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A review of parallel computing applications in calibrating watershed hydrologic models
Abstract
In recent decades, parallel computing has been increasingly applied to address the computational challenges of calibrating watershed hydrologic models. The purpose of this paper is to review these parallelization studies to summarize their contributions, identify knowledge gaps, and propose future research directions. These studies parallelized models based on either random-sampling-based algorithms or optimization algorithms and demonstrated considerable parallel speedup gain and parallel efficiency. However, the speedup gain/efficiency decreases as the number of parallel processing units increases, particularly after a threshold. In future, various combinations of hydrologic models, optimization algorithms, parallelization strategies, parallelization architectures, and communication modes need to be implemented to systematically evaluate a suite of parallelization scenarios for improving speedup gain, efficiency, and solution quality. A standardized suite of performance evaluation metrics needs to be developed to evaluate these parallelization approaches. Interactive multi-objective optimization algorithms and/or integrated sensitivity analysis and calibration algorithms are potential future research fields, as well.
... Um modelo é uma representação de algum objeto ou sistema de forma facilmente acessível e utilizável com o objetivo de entendê-lo e buscar suas respostas para diferentes entradas. Quanto mais complexo o sistema, mais desafiador e necessário será o modelo, como, por exemplo, os modelos hidrológicos (Asgari et al., 2022). Esses modelos são uma das ferramentas desenvolvidas cientificamente para melhor compreender e representar o comportamento das bacias hidrográficas e prever condições diferentes das observadas a partir de cenários propostos. ...
... Em geral, uma vantagem importante dos modelos hidrológicos é que vários cenários diferentes podem ser rapidamente investigados, muitos dos quais ainda não foram explorados em experimentos reais. Podem ainda ser mencionadas as vantagens associadas ao seu baixo custo, se compararmos o custo correspondente de uma investigação experimental Asgari et al., 2022). ...
... Nos modelos semiconceituais, os parâmetros utilizados são calibráveis, embora sejam aplicadas formulações para a descrição física do processo (Horton et al., 2022). Segundo Asgari et al. (2022) quando se trata de modelagem hidrossedimentológica, modelos de base física ganham destaque porque as equações representam os processos físicos envolvidos no evento. Ademais, com a valoração da informação espacial, o Sistema de Informação Geográfica (SIG) vem se destacando dentro da modelagem hidrológica e auxiliando na interface de programas e modelos. ...
As bacias hidrográficas são importantes unidades para a gestão dos recursos hídricos, mas devido as ações antrópicas essas unidades estão sendo impactadas, seja pelas mudanças climáticas, alterações na cobertura da terra, processos erosivos e entre outros. A transferência de água entre bacias surge como uma das alternativas para minimizar os efeitos dessas perturbações, especialmente, quando se trata de déficit hídrico na bacia receptora. Dentre as técnicas disponíveis para permitir a análise dos impactos das ações humanas nas bacias hidrográficas estão os modelos hidrológicos, tais como o modelo Soil and Water Assessment Tool (SWAT) que está sendo utilizado para diversas finalidades, dentre elas, na análise de transposição de bacias. Nesse contexto, o presente trabalho teve como objetivo realizar um levantamento literário sobre a temática da transposição de água entre bacias, abordando a aplicabilidade do modelo SWAT como ferramenta de suporte a decisão em estudos de simulação hidrológica quando a transferência de água entre bacias é considerada. Os resultados mostram que, apesar de pouco utilizado na avaliação dos impactos (positivos e negativos) causados pela transferência de água entre bacias, o modelo hidrológico SWAT demostrou bom desempenho quando aplicado isoladamente nesse tipo de simulação, bem como quando acoplado a outro modelo.
... High-performance computing (HPC) is a method for solving computational problems by simultaneously using multiple computing resources (Matloff, 2011). The technique has been widely researched and developed for calibrating gridded hydrological or agricultural system models and has shown promising results in reducing the simulation time for various models compared to conventional serialized computing (Tang et al., 2007;Zhang et al.,2016;Kim and Rye, 2019;Yin et al., 2020;Kim et al., 2021;Asgari et al., 2022;Hyun et al., 2022). Recently, HPC has also been applied to field-scale agricultural system models. ...
... A potential solution could be combining parallel computing with distributed computing to run computational tasks on multiple cores on different computers to provide a scalable HPC system for IO-bound and CPU-bound tasks (Dalcin et al., 2011;Asgari et al., 2022). For instance, Liu et al. (2016) demonstrated improved scalability and parallel performance for the SWAT model through a two-layer parallelization approach with a parallel-distributed computing setup. ...
... Li et al. (2022) developed a random-sampling-based auto-calibration script for the RZ-SHAW model to facilitate unsupervised automatic calibration. The script had a similar workflow as previous random--sampling-based model calibration studies, which includes parameter identification, parameter generation using random-sampling methods, model iteration with the generated parameters, and finally, selecting the parameters with the lowest error (Rouholahnejad et al., 2012;Zhang et al., 2016;Asgari et al., 2022). The script developed by Li et al. (2022) used the Sobol sequence as the random sampling method (provided by PyTorch, 2019) to generate the parameter sets. ...
The winter/spring season in cold climate regions has been recognized as a critical period for cropland nutrient loss and greenhouse gas emissions and has been predicted to be vulnerable to climate change. Agricultural system models serve as essential tools in anticipating potential outcomes. The RZ-SHAW model, combining the Simultaneous Heat and Water Model (SHAW) with the Root Zone Water Quality Model 2 (RZWQM2), comprehensively describes snow and soil freezing dynamics. Showing promising results in simulating winter frost dynamics it, however, remains computationally intensive. High-performance computing has been recognized as a promising approach to reducing simulation runtimes. A modularized parallel distributed high-performance computing (HPC) framework (RS-DPCF) with both multi-threading (MT) and multi-processing (MP) capability was developed for RZ–SHAW calibration and simulation. Two case studies demonstrated that the developed RS-DPCF program could significantly reduce RZ–SHAW calibration time, allow improved computation resource scalability, and perform field-scale simulations of overwintering conditions for multiple croplands across Canada. For the RZ–SHAW model, MT provided a parallel-computing efficiency superior to that of MP.
... Parallel computing aids in the calibration of hydrologic models by enabling the rapid exploration of this parameter space through the execution of several simulations with various parameter combinations, ultimately leading to the best possible calibration procedure. According to [43], the efficiency of hydrologic models may be accelerated up to a certain point by the number of parallel processing units. Using parallel computing, [44] was able to improve run time for the Soil and Water Assessment Tool (SWAT) by 28-35%. ...
... 4. To systematically evaluate a suite of parallelization scenarios for improving speedup gain, efficiency, and solution quality, different combinations of hydrologic models, optimization algorithms, parallelization strategies, parallelization architectures, and communication modes must be implemented [43]. If this can be accomplished using various machine-learning techniques, it would have a significant impact on the field of hydrology. ...
The application of Machine Learning (ML) to hydrologic modeling is fledgling. Its applicability to capture the dependencies on watersheds to forecast better within a short period is fascinating. One of the key reasons to adopt ML algorithms over physics-based models is its computational efficiency advantage and flexibility to work with various data sets. The diverse applications, particularly in emergency response and expanding over a large scale, demand the hydrological model in a short time and make researchers adopt data-driven modeling approaches unhesitatingly. In this work, in the era of ML and deep learning (DL), how it can help to improve the overall run time of physics-based model and potential constraints that should be addressed while modeling. This paper covers the opportunities and challenges of adopting ML for hydrological modeling and subsequently how it can help to improve the simulation time of physics-based models and future works that should be addressed.
... Hydrological modeling has been instrumental in simulating, forecasting, and managing water resources [1][2][3]. These models enable researchers to predict water availability, assess the impacts of land use and climate change, and devise strategies for sustainable water management. ...
The accessibility and deployment of complex hydrological models remain significant challenges in water resource management and research. This study presents a comprehensive workflow for converting Python-based hydrological models into web APIs, addressing the need for more accessible and interoperable modeling tools. The workflow leverages modern web technologies and containerization to streamline the deployment process. The workflow was applied to three distinct models: a GRACE downscaling model, a synthetic time series generator, and a MODFLOW groundwater model. The implementation process for each model was completed in approximately 15 min with a reliable internet connection, demonstrating the efficiency of the approach. The resulting APIs provide standardized interfaces for model execution, progress tracking, and result retrieval, facilitating integration with various applications. This workflow significantly reduces barriers to model deployment and usage, potentially broadening the user base for sophisticated hydrological tools. The approach aligns hydrological modeling with contemporary software development practices, opening new avenues for collaboration and innovation. While challenges such as performance scaling and security considerations remain, this work provides a blueprint for making complex hydrological models more accessible and operational, paving the way for enhanced research and practical applications in hydrology.
... > REPLACE THIS LINE WITH YOUR MANUSCRIPT ID NUMBER (DOUBLE-CLICK HERE TO EDIT) < computational efficiency [4] , which is a key reason why DADF has not yet been applied for hourly, all-weather LST product generation. Land surface process models are inherently less efficient when simulating at high spatial resolution, and the computational cost increases exponentially with the number of ensembles in the assimilation algorithm [98,99]. However, the lightweight Noah-MP land surface model and the efficient EnKF algorithm used in this study strike a good balance between accuracy and computational efficiency. ...
