토양수분 저류 기반의 간결한 준분포형 수문분할모형 개발

최정현 ( Jeonghyeon Choi ),김령은 ( Ryoungeun Kim ),김상단 ( Sangdan Kim ) 한국물환경학회 2020 한국물환경학회지 Vol.36 No.3

Hydrologic models, as a useful tool for understanding the hydrologic phenomena in the watershed, have become more complex with the increase of computer performance. The hydrologic model, with complex configurations and powerful performance, facilitates a broader understanding of the effects of climate and soil in hydrologic partitioning. However, the more complex the model is, the more effort and time is required to drive the model, and the more parameters it uses, the less accessible to the user and less applicable to the ungauged watershed. Rather, a parsimonious hydrologic model may be effective in hydrologic modeling of the ungauged watershed. Thus, a semi-distributed hydrologic partitioning model was developed with minimal composition and number of parameters to improve applicability. In this study, the validity and performance of the proposed model were confirmed by applying it to the Namgang Dam, Andong Dam, Hapcheon Dam, and Milyang Dam watersheds among the Nakdong River watersheds. From the results of the application, it was confirmed that despite the simple model structure, the hydrologic partitioning process of the watershed can be modeled relatively well through three vertical layers comprising the surface layer, the soil layer, and the aquifer. Additionally, discussions were conducted on antecedent soil moisture conditions widely applied to stormwater estimation using the soil moisture data simulated by the proposed model.

도시화에 따른 물순환 영향 평가 모형의 개발 및 적용(I) - 모형 개발 -

김현준,장철희,노성진 한국수자원학회 2012 한국수자원학회논문집 Vol.45 No.2

본 연구의 목적은 도시개발의 영향을 평가하고 물순환 개선시설의 적절한 배치를 설계하기 위한 물순환 해석 모형을 개발하는 것이다. 개념적 매개변수를 사용하는 기존의 집중형 수문모형으로는 도시개발로 인한 토지이용 변화 등의 유역 특성 변화를 적절히 모의하는데 한계가 있으며, 최근 활발히 연구되고 있는 분포형 수문모형은 입력자료 구축 및 모형구동에 많은 시간과 노력이 필요하여 다양한 도시설계 대안을 평가하기에는적절하지못하다. 유역 물순환 해석 모형(Catchment hydrologic cycle Analysis Tool, 이하 CAT)은 이러한 배경을 토대로 개발된 물리적 매개변수 기반의 링크-노드 방식의 물순환 정량화 모형이다. CAT은 기존 개념적 매개변수 기반의 집중형 수문모형과 물리적 매개변수 기반의 분포형 수문모형의 장단점을 최대한 보완하여, 도시유역 개발 전 후의 장 단기적인 물순환 변화 특성을 정량적으로 평가하고 물순환 개선시설의 효과적인 설계를 지원하기 위한 물순환 해석 모형이다. 개발된 모형의 평가를 위하여 설마천 유역을 대상으로 모의를 수행하였으며 출구점인 전적비교의 6개년(2002~2007) 동안의 시간별 하천 유출량 자료를 이용하여 모형의 보정(2002~2004)과 검정(2005~2007)을 실시한 결과, 보정과 검정기간의 Nash-Sutcliffe 모형효율계수는 각각 0.75와 0.89로 나타났다. The objective of this study is to develop a catchment hydrologic cycle assessment model which can assess the impact of urban development and designing water cycle improvement facilities. Developed model might contribute to minimize the damage caused by urban development and to establish sustainable urban environments. The existing conceptual lumped models have a potential limitation in their capacity to simulate the hydrologic impacts of land use changes and assess diverse urban design. The distributed physics-based models under active study are data demanding; and much time is required to gather and check input data; and the cost of setting up a simulation and computational demand are required. The Catchment Hydrologic Cycle Assessment Tool (hereinafter the CAT) is a water cycle analysis model based on physical parameters and it has a link-node model structure. The CAT model can assess the characteristics of the short/long-term changes in water cycles before and after urbanization in the catchment. It supports the effective design of water cycle improvement facilities by supplementing the strengths and weaknesses of existing conceptual parameter-based lumped hydrologic models and physical parameterbased distributed hydrologic models. the model was applied to Seolma-cheon catchment, also calibrated and validated using 6 years (2002~2007) hourly streamflow data in Jeonjeokbigyo station, and the Nash-Sutcliffe model efficiencies were 0.75 (2002~2004) and 0.89 (2005~2007).

