Challenges of Operational River Forecasting

Abstract

Skillful and timely streamflow forecasts are critically important to water managers and emergency protection services. To provide these forecasts, hydrologists must predict the behavior of complex coupled human-natural systems using incomplete and uncertain information and imperfect models. Moreover, operational predictions often integrate anecdotal information and unmodeled factors. Forecasting agencies face four key challenges: 1) making the most of available data, 2) making accurate predictions using models, 3) turning hydrometeorological forecasts into effective warnings, and 4) administering an operational service. Each challenge presents a variety of research opportunities, including the development of automated quality-control algorithms for the myriad of data used in operational streamflow forecasts, data assimilation, and ensemble forecasting techniques that allow for forecaster input, methods for using humangenerated weather forecasts quantitatively, and quantification of human interference in the hydrologic cycle. Furthermore, much can be done to improve the communication of probabilistic forecasts and to design a forecasting paradigm that effectively combines increasingly sophisticated forecasting technology with subjective forecaster expertise. These areas are described in detail to share a real-world perspective and focus for ongoing research endeavors.

Rain or snow: hydrologic processes, observations, prediction, and research needs

“…Hydrologists are increasingly using output from atmospheric models to drive hydrologic models from daily to climatic or multi-decadal timescales (Tung and Haith, 1995;Pachauri, 2002;Wood et al, 2004;Rojas et al, 2011;Yucel et al, 2015). These atmospheric models suffer from the same data paucity and scale issues that likewise challenge the implementation and validation of hydrologic models.…”

“…For example, nearly all operational models used by the National Weather Service River Forecast Centers in the United States use some type of temperature-based precipitation phase partitioning method (PPM) (Pagano et al, 2014). These are often single or double temperature threshold models that do not consider other conditions important to the hydrometeor's energy balance.…”

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

“…A one-way coupling between the two model compartments is implemented; i.e., once runoff has entered the river channel, the water cannot move back into the land phase of the hydrological cycle. We use the well-known SWAT hydrological model, version 2009 (Gassman et al, 2005;Neitsch et al, 2011), for rainfall-runoff modeling. SWAT is a semi-distributed, physically based hydrological model which operates at a daily time step.…”

“…Operational probabilistic hydrological modeling and river discharge forecasting is an active research topic in water resources engineering and applied hydrology (Pagano et al, 2014). Sharp and reliable forecasts of river discharge are required over a range of forecasting horizons for flood and drought management.…”

The Integrated System of Hydrological Forecasting in the Ussuri River Basin Based on the ECOMAG Model

“…It is crucial for the operation that the most accurate data are used. Both data-rich and data-poor national hydrometeorological services struggle with retrieving, quality controlling, infilling, formatting, archiving and redistributing data [1]. Robust algorithms are required to overcome the data processing and preparation issues.…”