Soroosh Sorooshian | University of California, Irvine (original) (raw)

Papers by Soroosh Sorooshian

Journal of Hydrology, Mar 1, 2006

The Model Parameter Estimation Experiment (MOPEX) is an international project aimed at developing... more The Model Parameter Estimation Experiment (MOPEX) is an international project aimed at developing enhanced techniques for the a priori estimation of parameters in hydrologic models and in land surface parameterization schemes of atmospheric models. The MOPEX science strategy involves three major steps: data preparation, a priori parameter estimation methodology development, and demonstration of parameter transferability. A comprehensive MOPEX database has been developed that contains historical hydrometeorological data and land surface characteristics data for many hydrologic basins in the United States (US) and in other countries. This database is being continuously expanded to include more basins in all parts of the world. A number of international MOPEX workshops have been convened to bring together interested hydrologists and land surface modelers from all over world to exchange knowledge and experience in developing a priori parameter estimation techniques.

Model Parameter Estimation Experiment (MOPEX) is an international project aimed to develop enhanc... more Model Parameter Estimation Experiment (MOPEX) is an international project aimed to develop enhanced techniques for the a priori estimation of parameters in hydrologic models and in land surface parameterization schemes of atmospheric models. MOPEX science strategy involves three major steps: data preparation, a priori parameter estimation methodology development, and demonstration of parameter transferability. A comprehensive MOPEX database has been developed that contains historical hydrometeorological data and land surface characteristics data for many hydrologic basins in the United States and in other countries. This database is continuing to be expanded to include more basins in all parts of the world. A number of international MOPEX workshops have been convened to bring together interested hydrologists and land surface modelers from all over world to exchange knowledge and experience in developing a priori parameter estimation techniques. This paper describes the results from the second and third MOPEX workshops. The specific objective of those workshops is to examine the state of a priori parameter estimation techniques and how they can be potentially improved with observations from well-monitored hydrologic basins. Participants of these MOPEX workshops were given data for 12 basins in the Southeastern United States and were asked to carry out a series of numerical experiments using a priori parameters as well as calibrated parameters developed for their respective hydrologic models. Eight different models have carried all out the required numerical experiments and the results from those models have been assembled for analysis in this paper. This paper presents an overview of the MOPEX experiment design. The experimental results are analyzed and the important lessons from the two workshops are discussed. Finally, a discussion of further work and future strategy is given.

Reviews of Geophysics, Jan 5, 2018

In this paper, we present a comprehensive review of the data sources and estimation methods of 30... more In this paper, we present a comprehensive review of the data sources and estimation methods of 30 currently available global precipitation data sets, including gauge-based, satellite-related, and reanalysis data sets. We analyzed the discrepancies between the data sets from daily to annual timescales and found large differences in both the magnitude and the variability of precipitation estimates. The magnitude of annual precipitation estimates over global land deviated by as much as 300 mm/yr among the products. Reanalysis data sets had a larger degree of variability than the other types of data sets. The degree of variability in precipitation estimates also varied by region. Large differences in annual and seasonal estimates were found in tropical oceans, complex mountain areas, northern Africa, and some high-latitude regions. Overall, the variability associated with extreme precipitation estimates was slightly greater at lower latitudes than at higher latitudes. The reliability of precipitation data sets is mainly limited by the number and spatial coverage of surface stations, the satellite algorithms, and the data assimilation models. The inconsistencies described limit the capability of the products for climate monitoring, attribution, and model validation. REVIEW ARTICLE 10.1002/2017RG000574 Key Points: • We conduct a comprehensive review of precipitation data sets • We evaluate the differences between data sets at different spatial and temporal scales • We explore the opportunities and challenges in generating reliable precipitation estimates

Scientific Data, Jun 23, 2021

Accurate long-term global precipitation estimates, especially for heavy precipitation rates, at f... more Accurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