High-resolution, all-weather Land Surface Temperature (LST) data on a global scale are pivotal for accurately reflecting the thermal feedback from the underlying surface. We developed a novel physically constrained Land Surface Model (LSM)-simulated LST downscaling and Polar-Orbiting Satellite (POLS)-LST for reconstructing time series framework that is particularly effective in addressing the challenges associated with frequent cloud cover and precipitation in mountainous regions. The framework employs observations from 11 stations and MODIS data, integrating the Noah-MP and the ensemble Kalman filter to construct the Data Assimilation Downscaling Framework (DADF). This approach provides a comprehensive solution for acquiring high-resolution, all-weather LST, with both temporal (hourly) and spatial (1 km) precision. To improve the accuracy of LSMs and DADF, we corrected the momentum roughness lengths for each of the six surface categories and soil emissivity based on site observations. Six underlying surface parameters derived from measured data serve as a valuable reference for investigating land surface processes in this region. In a validation of MODIS day and night LST with data from 11 measurements, we observed high precision in high-altitude alpine terrains (Kalasai and Arou) and undulating desert terrains (Tazhong sites A–E). The sand–air admixture in desert terrain, caused by wind, introduces errors in site-based observations. The average RMSE of DADF-LST is 3.85 K (compared to the RMSE of 4.72 K for MODIS LST), and the R2 > 0.82 across six categories. The DADF incorporates data assimilation algorithms for LST downscaling, enabling accurate capture of actual LST under cloudy conditions. It provides a physically constrained solution for obtaining LST data with high spatial and temporal resolution globally.
... This model places particular emphasis on the simulation of thermal processes, addressing a key limitation of traditional hydrological models. The main components of the VIC model include evapotranspiration, runoff generation, and streamflow routing (Lohmann et al., 1998;Asgari et al., 2022). The model divides the watershed into multiple grid cells, calculates runoff generation at each grid, and routes the output data into streamflow processes at the watershed outlet using the routing module (Castaneda-Gonzalez et al., 2023). ...
The Tianshan Mountains of Central Asia, highly sensitive to climate change, has been comprehensively assessed for its ecosystem vulnerability across multiple aspects. However, studies on the region’s main river systems and hydropower resources remain limited. Thus, examining the impact of climate change on the runoff and gross hydropower potential (GHP) of this region is essential for promoting sustainable development and effective management of water and hydropower resources. This study focused on the Kaidu River Basin that is situated above the Dashankou Hydropower Station on the southern slope of the Tianshan Mountains, China. By utilizing an ensemble of bias-corrected global climate models (GCMs) from Coupled Model Intercomparison Project Phase 6 (CMIP6) and the Variable Infiltration Capacity (VIC) model coupled with a glacier module (VIC–Glacier), we examined the variations in future runoff and GHP during 2017–2070 under four shared socio-economic pathway (SSP) scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) compared to the baseline period (1985–2016). The findings indicated that precipitation and temperature in the Kaidu River Basin exhibit a general upward trend under the four SSP scenarios, with the fastest rate of increase in precipitation under the SSP2-4.5 scenario and the most significant changes in mean, maximum, and minimum temperatures under the SSP5-8.5 scenario, compared to the baseline period (1980–2016). Future runoff in the basin is projected to decrease, with rates of decline under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios being 3.09, 3.42, 7.04, and 7.20 m ³ /s per decade, respectively. The trends in GHP are consistent with runoff, with rates of decline in GHP under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios at 507.74, 563.33, 1158.44, and 1184.52 MW/10a, respectively. Compared to the baseline period (1985–2016), the rates of change in GHP under the SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 scenarios are −20.66%, −20.93%, −18.91%, and −17.49%, respectively. The Kaidu River Basin will face significant challenges in water and hydropower resources in the future, underscoring the need to adjust water resource management and hydropower planning within the basin.
... Its extensive utilization for parameter estimation within hydrological models of varying complexity stems from its robustness, efficiency, and straightforward applications. Unlike other methods, SCE-UA requires only a minimal set of user-tunable control parameters (e.g., Wood et al., 1992;Van Griensven and Meixner, 2007;Yang et al., 2008;Asgari et al., 2022), making it particularly appealing. The SCE-UA method combines the strengths of genetic algorithms and complex shuffling, effectively searching the parameter space to find optimal solutions. ...
Plant hydraulics substantially affects terrestrial water and carbon cycles by modulating water transport and carbon assimilation. Despite improved drought simulations in certain ecosystems through their integration into land surface models (LSMs), the broader application of plant hydraulics in diverse ecosystems and hydroclimates is still underexplored. In this study, we implemented the recently developed Noah-Multiparameterization Land Surface Model (Noah-MP LSM) equipped with a plant hydraulics scheme (Noah-MP-PHS) across 40 FLUXNET sites globally. Employing the Shuffled Complex Evolution-University of Arizona (SCE-UA) auto-calibration algorithm , we optimized key plant hydraulics parameters for these sites spanning eight vegetation types in both arid and humid climates. Noah-MP-PHS significantly improves the simulation of evapotranspiration (ET) and gross primary production (GPP) by better representing atmospheric and soil water stress compared to traditional soil hydraulic schemes (SHSs, such as Noah and CLM). The augmented Noah-MP-PHS models reduce surface flux overestimation and underestimation, exhibiting an average increase of 0.14 and 0.15 in Kling-Gupta Efficiency (KGE) compared to Noah and CLM, respectively. The explicit consideration of plant capacitance in PHS reveals substantial deep-layer and nocturnal root water uptake especially under dry conditions. We employed eXplainable Machine learning (XML) to quantify the model's relative sensitivity to newly introduced leaf-, stem-and root-related parameters in PHS. The sensitivity analysis reveals a rise in root parameter importance and a decline in leaf and stem parameters as conditions shift from humid to arid. These findings indicate that as aridity states vary, the most influential parameters affecting surface fluxes variation may change in parameter calibration for PHS applications. Our findings underscore the importance of incorporating plant hydraulics into LSMs to enhance simulations of terrestrial water and carbon dynamics. These findings are crucial for understanding ecosystem responses to global climate changes and guide the broader application of PHS at larger scales.
... Intelligent computing extends the traditional computing paradigm by incorporating theoretical methods, architecture systems, and technical capabilities with the goal of computing with minimum cost (Zhu et al. 2023). Parallel computing, as a core technology for intelligent computing, has been widely used in the geographic information science (GIS) field, such as spatial data processing (Wang et al. 2022a(Wang et al. , 2022b, spatial pattern analysis , and watershed hydrologic monitoring (Asgari et al. 2022). However, parallel computing is required to complete tasks in the shortest time with the most efficient resource utilization within the intelligent computing paradigm. ...
Predicting computational intensity (CI) is essential for domain decomposition and load balance in parallel geoprocessing. However, traditional CI prediction is limited in capturing the heterogeneity of spatial domain, leading to poor accuracy and load imbalance. Leveraging recent advancements in deep learning from Artificial Intelligence (AI), this paper proposes a deep learning-based approach for predicting CI and enhancing domain decomposition, which reduces the dependency on expert knowledge through automatic feature learning. In the approach, Convolutional Neural Networks are employed to capture the heterogeneity of spatial domain, encompassing structural, distribution, and topological characteristics. A fully connected layer is then utilized for CI prediction and optimized domain decomposition. Comparative experiments were implemented between the proposed approach and three traditional methods, using two cases: spatial intersection on vector data and peak perilousness assessment. The results demonstrate that the proposed approach achieves a speedup ratio of 19.8 and a parallel efficiency of 0.82. The findings highlight the advantages of the proposed approach in terms of parallel performance and usability. This study serves as a valuable reference for illustrating how deep learning can enhance parallel geoprocessing, providing a roadmap for applying deep learning techniques to geocomputations and fostering further advancements of AI GIS.
... Before informing decision-making, model outputs, particularly those derived from ensemble-based approaches, typically undergo post-processing [9][10][11]. Progress in computing, such as the utilization of cloud-based systems and parallelization, along with advancements in information technologies like artificial intelligence, create new prospects for enhancing hydrological modeling [12][13][14]. Moreover, the persisting and anticipated environmental shifts, such as global warming and intensified extreme events, present novel hurdles for hydrological modeling [15]. ...
Hydrological models play a crucial role as essential tools in the realms of water resources operations, planning, and management practices [...]
... The hydrological model contains many parameters that will affect hydrological simulation results. The optimized model has better potential to characterize the conditions and processes of hydrological systems [35]. In this study, the Shuffled Complex Evolution-University of Arizona (SCE-UA) optimization algorithm, developed by Duan et al. [36], is used for the parameter calibration of these three hydrological models. ...
The natural hydrological cycle of basins has been significantly altered by climate change and human activities, leading to considerable uncertainties in attributing runoff. In this study, the impact of climate change and human activities on runoff of the Ganjiang River Basin was analyzed, and a variety of models with different spatio-temporal scales and complexities were used to evaluate the influence of model choice on runoff attribution and to reduce the uncertainties. The results show the following: (1) The potential evapotranspiration in the Ganjiang River Basin showed a significant downward trend, precipitation showed a significant upward trend, runoff showed a nonsignificant upward trend, and an abrupt change was detected in 1968; (2) The three hydrological models used with different temporal scales and complexity, GR1A, ABCD, DTVGM, can simulate the natural distribution of water resources in the Ganjiang River Basin; and (3) The impact of climate change on runoff change ranges from 60.07% to 82.88%, while human activities account for approximately 17.12% to 39.93%. The results show that climate change is the main driving factor leading to runoff variation in the Ganjiang River Basin.
... In this regard, semi-distributed, distributed, process-based deterministic, and stochastic hydrological models (Nesru 2023) were employed to simulate these complex hydrological processes. However, high spatio-temporal dynamics (Ibrahim et al. 2021;Markhali et al. 2022), the necessity of advanced calibration procedures (Asgari et al. 2022;Jin 2022), and the accurate estimation of initial hydrologic conditions (Manikanta & Umamahesh 2023) pose challenges to the simulation efficacy of these models. ...
The present study investigates the ability of five boosting algorithms, namely Adaptive Boosting (AdaBoost), Categorical Boosting (CatBoost),
Light Gradient Boosting (LGBoost), Natural Gradient Boosting (NGBoost), and eXtreme Gradient Boosting (XGBoost) for simulating streamflow
in the Lower Godavari Basin, India. Monthly rainfall, temperatures, and streamflow from 1982 to 2020 were used for training and testing. Kling
Gupta Efficiency (KGE) was deployed to assess the ability of the boosting algorithms. It was observed that all the boosting algorithms had
shown good simulating ability, having KGE values of AdaBoost (0.87, 0.85), CatBoost (0.90, 0.78), LGBoost (0.95, 0.93), NGBoost (0.95,
0.95), and XGBoost (0.91, 0.90), respectively, in training and testing. Thus, all the algorithms were used for projecting streamflow in a climate
change perspective for the short-term projections (2025–2050) and long-term projections (2051–2075) for four Shared Socioeconomic Pathways (SSPs). The highest streamflow for all four SSPs in the case of NGBoost is more than the historical scenario (9382 m3
/s), whereas viceversa for the remaining four. The effect of ensembling the outputs of five algorithms is also studied and compared with that of individual
algorithms.
