Distributed Hydrologic Modeling for Streamflow Prediction at Ungauged Basins

Hydrologic modeling and streamflow prediction of ungauged basins is an unsolved scientific problem as well as a policy-relevant science theme emerging as a major challenge to the hydrologic community. One way to address this problem is to improve hydrologic modeling capability through the use of spatial data and spatially distributed physically based models. This dissertation is composed of three papers focused on 1) the use of spatially distributed hydrologic models with spatially distributed precipitation inputs, 2) advanced multi-objective calibration techniques that estimate parameter uncertainty and use stream gauge and temperature data from multiple locations, and 3) an examination of the relationship between high-resolution soils data and streamflow recession for use in a priori parameter estimation in ungauged catchments. This research contributes to the broad quest to reduce uncertainty in predictions at ungauged basins by integrating developments of innovative modeling techniques with analyses that advance our understanding of natural systems

Effective and efficient algorithm for multiobjective optimization of hydrologic models

Practical experience with the calibration of hydrologic models suggests that any single-objective function, no matter how carefully chosen, is often inadequate to properly measure all of the characteristics of the observed data deemed to be important. One strategy to circumvent this problem is to define several optimization criteria (objective functions) that measure different (complementary) aspects of the system behavior and to use multicriteria optimization to identify the set of nondominated, efficient, or Pareto optimal solutions. In this paper, we present an efficient and effective Markov Chain Monte Carlo sampler, entitled the Multiobjective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm, which is capable of solving the multiobjective optimization problem for hydrologic models. MOSCEM is an improvement over the Shuffled Complex Evolution Metropolis (SCEM-UA) global optimization algorithm, using the concept of Pareto dominance (rather than direct single-objective function evaluation) to evolve the initial population of points toward a set of solutions stemming from a stable distribution (Pareto set). The efficacy of the MOSCEM-UA algorithm is compared with the original MOCOM-UA algorithm for three hydrologic modeling case studies of increasing complexity

Integrated High-Resolution Modeling for Operational Hydrologic Forecasting

Current advances in Earth-sensing technologies, physically-based modeling, and computational processing, offer the promise of a major revolution in hydrologic forecasting—with profound implications for the management of water resources and protection from related disasters. However, access to the necessary capabilities for managing information from heterogeneous sources, and for its deployment in robust-enough modeling engines, remains the province of large governmental agencies. Moreover, even within this type of centralized operations, success is still challenged by the sheer computational complexity associated with overcoming uncertainty in the estimation of parameters and initial conditions in large-scale or high-resolution models. In this dissertation we seek to facilitate the access to hydrometeorological data products from various U.S. agencies and to advanced watershed modeling tools through the implementation of a lightweight GIS-based software package. Accessible data products currently include gauge, radar, and satellite precipitation; stream discharge; distributed soil moisture and snow cover; and multi-resolution weather forecasts. Additionally, we introduce a suite of open-source methods aimed at the efficient parameterization and initialization of complex geophysical models in contexts of high uncertainty, scarce information, and limited computational resources. The developed products in this suite include: 1) model calibration based on state of the art ensemble evolutionary Pareto optimization, 2) automatic parameter estimation boosted through the incorporation of expert criteria, 3) data assimilation that hybridizes particle smoothing and variational strategies, 4) model state compression by means of optimized clustering, 5) high-dimensional stochastic approximation of watershed conditions through a novel lightweight Gaussian graphical model, and 6) simultaneous estimation of model parameters and states for hydrologic forecasting applications. Each of these methods was tested using established distributed physically-based hydrologic modeling engines (VIC and the DHSVM) that were applied to watersheds in the U.S. of different sizes—from a small highly-instrumented catchment in Pennsylvania, to the basin of the Blue River in Oklahoma. A series of experiments was able to demonstrate statistically-significant improvements in the predictive accuracy of the proposed methods in contrast with traditional approaches. Taken together, these accessible and efficient tools can therefore be integrated within various model-based workflows for complex operational applications in water resources and beyond

Structural Best Management Practices (BMPs) and hydrological effects modelling using swat for urban watershed

