Probabilistic Streamflow forecast for Narmada River Basin

2021 ◽  
Author(s):  
Urmin Vegad ◽  
Vimal Mishra
Keyword(s):  
River Basin ◽  
India Meteorological Department ◽  
Tropical Meteorology ◽  
Ensemble Prediction ◽  
Streamflow Prediction ◽  
Infiltration Capacity ◽  
Daily Streamflow ◽  
Operating Rules ◽  
Narmada River ◽  
Ensemble Streamflow Prediction

<p>Ensemble Streamflow Prediction (ESP) is a widely used method in forecasting streamflow, particularly for extremely low or high flows. However, the incorporation of reservoir operations in using ensemble streamflow prediction has not been investigated till yet. We calibrated Variable Infiltration Capacity (VIC) model for daily streamflow for Narmada river basin at four stations (Sandia, Handia, Mandleshwar and Garudeshwar) considering the effect of four reservoirs (Bargi, Tawa, Indira Sagar and Sardar Sarovar). The model is well-calibrated for the selected river basin (R2>0.55) at all locations. Further, routing of streamflow is done considering the reservoir storage dynamics and operating rules. Input data for ensemble prediction is taken from all 16 members of the Extended Range Forecast System (ERFS) developed by Indian Institute of Tropical Meteorology (IITM) and implemented by India Meteorological Department (IMD). Post-processing of the results gave us probabilities of uncertainties associated with streamflow prediction using ERFS members. This study provides key information in predictions of streamflow by incorporating the reservoirs based on the ERFS ensemble members, which can be used to effectively mitigate life and property losses associated with extreme flows in rivers.</p>

scholarly journals Correcting Errors in Streamflow Forecast Ensemble Mean and Spread

2008 ◽  
Vol 9 (1) ◽  
pp. 132-148 ◽  
Author(s):  
Andrew W. Wood ◽  
John C. Schaake
Keyword(s):  
River Basin ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Streamflow Prediction ◽  
Summer Period ◽  
Streamflow Forecast ◽  
Ensemble Mean ◽  
Ensemble Streamflow Prediction ◽  
Deterministic Components ◽  
Seasonal Streamflow

Abstract When hydrological models are used for probabilistic streamflow forecasting in the Ensemble Streamflow Prediction (ESP) framework, the deterministic components of the approach can lead to errors in the estimation of forecast uncertainty, as represented by the spread of the forecast ensemble. One avenue for correcting the resulting forecast reliability errors is to calibrate the streamflow forecast ensemble to match observed error characteristics. This paper outlines and evaluates a method for forecast calibration as applied to seasonal streamflow prediction. The approach uses the correlation of forecast ensemble means with observations to generate a conditional forecast mean and spread that lie between the climatological mean and spread (when the forecast has no skill) and the raw forecast mean with zero spread (when the forecast is perfect). Retrospective forecasts of summer period runoff in the Feather River basin, California, are used to demonstrate that the approach improves upon the performance of traditional ESP forecasts by reducing errors in forecast mean and improving spread estimates, thereby increasing forecast reliability and skill.

scholarly journals On the sources of hydrological prediction uncertainty in the Amazon

2012 ◽  
Vol 16 (9) ◽  
pp. 3127-3137 ◽  
Author(s):  
R. C. D. Paiva ◽  
W. Collischonn ◽  
M. P. Bonnet ◽  
L. G. G. de Gonçalves
Keyword(s):  
Data Assimilation ◽  
River Basin ◽  
Hydrological Model ◽  
Initial Conditions ◽  
State Variables ◽  
Streamflow Prediction ◽  
Amazon River Basin ◽  
Forecast Uncertainty ◽  
Ensemble Streamflow Prediction ◽  
Hydrological Forecast

