Articles | Volume 23, issue 11
https://doi.org/10.5194/hess-23-4803-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-23-4803-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
25 Nov 2019
Research article |
| 25 Nov 2019
| 25 Nov 2019
Selection of multi-model ensemble of general circulation models for the simulation of precipitation and maximum and minimum temperature based on spatial assessment metrics
Kamal Ahmed
Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM),
Johor Bahru, 81310, Malaysia
Faculty of Water Resource Management, Lasbela University of
Agriculture, Water and Marine Sciences, Balochistan, 90150, Pakistan
Dhanapala A. Sachindra
Institute for Sustainability and Innovation, College of Engineering
and Science, Victoria University, P.O. Box 14428, Melbourne, Victoria 8001,
Australia
Shamsuddin Shahid
Faculty of Civil Engineering, Universiti Teknologi Malaysia (UTM),
Johor Bahru, 81310, Malaysia
Mehmet C. Demirel
Department of Civil Engineering, Istanbul Technical University, 34469
Maslak, Istanbul, Turkey
Department of Civil Engineering, Seoul National University of Science
and Technology, Seoul, 01811, Republic of Korea
Related authors
Kamal Ahmed, Shamsuddin Shahid, Xiaojun Wang, Nadeem Nawaz, and Najeebullah Khan
Hydrol. Earth Syst. Sci., 23, 3081–3096, https://doi.org/10.5194/hess-23-3081-2019, https://doi.org/10.5194/hess-23-3081-2019, 2019
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The long-term changes (1901–2016) in annual and seasonal aridity in Pakistan and its causes are analyzed in this paper. Gauge-based gridded precipitation and PET data are used to show the spatial and temporal patterns of the changes in aridity over the diverse climate of the country. The present study suggests that the relative influence of precipitation and temperature on aridity determines its trends in the context of climate change.
A. Dewan, S. Shahid, M. Asaduzzaman, and F. Faruque
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2022, 627–633, https://doi.org/10.5194/isprs-annals-V-3-2022-627-2022, https://doi.org/10.5194/isprs-annals-V-3-2022-627-2022, 2022
Young Hoon Song, Eun-Sung Chung, and Shamsuddin Shahid
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-107, https://doi.org/10.5194/hess-2022-107, 2022
Manuscript not accepted for further review
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This study proposed two new concepts for the previous Double Gamma Quantile Mapping (DGQM), such as the inclusion of flexible dividing point between two individual gamma functions and the use of more probability distributions. As a result, F-DDQM method performed the better bias correction for the GCMs very clearly. This new F-DDQM method can be also applied to the various fields such as the use of satellite climate data, reanalysis climate data and spatial downscaling or interpolation.
E. Bontempo, M. C. Demirel, C. Corsini, F. Martins, and D. Valeriano
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3-W12-2020, 201–206, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-201-2020, https://doi.org/10.5194/isprs-archives-XLII-3-W12-2020-201-2020, 2020
Kamal Ahmed, Shamsuddin Shahid, Xiaojun Wang, Nadeem Nawaz, and Najeebullah Khan
Hydrol. Earth Syst. Sci., 23, 3081–3096, https://doi.org/10.5194/hess-23-3081-2019, https://doi.org/10.5194/hess-23-3081-2019, 2019
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The long-term changes (1901–2016) in annual and seasonal aridity in Pakistan and its causes are analyzed in this paper. Gauge-based gridded precipitation and PET data are used to show the spatial and temporal patterns of the changes in aridity over the diverse climate of the country. The present study suggests that the relative influence of precipitation and temperature on aridity determines its trends in the context of climate change.
Julian Koch, Mehmet Cüneyd Demirel, and Simon Stisen
Geosci. Model Dev., 11, 1873–1886, https://doi.org/10.5194/gmd-11-1873-2018, https://doi.org/10.5194/gmd-11-1873-2018, 2018
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Our work addresses a key challenge in earth system modelling: how to optimally exploit the information contained in satellite remote sensing observations in the calibration of such models. For this we thoroughly test a number of measures that quantify the fit between an observed and a simulated spatial pattern. We acknowledge the difficulties associated with such a comparison and suggest using measures that regard multiple aspects of spatial information, i.e. magnitude and variability.
Mehmet C. Demirel, Juliane Mai, Gorka Mendiguren, Julian Koch, Luis Samaniego, and Simon Stisen
Hydrol. Earth Syst. Sci., 22, 1299–1315, https://doi.org/10.5194/hess-22-1299-2018, https://doi.org/10.5194/hess-22-1299-2018, 2018
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Satellite data offer great opportunities to improve spatial model predictions by means of spatially oriented model evaluations. In this study, satellite images are used to observe spatial patterns of evapotranspiration at the land surface. These spatial patterns are utilized in combination with streamflow observations in a model calibration framework including a novel spatial performance metric tailored to target the spatial pattern performance of a catchment-scale hydrological model.
Xiao-Jun Wang, Jian-Yun Zhang, Shamsuddin Shahid, Yu-Xuan Xie, and Xu Zhang
Proc. IAHS, 376, 51–55, https://doi.org/10.5194/piahs-376-51-2018, https://doi.org/10.5194/piahs-376-51-2018, 2018
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A statistical model has been developed for forecasting domestic water demand in Haihe river basin of China due to population growth, technological advances and climate change. Historical records of domestic water use, climate, population and urbanization are used for the development of model. An ensemble of seven general circulation models (GCMs) namely, BCC-CSM1-1, BNU-ESM, CNRM-CM5, GISS-E2-R, MIROC-ESM, PI-ESM-LR, MRI-CGCM3 were used for the projection of climate and the changes in water demand.
G. Lee, K. S. Jun, and E.-S. Chung
Nat. Hazards Earth Syst. Sci., 15, 863–874, https://doi.org/10.5194/nhess-15-863-2015, https://doi.org/10.5194/nhess-15-863-2015, 2015
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This study proposes an improved group decision making (GDM) framework that combines VIKOR method with data fuzzification to quantify the spatial flood vulnerability, including multiple criteria. The combination of the GDM approach with the fuzzy VIKOR method can provide robust prioritization because it actively reflects the opinions of various groups and considers uncertainty in the input data.
M. C. Demirel, M. J. Booij, and A. Y. Hoekstra
Hydrol. Earth Syst. Sci., 19, 275–291, https://doi.org/10.5194/hess-19-275-2015, https://doi.org/10.5194/hess-19-275-2015, 2015
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This paper investigates the skill of 90-day low-flow forecasts using three models. From the results, it appears that all models are prone to over-predict runoff during low-flow periods using ensemble seasonal meteorological forcing. The largest range for 90-day low-flow forecasts is found for the GR4J model. Overall, the uncertainty from ensemble P forecasts has a larger effect on seasonal low-flow forecasts than the uncertainty from ensemble PET forecasts and initial model conditions.
J. H. Sung and E.-S. Chung
Hydrol. Earth Syst. Sci., 18, 3341–3351, https://doi.org/10.5194/hess-18-3341-2014, https://doi.org/10.5194/hess-18-3341-2014, 2014
G. Lee, K.-S. Jun, and E.-S. Chung
Nat. Hazards Earth Syst. Sci., 13, 1293–1312, https://doi.org/10.5194/nhess-13-1293-2013, https://doi.org/10.5194/nhess-13-1293-2013, 2013
Related subject area
Subject: Hydrometeorology | Techniques and Approaches: Uncertainty analysis
On the visual detection of non-natural records in streamflow time series: challenges and impacts
Historical rainfall data in northern Italy predict larger meteorological drought hazard than climate projections
Daytime-only mean data enhance understanding of land–atmosphere coupling
Quantifying the uncertainty of precipitation forecasting using probabilistic deep learning
Unraveling the contribution of potential evaporation formulation to uncertainty under climate change
Exploring hydrologic post-processing of ensemble streamflow forecasts based on affine kernel dressing and non-dominated sorting genetic algorithm II
Choosing between post-processing precipitation forecasts or chaining several uncertainty quantification tools in hydrological forecasting systems
Performance of the Global Forecast System's medium-range precipitation forecasts in the Niger river basin using multiple satellite-based products
Uncertainties and their interaction in flood hazard assessment with climate change
Bias-correcting input variables enhances forecasting of reference crop evapotranspiration
Uncertainty of gridded precipitation and temperature reference datasets in climate change impact studies
At which timescale does the complementary principle perform best in evaporation estimation?