... The swift advancement of computer science has led to the adoption of parallelization as a viable technique for enhancing computing efficiency, as opposed to serial computing (Asgari et al., 2022). A crucial method for parallel computing involves the division of a complex computational task into multiple independent loads, which can be allocated to several processors concurrently. ...
Physically-based hydrological-geotechnical modeling at large scales is difficult, especially due to the time-consuming nature of flow routing and 3D soil stability models. Although parallelization techniques are commonly used for each model individually, there is currently no concurrent parallelization strategy for both. This study proposed an open-source, Parallelized, and modular modeling software for regional Hydrologic processes and Landslides simulation and prediction (PHyL v1.0). It offers parallel computation in both hydrological and 3D slope stability modules, cross-scale modeling ability via a soil moisture downscaling method, and advanced input/output (I/O) and post-processing visualization. Additionally, PHyL v1.0 is flexible and extensible, making it compatible with all mainstream operating systems. We applied PHyL v1.0 in the Yuehe River Basin, where the computational efficiencies, parallel performance, parameter sensitivity analysis, and predictive capabilities were evaluated. The PHyL v1.0 is therefore appropriately used as an advanced software for high-resolution and complex simulations of regional floods and landslides.
... With the rapid development of high-performance computing, parallel computing has become an effective means to improve the efficiency of largescale data processing, and combining optimization algorithms with parallelism can further improve the feasibility. [19][20][21][22] And yet, in terms of specific practical applications with optimization problems of high dimensionality and complexity, the curse of dimensionality causes slow convergence of the solution for parameters optimization and the individuals makes the search easily fall into local optimal in the optimization process. In addition, the parallelization for calibrating hydrologic models requires load balancing and communication minimization. ...
Parameter calibration is an important part of hydrological simulation and affects the final simulation results. In this paper, we introduce heuristic optimization algorithms, genetic algorithm (GA) to cope with the complexity of the parameter calibration problem, and use particle swarm optimization algorithm (PSO) as a comparison. For large scale hydrological simulations, we use a multilevel parallel parameter calibration framework to make full use of processor resources, accelerate the process of solving high-dimensional parameter calibration. Further, we test and apply the experiments on domestic supercomputers. The results of parameter calibration with GA and PSO can basically reach the ideal value of 0.65 and above, with PSO achieving a speedup of 58.52 on TianHe-2 supercomputer. The experimental results indicate that by using a parallel implementation on multicore CPUs, high-dimensional parameter calibration in large scale hydrological simulation is possible. Moreover, our comparison of the two algorithms shows that the GA obtains better calibration results, and the PSO has a more pronounced acceleration effect.
... With the in-depth understanding of regional hydrological cycles and the rapid development of computer technology, basin hydrological simulation technology has become an important and effective tool for evaluating regional water resources [38]. At present, hundreds of basin hydrological models have been proposed worldwide and broadly applied in flood forecasting, water resource simulation, and impact assessment of environmental change [39]. However, due to differences in hydroclimatic characteristics among basins, basin hydrological models based on different runoff mechanisms also have corresponding regional applicability. ...
The global climate shows an obvious warming trend, and the impact on water resources is increasing. Abrupt temperature change and warming hiatus are two important states of temperature change. The quantitative impacts of temperature change and warming hiatus on surface runoff remain unclear. Based on the measured runoff data from 60 representative hydrological stations in China from 1956 to 2016 and the Water Balance Model developed by the Research Center for Climate Change (RCCC-WBC), this paper analyzes the quantitative impacts of abrupt temperature change and warming hiatus on surface runoff. The results showed that the effects of three types of abrupt temperature changes on runoff in different basins in China are significantly different. The effects of abrupt temperature changes and warming stagnation on runoff in northern China are greater than those in southern China, and the effects of abrupt temperature changes and warming stagnation on runoff in the upper, middle, and lower reaches of the same basin are also different. Before the abrupt change in temperature, the influence of temperature on the surface runoff was less than 9%, and the influence of temperature on the runoff in some southern areas was weaker, only affecting less than 3% of the runoff. When the temperature changes abruptly, the influence of air temperature on the surface runoff in a small part of the arid region is up to 30%. The abrupt change in mean maximum temperature has both positive and negative driving effects on runoff in China, and the negative driving effect is concentrated in the areas with abrupt warming, affecting about 8% of the runoff on average. The average influence of abrupt mean temperature change on runoff in China is about 10%, and the area with a large influence on runoff change is concentrated in the area north of 40° N. The abrupt change in temperature in the middle and lower reaches of the Yellow River Basin has a great influence on the runoff change, up to 13%. The maximum impact of abrupt mean minimum temperature on runoff is concentrated in Northeast China, ranging from 9% to 12%. During the period of temperature stagnation, air temperature and runoff showed an obvious reverse trend. During this period, the average negative influence of drastic changes in air temperature on runoff was about 15%, but precipitation and runoff still maintained a good consistency, which may be due to the effect of other influencing factors which offset the negative driving effect of air temperature.
This book constitutes the refereed post-conference proceedings of the 5th International Conference for Next Generation Arithmetic, CoNGA 2024, held in Sydney, NSW, Australia, during February 20–21, 2024.
The 5 revised full papers presented were carefully selected from 9 submissions. CoNGA is the leading conference on emerging technologies for computer arithmetic. The demands of both AI and HPC have led the community to realize that something better than traditional floating-point arithmetic is needed to reach the speed, accuracy, and energy-efficiency that are needed for today's most challenging workloads. In particular, posit arithmetic is achieving rapid adoption as a non-proprietary format, but CoNGA welcomes papers about any arithmetic format that breaks from the past and shows merit and promise.
Plant hydraulics, governing the fundamental processes of water transport and storage in plants, plays a crucial role in shaping terrestrial water and carbon cycles across various climate regimes. While the integration of plant hydraulics into land surface models (LSMs) has shown promising results in improving simulations for specific ecosystems under drought conditions, its broader potential for enhancing water and carbon modeling across diverse ecosystems and hydroclimate conditions remains insufficiently explored. In this study, we implemented the recently developed Noah-MP LSM equipped with a plant hydraulics scheme (Noah-MP-PHS) across 40 FLUXNET sites worldwide, spanning eight vegetation types in both arid and humid climates. Employing the Shuffled Complex Evolution-University of Arizona (SCE-UA) auto-calibration algorithm, we optimized key plant hydraulics parameters and employed eXplainable Machine learning (XML) to quantify the model’s sensitivity to these parameters. The results indicate that Noah-MP-PHS significantly improves the simulation of evapotranspiration (ET) and gross primary production (GPP) compared to models using traditional soil hydraulics schemes (SHSs, i.e., Noah and CLM). The XML sensitivity analysis demonstrates a notable shift in influential parameters as aridity transitions from wet to dry conditions, with leaf- and stem-related factors prevailing in humid hydroclimates. Conversely, during the transition of the aridity conditions from humid to dry, a gradual increase in the significance of root-related parameters occurs, accompanied by a corresponding decrease in the significance of leaf- and stem-related parameters. Furthermore, integrating PHS with explicit consideration of plant capacitance reveals substantial nocturnal root water uptake under diverse moisture conditions, particularly amplified in dry compared to wet conditions. This study underscores the importance of incorporating plant hydraulics in LSMs to enhance terrestrial water and carbon simulations, with significant implications for understanding ecosystem response and feedback to global climate system changes.
Automatic calibration (autocalibration) of models is a standard practice in hydrologic sciences. However, hydrologic modelers, while performing autocalibrations, spend considerable amount of time in data pre-processing, coding, and running simulations rather than focusing on science questions. Such inefficiency, as this paper outlines, stems from: (i) platform dependence, (ii) limited computational resource, (iii) limited programming literacy, (iv) limited model structure and source code literacy, and (v) lack of data-model interoperability in the so-called autocalibration process. By expanding and enhancing an existing web-based modeling platform SWATShare, developed for the Soil and Water Assessment Tool (SWAT) hydrologic model, this paper demonstrates a generalizable pathway to making autocalibration efficient via cyberinfrastructure (CI) solutions. SWATShare is a collaborative platform for sharing and visualization of SWAT models, model results, and metadata online. This paper describes the front and back end architectures of SWATShare for enabling efficient SWAT model autocalibration on the web. In addition, this paper also demonstrates three implementation case studies to validate the autocalibration workflow and results. Results from these implementations show that SWATShare autocalibration can produce streamflow hydrograph and parameters that are comparable with commonly used offline SWATCUP calibration outputs. In some instances, the parameter values from SWATShare calibration are more physically relevant than those from SWATCUP. Although the discussion in this paper is in the context of SWAT and SWATShare, the conceptual and technical design presented here can be used as an Open Science blueprint for similar CI-enabled developments in other hydrologic models, and more importantly, in other domains of Earth system sciences.