무유출의 고려를 통한 간헐하천 유역에 확률기반의 격자형 수문모형의 구축

이동기,안국현 한국수자원학회 2020 한국수자원학회논문집 Vol.53 No.6

This study presents a probabilistic distributed hydrological model for Ephemeral catchment, where zero flow often occurs due to the influence of distinct climate characteristics in South Korea. The gridded hydrological model is developed by combining the Sacramento Soil Moisture Accounting Model (SAC-SMA) runoff model with a routing model. In addition, an error model is employed to represent a probabilistic hydrologic model. To be specific, the hydrologic model is coupled with a censoring error model to properly represent the features of ephemeral catchments. The performance of the censoring error model is evaluated by comparing it with the Gaussian error model, which has been utilized in a probabilistic model. We first address the necessity to consider ephemeral catchments through a review of the extensive research conducted over the recent decade. Then, the Yongdam Dam catchment is selected for our study area to confirm the usefulness of the hydrologic model developed in this study. Our results indicate that the use of the censored error model provides more reliable results, although the two models considered in this study perform reliable results. In addition, the Gaussian model delivers many negative flow values, suggesting that it occasionally offers unrealistic estimations in hydrologic modeling. In an in-depth analysis, we find that the efficiency of the censored error model may increase as the frequency of zero flow increases. Finally, we discuss the importance of utilizing the censored error model when the hydrologic model is applied for ephemeral catchments in South Korea. 본 연구에서는 우리나라의 기후 특성의 영향으로 종종 발생하는 무유출량의 간헐하천 유역(Ephemeral catchment)에 확률기반 격자형 수문 모형을 구축하였다. 격자형 모형의 구축을 위하여 Sacramento Soil Moisture Accounting Model (SAC-SMA) 유출 모형을 사용하였으며 라우팅 모형의 결합으로 격자형 강우-유출 모형을 구축하였다. 확률 모형의 표현을 위하여 에러 모형을 결합시켰으며 간헐하천 유역에 적합하게 표현하기 위해서 검열된 오류 모형(censoring error model)을 사용하였다. 기존에 많이 사용되는 정규화된 오류 모형과의 비교를 통하여 본 연구에서 구축한 모형의 적합성을 평가하였다. 먼저 과거 주된 연구와 유역에 대한 검토를 통하여 그 필요성을 논하였으며 우리나라에서 수문 모형에 많이 사용되는 용담댐을 선정하여 수문 모형을 구축하였다. 결과적으로 본 연구에서 구축한 두개의 모형이 둘 다 신뢰할 만한 결과를 보여주지만 검열된 오류 모형의 사용이 더욱 적합한 결과를 보여주는 것을 확인하였다. 이 과정에서 기존의 방법론은 확률 기반의 유출량의 표현에 있어서 0 이하의 음수값을 상당히 표현하였으며 이는 현실이지 못한 수문 모델링의 표현을 의미한다. 본 연구에서는 또한 두 모형의 심층적인 비교를 위하여 심화된 간헐하천 유역을 구축하고 수문 모델링을 하였다. 결과적으로 무유출의 빈도 증가에 따라 무유출량을 고려하는 검열된 오류 모형의 효율이 증가하는 것을 알 수 있었다. 본 연구에서 얻은 결과는 우리나라의 수문 모델링에 있어서 간헐하천 유역에 대한 고려가 필요하다는 것을 의미한다.