Uncertainty in nonstationary frequency analysis of South Korea's daily rainfall peak over threshold excesses associated with covariates
Assessment of extreme flows and uncertainty under climate change: disentangling the uncertainty contribution of representative concentration pathways, global climate models and internal climate variability
The accuracy of weather radar in heavy rain: a comparative study for Denmark, the Netherlands, Finland and Sweden
A new uncertainty estimation approach with multiple datasets and implementation for various precipitation products
A crash-testing framework for predictive uncertainty assessment when forecasting high flows in an extrapolation context
Required sampling density of ground-based soil moisture and brightness temperature observations for calibration and validation of L-band satellite observations based on a virtual reality
Response of global evaporation to major climate modes in historical and future Coupled Model Intercomparison Project Phase 5 simulations
Cross-validating precipitation datasets in the Indus River basin
Assessment of spatial uncertainty of heavy rainfall at catchment scale using a dense gauge network
Influence of three phases of El Niño–Southern Oscillation on daily precipitation regimes in China
Dual-polarized quantitative precipitation estimation as a function of range
Reconstruction of droughts in India using multiple land-surface models (1951–2015)
Relative effects of statistical preprocessing and postprocessing on a regional hydrological ensemble prediction system
Exploratory studies into seasonal flow forecasting potential for large lakes
Evaluation of multiple forcing data sets for precipitation and shortwave radiation over major land areas of China
Verification of ECMWF System 4 for seasonal hydrological forecasting in a northern climate
Providing a non-deterministic representation of spatial variability of precipitation in the Everest region
Inter-comparison of daily precipitation products for large-scale hydro-climatic applications over Canada
Sensitivity of potential evapotranspiration to changes in climate variables for different Australian climatic zones
Characteristics of rainfall events in regional climate model simulations for the Czech Republic
The rainfall erosivity factor in the Czech Republic and its uncertainty
Hierarchy of climate and hydrological uncertainties in transient low-flow projections
Willingness-to-pay for a probabilistic flood forecast: a risk-based decision-making game
Assessment of small-scale variability of rainfall and multi-satellite precipitation estimates using measurements from a dense rain gauge network in Southeast India
Comparing CFSR and conventional weather data for discharge and soil loss modelling with SWAT in small catchments in the Ethiopian Highlands
Uncertainties in calculating precipitation climatology in East Asia
Measurement and interpolation uncertainties in rainfall maps from cellular communication networks
Characterization of precipitation product errors across the United States using multiplicative triple collocation
Exploring the impact of forcing error characteristics on physically based snow simulations within a global sensitivity analysis framework
Evaluation of land surface model simulations of evapotranspiration over a 12-year crop succession: impact of soil hydraulic and vegetation properties
Multi-objective parameter optimization of common land model using adaptive surrogate modeling
Testing gridded land precipitation data and precipitation and runoff reanalyses (1982–2010) between 45° S and 45° N with normalised difference vegetation index data
Evaluation of high-resolution precipitation analyses using a dense station network
Prediction of extreme floods based on CMIP5 climate models: a case study in the Beijiang River basin, South China
Estimating the water needed to end the drought or reduce the drought severity in the Carpathian region
Alternative configurations of quantile regression for estimating predictive uncertainty in water level forecasts for the upper Severn River: a comparison
Comparison of drought indicators derived from multiple data sets over Africa
The potential of radar-based ensemble forecasts for flash-flood early warning in the southern Swiss Alps
Laurent Strohmenger, Eric Sauquet, Claire Bernard, Jérémie Bonneau, Flora Branger, Amélie Bresson, Pierre Brigode, Rémy Buzier, Olivier Delaigue, Alexandre Devers, Guillaume Evin, Maïté Fournier, Shu-Chen Hsu, Sandra Lanini, Alban de Lavenne, Thibault Lemaitre-Basset, Claire Magand, Guilherme Mendoza Guimarães, Max Mentha, Simon Munier, Charles Perrin, Tristan Podechard, Léo Rouchy, Malak Sadki, Myriam Soutif-Bellenger, François Tilmant, Yves Tramblay, Anne-Lise Véron, Jean-Philippe Vidal, and Guillaume Thirel
Hydrol. Earth Syst. Sci., 27, 3375–3391, https://doi.org/10.5194/hess-27-3375-2023, https://doi.org/10.5194/hess-27-3375-2023, 2023
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We present the results of a large visual inspection campaign of 674 streamflow time series in France. The objective was to detect non-natural records resulting from instrument failure or anthropogenic influences, such as hydroelectric power generation or reservoir management. We conclude that the identification of flaws in flow time series is highly dependent on the objectives and skills of individual evaluators, and we raise the need for better practices for data cleaning.
Rui Guo and Alberto Montanari
Hydrol. Earth Syst. Sci., 27, 2847–2863, https://doi.org/10.5194/hess-27-2847-2023, https://doi.org/10.5194/hess-27-2847-2023, 2023
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The present study refers to the region of Bologna, where the availability of a 209-year-long daily rainfall series allows us to make a unique assessment of global climate models' reliability and their predicted changes in rainfall and multiyear droughts. Our results suggest carefully considering the impact of uncertainty when designing climate change adaptation policies for droughts. Rigorous use and comprehensive interpretation of the available information are needed to avoid mismanagement.
Zun Yin, Kirsten L. Findell, Paul Dirmeyer, Elena Shevliakova, Sergey Malyshev, Khaled Ghannam, Nina Raoult, and Zhihong Tan
Hydrol. Earth Syst. Sci., 27, 861–872, https://doi.org/10.5194/hess-27-861-2023, https://doi.org/10.5194/hess-27-861-2023, 2023
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Land–atmosphere (L–A) interactions typically focus on daytime processes connecting the land state with the overlying atmospheric boundary layer. However, much prior L–A work used monthly or daily means due to the lack of daytime-only data products. Here we show that monthly smoothing can significantly obscure the L–A coupling signal, and including nighttime information can mute or mask the daytime processes of interest. We propose diagnosing L–A coupling within models or archiving subdaily data.
Lei Xu, Nengcheng Chen, Chao Yang, Hongchu Yu, and Zeqiang Chen
Hydrol. Earth Syst. Sci., 26, 2923–2938, https://doi.org/10.5194/hess-26-2923-2022, https://doi.org/10.5194/hess-26-2923-2022, 2022
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Precipitation forecasting has potential uncertainty due to data and model uncertainties. Here, an integrated predictive uncertainty modeling framework is proposed by jointly considering data and model uncertainties through an uncertainty propagation theorem. The results indicate an effective predictive uncertainty estimation for precipitation forecasting, indicating the great potential for uncertainty quantification of numerous predictive applications.
Thibault Lemaitre-Basset, Ludovic Oudin, Guillaume Thirel, and Lila Collet
Hydrol. Earth Syst. Sci., 26, 2147–2159, https://doi.org/10.5194/hess-26-2147-2022, https://doi.org/10.5194/hess-26-2147-2022, 2022
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Increasing temperature will impact evaporation and water resource management. Hydrological models are fed with an estimation of the evaporative demand of the atmosphere, called potential evapotranspiration (PE). The objectives of this study were (1) to compute the future PE anomaly over France and (2) to determine the impact of the choice of the method to estimate PE. Our results show that all methods present similar future trends. No method really stands out from the others.
Jing Xu, François Anctil, and Marie-Amélie Boucher
Hydrol. Earth Syst. Sci., 26, 1001–1017, https://doi.org/10.5194/hess-26-1001-2022, https://doi.org/10.5194/hess-26-1001-2022, 2022
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The performance of the non-dominated sorting genetic algorithm II (NSGA-II) is compared with a conventional post-processing method of affine kernel dressing. NSGA-II showed its superiority in improving the forecast skill and communicating trade-offs with end-users. It allows the enhancement of the forecast quality since it allows for setting multiple specific objectives from scratch. This flexibility should be considered as a reason to implement hydrologic ensemble prediction systems (H-EPSs).
Emixi Sthefany Valdez, François Anctil, and Maria-Helena Ramos
Hydrol. Earth Syst. Sci., 26, 197–220, https://doi.org/10.5194/hess-26-197-2022, https://doi.org/10.5194/hess-26-197-2022, 2022
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We investigated how a precipitation post-processor interacts with other tools for uncertainty quantification in a hydrometeorological forecasting chain. Four systems were implemented to generate 7 d ensemble streamflow forecasts, which vary from partial to total uncertainty estimation. Overall analysis showed that post-processing and initial condition estimation ensure the most skill improvements, in some cases even better than a system that considers all sources of uncertainty.
