ORCID Profile
0000-0002-2889-999X
Current Organisations
University of California, Irvine
,
University of Alabama
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Publisher: Springer Science and Business Media LLC
Date: 15-12-2015
Publisher: Elsevier BV
Date: 08-2016
Publisher: American Geophysical Union (AGU)
Date: 11-2020
DOI: 10.1029/2020EF001671
Publisher: Elsevier BV
Date: 03-2022
Publisher: Elsevier BV
Date: 03-2018
Publisher: American Geophysical Union (AGU)
Date: 02-2018
DOI: 10.1002/2018WR022627
Publisher: Copernicus GmbH
Date: 16-05-2018
DOI: 10.5194/HESS-22-2903-2018
Abstract: Abstract. Rapid population and economic growth in Southeast Asia has been accompanied by extensive land use change with consequent impacts on catchment hydrology. Modeling methodologies capable of handling changing land use conditions are therefore becoming ever more important and are receiving increasing attention from hydrologists. A recently developed data-assimilation-based framework that allows model parameters to vary through time in response to signals of change in observations is considered for a medium-sized catchment (2880 km2) in northern Vietnam experiencing substantial but gradual land cover change. We investigate the efficacy of the method as well as the importance of the chosen model structure in ensuring the success of a time-varying parameter method. The method was used with two lumped daily conceptual models (HBV and HyMOD) that gave good-quality streamflow predictions during pre-change conditions. Although both time-varying parameter models gave improved streamflow predictions under changed conditions compared to the time-invariant parameter model, persistent biases for low flows were apparent in the HyMOD case. It was found that HyMOD was not suited to representing the modified baseflow conditions, resulting in extreme and unrealistic time-varying parameter estimates. This work shows that the chosen model can be critical for ensuring the time-varying parameter framework successfully models streamflow under changing land cover conditions. It can also be used to determine whether land cover changes (and not just meteorological factors) contribute to the observed hydrologic changes in retrospective studies where the lack of a paired control catchment precludes such an assessment.
Publisher: Elsevier BV
Date: 05-2014
Publisher: Springer Berlin Heidelberg
Date: 2018
Publisher: American Geophysical Union (AGU)
Date: 11-2022
DOI: 10.1029/2022WR032658
Abstract: Precipitation intensity–duration–frequency (IDF) curves play a crucial role in the design and planning of urban infrastructure to reduce the risk of urban flooding and rainfall‐triggered landslides. However, changing rainfall characteristics in a warming climate render conventional IDF curves inappropriate due to the statistical assumption of stationarity. In this study, we develop vine copula‐based projections of future IDF curves at sub‐daily to multi‐day time scales with a multi‐model ensemble of five regional climate simulations over China. Stochastic spatiotemporal downscaling of precipitation is achieved to generate extreme precipitation simulations at a high spatial (0.1°) and temporal (3 hourly) resolution. These downscaled simulations are combined by the vine copula to improve the reliability and accuracy of climate‐model‐based IDF curves relative to historical observations. Our findings reveal that climate‐model‐based stochastic downscaling of precipitation reproduces the IDF curves well based on historical observations in China. The vine copula multi‐model ensemble approach outperforms Bayesian model averaging by generating more accurate and reliable IDF curves. The urban areas of 196 Chinese cities are projected to experience an increase in extreme precipitation of up to 30% in intensity and nearly two times the frequency of historical events under a high emission scenario (RCP8.5). The current urban infrastructure of more than half of the 196 cities would thus be inadequate to prevent losses caused by rainfall‐triggered hazards if designed solely based on historical precipitation observations. Compared to the climate‐model‐based IDF curves, we find that statistical IDF curves based on a nonstationary time covariate (i.e., extrapolating historical trends) are likely to underestimate the risk of urban infrastructure failures under a warming climate. This work highlights that urban infrastructure design guidelines in China should be upgraded to adapt existing IDF curves to the changing climate.