수리모델과 GIS 데이터를 이용한 최적관리방안의 평가에 대한 불확실성의 재고

이태수(Tae Soo Lee) 한국지역지리학회 2011 한국지역지리학회지 Vol.17 No.2

최적관리방안 (Best Management Practices)은 토양 침식과 비점오염원으로 인한 수질악화를 개선하는 방안으로 널리 이용된다. 모델을 이용하여 토양침식이나 최적관리관행의 잠재적 효과를 추정하는 것은 해당 지역의 전반적인 조건과 문제점을 식별하고 이에 대한 보전계획을 수립하는 데 도움이 된다. 그러나 데이터, 특히 GIS (Geographic Information System) 데이터, 데이터 스케일의 문제, 혹은 모델의 선택 등에서 오는 불확실성은 최적관리방안의 효과를 예측하는데 있어서 정확성과 신뢰성을 떨어뜨리고 있다. 따라서 이 논문에서는 수리모델의 발전과 배경, 데이터의 불확실성, 모델의 선택, 그리고 데이터의 스케일 등을 참고문헌을 통하여 전반적으로 정리하고 살펴봄으로서 불확실성의 전반적인 이해를 돕고자 하였다. 또한 모델을 이용한 최적관리방안의 효과를 예측함에 있어서 소규모(small scale) 모델과 분포형(spatially distributed) 모델의 장점에 대해서도 논의 하였다. Best management practices (BMPs) are widely accepted and implemented as a mitigation method for soil erosion and non-point source problems. Estimating the amount of soil erosion and the effectiveness of BMPs using hydrological models help to understand the condition, identify the problems, and make plans for conservation practices in an area, typically a watershed. However, the accuracy and reliability of assessment of BMP impacts estimated by hydrological models can be often questionable due to the uncertainties from various sources including GIS(Geographic Information System) data, scale, and model. This study reviewed the development and the background of hydrological models, and the modeling issues such as the selection of models, scale, and uncertainties of data and models. This study also discussed the advantage of a small scale and spatially distributed model to estimate the impacts of BMPs.

Markov-Chain Monte Carlo 기법을 이용한 준 분포형 수문모형의 매개변수 및 모형 불확실성 분석

최정현 ( Choi Jeonghyeon ),장수형 ( Jang Suhyung ),김상단 ( Kim Sangdan ) 한국물환경학회 2020 한국물환경학회지 Vol.36 No.5

Hydrological models are based on a combination of parameters that describe the hydrological characteristics and processes within a watershed. For this reason, the model performance and accuracy are highly dependent on the parameters. However, model uncertainties caused by parameters with stochastic characteristics need to be considered. As a follow-up to the study conducted by Choi et al (2020), who developed a relatively simple semi-distributed hydrological model, we propose a tool to estimate the posterior distribution of model parameters using the Metropolis-Hastings algorithm, a type of Markov-Chain Monte Carlo technique, and analyze the uncertainty of model parameters and simulated stream flow. In addition, the uncertainty caused by the parameters of each version is investigated using the lumped and semi-distributed versions of the applied model to the Hapcheon Dam watershed. The results suggest that the uncertainty of the semi-distributed model parameters was relatively higher than that of the lumped model parameters because the spatial variability of input data such as geomorphological and hydrometeorological parameters was inherent to the posterior distribution of the semi-distributed model parameters. Meanwhile, no significant difference existed between the two models in terms of uncertainty of the simulation outputs. The statistical goodness of fit of the simulated stream flows against the observed stream flows showed satisfactory reliability in both the semi-distributed and the lumped models, but the seasonality of the stream flow was reproduced relatively better by the distributed model.

마코프 체인 몬테카를로 및 앙상블 칼만필터와 연계된 추계학적 단순 수문분할모형

최정현 ( Choi Jeonghyeon ),이옥정 ( Lee Okjeong ),원정은 ( Won Jeongeun ),김상단 ( Kim Sangdan ) 한국물환경학회 2020 한국물환경학회지 Vol.36 No.5

Hydrologic models can be classified into two types: those for understanding physical processes and those for predicting hydrologic quantities. This study deals with how to use the model to predict today's stream flow based on the system's knowledge of yesterday's state and the model parameters. In this regard, for the model to generate accurate predictions, the uncertainty of the parameters and appropriate estimates of the state variables are required. In this study, a relatively simple hydrologic partitioning model is proposed that can explicitly implement the hydrologic partitioning process, and the posterior distribution of the parameters of the proposed model is estimated using the Markov chain Monte Carlo approach. Further, the application method of the ensemble Kalman filter is proposed for updating the normalized soil moisture, which is the state variable of the model, by linking the information on the posterior distribution of the parameters and by assimilating the observed steam flow data. The stochastically and recursively estimated stream flows using the data assimilation technique revealed better representation of the observed data than the stream flows predicted using the deterministic model. Therefore, the ensemble Kalman filter in conjunction with the Markov chain Monte Carlo approach could be a reliable and effective method for forecasting daily stream flow, and it could also be a suitable method for routinely updating and monitoring the watershedaveraged soil moisture.