Haowen Yue, Mekonnen Gebremichael, and Vahid Nourani
Hydrol. Earth Syst. Sci., 26, 167–181, https://doi.org/10.5194/hess-26-167-2022, https://doi.org/10.5194/hess-26-167-2022, 2022
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The development of high-resolution global precipitation forecasts and the lack of reliable precipitation forecasts over Africa motivates this work to evaluate the precipitation forecasts from the Global Forecast System (GFS) over the Niger river basin in Africa. The GFS forecasts, at a 15 d accumulation timescale, have an acceptable performance; however, the forecasts are highly biased. It is recommended to apply bias correction to GFS forecasts before their application.
Hadush Meresa, Conor Murphy, Rowan Fealy, and Saeed Golian
Hydrol. Earth Syst. Sci., 25, 5237–5257, https://doi.org/10.5194/hess-25-5237-2021, https://doi.org/10.5194/hess-25-5237-2021, 2021
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The assessment of future impacts of climate change is associated with a cascade of uncertainty linked to the modelling chain employed in assessing local-scale changes. Understanding and quantifying this cascade is essential for developing effective adaptation actions. We find that not only do the contributions of different sources of uncertainty vary by catchment, but that the dominant sources of uncertainty can be very different on a catchment-by-catchment basis.
Qichun Yang, Quan J. Wang, Kirsti Hakala, and Yating Tang
Hydrol. Earth Syst. Sci., 25, 4773–4788, https://doi.org/10.5194/hess-25-4773-2021, https://doi.org/10.5194/hess-25-4773-2021, 2021
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Forecasts of water losses from land surface to the air are highly valuable for water resource management and planning. In this study, we aim to fill a critical knowledge gap in the forecasting of evaporative water loss. Model experiments across Australia clearly suggest the necessity of correcting errors in input variables for more reliable water loss forecasting. We anticipate that the strategy developed in our work will benefit future water loss forecasting and lead to more skillful forecasts.
Mostafa Tarek, François Brissette, and Richard Arsenault
Hydrol. Earth Syst. Sci., 25, 3331–3350, https://doi.org/10.5194/hess-25-3331-2021, https://doi.org/10.5194/hess-25-3331-2021, 2021
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It is not known how much uncertainty the choice of a reference data set may bring to impact studies. This study compares precipitation and temperature data sets to evaluate the uncertainty contribution to the results of climate change studies. Results show that all data sets provide good streamflow simulations over the reference period. The reference data sets also provided uncertainty that was equal to or larger than that related to general circulation models over most of the catchments.
Liming Wang, Songjun Han, and Fuqiang Tian
Hydrol. Earth Syst. Sci., 25, 375–386, https://doi.org/10.5194/hess-25-375-2021, https://doi.org/10.5194/hess-25-375-2021, 2021
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It remains unclear at which timescale the complementary principle performs best in estimating evaporation. In this study, evaporation estimation was assessed over 88 eddy covariance monitoring sites at multiple timescales. The results indicate that the generalized complementary functions perform best in estimating evaporation at the monthly scale. This study provides a reference for choosing a suitable time step for evaporation estimations in relevant studies.
Okjeong Lee, Jeonghyeon Choi, Jeongeun Won, and Sangdan Kim
Hydrol. Earth Syst. Sci., 24, 5077–5093, https://doi.org/10.5194/hess-24-5077-2020, https://doi.org/10.5194/hess-24-5077-2020, 2020
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The uncertainty of the model interpreting rainfall extremes with temperature is analyzed. The performance of the model focuses on the reliability of the output. It has been found that the selection of temperatures suitable for extreme levels plays an important role in improving model reliability. Based on this, a methodology is proposed to quantify the degree of uncertainty inherent in the change in rainfall extremes due to global warming.
Chao Gao, Martijn J. Booij, and Yue-Ping Xu
Hydrol. Earth Syst. Sci., 24, 3251–3269, https://doi.org/10.5194/hess-24-3251-2020, https://doi.org/10.5194/hess-24-3251-2020, 2020
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This paper studies the impact of climate change on high and low flows and quantifies the contribution of uncertainty sources from representative concentration pathways (RCPs), global climate models (GCMs) and internal climate variability in extreme flows. Internal climate variability was reflected in a stochastic rainfall model. The results show the importance of internal climate variability and GCM uncertainty in high flows and GCM and RCP uncertainty in low flows especially for the far future.
Marc Schleiss, Jonas Olsson, Peter Berg, Tero Niemi, Teemu Kokkonen, Søren Thorndahl, Rasmus Nielsen, Jesper Ellerbæk Nielsen, Denica Bozhinova, and Seppo Pulkkinen
Hydrol. Earth Syst. Sci., 24, 3157–3188, https://doi.org/10.5194/hess-24-3157-2020, https://doi.org/10.5194/hess-24-3157-2020, 2020
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A multinational assessment of radar's ability to capture heavy rain events is conducted. In total, six different radar products in Denmark, the Netherlands, Finland and Sweden were considered. Results show a fair agreement, with radar underestimating by 17 %-44 % on average compared with gauges. Despite being adjusted for bias, five of six radar products still exhibited strong conditional biases with intensities of 1–2% per mm/h. Median peak intensity bias was significantly higher, reaching 44 %–67%.
Xudong Zhou, Jan Polcher, Tao Yang, and Ching-Sheng Huang
Hydrol. Earth Syst. Sci., 24, 2061–2081, https://doi.org/10.5194/hess-24-2061-2020, https://doi.org/10.5194/hess-24-2061-2020, 2020
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This article proposes a new estimation approach for assessing the uncertainty with multiple datasets by fully considering all variations in temporal and spatial dimensions. Comparisons demonstrate that classical metrics may underestimate the uncertainties among datasets due to an averaging process in their algorithms. This new approach is particularly suitable for overall assessment of multiple climatic products, but can be easily applied to other spatiotemporal products in related fields.
Lionel Berthet, François Bourgin, Charles Perrin, Julie Viatgé, Renaud Marty, and Olivier Piotte
Hydrol. Earth Syst. Sci., 24, 2017–2041, https://doi.org/10.5194/hess-24-2017-2020, https://doi.org/10.5194/hess-24-2017-2020, 2020
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An increasing number of flood forecasting services assess and communicate the uncertainty associated with their forecasts. We present a crash-testing framework that evaluates the quality of hydrological forecasts in an extrapolation context. Overall, the results highlight the challenge of uncertainty quantification when forecasting high flows. They show a significant drop in reliability when forecasting high flows and considerable variability among catchments and across lead times.
Shaoning Lv, Bernd Schalge, Pablo Saavedra Garfias, and Clemens Simmer
Hydrol. Earth Syst. Sci., 24, 1957–1973, https://doi.org/10.5194/hess-24-1957-2020, https://doi.org/10.5194/hess-24-1957-2020, 2020
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Passive remote sensing of soil moisture has good potential to improve weather forecasting via data assimilation in theory. We use the virtual reality data set (VR01) to infer the impact of sampling density on soil moisture ground cal/val activity. It shows how the sampling error is growing with an increasing sampling distance for a SMOS–SMAP scale footprint in about 40 km, 9 km, and 3 km. The conclusion will help in understanding the passive remote sensing soil moisture products.
Thanh Le and Deg-Hyo Bae
Hydrol. Earth Syst. Sci., 24, 1131–1143, https://doi.org/10.5194/hess-24-1131-2020, https://doi.org/10.5194/hess-24-1131-2020, 2020
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Here we investigate the response of global evaporation to main climate modes, including the Indian Ocean Dipole (IOD), the North Atlantic Oscillation (NAO) and the El Niño–Southern Oscillation (ENSO). Our results indicate that ENSO is an important driver of evaporation for many regions, while the impacts of NAO and IOD are substantial. This study allows us to obtain insight about the predictability of evaporation and, hence, may help to improve the early-warning systems of climate extremes.
Jean-Philippe Baudouin, Michael Herzog, and Cameron A. Petrie
Hydrol. Earth Syst. Sci., 24, 427–450, https://doi.org/10.5194/hess-24-427-2020, https://doi.org/10.5194/hess-24-427-2020, 2020
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The amount of precipitation falling in the Indus River basin remains uncertain while its variability impacts 100 million inhabitants. A comparison of datasets from diverse sources (ground remote observations, model outputs) reduces this uncertainty significantly. Grounded observations offer the most reliable long-term variability but with important underestimation in winter over the mountains. By contrast, recent model outputs offer better estimations of total amount and short-term variability.
