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S. Samadi
Department of Agricultural Sciences, Clemson University, USA

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Preprint content
Published: 04 March 2021
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Deep Learning (DL) is becoming an increasingly important tool to produce accurate streamflow prediction across a wide range of spatial and temporal scales. However, classical DL networks do not incorporate uncertainty information but only return a point prediction. Monte-Carlo Dropout (MC-Dropout) approach offers a mathematically grounded framework to reason about DL uncertainty which was used here as random diagonal matrices to introduce randomness to the streamflow prediction process. This study employed Recurrent Neural Networks (RNNs) to simulate daily streamflow records across a coastal plain drainage system, i.e., the Northeast Cape Fear River Basin, North Carolina, USA. We employed MC-Dropout approach with the DL algorithm to make streamflow simulation more robust to potential overfitting by introducing random perturbation during training period. Daily streamflow was calibrated during 2000-2010 and validated during 2010-2014 periods. Our results provide a unique and strong evidence that variational sampling via MC-Dropout acts as a dissimilarity detector. The MC-Dropout method successfully captured the predictive error after tuning a hyperparameter on a representative training dataset. This approach was able to mitigate the problem of representing model uncertainty in DL simulations without sacrificing computational complexity or accuracy metrics and can be used for all kind of DL-based streamflow (time-series) model training with dropout.

ACS Style

Sadegh Sadeghi Tabas; Vidya Samadi. Model Uncertainty in Deep Learning Simulation of Daily Streamflow with Monte Carlo Dropout. 2021, 1 .

AMA Style

Sadegh Sadeghi Tabas, Vidya Samadi. Model Uncertainty in Deep Learning Simulation of Daily Streamflow with Monte Carlo Dropout. . 2021; ():1.

Chicago/Turabian Style

Sadegh Sadeghi Tabas; Vidya Samadi. 2021. "Model Uncertainty in Deep Learning Simulation of Daily Streamflow with Monte Carlo Dropout." , no. : 1.

Preprint content
Published: 04 March 2021
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Floods are among the most destructive natural hazard that affect millions of people across the world leading to severe loss of life and damage to property, critical infrastructure, and agriculture. Internet of Things (IoTs), machine learning (ML), and Big Data are exceptionally valuable tools for collecting the catastrophic readiness and countless actionable data. The aim of this presentation is to introduce Flood Analytics Information System (FAIS) as a data gathering and analytics system.  FAIS application is smartly designed to integrate crowd intelligence, ML, and natural language processing of tweets to provide warning with the aim to improve flood situational awareness and risk assessment. FAIS has been Beta tested during major hurricane events in US where successive storms made extensive damage and disruption. The prototype successfully identifies a dynamic set of at-risk locations/communities using the USGS river gauge height readings and geotagged tweets intersected with watershed boundary. The list of prioritized locations can be updated, as the river monitoring system and condition change over time (typically every 15 minutes).  The prototype also performs flood frequency analysis (FFA) using various probability distributions with the associated uncertainty estimation to assist engineers in designing safe structures. This presentation will discuss about the FAIS functionalities and real-time implementation of the prototype across south and southeast USA. This research is funded by the US National Science Foundation (NSF).

ACS Style

Vidya Samadi; Rakshit Pally. The Convergence of IoT, Machine Learning, and Big Data for Advancing Flood Analytics Knowledge  . 2021, 1 .

AMA Style

Vidya Samadi, Rakshit Pally. The Convergence of IoT, Machine Learning, and Big Data for Advancing Flood Analytics Knowledge  . . 2021; ():1.

Chicago/Turabian Style

Vidya Samadi; Rakshit Pally. 2021. "The Convergence of IoT, Machine Learning, and Big Data for Advancing Flood Analytics Knowledge  ." , no. : 1.

