scholarly journals Fuzzy Postprocessing to Advance the Quality of Continental Seasonal Hydrological Forecasts for River Basin Management

2020 ◽  
Vol 21 (10) ◽  
pp. 2375-2389
Author(s):  
Hector Macian-Sorribes ◽  
Ilias Pechlivanidis ◽  
Louise Crochemore ◽  
Manuel Pulido-Velazquez
Keyword(s):  
River Basin ◽  
Large Scale ◽  
Hydrological Model ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Seasonal Forecasts ◽  
Quantile Mapping ◽  
River Basin Scale ◽  
Basin Scale ◽  
Streamflow Forecasts

AbstractStreamflow forecasting services driven by seasonal meteorological forecasts from dynamic prediction systems deliver valuable information for decision-making in the water sector. Moving beyond the traditional river basin boundaries, large-scale hydrological models enable a coordinated, efficient, and harmonized anticipation and management of water-related risks (droughts, floods). However, the use of forecasts from such models at the river basin scale remains a challenge, depending on how the model reproduces the hydrological features of each particular river basin. Consequently, postprocessing of forecasts is a crucial step to ensure usefulness at the river basin scale. In this paper we present a methodology to postprocess seasonal streamflow forecasts from large-scale hydrological models and advance their quality for local applications. It consists of fuzzy logic systems that bias-adjust seasonal forecasts from a large-scale hydrological model by comparing its modeled streamflows with local observations. The methodology is demonstrated using forecasts from the pan-European hydrological model E-HYPE at the Jucar River basin (Spain). Fuzzy postprocessed forecasts are compared to postprocessed forecasts derived from a quantile mapping approach as a benchmark. Fuzzy postprocessing was able to provide skillful streamflow forecasts for the Jucar River basin, keeping most of the skill of raw E-HYPE forecasts and also outperforming quantile-mapping-based forecasts. The proposed methodology offers an efficient one-to-one mapping between large-scale modeled streamflows and basin-scale observations preserving its temporal dependence structure and can adapt its input set to increase the skill of postprocessed forecasts.

scholarly journals Using radar altimetry to update a large-scale hydrological model of the Brahmaputra river basin

2013 ◽  
Vol 45 (1) ◽  
pp. 148-164 ◽  
Author(s):  
Flemming Finsen ◽  
Christian Milzow ◽  
Richard Smith ◽  
Philippa Berry ◽  
Peter Bauer-Gottwein
Keyword(s):  
Real Time ◽  
River Basin ◽  
Large Scale ◽  
Hydrological Model ◽  
Water Levels ◽  
Hydrological Models ◽  
Altimetry Data ◽  
Brahmaputra River ◽  
Radar Altimetry ◽  
Rating Curves

Measurements of river and lake water levels from space-borne radar altimeters (past missions include ERS, Envisat, Jason, Topex) are useful for calibration and validation of large-scale hydrological models in poorly gauged river basins. Altimetry data availability over the downstream reaches of the Brahmaputra is excellent (17 high-quality virtual stations from ERS-2, 6 from Topex and 10 from Envisat are available for the Brahmaputra). In this study, altimetry data are used to update a large-scale Budyko-type hydrological model of the Brahmaputra river basin in real time. Altimetry measurements are converted to discharge using rating curves of simulated discharge versus observed altimetry. This approach makes it possible to use altimetry data from river cross sections where both in-situ rating curves and accurate river cross section geometry are not available. Model updating based on radar altimetry improved model performance considerably. The Nash–Sutcliffe model efficiency increased from 0.77 to 0.83. Real-time river basin modelling using radar altimetry has the potential to improve the predictive capability of large-scale hydrological models elsewhere on the planet.

Does the application of multiple hydrological models improve seasonal streamflow forecasting skill?

2020 ◽  
Author(s):  
Bastian Klein ◽  
Ilias Pechlivanidis ◽  
Louise Arnal ◽  
Louise Crochemore ◽  
Dennis Meissner ◽  
...  
Keyword(s):  
Central Europe ◽  
Bayesian Model Averaging ◽  
Forecast Skill ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Post Processing ◽  
Seasonal Forecasts ◽  
Streamflow Forecasts ◽  
Forecasting System ◽  
Seasonal Streamflow

