Hamid Moradkhani | University of Alabama - Tuscaloosa (original) (raw)

Papers by Hamid Moradkhani

Water Resources Research, 2016

The transferability of conceptual hydrologic models in time is often limited by both their struct... more The transferability of conceptual hydrologic models in time is often limited by both their structural deficiencies and adopted parameterizations. Adopting a stationary set of model parameters ignores biases introduced by the data used to derive them, as well as any future changes to catchment conditions. Although time invariance of model parameters is one of the hallmarks of a high quality hydrologic model, very few (if any) models can achieve this due to their inherent limitations. It is therefore proposed to consider parameters as potentially time varying quantities, which can evolve according to signals in hydrologic observations. In this paper, we investigate the potential for Data Assimilation (DA) to detect known temporal patterns in model parameters from streamflow observations. It is shown that the success of the DA algorithm is strongly dependent on the method used to generate background (or prior) parameter ensembles (also referred to as the parameter evolution model). A range of traditional parameter evolution techniques are considered and found to be problematic when multiple parameters with complex time variations are estimated simultaneously. Two alternative methods are proposed, the first is a Multilayer approach that uses the EnKF to estimate hyperparameters of the temporal structure, based on apriori knowledge of the form of nonstationarity. The second is a Locally Linear approach that uses local linear estimation and requires no assumptions of the form of parameter nonstationarity. Both are shown to provide superior results in a range of synthetic case studies, when compared to traditional parameter evolution techniques.

Materials Today Advances, 2022

Advances in Water Resources, 2020

Flood is one of the most catastrophic natural disasters in the United States, particularly in the... more Flood is one of the most catastrophic natural disasters in the United States, particularly in the Southeast states where hurricanes and tropical storms are most prevalent, causing billions of dollars in damage annually and significant losses of life and property. The Weather Research and Forecasting Hydrological model (WRF-Hydro) is a community-based hydrologic model designed to improve the skill of hydrometeorological forecasts, such as river discharge, through simulating hydrologic prognostic (e.g., soil moisture) and diagnostic (e.g., energy fluxes) variables. These quantities are potentially biased or erroneous due to the uncertainties involved in all layers of hydrologic predictions. In this study, we use an ensemble based Data Assimilation (DA) approach to explore the benefit of independently and jointly assimilating remotely sensed SMAP (Soil Moisture Active Passive) soil moisture (at different spatial resolutions) and USGS streamflow observations to improve the accuracy and reliability of WRF-Hydro model predictions while accounting for uncertainties. This study is conducted over a large region near to Houston, Texas where heavy rainfall from hurricane Harvey caused flooding in 2017. Before implementing DA, we first calibrated the WRF-Hydro model parameters using four United States Geological Survey (USGS) stream gauges installed within the watershed. In this step, we identified the most dominant model parameters, which were used later in the development of joint state-parameter DA. The findings of this study showed that the multivariate assimilation of soil moisture and streamflow observations results in improved prediction of streamflow as compared to univariate assimilation configurations and regardless of the watershed's streamflow regime. The results also revealed that, during the normal streamflow condition, assimilation of downscaled SMAP soil moisture at 1 km spatial resolution, would improve the accuracy of streamflow simulation more than the assimilation of coarse resolution products (i.e., the native SMAP at 36 km spatial resolution and its interpolated version at 9 km spatial resolution). However, during the period of hurricane Harvey, the soil moisture observations at different resolutions showed a similar impact on improving the streamflow prediction.

Environmental Research Letters, 2020

Tropical cyclones are among the most devastating natural disasters that pose risk to people and a... more Tropical cyclones are among the most devastating natural disasters that pose risk to people and assets all around the globe. The Saffir-Simpson scale is commonly used to inform threatened communities about the severity of hazard, but lacks consideration of other potential drivers of a hazardous situation (e.g. terrestrial and coastal flooding). Here, we propose an alternative approach that accounts for multiple components and their likelihood of coincidence for appropriate characterization of hurricane hazard. We assess the marginal and joint probability of wind-speed and rainfall from landfalling Atlantic tropical cyclones in the United States between 1979 ∼ 2017 to characterize the hazard associated with these events. We then integrate the vulnerability of affected communities to have a better depiction of risk that is comparable to the actual cost of these hurricanes. Our results show that the multihazard indexing approach significantly better characterizes the hurricane hazard, ...