Preprint content
Published: 03 March 2021
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Due to the importance of object detection in video analysis and image annotation, it is widely utilized in a number of computer vision tasks such as face recognition, autonomous vehicles, activity recognition, tracking objects and identity verification. Object detection does not only involve classification and identification of objects within images, but also involves localizing and tracing the objects by creating bounding boxes around the objects and labelling them with their respective prediction scores. Here, we leverage and discuss how connected vision systems can be used to embed cameras, image processing, Edge Artificial Intelligence (AI), and data connectivity capabilities for flood label detection. We favored the engineering definition of label detection that a label is a sequence of discrete measurable observations obtained using a capturing device such as web cameras, smart phone, etc. We built a Big Data service of around 1000 images (image annotation service) including the image geolocation information from various flooding events in the Carolinas (USA) with a total of eight different object categories. Our developed platform has several smart AI tools and task configurations that can detect objects’ edges or contours which can be manually adjusted with a threshold setting so as to best segment the image. The tool has the ability to train the dataset and predict the labels for large scale datasets which can be used as an object detector to drastically reduce the amount of time spent per object particularly for real-time image-based flood forecasting.  This research is funded by the US National Science Foundation (NSF).

ACS Style

Jaku Rabinder Rakshit Pally; Vidya Samadi. Application of Image Processing and Big Data Science for Flood Label Detection. 2021, 1 .

AMA Style

Jaku Rabinder Rakshit Pally, Vidya Samadi. Application of Image Processing and Big Data Science for Flood Label Detection. . 2021; ():1.

Chicago/Turabian Style

Jaku Rabinder Rakshit Pally; Vidya Samadi. 2021. "Application of Image Processing and Big Data Science for Flood Label Detection." , no. : 1.

Journal article
Published: 26 August 2020 in Environmental Modelling & Software
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With the rapid development of the Internet of Things (IoT) and Big data infrastructure, crowdsourcing techniques have emerged to facilitate data processing and problem solving particularly for flood emergences purposes. A Flood Analytics Information System (FAIS) has been developed as a Python Web application to gather Big data from multiple servers and analyze flooding impacts during historical and real-time events. The application is smartly designed to integrate crowd intelligence, machine learning (ML), and natural language processing of tweets to provide flood warning with the aim to improve situational awareness for flood risk management. This national scale prototype combines flood peak rates and river level information with geotagged tweets to identify a dynamic set of at-risk locations to flooding. FAIS is successfully tested in real-time during Hurricane Dorian flooding as well as for historical event (Hurricanes Florence) across the Carolinas, USA where the storm made extensive disruption to infrastructure and communities.

ACS Style

N. Donratanapat; S. Samadi; J.M. Vidal; S. Sadeghi Tabas. A national scale big data analytics pipeline to assess the potential impacts of flooding on critical infrastructures and communities. Environmental Modelling & Software 2020, 133, 104828 .

AMA Style

N. Donratanapat, S. Samadi, J.M. Vidal, S. Sadeghi Tabas. A national scale big data analytics pipeline to assess the potential impacts of flooding on critical infrastructures and communities. Environmental Modelling & Software. 2020; 133 ():104828.

Chicago/Turabian Style

N. Donratanapat; S. Samadi; J.M. Vidal; S. Sadeghi Tabas. 2020. "A national scale big data analytics pipeline to assess the potential impacts of flooding on critical infrastructures and communities." Environmental Modelling & Software 133, no. : 104828.

Journal article
Published: 06 July 2020 in Journal of Advances in Modeling Earth Systems
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This paper introduces for the first time the concept of Bayesian Model Averaging (BMA) with multiple prior structures, for rainfall‐runoff modeling applications. The original BMA model proposed by Raftery et al. (2005) assumes that the prior probability density function (pdf) is adequately described by a mixture of Gamma and Gaussian distributions. Here we discuss the advantages of using BMA with fixed and flexible prior distributions. Uniform, Binomial, Binomial‐Beta, Benchmark, and Global Empirical Bayes priors along with Informative Prior Inclusion and Combined Prior Probabilities were applied to calibrate daily streamflow records of a coastal plain watershed in the South‐East USA. Various specifications for Zellner's g prior including Hyper, Fixed, and Empirical Bayes Local (EBL) g priors were also employed to account for the sensitivity of BMA and derive the conditional pdf of each constituent ensemble member. These priors were examined using the simulation results of conceptual and semi‐distributed rainfall‐runoff models. The hydrologic simulations were first coupled with a new sensitivity analysis model and a parameter uncertainty algorithm to assess the sensitivity and uncertainty associated with each model. BMA was then used to subsequently combine the simulations of the posterior pdf of each constituent hydrological model. Analysis suggests that a BMA based on combined fixed and flexible priors provides a coherent mechanism and promising results for calculating a weighted posterior probability compared to individual model calibration. Furthermore, the probability of Uniform and Informative Prior Inclusion priors received significantly lower predictive error whereas more uncertainty resulted from a fixed g prior (i.e. EBL).

