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Journal articles on the topic 'Hydrologic Method'
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1
Vu, T. T., J. Kiesel, B. Guse, and N. Fohrer. "Towards an improved understanding of hydrological change – linking hydrologic metrics and multiple change point tests." Journal of Water and Climate Change 10, no. 4 (2018): 743–58. http://dx.doi.org/10.2166/wcc.2018.068.
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Abstract Understanding the connections between climate, anthropogenic impacts, and hydrology is fundamental for assessing future climate change. However, a comprehensive methodology is lacking to understand significant changes in the discharge regime and their causes. We propose an approach that links change point tests with hydrologic metrics applied to two Vietnamese catchments where both climatic and anthropogenic changes are observed. The change points in discharge series are revealed by six widely used change point tests. Then, 171 hydrologic metrics are investigated to evaluate all possible hydrological changes that occurred between the pre- and post-change point period. The tests showed sufficient capabilities to detect hydrological changes caused by precipitation alterations and damming. Linking the change point tests to the hydrological metrics had three benefits: (1) the significance of each detected change point was evaluated, (2) we found which test responds to which hydrologic metric, and (3) we were able to disentangle the hydrological impacts of the climatic and anthropogenic changes. Due to its objectivity, the presented method can improve the interpretation of anthropogenic changes and climate change impacts on the hydrological system.
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Meng, Xiao, Wu Qun Cheng, and Xian Bing Wu. "Application of Progressive Teaching Model in Engineering Hydrology and Hydrologic Calculation." Advanced Materials Research 919-921 (April 2014): 2185–88. http://dx.doi.org/10.4028/www.scientific.net/amr.919-921.2185.
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Engineering hydrology and hydrologic calculation is a core professional course of agricultural hydrologic engineering, in order to realize the implementation of quality education in higher school teaching purposes, with the teaching practice of engineering hydrology and hydrologic calculation, puts forward the progressive teaching mode of engineering hydrology and hydrologic calculation, and applied in teaching activities. The conception of progressive teaching mode and practice was summarized from four aspects of progressive teaching objective, teaching content, gradual progressive teaching method, and progressive ability.
3
Zuo, Q., and S. Liang. "Effects of dams on river flow regime based on IHA/RVA." Proceedings of the International Association of Hydrological Sciences 368 (May 7, 2015): 275–80. http://dx.doi.org/10.5194/piahs-368-275-2015.
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Abstract. The river hydrologic regime is a driving force of the river ecosystem. Operation of dams and sluices has significant impacts on rivers’ hydrological situation. Taking the example of the Shaying River, the Jieshou hydrologic section was selected to study the influence of the sluice and all its upstream dams on the hydrologic regime. Using 55 years of measured daily flows at Jieshou hydrologic station, the hydrological date were divided into two series as pre- and post-impact periods. Based on the IHA, the range of variability in 33 flow parameters was calculated, and the hydrologic alteration associated with dams and sluices operation was quantified. Using the RVA method, hydrologic alteration at the stream gauge site was assessed to demonstrate the influence of dams on the hydrological condition. The results showed that dams have a strong influence on the regime; the river eco-hydrological targets calculated in this study can afford some support for water resources and ecosystem management of Shaying River.
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Bauser, Hannes H., Daniel Berg, Ole Klein, and Kurt Roth. "Inflation method for ensemble Kalman filter in soil hydrology." Hydrology and Earth System Sciences 22, no. 9 (2018): 4921–34. http://dx.doi.org/10.5194/hess-22-4921-2018.
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Abstract. The ensemble Kalman filter (EnKF) is a popular data assimilation method in soil hydrology. In this context, it is used to estimate states and parameters simultaneously. Due to unrepresented model errors and a limited ensemble size, state and parameter uncertainties can become too small during assimilation. Inflation methods are capable of increasing state uncertainties, but typically struggle with soil hydrologic applications. We propose a multiplicative inflation method specifically designed for the needs in soil hydrology. It employs a Kalman filter within the EnKF to estimate inflation factors based on the difference between measurements and mean forecast state within the EnKF. We demonstrate its capabilities on a small soil hydrologic test case. The method is capable of adjusting inflation factors to spatiotemporally varying model errors. It successfully transfers the inflation to parameters in the augmented state, which leads to an improved estimation.
5
Javadinejad, Safieh. "A review on homogeneity across hydrological regions." Resources Environment and Information Engineering 3, no. 1 (2021): 124–37. http://dx.doi.org/10.25082/reie.2021.01.004.
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Hydrologic classification is the method of scientifically arranging streams, rivers or catchments into groups with the most similarity of flow regime features and use it to recognize hydrologically homogenous areas. Previous homogeneous attempts were depended on overabundance of hydrologic metrics that considers features of variability of flows that are supposed to be meaningful in modelling physical progressions in the basins. This research explains the techniques of hydrological homogeneity through comparing past and existing methods; in addition it provides a practical framework for hydrological homogeneity that illustrates serious elements of the classification process.
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Herman, J. D., J. B. Kollat, P. M. Reed, and T. Wagener. "Technical Note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models." Hydrology and Earth System Sciences 17, no. 7 (2013): 2893–903. http://dx.doi.org/10.5194/hess-17-2893-2013.
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Abstract. The increase in spatially distributed hydrologic modeling warrants a corresponding increase in diagnostic methods capable of analyzing complex models with large numbers of parameters. Sobol' sensitivity analysis has proven to be a valuable tool for diagnostic analyses of hydrologic models. However, for many spatially distributed models, the Sobol' method requires a prohibitive number of model evaluations to reliably decompose output variance across the full set of parameters. We investigate the potential of the method of Morris, a screening-based sensitivity approach, to provide results sufficiently similar to those of the Sobol' method at a greatly reduced computational expense. The methods are benchmarked on the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) over a six-month period in the Blue River watershed, Oklahoma, USA. The Sobol' method required over six million model evaluations to ensure reliable sensitivity indices, corresponding to more than 30 000 computing hours and roughly 180 gigabytes of storage space. We find that the method of Morris is able to correctly screen the most and least sensitive parameters with 300 times fewer model evaluations, requiring only 100 computing hours and 1 gigabyte of storage space. The method of Morris proves to be a promising diagnostic approach for global sensitivity analysis of highly parameterized, spatially distributed hydrologic models.
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Herman, J. D., J. B. Kollat, P. M. Reed, and T. Wagener. "Technical note: Method of Morris effectively reduces the computational demands of global sensitivity analysis for distributed watershed models." Hydrology and Earth System Sciences Discussions 10, no. 4 (2013): 4275–99. http://dx.doi.org/10.5194/hessd-10-4275-2013.
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Abstract. The increase in spatially distributed hydrologic modeling warrants a corresponding increase in diagnostic methods capable of analyzing complex models with large numbers of parameters. Sobol' sensitivity analysis has proven to be a valuable tool for diagnostic analyses of hydrologic models. However, for many spatially distributed models, the Sobol' method requires a prohibitive number of model evaluations to reliably decompose output variance across the full set of parameters. We investigate the potential of the method of Morris, a screening-based sensitivity approach, to provide results sufficiently similar to those of the Sobol' method at a greatly reduced computational expense. The methods are benchmarked on the Hydrology Laboratory Research Distributed Hydrologic Model (HL-RDHM) model over a six-month period in the Blue River Watershed, Oklahoma, USA. The Sobol' method required over six million model evaluations to ensure reliable sensitivity indices, corresponding to more than 30 000 computing hours and roughly 180 gigabytes of storage space. We find that the method of Morris is able to correctly identify sensitive and insensitive parameters with 300 times fewer model evaluations, requiring only 100 computing hours and 1 gigabyte of storage space. Method of Morris proves to be a promising diagnostic approach for global sensitivity analysis of highly parameterized, spatially distributed hydrologic models.
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Wang, Jie, Guoqing Wang, Amgad Elmahdi, et al. "Comparison of hydrological model ensemble forecasting based on multiple members and ensemble methods." Open Geosciences 13, no. 1 (2021): 401–15. http://dx.doi.org/10.1515/geo-2020-0239.
