Developing a Decision Support System for Water Resources Dispatching

Contradiction between water demand and water supply have a huge impact on social and economic development. This paper presents the development of a water resources dispatch decision support system. The system integrates models related to water dispatch such as streamflow forecast model, water allocation model and water dispatch model. Each model runs as an independent service and is registered in the model platform. The model platform interacts with the service layer and data layer through the model adapter. The model adapter is designed for converting the model input data sent by the service layer and the basic data and observation data queried by the data layer into the format required by the model. In case study, we took the Fu River Basin as an example to demonstrate an application of the system. The system realizes the complete process of data collection, streamflow forecast, water demand declaration, water distribution and water dispatch. User can get the recommended operation plan of the reservoir and the corresponding water supply result through the user interface. Process variables can also be viewed through the system, such as streamflow forecast results and water distribution results, etc. The proposed system can provide technical support and assistance for the decision makers, which also provide an effective demonstration for water resources management in other rivers.

Quantifying streamflow predictability across North America on sub-seasonal to seasonal timescales

<div> <p><span><span>Sub-seasonal to seasonal streamflow forecasts represent critical operational inputs for many water sector applications of societal relevance, such as spring flood early warning, water supply, hydropower generation, and irrigation scheduling. However, the skill of such forecasts has not risen greatly in recent decades despite recognizable advances in many relevant capabilities, including hydrologic modeling and S2S climate prediction. In order to build a continental-scale forecasting system that has value at the local scale, the sources and nature of predictability in the forecasts should be quantified and communicated. This can additionally help to target science investments for tangible improvements in the sub-seasonal to seasonal streamflow forecasting skill.</span></span></p> </div><div> <p><span><span>As part of the Canada-based Global Water Futures (GWF) program, we are advancing capabilities for probabilistic sub-seasonal to seasonal streamflow forecasts over North America. The overall aim is to improve sub-seasonal to seasonal streamflow forecasts for a range of water sector applications. We are implementing an array of forecasting methods that integrate state-of-the-art mechanistic models and statistical methods. These include, for instance, a </span></span><span>probabilistic sub-seasonal to seasonal streamflow forecasting system based on quantile regression of snow water equivalent observations, and a system based on the ESP approach (Day, 1985). </span></p> <p><span><span>To guide forecast system developments over North America, we are currently quantifying streamflow predictability for different hydroclimatic regimes, forecast initialization times, and lead times, against both streamflow simulations and observations to quantify the effect of model errors. Building on the work from Wood et al. (2016) and Arnal et al. (2017), we are disentangling the dominant predictability sources (i.e., initial hydrological conditions and atmospheric forcings) of sub-seasonal to seasonal streamflow across North American watersheds. The results provide insights into the elasticity of predictability, i.e., the increase in streamflow forecast skill possible by improving a specific component of the forecast system, and will inform the forecasting system development.</span></span></p> </div><div> <p><span><span>Arnal Louise, Wood Andrew W., Stephens Elisabeth, Cloke Hannah L., Pappenberger Florian, 2017: An Efficient Approach for Estimating Streamflow Forecast Skill Elasticity. Journal of Hydrometeorology, doi: 10.1175/JHM-D-16-0259.1</span></span></p> </div><div> <p>Day, Gerald N., 1985: Extended streamflow forecasting using NWSRFS. Journal of Water Resources Planning and Management, doi:10.1061/(ASCE)0733-9496(1985)111:2(157)</p> </div><p>Wood, Andrew W., Tom Hopson, Andy Newman, Levi Brekke, Jeff Arnold, and Martyn Clark, 2016: Quantifying streamflow forecast skill elasticity to initial condition and climate prediction skill. Journal of Hydrometeorology, doi: 10.1175/JHM-D-14-0213.1</p>

Influence of bias correction of meteorological and streamflow forecast on hydrological prediction in India

The efforts to develop a hydrologic model-based operational streamflow forecast in India are limited. We evaluate the role of bias correction of meteorological forecast and streamflow post-processing on hydrological prediction skill in India. We use the Variable Infiltration Capacity (VIC) model to simulate runoff and root zone soil moisture in the Narmada basin (drainage area: 97,410 km2), which was used as a testbed to examine the forecast skill along with the observed streamflow. We evaluated meteorological and hydrological forecasts during the monsoon (June-September) season for 2000-2018 period. The raw meteorological forecast displayed relatively low skill against the observed precipitation at 1-3 day lead time during the monsoon season. Similarly, the forecast skill was low with mean normalized root mean squared error (NRMSE) more than 0.9 and mean absolute bias larger than 60% for extreme precipitation at the 1-3-day lead time. We used Empirical Quantile Mapping (EQM) to bias correct precipitation forecast. The bias correction of precipitation forecast resulted in significant improvement in the precipitation forecast skill. Runoff and root zone soil moisture forecast was also significantly improved due to bias correction of precipitation forecast where the forecast evaluation is performed against the reference model run. However, bias correction of precipitation forecast did not cause considerable improvement in the streamflow prediction. Bias correction of streamflow forecast performs better than the streamflow forecast simulated using the bias-corrected meteorological forecast. The combination of the bias correction of precipitation forecast and post-processing of streamflow resulted in a significant improvement in the streamflow prediction (reduction in bias from 40% to 5%).