A digitally simulated electronic watershed analog has been developed for the analysis of the hydrologic regime of a watershed. Individual electrical circuits were designed to synthesize the physical characteristics of the hydrologic components of a watershed: interception, surface storage, runoff, infiltration, and subsurface storage. These circuits were related to pertinent empirical studies of significance to each component. Electrical circuit analogies, despite advantages inherent in their direct physical correspondence to hydrologic systems, have fallen into disuse due to the inflexibility of fixed component networks. A digital simulation program developed by the electrical engineering profession to provide flexibility in the design of electronic circuitry has been adapted for the simulation of the electronic watershed analog. The typical digital circuit analysis program is "canned" and the user need not understand its intricacies. Input is in the form of circuit parameters on punched cards. The output is in numeric or graphic form. Using digital simulation methodology, the electronic watershed analog has been used to analyze a 1.63 acre forested watershed.

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京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第20352号 / 工博第4289号 / 新制||工||1664(附属図書館) / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 清水 芳久, 教授 米田 稔, 准教授 KIM,SUNMIN / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Debris flows occur frequently in remote areas of Great Smoky Mountains National Park, Tennessee. Rainfall gauges are not adequate for modeling infiltration required for triggering debris flows. Weather radar, providing frequently updated, continuous coverage, is a valuable tool for estimating rainfall intensity, duration, runoff, and infiltration. Daily rainfall from a sole gauge was compared with hourly rainfall from the Digital Precipitation Array weather radar product to model infiltration on August 5, 2012, the day before a debris flow was known to have occurred in the 91-km2West Prong Little Pigeon River watershed. Additionally, both gauge and radar data were used for rainfall-runoff-infiltration modeling for a 42-day period in July and August 2012. Runoff and infiltration were simulated using the conventional semi-distributed hydrological model HEC-HMS. A local bias correction of radar rainfall at the gauge location improved correlation between the radar rainfall and the gauge data. Peak daily rainfall for the August 5 storm was 93 mm (gauge) and 98 mm (radar), whereas average daily rainfall for the 42-day period was 10 mm and 7.75 mm, respectively. Over the study period, simulated daily infiltration declined from 28 mm to 0.5 mm for the gauge and from 15 mm to 0.14 mm for radar, indicating essentially saturated conditions on the day of the debris flow.

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Modelling surface runoff can be beneficial to operations within many fields, such as agriculture planning, flood and drought risk assessment, and water resource management. In this study, we built a data-driven model that can reproduce monthly surface runoff at a 4-km grid network covering 13 watersheds in the Chesapeake Bay area. We used a random forest algorithm to build the model, where monthly precipitation, temperature, land cover, and topographic data were used as predictors, and monthly surface runoff generated by the SWAT hydrological model was used as the response. A sub-model was developed for each of 12 monthly surface runoff estimates, independent of one another. Accuracy statistics and variable importance measures from the random forest algorithm reveal that precipitation was the most important variable to the model, but including climatological data from multiple months as predictors significantly improves the model performance. Using 3-month climatological, land cover, and DEM derivatives from 40% of the 4-km grids as the training dataset, our model successfully predicted surface runoff for the remaining 60% of the grids (mean R2 (RMSE) for the 12 monthly models is 0.83 (6.60 mm)). The lowest R2 was associated with the model for August, when the surface runoff values are least in a year. In all studied watersheds, the highest predictive errors were found within the watershed with greatest topographic complexity, for which the model tended to underestimate surface runoff. For the other 12 watersheds studied, the data-driven model produced smaller and more spatially consistent predictive errors. / Master of Science / Surface runoff data can be valuable to many fields, such as agriculture planning, water resource management, and flood and drought risk assessment. The traditional approach to acquire the surface runoff data is by simulating hydrological models. However, running such models always requires advanced knowledge to watersheds and computation technologies. In this study, we build a statistical model that can reproduce monthly surface runoff at 4-km grid covering 13 watersheds in Chesapeake Bay area. This model uses publicly accessible climate, land cover, and topographic datasets as predictors, and monthly surface runoff from the SWAT model as the response. We develop 12 monthly models for each month, independent to each other. To test whether the model can be applied to generalize the surface runoff for the entire study area, we use 40% of grid data as the training sample and the remainder as validation. The accuracy statistics, the annual mean R2 and RMSE are 0.83 and 6.60 mm, show our model is capable to accurately reproduce monthly surface runoff of our study area. The statistics for August model are not as satisfying as other months’ models. The possible reason is the surface runoff in August is the lowest among the year, thus there is no enough variation for the algorithm to distinguish the minor difference of the response in model building process. When applying the model to watersheds in steep terrain conditions, we need to pay attention to the results in which the error may be relatively large.

