scholarly journals Operational river discharge forecasting in poorly gauged basins: the Kavango River basin case study

2015 ◽  
Vol 19 (3) ◽  
pp. 1469-1485 ◽  
Author(s):  
P. Bauer-Gottwein ◽  
I. H. Jensen ◽  
R. Guzinski ◽  
G. K. T. Bredtoft ◽  
S. Hansen ◽  
...  
Keyword(s):  
Water Resources ◽  
Data Assimilation ◽  
Open Source ◽  
Water Resources Management ◽  
Open Source Software ◽  
River Discharge ◽  
Lead Times ◽  
Probabilistic Forecasting ◽  
Resources Management ◽  
Probabilistic Forecasts

Abstract. Operational probabilistic forecasts of river discharge are essential for effective water resources management. Many studies have addressed this topic using different approaches ranging from purely statistical black-box approaches to physically based and distributed modeling schemes employing data assimilation techniques. However, few studies have attempted to develop operational probabilistic forecasting approaches for large and poorly gauged river basins. The objective of this study is to develop open-source software tools to support hydrologic forecasting and integrated water resources management in Africa. We present an operational probabilistic forecasting approach which uses public-domain climate forcing data and a hydrologic–hydrodynamic model which is entirely based on open-source software. Data assimilation techniques are used to inform the forecasts with the latest available observations. Forecasts are produced in real time for lead times of 0–7 days. The operational probabilistic forecasts are evaluated using a selection of performance statistics and indicators and the performance is compared to persistence and climatology benchmarks. The forecasting system delivers useful forecasts for the Kavango River, which are reliable and sharp. Results indicate that the value of the forecasts is greatest for intermediate lead times between 4 and 7 days.

scholarly journals Operational river discharge forecasting in poorly gauged basins: the Kavango River Basin case study

2014 ◽  
Vol 11 (10) ◽  
pp. 11071-11108
Author(s):  
P. Bauer-Gottwein ◽  
I. H. Jensen ◽  
R. Guzinski ◽  
G. K. T. Bredtoft ◽  
S. Hansen ◽  
...  
Keyword(s):  
Water Resources ◽  
Data Assimilation ◽  
Open Source ◽  
Water Resources Management ◽  
Open Source Software ◽  
River Discharge ◽  
Lead Times ◽  
Probabilistic Forecasting ◽  
Resources Management ◽  
Probabilistic Forecasts

Abstract. Operational probabilistic forecasts of river discharge are essential for effective water resources management. Many studies have addressed this topic using different approaches ranging from purely statistical black-box approaches to physically-based and distributed modelling schemes employing data assimilation techniques. However, few studies have attempted to develop operational probabilistic forecasting approaches for large and poorly gauged river basins. This study is funded by the European Space Agency under the TIGER-NET project. The objective of TIGER-NET is to develop open-source software tools to support integrated water resources management in Africa and to facilitate the use of satellite earth observation data in water management. We present an operational probabilistic forecasting approach which uses public-domain climate forcing data and a hydrologic–hydrodynamic model which is entirely based on open-source software. Data assimilation techniques are used to inform the forecasts with the latest available observations. Forecasts are produced in real time for lead times of 0 to 7 days. The operational probabilistic forecasts are evaluated using a selection of performance statistics and indicators. The forecasting system delivers competitive forecasts for the Kavango River, which are reliable and sharp. Results indicate that the value of the forecasts is greatest for intermediate lead times between 4 and 7 days.

scholarly journals Conditioning Ensemble Streamflow Prediction with the North Atlantic Oscillation improves skill at longer lead times

2020 ◽  
Author(s):  
Seán Donegan ◽  
Conor Murphy ◽  
Shaun Harrigan ◽  
Ciaran Broderick ◽  
Saeed Golian ◽  
...  
Keyword(s):  
Decision Making ◽  
Water Resources ◽  
North Atlantic Oscillation ◽  
Water Resources Management ◽  
North Atlantic ◽  
Lead Time ◽  
Lead Times ◽  
Streamflow Prediction ◽  
Resources Management ◽  
Ensemble Streamflow Prediction

Abstract. Skilful hydrological forecasts can benefit decision-making in water resources management and other water-related sectors that require long-term planning. In Ireland, no such service exists to deliver forecasts at the catchment scale. In order to understand the potential for hydrological forecasting in Ireland, we benchmark the skill of Ensemble Streamflow Prediction (ESP) for a diverse sample of 46 catchments using the GR4J hydrological model. Skill is evaluated within a 52-year hindcast study design over lead times of 1 day to 12 months for each of 12 initialisation months, January to December. Our results show that ESP is skilful against a probabilistic climatology benchmark in the majority of catchments up to several months ahead. However, the level of skill was strongly dependent on lead time, initialisation month, and individual catchment location and storage properties. Mean ESP skill was found to decay rapidly as a function of lead time, with continuous ranked probability skill scores of 0.8 (1-day), 0.32 (2-week), 0.18 (1-month), 0.05 (3-month), and 0.01 (12-month). Forecasts were generally more skilful when initialised in summer than other seasons. A strong correlation (ρ = 0.94) was observed between forecast skill and catchment storage capacity (baseflow index), with the most skilful regions, the Midlands and East, being those where slowly responding, high storage catchments are located. Results also highlight the potential utility of ESP for decision-making, as measured by its ability to forecast low and high flow events. In addition to our benchmarking experiment, we conditioned ESP on the winter North Atlantic Oscillation (NAO) using adjusted hindcasts from the Met Office's Global Seasonal Forecasting System version 5. We found gains in winter forecast skill of 7–18 % were possible over lead times of 1 to 3 months, and that NAO-conditioned ESP is particularly effective at forecasting dry winters, a critical season for water resources management. We conclude that ESP is skilful in a number of different contexts and thus should be operationalised in Ireland given its potential benefits for water managers and other stakeholders.