Atmospheric Research, Sep 1, 2017

In the first part of this paper, monthly precipitation data from Precipitation Estimation from Re... more In the first part of this paper, monthly precipitation data from Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR) and Tropical Rainfall Measuring Mission 3B42 algorithm Version 7 (TRMM-3B42V7) are evaluated over Iran using the Generalized Three-Cornered Hat (GTCH) method which is self-sufficient of reference data as input. Climate Data Unit (CRU) is added to the GTCH evaluations as an independent gauge-based dataset thus, the minimum requirement of three datasets for the model is satisfied. To ensure consistency of all datasets, the two satellite products were aggregated to 0.5°spatial resolution, which is the minimum resolution of CRU. The results show that the PERSIANN-CDR has higher Signal to Noise Ratio (SNR) than TRMM-3B42V7 for the monthly rainfall estimation, especially in the northern half of the country. All datasets showed low SNR in the mountainous area of southwestern Iran, as well as the arid parts in the southeast region of the country. Additionally, in order to evaluate the efficacy of PERSIANN-CDR and TRMM-3B42V7 in capturing extreme daily-precipitation amounts, an in-situ rain-gauge dataset collected by the Islamic Republic of the Iran Meteorological Organization (IRIMO) was employed. Given the sparsity of the rain gauges, only 0.25°pixels containing three or more gauges were used for this evaluation. There were 228 such pixels where daily and extreme rainfall from PERSIANN-CDR and TRMM-3B42V7 could be compared. However, TRMM-3B42V7 overestimates most of the intensity indices (correlation coefficients; R between 0.7648-0.8311, Root Mean Square Error; RMSE between 3.29mm/day-21.2mm/5day); PERSIANN-CDR underestimates these extremes (R between 0.6349-0.7791 and RMSE between 3.59mm/day-30.56mm/5day). Both satellite products show higher correlation coefficients and lower RMSEs for the annual mean of consecutive dry spells than wet spells. The results show that TRMM-3B42V7 can capture the annual mean of the absolute indices (the number of wet days in which daily precipitation > 10 mm, 20 mm) better than PERSIANN-CDR. The results of daily evaluations show that the similarity of Empirical Cumulative Density Function (ECDF) of satellite products and IRIMO gauges daily precipitation, as well as dry spells with different thresholds in some selected pixels (include at least five gauges), are significant. The results also indicate that ECDFs become more significant when threshold increases. In terms of regional analyses, the higher SNR of the products on monthly (based on the GTCH method) and daily evaluations (significant ECDFs) is mostly consistent.

Journal Of Geophysical Research: Atmospheres, Apr 12, 2017

This study explores using Passive Microwave (PMW) rainfall estimation for spatial and temporal ad... more This study explores using Passive Microwave (PMW) rainfall estimation for spatial and temporal adjustment of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System (PERSIANN-CCS). The PERSIANN-CCS algorithm collects information from infrared images to estimate rainfall. PERSIANN-CCS is one of the algorithms used in the Integrated Multisatellite Retrievals for GPM (Global Precipitation Mission) estimation for the time period PMW rainfall estimations are limited or not available. Continued improvement of PERSIANN-CCS will support Integrated Multisatellite Retrievals for GPM for current as well as retrospective estimations of global precipitation. This study takes advantage of the high spatial and temporal resolution of GEO-based PERSIANN-CCS estimation and the more effective, but lower sample frequency, PMW estimation. The Probability Matching Method (PMM) was used to adjust the rainfall distribution of GEO-based PERSIANN-CCS toward that of PMW rainfall estimation. The results show that a significant improvement of global PERSIANN-CCS rainfall estimation is obtained.

