A document by Larisa Tarasova. Click on the document to view its contents.

Stochastic precipitation generators (SPGs) are often used to produce synthetic precipitation series for water resource management. Typically, an SPG assumes a stationary climate. We present an hourly precipitation generation algorithm for non-stationary conditions informed by the Global Climate Model (GCM) forecasted average monthly temperature (AMT). The physical basis for precipitation formation is considered explicitly in the design of the algorithm using hourly Pressure Change Events (PCE) to define the relationship between hourly precipitation and AMT. The algorithm consists of a multi-variable Markov Chain and a moving window driven by time, temperature, and pressure change. We demonstrate the methodology by generating a 100-year, continuous, synthetic hourly precipitation time series using GCM AMT projections for the Northeast US. When compared with historical observations, the synthetic results suggest that future precipitation in this region will be more variable, with more frequent mild events and fewer but intensified extremes, especially in warm seasons. The synthetic time series suggests that there will be less precipitation in the summers, while winters will be wetter, consistent with other research on climate change projections for the northeast US. This SPG provides physically plausible weather ensembles for water resource studies involving climate change.

Climate change, inadequate maintenance, and aging beyond the design life increase the probability of dam failure. Dam failures can have significant social, financial, and environmental impacts. Financial losses can extend beyond infrastructure replacement costs, with cascading effects in multiple sectors such as electricity, transportation, water supply, and environmental services. The existing dam hazard classifications in the United States do not formally characterize “hazard hotspots” considering these impacts. Given that there are over 90,000 dams with different states of disrepair, maintenance, and budgetary constraints, a better way to rank their potential hazard and allocate resources for risk mitigation is needed. We present an approach that is scalable over many regions for rapidly assessing the magnitude and exposure of a dam failure for a preliminary ranking of the priority areas of concern. The estimation of the consequences of a dam failure including financial losses, affected critical infrastructure, and population is addressed using publicly available dam break and consequence tools and national infrastructure datasets. Dams can be ranked using seven criteria following the Analytical Hierarchical Process. The application of the framework is demonstrated with dams in the Cumberland River Basin. The main barrier to applying this approach at a national scale is the estimation of the inundation area upon dam failure, and we outline a strategy to implement it. The importance of increasing the resilience of dams is becoming more critical given the increasing interest in hydropower as a renewable energy source in the face of climate change.

A novel Bayesian Hierarchical Network Model (BHNM) for ensemble forecasts of daily streamflow that uses the spatial dependence induced by the river network topology and hydrometeorological variables from the upstream contributing area between station gauges is presented. Model parameters are allowed to vary with time as functions of selected covariates for each day. Using the network structure to incorporate flow information from upstream gauges and precipitation from the immediate contributing area as covariates allows one to model the spatial correlation of flows simultaneously and parsimoniously. An application to daily monsoon period (July-August) streamflow at four gauges in the Narmada basin in central India for the period 1978 – 2014 is presented. The covariates include daily streamflow from upstream gauges or from the gauge above of the upstream gauges depending on travel times and daily, 2-day, or 3-day precipitation from the area between two stations. The model validation indicates that the model is highly skillful relative to climatology and relative to a null-model of linear regression. We applied the BHNM out of sample to two high flooding years. High skill in both the timing and magnitude of the events is demonstrated.

We developed a novel Bayesian Hierarchical Network Model (BHNM) for daily streamflow, which uses the spatial dependence induced by the river network topology, and average daily precipitation from the upstream contributing area between station gauges. In this, daily streamflow at each station is assumed to be distributed as Gamma distribution with temporal non-stationary parameters. The mean and standard deviation of the Gamma distribution for each day are modeled as a linear function of suitable covariates. The covariates include daily streamflow from upstream gauges or from the gauge above of the upstream gauges depending on the travel times, and daily, 2-day, or 3-day precipitation from the area between two stations that attempts to reflect the antecedent land conditions. Intercepts and slopes of the mean and standard deviation parameters are modeled as a Multivariate Normal distribution (MVN) to capture their dependence structure. To ensure that the covariance matrix of MVN is positive definite, it is model as an Inverse Wishart distribution. Non-informative priors for each parameter were considered. Using the network structure in incorporating flow information from upstream gauges and precipitation from the immediate contributing area as covariates, enables to capture the spatial correlation of flows simultaneously and parsimoniously. The posterior distribution of the model parameters and, consequently, the predictive posterior Gamma distribution of the daily streamflow at each station and for each day are obtained. The model is demonstrated by its application to daily summer (July-August) streamflow at 4 gauges in the Narmada basin network in central India for the period 1978 – 2014. The skill of the probabilistic forecast is carried out by rank histograms and the Continuous Ranked Probability Score (CRPS). The model validation indicates that the model is highly skillful relative to climatology and relative to a null-model of linear regression. The forecasts present an adequate spread of uncertainty and non-bias. Since flooding is of major concern in this basin, we applied the BHNM in a cross-validated mode on two high flooding years – in that, the model was fitted on other years, and forecasts were made for the dropped-out high flooding year. The skill of the model in forecasting the high flood events was very good across the network – in both the timing and magnitude of the events. The model will be of immense help to policy makers in risk-based flood mitigation. The BHNM framework is general in nature and can be applied to any river network with other covariates as appropriate.

