Hydrological monitoring and seasonal forecasting: Progress and perspectives

Abstract

Hydrological monitoring and seasonal forecasting is an active research field because of its potential applications in hydrological risk assessment, preparedness and mitigation. In recent decades, developments in ground and satellite measurements have made the hydrometeorological information readily available, and advances in information technology have facilitated the data analysis in a real-time manner. New progress in climate research and modeling has enabled the prediction of seasonal climate with reasonable accuracy and increased resolution. These emerging techniques and advances have enabled more timely acquisition of accurate hydrological fluxes and status, and earlier warning of extreme hydrological events such as droughts and floods. This paper gives current state-of-the-art understanding of the uncertainties in hydrological monitoring and forecasting, reviews the efforts and progress in operational hydrological monitoring system assisted by observations from various sources and experimental seasonal hydrological forecasting, and briefly introduces the current monitoring and forecasting practices in China. The grand challenges and perspectives for the near future are also discussed, including acquiring and extracting reliable information for monitoring and forecasting, predicting realistic hydrological fluxes and states in the river basin being significantly altered by human activity, and filling the gap between numerical models and the end user. We highlight the importance of understanding the needs of the operational water management and the priority to transfer research knowledge to decision-makers.

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

“…Apart from enhancing the skill from weather or climate forecast, the increased skill of hydrological forecast likely necessitates improved precipitation estimation and initial hydrological conditions (Shukla et al, ; van Dijk et al, ). Data assimilation (DA) provides a useful method in this regard to merge different observations with model simulations to provide accurate initial conditions for hydroclimatic forecasting (including drought prediction) (Kumar et al, ; Liu et al, ; Tang et al, ). A variety of land data assimilation systems (LDAS) have been developed in recent decades, such as the NLDAS in the United States (Mitchell et al, ; Xia, Peters‐Lidard, et al, ; Xia, Ek, et al, ) and the Global Land Data Assimilation System (GLDAS) (Rodell et al, ), and they play an important role in drought early warning.…”

Drought: Progress in broadening its understanding

“…Decision support systems for drought forecasting are also emerging as useful tools in terms of issuing early warnings, assessing risk, and taking precautionary measures. As different factors exert direct and indirect influences on the occurrence of drought events, quantification and prediction of drought is challenging (DeChant & Moradkhani, 2015;Tang et al, 2009Tang et al, , 2016Touma, Ashfaq, Nayak, Kao, & Diffenbaugh, 2015). Hence, drought forecasting is often interlinked with decision support systems for the implementers and users on the ground.…”

Satellite Remote Sensing for Water Resources Management: Potential for Supporting Sustainable Development in Data‐Poor Regions

“…For floods, data on riverine flows and inundated areas are required to assess impacts. For early warning, short‐term and seasonal forecasts of hydrologic variables and potential agricultural impacts are also necessary to provide the lead time to enact measures to reduce impacts, and these forecasts need to be initialized and verified with observational data (Sheffield et al, ; Tang et al, ; Yuan et al, ). At the same time, long‐term, multisite time series (several decades) of relevant variables are required to estimate risk of flood/drought hazards as inputs into resource utilization and design of infrastructure for reduction of the hazard itself and mitigation of impacts (e.g., Serinaldi & Kilsby, ).…”