Sungmin O and Ulrich Foelsche
Hydrol. Earth Syst. Sci., 23, 2863–2875, https://doi.org/10.5194/hess-23-2863-2019, https://doi.org/10.5194/hess-23-2863-2019, 2019
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We analyze heavy local rainfall to address questions regarding the spatial uncertainty due to the approximation of areal rainfall using point measurements. Ten years of rainfall data from a dense network of 150 rain gauges in southeastern Austria are employed, which permits robust examination of small-scale rainfall at various horizontal resolutions. Quantitative uncertainty information from the study can guide both data users and producers to estimate uncertainty in their own rainfall dataset.
Aifeng Lv, Bo Qu, Shaofeng Jia, and Wenbin Zhu
Hydrol. Earth Syst. Sci., 23, 883–896, https://doi.org/10.5194/hess-23-883-2019, https://doi.org/10.5194/hess-23-883-2019, 2019
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ENSO-related changes in daily precipitation regimes are currently ignored by the scientific community. We analyzed the anomalies of daily precipitation and hydrological extremes caused by different phases of ENSO events, as well as the possible driving mechanisms, to reveal the influence of ENSO on China's daily precipitation regimes. Our results provide a valuable tool for daily precipitation prediction and enable the prioritization of adaptation efforts ahead of extreme events in China.
Micheal J. Simpson and Neil I. Fox
Hydrol. Earth Syst. Sci., 22, 3375–3389, https://doi.org/10.5194/hess-22-3375-2018, https://doi.org/10.5194/hess-22-3375-2018, 2018
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Many researchers have expressed that one of the main difficulties in modeling watershed hydrology is that of obtaining continuous, widespread weather input data, especially precipitation. The overarching objective of this study was to provide a comprehensive study of three weather radars as a function of range. We found that radar-estimated precipitation was best at ranges between 100 and 150 km from the radar, with different radar parameters being superior at varying distances from the radar.
Vimal Mishra, Reepal Shah, Syed Azhar, Harsh Shah, Parth Modi, and Rohini Kumar
Hydrol. Earth Syst. Sci., 22, 2269–2284, https://doi.org/10.5194/hess-22-2269-2018, https://doi.org/10.5194/hess-22-2269-2018, 2018
Sanjib Sharma, Ridwan Siddique, Seann Reed, Peter Ahnert, Pablo Mendoza, and Alfonso Mejia
Hydrol. Earth Syst. Sci., 22, 1831–1849, https://doi.org/10.5194/hess-22-1831-2018, https://doi.org/10.5194/hess-22-1831-2018, 2018
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We investigate the relative roles of statistical weather preprocessing and streamflow postprocessing in hydrological ensemble forecasting at short- to medium-range forecast lead times (day 1–7). For this purpose, we develop and implement a regional hydrologic ensemble prediction system (RHEPS). Overall analysis shows that implementing both preprocessing and postprocessing ensures the most skill improvements, but postprocessing alone can often be a competitive alternative.
Kevin Sene, Wlodek Tych, and Keith Beven
Hydrol. Earth Syst. Sci., 22, 127–141, https://doi.org/10.5194/hess-22-127-2018, https://doi.org/10.5194/hess-22-127-2018, 2018
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The theme of the paper is exploration of the potential for seasonal flow forecasting for large lakes using a range of stochastic transfer function techniques with additional insights gained from simple analytical approximations. The methods were evaluated using records for two of the largest lakes in the world. The paper concludes with a discussion of the relevance of the results to operational flow forecasting systems for other large lakes.
Fan Yang, Hui Lu, Kun Yang, Jie He, Wei Wang, Jonathon S. Wright, Chengwei Li, Menglei Han, and Yishan Li
Hydrol. Earth Syst. Sci., 21, 5805–5821, https://doi.org/10.5194/hess-21-5805-2017, https://doi.org/10.5194/hess-21-5805-2017, 2017
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In this paper, we show that CLDAS has the highest spatial and temporal resolution, and it performs best in terms of precipitation, while it overestimates the shortwave radiation. CMFD also has high resolution and its shortwave radiation data match well with the station data; its annual-mean precipitation is reliable but its monthly precipitation needs improvements. Both GLDAS and CN05.1 over mainland China need to be improved. The results can benefit researchers for forcing data selection.
Rachel Bazile, Marie-Amélie Boucher, Luc Perreault, and Robert Leconte
Hydrol. Earth Syst. Sci., 21, 5747–5762, https://doi.org/10.5194/hess-21-5747-2017, https://doi.org/10.5194/hess-21-5747-2017, 2017
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Meteorological forecasting agencies constantly work on pushing the limit of predictability farther in time. However, some end users need proof that climate model outputs are ready to be implemented operationally. We show that bias correction is crucial for the use of ECMWF System4 forecasts for the studied area and there is a potential for the use of 1-month-ahead forecasts. Beyond this, forecast performance is equivalent to using past climatology series as inputs to the hydrological model.
Judith Eeckman, Pierre Chevallier, Aaron Boone, Luc Neppel, Anneke De Rouw, Francois Delclaux, and Devesh Koirala
Hydrol. Earth Syst. Sci., 21, 4879–4893, https://doi.org/10.5194/hess-21-4879-2017, https://doi.org/10.5194/hess-21-4879-2017, 2017
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The central part of the Himalayan Range presents tremendous heterogeneity in terms of topography and climatology, but the representation of hydro-climatic processes for Himalayan catchments is limited due to a lack of knowledge in such poorly instrumented environments. The proposed approach is to characterize the effect of altitude on precipitation by considering ensembles of acceptable altitudinal factors. Ensembles of acceptable values for the components of the water cycle are then provided.
Jefferson S. Wong, Saman Razavi, Barrie R. Bonsal, Howard S. Wheater, and Zilefac E. Asong
Hydrol. Earth Syst. Sci., 21, 2163–2185, https://doi.org/10.5194/hess-21-2163-2017, https://doi.org/10.5194/hess-21-2163-2017, 2017
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This study was conducted to quantify the spatial and temporal variability of the errors associated with various gridded precipitation products in Canada. Overall, WFDEI [GPCC] and CaPA performed best with respect to different performance measures, followed by ANUSPLIN and WEDEI [CRU]. Princeton and NARR demonstrated the lowest quality. Comparing the climate model-simulated products, PCIC ensembles generally performed better than NA-CORDEX ensembles in terms of reliability in four seasons.
Danlu Guo, Seth Westra, and Holger R. Maier
Hydrol. Earth Syst. Sci., 21, 2107–2126, https://doi.org/10.5194/hess-21-2107-2017, https://doi.org/10.5194/hess-21-2107-2017, 2017
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This study assessed the impact of baseline climate conditions on the sensitivity of potential evapotranspiration (PET) to a large range of plausible changes in temperature, relative humidity, solar radiation and wind speed at 30 Australian locations. Around 2-fold greater PET changes were observed at cool and humid locations compared to others, indicating potential for elevated water loss in the future. These impacts can be useful to inform the selection of PET models under a changing climate.
Vojtěch Svoboda, Martin Hanel, Petr Máca, and Jan Kyselý
Hydrol. Earth Syst. Sci., 21, 963–980, https://doi.org/10.5194/hess-21-963-2017, https://doi.org/10.5194/hess-21-963-2017, 2017
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The study presents validation of precipitation events as simulated by an ensemble of regional climate models for the Czech Republic. While the number of events per season, seasonal total precipitation due to heavy events and the distribution of rainfall depths are simulated relatively well, event maximum precipitation and event intensity are strongly underestimated. This underestimation cannot be explained by scale mismatch between point observations and area average (climate model simulations).
Martin Hanel, Petr Máca, Petr Bašta, Radek Vlnas, and Pavel Pech
Hydrol. Earth Syst. Sci., 20, 4307–4322, https://doi.org/10.5194/hess-20-4307-2016, https://doi.org/10.5194/hess-20-4307-2016, 2016
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The paper is focused on assessment of the contribution of various sources of uncertainty to the estimated rainfall erosivity factor. It is shown that the rainfall erosivity factor can be estimated with reasonable precision even from records shorter than recommended, provided good spatial coverage and reasonable explanatory variables are available. The research was done as an update of the R factor estimates for the Czech Republic, which were later used for climate change assessment.