Publisher: American Geophysical Union (AGU)
Date: 2022
DOI: 10.1029/2021EF002417
Abstract: Drought and tropical storm (TS) are associated with water deficit and surplus, respectively. Soil moisture is a key component in the hydrological cycle that plays an important role in monitoring drought and reflects the infiltrated or stored water due to TS rainfall. Therefore, soil moisture information can be used for the assessment of whether TS can ameliorate severe drought conditions. Here, we use downscaled 1 km Soil Moisture Active Passive data set generated at the Center for Complex Hydrosystems Research at the University of Alabama, and Hurricane Database 2nd generation to examine the coincidence of extreme events between agricultural droughts and Atlantic TS in the contiguous United States from 2015 to 2019. The Cumulative Distribution Function (CDF) matching approach is employed to correct the bias using the root zone soil moisture data provided by the North American Land Data Assimilation System Phase 2 (NLDAS‐2), and then weekly Standardized Soil Moisture Index is calculated for characterization of agricultural drought. As a result, we estimate the frequency of TS impacted regions in the US, the ratio of droughts ameliorated and exacerbated by TS, and the regions where TS highly affect the offset of drought. Our findings indicate detailed spatial information of the offset of drought conditions based on a high‐resolution data set and provide potential information in terms of mitigating drought and TS for the future.
Publisher: American Geophysical Union (AGU)
Date: 27-10-2023
DOI: 10.1029/2023GL105640
Publisher: Copernicus GmbH
Date: 04-07-2017
Abstract: Abstract. Rapid population and economic growth in South-East-Asia has been accompanied by extensive land use change with consequent impacts on catchment hydrology. Modelling methodologies capable of handling changing land use conditions are therefore becoming ever more important, and are receiving increasing attention from hydrologists. A recently developed Data Assimilation based framework that allows model parameters to vary through time in response to signals of change in observations is considered for a medium sized catchment (2880 km2) in Northern Vietnam experiencing substantial but gradual land cover change. We investigate the efficacy of the method as well as the importance of the chosen model structure in ensuring the success of time varying parameter methods. The framework was utilized with two conceptual models (HBV and HyMOD) that gave good quality streamflow predictions during pre-change conditions. Although both time varying parameter models gave improved streamflow predictions under changed conditions compared to the time invariant parameter model, persistent biases for low flows were apparent in the HyMOD case. It was found that HyMOD was not suited to representing the modified baseflow conditions, resulting in extreme and unrealistic time varying parameter estimates. This work shows that the chosen model can be critical for ensuring the time varying parameter framework successfully models streamflow under changed land cover conditions. It also serves as an effective tool for separating the influence of climatic and land use change in retrospective studies where the lack of a paired control catchment precludes such an assessment.
Publisher: American Geophysical Union (AGU)
Date: 05-2016
DOI: 10.1002/2015WR017192
Publisher: Copernicus GmbH
Date: 29-10-2012
DOI: 10.5194/HESS-16-3863-2012
Abstract: Abstract. Data assimilation (DA) holds considerable potential for improving hydrologic predictions as demonstrated in numerous research studies. However, advances in hydrologic DA research have not been adequately or timely implemented in operational forecast systems to improve the skill of forecasts for better informed real-world decision making. This is due in part to a lack of mechanisms to properly quantify the uncertainty in observations and forecast models in real-time forecasting situations and to conduct the merging of data and models in a way that is adequately efficient and transparent to operational forecasters. The need for effective DA of useful hydrologic data into the forecast process has become increasingly recognized in recent years. This motivated a hydrologic DA workshop in Delft, the Netherlands in November 2010, which focused on advancing DA in operational hydrologic forecasting and water resources management. As an outcome of the workshop, this paper reviews, in relevant detail, the current status of DA applications in both hydrologic research and operational practices, and discusses the existing or potential hurdles and challenges in transitioning hydrologic DA research into cost-effective operational forecasting tools, as well as the potential pathways and newly emerging opportunities for overcoming these challenges. Several related aspects are discussed, including (1) theoretical or mathematical aspects in DA algorithms, (2) the estimation of different types of uncertainty, (3) new observations and their objective use in hydrologic DA, (4) the use of DA for real-time control of water resources systems, and (5) the development of community-based, generic DA tools for hydrologic applications. It is recommended that cost-effective transition of hydrologic DA from research to operations should be helped by developing community-based, generic modeling and DA tools or frameworks, and through fostering collaborative efforts among hydrologic modellers, DA developers, and operational forecasters.
No related grants have been discovered for Hamid Moradkhani.