Contaminated groundwater resources threaten human health and destroy ecosystems in many areas worldwide. Groundwater remediation is crucial for environmental recovery; however, it can be cost prohibitive. Planning a cost-effective remediation design can take a long time, as it may involve the evaluation of many management decisions, and the corresponding simulation models are computationally demanding. Parallel optimization can facilitate much faster management decisions for cost-effective groundwater remediation design using complex pollutant transport models. However, the efficiency of different parallel optimization algorithms varies depending on both the search strategy and parallelism. In this paper, we show the performance of a parallel surrogate-based optimization algorithm called parallel stochastic radial basis function (p-SRBF), which has not been previously used on contaminant remediation problems. The two case problems involve two superfund sites (i.e., the Umatilla Aquifer and Blaine Aquifer), and one objective evaluation takes 5 and 30 min for the two problems, respectively. Exceptional speedup (superlinear) is achieved with 4 to 16 cores, and excellent speedup is achieved using up to 64 cores, obtaining a good solution at 80 % efficiency. We compare our p-SRBF with three different parallel derivative-free optimization algorithms, including genetic algorithm, mesh adaptive direct search, and asynchronous parallel pattern search optimization, in terms of objective quality, computational reduction and robust behavior across multiple trials. p-SRBF outperforms the other algorithms, as it finds the best solution in both the Umatilla and Blaine cases and reduces the computational budget by at least 50 % in both problems. Additionally, statistical comparisons show that the p-SRBF results are better than those of the alternative algorithms at the 5 % significant level. This study enriches theoretical real-world groundwater remediation methods. The results demonstrate that p-SRBF is promising for environmental management problems that involve computationally expensive models.
In recent years, the development of new algorithms for multiobjective optimization has considerably grown. A large number of performance indicators has been introduced to measure the quality of Pareto front approximations produced by these algorithms. In this work, we propose a review of a total of 63 performance indicators partitioned into four groups according to their properties: cardinality, convergence, distribution and spread. Applications of these indicators are presented as well.
Parameter uncertainty analysis is one of the hot issues in hydrology studies, and the Generalized Likelihood Uncertainty Estimation (GLUE) is one of the most widely used methods. However, the scale of the existing research is relatively small, which results from computational complexity and limited computing resources. In this study, a parallel GLUE method based on a Message-Passing Interface (MPI) was proposed and implemented on a supercomputer system. The research focused on the computational efficiency of the parallel algorithm and the parameter uncertainty of the Xinanjiang model affected by different threshold likelihood function values and sampling sizes. The results demonstrated that the parallel GLUE method showed high computational efficiency and scalability. Through the large-scale parameter uncertainty analysis, it was found that within an interval of less than 0.1%, the proportion of behavioral parameter sets and the threshold value had an exponential relationship. A large sampling scale is more likely than a small sampling scale to obtain behavioral parameter sets at high threshold values. High threshold values may derive more concentrated posterior distributions of the sensitivity parameters than low threshold values.
In this article, we encompass an analysis of the recent advances in parallel genetic algorithms (PGAs). We have selected these algorithms because of the deep interest in many research fields for techniques that can face complex applications where running times and other computational resources are greedily consumed by present solvers, and PGAs act then as efficient procedures that fully use modern computational platforms at the same time that allow the resolution of cutting-edge open problems. We have faced this survey on PGAs with the aim of helping newcomers or busy researchers who want to have a wide vision on the field. Then, we discuss the most well-known models and their implementations from a recent (last six years) and useful point of view: We discuss on highly cited articles, keywords, the venues where they can be found, a very comprehensive (and new) taxonomy covering different research domains involved in PGAs, and a set of recent applications. We also introduce a new vision on open challenges and try to give hints that guide practitioners and specialized researchers. Our conclusion is that there are many advantages to using these techniques and lots of potential interactions to other evolutionary algorithms; as well, we contribute to creating a body of knowledge in PGAs by summarizing them in a structured way, so the reader can find this article useful for practical research, graduate teaching, and as a pedagogical guide to this exciting domain.
During the last three decades, the water resources engineering field has received a tremendous increase in the development and use of meta-heuristic algorithms like evolutionary algorithms (EA) and swarm intelligence (SI) algorithms for solving various kinds of optimization problems. The efficient design and operation of water resource systems is a challenging task and requires solutions through optimization. Further, real-life water resource management problems may involve several complexities like nonconvex, nonlinear and discontinuous functions, discrete variables, a large number of equality and inequality constraints, and often associated with multi-modal solutions. The objective function is not known analytically, and the conventional methods may face difficulties in finding optimal solutions. The issues lead to the development of various types of heuristic and meta-heuristic algorithms, which proved to be flexible and potential tools for solving several complex water resources problems. This article provides a review of state-of-the-art methods and their use in planning and management of hydrological and water resources systems. It includes a brief overview of EAs (genetic algorithms, differential evolution, evolutionary strategies, etc.) and SI algorithms (particle swarm optimization, ant colony optimization, etc.), and applications in the areas of water distribution networks, water supply, and wastewater systems, reservoir operation and irrigation systems, watershed management, parameter estimation of hydrological models, urban drainage and sewer networks, and groundwater systems monitoring network design and groundwater remediation. This paper also provides insights, challenges, and need for algorithmic improvements and opportunities for future applications in the water resources field, in the face of rising problem complexities and uncertainties.
Hydrological model parameters are generally considered to be a simplified representation that characterizes hydrologic processes, As hydrological models continue to deepen the application of hydrological processes in the basin, they face enormous calculations. Meanwhile, in pursuit calibrating the model parameters by optimal algorithms for higher accuracy, the computation burden of optical techniques has become much heavier. Therefore, in order to solve this problem of low efficiency of hydrological model calculation, this paper uses parallel PSO algorithm to calibrate the TOPMODEL model parameters, and then uses parallel computing to process the flow generation in each sub-basin. The results show that the daily runoff simulation value of tangnaihai hydrological station fits well with the measured hydrological process; Whether PSO or sub-basin all can improve computational efficiency by using parallel optimization techniques, the former and the latter increased by 3.22 and 2.57 times, respectively. The results provide a reference for further understanding the application of parallel computing in hydrological models.
The research features how parallel computing can advance hydrological performances associated with different calibration schemes (SCOs). The result shows that parallel computing can save up to 90% execution time, while achieving 81% simulation improvement. Basic statistics, including (1) index of agreement (D), (2) coefficient of determination (R²), (3) root mean square error (RMSE), and (4) percentage of bias (PBIAS) are used to evaluate simulation performances after model calibration in computer parallelism. Once the best calibration scheme is selected, additional efforts are made to improve model performances at the selected calibration target points, while the Rescaled Adjusted Partial Sums (RAPS) is used to evaluate the trend in annual streamflow. The qualitative result of reducing execution time by 86% on average indicates that parallel computing is another avenue to advance hydrologic simulations in the urban-rural interface, such as the Boise River Watershed, Idaho. Therefore, this research will provide useful insights for hydrologists to design and set up their own hydrological modeling exercises using the cost-effective parallel computing described in this case study.
Complexity and variety of modern multiobjective optimisation problems result in the emergence of numerous search techniques, from traditional mathematical programming to various randomised heuristics. A key issue raised consequently is how to evaluate and compare solution sets generated by these multiobjective search techniques. In this article, we provide a comprehensive review of solution set quality evaluation. Starting with an introduction of basic principles and concepts of set quality evaluation, this article summarises and categorises 100 state-of-the-art quality indicators, with the focus on what quality aspects these indicators reflect. This is accompanied in each category by detailed descriptions of several representative indicators and in-depth analyses of their strengths and weaknesses. Furthermore, issues regarding attributes that indicators possess and properties that indicators are desirable to have are discussed, in the hope of motivating researchers to look into these important issues when designing quality indicators and of encouraging practitioners to bear these issues in mind when selecting/using quality indicators. Finally, future trends and potential research directions in the area are suggested, together with some guidelines on these directions.
The Generalized Likelihood Uncertainty Estimation (GLUE) is a famous and widely used sensitivity and uncertainty analysis method. It provides a new way to solve the “equifinality” problem encountered in the hydrological model parameter estimation. In this research, we focused on the computational efficiency issue of the GLUE method. Inspired by the emerging heterogeneous parallel computing technology, we parallelized the GLUE in algorithmic level and then implemented the parallel GLUE algorithm on a multi-core CPU and many-core GPU hybrid heterogeneous hardware system. The parallel GLUE was implemented by using OpenMP and CUDA software ecosystems for multi-core CPU and many-core GPU systems, respectively. Application of the parallel GLUE for the Xinanjiang hydrological model parameter sensitivity analysis proved its much better computational efficiency than the traditional serial computing technology, and the correctness was also verified. The heterogeneous parallel computing accelerated GLUE method has very good application prospects for theoretical analysis and real-world applications.
Robust calibration of hydrologic models is critical for simulating water resource components; however, the time-consuming process of calibration sometimes impedes the accurate parameters’ estimation. The present study compares the performance of two approaches applied to overcome the computational costs of automatic calibration of the HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) model constructed for the Tamar basin located in northern Iran. The model is calibrated using the Particle Swarm Optimization (PSO) algorithm. In the first approach, a machine learning algorithm, i.e., Artificial Neural Network (ANN) was trained to act as a surrogate for the original HMS (ANN-PSO), while in the latter, the computational tasks were distributed among different processors. Due to inefficacy of preliminary ANN-PSO, an efficient adaptive technique was employed to boost training and accelerate the convergence of optimization. We found that both approaches were helpful in improving computational efficiency. For jointly-events calibrations schemes, meta-models outperformed parallelization due to effective exploration of calibration space, where parallel processing was not practical owing to the time required for data sharing and collecting among many clients. Model approximation using meta-models becomes highly complex, particularly in the presence of combining more events, because larger numbers of samples and much longer training times are required.
This paper looks into the two forms of parallel computing which are Single Instruction Multiple Data (SIMD) and Multiple Instruction Multiple Data (MIMD), and further diving into the sub categories of MIMD; loosely coupled and tightly coupled with an overview of their individual architectures and their implementation in the computing industry. For a case study of their industrial application, this paper looked into the Intel Iris processor as a practical implementation of SIMD architecture in the industry, IBM Power8 for tightly coupled MIMD systems which is a supercomputer, and lastly the Beowulf cluster system which is an implementation of loosely coupled MIMD. Each architecture implementation is looked into regarding why it is being chosen to be used in the industry.