Abstract. Recent extreme events in the Amazon River basin and the vulnerability of local population motivate the development of hydrological forecast systems using process based models for this region. In this direction, the knowledge of the source of errors in hydrological forecast systems may guide the choice on improving model structure, model forcings or developing data assimilation systems for estimation of initial model states. We evaluate the relative importance of hydrologic initial conditions and model meteorological forcings errors (precipitation) as sources of stream flow forecast uncertainty in the Amazon River basin. We used a hindcast approach that compares Ensemble Streamflow Prediction (ESP) and a reverse Ensemble Streamflow Prediction (reverse-ESP). Simulations were performed using the physically-based and distributed hydrological model MGB-IPH, comprising surface energy and water balance, soil water, river and floodplain hydrodynamics processes. The model was forced using TRMM 3B42 precipitation estimates. Results show that uncertainty on initial conditions plays an important role for discharge predictability, even for large lead times (∼1 to 3 months) on main Amazonian Rivers. Initial conditions of surface waters state variables are the major source of hydrological forecast uncertainty, mainly in rivers with low slope and large floodplains. Initial conditions of groundwater state variables are important, mostly during low flow period and in the southeast part of the Amazon where lithology and the strong rainfall seasonality with a marked dry season may be the explaining factors. Analyses indicate that hydrological forecasts based on a hydrological model forced with historical meteorological data and optimal initial conditions may be feasible. Also, development of data assimilation methods is encouraged for this region.

scholarly journals On the Impact of Short-Range Meteorological Forecasts for Ensemble Streamflow Predictions

2008 ◽  
Vol 9 (6) ◽  
pp. 1301-1317 ◽  
Author(s):  
Guillaume Thirel ◽  
Fabienne Rousset-Regimbeau ◽  
Eric Martin ◽  
Florence Habets
Keyword(s):  
Short Range ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Streamflow Prediction ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Ensemble Streamflow Prediction ◽  
Prediction Systems ◽  
Set Up ◽  
The Impact

Abstract Ensemble streamflow prediction systems are emerging in the international scientific community in order to better assess hydrologic threats. Two ensemble streamflow prediction systems (ESPSs) were set up at Météo-France using ensemble forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Ensemble Prediction System for the first one, and from the Prévision d’Ensemble Action de Recherche Petite Echelle Grande Echelle (PEARP) ensemble prediction system of Météo-France for the second. This paper presents the evaluation of their capacities to better anticipate severe hydrological events and more generally to estimate the quality of both ESPSs on their globality. The two ensemble predictions were used as input for the same hydrometeorological model. The skills of both ensemble streamflow prediction systems were evaluated over all of France for the precipitation input and streamflow prediction during a 569-day period and for a 2-day short-range scale. The ensemble streamflow prediction system based on the PEARP data was the best for floods and small basins, and the ensemble streamflow prediction system based on the ECMWF data seemed the best adapted for low flows and large basins.

scholarly journals Uncertainty and Bias in Satellite-Based Precipitation Estimates over Indian Subcontinental Basins: Implications for Real-Time Streamflow Simulation and Flood Prediction*

2016 ◽  
Vol 17 (2) ◽  
pp. 615-636 ◽  
Author(s):  
Harsh L. Shah ◽  
Vimal Mishra
Keyword(s):  
Real Time ◽  
River Basin ◽  
Large Population ◽  
River Basins ◽  
The Other ◽  
Climate Data ◽  
Infiltration Capacity ◽  
Mahanadi River ◽  
Daily Streamflow ◽  
Better Than

Abstract Real-time streamflow monitoring is essential over the Indian subcontinental river basins, as a large population is affected by floods. Moreover, streamflow monitoring helps in managing water resources in the agriculture-dominated region. In this study, the authors systematically investigated the bias and uncertainty in satellite-based precipitation products [Climate Prediction Center morphing technique (CMORPH); Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN); PERSIANN Climate Data Record (PERSIANN-CDR); and Tropical Rainfall Measuring Mission (TRMM), version 7, real-time (3B42RTV7) and gauge-adjusted (3B42V7) products] over the Indian subcontinental river basins for the period of 2000–13. Moreover, the authors evaluated the influence of bias in the satellite precipitation on real-time streamflow monitoring and flood assessment over the Mahanadi river basin. Results showed that CMORPH and PERSIANN underestimated daily mean precipitation over the majority of the subcontinental river basins. On the other hand, TRMM-3B42RTV7 overestimated daily mean precipitation over most of the river basins in the subcontinent. While gauge-adjusted products of PERSIANN (PERSIANN-CDR) and TRMM (TRMM-3B42V7) performed better than their real-time products, large biases remain in their performance to capture extreme precipitation (both frequency and magnitudes) over the subcontinental basins. Among the real-time precipitation products, TRMM-3B42RTV7 performed better than CMORPH and PERSIANN over the majority of the Indian subcontinental basins. Daily streamflow simulations using the Variable Infiltration Capacity model (VIC) for the Mahanadi river basin showed a better performance by the TRMM-3B42RTV7 product than the other real-time datasets. Moreover, daily streamflow simulations over the Mahanadi river basin showed that bias in real-time precipitation products affects the initial condition and precipitation forcing, which in turn affects flood peak timing and magnitudes.

