Uncertainty Modeling in The Assessment of Climate Change Impacts on Water Resources Management

Climate change has significant impacts on water resource systems. The objective of this study is to assess climate change impacts on water resource management. The methodology includes (a) the assessment of uncertainty introduced by choice of precipitation downscaling methods; (b) uncertainty assessment and quantification of the impact of climate change on projected streamflow; and (c) uncertainty in and impact of climate change on the management of reservoirs used for hydropower production. The assessment is conducted for two future time periods (2036 to 2065 and 2066 to 2095). The study area, Campbell River basin, British Columbia, Canada, consists of three reservoirs (Strathcona, Ladore and John Hart). A new multisite statistical downscaling method based on beta regression (BR) is developed for generating synthetic precipitation series, which can preserve temporal and spatial dependence along with other historical statistics (e.g. mean, standard deviation). To account for different uncertainty sources, four global climate models (GCMs), three greenhouse gas emission scenarios (RCPs), six downscaling models (DSMs), are considered, and the differences in projected variables of interest are analyzed. For streamflow generation a hydrologic model is used. The results show that the downscaling models contribute the highest amount of uncertainty to future streamflow predictions when compared to the contributions by GCMs or RCPs. It is also observed that the summer (June, July & August) and fall (September, October & December) flows into Strathcona dam (British Columbia) will decrease, while winter (December, January & February) flows will increase in both future time periods. In addition, the flow magnitude becomes more uncertain for higher return period flooding events in the Campbell River system under climate change than the low return period flooding events. To assess the climate change impacts on reservoir operation, in this study a system dynamics model is used for reservoir flow simulation. Results from system dynamics model show that as the inflow decreases in summer and fall, it also affects reservoir release and power production. It is projected that power production from downstream reservoirs (LDR & JHT) will decrease more drastically than the upstream reservoir (SCA) in both future time periods considered in this study

An Integrated Framework for Modeling and Mitigating Water Temperature Impacts in the Sacramento River

Increasing demands on the limited and variable water supply across the West can result in insufficient streamflow to sustain healthy fish habitat. In addition, construction of dams and diversions along rivers for the purpose of storing and distributing the limited supply of water can further deteriorate natural flow regimes and, often, obstruct important migratory pathways for cold water fish reproduction and development. The thermal impacts on the ecology of river ecosystems have been well documented, yet there is no comprehensive modeling framework in place for skillfully modeling climate-related impacts. In regulated systems, such as the Sacramento River system, these impacts are an interaction of volume and temperature of water release from the reservoir and the subsequent exchange with the environment downstream. We develop an integrated framework for modeling and mitigating water temperature impacts and demonstrate it on the Sacramento River system. The approach has four broad components that can be coupled to produce decision tools towards efficient management of water resources for stream temperature mitigation: (i) a suite of statistical models for modeling stream temperature attributes using hydrology and climate variables of critical importance to fish habitat; (ii) a reservoir thermal model for modeling the thermal structure and, consequently, the water release temperature, (iii) a stochastic weather generator to simulate weather sequences consistent with long-range (e.g., seasonal) outlooks; and, (iv) a set of decision rules (i.e., rubric) for reservoir water releases in response to outputs from the above components. The statistical stream temperature models and stochastic weather generators are coupled to the reservoir thermal model and validated for their ability to reproduce observed stream temperature variability along with characterizing the uncertainty at a compliance point downstream. We develop and validate a Decision Support Tool (DST) developed by coupling the stream temperature forecast model with the stochastic weather generator to the decision rubric. The DST incorporates forecast uncertainties and reservoir operating options to help mitigate stream temperature impacts for fish habitat, while efficiently using the reservoir water supply and cold pool storage. The use of these coupled tools in simulating impacts of future climate on stream temperature variability is also demonstrated

Application of a K-Nearest Neighbour weather generator for simulation of historical and future climate variables in the Upper Thames River basin

Climate change has the potential to significantly alter the hydrologic cycle, changing the frequency and intensity of precipitation events in an area. It is necessary to quantify these effects to effectively manage water resources in the future. Atmosphere-Ocean coupled Global Circulation Models (AOGCMs), often used in climate change research, have spatial resolutions that are too large to capture the local climate characteristics of a watershed. As a result, several downscaling tools have been developed, including stochastic weather generators. A methodology for the simulation of historical and future climate data using a nonparametric K-Nearest Neighbour block resampling weather generator with perturbation is presented (KnnCAD Version 4). The proposed approach is illustrated using a case study of the Upper Thames River basin in Ontario, Canada. KnnCAD V4 is shown to effectively reproduce the historical climate and can produce future climate change scenarios based on AOGCM data