As opposed to other disciplines, automated calibration procedures are not common practice for full hydrodynamic river models, mainly because of the long computation times impeding the accurate assessment of parameter values. Default or text-book values are therefore often used. This paper introduces a methodology to optimize hydrodynamic model parameter values, based on the use of a surrogate conceptual model. Thanks to the spatial lumping and the explicit calculation schemes of these conceptual models, very short calculation times and a large number of simulation runs can be achieved. The surrogate model is coupled with the Shuffled Complex Evolution Metropolis algorithm of the University of Arizona (SCEM-UA) to identify the optimal parameter sets and their uncertainty. Afterwards, the optimized parameter values are transferred to the full hydrodynamic model. The methodology is demonstrated on a case study of the river Molenbeek in Belgium, using streamflow, water level and gate level observations. Results show a decrease of the hydrodynamic model residuals by about 60 percent. © 2018 International Association for Hydro-environment Engineering and Research, Asia Pacific Division
Hydrological model calibration has been a hot issue for decades. The shuffled complex evolution method developed at the University of Ari-zona (SCE-UA) has been proved to be an effective and robust optimization approach. However, its computational efficiency deteriorates significantly when the amount of hydrometeorological data increases. In recent years, the rise of heterogeneous parallel computing has brought hope for the acceleration of hydrological model calibration. This study proposed a parallel SCE-UA method and applied it to the calibration of a watershed rainfall–runoff model, the Xinanjiang model. The parallel method was implemented on heterogeneous computing systems using OpenMP and CUDA. Performance testing and sensitivity analysis were carried out to verify its correctness and efficiency. Comparison results indicated that heterogeneous parallel computing-accelerated SCE-UA converged much more quickly than the original serial version and possessed satisfactory accuracy and stability for the task of fast hydrological model calibration.
Robust calibration of an agricultural-hydrological model is critical for simulating crop yield and water quality and making reasonable agricultural management. However, calibration of the agricultural-hydrological system models is challenging because of model complexity, the existence of strong parameter correlation, and significant computational requirements. Therefore, only a limited number of simulations can be allowed in any attempt to find a near-optimal solution within an affordable time, which greatly restricts the successful application of the model. The goal of this study is to locate the optimal solution of the Root Zone Water Quality Model (RZWQM2) given a limited simulation time, so as to improve the model simulation and help make rational and effective agricultural-hydrological decisions. To this end, we propose a computationally efficient global optimization procedure using sparse-grid based surrogates. We first used advanced sparse grid (SG) interpolation to construct a surrogate system of the actual RZWQM2, and then we calibrate the surrogate model using the global optimization algorithm, Quantum-behaved Particle Swarm Optimization (QPSO). As the surrogate model is a polynomial with fast evaluation, it can be efficiently evaluated with a sufficiently large number of times during the optimization, which facilitates the global search. We calibrate seven model parameters against five years of yield, drain flow, and loss data from a subsurface-drained corn-soybean field in Iowa. Results indicate that an accurate surrogate model can be created for the RZWQM2 with a relatively small number of SG points (i.e., RZWQM2 runs). Compared to the conventional QPSO algorithm, our surrogate-based optimization method can achieve a smaller objective function value and better calibration performance using a fewer number of expensive RZWQM2 executions, which greatly improves computational efficiency.
For multiobjective optimization problems, different optimization variables have different influences on objectives, which implies that attention should be paid to the variables according to their sensitivity. However, previous optimization studies have not considered the variables sensitivity or conducted sensitivity analysis independent of optimization. In this paper, an integrated algorithm is proposed, which combines the optimization method SPEA (Strength Pareto Evolutionary Algorithm) with the sensitivity analysis method SRCC (Spearman Rank Correlation Coefficient). In the proposed algorithm, the optimization variables are worked as samples of sensitivity analysis, and the consequent sensitivity result is used to guide the optimization process by changing the evolutionary parameters. Three cases including a mathematical problem, an airship envelope optimization, and a truss topology optimization are used to demonstrate the computational efficiency of the integrated algorithm. The results showed that this algorithm is able to simultaneously achieve parameter sensitivity and a well-distributed Pareto optimal set, without increasing the computational time greatly in comparison with the SPEA method.
Big Data (BD), with their potential to ascertain valued insights for enhanced decision-making process, have recently attracted substantial interest from both academics and practitioners. Big Data Analytics (BDA) is increasingly becoming a trending practice that many organizations are adopting with the purpose of constructing valuable information from BD. The analytics process, including the deployment and use of BDA tools, is seen by organizations as a tool to improve operational efficiency though it has strategic potential, drive new revenue streams and gain competitive advantages over business rivals. However, there are different types of analytic applications to consider. Therefore, prior to hasty use and buying costly BD tools, there is a need for organizations to first understand the BDA landscape. Given the significant nature of the BD and BDA, this paper presents a state-of-the-art review that presents a holistic view of the BD challenges and BDA methods theorized/proposed/employed by organizations to help others understand this landscape with the objective of making robust investment decisions. In doing so, systematically analysing and synthesizing the extant research published on BD and BDA area. More specifically, the authors seek to answer the following two principal questions: Q1 – What are the different types of BD challenges theorized/proposed/confronted by organizations? and Q2 – What are the different types of BDA methods theorized/proposed/employed to overcome BD challenges?. This systematic literature review (SLR) is carried out through observing and understanding the past trends and extant patterns/themes in the BDA research area, evaluating contributions, summarizing knowledge, thereby identifying limitations, implications and potential further research avenues to support the academic community in exploring research themes/patterns. Thus, to trace the implementation of BD strategies, a profiling method is employed to analyze articles (published in English-speaking peer-reviewed journals between 1996 and 2015) extracted from the Scopus database. The analysis presented in this paper has identified relevant BD research studies that have contributed both conceptually and empirically to the expansion and accrual of intellectual wealth to the BDA in technology and organizational resource management discipline.
In the field of hydrological modelling, the global and automatic parameter calibration has been a hot issue for many years. Among automatic parameter optimization algorithms, the shuffled complex evolution developed at the University of Arizona (SCE-UA) is the most successful method for stably and robustly locating the global 'best' parameter values. Ever since the invention of the SCE-UA, the profession suddenly has a consistent way to calibrate watershed models. However, the computational efficiency of the SCE-UA significantly deteriorates when coping with big data and complex models. For the purpose of solving the efficiency problem, the recently emerging heterogeneous parallel computing (parallel computing by using the multi-core CPU and many-core GPU) was applied in the parallelization and acceleration of the SCE-UA. The original serial and proposed parallel SCE-UA were compared to test the performance based on the Griewank benchmark function. The comparison results indicated that the parallel SCE-UA converged much faster than the serial version and its optimization accuracy was the same as the serial version. It has a promising application prospect in the field of fast hydrological model parameter optimization.
Parameter specification usually has significant influence on the performance
of land surface models (LSMs). However, estimating the parameters properly
is a challenging task due to the following reasons: (1) LSMs usually have
too many adjustable parameters (20 to 100 or even more), leading to the
curse of dimensionality in the parameter input space; (2) LSMs usually have
many output variables involving water/energy/carbon cycles, so that
calibrating LSMs is actually a multi-objective optimization problem; (3)
Regional LSMs are expensive to run, while conventional multi-objective
optimization methods need a large number of model runs (typically
~105–106). It makes parameter optimization
computationally prohibitive. An uncertainty quantification framework was
developed to meet the aforementioned challenges, which include the following
steps: (1) using parameter screening to reduce the number of adjustable
parameters, (2) using surrogate models to emulate the responses of dynamic
models to the variation of adjustable parameters, (3) using an adaptive
strategy to improve the efficiency of surrogate modeling-based optimization;
(4) using a weighting function to transfer multi-objective optimization to
single-objective optimization. In this study, we demonstrate the uncertainty
quantification framework on a single column application of a LSM
– the Common Land Model (CoLM), and evaluate the effectiveness and
efficiency of the proposed framework. The result indicate that this
framework can efficiently achieve optimal parameters in a more effective
way. Moreover, this result implies the possibility of calibrating other
large complex dynamic models, such as regional-scale LSMs,
atmospheric models and climate models.
Many heuristic algorithms for solving optimization problems have been proposed, and efforts have been made to improve their efficiency in finding optimal solutions. However, the sampling-based search strategies of the heuristic optimization algorithms still require considerable time to locate optima, particularly in the hyper-dimensional parameter space that are typical for distributed hydrologic models. This study evaluated the performance of parallel computing methods, ‘spmd’ and ‘parfor,’ of Matrix Laboratory (MATLAB) in improving computational efficiency of solving optimization problems using sampling-based algorithms in hydrologic and water quality modeling applications. The parallel computing methods were applied in calibrating 34 parameters for hydrologic simulation of the Soil and Water Assessment Tool (SWAT) model and in identifying watershed-scale optimum spatial distributions of corn stover removal using the AMALGAM optimization algorithm. In the applications, the SWAT model was calibrated with NSE of 0.86 and R2 of 0.93 at the watershed outlet for the calibration period of 1993 to 1999 using the parallel computing methods, and the
calibrated model provided NSE of 0.84 and R2 of 0.92 for the validation period of 2000 to 2009. The results clearly demonstrated the effectiveness of the methods in reducing computational time of the parameter calibration and spatial optimization. The SWAT model code modification was not required, and changes in the source code of the original sequential optimization algorithm were minimal. Thus, the parallel computing methods of MATLAB are expected to provide simple and quick solutions for improving efficiency of solving the optimization problems in watershed model applications.
Parameter specification usually has significant influence on the performance of land surface models (LSMs). However, estimating the parameters properly is a challenging task due to the following reasons: (1) LSMs usually have too many adjustable parameters (20 to 100 or even more), leading to the curse of dimensionality in the parameter input space; (2) LSMs usually have many output variables involving water/energy/carbon cycles, so that calibrating LSMs is actually a multi-objective optimization problem; (3) Regional LSMs are expensive to run, while conventional multi-objective optimization methods need a large number of model runs (typically ~105–106). It makes parameter optimization computationally prohibitive. An uncertainty quantification framework was developed to meet the aforementioned challenges, which include the following steps: (1) using parameter screening to reduce the number of adjustable parameters, (2) using surrogate models to emulate the responses of dynamic models to the variation of adjustable parameters, (3) using an adaptive strategy to improve the efficiency of surrogate modeling-based optimization; (4) using a weighting function to transfer multi-objective optimization to single-objective optimization. In this study, we demonstrate the uncertainty quantification framework on a single column application of a LSM – the Common Land Model (CoLM), and evaluate the effectiveness and efficiency of the proposed framework. The result indicate that this framework can efficiently achieve optimal parameters in a more effective way. Moreover, this result implies the possibility of calibrating other large complex dynamic models, such as regional-scale LSMs, atmospheric models and climate models.