Il est connu qu’avoir un système d’observation de la pluie de haute résolution spatio – temporelle est crucial pour obtenir de bons résultats dans la modélisation pluie – écoulement. Le radar est un outil qui donne des estimations quantitatives de precipitation avec une très bonne résolution. Lorsqu’il est fusionné avec un réseau des pluviomètres les avantages des deux systèmes sont obtenus. Cependant, les estimations fournies par le radar ont des incertitudes différentes à celles qui sont obtenus avec les pluviomètres. Dans le processus de calcul pluie – écoulement l'incertitude des précipitations interagit avec l'incertitude du modèle hydrologique. L’objectif de ce travail est d’étudier les méthodes utilisées pour quantifier l'incertitude dans l'estimation des précipitations par fusion radar – pluviomètres et de l'incertitude dans la modélisation hydrologique, afin de développer une méthodologie d'analyse de leurs contributions individuelles au traitement pluie - écoulement.Le travail est divisé en deux parties, la première cherche à évaluer: Comment peut-on quantifier l'incertitude de l'estimation des précipitations par radar? Pour répondre à la question, l'approche géostatistique par Krigeage avec Dérive Externe (KED) et Génération Stochastique de la précipitation a été utilisée, qui permet de modéliser la structure spatio – temporaire de l’erreur. La méthode a été appliquée dans la région des Cévennes - Vivarais (France), où il y a un système très dense d'observation. La deuxième partie explique: Comment pourrais être quantifiée l'incertitude de la simulation hydrologique qui provient de l'estimation de précipitation par radar et du processus de modélisation hydrologique? Dans ce point, l'outil de calcul hydrologique à Mesoéchelle (HCHM) a été développé, c’est un logiciel hydrologique distribuée et temps continu, basé sur le Numéro de Courbe et l’Hydrographe Unitaire. Il a été appliqué dans 20 résolutions spatio - temporelles allant de 10 à 300 km2 et 1 à 6 heures dans les bassins de l’Ardèche (~ 1971 km2) et le Gardon (1810 km2). Apres une analyse de sensibilité, le modèle a été simplifié avec 4 paramètres et l’incertitude de la chaîne de processus a été analysée: 1) Estimation de precipitation; 2) Modélisation hydrologique; et 3) Traitement pluie - écoulement, par l’utilisation du coefficient de variation de l'écoulement simulé.Il a été montré que KED est une méthode qui fournit l’écart type de l’estimation des précipitations, lequel peut être transformé dans une estimation stochastique de l’erreur locale. Dans la chaîne des processus: 1) L'incertitude dans l'estimation de précipitation augmente avec la réduction de l’échelle spatio – temporelle, et son effet est atténué par la modélisation hydrologique, vraisemblablement par les propriétés de stockage et de transport du bassin ; 2) L'incertitude de la modélisation hydrologique dépend de la simplification des processus hydrologiques et pas de la surface du bassin ; 3) L'incertitude dans le traitement pluie - écoulement est le résultat de la combinaison amplifiée des incertitudes de la précipitation et la modélisation hydrologique. / It is known that having a precipitation observation system at high space - time resolution is crucial to obtain good results in rainfall - runoff modeling. Radar is a tool that offers quantitative precipitation estimates with very good resolution. When it is merged with a rain gauge network the advantages of both systems are achieved. However, radars estimates have different uncertainties than those obtained with the rain gauge. In the modeling process, uncertainty of precipitation interacts with uncertainty of the hydrological model. The objective of this work is: To study methods used to quantify the uncertainty in radar – raingauge merge precipitation estimation and uncertainty in hydrological modeling, in order to develop a methodology for the analysis of their individual contributions in the uncertainty of rainfall - runoff estimation.The work is divided in two parts, the first one evaluates: How the uncertainty of radar precipitation estimation can be quantified? To address the question, the geostatistical approach by Kriging with External Drift (KED) and Stochastic Generation of Precipitation was used, which allows to model the spatio - temporal structure of errors. The method was applied in the Cévennes - Vivarais region (France), where there is a very rich observation system. The second part explains: How can it be quantified the uncertainty of the hydrological simulation coming from the radar precipitation estimates and hydrological modeling process? In this point, the hydrological mesoscale computation tool was developed; it is distributed hydrological software in time continuous, within the basis of the Curve Number and the Unit Hydrograph. It was applied in 20 spatio-temporal resolutions ranging from 10 to 300 km2 and 1 to 6 hours in the Ardèche (~ 1971 km2) and the Gardon (1810 km2) basins. After a sensitivity analysis, the model was simplified with 4 parameters and the uncertainty of the chain of process was analyzed: 1) Precipitation estimation; 2) Hydrological modeling; and 3) Rainfall - runoff estimation, by using the coefficient of variation of the simulated flow.It has been shown that KED is a method that provides the standard deviation of the precipitation estimation, which can be transformed into a stochastic estimation of the local error. In the chain of processes: 1) Uncertainty in precipitation estimation increases with decreasing spatio-temporal scale, and its effect is attenuated by hydrological modeling, probably due by storage and transport properties of the basin; 2) The uncertainty of hydrological modeling depends on the simplification of hydrological processes and not on the surface of the basin; 3) Uncertainty in rainfall - runoff treatment is the result of the amplified combination of precipitation and hydrologic modeling uncertainties.