ACS Style

S. Samadi; M. Pourreza‐Bilondi; C. A. M. E. Wilson; D. B. Hitchcock. Bayesian Model Averaging With Fixed and Flexible Priors: Theory, Concepts, and Calibration Experiments for Rainfall‐Runoff Modeling. Journal of Advances in Modeling Earth Systems 2020, 12, 1 .

AMA Style

S. Samadi, M. Pourreza‐Bilondi, C. A. M. E. Wilson, D. B. Hitchcock. Bayesian Model Averaging With Fixed and Flexible Priors: Theory, Concepts, and Calibration Experiments for Rainfall‐Runoff Modeling. Journal of Advances in Modeling Earth Systems. 2020; 12 (7):1.

Chicago/Turabian Style

S. Samadi; M. Pourreza‐Bilondi; C. A. M. E. Wilson; D. B. Hitchcock. 2020. "Bayesian Model Averaging With Fixed and Flexible Priors: Theory, Concepts, and Calibration Experiments for Rainfall‐Runoff Modeling." Journal of Advances in Modeling Earth Systems 12, no. 7: 1.

Journal article
Published: 06 April 2019 in Water
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Accurate prediction of daily streamflow plays an essential role in various applications of water resources engineering, such as flood mitigation and urban and agricultural planning. This study investigated a hybrid ensemble decomposition technique based on ensemble empirical mode decomposition (EEMD) and variational mode decomposition (VMD) with gene expression programming (GEP) and random forest regression (RFR) algorithms for daily streamflow simulation across three mountainous stations, Siira, Bilghan, and Gachsar, in Karaj, Iran. To determine the appropriate corresponding input variables with optimal lag time the partial auto-correlation function (PACF) and auto-correlation function (ACF) were used for streamflow prediction purpose. Calibration and validation datasets were separately decomposed by EEMD that eventually improved standalone predictive models. Further, the component of highest pass (IMF1) was decomposed by the VMD approach to breakdown the distinctive characteristic of the variables. Results suggested that the EEMD-VMD algorithm significantly enhanced model calibration. Moreover, the EEMD-VMD-RFR algorithm as a hybrid ensemble model outperformed better than other techniques (EEMD-VMD-GEP, RFR and GEP) for daily streamflow prediction of the selected gauging stations. Overall, the proposed methodology indicated the superiority of hybrid ensemble models compare to standalone in predicting streamflow time series particularly in case of high fluctuations and different patterns in datasets.

ACS Style

Mohammad Rezaie-Balf; Sajad Fani Nowbandegani; S. Zahra Samadi; Hossein Fallah; Sina Alaghmand. An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction. Water 2019, 11, 709 .

AMA Style

Mohammad Rezaie-Balf, Sajad Fani Nowbandegani, S. Zahra Samadi, Hossein Fallah, Sina Alaghmand. An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction. Water. 2019; 11 (4):709.

Chicago/Turabian Style

Mohammad Rezaie-Balf; Sajad Fani Nowbandegani; S. Zahra Samadi; Hossein Fallah; Sina Alaghmand. 2019. "An Ensemble Decomposition-Based Artificial Intelligence Approach for Daily Streamflow Prediction." Water 11, no. 4: 709.

Journal article
Published: 01 July 2018 in Journal of Hydrology
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This paper examines the frequency, distribution tails, and peak-over-threshold (POT) of extreme floods through analysis that centers on the October 2015 flooding in North Carolina (NC) and South Carolina (SC), United States (US). The most striking features of the October 2015 flooding were a short time to peak (Tp) and a multi-hour continuous flood peak which caused intensive and widespread damages to human lives, properties, and infrastructure. The 2015 flooding was produced by a sequence of intense rainfall events which originated from category 4 hurricane Joaquin over a period of four days. Here, the probability distribution and distribution parameters (i.e., location, scale, and shape) of floods were investigated by comparing the upper part of empirical distributions of the annual maximum flood (AMF) and POT with light- to heavy- theoretical tails: Fréchet, Pareto, Gumbel, Weibull, Beta, and Exponential. Specifically, four sets of U.S. Geological Survey (USGS) gauging data from the central Carolinas with record lengths from approximately 65 to 125 years were used. Analysis suggests that heavier-tailed distributions are in better agreement with the POT and somewhat AMF data than more often used exponential (light) tailed probability distributions. Further, the threshold selection and record length affect the heaviness of the tail and fluctuations of the parent distributions. The shape parameter and its evolution in the period of record play a critical and poorly understood role in determining the scaling of flood response to intense rainfall.