Agricultural and Forest Meteorology, 2020

Natural disasters may act as harmful causes of food insecurity in the Middle East. Frequent droug... more Natural disasters may act as harmful causes of food insecurity in the Middle East. Frequent drought events, water scarcity, and unsustainable intensive agricultural practices may impact food security in the region. This paper investigates a causal relationship between drought and food security across the Middle East. Meteorological, agricultural, and hydrological droughts are analyzed at multiple timescales over the region for seven decades during the period of 1948-2017. We simulate food security in the Middle East as a function of drought (representing a water stress factor) as well as several other socioeconomic drivers. A Bayesian approach is implemented to integrate these drivers in order to accurately predict food security in the region. Results reveal that hydrological drought is the most intensified drought type over the region, especially in Egypt, during the study period. Moreover, the results demonstrate the significant impacts of livestock, population growth, agricultural products, and drought on food security in the Middle East. Our findings further indicate that the agricultural products decreased in the Middle East following the recent drought event that happened in 2010.

Water Research, 2019

Hydrological droughts have considerable negative impacts on water quantity and quality, and under... more Hydrological droughts have considerable negative impacts on water quantity and quality, and understanding their regional characteristics is of crucial importance. This study presents a multistage framework to detect and characterize hydrological droughts considering both streamflow and water quality changes. Hydrological droughts are categorized into three stages of growth, persistence, retreat, and water quality variables (i.e., water temperature, dissolved oxygen concentration, and turbidity) are utilized to further investigate drought recovery. The framework is applied to 400 streamflow gauges across the Contiguous United States (CONUS) over the study period of 1950-2016. The method is illustrated for the 2012 US drought, which affected most of the nation. Results reveal the duration, frequency, and severity of historical droughts in various regions as well as their spatial consistencies and heterogeneities. Furthermore, duration of each stage of drought (i.e., growth, persistence, and retreat) is also assessed and the spatial patterns are diagnosed across the CONUS. Considering the water quality variables, increased water temperature (4 o C on average) and reduced dissolved oxygen concentration (2.5 mg/L on average) were observed during drought episodes, both of which impose severe consequences on ecology of natural habitats. On the contrary, turbidity was found to decrease during droughts, and indicate a sudden increase when drought terminates, due to increase in runoff. Varied drought recovery durations are perceived for different water quality variables, and in general, it takes about two more months for water quality variables to recover from a drought, following the hydrologica l drought termination.

Water Resources Research, 2018

We present a general Bayesian hierarchical framework for conducting nonstationary frequency analy... more We present a general Bayesian hierarchical framework for conducting nonstationary frequency analysis of multiple hydrologic variables. In this, annual maxima from each variable are assumed to follow a generalized extreme value (GEV) distribution in which the location parameter is allowed to vary in time. A Gaussian elliptical copula is used to model the joint distribution of all variables. We demonstrate the utility of this framework with a joint frequency analysis model of annual peak snow water equivalent (SWE), annual peak flow, and annual peak reservoir elevation at Taylor Park dam in Colorado, USA. Indices of largescale climate drivers-El Niño Southern Oscillation (ENSO), Pacific Decadal Oscillation (PDO), and Atlantic Multidecadal Oscillation (AMO) are used as covariates to model temporal nonstationarity. The Bayesian framework provides the posterior distribution of the model parameters and consequently the return levels. Results show that performing a multivariate joint frequency analysis reduces the uncertainty in return level estimates and better captures multivariate dependence compared to an independent model. Plain Language Summary In this study, we develop a method for determining the probability of occurrence of rare hydrologic events (e.g., floods). Utilizing modern statistical methods, we are able to estimate occurrence probabilities for multiple hydrologic variables simultaneously while incorporating climate information that changes in time. We apply this technique to estimate occurrence probabilities for streamflow, reservoir elevation, and snow levels for the Taylor Park reservoir in Colorado, USA. This method provides several benefits over traditional methods including reduction of uncertainty and a flexible model structure which allows for the incorporation of climate information.

Advances in Water Resources, 2018

This is a PDF file of an unedited manuscript that has been accepted for publication. As a service... more This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. Highlights  Systematic errors in observations or model simulations have traditionally been shown to degrade data assimilation quality.  We investigate this issue in the context of data assimilation and prediction in catchments with changing system properties (i.e. land cover conditions).  Experiments on a range of catchments show that the impacts of systematic errors due to unknown land cover changes are dependent on the inherent model prediction uncertainty that persists even in pre-change conditions.  Systematic errors introduced by unresolved dynamic system properties do not always negatively impact assimilation/forecast quality.