scholarly journals Potential application of hydrological ensemble prediction in forecasting flood and its components over the Yarlung Zangbo River Basin, China

2018 ◽  
Author(s):  
Li Liu ◽  
Su L. Pan ◽  
Zhi X. Bai ◽  
Yue P. Xu
Keyword(s):  
River Basin ◽  
Lead Time ◽  
Flood Forecasting ◽  
Ensemble Prediction ◽  
Infiltration Capacity ◽  
Weather Forecasts ◽  
Flood Volume ◽  
Yarlung Zangbo River ◽  
Forecasting System

Abstract. In recent year, flood becomes a serious issue in Tibetan Plateau (TP) due to climate change. Many studies have shown that ensemble flood forecasting based on numerical weather predictions can provide early warning with extended lead time. However, the role of hydrological ensemble prediction in forecasting flood volume and its components over the Yarlung Zangbo River Basin (YZR), China has not been systematically investigated. This study adopts Variable Infiltration Capacity (VIC) model to forecast annual maximum floods (MF) and annual first floods (FF) in YZR based on precipitation, maximum and minimum temperature from European Centre for Medium-Range Weather Forecasts (ECMWF). N-simulations is proposed to account for more scenarios of parameters in VIC and shows improved flood simulation. Ensemble flood forecasting system can skilfully predict MF with a lead time of more than10 days, and has skill in forecasting the snowmelt-related components in about 7 days ahead. The accuracy of forecasts for FF is inferior with a lead time of only 5 days. The performance in 7-day accumulated flood volumes is better than the peak flows. The components in baseflow for FF are irrelevant to lead time, whilst for MF an obvious deterioration in performance with lead time can be perceived. The snowmelt-induced surface runoff is the most poorly captured component by the system, and the well-predicted rainfall-related components are the major contributor for good performance. From this study, it is concluded that snowmelt-induced flood volume plays an important role in YZR Basin especially in FF.

scholarly journals Benchmarking ensemble streamflow prediction skill in the UK

2018 ◽  
Vol 22 (3) ◽  
pp. 2023-2039 ◽  
Author(s):  
Shaun Harrigan ◽  
Christel Prudhomme ◽  
Simon Parry ◽  
Katie Smith ◽  
Maliko Tanguy
Keyword(s):  
Soil Moisture ◽  
Lead Time ◽  
Base Flow ◽  
Ensemble Prediction ◽  
Flow Index ◽  
Lead Times ◽  
Streamflow Prediction ◽  
Skill Scores ◽  
Ensemble Streamflow Prediction ◽  
The Uk