ACS Style

R.C. Phillips; S.Z. Samadi; M.E. Meadows. How extreme was the October 2015 flood in the Carolinas? An assessment of flood frequency analysis and distribution tails. Journal of Hydrology 2018, 562, 648 -663.

AMA Style

R.C. Phillips, S.Z. Samadi, M.E. Meadows. How extreme was the October 2015 flood in the Carolinas? An assessment of flood frequency analysis and distribution tails. Journal of Hydrology. 2018; 562 ():648-663.

Chicago/Turabian Style

R.C. Phillips; S.Z. Samadi; M.E. Meadows. 2018. "How extreme was the October 2015 flood in the Carolinas? An assessment of flood frequency analysis and distribution tails." Journal of Hydrology 562, no. : 648-663.

Original paper
Published: 11 November 2017 in Stochastic Environmental Research and Risk Assessment
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Hydrologic models provide a comprehensive tool to estimate streamflow response to environmental variables. Yet, an incomplete understanding of physical processes and challenges associated with scaling processes to a river basin, introduces model uncertainty. Here, we apply generalized additive models of location, scale and shape (GAMLSS) to characterize this uncertainty in an Atlantic coastal plain watershed system. Specifically, we describe distributions of residual errors in a two-step procedure that includes model calibration of the soil and water assessment tool (SWAT) using a sequential Bayesian uncertainty algorithm, followed by time-series modeling of residual errors of simulated daily streamflow. SWAT identified dominant hydrological processes, performed best during moderately wet years, and exhibited less skill during times of extreme flow. Application of GAMLSS to model residuals efficiently produced a description of the error distribution parameters (mean, variance, skewness, and kurtosis), differentiating between upstream and downstream outlets of the watershed. Residual error distribution is better described by a non-parametric polynomial loess curve with a smooth transition from a Box–Cox t distribution upstream to a skew t type 3 distribution downstream. Overall, the fitted models show that low flow events more strongly influence the residual probability distribution, and error variance increases with streamflow discharge, indicating correlation and heteroscedasticity of residual errors. These results provide useful insights into the complexity of error behavior and highlight the value of using GAMLSS models to conduct Bayesian inference in the context of a regression model with unknown skewness and/or kurtosis.

ACS Style

S. Samadi; D. L. Tufford; Gregory Carbone. Estimating hydrologic model uncertainty in the presence of complex residual error structures. Stochastic Environmental Research and Risk Assessment 2017, 32, 1259 -1281.

AMA Style

S. Samadi, D. L. Tufford, Gregory Carbone. Estimating hydrologic model uncertainty in the presence of complex residual error structures. Stochastic Environmental Research and Risk Assessment. 2017; 32 (5):1259-1281.

Chicago/Turabian Style

S. Samadi; D. L. Tufford; Gregory Carbone. 2017. "Estimating hydrologic model uncertainty in the presence of complex residual error structures." Stochastic Environmental Research and Risk Assessment 32, no. 5: 1259-1281.

Journal article
Published: 26 October 2017 in JAWRA Journal of the American Water Resources Association
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This paper examines the performance of a semi-distributed hydrology model (i.e., Soil and Water Assessment Tool [SWAT]) using Sequential Uncertainty FItting (SUFI-2), generalized likelihood uncertainty estimation (GLUE), parameter solution (ParaSol), and particle swarm optimization (PSO). We applied SWAT to the Waccamaw watershed, a shallow aquifer dominated Coastal Plain watershed in the Southeastern United States (U.S.). The model was calibrated (2003-2005) and validated (2006-2007) at two U.S. Geological Survey gaging stations, using significant parameters related to surface hydrology, hydrogeology, hydraulics, and physical properties. SWAT performed best during intervals with wet and normal antecedent conditions with varying sensitivity to effluent channel shape and characteristics. In addition, the calibration of all algorithms depended mostly on Manning's n-value for the tributary channels as the surface friction resistance factor to generate runoff. SUFI-2 and PSO simulated the same relative probability distribution tails to those observed at an upstream outlet, while all methods (except ParaSol) exhibited longer tails at a downstream outlet. The ParaSol model exhibited large skewness suggesting a global search algorithm was less capable of characterizing parameter uncertainty. Our findings provide insights regarding parameter sensitivity and uncertainty as well as modeling diagnostic analysis that can improve hydrologic theory and prediction in complex watersheds. Editor's note: This paper is part of the featured series on SWAT Applications for Emerging Hydrologic and Water Quality Challenges. See the February 2017 issue for the introduction and background to the series.