scholarly journals Conditioning ensemble streamflow prediction with the North Atlantic Oscillation improves skill at longer lead times

2021 ◽  
Vol 25 (7) ◽  
pp. 4159-4183
Author(s):  
Seán Donegan ◽  
Conor Murphy ◽  
Shaun Harrigan ◽  
Ciaran Broderick ◽  
Dáire Foran Quinn ◽  
...  
Keyword(s):  
Water Resources ◽  
North Atlantic Oscillation ◽  
Water Resources Management ◽  
North Atlantic ◽  
Lead Time ◽  
Forecast Skill ◽  
Lead Times ◽  
Streamflow Prediction ◽  
Resources Management ◽  
Ensemble Streamflow Prediction

Abstract. Skilful hydrological forecasts can benefit decision-making in water resources management and other water-related sectors that require long-term planning. In Ireland, no such service exists to deliver forecasts at the catchment scale. In order to understand the potential for hydrological forecasting in Ireland, we benchmark the skill of ensemble streamflow prediction (ESP) for a diverse sample of 46 catchments using the GR4J (Génie Rural à 4 paramètres Journalier) hydrological model. Skill is evaluated within a 52-year hindcast study design over lead times of 1 d to 12 months for each of the 12 initialisation months, January to December. Our results show that ESP is skilful against a probabilistic climatology benchmark in the majority of catchments up to several months ahead. However, the level of skill was strongly dependent on lead time, initialisation month, and individual catchment location and storage properties. Mean ESP skill was found to decay rapidly as a function of lead time, with a continuous ranked probability skill score (CRPSS) of 0.8 (1 d), 0.32 (2-week), 0.18 (1-month), 0.05 (3-month), and 0.01 (12-month). Forecasts were generally more skilful when initialised in summer than other seasons. A strong correlation (ρ=0.94) was observed between forecast skill and catchment storage capacity (baseflow index), with the most skilful regions, the Midlands and the East, being those where slowly responding, high-storage catchments are located. Forecast reliability and discrimination were also assessed with respect to low- and high-flow events. In addition to our benchmarking experiment, we conditioned ESP with the winter North Atlantic Oscillation (NAO) using adjusted hindcasts from the Met Office's Global Seasonal Forecasting System version 5. We found gains in winter forecast skill (CRPSS) of 7 %–18 % were possible over lead times of 1 to 3 months and that improved reliability and discrimination make NAO-conditioned ESP particularly effective at forecasting dry winters, a critical season for water resources management. We conclude that ESP is skilful in a number of different contexts and thus should be operationalised in Ireland given its potential benefits for water managers and other stakeholders.

ECOLOGY AND SUSTAINABLE DEVELOPMENT IN LOCAL BUSINESS:SIMULATION OF LOW IMPACT DEVELOPMENT IN SURAKARTA REGENCY

2018 ◽  
Vol 4 (1) ◽  
pp. 32-38
Author(s):  
Bhimo Rizky Samudro ◽  
Yogi Pasca Pratama
Keyword(s):  
Water Resources ◽  
Water Resources Management ◽  
River Flow ◽  
Low Impact Development ◽  
Development Model ◽  
Business Activity ◽  
Resources Management ◽  
Central Java ◽  
Specific Culture ◽  
Central Java Province

This paper will describe the function of water resources to support business activities in Surakarta regency, Central Java province. Surakarta is a business city in Central Java province with small business enterprises and specific culture. This city has a famous river with the name is Bengawan Solo. Bengawan Solo is a River Flow Regional (RFR) to support business activities in Surakarta regency. Concious with the function, societies and local government in Surakarta must to manage the sustainability of River Flow Regional (RFR) Bengawan Solo. It is important to manage the sustainability of business activity in Surakarta regency.   According to the condition in Surakarta regency, this paper will explain how the simulation of Low Impact Development Model in Surakarta regency. Low Impact Development is a model that can manage and evaluate sustainability of water resources in River Flow Regional (RFR). Low Impact Development can analys goals, structures, and process water resources management. The system can also evaluate results and impacts of water resources management. From this study, we hope that Low Impact Development can manage water resources in River Flow Regional (RFR) Bengawan Solo.  

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