[Assessment of the lagging effect of vegetation response and loss probability in the Pearl River basin under drought stress].

We quantified the lag time of vegetation response to drought in the Pearl River basin (PRB) based on the standardized precipitation evapotranspiration index (SPEI) and normalized difference vegetation index (NDVI), and constructed a vegetation loss probability model under drought stress based on the Bayesian theory and two-dimensional joint distribution. We further quantitatively evaluated the spatial variations of loss probability of four vegetation types (evergreen broadleaf forest, mixed forest, grassland, and cropland) under different drought intensities. The results showed that the drought risk in eastern West River, the upper reaches of North River and East River, and southern Pearl River Delta was obviously higher than that in other regions during 1982-2020.

Unraveling the drivers for interannual variabilities of NO fluxes from forests soils across climatic zones.

Forest soils are an important source of nitrous oxide (NO), however, field observations of NO emission have often exhibited large variabilities when compared with managed agricultural lands. In the last decade, the number of forest NO studies has increased more than tenfold, but only a few of them have looked into the interannual flux variabilities from the regional scale. Here, we have collected 30 long-term NO monitoring studies (≥ 2 years) based on a global database, and extracted variabilities (VAR) as well as relative variabilities (VAR, in proportions) of annual NO fluxes.

Prediction of evapotranspiration variance in the Budyko framework with the incorporation of soil storage and runoff.

Water availability needs to be accurately assessed to understand and effectively manage hydrologic environments. However, the estimation of evapotranspiration (ET) is prone to errors due to the complex interactions that occur between the atmosphere, the Earth's surface, and vegetation cover. This paper proposes a novel approach for analyzing the sources of inaccuracy in estimating the annual ET using the Budyko framework (BF), particularly temporal variability in precipitation (P), potential evapotranspiration (E), runoff (R), and the change in soil storage (ΔS).

An evaluation framework for quantifying vegetation loss and recovery in response to meteorological drought based on SPEI and NDVI.

Drought affects vegetation growth to a large extent. Understanding the dynamic changes of vegetation during drought is of great significance for agricultural and ecological management and climate change adaptation. The relations between vegetation and drought have been widely investigated, but how vegetation loss and restoration in response to drought remains unclear.

Anthropogenic climate change exacerbates the risk of successive flood-heat extremes: Multi-model global projections based on the Inter-Sectoral Impact Model Intercomparison Project.

The successive flood-heat extreme (SFHE) event, which threatens the securities of human health, economy, and building environment, has attracted extensive research attention recently. However, the potential changes in SFHE characteristics and the global population exposure to SFHE under anthropogenic warming remain unclear. Here, we present a global-scale evaluation of the projected changes and uncertainties in SFHE characteristics (frequency, intensity, duration, land exposure) and population exposure under the Representative Concentration Pathway (RCP) 2.

Multivariate frequency analysis of urban rainfall characteristics using three-dimensional copulas.

Urban runoff is a major cause of urban flooding and is difficult to monitor in the long term. In contrast, long term continuous rainfall data are generally available for any given region. As a result, it has become customary to use design rainfall depth as a proxy for runoff in urban hydrological analyses, with an assumption of the same frequency for runoff and rainfall.

Application of the stormwater management model to a piedmont city: a case study of Jinan City, China.

The stormwater management model (SWMM) was adapted and calibrated to Jinan, a typical piedmont city in China, to verify the large-scale applicability of the model to piedmont cities. Fourteen storms were used for model calibration and validation. The calibrated model predicted the measured data with satisfactory accuracy and reliability.