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Abstract Ensemble hydrologic forecasting which takes advantages of multiple hydrologic models has made much contribution to water resource management. In this study, four hydrological models (the Xin’anjiang model (XAJ), Simhyd, GR4J, and artificial neural network (ANN) models) and three ensemble methods (the simple average, black box-based, and binomial-based methods) were applied and compared to simulate the hydrological process during 1979–1983 in three representative catchments (Daixi, Hengtangcun, and Qiaodongcun). The results indicate that for a single model, the XAJ model and the GR4J model performed relatively well with averaged Nash and Sutcliffe efficiency coefficient (NSE) values of 0.78 and 0.83, respectively. For the ensemble models, the results show that the binomial-based ensemble method (dynamic weight) outperformed with water volume error reduced by 0.8% and NSE value increased by 0.218. The best performance on runoff forecasting occurs in the Hengtang catchment by integrating four hydrologic models based on binomial ensemble method, achieving the water volume error of 2.73% and NSE value of 0.923. Finding would provide scientific support to water engineering design and water resources management in the study areas.
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He, Shaokun, Shenglian Guo, Zhangjun Liu, Jiabo Yin, Kebing Chen, and Xushu Wu. "Uncertainty analysis of hydrological multi-model ensembles based on CBP-BMA method." Hydrology Research 49, no. 5 (2018): 1636–51. http://dx.doi.org/10.2166/nh.2018.160.
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Abstract Quantification of the inherent uncertainty in hydrologic forecasting is essential for flood control and water resources management. The existing approaches, such as Bayesian model averaging (BMA), hydrologic uncertainty processor (HUP), copula-BMA (CBMA), aim at developing reliable probabilistic forecasts to characterize the uncertainty induced by model structures. In the probability forecast framework, these approaches either assume the probability density function (PDF) to follow a certain distribution, or are unable to reduce bias effectively for complex hydrological forecasts. To overcome these limitations, a copula Bayesian processor associated with BMA (CBP-BMA) method is proposed with ensemble lumped hydrological models. Comparing with the BMA and CBMA methods, the CBP-BMA method relaxes any assumption on the distribution of conditional PDFs. Several evaluation criteria, such as containing ratio, average bandwidth and average deviation amplitude of probabilistic application, are utilized to evaluate the model performance. The case study results demonstrate that the CBP-BMA method can improve hydrological forecasting precision with higher cover ratios more than 90%, which are increased by 4.4% and 3.2%, 2.2% and 1.7% over those of BMA and CBMA during the calibration and validation periods, respectively. The proposed CBP-BMA method provides an alternative approach for uncertainty estimation of hydrological multi-model forecasts.
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Patil, Vaishnavi Kiran, Vidya R. Saraf, Omkesh V. Karad, Swapnil B. Ghodke, Dnyanesvar Gore, and Shweta S. Dhekale. "Simulation of Rainfall Runoff Process Using HEC-HMS Model for Upper Godavari Basin Maharashtra, India." European Journal of Engineering Research and Science 4, no. 4 (2019): 102–7. http://dx.doi.org/10.24018/ejers.2019.4.4.927.
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The Hydrologic Engineering Centers Hydrologic Modeling System (HEC-HMS) is a popularly used watershed model to simulate rainfall- runoff process. Hydrological modeling is a commonly used tool to estimate the basin’s hydrological response due to precipitation. It allows to predict the hydrologic response to various watershed management practices and to have a better understanding of the impacts of these practices. It is evident from the extensive review of the literature that the studies on comparative assessment of watershed models for hydrologic simulations are very much limited in developing countries including India. In this study, modified SCS Curve Number method is applied to determine loss model as a major component in rainfall-runoff modeling. The study of HEC-HMS model is used to simulate rainfallrunoff process in Nashik region (Upper Godavari basin), Maharashtra. To compute runoff volume, peak runoff rate, and flow routing methods SCS curve number, SCS unit hydrograph, Exponential recession and Muskingum routing methods are chosen, respectively. The results of the present study indicate that HEC-HMS tool applied to watershed proved to be useful in achieving the various objectives. The study confirmed a significant increase in runoff as a result of urbanization. It is a powerful tool for flood forecasting Index
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Patil, Vaishnavi Kiran, Vidya R. Saraf, Omkesh V. Karad, Swapnil B. Ghodke, Dnyanesvar Gore, and Shweta S. Dhekale. "Simulation of Rainfall Runoff Process Using HEC-HMS Model for Upper Godavari Basin Maharashtra, India." European Journal of Engineering and Technology Research 4, no. 4 (2019): 102–7. http://dx.doi.org/10.24018/ejeng.2019.4.4.927.
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The Hydrologic Engineering Centers Hydrologic Modeling System (HEC-HMS) is a popularly used watershed model to simulate rainfall- runoff process. Hydrological modeling is a commonly used tool to estimate the basin’s hydrological response due to precipitation. It allows to predict the hydrologic response to various watershed management practices and to have a better understanding of the impacts of these practices. It is evident from the extensive review of the literature that the studies on comparative assessment of watershed models for hydrologic simulations are very much limited in developing countries including India. In this study, modified SCS Curve Number method is applied to determine loss model as a major component in rainfall-runoff modeling. The study of HEC-HMS model is used to simulate rainfallrunoff process in Nashik region (Upper Godavari basin), Maharashtra. To compute runoff volume, peak runoff rate, and flow routing methods SCS curve number, SCS unit hydrograph, Exponential recession and Muskingum routing methods are chosen, respectively. The results of the present study indicate that HEC-HMS tool applied to watershed proved to be useful in achieving the various objectives. The study confirmed a significant increase in runoff as a result of urbanization. It is a powerful tool for flood forecasting Index
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Dogulu, N., P. López López, D. P. Solomatine, A. H. Weerts, and D. L. Shrestha. "Estimation of predictive hydrologic uncertainty using the quantile regression and UNEEC methods and their comparison on contrasting catchments." Hydrology and Earth System Sciences 19, no. 7 (2015): 3181–201. http://dx.doi.org/10.5194/hess-19-3181-2015.
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Abstract. In operational hydrology, estimation of the predictive uncertainty of hydrological models used for flood modelling is essential for risk-based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analysing and predicting uncertainty. However, studies devoted to comparing the performance of the methods in predicting uncertainty are limited. This paper focuses on the methods predicting model residual uncertainty that differ in methodological complexity: quantile regression (QR) and UNcertainty Estimation based on local Errors and Clustering (UNEEC). The comparison of the methods is aimed at investigating how well a simpler method using fewer input data performs over a more complex method with more predictors. We test these two methods on several catchments from the UK that vary in hydrological characteristics and the models used. Special attention is given to the methods' performance under different hydrological conditions. Furthermore, normality of model residuals in data clusters (identified by UNEEC) is analysed. It is found that basin lag time and forecast lead time have a large impact on the quantification of uncertainty and the presence of normality in model residuals' distribution. In general, it can be said that both methods give similar results. At the same time, it is also shown that the UNEEC method provides better performance than QR for small catchments with the changing hydrological dynamics, i.e. rapid response catchments. It is recommended that more case studies of catchments of distinct hydrologic behaviour, with diverse climatic conditions, and having various hydrological features, be considered.
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Carrillo, G., P. A. Troch, M. Sivapalan, T. Wagener, C. Harman, and K. Sawicz. "Catchment classification: hydrological analysis of catchment behavior through process-based modeling along a climate gradient." Hydrology and Earth System Sciences Discussions 8, no. 3 (2011): 4583–640. http://dx.doi.org/10.5194/hessd-8-4583-2011.