Hydrologic Simulation Program-Fortran (HSPF) model calibration is typically done manually due to the lack of an automated calibration tool as well as the difficulty of balancing objective functions to be considered. This paper discusses the development and demonstration of an automated calibration tool for HSPF (HSPF-SCE). HSPF-SCE was developed using the open source software “R”. The tool employs the Shuffled Complex Evolution optimization algorithm (SCE-UA) to produce a pool of qualified calibration parameter sets from which the modeler chooses a single set of calibrated parameters. Six calibration criteria specified in the Expert System for the Calibration of HSPF (HSPEXP) decision support tool were combined to develop a single, composite objective function for HSPF-SCE. The HSPF-SCE tool was demonstrated, and automated and manually calibrated model performance were compared using three Virginia watersheds, where HSPF models had been previously prepared for bacteria total daily maximum load (TMDL) development. The example applications demonstrate that HSPF-SCE can be an effective tool for calibrating HSPF.
With the development of large-scale hydrologic modeling, computational efficiency is becoming more and more important. Rapid modeling and analysis are needed to deal with emergency environmental disasters. The Soil and Water Assessment Tool (SWAT) is a popular hydrologic model, which is less applied in large-scale watershed simulation because of its sequential characteristics. For improving the computational efficiency of the SWAT model, we present a new parallel processing solution for hydrologic cycle and calibration based on MPI (Message Passing Interface). We partitioned sub-basins during the processes based on a load balancing method. Then the calibration was parallelized using a master-slave scheme, in which different input parameters were allocated to different processes to run the hydrologic cycle and compute the function value. Because of the slow convergence and local optimization of the SCE-UA (Shuffled Complex Evolution-developed by University of Arizona) algorithm in SWAT calibration, a genetic algorithm (GA) is developed to optimize the calibration step. Then by dividing the default communicator into several sub-communicators, all the hydrologic cycles were parallelized in their own sub-communicators to achieve further acceleration. In this paper the results show speedups for the hydrologic cycle calculations, as well as in the optimized calibration step. In the case study, we tested the parallel hydrologic cycle by four processes, and got a speedup of 3.06. In the calibration section, after applying the GA optimization, with 10 cores, we got a speed increase of 8.0 in our GA parallel framework compared with the GA sequential calibration, which is much better than the original SWAT calibration. After the sub-communicators added, this process was speeded up even further. The study demonstrated that the GA parallel framework with multi-sub-communicators is an effective and efficient solution for the hydrologists in large scale hydrology simulations.
Ten stochastic optimization methods—adaptive simulated annealing (ASA), covariance matrix adaptation evolution strategy (CMAES), cuckoo search (CS), dynamically dimensioned search (DDS), differential evolution (DE), genetic algorithm (GA), harmony search (HS), pattern search (PS), particle swarm optimization (PSO), and shuffled complex evolution–University of Arizona (SCE–UA)—were used to calibrate parameter sets for three hydrological models on 10 different basins. Optimization algorithm performance was compared for each of the available basin-model combinations. For each model-basin pair, 40 calibrations were run with the 10 algorithms. Results were tested for statistical significance using a multicomparison procedure based on Friedman and Kruskal-Wallis tests. A dispersion metric was used to evaluate the fitness landscape underlying the structure on each test case. The trials revealed that the dimensionality and general fitness landscape characteristics of the model calibration problem are important when considering the use of an automatic optimization method. The ASA, CMAES, and DDS algorithms were either as good as or better than the other methods for finding the lowest minimum, with ASA being consistently among the best. The SCE–UA method performs better when the model complexity is reduced, whereas the opposite is true for DDS. Convergence speed was also studied, and the same three methods (CMAES, DDS, and ASA) were shown to converge faster than the other methods. The SCE–UA method converged nearly as fast as the best methods when the model with the smallest parameter space was used but was not as worthy in the higher-dimension parameter space of the other models. Convergence speed has little impact on algorithm efficiency. The methods offering the worst performance were DE, CS, GA, HS, and PSO, although they did manage to find good local minima in some trials. However, the other available methods generally outperformed these algorithms.
This paper reviews the use of the Generalized Likelihood Uncertainty Estimation (GLUE) methodology in the 20 years since the paper by Beven and Binley in Hydrological Processes in (1992), which is now one of the most highly cited papers in hydrology. The original conception, the on-going controversy it has generated, the nature of different sources of uncertainty and the meaning of the GLUE prediction uncertainty bounds are discussed. The hydrological, rather than statistical, arguments about the nature of model and data errors and uncertainties that are the basis for GLUE are emphasized. The application of the Institute of Hydrology distributed model to the Gwy catchment at Plynlimon presented in the original paper is revisited, using a larger sample of models, a wider range of likelihood evaluations and new visualization techniques. It is concluded that there are good reasons to reject this model for that data set. This is a positive result in a research environment in that it requires improved models or data to be made available. In practice, there may be ethical issues of using outputs from models for which there is evidence for model rejection in decision making. Finally, some suggestions for what is needed in the next 20 years are provided. © 2013 The Authors. Hydrological Processes published by John Wiley & Sons, Ltd.
The Silences of the Archives, the Reknown of the Story.
The Martin Guerre affair has been told many times since Jean de Coras and Guillaume Lesueur published their stories in 1561. It is in many ways a perfect intrigue with uncanny resemblance, persuasive deception and a surprizing end when the two Martin stood face to face, memory to memory, before captivated judges and a guilty feeling Bertrande de Rols. The historian wanted to go beyond the known story in order to discover the world of the heroes. This research led to disappointments and surprizes as documents were discovered concerning the environment of Artigat’s inhabitants and bearing directly on the main characters thanks to notarial contracts. Along the way, study of the works of Coras and Lesueur took a new direction. Coming back to the affair a quarter century later did not result in finding new documents (some are perhaps still buried in Spanish archives), but by going back over her tracks, the historian could only be struck by the silences of the archives that refuse to reveal their secrets and, at the same time, by the possible openings they suggest, by the intuition that almost invisible threads link here and there characters and events.
Large-scale hydrologic models are being used more and more in watershed management and decision making. Sometimes rapid modeling and analysis is needed to deal with emergency environmental disasters. However, time is often a major impediment in the calibration and application of these models. To overcome this, most projects are run with fewer simulations, resulting in less-than-optimum solutions. In recent years, running time-consuming projects on gridded networks or clouds in Linux systems has become more and more prevalent. But this technology, aside from being tedious to use, has not yet become fully available for common usage in research, teaching, and small to medium-size applications. In this paper we explain a methodology where a parallel processing scheme is constructed to work in the Windows platform. We have parallelized the calibration of the SWAT (Soil and Water Assessment Tool) hydrological model, where one could submit many simultaneous jobs taking advantage of the capabilities of modern PC and laptops. This offers a powerful alternative to the use of grid or cloud computing. Parallel processing is implemented in SWAT-CUP (SWAT Calibration and Uncertainty Procedures) using the optimization program SUFI2 (Sequential Uncertainty FItting ver. 2). We tested the program with large, medium, and small-size hydrologic models on several computer systems, including PCs, laptops, and servers with up to 24 CPUs. The performance was judged by calculating speedup, efficiency, and CPU usage. In each case, the parallelized version performed much faster than the non-parallelized version, resulting in substantial time saving in model calibration.
The usefulness of a hydrologic model depends on how well the model is calibrated. Therefore, the calibration procedure must be conducted carefully to maximize the reliability of the model. In general, manual procedures for calibration can be extremely time-consuming and frustrating, and this has been a major factor inhibiting the widespread use of the more sophisticated and complex hydrologic models. A global optimization algorithm entitled shuffled complex evolution recently was developed that has proved to be consistent, effective, and efficient in locating the globally optimal model parameters of a hydrologic model. In this paper, the capability of the shuffled complex evolution automatic procedure is compared with the interactive multilevel calibration multistage semiautomated method developed for calibration of the Sacramento soil moisture accounting streamflow forecasting model of the U.S. National Weather Service. The results suggest that the state of the art in automatic calibration now can be expected to perform with a level of skill approaching that of a well-trained hydrologist. This enables the hydrologist to take advantage of the power of automated methods to obtain good parameter estimates that are consistent with the historical data and to then use personal judgment to refine these estimates and account for other factors and knowledge not incorporated easily into the automated procedure. The analysis also suggests that simple split-sample testing of model performance is not capable of reliably indicating the existence of model divergence and that more robust performance evaluation criteria are needed.
ABSTRACT,and Erofeeva, 2002), and seawater intrusion (Iribar et al., 1997). Inversely obtained hydrologic parameters are always uncertain Inverse modeling has brought new opportunities, but (nonunique) because of errors associated with the measurements and also new challenges. Some of the advantages are savings the invoked conceptual model, among other factors. Quantification of in time and cost of laboratory,and/or field experiments this uncertainty in multidimensional parameter space is often difficult generallyneededtoobtaintheunknownparametersand because of complexities in the structure of the objective function. In this study we describe parameter uncertainties using chastic inverse methods are based on the philosophy that there is no such unique,set of effective parameter values. Beven and Binley (1992), among others, explain I
In this study, the hybrid particle swarm optimization (HPSO) algorithm was proposed and practised for the calibration of two conceptual rainfall–runoff models (dynamic water balance model and abcde). The performance of the developed method was compared with those of several metaheuristics. The models were calibrated for three sub-basins, and multiple performance criteria were taken into consideration in comparison. The results indicated that HPSO was derived significantly better and more consistent results than other algorithms with respect to hydrological model errors and convergence speed. A variance decomposition-based method – analysis of variance – was also used to quantify the dynamic sensitivity of HPSO parameters. Accordingly, the individual and interactive uncertainties of the parameters defined in the HPSO are relatively low.