Jean-Philippe Vidal, Benoît Hingray, Claire Magand, Eric Sauquet, and Agnès Ducharne
Hydrol. Earth Syst. Sci., 20, 3651–3672, https://doi.org/10.5194/hess-20-3651-2016, https://doi.org/10.5194/hess-20-3651-2016, 2016
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Possible transient futures of winter and summer low flows for two snow-influenced catchments in the southern French Alps show a strong decrease signal. It is however largely masked by the year-to-year variability, which should be the main target for defining adaptation strategies. Responses of different hydrological models strongly diverge in the future, suggesting to carefully check the robustness of evapotranspiration and snowpack components under a changing climate.
Louise Arnal, Maria-Helena Ramos, Erin Coughlan de Perez, Hannah Louise Cloke, Elisabeth Stephens, Fredrik Wetterhall, Schalk Jan van Andel, and Florian Pappenberger
Hydrol. Earth Syst. Sci., 20, 3109–3128, https://doi.org/10.5194/hess-20-3109-2016, https://doi.org/10.5194/hess-20-3109-2016, 2016
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Forecasts are produced as probabilities of occurrence of specific events, which is both an added value and a challenge for users. This paper presents a game on flood protection, "How much are you prepared to pay for a forecast?", which investigated how users perceive the value of forecasts and are willing to pay for them when making decisions. It shows that users are mainly influenced by the perceived quality of the forecasts, their need for the information and their degree of risk tolerance.
K. Sunilkumar, T. Narayana Rao, and S. Satheeshkumar
Hydrol. Earth Syst. Sci., 20, 1719–1735, https://doi.org/10.5194/hess-20-1719-2016, https://doi.org/10.5194/hess-20-1719-2016, 2016
Vincent Roth and Tatenda Lemann
Hydrol. Earth Syst. Sci., 20, 921–934, https://doi.org/10.5194/hess-20-921-2016, https://doi.org/10.5194/hess-20-921-2016, 2016
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The Soil and Water Assessment Tool (SWAT) suggests using the CFSR global rainfall data for modelling discharge and soil erosion in data-scarce parts of the world. These data are freely available and ready to use for SWAT modelling. However, simulations with the CFSR data in the Ethiopian Highlands were unable to represent the specific regional climates and showed high discrepancies. This article compares SWAT simulations with conventional rainfall data and with CFSR rainfall data.
J. Kim and S. K. Park
Hydrol. Earth Syst. Sci., 20, 651–658, https://doi.org/10.5194/hess-20-651-2016, https://doi.org/10.5194/hess-20-651-2016, 2016
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This study examined the uncertainty in climatological precipitation in East Asia, calculated from five gridded analysis data sets based on in situ rain gauge observations from 1980 to 2007. It is found that the regions of large uncertainties are typically lightly populated and are characterized by severe terrain and/or very high elevations. Thus, care must be taken in using long-term trends calculated from gridded precipitation analysis data for climate studies over such regions in East Asia.
M. F. Rios Gaona, A. Overeem, H. Leijnse, and R. Uijlenhoet
Hydrol. Earth Syst. Sci., 19, 3571–3584, https://doi.org/10.5194/hess-19-3571-2015, https://doi.org/10.5194/hess-19-3571-2015, 2015
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Commercial cellular networks are built for telecommunication purposes. These kinds of networks have lately been used to obtain rainfall maps at country-wide scales. From previous studies, we now quantify the uncertainties associated with such maps. To do so, we divided the sources or error into two categories: from microwave link measurements and from mapping. It was found that the former is the source that contributes the most to the overall error in rainfall maps from microwave link network.
S. H. Alemohammad, K. A. McColl, A. G. Konings, D. Entekhabi, and A. Stoffelen
Hydrol. Earth Syst. Sci., 19, 3489–3503, https://doi.org/10.5194/hess-19-3489-2015, https://doi.org/10.5194/hess-19-3489-2015, 2015
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This paper introduces a new variant of the triple collocation technique with multiplicative error model. The method is applied, for the first time, to precipitation products across the central part of continental USA. Results show distinctive patterns of error variance in each product that are estimated without a priori assumption of any of the error distributions. The correlation coefficients between each product and the truth are also estimated, which provides another performance perspective.
M. S. Raleigh, J. D. Lundquist, and M. P. Clark
Hydrol. Earth Syst. Sci., 19, 3153–3179, https://doi.org/10.5194/hess-19-3153-2015, https://doi.org/10.5194/hess-19-3153-2015, 2015
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A sensitivity analysis is used to examine how error characteristics (type, distributions, and magnitudes) in meteorological forcing data impact outputs from a physics-based snow model in four climates. Bias and error magnitudes were key factors in model sensitivity and precipitation bias often dominated. However, the relative importance of forcings depended somewhat on the selected model output. Forcing uncertainty was comparable to model structural uncertainty as found in other studies.
S. Garrigues, A. Olioso, J. C. Calvet, E. Martin, S. Lafont, S. Moulin, A. Chanzy, O. Marloie, S. Buis, V. Desfonds, N. Bertrand, and D. Renard
Hydrol. Earth Syst. Sci., 19, 3109–3131, https://doi.org/10.5194/hess-19-3109-2015, https://doi.org/10.5194/hess-19-3109-2015, 2015
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Land surface model simulations of evapotranspiration are assessed over a 12-year Mediterranean crop succession. Evapotranspiration mainly results from soil evaporation when it is simulated over a Mediterranean crop succession. This leads to a high sensitivity to the soil parameters. Errors on soil hydraulic properties can lead to a large bias in cumulative evapotranspiration over a long period of time. Accounting for uncertainties in soil properties is essential for land surface modelling.
W. Gong, Q. Duan, J. Li, C. Wang, Z. Di, Y. Dai, A. Ye, and C. Miao
Hydrol. Earth Syst. Sci., 19, 2409–2425, https://doi.org/10.5194/hess-19-2409-2015, https://doi.org/10.5194/hess-19-2409-2015, 2015
S. O. Los
Hydrol. Earth Syst. Sci., 19, 1713–1725, https://doi.org/10.5194/hess-19-1713-2015, https://doi.org/10.5194/hess-19-1713-2015, 2015
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The study evaluates annual precipitation (largely rainfall) amounts for the tropics and subtropics; precipitation was obtained from ground observations, satellite observations and numerical weather forecasting models.
- Annual precipitation amounts from ground and satellite observations were the most realistic.
- Newer weather forecasting models better predicted annual precipitation than older models.
- Weather forecasting models predicted inaccurate precipitation amounts for Africa.
A. Kann, I. Meirold-Mautner, F. Schmid, G. Kirchengast, J. Fuchsberger, V. Meyer, L. Tüchler, and B. Bica
Hydrol. Earth Syst. Sci., 19, 1547–1559, https://doi.org/10.5194/hess-19-1547-2015, https://doi.org/10.5194/hess-19-1547-2015, 2015
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The paper introduces a high resolution precipitation analysis system which operates on 1 km x 1 km resolution with high frequency updates of 5 minutes. The ability of such a system to adequately assess the convective precipitation distribution is evaluated by means of an independant, high resolution station network. This dense station network allows for a thorough evaluation of the analyses under different convective situations and of the representativeness error of raingaue measurements.
C. H. Wu, G. R. Huang, and H. J. Yu
Hydrol. Earth Syst. Sci., 19, 1385–1399, https://doi.org/10.5194/hess-19-1385-2015, https://doi.org/10.5194/hess-19-1385-2015, 2015
T. Antofie, G. Naumann, J. Spinoni, and J. Vogt
Hydrol. Earth Syst. Sci., 19, 177–193, https://doi.org/10.5194/hess-19-177-2015, https://doi.org/10.5194/hess-19-177-2015, 2015
P. López López, J. S. Verkade, A. H. Weerts, and D. P. Solomatine
Hydrol. Earth Syst. Sci., 18, 3411–3428, https://doi.org/10.5194/hess-18-3411-2014, https://doi.org/10.5194/hess-18-3411-2014, 2014
G. Naumann, E. Dutra, P. Barbosa, F. Pappenberger, F. Wetterhall, and J. V. Vogt
Hydrol. Earth Syst. Sci., 18, 1625–1640, https://doi.org/10.5194/hess-18-1625-2014, https://doi.org/10.5194/hess-18-1625-2014, 2014
K. Liechti, L. Panziera, U. Germann, and M. Zappa
Hydrol. Earth Syst. Sci., 17, 3853–3869, https://doi.org/10.5194/hess-17-3853-2013, https://doi.org/10.5194/hess-17-3853-2013, 2013
Cited articles
Abbasian, M., Moghim, S., and Abrishamchi, A.: Performance of the general
circulation models in simulating temperature and precipitation over Iran,
Theor. Appl. Climatol., 135, 1465–1483, https://doi.org/10.1007/s00704-018-2456-y, 2019.
