15 results on '"Zaherpour, Jamal"'
Search Results
2. Improving global and catchment estimates of runoff through computationally-intelligent ensemble approaches : applications of intelligent multi-model combination, cross-scale model comparisons, ensemble analyses, and new model parameterisations
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Zaherpour, Jamal
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551.48 ,GB Physical geography - Abstract
Water related problems (scarcity, availability and hazards) together form one of the three major crises (the two other are food and energy) for today and the future across the globe (World Economic Forum, 2016, Schewe et al., 2014a, Hanasaki et al., 2013, Rockstrom et al., 2009). Water crises are widespread and heterogeneous around the world and climate change and socioeconomic drivers are expected to accelerate these problems (Veldkamp, 2017). To deal with the above concerns, mitigation and adaptation strategies are developed at different scales (global, regional and local). Developing these strategies, as well as selecting the most appropriate one to the problem of interest, should ideally benefit from the highest possible accuracy in estimates of the hydrological cycle and water resources. More reliable decisions can in turn be made by applying tools and techniques that enhance decision makers' perception of the hydrological cycle, particularly extreme events i.e. droughts and floods. These tools should also facilitate insights into the cycle: ideally by reference to hydrological indicators, spatially (globally and locally) and temporally (present day and future). Global and catchment scale hydrological models (GHMs and CHMs) have been used as such tools that along with advances in data acquisition, analytical techniques and computation power offer powerful tools for modelling natural processes and provide useful insights into the hydrological cycle. GHMs have a shorter history of emergence and application than CHMs. GHMs have been developed and applied from 1986 in recognition of the fact that hydrological processes and water resources are global phenomena and should be treated at global scale (Bierkens, 2015). A GHM is a pragmatic trade-off between a faithful representation of the diversity of hydrological processes found across the world's catchments, and a generalised and simplified representation of hydrological processes that can support multi-decadal, generalised hydrological simulations at global scales. Compared to hydrological models designed for catchment-scale simulations (Arnold et al., 1993; Krysanova et al., 1998; Lindstrom et al., 2010), GHMs employ coarser spatial discretisation and model the global land surface in a single instantiation. The global scope of GHMs, limited availability and quality of observed discharge data across the global domain and their use of spatially generalised parameters make them more difficult to calibrate than catchment hydrological models. Whilst examples of calibrated GHMs do exist (Müller Schmied et al., 2016), the majority of GHMs are uncalibrated (Gosling et al., 2016; Hattermann et al., 2017). This lack of calibration, coupled with the diversity of simplifications employed in the hydrological process representations, means that there can be large inconsistency in the skill, bias and uncertainty of an individual GHM at different locations, as well as large inconsistencies between different GHMs at any given location (van Huijgevoort et al., 2013). This spatial inconsistency means that GHMs risk becoming a "jungle of models" (Kundzewicz, 1986) in which it can be difficult to determine where a particular GHM output is likely to be capable of delivering optimal hydrological simulations. It also makes it dangerous to assume that any individual GHM will be an adequate basis for making projections at any given location, even if the model's ability to replicate observed data in particular catchments is enhanced through the acquisition of higher quality input data or efforts to improve process representations (Liu et al., 2007). To an extent, these arguments are also applicable to CHMs because whilst they have been shown to generally perform better than GHMs in model evaluation studies, ensembles of such models still result in an uncertainty range when the models are run with identical inputs (Hattermann et al., 2017; Hattermann