Abstract. Skilful hydrological forecasts at sub-seasonal to seasonal lead times would be extremely beneficial for decision-making in water resources management, hydropower operations, and agriculture, especially during drought conditions. Ensemble streamflow prediction (ESP) is a well-established method for generating an ensemble of streamflow forecasts in the absence of skilful future meteorological predictions, instead using initial hydrologic conditions (IHCs), such as soil moisture, groundwater, and snow, as the source of skill. We benchmark when and where the ESP method is skilful across a diverse sample of 314 catchments in the UK and explore the relationship between catchment storage and ESP skill. The GR4J hydrological model was forced with historic climate sequences to produce a 51-member ensemble of streamflow hindcasts. We evaluated forecast skill seamlessly from lead times of 1 day to 12 months initialized at the first of each month over a 50-year hindcast period from 1965 to 2015. Results showed ESP was skilful against a climatology benchmark forecast in the majority of catchments across all lead times up to a year ahead, but the degree of skill was strongly conditional on lead time, forecast initialization month, and individual catchment location and storage properties. UK-wide mean ESP skill decayed exponentially as a function of lead time with continuous ranked probability skill scores across the year of 0.75, 0.20, and 0.11 for 1-day, 1-month, and 3-month lead times, respectively. However, skill was not uniform across all initialization months. For lead times up to 1 month, ESP skill was higher than average when initialized in summer and lower in winter months, whereas for longer seasonal and annual lead times skill was higher when initialized in autumn and winter months and lowest in spring. ESP was most skilful in the south and east of the UK, where slower responding catchments with higher soil moisture and groundwater storage are mainly located; correlation between catchment base flow index (BFI) and ESP skill was very strong (Spearman's rank correlation coefficient =0.90 at 1-month lead time). This was in contrast to the more highly responsive catchments in the north and west which were generally not skilful at seasonal lead times. Overall, this work provides scientific justification for when and where use of such a relatively simple forecasting approach is appropriate in the UK. This study, furthermore, creates a low cost benchmark against which potential skill improvements from more sophisticated hydro-meteorological ensemble prediction systems can be judged.

scholarly journals Multiscale Postprocessor for Ensemble Streamflow Prediction for Short to Long Ranges

2020 ◽  
Vol 21 (2) ◽  
pp. 265-285 ◽  
Author(s):  
Babak Alizadeh ◽  
Reza Ahmad Limon ◽  
Dong-Jun Seo ◽  
Haksu Lee ◽  
James Brown
Keyword(s):  
The United States ◽  
Skill Score ◽  
Error Modeling ◽  
Ensemble Prediction ◽  
Mean Annual Precipitation ◽  
Streamflow Prediction ◽  
Ensemble Mean ◽  
Ensemble Streamflow Prediction ◽  
Full Utilization ◽  
Simulated Flow

AbstractA novel multiscale postprocessor for ensemble streamflow prediction, MS-EnsPost, is described and comparatively evaluated with the existing postprocessor in the National Weather Service’s Hydrologic Ensemble Forecast Service, EnsPost. MS-EnsPost uses data-driven correction of magnitude-dependent bias in simulated flow, multiscale regression using observed and simulated flows over a range of temporal aggregation scales, and ensemble generation using parsimonious error modeling. For comparative evaluation, 139 basins in eight River Forecast Centers in the United States were used. Streamflow predictability in different hydroclimatological regions is assessed and characterized, and gains by MS-EnsPost over EnsPost are attributed. The ensemble mean and ensemble prediction results indicate that, compared to EnsPost, MS-EnsPost reduces the root-mean-square error and mean continuous ranked probability score of day-1 to day-7 predictions of mean daily flow by 5%–68% and by 2%–62%, respectively. The deterministic and probabilistic results indicate that for most basins the improvement by MS-EnsPost is due to both magnitude-dependent bias correction and full utilization of hydrologic memory through multiscale regression. Comparison of the continuous ranked probability skill score results with hydroclimatic indices indicates that the skill of ensemble streamflow prediction with post processing is modulated largely by the fraction of precipitation as snowfall and, for non-snow-driven basins, mean annual precipitation.

Developing semi-distributed hydrological models for streamflow forecasting in upper Narmada River basin

2021 ◽  
Author(s):  
Ankit Singh ◽  
Soubhik Mondal ◽  
Sanjeev Kumar Jha
Keyword(s):  
River Basin ◽  
Stream Flow ◽  
Hydrological Model ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Streamflow Forecasting ◽  
Vic Model ◽  
Narmada River ◽  
Medium Range ◽  
Streamflow Forecasts

