A Comparative Evaluation of Short-Term Streamflow Forecasting Using Time Series Analysis and Rainfall-Runoff Models in eWater Source

Dutta, Dushmanta; Welsh, Wendy; Vaze, Jai; Kim, Shaun; Nicholls, David

doi:10.1007/s11269-012-0151-9

Cited by 35 publications

(16 citation statements)

References 39 publications

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“…On the other hand, the hydrological statistical models well express the hydrological prototypes by means of statistical relationships between inputs and outputs (Dutta et al, 2012) without explicit consideration of mechanisms of hydrological processes (Box and Jenkins, 1976;Castellano-M endez et al, 2004;Can et al, 2012). Compared with the physical models, mathematical statistical models require much less hydrological data and parameters (Adamowski et al, 2012).…”

Section: Introductionmentioning

confidence: 99%

Analyzing effects of climate change on streamflow in a glacier mountain catchment using an ARMA model

Liu

et al. 2015

Quaternary International

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Section: Introductionmentioning

confidence: 99%

Analyzing effects of climate change on streamflow in a glacier mountain catchment using an ARMA model

Liu

et al. 2015

Quaternary International

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“…In order to provide variable source area (VSA) forecasts to a large audience of end users, and tools to interested scientist developing solutions to similar problems, this study introduces a DSS that provides real-time and spatially detailed, short-term forecasts in a web based interface built on free and open source software (FOSS). Although DSS platforms with similar goals have been introduced (e.g., predicting stream flow or reservoir status [12][13][14][15]) and software tools have been designed to communicate geospatial information including real time data and forecasts from earth systems models [16][17][18] we describe a system that is developed for the particular problem of rapid refresh, high resolution, forecast products capable of displaying raster model outputs anywhere in the world at very fine resolutions.…”

Section: Introductionmentioning

confidence: 99%

A Web Based Interface for Distributed Short-Term Soil Moisture Forecasts

Sommerlot

Easton

2017

Water

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Agricultural non-point source (NPS) pollution is a source of water quality impairment, and demonstrates widely varying spatial and temporal pollution potential. Many efforts to protect water quality are based on seasonal and annual estimates of pollutant loss potential (NRCS 590 nutrient management standard, P-Index) that inadequately address the hydrologic processes driving NPS pollution. One barrier to adopting practices that address NPS pollution is a lack of tools capable of transferring information at sufficient spatial and temporal resolution so that end-users can make informed decisions. We introduce a web-based system displaying distributed hydrologic forecasts using free and open source software. The system consists of three primary components: (1) a hydrology model that provides short-term distributed forecasts; (2) a data structure capable of re-structuring large, high resolution rasters; (3) an interface employing adaptive map-viewing technology that allows end-users to interact with the data to avoid high-risk areas when planning agricultural practices.

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“…Numerical weather predictions are usually incorporated into ensemble streamflow prediction (ESP) to provide climate information (e.g., Clark and Hay 2004;Roulin and Vannitsem 2005;Ghile and Schulze 2010;Tang et al 2010;Şensoy and Uysal 2012;Dutta et al 2012). A hydrological forecasting system based on coupling the ensemble weather forecast (EWF) with hydrological models is able to capture the uncertainties associated with the weather forecast and thus to better predict river flows.…”

Section: Introductionmentioning

confidence: 99%

Combining Stochastic Weather Generation and Ensemble Weather Forecasts for Short-Term Streamflow Prediction

Chen

Brissette

2015

Water Resour Manage

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Ensemble streamflow predictions (ESPs) offer great potential benefits for water resource management, as they contain key probabilistic information for analyzing prediction uncertainty. Ensemble weather forecasts (EWFs) are usually incorporated into ESPs to provide climate information. However, there is no simple way to combine both of them, since EWFs are generally biased and under-dispersed. This study presents a new short-term (1 to 7 lead days) probabilistic streamflow prediction system combining stochastic weather generation and EWFs. The bias and under-dispersion of EWFs were first corrected using a weather generatorbased post-processing approach (GPP). The corrected weather forecasts were then coupled with a hydrological model for streamflow forecasts. The proposed GPP forecast was compared against two other forecasts, one using the raw EWF (GFS), and the other using a stochastic weather generator (WG). The comparison was carried out over two Quebec watersheds, using a set of deterministic and probabilistic verification metrics. The deterministic metrics showed that the GPP forecast is consistently the best at predicting the ensemble mean streamflow for both watersheds and for all the leads ranging between 1 and 7 days, followed by the WG forecast. The probabilistic metrics showed negative or near zero skill retained by the GFS forecast for the first 7 lead days. The WG system was much more skillful than the GFS. The GPP forecast consistently displayed the highest skill and reliability in terms of all the metrics applied. With increasing lead days, the skill and reliability of the GPP forecast tend to converge toward that of the WG forecast, indicating that the short-term GPP forecast could easily be linked to a pure WG forecast to extend the forecast horizon.

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A Comparative Evaluation of Short-Term Streamflow Forecasting Using Time Series Analysis and Rainfall-Runoff Models in eWater Source

Cited by 35 publications

References 39 publications

Analyzing effects of climate change on streamflow in a glacier mountain catchment using an ARMA model

Analyzing effects of climate change on streamflow in a glacier mountain catchment using an ARMA model

A Web Based Interface for Distributed Short-Term Soil Moisture Forecasts

Combining Stochastic Weather Generation and Ensemble Weather Forecasts for Short-Term Streamflow Prediction

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