복합형 유역모델 STREAM의 개발(1): 모델 구조 및 이론

조홍래 ( Honglae Cho ),정의상 ( Euisang Jeong ),구본경 ( Bhonkyoung Koo ) 한국물환경학회 2015 한국물환경학회지 Vol.31 No.5

Distributed models represent watersheds using a network of numerous, uniform calculation units to provide spatially detailed and consistent evaluations across the watershed. However, these models have a disadvantage in general requiring a high computing cost. Semi-distributed models, on the other hand, delineate watersheds using a simplified network of non-uniform calculation units requiring a much lower computing cost than distributed models. Employing a simplified network of non-uniform units, however, semi-distributed models cannot but have limitations in spatially-consistent simulations of hydrogeochemical processes and are often not favoured for such a task as identifying critical source areas within a watershed. Aiming to overcome these shortcomings of both groups of models, a hybrid watershed model STREAM (Spatio-Temporal River-basin Ecohydrology Analysis Model) was developed in this study. Like a distributed model, STREAM divides a watershed into square grid cells of a same size each of which may have a different set of hydrogeochemical parameters reflecting the spatial heterogeneity. Like many semi-distributed models, STREAM groups individual cells of similar hydrogeochemical properties into representative cells for which real computations of the model are carried out. With this hybrid structure, STREAM requires a relatively small computational cost although it still keeps the critical advantage of distributed models.

다중최적화기법을 이용한 분포형 수문모형의 최적 분포형 선택

김연수(Yonsoo Kim),김태균(Taegyun Kim) 한국습지학회 2020 한국습지학회지 Vol.22 No.1

본 연구에서는 다중최적화기법을 이용하여 분포형 수문모형의 매개변수 보정 과정에서 분포형의 정도가 융설과 유량의 최적화에 어떠한 영향을 미치고 있는 가를 연구하였다. 분포형 수문모형으로는 HL-RDHM를 이용하였고, 분포형 정도에 따라 집중형, 준분포형, 완전분포형 등 3개의 모형을 구성하여 최적 매개변수를 산정하였다. 유역은 108개의 격자로 구성되며, 격자별로 융설과 관련하여 15개, 유출량 관련 13개의 매개변수를 다중최적화기법인 MOSCEM를 이용하여 최적화하였다. 최적 매개변수 산정을 위하여 2004-2005년의 기상학적 자료와 융설량과 유출량 관측자료가 이용되었고, 최적화된 매개변수를 2001-2004년의 자료를 이용하여 검증하였다. 다중최적화기법 적용 결과 집중형의 경우, 초기 값에 의한 결과로부터 RMSE 값이 융설량은 평균 35%, 유출량은 약 42% 개선되었고, 준분포형과 완전분포형의 경우는 융설량은 평균40%, 유출량은 약 43% 정도의 RSME 값이 향상되었다. 전반적으로 집중형보다는 분포형 모형이 최적화 과정에서 융설과유출량 예측에 더 나은 성과를 보여주었지만, 준포형과 완전분포형의 경우 최적화 성과에서 큰 차이를 보이지 않았고, 유출보다는 융설에서 분포형 정도에 따른 모형의 민감도가 더 높은 것을 확인되었다. The purpose of this study is to investigate how the degree of distribution influences the calibration of snow and runoff in distributed hydrological models using a multi-criteria calibration method. The Hydrology Laboratory-Research Distributed Hydrologic Model (HL-RDHM) developed by NOAA-National Weather Service (NWS) is employed to estimate optimized parameter sets. We have 3 scenarios depended on the model complexity for estimating best parameter sets: Lumped, Semi-Distributed, and Fully-Distributed. For the case study, the Durango River Basin, Colorado is selected as a study basin to consider both snow and water balance components. This study basin is in the mountainous western U.S. area and consists of 108 Hydrologic Rainfall Analysis Project (HRAP) grid cells. 5 and 13 parameters of snow and water balance models are calibrated with the Multi-Objective Shuffled Complex Evolution Metropolis (MOSCEM) algorithm. Model calibration and validation are conducted on 4km HRAP grids with 5 years (2001-2005) meteorological data and observations. Through case study, we show that snow and streamflow simulations are improved with multiple criteria calibrations without considering model complexity. In particular, we confirm that semi- and fully distributed models are better performances than those of lumped model. In case of lumped model, the Root Mean Square Error (RMSE) values improve by 35% on snow average and 42% on runoff from a priori parameter set through multi-criteria calibrations. On the other hand, the RMSE values are improved by 40% and 43% for snow and runoff on semi- and fully-distributed models.