ACS Style

S. Samadi; D.L. Tufford; Gregory Carbone. Assessing Parameter Uncertainty of a Semi-Distributed Hydrology Model for a Shallow Aquifer Dominated Environmental System. JAWRA Journal of the American Water Resources Association 2017, 53, 1368 -1389.

AMA Style

S. Samadi, D.L. Tufford, Gregory Carbone. Assessing Parameter Uncertainty of a Semi-Distributed Hydrology Model for a Shallow Aquifer Dominated Environmental System. JAWRA Journal of the American Water Resources Association. 2017; 53 (6):1368-1389.

Chicago/Turabian Style

S. Samadi; D.L. Tufford; Gregory Carbone. 2017. "Assessing Parameter Uncertainty of a Semi-Distributed Hydrology Model for a Shallow Aquifer Dominated Environmental System." JAWRA Journal of the American Water Resources Association 53, no. 6: 1368-1389.

Article
Published: 16 January 2017 in River Research and Applications
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The challenges posed by nonstationarity in predicting catchment water balance components motivated this study to test the stationary versus nonstationarity hypothesis and detect changes in the watershed response to land use land cover (LULC) alterations, and climate variability and change. The focus is on a two-step procedure that includes model calibration of Soil and Water Assessment Tool using a sequential Bayesian uncertainty algorithm (i.e. sequential uncertainty fitting), followed by nonstationary assessment of water balance component using extreme value analysis over an Atlantic coastal plain watershed in the southeastern USA. Analysis suggests that the uncertainty of Soil and Water Assessment Tool model is statistically aligned with LULC alterations that increased the sensitivity of Manning's roughness coefficient, transmission loss and the resistance of the soil matrix to water flow. Changes in LULC along with variability in the magnitude, timing and frequency of precipitation diminished surface runoff and groundwater contribution to the river system whereas it increased evapotranspiration with a substantial decline in water storage capacity. Nonstationary assessment of water balance using extreme value analysis model further revealed a functional form of stationary behaviour (no trends) prior to LULC alteration while large amplification was detected during post-changes. The results and findings presented in this paper confirm our hypothesis about a combined effect of climate and LULC changes on hydrological functions and that variation of these fingerprints elucidates the presence of nonstationarity in the watershed system. Copyright © 2017 John Wiley & Sons, Ltd.

ACS Style

S. Z. Samadi; M. E. Meadows. The Transferability of Terrestrial Water Balance Components under Uncertainty and Nonstationarity: A Case Study of the Coastal Plain Watershed in the Southeastern USA. River Research and Applications 2017, 33, 796 -808.

AMA Style

S. Z. Samadi, M. E. Meadows. The Transferability of Terrestrial Water Balance Components under Uncertainty and Nonstationarity: A Case Study of the Coastal Plain Watershed in the Southeastern USA. River Research and Applications. 2017; 33 (5):796-808.

Chicago/Turabian Style

S. Z. Samadi; M. E. Meadows. 2017. "The Transferability of Terrestrial Water Balance Components under Uncertainty and Nonstationarity: A Case Study of the Coastal Plain Watershed in the Southeastern USA." River Research and Applications 33, no. 5: 796-808.