<p>Many sectors, such as hydropower, agriculture, water supply and waterway transport, need information about the possible evolution of meteorological and hydrological conditions in the next weeks and months to optimize their decision processes on a long term. With increasing availability of meteorological seasonal forecasts, hydrological seasonal forecasting systems have been developed all over the world in the last years. Many of them are running in operational mode. On European scale the European Flood Awareness System EFAS and SMHI are operationally providing seasonal streamflow forecasts. In the context of the EU-Horizon2020 project IMPREX additionally a national scale forecasting system for German waterways operated by BfG was available for the analysis of seasonal forecasts from multiple hydrological models.</p><p>Statistical post processing tools could be used to estimate the predictive uncertainty of the forecasted variable from deterministic / ensemble forecasts of a single / multi-model forecasting system. Raw forecasts shouldn’t be used directly by users without statistical post-processing because of various biases. To assess the added potential benefit of the application of a hydrological multi-model ensemble, the forecasting systems from EFAS, SMHI and BfG were forced by re-forecasts of the ECMWF’s Seasonal Forecast System 4 and the resulting seasonal streamflow forecasts have been verified for 24 gauges across Central Europe. Additionally two statistical forecasting methods - Ensemble Model Output Statistics EMOS and Bayesian Model Averaging BMA - have been applied to post-process the forecasts.</p><p>Overall, seasonal flow forecast skill is limited in Central Europe before and after post-processing with a current predictability of 1-2 months. The results of the multi-model analysis indicate that post-processing of raw forecasts is necessary when observations are used as reference. Post-processing improves forecast skill significantly for all gauges, lead times and seasons. The multi-model combination of all models showed the highest skill compared to the skill of the raw forecasts and the skill of the post-processed results of the individual models, i.e. the application of several hydrological models for the same region improves skill, due to the different model strengths.</p>

Perspectives in using a remotely sensed dryness index in distributed hydrological models at the river-basin scale

2002 ◽  
Vol 16 (15) ◽  
pp. 2973-2987 ◽  
Author(s):  
J. Andersen ◽  
I. Sandholt ◽  
K. H. Jensen ◽  
J. C. Refsgaard ◽  
H. Gupta
Keyword(s):  
River Basin ◽  
Remotely Sensed ◽  
Hydrological Models ◽  
River Basin Scale ◽  
Basin Scale ◽  
Dryness Index ◽  
Distributed Hydrological Models

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 Seasonal streamflow forecasting by conditioning climatology with precipitation indices

2016 ◽  
Author(s):  
Louise Crochemore ◽  
Maria-Helena Ramos ◽  
Florian Pappenberger ◽  
Charles Perrin
Keyword(s):  
Time Series ◽  
Hydrological Model ◽  
Historical Data ◽  
Standardized Precipitation Index ◽  
Low Flow ◽  
Drought Risk ◽  
Hydrological Models ◽  
Streamflow Forecasting ◽  
Streamflow Forecasts ◽  
Seasonal Streamflow

Abstract. Many fields such as drought risk assessment or reservoir management can benefit from long-range streamflow forecasts. The simplest way to make probabilistic streamflow forecasts can be to use historical streamflow time series, if available. Another approach is to use ensemble climate scenarios as input to a hydrological model. Climatology (i.e. time series of climate conditions recorded over a long time period) has long been used in long-range streamflow forecasting. However, in the last decade, the use of general circulation model (GCM) outputs as input to hydrological models has developed. While precipitation climatology and historical streamflows offer reliable ensembles, forecasts based on GCM outputs can offer sharper ensembles, partly due to the initialisation of GCMs and hydrological models on current conditions. This study proposes to condition historical data based on GCM precipitation forecasts to get the most out of both data sources and improve seasonal streamflow forecasting in France. Four conditioning statistics based on ECMWF System 4 forecasts of cumulative precipitation and of the Standardized Precipitation Index (SPI) were used to select traces within historical streamflows and historical precipitations. The four conditioned precipitation scenarios were used as input to the GR6J hydrological model to obtain eight conditioned streamflow forecast scenarios. These streamflow scenarios were compared to three references: an ensemble based on historical streamflows, the widespread Ensemble Streamflow Prediction (ESP) ensemble, and System 4 precipitation forecasts. These ensembles were evaluated based on their sharpness, reliability and overall performance. An overall comparison of forecast ensembles showed that conditioning past observations based on the three-month Standardized Precipitation Index (SPI3) improved the sharpness of ensembles based on historical data, while maintaining a good reliability. An evaluation of forecast ensembles for low-flow forecasting showed that the SPI3-conditioned ensembles provided reliable forecasts of low-flow duration and deficit volume based on the 80th exceedance percentile. Last, drought risk forecasting was illustrated for the 2003 drought.

Irrigation improvement projects in Tanzania; scale impacts and policy implications

Water Policy ◽  
2004 ◽  
Vol 6 (2) ◽  
pp. 89-102 ◽  
Author(s):  
Bruce Lankford
Keyword(s):  
River Basin ◽  
Large Scale ◽  
River Basins ◽  
Policy Implications ◽  
River Basin Scale ◽  
Basin Scale ◽  
Smallholder Irrigation

Observations in Tanzania indicate that the improvement of traditional smallholder irrigation does not necessarily result in improved water performance, greater equity and reduced conflict. The usual outcomes of such projects is a gain in water for the system being upgraded, especially if located upstream, accompanied by less ability to share water at the river basin scale. This paper concludes that these projects do not commonly understand, match and respond to the complexities of well-developed and evolving smallholder irrigation found in multi-user river basins. Without re-appraisal, the risk is that donors will be unsuccessful with smallholder irrigation and turn away from this sector, as they did with large-scale irrigation.