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Abstract. Catchment classification is an efficient method to synthesize our understanding of how climate variability and catchment characteristics interact to define hydrological response. One way to accomplish catchment classification is to empirically relate climate and catchment characteristics to hydrologic behavior and to quantify the skill of predicting hydrologic response based on the combination of climate and catchment characteristics. Since there are important subsurface properties that cannot be readily measured, the skill of classification reflects (the lack of) the amount of cross-correlation between observable landscape features and unobservable subsurface features. The resulting empirical approach is also strongly controlled by the dataset used, and therefore lacks the power to generalize beyond the heterogeneity of characteristics found in the dataset. An alternative approach, that can partially alleviate the above-mentioned issue of observability, uses our current level of hydrological understanding, expressed in the form of a process-based model, to interrogate how climate and catchment characteristics interact to produce the observed hydrologic response. In this paper we present a general method of hydrologic analysis by means of a process-based model to support a bottom-up catchment classification system complementary to top-down classification methods. The model uses topographic, geomorphologic, soil and vegetation information at the catchment scale and conditions parameter values using readily available data on precipitation, temperature and streamflow. It is applicable to a wide range of catchments in different climate settings. We have developed a step-by-step procedure to analyze the observed hydrologic response and to assign parameter values related to specific components of the model. We applied this procedure to 12 catchments across a climate gradient east of the Rocky Mountains, USA. We show that the model is capable of reproducing the observed hydrologic behavior measured through hydrologic signatures chosen at different temporal scales. Next, we analyze the dominant time scales of catchment response and their dimensionless ratios with respect to climate and observable landscape features in an attempt to explain hydrologic partitioning. We find that only a limited number of model parameters can be related to observable landscape features. However, several climate-model time scales, and the associated dimensionless numbers, show scaling relationships with respect to the investigated hydrological signatures (runoff coefficient, baseflow index, and slope of the flow duration curve). Moreover, our analysis revealed systematic co-variation of climate, vegetation and soil related time scales along the climate gradient. If such co-variation can be shown to be robust across many catchments along different climate gradients, it opens perspective for model parameterization in ungauged catchments as well as prediction of hydrologic response in a rapidly changing environment.
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Rahim, Akif, Xander Wang, Neelam Javed, Farhan Aziz, Amina Jahangir, and Tahira Khurshid. "The Perturbation of Mangla Watershed Ecosystem in Pakistan Due to Hydrological Alteration." Water 15, no. 4 (2023): 656. http://dx.doi.org/10.3390/w15040656.
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Hydrological regimes influence an aquatic ecosystem’s biotic composition, structure, and functioning. But construction of dams or anthropogenic activities substantially alter the hydrologic regimes. In this study, we used a method named as the “Indicators of Hydrologic Alteration” to examine the degree of hydrologic alteration at seven flow gauge stations in the Mangla watershed. The assessment of alteration is carried out according to the Range of Variability (RVA). This method relies on analyzing hydrologic data obtained from existing measurement points (e.g., stream gauges) within an ecosystem or model-generated data. We used 33 parameters categorized into 5 groups based on magnitude, duration, frequency, timing, and rate of change to characterize hydrologic variation within a year statistically. We then examine the hydrologic perturbations by comparing the measure of central tendency and dispersion for each parameter between the “pre-impact (1967–1994)” and “post-impact (1995–2014)” periods. The results show that within the Mangla watershed, the high alteration was noted in the magnitude of monthly flows and extreme flows at Azad Pattan, Gari Habibullah, Palote and at Muzafarabad stations. The flow at Domel and Kohala stations are found in low hydrological alteration among all groups of indicators. The study indicates that Neelum Basin at Muzaffarabad has significantly high alteration with maximum negative values. On the other hand, a high frequency of alteration observed in the monthly flows and extreme water conditions. Overall, a moderate alteration is observed in the whole watershed, which may produce adverse effects on the aquatic ecosystem of the Mangla watershed.
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Yu, Cui Song, and Xiao Na Guo. "Hydrological Frequency Calculation Method Study of Urban Rivers Runoff under Changing Environment." Applied Mechanics and Materials 170-173 (May 2012): 2023–26. http://dx.doi.org/10.4028/www.scientific.net/amm.170-173.2023.
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The consistency of hydrological series has been destroyed by the impact of human activities and climate change. Hydrological series is consist of certain component and random element. The random and certain components of hydrological series are identified and separated through statistic analysis. The certain element is determined by using hydrologic model while the consistancy of random element is confirmed directly by hydrological frequency curve. And then add them together. The runoff series of the Huangtai Hydrometric Station in the Xiaoqing River is for example. It proves effective and feasible and the result accord with the reality of the basin.
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Yu, Yufeng, Yuelong Zhu, Shijin Li, and Dingsheng Wan. "Time Series Outlier Detection Based on Sliding Window Prediction." Mathematical Problems in Engineering 2014 (2014): 1–14. http://dx.doi.org/10.1155/2014/879736.
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In order to detect outliers in hydrological time series data for improving data quality and decision-making quality related to design, operation, and management of water resources, this research develops a time series outlier detection method for hydrologic data that can be used to identify data that deviate from historical patterns. The method first built a forecasting model on the history data and then used it to predict future values. Anomalies are assumed to take place if the observed values fall outside a given prediction confidence interval (PCI), which can be calculated by the predicted value and confidence coefficient. The use ofPCIas threshold is mainly on the fact that it considers the uncertainty in the data series parameters in the forecasting model to address the suitable threshold selection problem. The method performs fast, incremental evaluation of data as it becomes available, scales to large quantities of data, and requires no preclassification of anomalies. Experiments with different hydrologic real-world time series showed that the proposed methods are fast and correctly identify abnormal data and can be used for hydrologic time series analysis.
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Nickman, Alireza, Steve W. Lyon, Per-Erik Jansson, and Bo Olofsson. "Simulating the impact of roads on hydrological responses: examples from Swedish terrain." Hydrology Research 47, no. 4 (2016): 767–81. http://dx.doi.org/10.2166/nh.2016.030.
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In this study, the potential impacts of road topography on hydrologic responses at the watershed scale were simulated. The method considered used a geographic information system to identify road embankment locations and subsequently remove them from the baseline elevation data. Starting from both the ‘with’ and ‘without’ road elevation model, the surface and near-surface hydrological responses for 20 watersheds in Sweden were modeled in HEC-HMS under three different storm intensities. Flow duration curves (FDCs) were used to compare hydrologic responses for the different modeling scenarios under the various storm intensities. Specifically, L-moment ratios of the FDCs were calculated and their variation compared. Results showed an increase in peak flow amounts and reduction in time to peak with increased storm intensity. In addition, variations of the L-moment ratios were larger in larger watersheds. However, the impact of the roads on the modeled hydrologic responses was much smaller than anticipated and only identifiable through detailed examination of the L-moment statistical descriptors. Our findings not only highlight the potential impacts of road topography on watershed-scale hydrology (especially concerning high intensity storms) but also provide a methodology for detecting the even rather small changes that could manifest, for example, under coupled road network and climatic changes.
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Carrillo, G., P. A. Troch, M. Sivapalan, T. Wagener, C. Harman, and K. Sawicz. "Catchment classification: hydrological analysis of catchment behavior through process-based modeling along a climate gradient." Hydrology and Earth System Sciences 15, no. 11 (2011): 3411–30. http://dx.doi.org/10.5194/hess-15-3411-2011.
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Abstract. Catchment classification is an efficient method to synthesize our understanding of how climate variability and catchment characteristics interact to define hydrological response. One way to accomplish catchment classification is to empirically relate climate and catchment characteristics to hydrologic behavior and to quantify the skill of predicting hydrologic response based on the combination of climate and catchment characteristics. Here we present results using an alternative approach that uses our current level of hydrological understanding, expressed in the form of a process-based model, to interrogate how climate and catchment characteristics interact to produce observed hydrologic response. The model uses topographic, geomorphologic, soil and vegetation information at the catchment scale and conditions parameter values using readily available data on precipitation, temperature and streamflow. It is applicable to a wide range of catchments in different climate settings. We have developed a step-by-step procedure to analyze the observed hydrologic response and to assign parameter values related to specific components of the model. We applied this procedure to 12 catchments across a climate gradient east of the Rocky Mountains, USA. We show that the model is capable of reproducing the observed hydrologic behavior measured through hydrologic signatures chosen at different temporal scales. Next, we analyze the dominant time scales of catchment response and their dimensionless ratios with respect to climate and observable landscape features in an attempt to explain hydrologic partitioning. We find that only a limited number of model parameters can be related to observable landscape features. However, several climate-model time scales, and the associated dimensionless numbers, show scaling relationships with respect to the investigated hydrological signatures (runoff coefficient, baseflow index, and slope of the flow duration curve). Moreover, some dimensionless numbers vary systematically across the climate gradient, possibly as a result of systematic co-variation of climate, vegetation and soil related time scales. If such co-variation can be shown to be robust across many catchments along different climate gradients, it opens perspective for model parameterization in ungauged catchments as well as prediction of hydrologic response in a rapidly changing environment.
