A probabilistic prediction network for hydrological drought identification and environmental flow assessment

Abstract

A general probabilistic prediction network is proposed for hydrological drought examination and environmental flow assessment. This network consists of three major components. First, we present the joint streamflow drought indicator (JSDI) to describe the hydrological dryness/wetness conditions. The JSDI is established based on a high‐dimensional multivariate probabilistic model. In the second part, a drought‐based environmental flow assessment method is introduced, which provides dynamic risk‐based information about how much flow (the environmental flow target) is required for drought recovery and its likelihood under different hydrological drought initial situations. The final part involves estimating the conditional probability of achieving the required environmental flow under different precipitation scenarios according to the joint dependence structure between streamflow and precipitation. Three watersheds from different countries (Germany, China, and the United States) with varying sizes from small to large were used to examine the usefulness of this network. The results show that the JSDI can provide an assessment of overall hydrological dryness/wetness conditions and performs well in identifying both drought onset and persistence. This network also allows quantitative prediction of targeted environmental flow required for hydrological drought recovery and estimation of the corresponding likelihood. Moreover, the results confirm that the general network can estimate the conditional probability associated with the required flow under different precipitation scenarios. The presented methodology offers a promising tool for water supply planning and management and for drought‐based environmental flow assessment. The network has no restrictions that would prevent it from being applied to other basins worldwide.

Seasonal Drought Prediction: Advances, Challenges, and Future Prospects

“…For the regression model, the probabilistic forecast of the predictand can be constructed through distributional assumptions of error terms (Hao, Hong, et al, ; Hwang & Carbone, ). For the conditional probability model, probabilistic forecasts can be achieved by constructing conditional distribution functions of the predictand with respect to predictors (Hao, Hao, Singh, Ouyang, & Cheng, ; Liu et al, ). Probabilistic drought prediction provides more values than a deterministic forecast and is easier for decision makers to understand.…”

Hydrological Drought Instantaneous Propagation Speed Based on the Variable Motion Relationship of Speed‐Time Process

“…The climate of Basin‐2 belongs to the temperate continental‐humid, characterized by synchronization of high temperature and ample precipitation. The third catchment is the Glan River basin located in southwestern Germany (the Basin‐3), which has a catchment area of 1,092 km 2 (Hellebrand et al, ; Liu et al, ). This area is located in inland Europe with continental climate characteristics.…”

Assessment of the Impact of Climate Change on Drought Characteristics in the Hwanghae Plain, North Korea Using Time Series SPI and SPEI: 1981–2100

“…The representative downscaling method is statistical downscaling, which uses different statistical approaches, such as the theoretical probability functions widely used in hydrology or climatology. These include normal, gamma, Weibull, lognormal, and generalized extreme value (GEV) probability functions [9,10]. However, in this study, we used the downscaled climate data from dynamical downscaling conducted by the National Institute of Meteorological Research (NIMR) at the Korea Meteorological Administration (KMA).…”