Multiple studies provide evidence on the impact of certain gene interactions in the occurrence of diseases. Due to the complexity of genotype–phenotype relationships, it is required the development of highly efficient algorithmic strategies that successfully identify high-order interactions attending to different evaluation criteria. This work investigates parallel evolutionary computation approaches for multiobjective gene interaction analysis. A multiobjective genetic algorithm, with novel optimized design features, is developed and parallelized under problem-independent and problem-dependent schemes. Experimental results show the relevant performance of the method for complex interaction orders, significantly accelerating execution time (up to 296×) with regard to other state-of-the-art multiobjective tools.
Solving optimization problems with parallel algorithms has a long tradition in OR. Its future relevance for solving hard optimization problems in many fields, including finance, logistics, production and design, is leveraged through the increasing availability of powerful computing capabilities. Acknowledging the existence of several literature reviews on parallel optimization, we did not find reviews that cover the most recent literature on the parallelization of both exact and (meta)heuristic methods. However, in the past decade substantial advancements in parallel computing capabilities have been achieved and used by OR scholars so that an overview of modern parallel optimization in OR that accounts for these advancements is beneficial. Another issue from previous reviews results from their adoption of different foci so that concepts used to describe and structure prior literature differ. This heterogeneity is accompanied by a lack of unifying frameworks for parallel optimization across methodologies, application fields and problems, and it has finally led to an overall fragmented picture of what has been achieved and still needs to be done in parallel optimization in OR. This review addresses the aforementioned issues with three contributions: First, we suggest a new integrative framework of parallel computational optimization across optimization problems, algorithms and application $ Invited review domains. The framework integrates the perspectives of algorithmic design and computational implementation of parallel optimization. Second, we apply the framework to synthesize prior research on parallel optimization in OR, focusing on computational studies published in the period 2008-2017. Finally, we suggest research directions for parallel optimization in OR.
Distributed watershed models should pass through a careful sensitivity analysis and calibration procedure before they are utilized as a decision making aid in the planning and management of water resources. Although manual approaches are still frequently used for sensitivity and calibration, they are tedious, time consuming, and require experienced personnel. This paper describes two typical and effective automatic approaches for sensitivity analysis and calibration for the Soil and Water Assessment Tool (SWAT). These included Sequential Uncertainty Fitting (SUFI-2) algorithm and Shuffled Complex Evolution (SCE-UA) algorithm. The results show that: (1) The main factor that influences the simulated accuracy of the Heihe River basin runoff is the Soil Conservation Service (SCS) runoff curve parameters; (2) SWAT performed very well in the Heihe River basin. According to the observed runoff data from 2005 to 2013, the determination coefficient R2 of the simulation and the efficiency coefficient (Ens) of the model was higher than 0.8; (3) Compared with Shuffled Complex Evolution, the SUFI-2 algorithm provides almost the same overall ranking of the sensitive parameters, but it is found to require less time with higher accuracy. The SUFI-2 provides a practical and flexible tool to attain reliable deterministic simulation and uncertainty analysis of SWAT, it can lead to a better understanding and to better estimated values and thus reduced uncertainty.
Environmental modelers using optimization algorithms for model calibration face an ambivalent choice. Some algorithms, for example, Newton-type methods, are fast but struggle to consistently find global parameter optima; other algorithms, for example, evolutionary methods, boast better global convergence but at much higher cost (e.g., requiring more objective function calls). Trade-offs between accuracy/robustness versus cost are ubiquitous in numerical computation, yet environmental modeling studies have lacked a systematic framework for quantifying these trade-offs. This study develops a framework for benchmarking stochastic optimization algorithms in the context of environmental model calibration, where multiple algorithm invocations are typically necessary. We define reliability as the probability of finding the desired (global or tolerable) optimum from random initial points and estimate the number of invocations to find the desired optimum with prescribed confidence (here 95%). A robust algorithm should achieve consistently high reliability across many problems. A characteristic efficiency metric for algorithm benchmarking is defined as the total cost (objective function calls over multiple invocations) to find the desired optimum with prescribed confidence. This approach avoids the pitfalls of existing approaches that compare costs without controlling the confidence in algorithm success. A case study illustrates the framework by benchmarking the Levenberg-Marquardt and Shuffled Complex Evolution (SCE) algorithms over three catchments and four hydrological models. In 8 of 12 scenarios, Levenberg-Marquardt is more efficient than SCE—by sacrificing some of its speed advantage to match SCE reliability through more invocations. The proposed framework is easy to apply and can help guide algorithm selection in environmental model calibration.
Parameter optimization and calibration play a crucial role in the overall performance of hydrological models and the quality of hydrologic forecast results. The hydrological model is characterized by high complexity, a large number of parameters, high dimensionality and a large amount of data processing. Therefore, there are many computationally intensive tasks in model parameter optimization that require a large CPU processing time. To improve the optimization precision and performance for parameters optimization of the Xinanjiang model, a parallel Multi-core Parallel Artificial Bee Colony algorithm (MPABC) was proposed based on the hybrid hierarchical model and Fork/Join framework. The algorithm is to introduce the multi-populations' parallel operation to guarantee the population's diversity, improve the global convergence ability and avoid falling into the local optimum. And also in order to divide the complex computing task into several independent parallel sub-tasks on different cores, so as to take all the performance advantages of multi-core CPU. The experiment is divided into two parts. In the first part, the performance of the original serial ABC algorithm and the MPABC algorithm is analyzed and compared based on four benchmark objective functions. The results show that the MPABC algorithm can achieve a speedup of 3.795 and an efficiency of 94.87% in solving complex problems. The MPABC algorithm could greatly improve the optimization efficiency. The second part is to select the Nash-Sutcliffe coefficient as the objective function and apply the MPABC and PPSO and PgGA algorithms to optimize the Xinanjiang hydrological model in the Heihe River Basin. The results showed that the MPABC algorithm can make full use of multi-core resources, improve the solution's quality and efficiency, and have the advantages of low parallel cost and simple realizing process. Thus, the MPABC algorithm is an effective and feasible method to solve the hydrological model parameters' optimization problem, and can provide a reliable parameter decision support for practical applications of hydrological forecasting.
Conceptual rainfall-runoff models (CRRMs) are widely used for flood forecasting and hydrologic simulations. However, parameter calibration poses a major challenge for using CRRMs, especially given that climate changes and effects of human activities often necessitate recalibration of CRRMs. Genetic algorithms (GAs) are one of the most widely used optimization techniques for hydrological model calibration, and have been widely and successfully used in model calibration; however, the complexity and high dimensionality of parameter calibration make them time-consuming and prone to local optima. Moreover, repetitive computation of fitness values in the GAs greatly reduces the efficiency. Therefore, new methods must be explored to improve the computational efficiency. Multicore parallel technology, which enables resource sharing and has low parallel costs and computing burdens, offers considerable benefits for parameter calibration. Thus, a multicore parallel genetic algorithm (MCPGA) based on the fork-join parallel framework with a tabu strategy is proposed in this paper for CRRM calibration. The method uses a multicore to divide the original task into several small subtasks and complete them concurrently, and adopts tabu strategy to avoid redundant computation in the GA to enhance the computing efficiency. The current methodology is applied to parameter calibration for the Xinanjiang model (which is a typical CRRM and has extensive applications in humid and semi-humid regions in China) of the Shuangpai Reservoir. Result comparisons between the MCPGA and serial GA indicate that the proposed method ensures a high degree of accuracy and significantly improves the computational efficiency.
Simulation-optimization method entails a large number of model simulations, which is computationally intensive or even prohibitive if the model simulation is extremely time-consuming. Statistical models have been examined as a surrogate of the high-fidelity physical model during simulation-optimization process to tackle this problem. Among them, Multivariate Adaptive Regression Splines (MARS), a non-parametric adaptive regression method, is superior in overcoming problems of high-dimensions and discontinuities of the data. Furthermore, the stability and accuracy of MARS model can be improved by bootstrap aggregating methods, namely, bagging. In this paper, Bagging MARS (BMARS) method is integrated to a surrogate-based simulation-optimization framework to calibrate a three-dimensional MODFLOW model, which is developed to simulate the groundwater flow in an arid hardrock-alluvium region in northwestern Oman. The physical MODFLOW model is surrogated by the statistical model developed using BMARS algorithm. The surrogate model, which is fitted and validated using training dataset generated by the physical model, can approximate solutions rapidly. An efficient Sobol’ method is employed to calculate global sensitivities of head outputs to input parameters, which are used to analyze their importance for the model outputs spatiotemporally. Only sensitive parameters are included in the calibration process to further improve the computational efficiency. Normalized root mean square error (NRMSE) between measured and simulated heads at observation wells is used as the objective function to be minimized during optimization. The reasonable history match between the simulated and observed heads demonstrated feasibility of this high-efficient calibration framework.
With enhanced availability of high spatial resolution data, hydrologic models such as the Soil and Water Assessment Tool (SWAT) are increasingly used to investigate effects of management activities and climate change on water availability and quality. The advantages come at a price of greater computational demand and run time. This becomes challenging to model calibration and uncertainty analysis as these routines involve a large number of model runs. For efficient modelling, a cloud-based Calibration and Uncertainty analysis Tool for SWAT (CUT-SWAT) was implemented using Hadoop, an open source cloud platform, and the Generalized Likelihood Uncertainty Estimation method. Test results on an enterprise cloud showed that CUT-SWAT can significantly speedup the calibration and uncertainty analysis processes with a speedup of 21.7–26.6 depending on model complexity and provides a flexible and fault-tolerant model execution environment (it can gracefully and automatically handle partial failure), thus would be an ideal method to solve computational demand problems in hydrological modelling.