Theoretical and Applied Climatology, Aug 3, 2016

In this study, satellite-based daily precipitation estimation data from precipitation estimation ... more In this study, satellite-based daily precipitation estimation data from precipitation estimation from remotely sensed information using artificial neural networks (PERSIANN)-climate data record (CDR) are being evaluated in Iran. This dataset (0.25°, daily), which covers over three decades of continuous observation beginning in 1983, is evaluated using rain-gauge data for the period of 1998-2007. In addition to categorical statistics and mean annual amount and number of rainy days, ten standard extreme indices were calculated to observe the behavior of daily extremes. The results show that PERSIANN-CDR exhibits reasonable performance associated with the probability of detection and false-alarm ratio, but it overestimates precipitation in the area. Although PERSIANN-CDR mostly underestimates extreme indices, it shows relatively high correlations (between 0.6316-0.7797) for intensity indices. PERSIANN-CDR data are also used to calculate the trend in annual amounts of precipitation, the number of rainy days, and precipitation extremes over Iran covering the period of 1983-2012. Our analysis shows that, although annual precipitation decreased in the western and eastern regions of Iran, the annual number of rainy days increased in the northern and northwestern areas. Statistically significant negative trends are identified in the 90th percentile daily precipitation, as well as the mean daily precipitation from wet days in the northern part of the study area. The positive trends of the maximum annual number of consecutive dry days in the eastern regions indicate that the dry periods became longer in these arid areas.

Journal of Hydrology, Dec 1, 2013

Results indicate that in the two study basins, no single model performed best in all cases. In ad... more Results indicate that in the two study basins, no single model performed best in all cases. In addition, no distributed model was able to consistently outperform the lumped model benchmark. However, one or more distributed models were able to outperform the lumped model benchmark in many of the analyses. Several calibrated distributed models achieved higher correlation and lower bias than the calibrated lumped benchmark in the calibration, validation, and combined periods. Evaluating a number of specific precipitation-runoff events, one calibrated distributed model was able to perform at a level equal to or better than the calibrated lumped model benchmark in terms of event-averaged peak and runoff volume error. However, three distributed models were able to provide improved peak timing compared to the lumped benchmark. Taken together, calibrated distributed models provided specific improvements over the lumped benchmark in 24% of the model-basin pairs for peak flow, 12% of the model-basin pairs for event runoff volume, and 41% of the model-basin pairs for peak timing. Model calibration improved the performance statistics of nearly all models (lumped and distributed). Analysis of several precipitation/runoff events indicates that distributed models may more accurately model the dynamics of the rain/snow line (and resulting hydrologic conditions) compared to the lumped benchmark model. Analysis of SWE simulations shows that better results were achieved at higher elevation observation sites. Although the performance of distributed models was mixed compared to the lumped benchmark, all calibrated models performed well compared to results in the DMIP 2 Oklahoma basins in terms of run period correlation and %Bias, and event-averaged peak and runoff error. This finding is noteworthy considering that these Sierra Nevada basins have complications such as orographicallyenhanced precipitation, snow accumulation and melt, rain on snow events, and highly variable topography. Looking at these findings and those from the previous DMIP experiments, it is clear that at this point in their evolution, distributed models have the potential to provide valuable information on specific flood events that could complement lumped model simulations.

Deep learning (DL) models are popular but computationally expensive, machine learning (ML) models... more Deep learning (DL) models are popular but computationally expensive, machine learning (ML) models are oldfashioned but more efficient. Their differences in hydrological probabilistic post-processing are not clear at the moment. This study conducts a systematic model comparison between the quantile regression forest (QRF) model and probabilistic long short-term memory (PLSTM) model as hydrological probabilistic post-processors. Specifically, we compare these two models 15 to deal with the biased streamflow simulation driven by three kinds of satellite precipitation products in 522 sub-basins of Yalong River basin of China. Model performance is comprehensively assessed by a series of scoring metrics from the probabilistic and deterministic perspectives, respectively. In general, the QRF model and the PLSTM model are comparable in terms of probabilistic prediction. Their performance is closely related to the flow accumulation area of the sub-basin. For sub-basins with flow accumulation area less than 60,000 km 2 , the QRF model outperforms the PLSTM model in most of the 20 sub-basins. For sub-basins with flow accumulation area larger than 60,000 km 2 , the PLSTM model has an undebatable advantage. In terms of deterministic predictions, the PLSTM model should be more preferred than the QRF model, especially when the raw streamflow is poorly simulated and used as an input. But if we put aside the model performance, the QRF model is more efficient in all cases, saving half the time than the PLSTM model. This study can deepen our understanding of ML and DL models in hydrological post-processing and enable more appropriate model selection in practice. 25

