Hamid Moradkhani

Hydrologic Remote Sensing and Land Surface Data Assimilation

Accurate, reliable and skillful forecasting of key environmental variables such as soil moisture and snow are of paramount importance due to their strong influence on many water resources applications including flood control, agricultural production and effective water resources management which collectively control the behavior of the climate system. Soil moisture is a key state variable in land surface–atmosphere interactions affecting surface energy fluxes, runoff and the radiation balance. Snow processes also have a large influence on land-atmosphere energy exchanges due to snow high albedo, low thermal conductivity and considerable spatial and temporal variability resulting in the dramatic change on surface and ground temperature. Measurement of these two variables is possible through variety of methods using ground-based and remote sensing procedures. Remote sensing, however, holds great promise for soil moisture and snow measurements which have considerable spatial and temporal variability. Merging these measurements with hydrologic model outputs in a systematic and effective way results in an improvement of land surface model prediction. Data Assimilation provides a mechanism to combine these two sources of estimation. Much success has been attained in recent years in using data from passive microwave sensors and assimilating them into the models. This paper provides an overview of the remote sensing measurement techniques for soil moisture and snow data and describes the advances in data assimilation techniques through the ensemble filtering, mainly Ensemble Kalman filter (EnKF) and Particle filter (PF), for improving the model prediction and reducing the uncertainties involved in prediction process. It is believed that PF provides a complete representation of the probability distribution of state variables of interests (according to sequential Bayes law) and could be a strong alternative to EnKF which is subject to some limitations including the linear updating rule and assumption of jointly normal distribution of errors in state variables and observation.

A Bayesian Framework for Probabilistic Seasonal Drought Forecasting

Seasonal drought forecasting is presented within a multivariate probabilistic framework. The standardized streamflow index (SSI) is used to characterize hydrologic droughts with different severities across the Gunnison River basin in the upper Colorado River basin. Since streamflow, and subsequently hydrologic droughts, are autocorrelated variables in time, this study presents a multivariate probabilistic approach using copula functions to perform drought forecasting within a Bayesian framework. The spring flow (April‐June) is considered as the forecast variable and found to have the highest correlations with the previous winter (January‐March) and fall (October‐December). Incorporating copula functions into the Bayesian framework, two different forecast models are established to estimate the hydrologic drought of spring given either the previous winter (first-order conditional model) or previous winter and fall (second-order conditional model). Conditional probability density functions (PDFs) and cumulative distribution functions (CDFs) are generated to characterize the significant probabilistic features of spring droughts. According to forecasts, the spring drought is more sensitive to the winter status than the fall status, which approves the results of prior correlation analysis. The 90% predictive bound of the spring-flow forecast indicates the efficiency of the proposed model in estimating the spring droughts. The proposed model is compared with the conventional forecast model, the ensemble streamflow prediction (ESP), and it is found that their forecasts are generally in agreement with each other. However, the forecast uncertainty of the new method is more reliable than the ESP method. The new probabilistic forecast model can provide insights to water resources managers and stakeholders to facilitate the decision making and developing drought mitigation plans.

Drought Analysis under Climate Change Using Copula

AbstractThe joint behavior of drought characteristics under climate change is evaluated using the copula method, which has recently attained popularity in the analysis of complex hydrologic systems with correlated variables. Trivariate copulas are applied, in this study, to analyze the major drought variables, including duration, severity, and intensity, in Oregon’s Upper Klamath River Basin. Among the variables, results show that duration severity exhibits the strongest correlation, whereas duration intensity exhibits the least correlation. The impact of climate change on future droughts is evaluated using five general circulation models (GCMs) under one emission scenario. Despite more intense extreme events that are expected to occur in most parts of the globe in the future, the results of this study show that the Upper Klamath River Basin in the Pacific Northwest will experience less intense droughts affected by climate change. Compared with historical events, an overall decrease in drought duration an...

Statistical Downscaling of Precipitation Using Machine Learning with Optimal Predictor Selection

Various methods have been proposed to downscale the coarse resolution general circulation model (GCM) climatological variables to the fine-scale regional variables; however, fewer studies have been focused on the selection of GCM predictors. Additionally, the results obtained from one downscaling technique may not be robust and the uncertainties related to the downscaling scheme are not realized. To address these issues, the writers employed independent component analysis (ICA) for predictor selection that determines spatially independent GCM variables. Cross-validation of the independent components is employed to find the predictor combination that describes the regional precipitation over the upper Willamette basin with minimum error. These climate variables, along with the observed precipitation, are used to calibrate three downscaling models: multilinear regression (MLR), support vector machine (SVM), and adaptive-network-based fuzzy inference system (ANFIS). The presented method incorporates several ...

Improved Bayesian multimodeling: Integration of copulas and Bayesian model averaging

Bayesian model averaging (BMA) is a popular approach to combine hydrologic forecasts from individual models and characterize the uncertainty induced by model structure. In the original form of BMA, the conditional probability density function (PDF) of each model is assumed to be a particular probability distribution (e.g., Gaussian, gamma, etc.). If the predictions of any hydrologic model do not follow certain distribution, a data transformation procedure is required prior to model averaging. Moreover, it is strongly recommended to apply BMA on unbiased forecasts, whereas it is sometimes difficult to effectively remove bias from the predictions of complex hydrologic models. To overcome these limitations, we develop an approach to integrate a group of multivariate functions, the so-called copula functions, into BMA. Here we introduce a copula-embedded BMA (Cop-BMA) method that relaxes any assumption on the shape of conditional PDFs. Copula functions have a flexible structure and do not restrict the shape of posterior distributions. Furthermore, copulas are effective tools in removing bias from hydrologic forecasts. To compare the performance of BMA with Cop-BMA, they are applied to hydrologic forecasts from different rainfall-runoff and land-surface models. We consider the streamflow observation and simulations for 10 river basins provided by the Model Parameter Estimation Experiment (MOPEX) project. Results demonstrate that the predictive distributions are more accurate and reliable, less biased, and more confident with small uncertainty after Cop-BMA application. It is also shown that the postprocessed forecasts have better correlation with observation after Cop-BMA application.