Monthly river flow simulation with a joint conditional density estimation network

River flow synthesizing and downscaling are required for the analysis of risks associated with water resources management plans and for regional impact studies of climate change. This paper presents a probabilistic model that synthesizes and downscales monthly river flow by estimating the joint distribution of flows of two adjacent months conditional on covariates. The covariates may consist of lagged and aggregated flow variables (synthesizing), exogenous climatic variables (downscaling), or combinations of these two types. The joint distribution is constructed by connecting two marginal distributions in terms of copulas. The relationship between covariates and distribution parameters is approximated by an artificial neural network, which is calibrated using the principle of maximum likelihood. Outputs of the neural network yield parameters of the joint distribution. From the estimated joint distribution, a conditional distribution of river flow of current month given the estimation of the previous month can be derived. Depending on the different types of covariate information, this conditional distribution may serve as the ‘‘engine’’ for synthesizing or downscaling river flow sequences. The idea of the proposed model is illustrated using three case studies. The first case deals with synthetic data and shows that the model is capable of fitting a nonstationary joint distribution. Second, the model is utilized to synthesize monthly river flow at four sample stations on the main stream of the Colorado River. Results reveal that the model reproduces essential evaluation statistics fairly well. Third, a simple illustrative example for river flow downscaling is presented. Analysis indicates that the model can be a viable option to downscale monthly river flow as well

Long-term-robust adaptation strategies for reservoir operation considering magnitude and timing of climate change: application to Diyala River Basin in Iraq

2020 Spring.Includes bibliographical references.Vulnerability assessment due to climate change impacts is of paramount importance for reservoir operation to achieve the goals of water resources management. This requires accurate forcing and basin data to build a valid hydrology model and assessment of the sensitivity of model results to the forcing data and uncertainty of model parameters. The first objective of this study is to construct the model and identify its sensitivity to the model parameters and uncertainty of the forcing data. The second objective is to develop a Parametric Regional Weather Generator (RP-WG) for use in areas with limited data availability that mimics observed characteristics. The third objective is to propose and assess a decision-making framework to evaluate pre-specified reservoir operation plans, determine the theoretical optimal plan, and identify the anticipated best timeframe for implementation by considering all possible climate scenarios. To construct the model, the Variable Infiltration Capacity (VIC) platform was selected to simulate the characteristics of the Diyala River Basin (DRB) in Iraq. Several methods were used to obtain the forcing data and they were validated using the Kling–Gupta efficiency (KGE) metric. Variables considered include precipitation, temperature, and wind speed. Model sensitivity and uncertainty were examined by the Generalized Likelihood Uncertainty Estimation (GLUE) and the Differential Evolution Adaptive Metropolis (DREAM) techniques. The proposed RP-WG was based on (1) a First-order, Two-state Markov Chain to simulate precipitation occurrences; (2) use of Wilks' technique to produce correlated weather variables at multiple sites with conservation of spatial, temporal, and cross correlations; and (3) the capability to produce a wide range of synthetic climate scenarios. A probabilistic decision-making framework under nonstationary hydroclimatic conditions was proposed with four stages: (1) climate exposure generation (2) supply scenario calculations, (3) demand scenario calculations, and (4) multi-objective performance assessment. The framework incorporated a new metric called Maximum Allowable Time to examine the timeframe for robust adaptations. Three synthetic pre-suggested plans were examined to avoid undesirable long-term climate change impacts, while the theoretical-optimal plan was identified by the Non-dominated Sorting Genetic Algorithm II. The multiplicative random cascade and Schaake Shuffle techniques were used to determine daily precipitation data, while a set of correction equations was developed to adjust the daily temperature and wind speed. The depth of the second soil layer caused most sensitivity in the VIC model, and the uncertainty intervals demonstrated the validity of the VIC model to generate reasonable forecasts. The daily VIC outputs were calibrated with a KGE average of 0.743, and they were free from non-normality, heteroscedasticity, and auto-correlation. Results of the PR-WG evaluation show that it exhibited high values of the KGE, preserved the statistical properties of the observed variables, and conserved the spatial, temporal, and cross correlations among the weather variables at all sites. Finally, risk assessment results show that current operational rules are robust for flood protection but vulnerable in drought periods. This implies that the project managers should pay special attention to the drought and spur new technologies to counteract. Precipitation changes were dominant in flood and drought management, and temperature and wind speed changes effects were significant during drought. The results demonstrated the framework's effectiveness to quantify detrimental climate change effects in magnitude and timing with the ability to provide a long-term guide (and timeframe) to avert the negative impacts

