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Item type:Publication, Access status: without files , The Caspian Sea has lost an area nearly the size of Sicily: human activities are a major reason whyPublicationtype: Journal Article9 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Iran’s policy priorities intensify water crisis(Springer, 2026-03-13); ; ; ; Mahoozi, SanamThe chronic water crisis in Iran stems from decades of water-intensive development, fragmented governance and national priorities that sidelined environmental protection. Without major governance reform and reprioritization, technical solutions alone cannot stop worsening water and environmental degradation.Publicationtype: Journal Article71 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Sustainability nexus analytics, informatics, and data (AID): drought(Springer, 2024-11-13); ; ; Jayasinghe, SusanthaPublicationtype: Journal Article16 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , The legacy of STAHY: milestones, achievements, challenges, and open problems in statistical hydrology(AHS Press, 2024-09-25); ; ; ; Chebana, FatehStatistical tools are crucial for a variety of hydrological applications, whether to model processes and enhance understanding and knowledge or to design infrastructure systems. Given the rapid evolution of statistical methods and the need for a solid theoretical foundation for their correct application, a multidisciplinary community STAtistics in HYdrology Working Group (STAHY-WG) aggregated under the International Association of Hydrological Sciences (IAHS) umbrella to contribute to this research field. Now, more than 15 years since its inception, this paper summarizes the main achievements of this productive community collaboration in four (of many) branches of statistical hydrology: extreme value analysis, multivariate analysis, time series analysis, and regionalization. The aim is to provide an overview of recent developments, offer practical suggestions (e.g. software packages), and outline future challenges to support scientists and practitioners in their endeavours within the realm of statistical hydrology studies.Publicationtype: Journal Article8 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Status and prospects for drought forecasting: opportunities in artificial intelligence and hybrid physical-statistical forecasting(Royal Society of London, 2022-12-12); ; ; Jiwa, ShakilDespite major improvements in weather and climate modelling and substantial increases in remotely sensed observations, drought prediction remains a major challenge. After a review of the existing methods, we discuss major research gaps and opportunities to improve drought prediction. We argue that current approaches are top-down, assuming that the process(es) and/or driver(s) are known - i.e. starting with a model and then imposing it on the observed events (reality). With the help of an experiment, we show that there are opportunities to develop bottom-up drought prediction models - i.e. starting from the reality (here, observed events) and searching for model(s) and driver(s) that work. Recent advances in artificial intelligence and machine learning provide significant opportunities for developing bottom-up drought forecasting models. Regardless of the type of drought forecasting model (e.g. machine learning, dynamical simulations, analogue based), we need to shift our attention to robustness of theories and outputs rather than event-based verification. A shift in our focus towards quantifying the stability of uncertainty in drought prediction models, rather than the goodness of fit or reproducing the past, could be the first step towards this goal. Finally, we highlight the advantages of hybrid dynamical and statistical models for improving current drought prediction models. This article is part of Science+ meeting issue 'Drought risk in the Anthropocene'.Publicationtype: Review Article11 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Maximum land surface temperatures under a changing climate: Modeling and global scale analyses(2022-12); ; ; ; Publicationtype: Conference Paper not in Proceedings38 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Biases beyond the mean in CMIP6 extreme precipitation: a global investigation(Wiley-Blackwell, 2021-10-01); ; ; Climate models are crucial for assessing climate variability and change. A reliable model for future climate should reasonably simulate the historical climate. Here, we assess the performance of CMIP6 models in reproducing statistical properties of observed annual maxima of daily precipitation. We go beyond the commonly used methods and assess CMIP6 simulations on three scales by performing: (a) univariate comparison based on L-moments and relative difference measures; (b) bivariate comparison using Kernel densities of mean and L-variation, and of L-skewness and L-kurtosis, and (c) comparison of the entire distribution function using the Generalized Extreme Value ((Formula presented.)) distribution coupled with a novel application of the Anderson-Darling Goodness-of-fit test. The results reveal that the statistical shape properties (related to the frequency and magnitude of extremes) of CMIP6 simulations match well with the observational datasets. The simulated mean and variation differ among the models with 70% of simulations having a difference within (Formula presented.) 10% from the observations. Biases are observed in the bivariate investigation of mean and variation. Several models perform well with the HadGEM3-GC31-MM model performing well in all three scales when compared to the ground-based Global Precipitation Climatology Centre data. Finally, the study highlights biases of CMIP6 models in simulating extreme precipitation in the Arctic, Tropics, arid and semi-arid regions.Publicationtype: Journal Article6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , 35 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Climate extremes and compound hazards in a warming world(Annual Reviews, 2020-02-19); ; ; ; Climate extremes threaten human health, economic stability, and the well-being of natural and built environments (e.g., 2003 European heat wave). As the world continues to warm, climate hazards are expected to increase in frequency and intensity. The impacts of extreme events will also be more severe due to the increased exposure (growing population and development) and vulnerability (aging infrastructure) of human settlements. Climate models attribute part of the projected increases in the intensity and frequency of natural disasters to anthropogenic emissions and changes in land use and land cover. Here, we review the impacts, historical and projected changes,and theoretical research gaps of key extreme events (heat waves, droughts, wildfires, precipitation, and flooding). We also highlight the need to improve our understanding of the dependence between individual and interrelated climate extremes because anthropogenic-induced warming increases the risk of not only individual climate extremes but also compound (co-occurring) and cascading hazards. Climate hazards are expected to increase in frequency and intensity in a warming world. Anthropogenic-induced warming increases the risk of compound and cascading hazards. We need to improve our understanding of causes and drivers of compound and cascading hazards.Publicationtype: Review Article15 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Access status: without files , Precise temporal Disaggregation Preserving Marginals and Correlations (DiPMaC) for stationary and nonstationary processes(Wiley, 2018-09-04); ; ; ; Hydroclimatic variables such as precipitation and temperature are often measured or simulated by climate models at coarser spatiotemporal scales than those needed for operational purposes. This has motivated more than half a century of research in developing disaggregation methods that break down coarse-scale time series into finer scales, with two primary objectives: (a) reproducing the statistical properties of the fine-scale process and (b) preserving the original coarse-scale data. Existing methods either preserve a limited number of statistical moments at the fine scale, which is often insufficient and can lead to an unrepresentative approximation of the actual marginal distribution, or are based on a limited number of a priori distributional assumptions, for example, lognormal. Additionally, they are not able to account for potential nonstationarity in the underlying fine-scale process. Here we introduce a novel disaggregation method, named Disaggregation Preserving Marginals and Correlations (DiPMaC), that is able to disaggregate a coarse-scale time series to any finer scale, while reproducing the probability distribution and the linear correlation structure of the fine-scale process. DiPMaC is also generalized for arbitrary nonstationary scenarios to reproduce time varying marginals. Additionally, we introduce a computationally efficient algorithm, based on Bernoulli trials, to optimize the disaggregation procedure and guarantee preservation of the coarse-scale values. We focus on temporal disaggregation and demonstrate the method by disaggregating monthly precipitation to hourly, and time series with trends (e.g., climate model projections), while we show its potential to disaggregate based on general nonstationary scenarios. The example applications demonstrate the performance and robustness of DiPMaC.Publicationtype: Journal Article10