et al., 2018). To minimise the challenge of varying outputs from different models, several model inter-comparison projects (MIPs) have been undertaken around the world (Henderson-Sellers et al., 1995, Entin et al., 1999, Guo and Dirmeyer, 2006, Koster et al., 2006, Harding et al., 2011). These projects usually use standard modelling baselines to deal with discrepancies between model outputs. This results in higher consistency in the climate forcings input to the models (where applicable), their process representations (e.g. the simulation of human impacts such as water abstractions), and the temporal and spatial resolutions of their simulations. This way, model outputs are directly comparable to each other, which supports diagnostic inter-comparisons between them (Bierkens, 2015). One of the largest, ongoing MIPs (whose data are used in this thesis) is the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP) (Schellnhuber et al., 2014, Warszawski et al., 2014). ISIMIP is a community-driven effort by more than 130 modelling groups, that covers different sectors including water (both global and catchment hydrological modelling communities). Outputs from ISIMIP are widely used in different projects, such as reports of the International Panel on Climate Change (IPCC) (http://www.ipcc.ch/). MIPs including ISIMIP provide a unique opportunity to access data from different models and to assess their relative performance. It also facilitates continuous model improvement via the inclusion of new schemes (e.g. human impacts such as dams, reservoirs and water abstractions) accompanied by dozens of models, as well as communication between modelling groups working in the same or different sectors. Nonetheless, they do not fully address the challenge of spatial inconsistencies between models, as well as the question of what ensemble representative to select for use when trying to improve the reliability of decision-making. There remain other shortcomings or unexplored aspects within MIPs (particularly ISIMIP as this research's focus MIP), hence areas of further research and potential improvement in model evaluation and application which will be addressed later in this introduction. The question of how to address the challenges of spatial inconsistency in hydrological models has been a feature of catchment-scale model research for several decades. In answering it, catchment modellers have recognised that reliance on a single, inconsistent model is inherently risky and should be avoided (Marshall et al., 2006; Shamseldin et al., 1997). Instead, they have developed ways to take advantage of the diversity of outputs (Clemen, 1989) generated by different models by using optimised mathematical combination methods to deliver a combined output that performs better than the individual models from which it was created (Hagedorn et al., 2005). This general approach-known as multi-model combination (MMC)-has been an important focus of catchment hydrological modelling studies, especially over the last two decades (Abrahart and See, 2002; Ajami et al., 2006; Arsenault et al., 2015; Azmi et al., 2010; de Menezes et al., 2000; Fernando et al., 2012; Jeong and Kim, 2009; Marshall et al., 2007; Marshall et al., 2006; Moges et al., 2016; Nasseri et al., 2014; Sanderson and Knutti, 2012; Shamseldin et al., 1997). Given its demonstrable potential in catchment studies, it is perhaps surprising that the potential of applying MMC to GHMs has yet to be explored. A wide range of techniques can be used to generate an MMC solution. The simplest example is the calculation of the arithmetic mean of the input models (commonly referred to as an Ensemble Mean (EM)). More sophisticated techniques employ weighted schemes (Arsenault et al., 2015), with the differential weightings applied to each input model reflecting their relative strengths or weaknesses. The mathematical approach taken to determining the weights depends on the objective of the MMC. Where the primary objective is to minimise the difference between the MMC solution and observed data (i.e. maximise the predictive performance), without explicitly accounting for model or parameter uncertainty, the use of multiple linear regression (Doblas-Reyes et al., 2005) or machine learning algorithms to 'learn' the optimal set weights to apply to each MMC input model is a popular approach (Marshall et al., 2007).