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Shu, Lele, Paul A. Ullrich, and Christopher J. Duffy. "Simulator for Hydrologic Unstructured Domains (SHUD v1.0): numerical modeling of watershed hydrology with the finite volume method." Geoscientific Model Development 13, no. 6 (2020): 2743–62. http://dx.doi.org/10.5194/gmd-13-2743-2020.
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Abstract. Hydrologic modeling is an essential strategy for understanding and predicting natural flows, particularly where observations are lacking in either space or time or where complex terrain leads to a disconnect in the characteristic time and space scales of overland and groundwater flow. However, significant difficulties remain for the development of efficient and extensible modeling systems that operate robustly across complex regions. This paper introduces the Simulator for Hydrologic Unstructured Domains (SHUD), an integrated, multiprocess, multiscale, flexible-time-step model, in which hydrologic processes are fully coupled using the finite volume method. SHUD integrates overland flow, snow accumulation/melt, evapotranspiration, subsurface flow, groundwater flow, and river routing, thus allowing physical processes in general watersheds to be realistically captured. SHUD incorporates one-dimensional unsaturated flow, two-dimensional groundwater flow, and a fully connected river channel network with hillslopes supporting overland flow and baseflow. The paper introduces the design of SHUD, from the conceptual and mathematical description of hydrologic processes in a watershed to the model's computational structures. To demonstrate and validate the model performance, we employ three hydrologic experiments: the V-catchment experiment, Vauclin's experiment, and a model study of the Cache Creek Watershed in northern California. Ongoing applications of the SHUD model include hydrologic analyses of hillslope to regional scales (1 m2 to 106 km2), water resource and stormwater management, and interdisciplinary research for questions in limnology, agriculture, geochemistry, geomorphology, water quality, ecology, climate and land-use change. The strength of SHUD is its flexibility as a scientific and resource evaluation tool where modeling and simulation are required.
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Dogulu, N., P. López López, D. P. Solomatine, A. H. Weerts, and D. L. Shrestha. "Estimation of predictive hydrologic uncertainty using quantile regression and UNEEC methods and their comparison on contrasting catchments." Hydrology and Earth System Sciences Discussions 11, no. 9 (2014): 10179–233. http://dx.doi.org/10.5194/hessd-11-10179-2014.
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Abstract. In operational hydrology, estimation of predictive uncertainty of hydrological models used for flood modelling is essential for risk based decision making for flood warning and emergency management. In the literature, there exists a variety of methods analyzing and predicting uncertainty. However, case studies comparing performance of these methods, most particularly predictive uncertainty methods, are limited. This paper focuses on two predictive uncertainty methods that differ in their methodological complexity: quantile regression (QR) and UNcertainty Estimation based on local Errors and Clustering (UNEEC), aiming at identifying possible advantages and disadvantages of these methods (both estimating residual uncertainty) based on their comparative performance. We test these two methods on several catchments (from UK) that vary in its hydrological characteristics and models. Special attention is given to the errors for high flow/water level conditions. Furthermore, normality of model residuals is discussed in view of clustering approach employed within the framework of UNEEC method. It is found that basin lag time and forecast lead time have great impact on quantification of uncertainty (in the form of two quantiles) and achievement of normality in model residuals' distribution. In general, uncertainty analysis results from different case studies indicate that both methods give similar results. However, it is also shown that UNEEC method provides better performance than QR for small catchments with changing hydrological dynamics, i.e. rapid response catchments. We recommend that more case studies of catchments from regions of distinct hydrologic behaviour, with diverse climatic conditions, and having various hydrological features be tested.
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Malekani, L., S. Khaleghi, and M. Mahmoodi. "APPLICATION OF GIS IN MODELING ZILBERCHAI BASIN RUNOFF." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XL-2/W3 (October 22, 2014): 181–86. http://dx.doi.org/10.5194/isprsarchives-xl-2-w3-181-2014.
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Runoff is one of most important hydrological variables that are used in many civil works, planning for optimal use of reservoirs, organizing rivers and warning flood. The runoff curve number (CN) is a key factor in determining runoff in the SCS (Soil Conservation Service) based hydrologic modeling method. The traditional SCS-CN method for calculating the composite curve number consumes a major portion of the hydrologic modeling time. Therefore, geographic information systems (GIS) are now being used in combination with the SCS-CN method. This work uses a methodology of determining surface runoff by Geographic Information System model and applying SCS-CN method that needs the necessary parameters such as land use map, hydrologic soil groups, rainfall data, DEM, physiographic characteristic of the basin. The model is built by implementing some well known hydrologic methods in GIS like as ArcHydro, ArcCN-Runoff for modeling of Zilberchai basin runoff. The results show that the high average weighted of curve number indicate that permeability of the basin is low and therefore likelihood of flooding is high. So the fundamental works is essential in order to increase water infiltration in Zilberchai basin and to avoid wasting surface water resources. Also comparing the results of the computed and observed runoff value show that use of GIS tools in addition to accelerate the calculation of the runoff also increase the accuracy of the results. This paper clearly demonstrates that the integration of GIS with the SCS-CN method provides a powerful tool for estimating runoff volumes in large basins.
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Golian, Saeed, Bahram Saghafian, and Ashkan Farokhnia. "Copula-based interpretation of continuous rainfall–runoff simulations of a watershed in northern Iran." Canadian Journal of Earth Sciences 49, no. 5 (2012): 681–91. http://dx.doi.org/10.1139/e2012-011.
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In the present work, the joint response of key hydrologic variables, including total precipitation depths and the corresponding simulated peak discharges, are investigated for different antecedent soil moisture conditions using the copula method. The procedure started with the calibration and validation of the soil moisture accounting (SMA) loss rate algorithm incorporated in the Hydrologic Engineering Center – hydrologic modeling system (HEC–HMS) model for the study watershed. A 1000 year long time series of hourly rainfall was then generated by the Neyman–Scott rectangular pulses (NSRP) rainfall generator, which was then transformed into the runoff rate by the HEC–HMS model. This long-term continuous hydrological simulation resulted in characterizing the response of the watershed for various input conditions such as initial soil moisture content (AMC), total rainfall depth, and rainfall duration. For each initial soil moisture class, the copula method was employed to determine the joint probability distribution of rainfall depth and peak discharge. For instance, for dry AMC condition and 1 h rainfall duration, the Joe family fitted best to the data, compared with six other one-parameter families of copulas. Results showed that the bivariate analysis of rainfall–runoff using the copula method can well characterize the watershed hydrological behavior. The derived offline curves could provide a probabilistic real-time peak discharge forecast.
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Apaydin, H., F. Ozturk, H. Merdun, and N. M. Aziz. "Determination of the drainage basin characteristics using vector GIS." Hydrology Research 37, no. 2 (2006): 129–42. http://dx.doi.org/10.2166/nh.2006.0011.
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Detailed geomorphologic characteristics need to be compiled for performing hydrologic modeling of a basin. Basin form and hydrologic characteristics are to be related so the basin form must also be represented by quantitative descriptors. The typical morphologic characteristics used in hydrological analyses are basin area, perimeter, mainstream length, total stream length, contour length, basin shape (form factor, circularity ratio, compactness ratio, basin elongation), slope, drainage density, relief (maximum relief, relief ratio, relative relief), effective basin width, and median elevation. The objective of this study is to propose an algorithm to automatically calculate basin characteristics using vector GIS. The results produced by the algorithm were compared to the manual method and the two methods were found statistically similar.
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Lee, Hanyong, Min Suh Chae, Jong-Yoon Park, Kyoung Jae Lim, and Youn Shik Park. "Development and Application of a QGIS-Based Model to Estimate Monthly Streamflow." ISPRS International Journal of Geo-Information 11, no. 1 (2022): 40. http://dx.doi.org/10.3390/ijgi11010040.
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Changes in rainfall pattern and land use have caused considerable impacts on the hydrological behavior of watersheds; a Long-Term Hydrologic Impact Analysis (L-THIA) model has been used to simulate such variations. The L-THIA model defines curve number according to the land use and hydrological soil group before calculating the direct runoff based on the amount of rainfall, making it a convenient method of analysis. Recently, a method was proposed to estimate baseflow using this model, which may be used to estimate the overall streamflow. Given that this model considers the spatial distribution of land use and hydrological soil groups and must use rainfall data at multiple positions, it requires the usage of a geographical information system (GIS). Therefore, a model that estimates streamflow using land use maps, hydrologic soil group maps, and rain gauge station maps in QGIS, a popular GIS software, was developed. This model was tested in 15 watersheds.
