Comparing global and local calibration schemes from a differential split-sample test perspective

This work explores the performances of the hydrologic model Hydrotel, applied to 36 catchments located in the Province of Quebec, Canada. A local calibration (each catchment taken individually) scheme and a global calibration (a single parameter set sought for all catchments) scheme are compared in a differential split-sample test perspective. Such a methodology is useful to gain insights on a model’s skills under different climatic conditions, in view of its use for climate change impact studies. The model was calibrated using both schemes on five non-continuous dry and cold years and then evaluated on five dissimilar humid and warm years. Results indicate that, as expected, local calibration leads to better performances than the global one. However, global calibration achieves satisfactory simulations while producing a better temporal robustness (i.e., model transposability to periods with different climatic conditions). Global calibration, in opposition to local calibration, thus imposes spatial consistency to the calibrated parameter values, while locally adjusted parameter sets can significantly vary from one catchment to another due to equifinality. It is hence stated that a global calibration scheme represents a good trade-off between local performance, temporal robustness, and the spatial consistency of parameter values, which is, for example, of interest in the context of ungauged catchments’ simulation, climate change impact studies, or even simply large-scale modeling.

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Is bias correction of Regional Climate Model (RCM) simulations possible for non-stationary conditions?

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-9-12765-2012 ◽

2012 ◽

Vol 9 (11) ◽

pp. 12765-12795 ◽

Cited By ~ 18

Author(s):

C. Teutschbein ◽

J. Seibert

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Change Impact ◽

Climate Model ◽

Regional Climate ◽

Climate Conditions ◽

Impact Studies ◽

Split Sample ◽

Sample Test ◽

Change Impact

Abstract. In hydrological climate-change impact studies, Regional Climate Models (RCMs) are commonly used to transfer large-scale Global Climate Model (GCM) data to smaller scales and to provide more detailed regional information. However, there are often considerable biases in RCM simulations, which have led to the development of a number of bias correction approaches to provide more realistic climate simulations for impact studies. Bias correction procedures rely on the assumption that RCM biases do not change over time, because correction algorithms and their parameterizations are derived for current climate conditions and assumed to apply also for future climate conditions. This underlying assumption of bias stationarity is the main concern when using bias correction procedures. It is in principle not possible to test whether this assumption is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well bias correction methods perform for conditions different from those used for calibration. For five Swedish catchments, several time series of RCM simulated precipitation and temperature were obtained from the ENSEMBLES data base and different commonly-used bias correction methods were applied. We then performed a differential split-sample test by dividing the data series into cold and warm respective dry and wet years. This enabled us to evaluate the performance of different bias correction procedures under systematically varying climate conditions. The differential split-sample test resulted in a large spread and a clear bias for some of the correction methods during validation years. More advanced correction methods such as distribution mapping performed relatively well even in the validation period, whereas simpler approaches resulted in the largest deviations and least reliable corrections for changed conditions. Therefore, we question the use of simple bias correction methods such as the widely used delta-change approach and linear scaling for RCM-based climate-change impact studies and recommend using higher-skill bias correction methods.

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Technical Note – RAT: a Robustness Assessment Test for calibrated and uncalibrated hydrological models

10.5194/hess-2021-147 ◽

2021 ◽

Author(s):

Pierre Nicolle ◽

Vazken Andréassian ◽

Paul Royer-Gaspard ◽

Charles Perrin ◽

Guillaume Thirel ◽

...

Keyword(s):

Climate Change ◽

Climate Change Impact ◽

Hydrological Model ◽

Technical Note ◽

New Method ◽

Hydrological Models ◽

Split Sample ◽

Sample Test ◽

Change Impact ◽

Robustness Assessment

Abstract. In this note, we present RAT, a new method to assess the robustness of hydrological models. RAT can be seen as an alternative to the classical split-sample test widely used in hydrology. And because the RAT method does not require multiple calibrations, we suggest that it can be applied even to uncalibrated models. The RAT method can be used to determine whether a hydrological model is "safe" for being used for climate change impact studies.

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Is bias correction of regional climate model (RCM) simulations possible for non-stationary conditions?