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- 2018
3. How evaluation of global hydrological models can help to improve credibility of river discharge projections under climate change
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Krysanova, Valentina, Zaherpour, Jamal, Didovets, Iulii, Gosling, Simon N., Gerten, Dieter, Hanasaki, Naota, Müller Schmied, Hannes, Pokhrel, Yadu, Satoh, Yusuke, Tang, Qiuhong, and Wada, Yoshihide
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- 2020
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4. A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1 °C, 2 °C and 3 °C
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Gosling, Simon N., Zaherpour, Jamal, Mount, Nick J., Hattermann, Fred F., Dankers, Rutger, Arheimer, Berit, Breuer, Lutz, Ding, Jie, Haddeland, Ingjerd, Kumar, Rohini, Kundu, Dipangkar, Liu, Junguo, van Griensven, Ann, Veldkamp, Ted I. E., Vetter, Tobias, Wang, Xiaoyan, and Zhang, Xinxin
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- 2017
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5. Erratum to: A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1 °C, 2 °C and 3 °C
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Gosling, Simon N., Zaherpour, Jamal, Mount, Nick J., Hattermann, Fred F., Dankers, Rutger, Arheimer, Berit, Breuer, Lutz, Ding, Jie, Haddeland, Ingjerd, Kumar, Rohini, Kundu, Dipangkar, Liu, Junguo, van Griensven, Ann, Veldkamp, Ted I. E., Vetter, Tobias, Wang, Xiaoyan, and Zhang, Xinxin
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- 2017
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6. Cross-scale evaluation of catchment- and global-scale hydrological model simulations of drought characteristics across eight large river catchments
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Kumar, Amit, primary, Gosling, Simon N, additional, Johnson, Matthew F, additional, Zaherpour, Jamal, additional, Leng, Guoyong, additional, Schmied, Hannes Muller, additional, Kupzig, Jenny, additional, Breuer, Lutz, additional, Hanasaki, Naota, additional, Tang, Qiuhong, additional, Kumar, Rohini, additional, Ostberg, Sebastian, additional, Stacke, Tobias, additional, Pokhrel, Yadu, additional, Wada, Yoshihide, additional, and Masaki, Yoshimitsu, additional
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- 2021
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7. Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models
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Mount, Nick, Zaherpour, Jamal, Gosling, Simon, Dankers, Rutger, Eisner, Stephanie, Gerten, Dieter, Liu, Xingcai, Masaki, Yoshimitsu, Tang, Qiuhong, and Wada, Yoshihide
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This study presents a novel application of machine learning to deliver optimised, multi-model combinations (MMCs) of Global Hydrological Model (GHM) simulations. We exemplify the approach using runoff simulations from five GHMs across 40 large global catchments. The benchmarked, median performance gain of the MMC solutions is 45% compared to the best performing GHM and exceeds 100% when compared to the EM. The performance gain offered by MMC suggests that future multimodel applications consider reporting MMCs, alongside the EM and intermodal range, to provide endusers of GHM ensembles with a better contextualised estimate of runoff. Importantly, the study highlights the difficulty of interpreting complex, non-linear MMC solutions in physical terms. This indicates that a pragmatic approach to future MMC studies based on machine learning methods is required, in which the allowable solution complexity is carefully constrained.
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- 2019
8. Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts
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Zaherpour, Jamal, Gosling, Simon N., Mount, Nick J., Veldkamp, Ted, Dankers, Rutger, Eisner, Stephanie, Gerten, Dieter, Gudmundsson, Lukas, Haddeland, I., Hanasaki, Naota, Kim, Hyungjun, Leng, Guoyong, Liu, Junguo, Masaki, Yoshimitsu, Oki, Taikan, Pokhrel, Yadu, Satoh, Yusuke, Schewe, Jacob, and Wada, Yoshihide
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global hydrological models, land surface models, human impacts, extreme events, model evaluation, model validation - Abstract
Global-scale hydrological models are routinely used to assess water scarcity, flood hazards and droughts worldwide. Recent efforts to incorporate anthropogenic activities in these models have enabled more realistic comparisons with observations. Here we evaluate simulations from an ensemble of six models participating in the second phase of the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP2a). We simulate observed monthly runoff in 40 catchments, spatially distributed across 8 global hydrobelts. The performance of each model and the ensemble mean is examined with respect to their ability to replicate observed mean and extreme runoff under human-influenced conditions. Application ofa novel integrated evaluation metric to quantify the models’ ability to simulate timeseries of monthly runoff suggests that the models generally perform better in the wetter equatorial and northern hydrobelts than in drier southern hydrobelts. When model outputs are temporally aggregated to assess mean annual and extreme runoff, the models perform better. Nevertheless, we find a general trend in the majority of models towards the overestimation of mean annual runoff and allindicators of upper and lower extreme runoff. There are particular challenges associated with reproducing both the timing and magnitude of seasonal cycles; the models struggle to capture the timing of the seasonal cycle, particularly in northern hydrobelts, while in southern hydrobelts the models struggle to reproduce the magnitude of the seasonal cycle. It is noteworthy that over all hydrological indicators, the ensemble mean fails to perform better than any individual model – afinding that challenges the commonly held perception that model ensemble estimates deliver superior performance over individual models. The study highlights the need for continued model development and improvement. It also suggests that caution should be taken when summarising the simulations from a model ensemble based upon its mean output.