NWS-PC 모형을 이용한 강우-유출 모의에서 적설 및 융설 영향

강신욱(Kang Shin Uk),유승엽(Rieu Seung Yup) 대한토목학회 2008 대한토목학회논문집 B Vol.28 No.1B

The impact of snow accumulation and snowmelt in rainfall-runoff modelling was analyzed for the Soyanggang dam basin by comparing the measured and simulated discharges simulated by the NWS-PC model. Sugawara's conceptual model was used to simulate the snow accumulation and snowmelt phenomena and NWS-PC model was employed to simulate rainfall-runoff. Parameters in model calibration were estimated by the Multi-step Automated Calibration Scheme and optimized using SCE-VA algorithm in each step. The results of the model calibration and verification show that the model considering snowmelt process is better than the one without consideration of snowmelt under the performance criteria such as RMSE, PBIAS, NSE, and PME. The measured discharge time series has over 60 days of persistence. Correlograms for each simulation showed that the simulated discharge with snowmelt model reproduce the persistence closely to the measured discharge's while the one without snow accumulation and snow-melt model reproduce only 20 days of persistence. The study result indicates that the inclusion of snow accumulation and snowmelt model is important for the accurate simulation of rainfall-runoff phenomena in the Soyanggang dam basin. 소양강댐 유역의 관측유입량과 융설 모의의 포함 유무에 따른 모의 결과를 비교함으로써 적설 및 융설 모형의 필요성을 분석하였다. 사용한 융설 모형은 Sugawara 등의 개념적 융설 모형이고, 강우-유출 모형은 NWS-PC를 사용하였다. 모형의 매개변수는 다단계 자동보정법에 의해 추정하였고, 각 단계별로 SCE-UA 알고리즘에 의해 최적화되었다. 매개변수 추정시와 검증 모의에서 RMSE, PBIAS, NSE, PME 통계량은 융설을 포함한 모의가 그렇지 않은 모의보다 좋은 결과를 나타내었다. 소양강댐의 관측유입량은 약 두 달 이상의 자기상관성을 나타내었고, 융설을 포함하지 않은 경우에 모의된 유량시계열은 20일 정도의 자기상관성을 나타내었다. 융설을 포함한 경우의 모의유량 시계열은 관측 유량시계열과 유사하게 약 두 달 이상의 자기상관성을 나타내었다. 이와 같은 결과로 소양강댐 유역의 강우-유출 모의시 적설 및 융설 모형을 포함하여야 모형의 정확성을 향상시킬 수 있다.

Streamflow, stomata, and soil pits: Sources of inference for complex models with fast, robust uncertainty quantification

Dwelle, M. Chase,Kim, Jongho,Sargsyan, Khachik,Ivanov, Valeriy Y. C.M.L. Publications 2019 ADVANCES IN WATER RESOURCES Vol. No.

<P><B>Abstract</B></P> <P>The scale and complexity of environmental and earth systems introduce an array of uncertainties that need to be systematically addressed. In numerical modeling, the ever-increasing complexity of representation of these systems confounds our ability to resolve relevant uncertainties. Specifically, the numerical representation of the governing processes involve many inputs and parameters that have been traditionally treated as deterministic. Considering them as uncertain introduces a large computational burden, stemming from the requirement of a prohibitive number of model simulations. Furthermore, within hydrology, most catchments are sparsely monitored, and there are limited, heterogeneous types of data available to confirm the model’s behavior. Here we present a blueprint of a general approach to uncertainty quantification for complex hydrologic models, taking advantage of recent methodological developments. We rely on polynomial chaos machinery to construct accurate surrogates that can be efficiently sampled for the ecohydrologic model tRIBS-VEGGIE to mimic its behavior with respect to a selected set of quantities of interest. The use of the Bayesian compressive sensing technique allows for fewer evaluations of the computationally expensive tRIBS-VEGGIE. The approach enables inference of model parameters using a set of observed hydrologic quantities including stream discharge, water table depth, evapotranspiration, and soil moisture from the Asu experimental catchment near Manaus, Brazil. The results demonstrate the flexibility of the framework for hydrologic inference in watersheds with sparse, irregular observations of varying accuracy. Significant computational savings imply that problems of greater computational complexity and dimension can be addressed using accurate, computationally cheap surrogates for complex hydrologic models. This will ultimately yield probabilistic representation of model behavior, robust parameter inference, and sensitivity analysis without the need for greater investment in computational resources.</P> <P><B>Highlights</B></P> <P> <UL> <LI> A general approach to uncertainty quantification with a complex, process-rich model. </LI> <LI> Construction of efficient surrogate models with Bayesian compressive sensing. </LI> <LI> Robust parametric inference using heterogeneous sources of process-scale data. </LI> <LI> Simultaneous characterization of sensitivity of hydrologic outputs to uncertain variables. </LI> </UL> </P>