Orientador: Prof. Dr. Cristovao V.S. FernandesDissertação (mestrado) - Universidade Federal do Paraná, Setor de Tecnologia, Programa de Pós-Graduação em Engenharia de Recursos Hídricos e Ambiental. Defesa : Curitiba, 15/03/2019Inclui referências: p. 128-141Resumo: As Best Management Practices (BMPs) têm sido usadas como solução para mitigação de condições de pós-desenvolvimento em bacias urbanas e rurais. Estes dispositivos regulam vazões e volumes, além de capturar poluentes do escoamento superficial usando vários mecanismos. Estes dispositivos têm sido estudados e seu uso disseminado em vários países. Concomitantemente, o melhoramento de modelos de transporte e destinação de constituintes para investigar os efeitos, algoritmos para otimizar a busca por locais ótimos de instalação e facilitação da avaliação de entradas e saídas trouxe à luz vários desafios no que tange a modelagem dos fenômenos, incluindo a seleção de escalas de dimensão e tempo adequadas à representação dos fenômenos. A revisão de literatura demonstra uma fronteira clara entre usar inputs massivos de dados e computação exaustiva em modelos para descrição detalhada dos processos ou a adoção de abordagens mais simplificadas que capturem áreas maiores a custos menores de levantamento de dados. Neste estudo o Soil and Water Assessment Tool (SWAT) é utilizado como solução harmônica para modelagem em bacias com usos do solo mistos. Para vencer os desafios acima citados, BMPs são tratadas como zonas de recarga, isto é, zonas com Números de Curva (CN) menores. A localização destes dispositivos no modelo é realizada utilizando critérios consolidados de viabilidade através de ferramentas já desenvolvidas. Quatro cenários de redução percentual são utilizados para avaliação das melhoras de fluxo nas escalas da Hydrological Response Unit (HRU), subbacia e curso do rio(reach): 10%, 30%, 50% e 70%. As mudanças foram avaliadas na escala diária e anual, usando aplicações desenvolvidas em Python para automatizar a parametrização do modelo e a entrada e saída de dados. O estudo foi bem-sucedido em conceber a geração de múltiplos cenários, assim como em produzir ferramentas que auxiliem a entrada e saída de dados. Os resultados demonstram que a criação de zonas de recarga é mais eficaz em regiões onde há mais capacidade de retenção do solo. Do contrário, a redução do escoamento superficial tende a chegar em um limite, a partir do qual não há mais roteamento do escoamento superficial. Em HRUs e subbacias onde as condições de solo são favoráveis, a dinâmica de roteamento superficial e subsuperficial é modificada, fazendo com que a recarga dos aquíferos aumente e as recessões sejam mais lentas. Em geral, não são visíveis efeitos na escala da subbacia e no curso principal do rio, uma vez que muito do escoamento superficial é roteado como escoamento lateral ou fluo de subsuperfície. Além disso, a superposição dos efeitos para o resto da bacia é muito pequena na escala diária. Palavras-chave: SWAT. Bacias Urbanas. Python. Best Management Practices Hidrologia.Abstract: Best Management Practice (BMP) devices have been employed as a solution for both agricultural and urban watershed post-development effect mitigation. These devices regulate flow and capture runoff pollutants using various mechanisms. Such devices have been studied and its use disseminated in several countries. Concurrently, the enhancement of pollutant fate and transport models to assess the effects, search for optimal locations and facilitate inputs has brought to light several challenges concerning the modelling of physical phenomena, especially the one related to selecting time and size scales for adequate representation. The literature revision demonstrates that a clear boundary between using massive data inputs and computation-exhaustive models for thorough process description or more simplified approaches that capture larger areas at a more affordable data cost has limited the comprehension and description of BMP hydrological processes at the subbasin and watershed scale. In this study, SWAT is used a harmonic solution for modelling mixed land-use watersheds. To overcome the challenges stated, BMPs are treated as recharge - lower Curve Number (CN) zones, in feasible scenarios generated using an pre-built-tool and consolidated feasibility topographic, hydrological and space-distribution features. Four scenarios were generated: 10, 30, 50 70% CN reductions were tested and evaluated at the daily HRU/subbasin and subbasin yearly average scales, using developed applications for automating the parameter change and Input/output operations. The study was successful in automating the BMP scenario generation and multiple scenario generation as well as output data analysis. Results show that the creation of recharge zones is more effective at regions where more soil storage is available. Otherwise, runoff reduction tends to reach a limit. In HRUs and subbasins where soil conditions are favorable, the entire soil water and groundwater flow dynamics is modified, causing aquifer recharge to increase on average and recessions to be slower. Generally, no effects can be noticed at the subbasin o reach scale, as much of the runoff is also routed either as lateral flow or groundwater flow. The superposition of such effects to the rest of the watershed results in small differences at the daily scale. Keywords: SWAT. Urban watersheds. Python. Best Management Practices. Hydrology