Essays on the Quantification and Propagation of Uncertainty in Climate Change Impact Assessments for Water Resource Systems

Sustainable water resources planning and management under climate change requires a proper treatment of uncertainties that emerge in an impacts analysis. A primary source of this uncertainty originates from the difficulties in projecting how anthropogenic greenhouse gas emissions will evolve over time and influence the climate system at regional and local scales. However, other sources of uncertainty, such as errors in modeling hydrologic response to climate and the influences of internal climate variability, compound the effects of climate change uncertainty and further obscure our understanding of water resources performance under future climate conditions. This work presents an approach to quantify the interactions, propagation, and relative contributions of different sources of uncertainty in a water resources impacts assessment under climate change. Hydrologic modeling uncertainty is addressed using Bayesian methods that can quantify both parametric and structural errors. Hydrologic uncertainties are propagated through an ensemble of climate projections to explore their joint uncertainty. A new stochastic weather generator is presented to develop a wide ensemble of climate projections that can extend beyond the limited range of change often afforded by global climate models and better explore climate risks. The weather generator also enables the development of multiple realizations of the same mean climate conditions, allowing an exploration of the effects of internal climate variability. The uncertainties from mean climate changes, internal climate variability, and hydrologic modeling errors are then integrated in two climate change analyses of a flood control facility and a multi-purpose surface reservoir system, respectively, to explore their separate and combined effect on future system performance. The primary goal of this work is to present methods that can better estimate the precision associated with future projections of water resource system performance under climate change, and through this provide information that can guide the development of adaptation strategies that are robust to these uncertainties

The Impact of Climate Change and Variability on Africa's Renewable Energy Development :A Hybrid Uncertainty Approach

This dissertation presents advanced methods that could be used to assess various impact of climate change to hydropower and reliability assessment of wind resource using alternative reservoir operation that maximizes the firm generation of integrated wind and hydropower. The first component of this study introduces a hybrid approach of risk based climate change impact assessment. This method combines uncertainties in historical climate variability with uncertainties in climate predictions to conduct more comprehensive climate change impact assessment on hydropower. Results from this study, illustrated in Zambezi and Congo River basins, indicate that the single basecase approach of delta-change technique substantially underestimates the potential impact of climate change. Particularly, assessments for water resource systems in areas with high natural hydroclimatic variability the combined effect natural variability and climate change is more pronounced. The second component utilizes the concept of Empirical Orthogonal Functions (EOFs) analysis technique to access the join spatio-temporal patterns of interannual variability hydropower generation between different power pools in Africa. EOF analysis of annual streamflow and hydropower generation was carried out followed by investigation of the resulting dominant spatial patterns to identify locations of existing and future potential hydropower sites which indicate a homogeneous or a heterogeneous pattern of variability. Results indicated a distinct out-of-phase pattern of variability between Southern and West African Power pools. Furthermore, the method was extended to conducted potential impact of climate change induced change in inter-annual variability. The third component presents a reliability assessment method of wind-hydropower integration. A water resources model combined with a single node power grid system model accompanied by a genetic algorithm solver is implemented to determine optimum operation strategy for each storage reservoir aiming at maximizing the 90th percentile power generation over the entire simulation period. This model is tested on the hydropower system in the Zambezi basin to demonstrate how storage reservoirs could be used to offset wind power intermittence in South Africa. Results show an increased level of wind penetration, a reduced level of coal power utilization and less cycling requirement in power system as a result of better regulation that is achieved through the combined operation.</p