Acharya, N., Singh, A., Mohanty, U. C., Nair, A., and Chattopadhyay, S.:
Performance of general circulation models and their ensembles for the
prediction of drought indices over India during summer monsoon, Nat.
Hazards, 66, 851–871, https://doi.org/10.1007/s11069-012-0531-8, 2013.
Afshar, A. A., Hasanzadeh, Y., Besalatpour, A. A., and Pourreza-Bilondi, M.:
Climate change forecasting in a mountainous data scarce watershed using
CMIP5 models under representative concentration pathways, Theor. Appl.
Climatol., 129, 683–699, https://doi.org/10.1007/s00704-016-1908-5, 2016.
Ahmadalipour, A., Rana, A., Moradkhani, H., and Sharma, A.: Multi-criteria
evaluation of CMIP5 GCMs for climate change impact analysis, Theor. Appl.
Climatol., 128, 71–87, https://doi.org/10.1007/s00704-015-1695-4, 2017.
Ahmed, K., Shahid, S., and Harun, S. B.: Spatial interpolation of climatic
variables in a predominantly arid region with complex topography,
Environment Systems and Decisions, 34, 555–563, 2014.
Ahmed, K., Shahid, S., Chung, E.-S., Ismail, T., and Wang, X.-J.: Spatial
distribution of secular trends in annual and seasonal precipitation over
Pakistan, Clim. Res., 74, 95–107, 2017.
Ahmed, K., Shahid, S., Chung, E.-S., Wang, X.-J., and Harun, S. B.: Climate
Change Uncertainties in Seasonal Drought Severity-Area-Frequency Curves:
Case of Arid Region of Pakistan, J. Hydrol., 570, 473–485,
https://doi.org/10.1016/j.jhydrol.2019.01.019, 2019a.
Ahmed, K., Shahid, S., Nawaz, N., and Khan, N.: Modeling climate change
impacts on precipitation in arid regions of Pakistan: a non-local model
output statistics downscaling approach, Theor. Appl. Climatol., 137,
1347–1364, https://doi.org/10.1007/s00704-018-2672-5, 2019b.
Ahmed, K., Shahid, S., Sachindra, D. A., Nawaz, N., and Chung, E.-S.:
Fidelity assessment of general circulation model simulated precipitation and
temperature over Pakistan using a feature selection method, J. Hydrol., 573,
281–298, https://doi.org/10.1016/j.jhydrol.2019.03.092, 2019c.
Ahmed, K., Shahid, S., Wang, X., Nawaz, N., and Khan, N.: Spatiotemporal changes in aridity of Pakistan during 1901–2016, Hydrol. Earth Syst. Sci., 23, 3081–3096, https://doi.org/10.5194/hess-23-3081-2019, 2019d.
Akhter, J., Das, L., and Deb, A.: CMIP5 ensemble-based spatial rainfall
projection over homogeneous zones of India, Clim. Dynam., 49, 1885–1916, https://doi.org/10.1007/s00382-016-3409-8, 2017.
Barfus, K. and Bernhofer, C.: Assessment of GCM capabilities to simulate
tropospheric stability on the Arabian Peninsula, Int. J. Climatol., 35,
1682–1696, 2015.
Breiman, L.: Random Forests, Mach. Learn., 45, 5–32, https://doi.org/10.1023/A:1010933404324, 2001.
Byg, A. and Salick, J.: Local perspectives on a global phenomenon – Climate
change in Eastern Tibetan villages, Global Environ. Chang., 19,
156–166, https://doi.org/10.1016/j.gloenvcha.2009.01.010, 2009.
Cameron, F.: Climate change as a complex phenomenon and the problem of
cultural governance, Museum Soc., 9, 84–89, 2011.
Chandler, R. E.: Exploiting strength, discounting weakness: combining
information from multiple climate simulators, Phil. Trans. R. Soc. A, 371,
20120388, https://doi.org/10.1098/rsta.2012.0388, 2013.
Chen, F.-W. and Liu, C.-W.: Estimation of the spatial rainfall distribution
using inverse distance weighting (IDW) in the middle of Taiwan, Paddy Water Environ., 10, 209–222, https://doi.org/10.1007/s10333-012-0319-1, 2012.
Chen, W., Jiang, Z., and Li, L.: Probabilistic projections of climate change
over China under the SRES A1B scenario using 28 AOGCMs, J. Climate, 24,
4741–4756, 2011.
Cramér, H.: Mathematical methods of statistics (PMS-9), Princeton
University Press, Princeton, USA, 1999.
Demirel, M. C., Mai, J., Mendiguren, G., Koch, J., Samaniego, L., and Stisen, S.: Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model, Hydrol. Earth Syst. Sci., 22, 1299–1315, https://doi.org/10.5194/hess-22-1299-2018, 2018.
Evans, T. E.: The effects of changes in the world hydrological cycle on
availability of water resources, Global Climate Change and Agricultural
Production: Direct and Indirect Effects of Changing Hydrological,
Pedological and Plant Physiological Processes, John Wiley & Sons Ltd,
Chichester, West Sussex, England, 1996.
Folberth, C., Baklanov, A., Balkovič, J., Skalský, R., Khabarov, N.,
and Obersteiner, M.: Spatio-temporal downscaling of gridded crop model yield
estimates based on machine learning, Agr. Forest Meteorol., 264, 1–15, 2019.
Gao, Y., Wang, H., and Jiang, D.: An intercomparison of CMIP5 and CMIP3
models for interannual variability of summer precipitation in Pan-Asian
monsoon region, Int. J. Climatol., 35, 3770–3780, 2015.
Giorgi, F. and Mearns, L. O.: Calculation of average, uncertainty range,
and reliability of regional climate changes from AOGCM simulations via the
“reliability ensemble averaging” (REA) method, J. Climate, 15, 1141–1158,
2002.
Gleckler, P. J., Taylor, K. E., and Doutriaux, C.: Performance metrics for
climate models, J. Geophys. Res.-Atmos., 113, D06104, https://doi.org/10.1029/2007JD008972, 2008.
Goodman, L. A. and Kruskal, W. H.: Measures of association for cross
classifications, J. Am. Stat. Assoc., 49,
732–764, 1954.
Gu, H., Yu, Z., Wang, J., Wang, G., Yang, T., Ju, Q., Yang, C., Xu, F., and
Fan, C.: Assessing CMIP5 general circulation model simulations of
precipitation and temperature over China, Int. J. Climatol., 35, 2431–2440,
2015.
Gupta, H. V., Kling, H., Yilmaz, K. K., and Martinez, G. F.: Decomposition
of the mean squared error and NSE performance criteria: Implications for
improving hydrological modelling, J. Hydrol., 377, 80–91, 2009.
Hargrove, W. W., Hoffman, F. M., and Hessburg, P. F.: Mapcurves: a
quantitative method for comparing categorical maps, J. Geog. Syst., 8, 187, https://doi.org/10.1007/s10109-006-0025-x, 2006.
Harris, I., Jones, P. D., Osborn, T. J., and Lister, D. H.: Updated
high-resolution grids of monthly climatic observations – the CRU TS3.10
Dataset, Int. J. Climatol., 34, 623–642, https://doi.org/10.1002/joc.3711, 2014.
Hayhoe, K., Edmonds, J., Kopp, R., LeGrande, A., Sanderson, B., Wehner, M.,
and Wuebbles, D.: Climate models, scenarios, and projections, US Global Change Research Program, Washington, D.C., USA, 133–160, https://doi.org/10.7930/J0WH2N54, 2017.
He, X., Chaney, N. W., Schleiss, M., and Sheffield, J.: Spatial downscaling
of precipitation using adaptable random forests, Water Resour. Res., 52,
8217–8237, 2016.
Herger, N., Abramowitz, G., Knutti, R., Angélil, O., Lehmann, K., and Sanderson, B. M.: Selecting a climate model subset to optimise key ensemble properties, Earth Syst. Dynam., 9, 135–151, https://doi.org/10.5194/esd-9-135-2018, 2018.