<p>Short-term streamflow forecast is important for various hydrological applications such as, estimating inflow to reservoirs, sending alarms in case of extreme events like flood and flash floods etc. Flooding events in last few years in the Indian subcontinent emphasized the importance of more accurate streamflow forecasts and the possible benefit of high-resolution Numerical Weather Prediction (NWP) models has been confirmed. In India, National Center for Medium Range Weather Forecasting (NCMRWF) provides rainfall forecasts from its UK Met office Unified Model based deterministic model (NCUM), and ensemble prediction system (NEPS). The comparison of NCMRWF with the forecast from other agencies such as Japan Metrological Agency (JMA)and European Center for Medium Range Forecast (ECMWF) have been addressed in this work. Global NWP models developed by different international agencies applydifferent algorithms, initial and boundaries conditions.The usefulness of several forecasts in streamflow forecasting is still being investigated in India. Recent studies on streamflow forecasting by using different NWP models shows that the performance of streamflow forecasts directly depends on the skill of NWP models. Hydrological model also plays a vital role in stream flow forecasting, because different hydrological model have different structure, parameters and algorithms to simulate the flow.</p><p>            In this study we use the Soil and Water Assessment Tool (SWAT) a Hydrological Response Unit (HRU’s) based hydrological model. HRU is the area that contains similar type of soil, land use and slope properties in a subbasin. For comparison, the streamflow generated from the forecasted rainfall by NWP, we select three different NWP models namely JMA, ECMWF and NCMRWF for streamflow forecasting. Manot watershed part of Narmada River basin in central India is selected as the study area for this study. Streamflow is examined for monsoon (June to September) period of 2018 at multiple lead times i.e. 1 to 5 days. Rain-gauge based gridded Indian Meteorological Department (IMD) rainfall product is used as observed data in SWAT. All rainfall products are at 0.25*0.25-degree spatial resolution. The preliminary comparison between the simulated streamflow and the observation shows that the stream flow patterns produced by various forecast products are in good comparison with high peaks. Our results also indicate that the forecast accuracy of NCMRWF is closely comparable with other forecast products for all lead time. In addition, the setup of Variable Infiltration Capacity (VIC), the hydrological model for Streamflow forecasting is in progress. The VIC model is a grid-based model with variable infiltration soil layers and each of this layer characterizes the soil hydrological responses and heterogeneity in land cover classes. For routing, VIC model divides the whole basin into grides and water balance is calculated at the outlet of each and every grid and the flow simulate according to the flow direction. This model considers both the baseflow and the surface flow. The detailed results of ongoing work will be presented at the conference.</p>

scholarly journals On the sources of hydrological prediction uncertainty in the Amazon

2012 ◽  
Vol 9 (3) ◽  
pp. 3739-3760 ◽  
Author(s):  
R. C. D. Paiva ◽  
W. Collischonn ◽  
M. P. Bonnet ◽  
L. G. G. Gonçalves
Keyword(s):  
River Basin ◽  
Hydrological Model ◽  
Initial Conditions ◽  
Amazon River ◽  
State Variables ◽  
Streamflow Prediction ◽  
Amazon River Basin ◽  
Forecast Uncertainty ◽  
Ensemble Streamflow Prediction ◽  
Hydrological Forecast

Abstract. Recent extreme events in the Amazon River basin and the vulnerability of local population motivate the development of hydrological forecast systems (HFSs) using process based models for this region. In this direction, the knowledge of the source of errors in HFSs may guide the choice on improving model structure, model forcings or developing data assimilation (DA) systems for estimation of initial model states. We evaluate the relative importance of hydrologic initial conditions (ICs) and model meteorological forcings (MFs) errors (precisely precipitation) as sources of stream flow forecast uncertainty in the Amazon River basin. We used a hindcast approach developed by Wood and Lettenmaier (2008) that contrasts Ensemble Streamflow Prediction (ESP) and a reverse Ensemble Streamflow Prediction (reverse-ESP). Simulations were performed using the physically-based and distributed hydrological model MGB-IPH, comprising surface energy and water balance, soil water, river and floodplain hydrodynamics processes. Model was forced using TRMM 3B42 precipitation estimates. Results show that uncertainty on initial conditions play an important role for discharge predictability even for large lead times (~1 to 3 months) on main Amazonian Rivers. ICs of surface waters state variables are the major source of hydrological forecast uncertainty, mainly in rivers with low slope and large floodplains. ICs of groundwater state variables are important mostly during low flow period and southeast part of the Amazon, where lithology and the strong rainfall seasonality with a marked dry season may be the explaining factors. Analyses indicate that hydrological forecasts based on a hydrological model forced with historical meteorological data and optimal initial conditions, may be feasible. Also, development of DA methods is encouraged for this region.

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