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- 2018
9. Climate impacts in Europe
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CISCAR MARTINEZ JUAN CARLOS, IBARRETA RUIZ DOLORES, SORIA RAMIREZ ANTONIO, DOSIO ALESSANDRO, TORETI ANDREA, CEGLAR ANDREJ, FUMAGALLI DAVIDE, DENTENER FRANCISCUS, LECERF REMI, ZUCCHINI ANTONIO, PANARELLO LORENZO, NIEMEYER STEFAN, PEREZ DOMINGUEZ IGNACIO, FELLMANN THOMAS, KITOUS ALBAN, DESPRÉS JACQUES, CHRISTODOULOU ARIS, DEMIREL HANDE, ALFIERI LORENZO, DOTTORI FRANCESCO, VOUSDOUKAS MICHAIL, MENTASCHI LORENZO, VOUKOUVALAS EVANGELOS, CAMMALLERI CARMELO, MARINHO FERREIRA BARBOSA PAULO, MICALE FABIO, VOGT JUERGEN, BARREDO CANO JOSE IGNACIO, CAUDULLO GIOVANNI, MAURI ACHILLE, DE RIGO DANIELE, LIBERTA' GIORGIO, DURRANT TRACY, ARTES VIVANCOS TOMAS, SAN-MIGUEL-AYANZ JESUS, GOSLING SIMON, ZAHERPOUR JAMAL, DE ROO ARIE, BISSELINK BERNARD, BERNHARD JEROEN, BIANCHI ALESSANDRA, RÓZSAI MÁTÉ, SZEWCZYK WOJCIECH, MONGELLI IGNAZIO, and FEYEN LUC
- Abstract
The study assesses how climate change could affect Europe in eleven impact areas. Under a high warming scenario, several climate impacts show a clear geographical north-south divide. Most of the welfare losses, assessed for six impact areas, would be greatly reduced under a 2ᵒC scenario., JRC.C.6-Economics of Climate Change, Energy and Transport
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- 2018
10. PESETA III: Climate change impacts on labour productivity
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GOSLING SIMON, ZAHERPOUR JAMAL, and IBARRETA RUIZ DOLORES
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This study uses five impact models that describe observed relationships between labour productivity and temperature, with climate model simulations from five climate models under a high emissions scenario (RCP8.5), i.e. 25 climate-impact model combinations, to assess the impact of climate change on outdoor and indoor labour productivity respectively, at the national-scale, across Europe. This is the first assessment to use multiple impact models with multiple climate models and to consider the potential effects of adaptation on lowering the impacts relative to no adaptation taking place. Impacts are estimated for the end of century (2071-2100) and near-term (2021-2050), relative to present-day (1981-2010). Impacts are also estimated under a mitigation scenario, where global-mean warming is 2°C relative to pre-industrial. Impacts are assessed with and without adaptation respectively. Planned adaptation is represented as an adjustment in work activities following recommendations by the US Occupational Safety & Health Administration to consider the adjustment of work shifts during hot periods – all labour takes place at night instead of day-time, under the adaptation assumption. Without climate change mitigation and adaptation, daily average outdoor labour productivity could decline by around 10-15% from present-day levels in several southern European countries by the end of the century (Bulgaria, Greece, Italy, Macedonia, Portugal, Spain and Turkey). Countries in northern Europe could also see declines in daily average outdoor labour productivity but the declines are considerably smaller than for the southern countries, at around 2-4% (Denmark, Estonia, Finland, Norway and Sweden). The magnitude of impact on indoor labour productivity is generally 2-4 percentage points lower than for impacts on outdoor labour productivity, for the three most sensitive impact models, while for the two least sensitive impact models, the differences are smaller. There is uncertainty in the magnitude of projected climate change impacts on labour productivity due to: 1) differences in the projections of climate between different climate models; and 2) the use of different impact models. Both sources of uncertainty are significant. The range in projected impacts due to using multiple climate models is comparable to the range in impacts from using multiple impact models with only one climate model. Adaptation and mitigation have the potential to significantly lessen the impacts of climate change on declines in labour productivity across Europe. For some countries the impacts can be up to around 10 percentage points lower with adaptation than without, for some climate-impact model combinations, at the end of the century under high emissions (e.g. Bulgaria, Croatia, Greece, Italy, Spain and Turkey). However, the declines in daily average outdoor labour productivity could still be around 5% relative to present-day in these countries (and up to 10% for Greece, with one climate-impact model combination). Whilst the potential benefits of adaptation are clear from this assessment, it is important to be aware of the caveats associated with the adaptation modelling approach employed. These include an assumption of the entire work force engaged in moderate to heavy labour shifting to night-time working, acknowledgment that night-time working can be associated with negative health effects, and potentially higher costs of night-time working due to energy requirements for lighting and higher wages for working unsocial hours. Such a change in working practices is optimistic, but not implausible, since currently around 20% of workers in Europe are employed on shift work involving night work. Limiting global warming to below 2°C (and assuming no adaptation) could avoid a substantial proportion of impacts in the European countries that see the largest impacts without mitigation (Bulgaria, Greece, Italy, Macedonia, Portugal, Spain and Turkey). With some climate-impact model combinations the declines in labour productivity can be up to 10 percentage points lower in these countries with mitigation when compared to without mitigation, JRC.C.6-Economics of Climate Change, Energy and Transport
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- 2018
11. Exploring the value of machine learning for weighted multi-model combination of an ensemble of global hydrological models
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Zaherpour, Jamal, primary, Mount, Nick, additional, Gosling, Simon N., additional, Dankers, Rutger, additional, Eisner, Stephanie, additional, Gerten, Dieter, additional, Liu, Xingcai, additional, Masaki, Yoshimitsu, additional, Müller Schmied, Hannes, additional, Tang, Qiuhong, additional, and Wada, Yoshihide, additional
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- 2019
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12. Erratum to: A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1 °C, 2 °C and 3 °C
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Gosling, Simon N., primary, Zaherpour, Jamal, additional, Mount, Nick J., additional, Hattermann, Fred F., additional, Dankers, Rutger, additional, Arheimer, Berit, additional, Breuer, Lutz, additional, Ding, Jie, additional, Haddeland, Ingjerd, additional, Kumar, Rohini, additional, Kundu, Dipangkar, additional, Liu, Junguo, additional, van Griensven, Ann, additional, Veldkamp, Ted I. E., additional, Vetter, Tobias, additional, Wang, Xiaoyan, additional, and Zhang, Xinxin, additional
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- 2016
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13. A comparison of changes in river runoff from multiple global and catchment-scale hydrological models under global warming scenarios of 1 °C, 2 °C and 3 °C
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Gosling, Simon N., primary, Zaherpour, Jamal, additional, Mount, Nick J., additional, Hattermann, Fred F., additional, Dankers, Rutger, additional, Arheimer, Berit, additional, Breuer, Lutz, additional, Ding, Jie, additional, Haddeland, Ingjerd, additional, Kumar, Rohini, additional, Kundu, Dipangkar, additional, Liu, Junguo, additional, van Griensven, Ann, additional, Veldkamp, Ted I. E., additional, Vetter, Tobias, additional, Wang, Xiaoyan, additional, and Zhang, Xinxin, additional
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- 2016
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14. Worldwide evaluation of mean and extreme runoff from six global-scale hydrological models that account for human impacts
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Zaherpour, Jamal, Gosling, Simon N., Mount, Nick, Schmied, Hannes M., Veldkamp, Ted I. E., Dankers, Rutger, Eisner, Stephanie, Gerten, Dieter, Gudmundsson, Lukas, Haddeland, Ingjerd, Hanasaki, Naota, Kim, Hyungjun, Leng, Guoyong, Liu, Junguo, Masaki, Yoshimitsu, Oki, Taikan, Pokhrel, Yadu, Satoh, Yusuke, Schewe, Jacob, and Wada, Yoshihide
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human impacts ,extreme events ,model evaluation ,model validation ,13. Climate action ,global hydrological models ,land surface models ,6. Clean water - Abstract
