Streamflow Response to Climate Change in the Brahmani River Basin, India
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The runoff analysis advocated by HINO and HASEGAWA was adopted the outflow through drain boring at Irahara landslide. The unit-hydrograph of surface runoff and interflow of No.1 carried out at 1989 changed after the drain boring executed at 1990. The unit-hydrograph of ground-water runoff did not change. The distance between No.1 and the drain boring at 1990 was about 100 m, therefore the sphere of drain boring influence was the same. The time constant of runoff became longer from about 9 hours at Aug. 1989 from about 30 or 40 hours at 1990, 1991 and 1992.It is clear that a small landslide is observed by extensometer and borehole-inclinometer. The unithydrograph of grount-water runoff does not change but the unit-hydrograph of surface runoff and interflow change. Therefore it seems to be the most probable that the ground-water gives rise to a small landslide.
Interflow
Extensometer
Tailwater
Outflow
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Flood forecasting
Statistic
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Abstract Analysis of flood and streamflow timing has recently gained prominence as a tool for attribution of climatic changes to flooding. Such studies generally apply circular statistics to the day of maximum flow in a calendar year and use nonparametric linear trend tests to investigate changes in flooding on a local or regional scale. Here we investigate both the center timing of streamflow and the day of maximum flow using a local water year. For each station, the start of the water year is defined as the month of lowest average monthly streamflow. This definition of water year prevents ambiguity in the direction of computed trends and enables flood and streamflow timing to be described by a normal distribution. Using the assumption of normality, we calculate the historical trend in both flood and streamflow timing using linear regression. While shifts in flood and streamflow timing are consistent with climate change and are shifting in a similar direction, shifts in the timing of the annual maxima flood are approximately three times that of streamflow timing. The results here have implications for water resources and environmental management where streamflow and flood timing are critical to planning. The applicability of the normal approximation to flood and streamflow timing will enable future analyses to use parametric statistics.
Flood forecasting
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Streamflow forecasting is of great significance for water resources planning and management. In recent years, numerous data-driven models have been widely used for streamflow forecasting. However, the traditional single data-driven model ignores the utilization of different streamflow regimes. This study proposed an integrated framework for daily streamflow forecasting based on the regime recognition of flow sequences. The framework integrates self-organizing maps (SOM) for identifying streamflow sub-sequences, the random forests (RF) algorithm to select input variables for different streamflow sub-sequences, and a deep belief network (DBN) for establishing complex relationships between the selected input variables and streamflows for different sub-sequences. Specifically, the integrated framework was applied to forecast daily streamflow at the Xiantao hydrological station in the Hanjiang River Basin, China. The results show that the developed integrated framework has higher streamflow prediction accuracy than the single data-driven model (i.e., the DBN model in this study), with Nash efficiency coefficient (NSE) of 0.91/0.81 and coefficient of determination (R2) of 0.93/0.89 for the integrated framework/DBN model during the validation period, respectively. Additionally, the prediction accuracy of the peak flood was also improved. The relative error of the peak flood derived from the integrated framework was reduced by 4.6%, compared with the single DBN model. Overall, the constructed integration framework, considering the complex characteristic of different flow regimes, could improve the accuracy for daily streamflow forecasting.
Flood forecasting
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Abstract Operational streamflow forecasting is critically important to managers of river basins that supply water, hydropower, and flood protection. While seasonal water supply forecasts (WSFs) are important for long‐term water resources planning operations, shorter term (e.g., 1–5 weeks) streamflow forecasts are critical for balancing water conservation with flood risk during wet periods. In this study, we designed a streamflow forecasting system with the water resources group at the Salt River Project (SRP), a provider of water and power to millions of customers in central Arizona (AZ), to provide streamflow forecasts for a diverse and operationally important set of watersheds in AZ. The forecast system uses machine learning to make seasonal WSFs, a rainfall–runoff model driven by ensemble meteorological forecasts to make 35‐day streamflow forecasts, and an innovative approach to improve the WSFs based on the 35‐day streamflow forecasts. This model integration allows for an assessment of the impact of different meteorological forecasts on WSFs, helping SRP to balance water conservation goals with shorter term flood risks. In addition, seasonal WSFs are improved in the early winter when they incorporate the 35‐day streamflow predictions. Furthermore, these improvements are larger than when they incorporate 7‐day streamflow predictions, demonstrating the value of using subseasonal to seasonal (S2S, >1–2 weeks) forecasts to improve seasonal WSFs in these watersheds.
Flood forecasting
Water balance
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Extended forecasting using the National Weather Service River Forecast System (NWSRFS) is done with the NWS Extended Streamflow Prediction (ESP) program. This paper examines the theory, capabilities, and potential applications of the ESP procedure. ESP uses conceptual hydrologic/hydraulic models to forecast future streamflow using. the current snow, soil moisture, river, and reservoir conditions with historical meteorological data. The ESP procedure assumes that meteorological events that occurred in the past are representative of events that may occur in the future. Each year of historical meteorological data is assumed to be a possible representation of the future and is used to simulate a streamflow trace. The simulated streamflow traces can be scanned for maximum flow, minimum flow, volume of flow, reservoir stage, etc., for any period in the future. ESP produces a probabilistic forecast for each streamflow variable and period of interest. The procedure was originally developed for water supply forecasting in snowmelt areas, but it can also be used to produce spring flood outlooks, forecasts for navigation, inflow hydrographs for reservoir operation, and time series needed for risk analysis during droughts.
Flood forecasting
Snowmelt
Inflow
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Accurate long-term streamflow forecast is essential to alleviate and solve the water security problems related to flood and drought disaster warnings. In this study, a new strategy for forecasting monthly streamflow is proposed and four scenarios are designed for the evaluation of different roles of baseflow and surface runoff on performances of long-term streamflow forecasting. The developed models are evaluated at multiple streamflow sites located in the Zhejiang Province of China. The results show that artificial intelligence (AI)-based models with two predictor variables (i.e. baseflow and surface runoff) performed better than that with a single predictor (streamflow) for all the months in a year, and the prediction accuracy of annual peak and monthly streamflow values is improved. Based on the comprehensive evaluations of all the models, the baseflow and surface runoff values are recommended as inputs to AI-based models for an improved prediction accuracy of streamflows.
Base flow
Flood forecasting
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