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Li, Zhe, Siyu Cai, Xiaohui Lei, and Lingmin Wang. "Diagnosis of Basin Eco-Hydrological Variation Based on Index Sensitivity of Similar Years: A Case Study in the Hanjiang River Basin." Water 14, no. 12 (2022): 1931. http://dx.doi.org/10.3390/w14121931.
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The variation of hydrological conditions in the basin affects the original stable state of the basin, and the change of eco-hydrological conditions also plays a decisive role in the stability of the basin. In this manuscript, Indicators of Hydrologic Alteration (IHA) was used to diagnose watershed variation from the eco-hydrological perspective, and a new diagnostic method was proposed in the current study, which was the extraction method of the most relevant eco-hydrological indicators based on a similar year sensitive index and the diagnosis method of variation period. This method used the sensitivity of statistical characteristics between similar years to provide the basis for the selection of the most ecologically-relevant hydrogeological indicators (ERHIs), then selected the strong variation indicators from the most relevant eco-hydrological indicators, and finally used the strong variation indicators to diagnose the watershed variation. The runoff data (1960 to 2020) in the Ankang gauging station of the Hanjiang River were analyzed, and the results showed that the indicators of high variation were the average duration index of low discharge in a year and the minimum discharge index of one day in a year. The variation period was from 1973 to 1986. It was concluded that the diagnosis results from the perspective of eco-hydrology were consistent with the actual hydrological situation changes, and this method had certain reliability.
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Cranmer, A. J., N. Kouwen, and S. F. Mousavi. "Proving WATFLOOD: modelling the nonlinearities of hydrologic response to storm intensities." Canadian Journal of Civil Engineering 28, no. 5 (2001): 837–55. http://dx.doi.org/10.1139/l01-049.
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This paper examines the effects of modelling the nonlinearities of hydrologic response to various storm intensities. Radar rainfall data, remotely sensed land use and land cover data, measured streamflows, and meteorological data were incorporated into the distributed flood forecasting model WATFLOOD to synthesize runoff hydrographs for three significant warm weather rainfall events occurring in 1995. The watershed selected for study was the 288 km2 Duffins Creek drainage basin in southern Ontario. The effects of scaling radar rainfall amounts to match regional storm intensities on the synthesized streamflow hydrographs were examined. Computations and analysis were performed in agreement with widely accepted hydrologic principles and assumptions. The observed and synthesized hydrographs were compared using the unit hydrograph method. The observed and composite unit hydrographs matched extremely well in terms of shape, timing, and peak flow magnitude. These results indicated that WATFLOOD is capable of accurately modelling the nonlinear rainfallrunoff processes for increasing rainfall intensities with respect to peak flow, basin lag, and time to peak flow. However, the arbitrariness of assessing the effective rainfall and base-flow separation for the unit hydrograph method can lead to uncertainties in computing peak flow magnitudes. The grid element size and number and the drainage areas above streamflow gauges are of critical importance to the accuracy of the model.Key words: hydrology, watershed model, flood forecasting, hydrological modelling, model validation, unit hydrograph, nonlinear response.
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Cai, Yaxi, and Xiaodong Yang. "Sediment Variation Characteristics of Major Rivers in the Middle Reaches of the Yellow River." Journal of Architectural Research and Development 5, no. 5 (2021): 20–26. http://dx.doi.org/10.26689/jard.v5i5.2519.
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The sediment sequence analysis of Mann-Kendall method based on major rivers of 10 hydrological station in the middle reaches of the Yellow River [1]. The results show that: The main rivers in the middle reaches of the Yellow River hydrologic station sediment overall showed a trend of decreased significantly. Sediment discharge of all stations except Gao Jiachuan station have reached the maximum in 1956-1969s [2-3]. Among various hydrologic station sediment discharge of inter-generational are generally shows the tendency of reducing year by year. Calculate the sediment transport of major river basin of Yellow River, which average is 0.63.
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Mohd Lokoman, Rahmah, Fadhilah Yusof, Nor Eliza Alias, and Zulkifli Yusop. "Construction of Dependence Structure for Rainfall Stations by Joining Time Series Models with Copula Method." Malaysian Journal of Fundamental and Applied Sciences 17, no. 4 (2021): 306–20. http://dx.doi.org/10.11113/mjfas.v17n4.2345.
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Copula model has applied in various hydrologic studies, however, most analyses conducted does not considering the non-stationary conditions that may exist in the time series. To investigate the dependence structure between two rainfall stations at Johor Bahru, two methods have been applied. The first method considers the non-stationary condition that exists in the data, while the second method assumes stationarity in the time series data. Through goodness-off-fit (GOF) and simulation tests, performance of both methods are compared in this study. The results obtained in this study highlight the importance of considering non-stationarity conditions in the hydrological data.
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Richter, Brian D., Jeffrey V. Baumgartner, Jennifer Powell, and David P. Braun. "A Method for Assessing Hydrologic Alteration within Ecosystems." Conservation Biology 10, no. 4 (1996): 1163–74. http://dx.doi.org/10.1046/j.1523-1739.1996.10041163.x.
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Agarwal, A., R. Maheswaran, V. Sehgal, R. Khosa, B. Sivakumar, and C. Bernhofer. "Hydrologic regionalization using wavelet-based multiscale entropy method." Journal of Hydrology 538 (July 2016): 22–32. http://dx.doi.org/10.1016/j.jhydrol.2016.03.023.
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Atencia, A., L. Mediero, M. C. Llasat, and L. Garrote. "Effect of radar rainfall time resolution on the predictive capability of a distributed hydrologic model." Hydrology and Earth System Sciences 15, no. 12 (2011): 3809–27. http://dx.doi.org/10.5194/hess-15-3809-2011.
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Abstract. The performance of a hydrologic model depends on the rainfall input data, both spatially and temporally. As the spatial distribution of rainfall exerts a great influence on both runoff volumes and peak flows, the use of a distributed hydrologic model can improve the results in the case of convective rainfall in a basin where the storm area is smaller than the basin area. The aim of this study was to perform a sensitivity analysis of the rainfall time resolution on the results of a distributed hydrologic model in a flash-flood prone basin. Within such a catchment, floods are produced by heavy rainfall events with a large convective component. A second objective of the current paper is the proposal of a methodology that improves the radar rainfall estimation at a higher spatial and temporal resolution. Composite radar data from a network of three C-band radars with 6-min temporal and 2 × 2 km2 spatial resolution were used to feed the RIBS distributed hydrological model. A modification of the Window Probability Matching Method (gauge-adjustment method) was applied to four cases of heavy rainfall to improve the observed rainfall sub-estimation by computing new Z/R relationships for both convective and stratiform reflectivities. An advection correction technique based on the cross-correlation between two consecutive images was introduced to obtain several time resolutions from 1 min to 30 min. The RIBS hydrologic model was calibrated using a probabilistic approach based on a multiobjective methodology for each time resolution. A sensitivity analysis of rainfall time resolution was conducted to find the resolution that best represents the hydrological basin behaviour.
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Werner, Arelia T., and Alex J. Cannon. "Hydrologic extremes – an intercomparison of multiple gridded statistical downscaling methods." Hydrology and Earth System Sciences 20, no. 4 (2016): 1483–508. http://dx.doi.org/10.5194/hess-20-1483-2016.
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Abstract. Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e. correlation tests) and distributional properties (i.e. tests for equality of probability distributions). Outputs from seven downscaling methods – bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), the climate imprint delta method (CI), and bias corrected CI (BCCI) – are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3-day peak flow and 7-day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational data sets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational data set. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7-day low-flow events, regardless of reanalysis or observational data set. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event-scale spatial gradients, passed the greatest number of tests for hydrologic extremes. Non-stationarity in the observational/reanalysis data sets complicated the evaluation of downscaling performance. Comparing temporal homogeneity and trends in climate indices and hydrological model outputs calculated from downscaled reanalyses and gridded observations was useful for diagnosing the reliability of the various historical data sets. We recommend that such analyses be conducted before such data are used to construct future hydro-climatic change scenarios.