Long-Lead Water Supply Forecast Using Large-Scale Climate Predictors and Independent Component Analysis

Interest in water supply forecasting has grown prominently due to population growth and increasing demands for water. One important aspect of successfully managing the supply of water is accurate and reliable forecasts of seasonal streamflow volumes. Much of the streamflow in mountainous regions is a result of the collection of seasonal snowpack over the winter months and the melting of this snowpack over the spring and summer. However, there has been increasing pressure on operational agencies to issue longer-lead water supply forecasts that would be released in late fall or early winter preceding the runoff season. Longer-lead forecasts are difficult to make due to the uncertainty in future winter and spring climate conditions and the lack of snowpack information. During the late fall and early winter, large-scale oceanic and atmospheric information can provide insight into future climate conditions and spring runoff and have shown to be useful in developing long-lead forecast. In this study, statistica...

Improving Soil Moisture Profile Prediction With the Particle Filter-Markov Chain Monte Carlo Method

Satellite soil moisture estimates have received increasing attention over the past decade. This paper examines the applicability of estimating soil moisture states and soil hydraulic parameters through two particle filter (PF) methods: The PF with commonly used sampling importance resampling (PF-SIR) and the PF with recently developed Markov chain Monte Carlo sampling (PF-MCMC) methods. In a synthetic experiment, the potential of assimilating remotely sensed near-surface soil moisture measurements into a 1-D mechanistic soil water model (HYDRUS-1D) using both the PF-SIR and PF-MCMC algorithms is analyzed. The effects of satellite temporal resolution and accuracy, soil type, and ensemble size on the assimilation of soil moisture are analyzed. In a real data experiment, we first validate the Advanced Microwave Scanning Radiometer--Earth Observing System (AMSR-E) soil moisture products in the Oklahoma Little Washita Watershed. Aside from rescaling the remotely sensed soil moisture, a bias correction algorithm is implemented to correct the deep soil moisture estimate. Both the ascending and descending AMSR-E soil moisture data are assimilated into the HYDRUS-1D model. The synthetic assimilation results indicated that, whereas both updating schemes showed the ability to correct the soil moisture state and estimate hydraulic parameters, the PF-MCMC scheme is consistently more accurate than PR-SIR. For real data case, the quality of remotely sensed soil moisture impacts the benefits of their assimilation into the model. The PF-MCMC scheme brought marginal gains than the open-loop simulation in RMSE at both surface and root-zone soil layer, whereas the PF-SIR scheme degraded the open-loop simulation.

A regional Bayesian hierarchical model for flood frequency analysis

In this study, we propose a regional Bayesian hierarchical model for flood frequency analysis. The Bayesian method is an alternative to the traditional regional flood frequency analysis. Instead of relying on the delineation of implicit homogeneous regions, the Bayesian hierarchical method describes the spatial dependence in its inner structure. Similar to the classical Bayesian hierarchical model, the process layer of our model presents the spatial variability of the parameters by considering different covariates (e.g., drainage area, elevation, precipitation). Beyond the three classical layers (data, process, and prior) of the Bayesian hierarchical model, we add a new layer referred to as the “L-moments layer”. The L-moments layer uses L-moments theory to select the best-fit probability distribution based on the available data. This new layer can overcome the subjective selection of the distribution based on extreme value theory and determine the distribution from the data instead. By adding this layer, we can combine the merits of regional flood frequency and Bayesian methods. A standard process of covariates selection is also proposed in the Bayesian hierarchical model. The performance of the Bayesian model is assessed by a case study over the Willamette River Basin in the Pacific Northwest, U.S. The uncertainty of different flood percentiles can be quantified from the posterior distributions using the Markov Chain Monte Carlo method. Temporal changes for the 100-year flood percentiles are also examined using a 20- and 30-year moving window method. The calculated shifts in flood risk can aid future water resources planning and management.

Ensemble Combination of Seasonal Streamflow Forecasts

AbstractVarious hydrologic models with different complexities have been developed to represent the characteristics of river basins, improve streamflow forecasts such as seasonal volumetric flow predictions, and meet other demands from different stakeholders. Because no single hydrologic model is able to perfectly simulate the observed flow, multimodel combination techniques are developed to combine forecasts obtained from different models and to quantify the uncertainties with the goal of improving upon single-model performance. In this study, a comprehensive set of multimodel ensemble averaging techniques with varying complexities are investigated for operational forecasting over four river basins in the Western United States. Ensemble merging models are divided into three categories of simple, intermediate, and complex, and comparison is made between each class by using a bootstrap approach. Analysis suggests that model combination effectively improves most of the individual seasonal forecasts and can o...

Hydrologic Modeling in Dynamic Catchments: A Data Assimilation Approach

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.