Hussain, M., Yusof, K. W., Mustafa, M. R. U., Mahmood, R., and Jia, S.:
Evaluation of CMIP5 models for projection of future precipitation change in
Bornean tropical rainforests, Theor. Appl. Climatol., 134, 423–440, https://doi.org/10.1007/s00704-017-2284-5, 2018.
IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Groups
I, II and III to the Fifth Assessment Report of the Intergovernmental Panel
on Climate Change, edited by: Core Writing Team, Pachauri, R. K., and
Meyer, L. A.,
IPCC, Geneva, Switzerland, 1–169, 2014.
Jena, P., Azad, S., and Rajeevan, M. N.: Statistical selection of the
optimum models in the CMIP5 dataset for climate change projections of Indian
monsoon rainfall, Climate, 3, 858–875, 2015.
Jiang, Z., Li, W., Xu, J., and Li, L.: Extreme precipitation indices over
China in CMIP5 models. Part I: Model evaluation, J. Climate, 28, 8603–8619,
2015.
Johnson, F. and Sharma, A.: Measurement of GCM skill in predicting
variables relevant for hydroclimatological assessments, J. Climate, 22,
4373–4382, 2009.
Johnson, F. and Sharma, A.: A nesting model for bias correction of
variability at multiple time scales in general circulation model
precipitation simulations, Water Resour. Res., 48, W01504, https://doi.org/10.1029/2011WR010464, 2012.
Khan, N., Shahid, S., Ahmed, K., Ismail, T., Nawaz, N., and Son, M.:
Performance Assessment of General Circulation Model in Simulating Daily
Precipitation and Temperature Using Multiple Gridded Datasets, Water, 10,
1793, https://doi.org/10.3390/w10121793 2018a.
Khan, N., Shahid, S., Ismail, T. B., and Wang, X.-J.: Spatial distribution
of unidirectional trends in temperature and temperature extremes in
Pakistan, Theor. Appl. Climatol., 136, 899–913, https://doi.org/10.1007/s00704-018-2520-7, 2018b.
Kim, J., Ivanov, V. Y., and Fatichi, S.: Climate change and uncertainty
assessment over a hydroclimatic transect of Michigan, Stoch. Environ. Res.
Risk Assess., 30, 923–944, 2015.
Kishore, P., Jyothi, S., Basha, G., Rao, S. V. B., Rajeevan, M., Velicogna,
I., and Sutterley, T. C.: Precipitation climatology over India: validation
with observations and reanalysis datasets and spatial trends, Clim. Dynam.,
46, 541–556, https://doi.org/10.1007/s00382-015-2597-y, 2015.
Knutti, R., Furrer, R., Tebaldi, C., Cermak, J., and Meehl, G. A.:
Challenges in combining projections from multiple climate models, J. Climate,
23, 2739–2758, 2010.
Knutti, R., Masson, D., and Gettelman, A.: Climate model genealogy:
Generation CMIP5 and how we got there, Geophys. Res. Lett., 40, 1194–1199,
2013.
Koch, J., Demirel, M. C., and Stisen, S.: The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models, Geosci. Model Dev., 11, 1873–1886, https://doi.org/10.5194/gmd-11-1873-2018, 2018.
Krishnamurti, T., Kishtawal, C., LaRow, T. E., Bachiochi, D. R., Zhang, Z.,
Williford, C. E., Gadgil, S., and Surendran, S.: Improved weather and
seasonal climate forecasts from multimodel superensemble, Science, 285,
1548–1550, 1999.
Krishnamurti, T. N., Kishtawal, C., Zhang, Z., LaRow, T., Bachiochi, D.,
Williford, E., Gadgil, S., and Surendran, S.: Multimodel ensemble forecasts
for weather and seasonal climate, J. Climate, 13, 4196–4216, 2000.
Kusunoki, S. and Arakawa, O.: Are CMIP5 Models Better than CMIP3 Models in
Simulating Precipitation over East Asia?, J. Climate, 28, 5601–5621, https://doi.org/10.1175/JCLI-D-14-00585.1, 2015.
Latif, M., Hannachi, A., and Syed, F.: Analysis of rainfall trends over
Indo-Pakistan summer monsoon and related dynamics based on CMIP5 climate
model simulations, Int. J. Climatol., 38, e577–e595, 2018.
Lutz, A. F., ter Maat, H. W., Biemans, H., Shrestha, A. B., Wester, P., and
Immerzeel, W. W.: Selecting representative climate models for climate change
impact studies: an advanced envelope-based selection approach, Int. J.
Climatol., 36, 3988–4005, 2016.
Mahmood, R., Jia, S., Tripathi, N. K., and Shrestha, S.: Precipitation Extended Linear Scaling Method for Correcting GCM Precipitation and Its Evaluation and Implication in the Transboundary Jhelum River Basin, Atmosphere, 9, 160, 2018.
McMahon, T. A., Peel, M. C., and Karoly, D. J.: Assessment of precipitation and temperature data from CMIP3 global climate models for hydrologic simulation, Hydrol. Earth Syst. Sci., 19, 361–377, https://doi.org/10.5194/hess-19-361-2015, 2015.
Mendlik, T. and Gobiet, A.: Selecting climate simulations for impact
studies based on multivariate patterns of climate change, Clim. Change, 135,
381–393, https://doi.org/10.1007/s10584-015-1582-0, 2016.
Miao, C., Duan, Q., Yang, L., and Borthwick, A. G.: On the applicability of
temperature and precipitation data from CMIP3 for China, PLoS One, 7,
e44659, https://doi.org/10.1371/journal.pone.0044659, 2012.
Min, S.-K. and Hense, A.: A Bayesian approach to climate model evaluation
and multi-model averaging with an application to global mean surface
temperatures from IPCC AR4 coupled climate models, Geophys. Res. Lett., 33, L08708, https://doi.org/10.1029/2006GL025779, 2006.
Murphy, J. M., Sexton, D. M., Barnett, D. N., Jones, G. S., Webb, M. J.,
Collins, M., and Stainforth, D. A.: Quantification of modelling
uncertainties in a large ensemble of climate change simulations, Nature,
430, 768–772, https://doi.org/10.1038/nature02771, 2004.
Noor, M., Ismail, T. B., Shahid, S., Ahmed, K., Chung, E.-S., and Nawaz, N.:
Selection of CMIP5 multi-model ensemble for the projection of spatial and
temporal variability of rainfall in peninsular Malaysia, Theor. Appl.
Climatol., 138, 999–1012, https://doi.org/10.1007/s00704-019-02874-0, 2019.
Nowosad, J. and Stepinski, T. F.: Spatial association between
regionalizations using the information-theoretical V-measure, Int. J. Geogr. Inf. Sci., 32, 2386–2401, 2018.
Oh, S.-G. and Suh, M.-S.: Comparison of projection skills of deterministic
ensemble methods using pseudo-simulation data generated from multivariate
Gaussian distribution, Theor. Appl. Climatol., 129, 243–262, 2017.
Pavan, V. and Doblas-Reyes, F.: Multi-model seasonal hindcasts over the
Euro-Atlantic: skill scores and dynamic features, Clim. Dynam., 16, 611–625,
2000.
Pearson, K.: Early statistical papers, Cambridge University Press, Cambridge, 1–40, 1948.
Perkins-Kirkpatrick, S. E. and Gibson, P. B.: Changes in regional heatwave
characteristics as a function of increasing global temperature, Sci.
Rep. UK, 7, 12256, https://doi.org/10.1038/s41598-017-12520-2, 2017.
Perkins, S., Pitman, A., Holbrook, N., and McAneney, J.: Evaluation of the
AR4 climate models' simulated daily maximum temperature, minimum
temperature, and precipitation over Australia using probability density
functions, J. Climate, 20, 4356–4376, 2007.
Pocernich, M. M.: The verification package, available at:
https://cran.r-project.org/ (last access: 15 September 2018), 2006.
Pour, S. H., Shahid, S., Chung, E.-S., and Wang, X.-J.: Model output
statistics downscaling using support vector machine for the projection of
spatial and temporal changes in rainfall of Bangladesh, Atmos. Res., 213,
149–162, https://doi.org/10.1016/j.atmosres.2018.06.006, 2018.
Prasanna, V.: Regional climate change scenarios over South Asia in the CMIP5
coupled climate model simulations, Meteorol. Atmos. Phys., 127, 561–578,
2015.
Raäisaänen, J.: How reliable are climate models?, Tellus A, 59, 2–29, 2007.