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Werner, A. T., and A. J. Cannon. "Hydrologic extremes – an intercomparison of multiple gridded statistical downscaling methods." Hydrology and Earth System Sciences Discussions 12, no. 6 (2015): 6179–239. http://dx.doi.org/10.5194/hessd-12-6179-2015.
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Abstract. Gridded statistical downscaling methods are the main means of preparing climate model data to drive distributed hydrological models. Past work on the validation of climate downscaling methods has focused on temperature and precipitation, with less attention paid to the ultimate outputs from hydrological models. Also, as attention shifts towards projections of extreme events, downscaling comparisons now commonly assess methods in terms of climate extremes, but hydrologic extremes are less well explored. Here, we test the ability of gridded downscaling models to replicate historical properties of climate and hydrologic extremes, as measured in terms of temporal sequencing (i.e., correlation tests) and distributional properties (i.e., tests for equality of probability distributions). Outputs from seven downscaling methods – bias correction constructed analogues (BCCA), double BCCA (DBCCA), BCCA with quantile mapping reordering (BCCAQ), bias correction spatial disaggregation (BCSD), BCSD using minimum/maximum temperature (BCSDX), climate imprint delta method (CI), and bias corrected CI (BCCI) – are used to drive the Variable Infiltration Capacity (VIC) model over the snow-dominated Peace River basin, British Columbia. Outputs are tested using split-sample validation on 26 climate extremes indices (ClimDEX) and two hydrologic extremes indices (3 day peak flow and 7 day peak flow). To characterize observational uncertainty, four atmospheric reanalyses are used as climate model surrogates and two gridded observational datasets are used as downscaling target data. The skill of the downscaling methods generally depended on reanalysis and gridded observational dataset. However, CI failed to reproduce the distribution and BCSD and BCSDX the timing of winter 7 day low flow events, regardless of reanalysis or observational dataset. Overall, DBCCA passed the greatest number of tests for the ClimDEX indices, while BCCAQ, which is designed to more accurately resolve event-scale spatial gradients, passed the greatest number of tests for hydrologic extremes. Non-stationarity in the observational/reanalysis datasets complicated the evaluation of downscaling performance. Comparing temporal homogeneity and trends in climate indices and hydrological model outputs calculated from downscaled reanalyses and gridded observations was useful for diagnosing the reliability of the various historical datasets. We recommend that such analyses be conducted before such data are used to construct future hydro-climatic change scenarios.
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Chen, Lina, Longxi Han, Junyi Tan, et al. "Water Environmental Capacity Calculated Based on Point and Non-Point Source Pollution Emission Intensity under Water Quality Assurance Rates in a Tidal River Network Area." International Journal of Environmental Research and Public Health 16, no. 3 (2019): 428. http://dx.doi.org/10.3390/ijerph16030428.
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A mathematical model for simulating hydrodynamics and pollutants migration in a tidal river network was constructed, which takes the temporal and spatial distribution of rainfall runoff and non-point pollutants into consideration. Under the design hydrologic conditions of a typical hydrological year, the daily concentration change process for the control section is obtained. Aiming at the uncertainty of hydrology and water quality parameters such as flow direction, flow rate and concentration change in tidal river network area, a statistical analysis method is used to obtain the maximum allowable concentration of pollutants in the control section under the condition of the water quality standard assurance rate of. Then, a formula for calculating the pollutions emission intensity of point and non-point sources is derived. The method was applied to the pollution source control in a typical region like Taihu in China.
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Newman, Andrew J., Amanda G. Stone, Manabendra Saharia, Kathleen D. Holman, Nans Addor, and Martyn P. Clark. "Identifying sensitivities in flood frequency analyses using a stochastic hydrologic modeling system." Hydrology and Earth System Sciences 25, no. 10 (2021): 5603–21. http://dx.doi.org/10.5194/hess-25-5603-2021.
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Abstract. This study employs a stochastic hydrologic modeling framework to evaluate the sensitivity of flood frequency analyses to different components of the hydrologic modeling chain. The major components of the stochastic hydrologic modeling chain, including model structure, model parameter estimation, initial conditions, and precipitation inputs were examined across return periods from 2 to 100 000 years at two watersheds representing different hydroclimates across the western USA. A total of 10 hydrologic model structures were configured, calibrated, and run within the Framework for Understanding Structural Errors (FUSE) modular modeling framework for each of the two watersheds. Model parameters and initial conditions were derived from long-term calibrated simulations using a 100 member historical meteorology ensemble. A stochastic event-based hydrologic modeling workflow was developed using the calibrated models in which millions of flood event simulations were performed for each basin. The analysis of variance method was then used to quantify the relative contributions of model structure, model parameters, initial conditions, and precipitation inputs to flood magnitudes for different return periods. Results demonstrate that different components of the modeling chain have different sensitivities for different return periods. Precipitation inputs contribute most to the variance of rare floods, while initial conditions are most influential for more frequent events. However, the hydrological model structure and structure–parameter interactions together play an equally important role in specific cases, depending on the basin characteristics and type of flood metric of interest. This study highlights the importance of critically assessing model underpinnings, understanding flood generation processes, and selecting appropriate hydrological models that are consistent with our understanding of flood generation processes.
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Zlatanović, Nikola, and Sonja Gavrić. "Comparison of an Automated and Manual Method for Calculating Storm Runoff Response in Ungauged Catchments in Serbia." Journal of Hydrology and Hydromechanics 61, no. 3 (2013): 195–201. http://dx.doi.org/10.2478/johh-2013-0025.
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Abstract Estimation of discharge from ungauged catchments based on rainfall-runoff analysis is a very frequent task in engineering hydrology. Very often, design discharges are needed for streams or small rivers where no streamflow data is available (river training works, culverts, small hydropower plants, etc). This study uses a well established lumped hydrologic rainfall-runoff model to compare two different approaches in data preparation. The traditional method of manual obtainment of catchment parameters was compared to a more contemporary methodology using automation with geographic information systems, digital terrain models and available datasets, with an emphasis on open-source tools and freely available datasets. Both techniques were implemented on more than 100 catchments in Serbia to calculate storm runoff response. The results show minor differences that are insignificant compared to the time and resources saved with the automated techniques. The use of such automated methods enables the hydrologist to direct more attention to other factors that influence discharge even more than catchment parameters, such as rainfall, soil and land use data.
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Hua, Xu, Xue Hengxin, and Chen Zhiguo. "Application of hydrologic forecast model." Water Science and Technology 66, no. 2 (2012): 239–46. http://dx.doi.org/10.2166/wst.2012.161.
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In order to overcome the shortcoming of the solution may be trapped into the local minimization in the traditional TSK (Takagi-Sugeno-Kang) fuzzy inference training, this paper attempts to consider the TSK fuzzy system modeling approach based on the visual system principle and the Weber law. This approach not only utilizes the strong capability of identifying objects of human eyes, but also considers the distribution structure of the training data set in parameter regulation. In order to overcome the shortcoming of it adopting the gradient learning algorithm with slow convergence rate, a novel visual TSK fuzzy system model based on evolutional learning is proposed by introducing the particle swarm optimization algorithm. The main advantage of this method lies in its very good optimization, very strong noise immunity and very good interpretability. The new method is applied to long-term hydrological forecasting examples. The simulation results show that the method is feasibile and effective, the new method not only inherits the advantages of traditional visual TSK fuzzy models but also has the better global convergence and accuracy than the traditional model.
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Domingo, N. D. Sto, A. Refsgaard, O. Mark, and B. Paludan. "Flood analysis in mixed-urban areas reflecting interactions with the complete water cycle through coupled hydrologic-hydraulic modelling." Water Science and Technology 62, no. 6 (2010): 1386–92. http://dx.doi.org/10.2166/wst.2010.365.