scholarly journals Using the Global Hydrodynamic Model and GRACE Follow-On Data to Access the 2020 Catastrophic Flood in Yangtze River Basin

2021 ◽  
Vol 13 (15) ◽  
pp. 3023
Author(s):  
Jinghua Xiong ◽  
Shenglian Guo ◽  
Jiabo Yin ◽  
Lei Gu ◽  
Feng Xiong
Keyword(s):  
River Basin ◽  
Hydrodynamic Model ◽  
Yangtze River ◽  
Large Scale ◽  
Yangtze River Basin ◽  
The Yangtze River ◽  
Ecosystem Monitoring ◽  
Catastrophic Flood ◽  
Basin Scale ◽  
The Yangtze River Basin

Flooding is one of the most widespread and frequent weather-related hazards that has devastating impacts on the society and ecosystem. Monitoring flooding is a vital issue for water resources management, socioeconomic sustainable development, and maintaining life safety. By integrating multiple precipitation, evapotranspiration, and GRACE-Follow On (GRAFO) terrestrial water storage anomaly (TWSA) datasets, this study uses the water balance principle coupled with the CaMa-Flood hydrodynamic model to access the spatiotemporal discharge variations in the Yangtze River basin during the 2020 catastrophic flood. The results show that: (1) TWSA bias dominates the overall uncertainty in runoff at the basin scale, which is spatially governed by uncertainty in TWSA and precipitation; (2) spatially, a field significance at the 5% level is discovered for the correlations between GRAFO-based runoff and GLDAS results. The GRAFO-derived discharge series has a high correlation coefficient with either in situ observations and hydrological simulations for the Yangtze River basin, at the 0.01 significance level; (3) the GRAFO-derived discharge observes the flood peaks in July and August and the recession process in October 2020. Our developed approach provides an alternative way of monitoring large-scale extreme hydrological events with the latest GRAFO release and CaMa-Flood model.

scholarly journals Water accounting for water management at the river basin scale in India: approaches and gaps

Water Policy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 768-788
Author(s):  
Nitin Bassi ◽  
Guido Schmidt ◽  
Lucia De Stefano
Keyword(s):  
Water Management ◽  
River Basin ◽  
Water Policy ◽  
Past Research ◽  
River Basin Scale ◽  
The Past ◽  
Basin Scale ◽  
Water Accounting ◽  
Accounting Approaches ◽  
National Water

Abstract The main objective of this research paper is to assess the extent to which the concept of water accounting has been applied for water management at the river basin scale in India. For this, the study first assesses the importance given to the use of water accounting for water management in India's national water policy. It then analyses the evolution of water accounting approaches in India through a systematic review of the past research studies on the theme. Further, it looks at their contribution to decision-making concerning allocation of water resources and resolving conflicts over water sharing. Finally, it identifies the existing gaps in the methodologies for water accounting so far used in India.

scholarly journals A New Tool to Estimate Inundation Depths by Spatial Interpolation (RAPIDE): Design, Application and Impact on Quantitative Assessment of Flood Damages

Water ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 1805 ◽  
Author(s):  
Anna Scorzini ◽  
Alessio Radice ◽  
Daniela Molinari
Keyword(s):  
River Basin ◽  
Water Depth ◽  
Spatial Interpolation ◽  
Large Scale ◽  
Quantitative Estimation ◽  
Digital Terrain Model ◽  
Water Levels ◽  
Flood Depth ◽  
Inundation Depth ◽  
Basin Scale

Rapid tools for the prediction of the spatial distribution of flood depths within inundated areas are necessary when the implementation of complex hydrodynamic models is not possible due to time constraints or lack of data. For example, similar tools may be extremely useful to obtain first estimates of flood losses in the aftermath of an event, or for large-scale river basin planning. This paper presents RAPIDE, a new GIS-based tool for the estimation of the water depth distribution that relies only on the perimeter of the inundation and a digital terrain model. RAPIDE is based on a spatial interpolation of water levels, starting from the hypothesis that the perimeter of the flooded area is the locus of points having null water depth. The interpolation is improved by (i) the use of auxiliary lines, perpendicular to the river reach, along which additional control points are placed and (ii) the possibility to introduce a mask for filtering interpolation points near critical areas. The reliability of RAPIDE is tested for the 2002 flood in Lodi (northern Italy), by comparing the inundation depth maps obtained by the rapid tool to those from 2D hydraulic modelling. The change of the results, related to the use of either method, affects the quantitative estimation of direct damages very limitedly. The results, therefore, show that RAPIDE can provide accurate flood depth predictions, with errors that are fully compatible with its use for river-basin scale flood risk assessments and civil protection purposes.

Sign in / Sign up

Export Citation Format

Share Document