ABSTRACT
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. The response time of vegetation to drought in high-altitude areas in the upper reaches of PRB (mostly<3 month) was generally shorter than that in low altitude areas (>8 month). Drought exacerbated the probability of vegetation loss, with higher vulnerability of mixed forest than the other three vegetation types. The loss probability of vegetation was lower in northwestern PRB than that in central PRB.
Subject(s)
Droughts , Ecosystem , Forests , Rivers , Trees , China , Trees/growth & development , Stress, Physiological , Grassland , Models, Theoretical , Bayes Theorem , Poaceae/growth & developmentABSTRACT
Forest soils are an important source of nitrous oxide (N2O), however, field observations of N2O emission have often exhibited large variabilities when compared with managed agricultural lands. In the last decade, the number of forest N2O 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 N2O monitoring studies (≥ 2 years) based on a global database, and extracted variabilities (VARFlux) as well as relative variabilities (VAR%, in proportions) of annual N2O fluxes. The relationship of mean annual precipitation (MAP), mean annual temperature (MAT), and nitrogen (N) deposition with flux variabilities was examined to explore the underlying mechanisms for N2O emission on a long-term scale. Our results show that mean VARFlux is 0.43 kg N ha-1 yr-1 and VAR% is 28.68%. Across climatic zones, the subtropical forests have the largest annual N2O fluxes, as well as the largest fluctuations among annual budgets, while the tropics were the smallest. We found that the regulating factors for VARFlux and VAR% are fundamentally different, i.e., MAT and N input determine the annual fluxes as well as VARFlux while MAP and other limiting soil parameters determine VAR%. The relative contributions of different seasons to flux variabilities were also explored, indicating that N2O fluxes of warm and cool seasons are more responsible for the fluctuations in annual fluxes of the (sub)tropical and temperate forests, respectively. Overall, despite the limitation in interpretations due to few long-term studies from literature, this work highlights that significant interannual variabilities are common phenomena for N2O emission from different climatic zones forest soils; by unraveling the divergent drivers for VARFlux and VAR%, we have provided the possibility of improving N2O simulation models for constraining the heterogeneity of N2O emission processes from climatic zones forest soils.
ABSTRACT
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 (EP), runoff (R), and the change in soil storage (ΔS). Error decomposition is employed to determine the individual contributions of P, R, EP, and ΔS to the ET error variance at 12 locations in the state of Illinois using a dataset covering a 22-year period. To the best of our knowledge, this study represents the first BF-based investigation that considers R in the error decomposition of the predicted ET variance. The ET error variance increases with the variance in the P and R in Illinois and decreases with the covariance between these two variables. In addition, when accounting for ΔS in the BF, the scenario in which ΔS affects the total available water (i.e., P) is reliable, with a low prediction error and a 13.87 % lower root mean square error compared with the scenario in which the effect of ΔS is negligible. We thus recommend the inclusion of ΔS and R as key variables in the BF to improve water budget estimations.
ABSTRACT
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) 2.6 and 6.0 scenarios, based on the multi-model ensembles (five global water models forced by four global climate models) within the Inter-Sectoral Impact Model Intercomparison Project 2b framework. The results reveal that, relative to the 1970-1999 baseline period, the SFHE frequency is projected to increase nearly globally by the end of this century, especially in the Qinghai-Tibet Plateau (>20 events/30-year) and the tropical regions (e.g., northern South America, central Africa, and southeastern Asia, >15 events/30-year). The projected higher SFHE frequency is generally accompanied by a larger model uncertainty. By the end of this century, the SFHE land exposure is expected to increase by 12 % (20 %) under RCP2.6 (RCP6.0), and the intervals between flood and heatwave in SFHE tend to decrease by up to 3 days under both RCPs, implying the more intermittent SFHE occurrence under future warming. The SFHE events will lead to the higher population exposure in the Indian Peninsula and central Africa (<10 million person-days) and eastern Asia (<5 million person-days) due to the higher population density and the longer SFHE duration. Partial correlation analysis indicates that the contribution of flood to the SFHE frequency is greater than that of heatwave for most global regions, but the SFHE frequency is dominated by the heatwave in northern North America and northern Asia.
Subject(s)
Climate Change , Hot Temperature , Humans , Models, Theoretical , Floods , TibetABSTRACT
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. However, this approach has lack of overall coordination and cannot fully reflect the variability of rainfall characteristics. To address this issue, this study presents a three-dimensional copula-based multivariate frequency analysis of rainfall characteristics based on a long term (1961-2012) rainfall data from Guangzhou, China. Firstly, continuous rainfall data were divided into individual rainfall events using the rainfall intensity method. Then the characteristic variables of rainfall (design rainfall depth, DRD; total rainfall depth, TRD; peak rainfall depth, PRD) were sampled using the annual maximum method. Finally, a copula method was used to develop the multivariate joint probability distribution and the conditional probability distribution of rainfall characteristics. The results showed that the copula-based method is easy to implement and can better reflect urban rainstorm characteristics. It can serve a scientific reference for urban flood control and drainage planning.
Subject(s)
Cities , Floods , Models, Theoretical , Rain , Sanitary Engineering , China , Hydrology , Multivariate Analysis , Water MovementsABSTRACT
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. A sensitivity analysis was then conducted to evaluate the impact of the model parameters; it showed that: (1) the model outputs were most sensitive to imperviousness and conduit roughness; and (2) infiltration parameters and depression storage play an important role in total runoff and peak flow. The urban drainage system of Jinan was assessed using urban design storms with the calibrated model, and the effects of engineered flood control measures were evaluated. The overall results demonstrate that SWMM is applicable on a large scale to piedmont cities.