TOWARDS IMPROVED HYDROLOGIC LAND SURFACE MODELLING: ENHANCED MODEL IDENTIFICATION AND INTEGRATION OF WATER MANAGEMENT

Large-scale hydrological models are essential tools for addressing emerging water security challenges. They enable us to understand and predict changes in water cycle at river-basin, continental, and global scales. This thesis aimed to improve ‘land surface models’ for large-scale hydrological modelling applications. Specifically, the research contributions were made across four fronts: (1) improving the conventional procedure for parameter identification of hydrological processes by using new sources of remotely-sensed data in addition to streamflow data within a multi-objective optimization and sensitivity analysis framework, (2) developing and integrating an efficient parameterization scheme for the representation of reservoirs into the land surface model for realistic representation of downstream flows, which can further feedback to land surface and atmospheric models, (3) demonstrating how precipitation uncertainty from multiple high-resolution precipitation products influences the performance of a land-surface based hydrological model, and (4) developing an enhanced and comprehensive large-scale hydrologic model for a complex and heavily regulated watershed. The analyses and results of this thesis illuminated important issues and their solutions in large-scale hydrological modelling. First, the multi-objective optimization and sensitivity analysis approach using multiple state and flux variables and performance criteria enables robust model parameterization and lessens issues around parameter equifinality in the highly-parameterized land surface models. Second, the dynamic parameterization of reservoir operation, based on multiple storage zones and reservoir release targets, improves the simulation of reservoir storage dynamics and downstream release, and subsequently, significantly improves the fidelity of land surface models when modeling managed basins. Third, there is a critical need for a rigorous evaluation of precipitation datasets widely used for forcing land surface models. The datasets investigated here showed considerable discrepancies, bringing their utility for land surface modelling into question. Fourth, effective parameterization and calibration of land surface models is critically important, particularly in large, complex, and highly-regulated basins

A Pareto-Based Sensitivity Analysis and Multiobjective Calibration Approach for Integrating Streamflow and Evaporation Data

Evaporation is gaining increasing attention as a calibration and evaluation variable in hydrologic studies that seek to improve the physical realism of hydrologic models and go beyond the long-established streamflow-only calibration. However, this trend is not yet reflected in sensitivity analyses aimed at determining the relevant parameters to calibrate, where streamflow has traditionally played a leading role. On the basis of a Pareto optimization approach, we propose a framework to integrate the temporal dynamics of streamflow and evaporation into the sensitivity analysis and calibration stages of the hydrologic modeling exercise, here referred to as “Pareto-based sensitivity analysis” and “multiobjective calibration.” The framework is successfully applied to a case study using the Variable Infiltration Capacity (VIC) model in three catchments located in Spain as representative of the different hydroclimatic conditions within the Iberian Peninsula. Several VIC vegetation parameters were identified as important to the performance estimates for evaporation during sensitivity analysis, and therefore were suitable candidates to improve the model representation of evaporative fluxes. Sensitivities for the streamflow performance, in turn, were mostly driven by the soil and routing parameters, with little contribution from the vegetation parameters. The multiobjective calibration experiments were carried out for the most parsimonious parameterization after a comparative analysis of the performance gains and losses for streamflow and evaporation, and yielded optimal adjustments for both hydrologic variables simultaneously. Results from this study will help to develop a better understanding of the trade-offs resulting from the joint integration of streamflow and evaporation data into modeling frameworks.ALHAMBRA cluster (http://alhambra. ugr.es) of the University of GranadaProject P20_00035, funded by the FEDER/ Junta de Andalucía-Consejería de Transformación Económica, Industria, Conocimiento y Universidades, the project CGL2017-89836-RThe Spanish Ministry of Economy and CompetitivenessEuropean Community Funds (FEDER)The project PID2021- 126401OB-I00MCIN/ AEI/10.13039/501100011033/FEDER Una manera de hacer Europa and the project LifeWatch-2019-10-UGR-01 funded by FEDER/Ministerio de Ciencia e InnovaciónThe Ministry of Education, Culture and Sport of Spain through an FPU Grant (reference FPU17/02098)Aid for Research Stays in the Hydrology and Quantitative Water Management Group of Wageningen University (reference EST19/00169)Universidad de Granada/CBU

Comparing multi-objective optimization techniques to calibrate a conceptual hydrological model using in situ runoff and daily GRACE data