Large-scale problems that demand high precision have remarkably increased the computational time of numerical simulation models. Therefore, the parallelization of models has been widely implemented in recent years. However, computing time remains a major challenge when a large model is calibrated using optimization techniques. To overcome this difficulty, we proposed a double-layer parallel system for hydrological model calibration using high-performance computing (HPC) systems. The lower-layer parallelism is achieved using a hydrological model, the Digital Yellow River Integrated Model, which was parallelized by decomposing river basins. The upper-layer parallelism is achieved by simultaneous hydrological simulations with different parameter combinations in the same generation of the genetic algorithm and is implemented using the job scheduling functions of an HPC system. The proposed system was applied to the upstream of the Qingjian River basin, a sub-basin of the middle Yellow River, to calibrate the model effectively by making full use of the computing resources in the HPC system and to investigate the model’s behavior under various parameter combinations. This approach is applicable to most of the existing hydrology models for many applications.
The efficiency of calibrating spatially distributed hydrologic models is a major concern in the application of these models to understand and manage natural and human activities that affect watershed systems. In this study, we developed a multi-core aware multi-objective evolutionary optimization tool, MAMEO, to calibrate the Soil and Water Assessment Tool (SWAT) model. The efficiency of MAMEO and that obtained with the sequential method were evaluated with data from the Little River Experimental Watershed. By using a 16-core machine, test results showed that calibrating SWAT with the MAMEO method required 80% less time than needed by the sequential method. MAMEO can provide multiple non-dominated parameter solutions in an efficient manner and enable modelers to simultaneously address multiple optimization objectives. © 2012 American Society of Agricultural and Biological Engineers.
There has been a modern confluence of environmental and water systems research towards a design paradigm that emphasizes multiple objectives for computationally intensive applications in areas including groundwater management, water distribution systems, and non-point source pollution. To date there have been very few parallel evolutionary multiobjective applications world-wide. This study seeks to explore new parallelization strategies for the Epsilon-Dominance Nondominated Sorted Genetic Algorithm-II that compare standard Master-Slave and multiple population parallelization approaches. Our analysis focuses on enhancing the efficiency of evolutionary multiobjective optimization by (1) using convergence based dynamic topologies and (2) enhancing search progress using archive-based migration strategies. The long-term goal of this research is to develop parallelization strategies that minimize processor population sizes and communication times while maintaining a proper load balance to achieve optimal speedups. Detailed results of the analysis will be available at the time of the conference.
EXTENDED ABSTRACT Distributed watershed models are increasingly being used to support decisions about alternative management strategies in the areas of landuse change, climate change, water allocation, and pollution control. For this reason it is important that these models pass through a careful calibration and uncertainty analysis. Furthermore, as calibration model parameters are always conditional in nature the meaning of a calibrated model, its domain of use, and its uncertainty should be clear to both the analyst and the decision maker. Large-scale distributed models are particularly difficult to calibrate and to interpret the calibration because of large model uncertainty, input uncertainty, and parameter non-uniqueness. To perform calibration and uncertainty analysis, in recent years many procedures have become available. As only one technique cannot be applied to all situations and different projects can benefit from different procedures, we have linked, for the time being, three programs to the hydrologic simulator Soil and Water Assessment Tools (SWAT) (Arnold et al., 1998) under the same platform, SWAT-CUP (SWAT Calibration Uncertainty Procedures). These procedures include: Generalized Likelihood Uncertainty Estimation (GLUE) (Beven and Binley, 1992), Parameter Solution (ParaSol) (van Griensven and Meixner, 2006), and Sequential Uncertainty FItting (SUFI-2) (Abbaspour, et al., 2007). In this paper we describe SWAT-CUP and the three procedures and provide an application example using SUFI-2. Inverse modelling (IM) has often been used to denote a calibration procedure which uses measured data to optimize an objective function for the purpose of finding the best parameters. In recent years IM has become a very popular method for calibration. IM is concerned with the problem of making inferences about physical systems from measured output variables of the model (e.g., river discharge, sediment concentration). This is attractive because direct measurement of parameters describing the physical system is time consuming, costly, tedious, and often has limited applicability. In large-scale distributed applications most parameters are almost impossible to measure as they are lumped and; hence, do not carry the same physical meaning as they did in their small-scale applications. For example, soil parameters such as hydraulic conductivity, bulk density, water storage capacity are but fitting parameters in the large scale. Because nearly all measurements are subject to some uncertainty and the models are only approximations, the inferences are usually statistical in nature. Furthermore, because one can only measure a limited number of (noisy) data and physical systems are usually modelled by continuum equations, no hydrological inverse problem is really uniquely solvable. In other words, if there is a single model that fits the measurements there will be many of them and a large number of parameter combinations can lead to acceptable modelling results. Our goal in inverse modelling is then to characterize the set of models, mainly through assigning distributions (uncertainties) to the parameters, which fit the data and satisfy our presumptions as well as other prior information. To make the parameter inferences quantitative, one must consider 1) the error in the measured data (driving variables such as rainfall and temperature), 2) the error in the measured variables used in model calibration (e.g., river discharges and sediment concentrations, nutrient loads, etc.), and 3) the error in the conceptual model (i.e., inclusion of all the physics in the model that contributes significantly to the data). The latter uncertainty could especially be large in large-scale watershed models.
The temptation to include model parameters and high resolution input data together with the availability of powerful optimization and uncertainty analysis algorithms has significantly enhanced the complexity of hydrologic and water quality modeling. However, the ability to take advantage of sophisticated models is hindered in those models that need a large number of input files, such as the Soil and Water Assessment Tool (SWAT). The process of reading large amount of input files containing spatial and computational units used in SWAT is cumbersome and time-consuming. In this study, the Consolidated SWAT (C-SWAT) was developed to consolidate 13 groups of SWAT input files from subbasin and Hydrologic Response Unit (HRU) levels into a single file for each category. The utility of the consolidated inputs of model is exhibited for auto-calibration of the Little Washita River Basin (611 km2). The results of this study show that the runtime of the SWAT model could be reduced considerably with consolidating input files. The advantage of proposed method was further promoted with application of the optimization method using a parallel computing technique. The concept is transferrable to other models that store input data in hundreds or thousands of files.
In this study, a solution to the school timetabling problem using parallel genetic algorithm with simulated annealing is presented. The hybridization of simulated annealing and parallel genetic algorithm is explained. Also, how these algorithms are run in parallel on a local network of workstations are discussed. Some comparative results among the different parallel models are exhibited. The implementation of the parallel algorithms are used to construct conflict-free and satisfying timetables for the Department of Mathematics of the University of the Philippines Diliman. The program output of this study can be easily modified to be used as a helpful and efficient guide to the decision-making process of the scheduler.
Understanding hydrologic systems at the scale of large watersheds and river basins is critically important to society when faced with extreme events, such as floods and droughts, or with concerns about water quality. A critical requirement of watershed modeling is model calibration, in which the computational model's parameters are varied during a search algorithm in order to find the best match against physically-observed phenomena such as streamflow. Because it is generally performed on a laptop computer, this calibration phase can be very time-consuming, significantly limiting the ability of a hydrologist to experiment with different models. In this paper, we describe our system for watershed model calibration using cloud computing, specifically Microsoft Windows Azure. With a representative watershed model whose calibration takes 11.4 hours on a commodity laptop, our cloud-based system calibrates the watershed model in 43.32 minutes using 16 cloud cores (15.78x speedup), 11.76 minutes using 64 cloud cores (58.13x speedup), and 5.03 minutes using 256 cloud cores (135.89x speedup). We believe that such speed-ups offer the potential toward real-time interactive model creation with continuous calibration, ushering in a new paradigm for watershed modeling.
Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on (1) a screening method (Morris) for qualitative ranking of parameters, and (2) a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model (RSMSobol). First, the Morris screening method was used to qualitatively identify the parameters’ sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model (RSM) were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.
Mathematical models are utilized to approximate various highly complex engineering, physical, environmental, social, and economic phenomena. Model parameters exerting the most influence on model results are identified through a 'sensitivity analysis'. A comprehensive review is presented of more than a dozen sensitivity analysis methods. This review is intended for those not intimately familiar with statistics or the techniques utilized for sensitivity analysis of computer models. The most fundamental of sensitivity techniques utilizes partial differentiation whereas the simplest approach requires varying parameter values one-at-a-time. Correlation analysis is used to determine relationships between independent and dependent variables. Regression analysis provides the most comprehensive sensitivity measure and is commonly utilized to build response surfaces that approximate complex models.
As the number of nodes in high performance computing (HPC) systems increases, collective I/O becomes an important issue and I/O aggregators are the key factors in improving the performance of collective I/O. When an HPC system uses non-exclusive scheduling, a different number of CPU cores per node can be assigned for MPI jobs; thus, I/O aggregators experience a disparity in their workloads and communication costs. Because the communication behaviors are influenced by the sequence of the I/O aggregators and by the number of CPU cores in neighbor nodes, changing the order of the nodes affects the communication costs in collective I/O. There are few studies, however, that seek to incorporate steps to adequately determine the node sequence. In this study, it was found that an inappropriate order of nodes results in an increase in the collective I/O communication costs. In order to address this problem, we propose the use of specific heuristic methods to regulate the node sequence. We also develop a prediction function in order to estimate the MPI-IO performance when using the proposed heuristic functions. The performance measurements indicated that the proposed scheme achieved its goal of preventing the performance degradation of the collective I/O process. For instance, in a multi-core cluster system with the Lustre file system, the read bandwidth of MPI-Tile-IO was improved by 7.61% to 17.21% and the write bandwidth of the benchmark was also increased by 17.05% to 26.49%.
The paper presents a comparative analysis between the Multicore and the Grid execution of SWAT hydrological model. We try to emphasize the advantages brought by the Grid infrastructure, especially for large scale applications which require large number of executions and huge data resources. We use as a case study a large scale hydrological model, built using the Arc SWAT program, which covers the Danube River Basin and we draw the conclusions derived from these experiments, pointing out the benefits brought by the Grid architecture.