Geophysical Research Letters, 2004

Using the MM5-4DVAR system, a monsoon rainstorm case over southern Arizona (5 -6 August 2002) was... more Using the MM5-4DVAR system, a monsoon rainstorm case over southern Arizona (5 -6 August 2002) was investigated for the influence of assimilating satellite rainfall estimates on precipitation forecasts. A set of numerical experiments was conducted with multiple configurations including using 20-km or 30-km grid distances and none or 3-h or 6-h assimilation time windows. Results show that satellite rainfall assimilation can improve the rainstorm-forecasting pattern and amount to some extent. The minimization procedure of 4DVAR is sensitive to model spatial resolution and the assimilation time window. The 3-h assimilation window with hourly rainfall data works well for the 6-h forecast, and for 12-h or longer forecasts, a 6-h assimilation window will be requested.

Arabian Journal of Geosciences, Sep 1, 2018

Precipitation is a key input variable for hydrological and climate studies. Rain gauges can provi... more Precipitation is a key input variable for hydrological and climate studies. Rain gauges can provide reliable precipitation measurements at a point of observations. However, the uncertainty of rain measurements increases when a rain gauge network is sparse. Satellite-based precipitation estimations SPEs appear to be an alternative source of measurements for regions with limited rain gauges. However, the systematic bias from satellite precipitation estimation should be estimated and adjusted. In this study, a method of removing the bias from the precipitation estimation from remotely sensed information using artificial neural networkscloud classification system (PERSIANN-CCS) over a region where the rain gauge is sparse is investigated. The method consists of monthly empirical quantile mapping of gauge and satellite measurements over several climate zones as well as inverseweighted distance for the interpolation of gauge measurements. Seven years (2010-2016) of daily precipitation estimation from PERSIANN-CCS was used to test and adjust the bias of estimation over Saudi Arabia. The first 6 years (2010)(2011)(2012)(2013)(2014)(2015) are used for calibration, while 1 year ( ) is used for validation. The results show that the mean yearly bias is reduced by 90%, and the yearly root mean square error is reduced by 68% during the validation year. The experimental results confirm that the proposed method can effectively adjust the bias of satellite-based precipitation estimations.

arXiv (Cornell University), Sep 27, 2018

Being able to effectively identify clouds and monitor their evolution is one important step towar... more Being able to effectively identify clouds and monitor their evolution is one important step toward more accurate quantitative precipitation estimation and forecast. In this study, a new gradient-based cloud-image segmentation technique is developed using tools from image processing techniques. This method integrates morphological image gradient magnitudes to separable cloud systems and patches boundaries. A varying scale-kernel is implemented to reduce the sensitivity of image segmentation to noise and capture objects with various finenesses of the edges in remote-sensing images. The proposed method is flexible and extendable from single-to multi-spectral imagery. Case studies were carried out to validate the algorithm by applying the proposed segmentation algorithm to synthetic radiances for channels of the Geostationary Operational Environmental Satellites (GOES-R) simulated by a high-resolution weather prediction model. The proposed method compares favorably with the existing cloud-patch-based segmentation technique implemented in the PERSIANN-CCS (Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network -Cloud Classification System) rainfall retrieval algorithm. Evaluation of event-based images indicates that the proposed algorithm has potential to improve rain detection and estimation skills with an average of more than 45% gain comparing to the segmentation technique used in PERSIANN-CCS and identifying cloud regions as objects with accuracy rates up to 98%.