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The potential devastating effects of urban flooding have given high importance to thorough understanding and management of water movement within catchments, and computer modelling tools have found widespread use for this purpose. The state-of-the-art in urban flood modelling is the use of a coupled 1D pipe and 2D overland flow model to simultaneously represent pipe and surface flows. This method has been found to be accurate for highly paved areas, but inappropriate when land hydrology is important. The objectives of this study are to introduce a new urban flood modelling procedure that is able to reflect system interactions with hydrology, verify that the new procedure operates well, and underline the importance of considering the complete water cycle in urban flood analysis. A physically-based and distributed hydrological model was linked to a drainage network model for urban flood analysis, and the essential components and concepts used were described in this study. The procedure was then applied to a catchment previously modelled with the traditional 1D-2D procedure to determine if the new method performs similarly well. Then, results from applying the new method in a mixed-urban area were analyzed to determine how important hydrologic contributions are to flooding in the area.
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Cho, Younghyun, and Bernard A. Engel. "Spatially distributed long-term hydrologic simulation using a continuous SCS CN method-based hybrid hydrologic model." Hydrological Processes 32, no. 7 (2018): 904–22. http://dx.doi.org/10.1002/hyp.11463.
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Wang, S., G. H. Huang, B. W. Baetz, and W. Huang. "Probabilistic Inference Coupled with Possibilistic Reasoning for Robust Estimation of Hydrologic Parameters and Piecewise Characterization of Interactive Uncertainties." Journal of Hydrometeorology 17, no. 4 (2016): 1243–60. http://dx.doi.org/10.1175/jhm-d-15-0131.1.
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Abstract This paper presents a factorial possibilistic–probabilistic inference (FPI) framework for estimation of hydrologic parameters and characterization of interactive uncertainties. FPI is capable of incorporating expert knowledge into the parameter adjustment procedure for enhancing the understanding of the nature of the calibration problem. As a component of the FPI framework, a Monte Carlo–based fractional fuzzy–factorial analysis (MFA) method is also proposed to identify the best parameter set and its underlying probability distributions in a fuzzy probability space. Factorial analysis of variance (ANOVA) coupled with its multivariate extensions are performed to explore potential interactions among model parameters and among hydrological metrics in a systematic manner. The proposed methodology is applied to the Xiangxi River watershed by using the conceptual hydrological model (HYMOD) to demonstrate its validity and applicability. Results reveal that MFA is capable of deriving probability density functions (PDFs) of hydrologic model parameters. Moreover, the sequential inferences derived from the F test and its multivariate approximations disclose the statistical significance of parametric interactions affecting individual and multiple hydrological metrics, respectively. The findings presented here indicate that parametric interactions are complex in a fuzzy stochastic environment, and the magnitude and direction of interaction effects vary in different regions of the parameter space as well as vary temporally because of the dynamic behavior of hydrologic systems.
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Honti, M., A. Scheidegger, and C. Stamm. "Importance of hydrological uncertainty assessment methods in climate change impact studies." Hydrology and Earth System Sciences Discussions 11, no. 1 (2014): 501–53. http://dx.doi.org/10.5194/hessd-11-501-2014.
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Abstract. Climate change impact assessments have become more and more popular in hydrology since the middle 1980's with a recent boost after the publication of the IPCC AR4 report. During hundreds of impact studies a quasi-standard methodology emerged, which is mainly shaped by the growing public demand for predicting how water resources management or flood protection should change in the following decades. The "standard" workflow relies on a model cascade from global circulation model (GCM) predictions for selected IPCC scenarios to future catchment hydrology. Uncertainty is present at each level and propagates through the model cascade. There is an emerging consensus between many studies on the relative importance of the different uncertainty sources. The prevailing perception is that GCM uncertainty dominates hydrological impact studies. Our hypothesis was that the relative importance of climatic and hydrologic uncertainty is (among other factors) heavily influenced by the uncertainty assessment method. To test this we carried out a climate change impact assessment and estimated the relative importance of the uncertainty sources. The study was performed on two small catchments in the Swiss Plateau with a lumped conceptual rainfall runoff model. In the climatic part we applied the standard ensemble approach to quantify uncertainty but in hydrology we used formal Bayesian uncertainty assessment with two different likelihood functions. One was a time-series error model that was able to deal with the complicated statistical properties of hydrological model residuals. The second was an approximate likelihood function for the flow quantiles. The results showed that the expected climatic impact on flow quantiles was small compared to prediction uncertainty. The source, structure and composition of uncertainty depended strongly on the uncertainty assessment method. This demonstrated that one could arrive to rather different conclusions about predictive uncertainty for the same hydrological model and calibration data when considering different objective functions for calibration.
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Sun, Jun, Feng Ye, Nadia Nedjah, Ming Zhang, and Dong Xu. "A Practical Yet Accurate Real-Time Statistical Analysis Library for Hydrologic Time-Series Big Data." Water 15, no. 4 (2023): 708. http://dx.doi.org/10.3390/w15040708.
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Using different statistical analysis methods to examine hydrologic time-series data is the basis of accurate hydrologic status analysis. With the wide application of the Internet of Things and sensor technologies, traditional statistical analysis methods are unable to meet the demand for real-time and accurate hydrologic data analysis. The existing mainstream big-data analysis platforms lack analysis methods oriented to hydrologic data. In this context, a real-time statistical analysis library based on the new generation of big data processing engine Flink, called HydroStreamingLib, was proposed and implemented. Furthermore, in order to prove the efficiency and handiness of the proposed library, a real-time statistical analysis system of hydrologic stream data was developed based on the concepts available in the proposed library. The results showed that HydroStreamingLib provides users with an efficient, real-time statistical verification method, thus extending the application capabilities of Flink Ecology in some specific fields.
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Gourley, Jonathan J., and Baxter E. Vieux. "A Method for Evaluating the Accuracy of Quantitative Precipitation Estimates from a Hydrologic Modeling Perspective." Journal of Hydrometeorology 6, no. 2 (2005): 115–33. http://dx.doi.org/10.1175/jhm408.1.
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Abstract A major goal in quantitative precipitation estimation and forecasting is the ability to provide accurate initial conditions for the purposes of hydrologic modeling. The accuracy of a streamflow prediction system is dependent upon how well the initial hydrometeorological states are characterized. A methodology is developed to objectively and quantitatively evaluate the skill of several different precipitation algorithms at the scale of application—a watershed. Thousands of hydrologic simulations are performed in an ensemble fashion, enabling an exploration of the model parameter space. Probabilistic statistics are then utilized to compare the relative skill of hydrologic simulations produced from the different precipitation inputs to the observed streamflow. The primary focus of this study is to demonstrate a methodology to evaluate precipitation algorithms that can be used to supplement traditional radar–rain gauge analyses. This approach is appropriate for the evaluation of precipitation estimates or forecasts that are intended to serve as inputs to hydrologic models.
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Liu, Yang, Shengle Cao, Yuheng Yang, and Xi Zhang. "Assessment of hydrologic regime considering the distribution of hydrologic parameters." Water Supply 18, no. 3 (2017): 875–85. http://dx.doi.org/10.2166/ws.2017.161.
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Abstract To assess hydrologic regime more comprehensively using the distribution of hydrologic parameters, the probability density function of each parameter is obtained from parameter estimations and goodness-of-fit tests based on the principle of maximum entropy. Then, the Shannon entropy and weights for a multi-attribute decision-making process are used to calculate the degree of hydrologic alteration. This method is applied to the Xiaoqing River in the city of Jinan, China. The results indicate that the diversities of the monthly mean flow and annual extreme flow show decreasing trends that are attributable to human impacts, while the diversities of the timing of annual extreme, high and low flows, and the rate and frequency of flooding show increasing trends. Meanwhile, the overall degree of hydrologic alteration of the Xiaoqing River in Jinan is 0.747, which belongs to a change in the height. Thus, we suggest that the timing and volume of inter-basin water transfer should be reasonably regulated and that the regulation of peak flooding times and peak flow should be strengthened to make them conform to ecological characteristics during the water resource management of the Xiaoqing River.
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Wang, Wei, Jia Liu, Chuanzhe Li, Yuchen Liu, and Fuliang Yu. "Data Assimilation for Rainfall-Runoff Prediction Based on Coupled Atmospheric-Hydrologic Systems with Variable Complexity." Remote Sensing 13, no. 4 (2021): 595. http://dx.doi.org/10.3390/rs13040595.