Coastal areas are highly vulnerable to flooding, due to hydrological extreme events such heavy rainfalls and/or storm surges which are supposed to be increasing in the next future due to the emission in atmosphere of anthropogenic greenhouse gases. In this study, in order to assess the future hydraulic risk in coastal regions, as well as, to identify optimal defense/adaptation policies, a risk analysis model is developed to calculate the present day and future flood risk, accounting for climate change uncertainties and mitigation measures. Such model juxtaposes a number of coupled/nested models as: a) a stacking daily rainfall downscaling model which combines simulations from multiple predictive models, as Random Forest, extreme gradient boosting and Non-homogeneous Hidden Markov Model (NHMM) (Cioffi et al. 2018); b) a Bivariate Point Process model (BPPM) (Zheng et al., 2014) that calculates Joint probability density function between extreme daily rainfall amount and daily extreme storm tide depth; c) a simulation-optimization model - in which multi-objective GA optimization model (Deb et al., 2002) and 2D hydraulic model are combined (Cioffi et al. 2018) - calculates sets of Pareto optimal solutions which are obtained by defining two optimality criteria consisting in: minimizing both the cost of the flood defense infrastructure system and the flooding hydraulic risk. ; d) a mathematical decision model which is aimed to identify the best policies of mitigation of hydraulic risk and the timing, taking into account the uncertainties in hydrological extreme event predictions. The risk analysis model is applied to the study case of Mazzocchio area which is the most depressed area (about 10000 ha) within the Pontinia Plain, a large reclamation region in the south of Lazio (Italy), particularly vulnerable to extreme events - as extreme rainfall amount and sea level rise due to storm surge at the sea outfall of the river- which in the past caused the crisis of hydraulic network system with flooding of large areas and collapse of levees. XXI Century projections of daily rainfall amount and sea level for the RCP 8.5-IPCC scenarios were performed using ensemble of 35 GCM simulations (CESM1 CAM5 BGC 20C + RCP8.5 Large Ensemble) (Kay et al., 2015).

Emerging parametric insurance products targeted at regional governments consider an index of flooding as the instrument for payoff and rate setting. Inundation extent from satellite remote sensing may provide a more direct measure of flood risk in this context than hydraulic modeling of flow and inundation. Here, we examine satellite-based fractional inundated area as a proxy for flood impact that can be used for index insurance payment at a regional scale. Typical methods for estimating return periods from unbounded distributions such as the GEV (generalized extreme value distribution) are not appropriate for fractional flooded area, which is bounded by 0 and 1. Here we examine alternative bounded distributions (2 parameter and a 4 parameter Beta) to estimate return periods and quantify uncertainty using a bootstrap sampling procedure for the short duration satellite record of fractional flooded area. We consider two examples with distinct flood dynamics i) a country (Bangladesh) where a flood can cover the majority of the land surface, and ii) a river basin (the Rio Salado basin in Argentina) where the worst flood covered only a modest fraction of the watershed. We explore how a parametric insurance policy based on fractional flooded area could be priced based on a typical approach used in the industry, that accounts for uncertainty for small sample estimation. Our exploratory approach to model selection illustrates how estimating the uncertainty price influences insurance contract pricing and is important to consider the choice of distribution beyond just the traditional measures of goodness of fit.

Urban pluvial flooding caused by extreme rainfall has been increasing globally, thus exacerbating loss of life and damage to property. Accurate and updated real-time forecasts are critical needs for urban flooding response and defense. Big data has opened new avenues for inundation depth prediction in complex urban settings. In this study, a new method for pluvial flood classification was proposed for an inundation depth change index (IDCI) by dividing floods into three types: pluvial persistent floods (PPFs), pluvial normal floods (PNFs) and pluvial flash floods (PFFs). Prediction models are identified for the three flood types using data from a network of sensors (109 rainfall stations and 80 flood water depth stations) in Shenzhen, China. The results show that backpropagation neural networks (BPNNs) and long short-term memory (LSTM) exhibit good performance in depth prediction but are not significantly different from one another. In addition, PPFs require a longer rainfall sequence to obtain a better forecast. The Nash-Sutcliffe efficiency (NSE) of the depth prediction results at all stations is 0.86. The prospects for generalizing this approach and its usage are discussed.