Raju, K. S., Sonali, P., and Kumar, D. N.: Ranking of CMIP5-based global
climate models for India using compromise programming, Theor. Appl.
Climatol., 128, 563–574, 2017.
Rees, W.: Comparing the spatial content of thematic maps, Int. J. Remote
Sens., 29, 3833–3844, 2008.
Rehman, N., Adnan, M., and Ali, S.: Assessment of CMIP5 climate models over
South Asia and climate change projections over Pakistan under representative
concentration pathways, Int. J. Global Warm., 16,
381–415, 2018.
Roberts, N. M. and Lean, H. W.: Scale-selective verification of rainfall
accumulations from high-resolution forecasts of convective events, Mon. Weather Rev., 136, 78–97, 2008.
Ruane, A. C. and McDermid, S. P.: Selection of a representative subset of
global climate models that captures the profile of regional changes for
integrated climate impacts assessment, Earth Perspectives, 4, 1, https://doi.org/10.1186/s40322-017-0036-4, 2017.
Sa'adi, Z., Shahid, S., Chung, E.-S., and bin Ismail, T.: Projection of
spatial and temporal changes of rainfall in Sarawak of Borneo Island using
statistical downscaling of CMIP5 models, Atmos. Res., 197, 446–460, 2017.
Sachindra, D., Huang, F., Barton, A., and Perera, B.: Statistical
downscaling of general circulation model outputs to precipitation – part 2:
bias-correction and future projections, Int. J. Climatol., 34, 3282–3303,
2014.
Salman, S. A., Shahid, S., Ismail, T., Ahmed, K., and Wang, X.-J.: Selection
of climate models for projection of spatiotemporal changes in temperature of
Iraq with uncertainties, Atmos. Res., 213, 509–522,
https://doi.org/10.1016/j.atmosres.2018.07.008, 2018a.
Salman, S. A., Shahid, S., Ismail, T., Al-Abadi, A. M., Wang, X.-J., and
Chung, E.-S.: Selection of gridded precipitation data for Iraq using
compromise programming, Measurement, 132, 87–98, 2018b.
Sansom, P. G., Stephenson, D. B., Ferro, C. A., Zappa, G., and Shaffrey, L.:
Simple uncertainty frameworks for selecting weighting schemes and
interpreting multimodel ensemble climate change experiments, J. Climate, 26,
4017–4037, 2013.
Sarthi, P. P., Kumar, P., and Ghosh, S.: Possible future rainfall over
Gangetic Plains (GP), India, in multi-model simulations of CMIP3 and CMIP5,
Theor. Appl. Climatol., 124, 691–701, https://doi.org/10.1007/s00704-015-1447-5, 2016.
Schneider, U., Becker, A., Finger, P., Meyer-Christoffer, A., Ziese, M., and
Rudolf, B.: GPCC's new land surface precipitation climatology based on
quality-controlled in situ data and its role in quantifying the global water
cycle, Theor. Appl. Climatol., 115, 15–40, https://doi.org/10.1007/s00704-013-0860-x, 2013.
Sheffield, J. and Wood, E. F.: Projected changes in drought occurrence
under future global warming from multi-model, multi-scenario, IPCC AR4
simulations, Clim. Dynam., 31, 79–105, https://doi.org/10.1007/s00382-007-0340-z, 2008.
Sheikh, M. M.: Drought management and prevention in Pakistan, COMSATS 1st
meeting on water resources in the south: present scenario and future
prospects, Islamabad, 117–131, 2001.
Shiru, M. S., Shahid, S., Alias, N., and Chung, E.-S.: Trend Analysis of
Droughts during Crop Growing Seasons of Nigeria, Sustainability, 10, 871,
2018.
Shukla, J., DelSole, T., Fennessy, M., Kinter, J., and Paolino, D.: Climate
model fidelity and projections of climate change, Geophys. Res. Lett., 33, L07702, https://doi.org/10.1029/2005GL025579, 2006.
Signorell, A.: DescTools: Tools for descriptive statistics, R package
version 0.99, 18, 2016.
Smith, I. and Chandler, E.: Refining rainfall projections for the Murray
Darling Basin of south-east Australia – the effect of sampling model results
based on performance, Clim. Change, 102, 377–393, 2010.
Smith, J. B., Hulme, M., Jaagus, J., Keevallik, S., Mekonnen, A., and
Hailemariam, K.: Climate change scenarios, UNEP Handbook on Methods for
Climate Change Impact Assessment and Adaptation Studies, UNEP, the Netherlands, 2, 3–1, 1998.
Sohoulande Djebou, D. and Singh, V.: Impact of climate change on the
hydrologic cycle and implications for society, Environ. Soc. Psychol., 1, 9–16,
2015.
Spinoni, J., Naumann, G., Carrao, H., Barbosa, P., and Vogt, J.: World
drought frequency, duration, and severity for 1951–2010, Int. J. Climatol.,
34, 2792–2804, https://doi.org/10.1002/joc.3875, 2014.
Taylor, K. E., Stouffer, R. J., and Meehl, G. A.: An overview of CMIP5 and
the experiment design, B. Am. Meteorol. Soc., 93, 485–498, 2012.
Tebaldi, C., Smith, R. L., Nychka, D., and Mearns, L. O.: Quantifying
uncertainty in projections of regional climate change: A Bayesian approach
to the analysis of multimodel ensembles, J. Climate, 18, 1524–1540, 2005.
Thober, S. and Samaniego, L.: Robust ensemble selection by multivariate
evaluation of extreme precipitation and temperature characteristics, J. Geophys. Res.-Atmos., 119, 594–613, 2014.
Wang, B., Zheng, L., Liu, D. L., Ji, F., Clark, A., and Yu, Q.: Using
multi-model ensembles of CMIP5 global climate models to reproduce observed
monthly rainfall and temperature with machine learning methods in Australia,
Int. J. Climatol., 38, 4891–4902, 2018.
Wang, Y., Shi, L., Zanobetti, A., and Schwartz, J. D.: Estimating and
projecting the effect of cold waves on mortality in 209 US cities,
Environ. Int., 94, 141–149, 2016.
Weigel, A. P., Knutti, R., Liniger, M. A., and Appenzeller, C.: Risks of
model weighting in multimodel climate projections, J. Climate, 23, 4175–4191,
2010.
Willmott, C. J.: On the validation of models, Phys. Geogr., 2,
184–194, https://doi.org/10.1080/02723646.1981.10642213, 1981.
Willmott, C. J.: Some comments on the evaluation of model performance, B. Am. Meteorol. Soc., 63, 1309–1313, 1982.
Wu, C., Huang, G., Yu, H., Chen, Z., and Ma, J.: Impact of Climate Change on
Reservoir Flood Control in the Upstream Area of the Beijiang River Basin,
South China, J. Hydrometeorol., 15, 2203–2218, https://doi.org/10.1175/jhm-d-13-0181.1,
2014.
Wu, Z., Chen, X., Lu, G., Xiao, H., He, H., and Zhang, J.: Regional response
of runoff in CMIP5 multi-model climate projections of Jiangsu Province,
China, Stoch. Environ. Res. Risk Assess., 31, 2627–2643, 2016.
Xuan, W., Ma, C., Kang, L., Gu, H., Pan, S., and Xu, Y.-P.: Evaluating
historical simulations of CMIP5 GCMs for key climatic variables in Zhejiang
Province, China, Theor. Appl. Climatol., 128, 207–222, https://doi.org/10.1007/s00704-015-1704-7, 2017.
You, Q., Jiang, Z., Wang, D., Pepin, N., and Kang, S.: Simulation of
temperature extremes in the Tibetan Plateau from CMIP5 models and comparison
with gridded observations, Clim. Dynam., 51, 355–369, 2018.
Zawadzka, J., Mayr, T., Bellamy, P., and Corstanje, R.: Comparing
physiographic maps with different categorisations, Geomorphology, 231,
94–100, 2015.
Short summary
This study evaluated the performance of 36 CMIP5 GCMs in simulating seasonal precipitation and maximum and minimum temperature over Pakistan using spatial metrics (SPAtial EFficiency, fractions skill score, Goodman–Kruskal's lambda, Cramer's V, Mapcurves, and Kling–Gupta efficiency) for the period 1961–2005. NorESM1-M, MIROC5, BCC-CSM1-1, and ACCESS1-3 were identified as the most suitable GCMs for simulating all three climate variables over Pakistan.
This study evaluated the performance of 36 CMIP5 GCMs in simulating seasonal precipitation and...