Stochastic Weather Generator Based Ensemble Streamflow Forecasting

On a seasonal time scale, forecast centers of National Weather Service produce streamflow forecasts via a method called Ensemble Streamflow Prediction (ESP). In conjunction with the physically-based Sacramento Soil Moisture Accounting model (SAC-SMA), ESP uses historical weather sequences for the forecasting period starting from model\u27s current initial conditions, to produce ensemble streamflow. There are two major drawbacks of this method|(i) the ensembles are limited to the length of historical record thereby producing limited variability and (ii) incorporating seasonal climate forecasts such as El Niño Southern Oscillation (ENSO) is done by selecting a subset of historical sequences which further reduces the variability of streamflow forecasts. The need for alleviating these drawbacks motivates the proposed research. To this end, this research effort has two components (i) an improved multi-site stochastic weather generator and (ii) coupling it to the SAC-SMA model for ensemble streamflow forecasting. We enhanced the traditional K-nearest neighbor semi-parametric stochastic weather generator (SWG). In SWG the daily precipitation state (wet or dry) is modeled as a Markov Chain and the weather vector on a given day is simulated conditioned on the previous day\u27s precipitation state and weather vector and current day\u27s precipitation state. A K-nearest neighbor resampling approach is used to simulate from the conditional probability density function. Our improvements to this stochastic generator include (i) clustering the locations into climatologically homogeneous regions and applying the weather generator separately for each region and jointly to better capture the spatial heterogeneity and, (ii) modifying the resampling approach to incorporate probabilistic seasonal climate forecast. We tested this enhanced weather generator by applying it to daily weather sequences at 66 locations in the San Juan River Basin. The proposed method generates a rich variety of weather sequences capturing the distributional properties at all the locations and the spatial dependence. It also simulates consistent weather sequences conditioned on seasonal climate forecasts. The multi-site stochastic weather generator was coupled with the SAC-SMA model (WGESP) within NWS\u27s new Community Hydrologic Prediction System (CHPS) to produce ensemble streamflow forecast. Spring season ensemble forecasts at several lead times from Nov through Apr for the period 1981{2010 were made from WG-ESP and the traditional ESP for the San Juan River Basin. We show that the weather generator based ensemble produces a rich variability in the flows including extremes and a higher skill at long lead times. Especially, skill in wet year forecast was found to be higher than dry years. The flexible and robust framework provides many opportunities to further improve the ESP system in enabling increased skills at longer lead times that will be of immense help to water resources managers

Multimodel regression-sampling algorithm for generating rich monthly streamflow scenarios

This paper presents a multimodel regression-sampling algorithm (MRS) for monthly streamflow simulation. MRS is motivated from the acknowledgment that typical nonparametric models tend to simulate sequences exhibiting too close a resemblance to historical records and parametric models have limitations in capturing complex distributional and dependence characteristics, such as multimodality and nonlinear autocorrelation. The aim of MRS is to generate streamflow sequences with rich scenarios while properly capturing complex distributional and dependence characteristics. The basic assumptions of MRS include: (1) streamflow of a given month depends on a feature vector consisting of streamflow of the previous month and the dynamic aggregated flow of the past 12 months and (2) streamflow can be multiplicatively decomposed into a deterministic expectation term and a random residual term. Given a current feature vector, MRS first relates the conditional expectation to the feature vector through an ensemble average of multiple regression models. To infer the conditional distribution of the residual, MRS adopts the k-nearest neighbor concept. More precisely, the conditional distribution is estimated by a gamma kernel smoothed density of historical residuals inside the k-neighborhood of the given feature vector. Rather than obtaining the residuals from the averaged model only, MRS retains all residuals from all the original regression models. In other words, MRS perceives that the original residuals put together would better represent the covariance structure between streamflow and the feature vector. By doing so, the benefit is that a kernel smoothed density of the residual with reliable accuracy can be estimated, which is hardly possible in a single-model framework. It is the smoothed density that ensures the generation of sequences with rich scenarios unseen in historical record. We evaluated MRS at selected stream gauges and compared with several existing models. Results show that (1) compared with typical nonparametric models, MRS is more apt at generating sequences with richer scenarios and (2) in contrast to parametric models, MRS can reproduce complex distributional and dependence characteristics. Since MRS is flexible at incorporating different covariates, it can be tailored for other potential applications, such as hydrologic forecasting, downscaling, as well as postprocessing deterministic forecasts into probabilistic ones