Hydrological models are necessary tools for simulating the water cycle and for understanding changes in water resources. To achieve realistic model simulation results, real-world observations are used to determine model parameters within a “calibration” procedure. Optimization techniques are usually applied in the model calibration step, which assures a maximum similarity between model outputs and observations. Practical experiences of hydrological model calibration have shown that single-objective approaches might not be adequate to tune different aspects of model simulations. These limitations can be as a result of (i) using observations that do not sufficiently represent the dynamics of the water cycle, and/or (ii) due to restricted efficiency of the applied calibration techniques. To address (i), we assess how adding daily Total Water Storage (dTWS) changes derived from the Gravity Recovery And Climate Experiment (GRACE) as an extra observations, besides the traditionally used runoff data, improves calibration of a simple 4-parameter conceptual hydrological model (GR4J, in French: mod`ele du G´enie Rural `a 4 param`etres Journalier) within the Danube River Basin. As selecting a proper calibration approach (in ii) is a challenging task and might have significant influence on the quality of model simulations, for the first time, four evolutionary optimization techniques, including the Non-dominated Sorting Genetic Algorithm II (NSGA-II), the Multi-objective Particle Swarm Optimization (MPSO), the Pareto Envelope-Based Selection Algorithm II (PESA-II), and the Strength Pareto Evolutionary Algorithm II (SPEA-II) along with the Combined objective function and Genetic Algorithm (CGA) are tested to calibrate the model in (i). A number of quality measures are applied to assess cardinality, accuracy, and diversity of solutions, which include the Number of Pareto Solutions (NPS), Generation Distance (GD), Spacing (SP), and Maximum Spread (MS). Our results indicate that according toMS and SP, NSGA-II performs better than other techniques for calibrating GR4J using GRACE dTWS and in situ runoff data. Considering GD as a measure of efficiency, MPSO is found to be the best technique. CGA is found to be an efficient method, while considering the statistics of the GR4J’s 4 calibrated parameters to rank the optimization techniques. The Nash-Sutcliffe model efficiency coefficient is also used to assess the predictive power of the calibrated hydrological models, for which our results indicate satisfactory performance of the assessed calibration experiments

Model Calibration in Watershed Hydrology

Hydrologic models use relatively simple mathematical equations to conceptualize and aggregate the complex, spatially distributed, and highly interrelated water, energy, and vegetation processes in a watershed. A consequence of process aggregation is that the model parameters often do not represent directly measurable entities and must, therefore, be estimated using measurements of the system inputs and outputs. During this process, known as model calibration, the parameters are adjusted so that the behavior of the model approximates, as closely and consistently as possible, the observed response of the hydrologic system over some historical period of time. This Chapter reviews the current state-of-the-art of model calibration in watershed hydrology with special emphasis on our own contributions in the last few decades. We discuss the historical background that has led to current perspectives, and review different approaches for manual and automatic single- and multi-objective parameter estimation. In particular, we highlight the recent developments in the calibration of distributed hydrologic models using parameter dimensionality reduction sampling, parameter regularization and parallel computing

Measurement and modeling of stormwater from small suburban watersheds in Vermont

Despite decades of U.S. water quality management efforts, over half of assessed waterbody units were threatened or impaired for designated uses in the most recent assessments, with urban runoff being a leading contributor to those impairments. This cumulative research explores several aspects of urban runoff dynamics through a combination of field study and modeling. Stormwater ponds are ubiquitous in developed landscapes due to their ability to provide multiple forms of treatment for stormwater runoff. However, evolving design goals have reduced the applicability of much of the early work that was done on pond effectiveness. In this study, we instrumented a recently constructed detention pond in Burlington, VT, USA. Flow gaging demonstrated that the pond achieved a 93% reduction in event peak flow rates over the monitoring period. Storm sampling showed that the pond significantly reduced total (TN) (1.45 mg/L median influent, 0.93 mg/L median effluent, p \u3c 0.001) and total phosphorus (TP) (0.498 mg/L median influent, 0.106 mg/L median effluent, p \u3c 0.001) concentrations over the events sampled. A loading analysis estimated the TN and TP removal efficiencies for the pond to be 23% and 77% respectively. Lastly, temperature data collected from the pond showed that during the summer the pond accumulates considerable heat energy. This study adds to the body of literature on detention pond performance, and raises concerns about the extensive use of stormwater ponds in watersheds where thermal stress is a concern. EPA SWMM is a widely used urban hydrologic, hydraulic and water quality model, though its application can be limited due to its deterministic nature, high dimensional parameter space, and the resulting implications for modelling uncertainty. In this work, I applied a global sensitivity analysis (SA) and evolutionary strategies (ES) calibration to SWMM to produce model predictions that account for parameter uncertainty in a headwater tributary case study in South Burlington, VT, USA. Parameter sensitivity was found to differ based on model structure, and the ES approach was generally successful at calibrating selected parameters, although less so as the number of concurrently varying parameters increased. A watershed water quality analysis using the calibrated model suggested that for different events in the record, the stream channel was alternately a source and a sink for sediment and nutrients, based on the predicted washoff loads and the measured loads from the stream sampling stations. These results add to the previous work on SWMM SA, auto-calibration, and parameter uncertainty assessment. Lastly, given the extent of eutrophication impairment in the U.S., I compared TN and TP data collected in these original works with national and regional datasets. TN concentrations sampled in this work were generally commensurate with values reported elsewhere, however TP data were not. Drainage area attributes and an event based rainfall runoff analysis of the study catchments provided circumstantial support for the idea that runoff from lawns is driving the high TP loads in Englesby Brook. The role of pet wastes is considered as a potentially fruitful area for further research