Hydrology and Earth System Sciences ◽

10.5194/hess-17-5061-2013 ◽

2013 ◽

Vol 17 (12) ◽

pp. 5061-5077 ◽

Cited By ~ 195

Author(s):

C. Teutschbein ◽

J. Seibert

Keyword(s):

Climate Change ◽

Bias Correction ◽

Climate Change Impact ◽

Climate Model ◽

Regional Climate ◽

Model Errors ◽

Climate Conditions ◽

Split Sample ◽

Sample Test ◽

Change Impact

Abstract. In hydrological climate-change impact studies, regional climate models (RCMs) are commonly used to transfer large-scale global climate model (GCM) data to smaller scales and to provide more detailed regional information. Due to systematic and random model errors, however, RCM simulations often show considerable deviations from observations. This has led to the development of a number of correction approaches that rely on the assumption that RCM errors do not change over time. It is in principle not possible to test whether this underlying assumption of error stationarity is actually fulfilled for future climate conditions. In this study, however, we demonstrate that it is possible to evaluate how well correction methods perform for conditions different from those used for calibration with the relatively simple differential split-sample test. For five Swedish catchments, precipitation and temperature simulations from 15 different RCMs driven by ERA40 (the 40 yr reanalysis product of the European Centre for Medium-Range Weather Forecasts (ECMWF)) were corrected with different commonly used bias correction methods. We then performed differential split-sample tests by dividing the data series into cold and warm respective dry and wet years. This enabled us to cross-evaluate the performance of different correction procedures under systematically varying climate conditions. The differential split-sample test identified major differences in the ability of the applied correction methods to reduce model errors and to cope with non-stationary biases. More advanced correction methods performed better, whereas large deviations remained for climate model simulations corrected with simpler approaches. Therefore, we question the use of simple correction methods such as the widely used delta-change approach and linear transformation for RCM-based climate-change impact studies. Instead, we recommend using higher-skill correction methods such as distribution mapping.

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Variations in the Simulation of Climate Change Impact Indices due to Different Land Surface Schemes over the Mediterranean, Middle East and Northern Africa

Atmosphere ◽

10.3390/atmos10010026 ◽

2019 ◽

Vol 10 (1) ◽

pp. 26 ◽

Cited By ~ 7

Author(s):

Katiana Constantinidou ◽

George Zittis ◽

Panos Hadjinicolaou

Keyword(s):

Climate Change ◽

Middle East ◽

Land Surface ◽

Climate Change Impact ◽

Climate Model ◽

Eastern Mediterranean ◽

Regional Climate ◽

Climatic Conditions ◽

Relative Dispersion ◽

Change Impact

The Eastern Mediterranean (EM) and the Middle East and North Africa (MENA) are projected to be exposed to extreme climatic conditions in the 21st century, which will likely induce adverse impacts in various sectors. Relevant climate change impact assessments utilise data from climate model projections and process-based impact models or simpler, index-based approaches. In this study, we explore the implied uncertainty from variations of climate change impact-related indices as induced by the modelled climate (WRF regional climate model) from different land surface schemes (Noah, NoahMP, CLM and RUC). The three climate change impact-related indicators examined here are the Radiative Index of Dryness (RID), the Fuel Dryness Index (Fd) and the Water-limited Yield (Yw). Our findings indicate that Noah simulates the highest values for both RID and Fd, while CLM gives the highest estimations for winter wheat Yw. The relative dispersion in the three indices derived by the different land schemes is not negligible, amounting, for the overall geographical domain of 25% for RID and Fd, and 10% for Yw. The dispersion is even larger for specific sub-regions.

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Climate Change Impacts on Agriculture, a Case Study of Bangladesh, India, Nepal, and Pakistan

SocioEconomic Challenges ◽

10.21272/sec.5(2).35-48.2021 ◽

2021 ◽

Vol 5 (2) ◽

pp. 35-48

Author(s):

Medani P. Bhandari

Keyword(s):

Climate Change ◽

Food Security ◽

South Asia ◽

Climate Change Impact ◽

Agricultural Land ◽

Farming Systems ◽

Climatic Conditions ◽

Future Research ◽

Cropping Pattern ◽

Change Impact

Climate change raises the risk on food security, alters the cropping pattern, and secondly, it also plays the triggering role to widen inequality. The South Asian region is home to nearly half of the poor and malnourished population of the world. In South Asia — Bangladesh, India, Nepal, and Pakistan encounter similar climate induced changes though they differ in their socio-political, economic, and cultural conditions. The physiological population densities (farming population per unit of agricultural land) suggest that these countries belong to the threat zone in terms of climate change impact on agriculture. It has been obvious that any unfavorable climatic conditions mean poor agricultural growth which will have serious ramification on the countries’ economies. Poverty induces poverty; because of the rudimentary technologies used in agriculture, more manpower is needed for farming thus encouraging couples to increase family manpower to invest on farming, which might lead to overpopulation. This paper evaluates how climate change has direct impact on the agricultural development and broader economic growth in the global context and South Asia (Bangladesh, India, Nepal, and Pakistan). Paper unveils the climate change induced challenges in agriculture with the empirical evidence, elaborates the consequences to the farmers livelihood and food security. Based on secondary information, this paper provides climate change risk scenario and recommends few coping strategies to minimize the climate change impact in farming systems and pathway for the future research.

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