Science of The Total Environment, 2018

Drought vulnerability is a complex concept that identifies the capacity to cope with drought, and... more Drought vulnerability is a complex concept that identifies the capacity to cope with drought, and reveals the susceptibility of a system to the adverse impacts of drought. In this study, a multi-dimensional modeling framework is carried out to investigate drought vulnerability at a national level across the African continent. Data from 28 factors in six different components (i.e. economy, energy and infrastructure, health, land use, society, and water resources) are collected for 46 African countries during 1960-2015, and a composite Drought Vulnerability Index (DVI) is calculated for each country. Various analyses are conducted to assess the reliability and accuracy of the proposed DVI, and the index is evaluated against historical observed drought impacts. Then, regression models are fitted to the historical time-series of DVI for each country, and the models are extrapolated for the period of 2020-2100 to provide three future scenarios of DVI projection (low, medium, and high) based on historical variations and trends. Results show that Egypt, Tunisia, and Algeria are the least drought vulnerable countries, and Chad, Niger, and Malawi are the most drought vulnerable countries in Africa. Future DVI projections indicate that the difference between low-and high-vulnerable countries will increase in future, with most of the southern and northern African countries becoming less vulnerable to drought, whereas the majority of central African countries indicate increasing drought vulnerability. The projected DVIs can be utilized for longterm drought risk analysis as well as strategic adaptation planning purposes.

Hydrological Sciences Journal, 2016

This paper investigates the impact of Hungry Horse Dam on streamflow dynamics in South Fork of th... more This paper investigates the impact of Hungry Horse Dam on streamflow dynamics in South Fork of the Flathead River, Montana in the USA. To this end, pre-and post-dam periods of raw and naturalised streamflow data were analysed. Pettitt's change point analysis indicated a significant change point in streamflow dynamics due to dam construction. Complexities in pre-and post-dam periods were evaluated by sample and multi-scale entropy analyses, and the entropies of the post-dam period were found to be higher than those of the pre-dam period. Possible reasons unrelated to the natural hydrologic cycle caused by the dam were analysed using wavelet analyses. The wavelet analyses showed a clear change in phase

Journal of Hydrology, 2014

Uncertainties are an unfortunate yet inevitable part of any forecasting system. Within the contex... more Uncertainties are an unfortunate yet inevitable part of any forecasting system. Within the context of seasonal hydrologic predictions, these uncertainties can be attributed to three causes: imperfect characterization of initial conditions, an incomplete knowledge of future climate and errors within computational models. This study proposes a method to account for all threes sources of uncertainty, providing a framework to reduce uncertainty and accurately convey persistent predictive uncertainty. In currently available forecast products, only a partial accounting of uncertainty is performed, with the focus primarily on meteorological forcing. For example, the Ensemble Streamflow Prediction (ESP) technique uses meteorological climatology to estimate total uncertainty, thus ignoring initial condition and modeling uncertainty. In order to manage all three sources of uncertainty, this study combines ESP with Ensemble Data Assimilation, to quantify initial condition uncertainty, and Sequential Bayesian Combination, to quantify model errors. This gives a more complete description of seasonal hydrologic forecasting uncertainty. Results from this experiment suggest that the proposed method increases the reliability of probabilistic forecasts, particularly with respect to the tails of the predictive distribution.

World Environmental and Water Resources Congress 2010, 2010

Journal of Hydrology, 2015

In this study multi-model ensemble analysis of extreme runoff is performed based on eight regiona... more In this study multi-model ensemble analysis of extreme runoff is performed based on eight regional climate models (RCMs) provided by the North American Regional Climate Change Assessment Program (NARCCAP). Hydrologic simulation is performed by driving the Variable Infiltration Capacity (VIC) model over the Pacific Northwest region, for historical and future time periods. Extreme event analysis is then conducted using spatial hierarchical Bayesian modeling (SHB). Ensemble merging of extreme runoff is carried out using Bayesian Model Averaging (BMA) in which spatially distributed weights corresponding to each regional climate model are obtained. Comparison of the residuals before and after the multi-model combination shows that the merged signal generally outperforms the best individual signal. The climate model simulations show close performance regarding maximum and minimum temperature and wind speed, however, the differences are more pronounced for precipitation and runoff. Between-model variances increase for the future time series compared to the historical ones indicating larger uncertainties in climate change projections. The combined model is then used to predict projected seasonal runoff extremes and compare them with historical simulations. Ensemble average results suggest that seasonal extreme runoff will increase in most regions in particular the Rockies and west of the Cascades.