Journal article
Published: 04 July 2016 in Hydrology Research
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One of the key inputs of a hydrologic budget is the potential evapotranspiration (PET), which represents the hypothetical upper limit to evapotranspirative water losses. However, different mathematical formulas proposed for defining PET often produce inconsistent results and challenge hydrological estimation. The objective of this study is to investigate the effects of the Priestley–Taylor (P–T), Hargreaves, and Penman–Monteith methods on daily streamflow simulation using the Soil and Water Assessment Tool (SWAT) for the southeastern United States. PET models are compared in terms of their sensitivity to the SWAT parameters and their ability to simulate daily streamflow over a five-year simulation period. The SWAT model forced by these three PET methods and by gauged climatic dataset showed more deficiency during low and peak flow estimates. Sensitive parameters vary in magnitudes with more skew and bias in saturated soil hydraulic conductivity and shallow aquifer properties. The results indicated that streamflow simulation using the P–T method performed well especially during extreme events’ simulation.

ACS Style

S. Zahra Samadi. Assessing the sensitivity of SWAT physical parameters to potential evapotranspiration estimation methods over a coastal plain watershed in the southeastern United States. Hydrology Research 2016, 48, 395 -415.

AMA Style

S. Zahra Samadi. Assessing the sensitivity of SWAT physical parameters to potential evapotranspiration estimation methods over a coastal plain watershed in the southeastern United States. Hydrology Research. 2016; 48 (2):395-415.

Chicago/Turabian Style

S. Zahra Samadi. 2016. "Assessing the sensitivity of SWAT physical parameters to potential evapotranspiration estimation methods over a coastal plain watershed in the southeastern United States." Hydrology Research 48, no. 2: 395-415.

Journal article
Published: 14 July 2015 in Theoretical and Applied Climatology
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Mangrove wetlands exist in the transition zone between terrestrial and marine environments and have remarkable ecological and socio-economic value. This study uses climate change downscaling to address the question of non-stationarity influences on mangrove variations (expansion and contraction) within an arid coastal region. Our two-step approach includes downscaling models and uncertainty assessment, followed by a non-stationary and trend procedure using the Extreme Value Analysis (extRemes code). The Long Ashton Research Station Weather Generator (LARS-WG) model along with two different general circulation model (GCMs) (MIRH and HadCM3) were used to downscale climatic variables during current (1968–2011) and future (2011–2030, 2045–2065, and 2080–2099) periods. Parametric and non-parametric bootstrapping uncertainty tests demonstrated that the LARS-WGS model skillfully downscaled climatic variables at the 95 % significance level. Downscaling results using MIHR model show that minimum and maximum temperatures will increase in the future (2011–2030, 2045–2065, and 2080–2099) during winter and summer in a range of +4.21 and +4.7 °C, and +3.62 and +3.55 °C, respectively. HadCM3 analysis also revealed an increase in minimum (∼+3.03 °C) and maximum (∼+3.3 °C) temperatures during wet and dry seasons. In addition, we examined how much mangrove area has changed during the past decades and, thus, if climate change non-stationarity impacts mangrove ecosystems. Our results using remote sensing techniques and the non-parametric Mann–Whitney two-sample test indicated a sharp decline in mangrove area during 1972,1987, and 1997 periods (p value = 0.002). Non-stationary assessment using the generalized extreme value (GEV) distributions by including mangrove area as a covariate further indicated that the null hypothesis of the stationary climate (no trend) should be rejected due to the very low p values for precipitation (p value = 0.0027), minimum (p value = 0.000000029) and maximum (p value = 0.00016) temperatures. Based on non-stationary analysis and an upward trend in downscaled temperature extremes, climate change may control mangrove development in the future.

ACS Style

Halimeh Etemadi; S. Zahra Samadi; Mohammad Sharifikia; Joseph M. Smoak. Assessment of climate change downscaling and non-stationarity on the spatial pattern of a mangrove ecosystem in an arid coastal region of southern Iran. Theoretical and Applied Climatology 2015, 126, 35 -49.

AMA Style

Halimeh Etemadi, S. Zahra Samadi, Mohammad Sharifikia, Joseph M. Smoak. Assessment of climate change downscaling and non-stationarity on the spatial pattern of a mangrove ecosystem in an arid coastal region of southern Iran. Theoretical and Applied Climatology. 2015; 126 (1-2):35-49.

Chicago/Turabian Style

Halimeh Etemadi; S. Zahra Samadi; Mohammad Sharifikia; Joseph M. Smoak. 2015. "Assessment of climate change downscaling and non-stationarity on the spatial pattern of a mangrove ecosystem in an arid coastal region of southern Iran." Theoretical and Applied Climatology 126, no. 1-2: 35-49.