Provided by the Department of Hydrology and Water Resources., Oct 1, 1999

Extensive flooding occurred throughout the northeastern United States during January of 1996. The... more Extensive flooding occurred throughout the northeastern United States during January of 1996. The flood event cost the lives of 33 people and over a billion dollars in flood damage. Following the `Blizzard of `96 ", a warm front moved into the Mid - Atlantic region bringing extensive rainfall and causing significant melting and flooding to occur. Flood forecasting is a vital part of the National Weather Service (NWS) hydrologic responsibilities. Currently, the NWS River Forecast Centers use either the Antecedent Precipitation Index (API) or the Sacramento Soil -Moisture Accounting Model (SAC -SMA). This study evaluates the API and SAC -SMA models for their effectiveness in flood forecasting during this rain -on -snow event. The SAC -SMA, in conjunction with the SNOW -17 model, is calibrated for five basins in the Mid -Atlantic region using the Shuffled Complex Evolution (SCE -UA) automatic algorithm developed at the University of Arizona. Nash -Sutcliffe forecasting efficiencies (Ef) for the calibration period range from 0.79 to 0.87, with verification values from 0.42 to 0.95. Flood simulations were performed on the five basins using the API and calibrated SAC - SMA model. The SAC -SMA model does a better job of estimating observed flood discharge on three of the five study basins, while two of the basins experience flood simulation problems with both models. Study results indicate the SAC -SMA has the potential for better flood forecasting during complex rain -on -snow events such as during the January 1996 floods in the Northeast. hydrologic services and was established in the 1940s as part of the Department of Commerce (Ingram, 1996). The first River Forecast Center (RFC) was established in the 1940s in the Ohio River Basin for the unique purpose of flood forecasting. By the 1960s, 13 River Forecast Centers were established across the United States with distinct areas of hydrologic responsibility (Figure 1.1). Today's National Weather Service, under the direction of the National Oceanic and Atmospheric Administration (NOAA), is charged with "the responsibility of providing accurate and timely hydrologic information and forecasts for watersheds and rivers throughout the United States" (Brazil and Hudlow, 1981). The NWS RFCs now issue forecasts for over 4,000 river locations across the United States . Along with flood forecasting, additional RFC responsibilities include daily operational forecasts for water supply, irrigation, reservoir operation, energy production, water quality, navigation, and recreation . RFCs in the northern climates of the U.S. also issue seasonal snowmelt forecasts for the spring melt (Brazil and Hudlow, 1981). The National Weather Service's 13 River Forecast Centers have access to an assembled system of forecasting techniques, including rainfall -runoff models, snow accumulation and ablation models, routing techniques, calibration methods, and other hydrologic procedures (Burnash, 1995). Twelve of the 13 RFCs use this centralized system known as the National Weather Service Forecast System (NWSRFS), a highly

On the Tibetan Plateau, the limited ground-based rainfall information owing to a harsh environmen... more On the Tibetan Plateau, the limited ground-based rainfall information owing to a harsh environment has brought great challenges to hydrological studies. Satellite-based rainfall products, which allow a better coverage than both radar network and rain gauges on the Tibetan Plateau, can be suitable observation alternatives for investigating the hydrological processes and climate change. In this study, a newly developed daily satellite-based precipitation product, termed Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), is used as input of a hydrologic model to simulate streamflow in the upper Yellow and Yangtze River Basin on the Tibetan Plateau. The results show that the simulated streamflow using PERSIANN-CDR precipitation is closer to observation than that using limited gauge-based precipitation interpolation in the upper Yangtze River Basin. The simulated streamflow using gauge-based precipitation are higher than the streamflow observation during the wet season. In the upper Yellow River Basin, PERSIANN-CDR precipitation and gauge-based precipitation have similar good performance in simulating streamflow. The evaluation of streamflow simulation capability in this study partly indicates that PERSIANN-CDR rainfall product has good potentials to be a reliable dataset and an alternative information source besides the sparse gauge network for conducting long term hydrological and climate studies on the Tibetan Plateau.

This talk will summarize the shifts in IMERG (Integrated Multi-satellitE Retrievals for GPM (Glob... more This talk will summarize the shifts in IMERG (Integrated Multi-satellitE Retrievals for GPM (Global Precipitation Measurement)) from Version 03 to 04 in early Spring 2016, and to Version 05 in late Summer 2017. For example, Version 04 replaced approximate pre-launch calibrations with GPM Core Observatory-based calibrations, while Version 05 introduced improved estimates for the primary GPM instrument products (DPR, GMI, and Combined Instrument). In Version 04 the IR estimates were routinely calibrated to the passive microwave estimates. As analysis showed that the Combined Instrument estimates (the IMERG calibration standard) tend to be biased high over land and low over ocean at higher latitudes, in Version 04 we climatologically calibrated IMERG to the Global Precipitation Climatology Project (GPCP) monthly Satellite-Gauge product, except in low- and mid-latitude ocean regions. This calibration leaves the relative time series intact, and only adjusts the mean of the entire series....