Global-scale hydrological models are routinely used to assess water scarcity, flood hazards and droughts worldwide. Recent efforts to incorporate anthropogenic activities in these models have enabled more realistic comparisons with observations. Here we evaluate simulations from an ensemble of six models participating in the second phase of the Inter-Sectoral Impact Model Inter-comparison Project (ISIMIP2a). We simulate monthly runoff in 40 catchments, spatially distributed across eight global hydrobelts. The performance of each model and the ensemble mean is examined with respect to their ability to replicate observed mean and extreme runoff under human-influenced conditions. Application of a novel integrated evaluation metric to quantify the models' ability to simulate timeseries of monthly runoff suggests that the models generally perform better in the wetter equatorial and northern hydrobelts than in drier southern hydrobelts. When model outputs are temporally aggregated to assess mean annual and extreme runoff, the models perform better. Nevertheless, we find a general trend in the majority of models towards the overestimation of mean annual runoff and all indicators of upper and lower extreme runoff. The models struggle to capture the timing of the seasonal cycle, particularly in northern hydrobelts, while in southern hydrobelts the models struggle to reproduce the magnitude of the seasonal cycle. It is noteworthy that over all hydrological indicators, the ensemble mean fails to perform better than any individual model—a finding that challenges the commonly held perception that model ensemble estimates deliver superior performance over individual models. The study highlights the need for continued model development and improvement. It also suggests that caution should be taken when summarising the simulations from a model ensemble based upon its mean output., Environmental Research Letters, 13 (6), ISSN:1748-9326, ISSN:1748-9318
15. Human impact parameterizations in global hydrological models improves estimates of monthly discharges and hydrological extremes: a multi-model validation study
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Veldkamp, Ted Isis Elize, Zhao, Fang, Ward, Philip J., Moel, Hans de, Aerts, Jeroen C.J.H., Portmann, Felix T., Masaki, Yoshimitsu, Pokhrel, Yadu, Liu, Xingcai, Satoh, Yusuke, Gerten, Dieter, Gosling, Simon N., Zaherpour, Jamal, and Wada, Y.
- Abstract
Human activities have a profound influence on river discharge, hydrological extremes, and water-related hazards. In this study, we compare the results of five state-of-the-art global hydrological models (GHMs) with observations to examine the role of human impact parameterizations (HIP) in the simulation of the mean, high, and low flows. The analysis is performed for 471 gauging stations across the globe and for the period 1971-2010. We find that the inclusion of HIP improves the performance of GHMs, both in managed and near-natural catchments. For near-natural catchments, the improvement in performance results from improvements in incoming discharges from upstream managed catchments. This finding is robust across GHMs, although the level of improvement and reasons for improvement vary greatly by GHM. The inclusion of HIP leads to a significant decrease in the bias of long-term mean monthly discharge in 36-73% of the studied catchments, and an improvement in modelled hydrological variability in 31-74% of the studied catchments. Including HIP in the GHMs also leads to an improvement in the simulation of hydrological extremes, compared to when HIP is excluded. Whilst the inclusion of HIP leads to decreases in simulated high-flows, it can lead to either increases or decreases in low-flows. This is due to the relative importance of the timing of return flows and reservoir operations and their associated uncertainties. Even with the inclusion of HIP, we find that model performance still not optimal. This highlights the need for further research linking the human management and hydrological domains, especially in those areas with a dominant human impact. The large variation in performance between GHMs, regions, and performance indicators, calls for a careful selection of GHMs, model components, and evaluation metrics in future model applications.
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