World Environmental and Water Resources Congress 2011, 2011

Ensemble Streamflow Prediction (ESP) provides the means for statistical post-processing of the fo... more Ensemble Streamflow Prediction (ESP) provides the means for statistical post-processing of the forecasts and estimating the inherent uncertainties. On the other hand large scale climate variables provide valuable information for hydrologic predictions. In this study we propose a post-processing procedure that assigns weights to streamflow ensemble members using these large scale climate signals. Analysis is performed over the snow dominated East River basin in Colorado to improve the spring ensemble streamflow volume forecast. We employ Fuzzy C-Means clustering method for the weighting and it is found that Principle Component Analysis (PCA) improve the accuracy of the weighting scheme considerably. The presented objective method can be applied to enhance the final ESPs; nevertheless the user expertise may change any of the process steps. The current predictions based on simple average or the median of the ensemble members may come with the weighted ensemble forecasts to better provide possible ranges and uncertainty bounds.

The impacts of climate change on the seasonality of extremes i.e. both high and low flows in the ... more The impacts of climate change on the seasonality of extremes i.e. both high and low flows in the Columbia River basin were analyzed using three seasonality indices, namely the seasonality ratio (SR), weighted mean occurrence day (WMOD) and weighted persistence (WP). These indices reflect the streamflow regime, timing and variability in timing of extreme events respectively. The three indices were estimated from: (1) observed streamflow; (2) simulated streamflow by the VIC model using simulated inputs from ten combinations of bias corrected and downscaled CMIP5 inputs for the current climate (1979–2005); (3) simulated streamflow using simulated inputs from ten combinations of CMIP5 inputs for the future climate (2040–2080) including two different pathways (RCP4.5 and RCP8.5). The hydrological model was calibrated at 1/16 latitude-longitude resolution and the simulated streamflow was routed to the subbasin outlets of interest. These three cases are compared to assess the effects of fo...

World Environmental and Water Resources Congress 2009, 2009

Accurate estimation of rainfall magnitudes and their spatial distribution plays an important role... more Accurate estimation of rainfall magnitudes and their spatial distribution plays an important role in hydrological applications such as flood risk analysis and river flow forecasts. In addition knowing the forcing data error structure is crucial part of hydrologic data assimilation system. Recent advances in radar and satellite based measurements have provided alternative methods for precipitation estimation. In this study, gauge, radar and satellite precipitation estimates are used to explore the impacts of different precipitation data sources on the accuracy of hydrological simulation over the Leaf River basin in Mississippi. The NEXRAD stage IV , 3B42 gridded TRMM, PERSIANN-CCS and rain-gauge data over the watershed are aggregated to similar resolution in time and forced to a conceptual hydrological model, SAC-SMA. The model performance with each of the rainfall estimates input has been assessed by statistical comparison of the simulated and measured streamflow at the watershed outlet. In this study, the standard error of satellite-based PERSIANN-CCS rainfall estimates conditioning on the assumed true field (i.e. radar rainfall) is obtained according to a multivariate function considering the spatial and temporal scales. Accepting the multiplicative nature of precipitation error, the Monte Carlo simulation based on log-normal distribution of error is conducted to generate the ensemble of precipitation and propagate them into a conceptual hydrologic model to investigate the impact of input error on streamflow simulation.

Water Resources Research, 2010

Improving robustness of hydrologic parameter estimation by the use of moving block bootstrap resa... more Improving robustness of hydrologic parameter estimation by the use of moving block bootstrap resampling, Water Resour. Res., 46, W07515.

Water Resources Research, 2006

The aim of this paper is to foster the development of an end-to-end uncertainty analysis framewor... more The aim of this paper is to foster the development of an end-to-end uncertainty analysis framework that can quantify satellite-based precipitation estimation error characteristics and to assess the influence of the error propagation into hydrological simulation. First, the error associated with the satellite-based precipitation estimates is assumed as a nonlinear function of rainfall space-time integration scale, rain intensity, and sampling frequency. Parameters of this function are determined by using high-resolution satellite-based precipitation estimates and gauge-corrected radar rainfall data over the southwestern United States. Parameter sensitivity analysis at 16 selected 5°Â 5°latitudelongitude grids shows about 12-16% of variance of each parameter with respect to its mean value. Afterward, the influence of precipitation estimation error on the uncertainty of hydrological response is further examined with Monte Carlo simulation. By this approach, 100 ensemble members of precipitation data are generated, as forcing input to a conceptual rainfall-runoff hydrologic model, and the resulting uncertainty in the streamflow prediction is quantified. Case studies are demonstrated over the Leaf River basin in Mississippi. Compared with conventional procedure, i.e., precipitation estimation error as fixed ratio of rain rates, the proposed framework provides more realistic quantification of precipitation estimation error and offers improved uncertainty assessment of the error propagation into hydrologic simulation. Further study shows that the radar rainfallgenerated streamflow sequences are consistently contained by the uncertainty bound of satellite rainfall generated streamflow at the 95% confidence interval.