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The data assimilation technique is an effective method for reducing initial condition errors in numerical weather prediction (NWP) models. This paper evaluated the potential of the weather research and forecasting (WRF) model and its three-dimensional data assimilation (3DVar) module in improving the accuracy of rainfall-runoff prediction through coupled atmospheric-hydrologic systems. The WRF model with the assimilation of radar reflectivity and conventional surface and upper-air observations provided the improved initial and boundary conditions for the hydrological process; subsequently, three atmospheric-hydrological systems with variable complexity were established by coupling WRF with a lumped, a grid-based Hebei model, and the WRF-Hydro modeling system. Four storm events with different spatial and temporal rainfall distribution from mountainous catchments of northern China were chosen as the study objects. The assimilation results showed a general improvement in the accuracy of rainfall accumulation, with low root mean square error and high correlation coefficients compared to the results without assimilation. The coupled atmospheric-hydrologic systems also provide more accurate flood forecasts, which depend upon the complexity of the coupled hydrological models. The grid-based Hebei system provided the most stable forecasts regardless of whether homogeneous or inhomogeneous rainfall was considered. Flood peaks before assimilation were underestimated more in the lumped Hebei model relative to the other coupling systems considered, and the model seems more applicable for homogeneous temporal and spatial events. WRF-Hydro did not exhibit desirable predictions of rapid flood process recession. This may reflect increasing infiltration due to the interaction of atmospheric and land surface hydrology at each integration, resulting in mismatched solutions for local runoff generation and confluence.
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Zhang, Hanchen, and Weijiang Zhang. "Effect of time scale on flood simulation: maximum rainfall intensity and fractal theory based time disaggregation method for rainfall." Water Supply 20, no. 8 (2020): 3585–96. http://dx.doi.org/10.2166/ws.2020.250.
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Abstract The time scale of rainfall data limits the accuracy and application scope of hydrologic models, especially when low-accuracy observed rainfall data are used in physically based distributed hydrologic models. In this study, an optimized rainfall method based on maximum rainfall intensity and self-similarity was established to provide different rainfall data for the physically based distributed hydrological model. The results showed the following: (1) the increase of time scale resulted in decreased rainfall intensity and an evenly distributed rainfall pattern; (2) the established disaggregation method for rainfall well described the uneven distribution in time; (3) the influence of time scale could be divided into 1–20, 20–120, and 120–360 min; (4) the conversion method between rainfall intensity and saturated hydraulic conductivity was effective at ensuring the physical meaning of the parameters on a time scale of 20–90 min. Furthermore, results showed that the reasonable time scale of application of the CASC2D model is less than 120 min. For longer time scales, the model was able to simulate peak discharge but unable to describe the flood process.
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Liu, Haifan, Heng Dai, Jie Niu, et al. "Hierarchical sensitivity analysis for a large-scale process-based hydrological model applied to an Amazonian watershed." Hydrology and Earth System Sciences 24, no. 10 (2020): 4971–96. http://dx.doi.org/10.5194/hess-24-4971-2020.
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Abstract. Sensitivity analysis methods have recently received much attention for identifying important uncertainty sources (or uncertain inputs) and improving model calibrations and predictions for hydrological models. However, it is still challenging to apply the quantitative and comprehensive global sensitivity analysis method to complex large-scale process-based hydrological models (PBHMs) because of its variant uncertainty sources and high computational cost. Therefore, a global sensitivity analysis method that is capable of simultaneously analyzing multiple uncertainty sources of PBHMs and providing quantitative sensitivity analysis results is still lacking. In an effort to develop a new tool for overcoming these weaknesses, we improved the hierarchical sensitivity analysis method by defining a new set of sensitivity indices for subdivided parameters. A new binning method and Latin hypercube sampling (LHS) were implemented for estimating these new sensitivity indices. For test and demonstration purposes, this improved global sensitivity analysis method was implemented to quantify three different uncertainty sources (parameters, models, and climate scenarios) of a three-dimensional large-scale process-based hydrologic model (Process-based Adaptive Watershed Simulator, PAWS) with an application case in an ∼ 9000 km2 Amazon catchment. The importance of different uncertainty sources was quantified by sensitivity indices for two hydrologic outputs of interest: evapotranspiration (ET) and groundwater contribution to streamflow (QG). The results show that the parameters, especially the vadose zone parameters, are the most important uncertainty contributors for both outputs. In addition, the influence of climate scenarios on ET predictions is also important. Furthermore, the thickness of the aquifers is important for QG predictions, especially in main stream areas. These sensitivity analysis results provide useful information for modelers, and our method is mathematically rigorous and can be applied to other large-scale hydrological models.
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Das, S. "Distribution selection for hydrologic frequency analysis using subsampling method." IOP Conference Series: Earth and Environmental Science 39 (August 2016): 012059. http://dx.doi.org/10.1088/1755-1315/39/1/012059.
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Tan, Yaogeng, Sandra M. Guzman, Zengchuan Dong, and Liang Tan. "Selection of Effective GCM Bias Correction Methods and Evaluation of Hydrological Response under Future Climate Scenarios." Climate 8, no. 10 (2020): 108. http://dx.doi.org/10.3390/cli8100108.
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Global climate change is presenting a variety of challenges to hydrology and water resources because it strongly affects the hydrologic cycle, runoff, and water supply and demand. In this study, we assessed the effects of climate change scenarios on hydrological variables (i.e., evapotranspiration and runoff) by linking the outputs from the global climate model (GCM) with the Soil and Water Assessment Tool (SWAT) for a case study in the Lijiang River Basin, China. We selected a variety of bias correction methods and their combinations to correct the lower resolution GCM outputs of both precipitation and temperature. Then, the SWAT model was calibrated and validated using the observed flow data and corrected historical GCM with the optimal correction method selected. Hydrological variables were simulated using the SWAT model under emission scenarios RCP2.6, RCP4.5, and RCP8.5. The results demonstrated that correcting methods have a positive effect on both daily precipitation and temperature, and a hybrid method of bias correction contributes to increased performance in most cases and scenarios. Based on the bias corrected scenarios, precipitation annual average, temperature, and evapotranspiration will increase. In the case of precipitation and runoff, projection scenarios show an increase compared with the historical trends, and the monthly distribution of precipitation, evapotranspiration, and runoff shows an uneven distribution compared with baseline. This study provides an insight on how to choose a proper GCM and bias correction method and a helpful guide for local water resources management.
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Mizukami, Naoki, Martyn P. Clark, Ethan D. Gutmann, et al. "Implications of the Methodological Choices for Hydrologic Portrayals of Climate Change over the Contiguous United States: Statistically Downscaled Forcing Data and Hydrologic Models." Journal of Hydrometeorology 17, no. 1 (2015): 73–98. http://dx.doi.org/10.1175/jhm-d-14-0187.1.
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Abstract Continental-domain assessments of climate change impacts on water resources typically rely on statistically downscaled climate model outputs to force hydrologic models at a finer spatial resolution. This study examines the effects of four statistical downscaling methods [bias-corrected constructed analog (BCCA), bias-corrected spatial disaggregation applied at daily (BCSDd) and monthly scales (BCSDm), and asynchronous regression (AR)] on retrospective hydrologic simulations using three hydrologic models with their default parameters (the Community Land Model, version 4.0; the Variable Infiltration Capacity model, version 4.1.2; and the Precipitation–Runoff Modeling System, version 3.0.4) over the contiguous United States (CONUS). Biases of hydrologic simulations forced by statistically downscaled climate data relative to the simulation with observation-based gridded data are presented. Each statistical downscaling method produces different meteorological portrayals including precipitation amount, wet-day frequency, and the energy input (i.e., shortwave radiation), and their interplay affects estimations of precipitation partitioning between evapotranspiration and runoff, extreme runoff, and hydrologic states (i.e., snow and soil moisture). The analyses show that BCCA underestimates annual precipitation by as much as −250 mm, leading to unreasonable hydrologic portrayals over the CONUS for all models. Although the other three statistical downscaling methods produce a comparable precipitation bias ranging from −10 to 8 mm across the CONUS, BCSDd severely overestimates the wet-day fraction by up to 0.25, leading to different precipitation partitioning compared to the simulations with other downscaled data. Overall, the choice of downscaling method contributes to less spread in runoff estimates (by a factor of 1.5–3) than the choice of hydrologic model with use of the default parameters if BCCA is excluded.