchange and radiation were chosen. A full year was simulated, with the history of the soil included (hindcast) to find the optimal parameter value. A different year has been used to give an independent assessment of the impact of the optimization process. Although the operational MeteoSwiss model is already a well-tuned configuration, the results showed that a slight model performance gain is obtained by using the Calibration of COSMO (CALMO) methodology.
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Chung, S. W., and J. K. Oh. "Calibration of CE-QUAL-W2 for a monomictic reservoir in a monsoon climate area." Water Science and Technology 54, no. 11-12 (December 1, 2006): 29–37. http://dx.doi.org/10.2166/wst.2006.841.
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The impact of inflow mixing on reservoir stratification is significant for reservoirs situated in a monsoon climate area. It cause difficulty in the calibration of a two-dimensional hydrodynamic and water quality model, CE-QUAL-W2 that was recently adopted for a real-time turbidity monitoring and modelling system (RTMMS) for a reservoir in Korea. This paper presents a systematic calibration and verification processe of the model for the reservoir. A sensitivity analysis showed that wind sheltering, Chezy, and sediment heat exchange coefficients are most sensitive to stratification structure. Inflow temperature was very sensitive during a year of normal precipitation, but it is not significant during a year of drought. Residual analysis revealed that the model has shortcomings in the simulation of water temperature near the metalimnetic zone without calibration. After calibration, however, the absolute mean errors between observed and simulated values were placed within 0.116–1.190 °C. Its performance was maintained under heavy flood events during the verification stage, which implies that the model is ready to use for the simulation of turbidity plume in the RTMMS under various hydrologic conditions. The suggested model calibration strategy and relevant results may be adopted for other reservoirs located in a monsoon climate area.
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Pokorný, Jan, Jan Fišer, and Miroslav Jícha. "Calibration of the heat balance model for prediction of car climate." EPJ Web of Conferences 25 (2012): 01077. http://dx.doi.org/10.1051/epjconf/20122501077.
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Minville, Marie, Dominique Cartier, Catherine Guay, Louis-Alexandre Leclaire, Charles Audet, Sébastien Le Digabel, and James Merleau. "Improving process representation in conceptual hydrological model calibration using climate simulations." Water Resources Research 50, no. 6 (June 2014): 5044–73. http://dx.doi.org/10.1002/2013wr013857.
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Vrac, M., P. Marbaix, D. Paillard, and P. Naveau. "Non-linear statistical downscaling of present and LGM precipitation and temperatures over Europe." Climate of the Past 3, no. 4 (December 19, 2007): 669–82. http://dx.doi.org/10.5194/cp-3-669-2007.
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Abstract. Local-scale climate information is increasingly needed for the study of past, present and future climate changes. In this study we develop a non-linear statistical downscaling method to generate local temperatures and precipitation values from large-scale variables of a Earth System Model of Intermediate Complexity (here CLIMBER). Our statistical downscaling scheme is based on the concept of Generalized Additive Models (GAMs), capturing non-linearities via non-parametric techniques. Our GAMs are calibrated on the present Western Europe climate. For this region, annual GAMs (i.e. models based on 12 monthly values per location) are fitted by combining two types of large-scale explanatory variables: geographical (e.g. topographical information) and physical (i.e. entirely simulated by the CLIMBER model). To evaluate the adequacy of the non-linear transfer functions fitted on the present Western European climate, they are applied to different spatial and temporal large-scale conditions. Local projections for present North America and Northern Europe climates are obtained and compared to local observations. This partially addresses the issue of spatial robustness of our transfer functions by answering the question "does our statistical model remain valid when applied to large-scale climate conditions from a region different from the one used for calibration?". To asses their temporal performances, local projections for the Last Glacial Maximum period are derived and compared to local reconstructions and General Circulation Model outputs. Our downscaling methodology performs adequately for the Western Europe climate. Concerning the spatial and temporal evaluations, it does not behave as well for Northern America and Northern Europe climates because the calibration domain may be too different from the targeted regions. The physical explanatory variables alone are not capable of downscaling realistic values. However, the inclusion of geographical-type variables – such as altitude, advective continentality and moutains effect on wind (W–slope) – as GAM explanatory variables clearly improves our local projections.