Water Resources Research, 2020

Understanding causal relations is of utmost importance in hydrology and climate research for syst... more Understanding causal relations is of utmost importance in hydrology and climate research for systems identification, prediction, and understanding systems behavior in a changing climate. Traditionally, researchers in hydrometeorology attempted to study causal questions by conducting controlled experiments using numerical models. This approach, however, in most cases of interest provides uncertain results because the models are approximate representation of the natural system. An alternative approach that has recently drawn significant attention in several fields is to infer causal relations from purely observational data. It possesses several traits to its utility particularly in hydrometeorology due to the rapid accumulation of in situ and remotely sensed data records. The first objective of this study is to present a brief description of four causal discovery methods (Granger causality, Transfer Entropy, graph‐based algorithms, and Convergent Cross Mapping) with special emphasis o...

The Nile river basin is one of the global hotspots vulnerable to climate change impacts due to fa... more The Nile river basin is one of the global hotspots vulnerable to climate change impacts due to fast growing population and geopolitical tensions. Previous studies demonstrated that general circulat...

Over the past two decades, Precipitation Estimation from Remotely Sensed Information using Artifi... more Over the past two decades, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) products have been incorporated in a wide range of studies. Currently, PERSIANN offers several precipitation products based on different algorithms available at various spatial and temporal scales, namely, PERSIANN, PERSIANN-CCS and PERSIANN-CDR. The goal of this article is to first provide an overview of the available PERSIANN precipitation retrieval algorithms and their differences. Secondly, we offer an evaluation of the available operational products over the Contiguous United States at different spatial and temporal scales using Climate Prediction Center (CPC) Unified gauge-based analysis as a benchmark. Finally, the available products are intercompared at a quasi-global scale. Furthermore, we highlight strength and limitations of the PERSIANN products and briefly discuss the expected future developments. Precipitation is an integral part of the Earth's hydrologic cycle, playing a foremost role in its water and energy balance. Accurate, uninterrupted and uniform observation of precipitation represent important inputs for research and operational applications. The resilience and capacity of societies to react and adapt to climate extremes such as storms, floods and droughts are greatly enhanced with a long term historical record of precipitation. Practical applications include use of precipitation Intensity-Duration-Frequency (IDF) information for infrastructure design and use of near real-time precipitation data in development of early warning systems and disaster management planning. Moreover, the observation of precipitation is essential for understanding Earth's climate, its underlying variabilities and trends. In turn, climatic understanding can improve our ability to forecast extreme events and enables informative strategic planning and decision making on issues related to water supply, both in quantity and quality. Precipitation measurement continues to represent a great challenge for the scientific community mainly due to its spatiotemporal variations in intensity and duration . The three primary instruments used for measurement of precipitation are gauges, radar and satellite. Rain gauges provides direct measurement of precipitation; however, it suffers from intermittent coverage over most continents. Radar technology is not available in many countries and even in places where the technology is available, radar blockage by mountains is a major challenge. Both rain gauges and radar do not provide measurements over oceans. On the other hand, satellite-based precipitation measurements seem to be the most promising method to accurately observe precipitation over both land and ocean.

Scientia Iranica, 2019

The Shuffled Complex Evolution (SCE-UA) method developed at the University of Arizona is a global... more The Shuffled Complex Evolution (SCE-UA) method developed at the University of Arizona is a global optimization algorithm, initially developed by [1] for the calibrationof conceptual rainfall-runoff (CRR) models. SCE-UA searches for the global optimumof a function by evolving clusters of samples drawn from the parameter space, via a systematiccompetitive evolutionary process. Being a general purpose global optimization algorithm, it has found widespread applications across a diverse range of science and engineering fields. Here, we recount the history of the development of the SCE-UA algorithm and its later advancements. We also present a survey of illustrative applications of the SCE-UA algorithm and discuss its extensions to multi-objective problems and touncertainty assessment. Finally, we suggest potential directions for future investigation.