A Nonstationary Standardized Precipitation Index incorporating climate indices as covariates

Li, J. Z.; Wang, Y. X.; Li, S. F.; Hu, Rong

doi:10.1002/2015jd023920

Cited by 60 publications

(31 citation statements)

References 58 publications

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“…So, for example, Li et al . () adjusted the gamma parameters used in their SPI calculations to incorporate known precipitation covariates affecting inter‐decadal and inter‐annual variability. By contrast, Dubrovsky et al .…”

Section: Introductionmentioning

confidence: 98%

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Estimating uncertainty associated with the standardized precipitation index

Carbone

Brunetti

2018

Intl Journal of Climatology

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We investigate methodological uncertainties associated with the standardized precipitation index (SPI) that result from limited record length, trends, and outliers. We use long, homogenous records from 14 Italian stations to investigate how specific features in the precipitation record affect construction of an underlying gamma probability function. We apply a resampling scheme to the long records in order to estimate confidence intervals associated with a range of precipitation characteristics. Stability in parameter estimation increases nonlinearly as record length increases. The resulting SPI estimates for 30‐year reference periods have considerably more uncertainty than those made from 60‐year records. In general, increasing record length beyond 60‐years has limited benefits and, in the presence of a trend, may increase uncertainty. Extreme events also have significant influence on SPI estimates, even for records exceeding 60 years. Despite using stations from different geographic regions, each with unique precipitation characteristics, we find consistent confidence interval estimates across stations. These confidence intervals can be applied to specific time series to identify how trends, changes in variability, and outliers during a particular reference period influence SPI values.

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Section: Introductionmentioning

confidence: 98%

“…The challenge is particularly relevant in the context of inter-decadal variability, trends, or projections of future drought. So, for example, Li et al (2015) adjusted the gamma parameters used in their SPI calculations to incorporate known precipitation covariates affecting inter-decadal and inter-annual variability. By e608 G.…”

Section: Introductionmentioning

confidence: 99%

Estimating uncertainty associated with the standardized precipitation index

Carbone

Brunetti

2018

Intl Journal of Climatology

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“…To model the runoff series, three types of GAMLSS models were built, including stationary models, nonstationary models with time t as a covariate, and nonstationary models with the observed precipitation series p as a covariate. In this study, we assume a linear relationship between only the parameters of the model distribution and the covariates (Li & Tan, ; Li, Wang, Li, & Hu, ).…”

Section: Resultsmentioning

confidence: 99%

A nonstationary runoff frequency analysis for future climate change and its uncertainties

Zhang

Feng

2019

Hydrological Processes

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With the intensification of climate change, its impact on runoff variations cannot be ignored. The main purpose of this study is to analyse the nonstationarity of runoff frequency adjusted for future climate change in the Luanhe River basin, China, and quantify the different sources of uncertainties in nonstationary runoff frequency analysis. The advantage of our method is the combination of generalized additive models in location, scale, and shape (GAMLSS) and downscaling models. The nonstationary GAMLSS models were established for the nonstationary frequency analysis of runoff (1961–2010) by using the observed precipitation as a covariate, which is closely related to runoff and contributes significantly to its nonstationarity. To consider the nonstationary effects of future climate change on future runoff variations, the downscaled precipitation series in the future (2011–2080) from the general circulation models (GCMs) were substituted into the selected nonstationary model to calculate the statistical parameters and runoff frequency in the future. A variance decomposition method was applied to quantify the impacts of different sources of uncertainty on the nonstationary runoff frequency analysis. The results show that the impacts of uncertainty in the GCMs, scenarios, and statistical parameters of the GAMLSS model increase with increasing runoff magnitude. In addition, GCMs and GAMLSS model parameters have the main impacts on runoff uncertainty, accounting for 14% and 83% of the total uncertainty sources, respectively. Conversely, the interactions and scenarios make limited contributions, accounting for 2% and 1%, respectively. Further analysis shows that the sources of uncertainty in the statistical parameters of the nonstationary model mainly result from the fluctuations in the precipitation sequence. This result indicates the necessity of considering the precipitation sequence as a covariate for runoff frequency analysis in the future.

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“…The SMDI uses soil moisture for indicating long-term drought conditions [23], whereas ETDI uses ET and PET for indicating short-term drought conditions [23]. Both SMDI and ETDI can be scaled between −2 and +2 to compare with Standardized Precipitation Index values [24][25][26][27][28][29] or between −4 and +4 to compare with Palmer Drought Severity Index values [23,30]. This study focused on the use of ETDI for estimating short-term drought because it reflects both soil moisture state and plant health.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Agricultural Drought Using Remotely Sensed Evapotranspiration in a Data-Scarce Catchment

Wambura

Dietrich

2020

Water

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Understanding spatial drought characteristics is vital for planning adaptation and mitigation measures in river catchments. In many parts of the world, spatial drought information is not available due to lack of adequate evenly distributed data for spatial drought analyses. This study elucidates a spatial drought analysis in a data-scarce tropical catchment using remote sensing actual evapotranspiration (ET) and potential evapotranspiration (PET) data. Firstly, the time series of 690 images of remotely sensed ET and PET between the years 2000 and 2014 were spatially analyzed using the evapotranspiration deficit index (ETDI) approach to obtain ETDIs in the Kilombero River catchment (Tanzania). Then, spatio-temporal patterns of ETDIs were used to characterize drought frequency, total drought durations, total drought severity, and drought intensity. The frequency, durations, severity, and intensity of drought increased from the year 2000 towards 2014, causing substantial drought changes in the catchment. However, drought intensity revealed that those changes were mainly from no drought and mild drought to moderate drought. Between the years 2000 and 2014, no-drought areas and mild drought areas declined from 10% to 0% and from 42% to 19%, respectively, whereas moderate drought areas increased from 47% to 81% of the catchment size. Those changes of drought conditions were partly attributed to anthropogenic land cover change, especially in the southwest grasslands, and were partly attributed to meteorological factors in other parts of the catchment. This information is crucial for further land cover change and climate change investigations, as well as planning water and land resources in the Kilombero River catchment. Moreover, the study also demonstrates the potential of using publicly available remote sensing ET products and the ETDI approach for spatially characterizing drought in ungauged regions.

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A Nonstationary Standardized Precipitation Index incorporating climate indices as covariates

Cited by 60 publications

References 58 publications

Estimating uncertainty associated with the standardized precipitation index

Estimating uncertainty associated with the standardized precipitation index

A nonstationary runoff frequency analysis for future climate change and its uncertainties

Analysis of Agricultural Drought Using Remotely Sensed Evapotranspiration in a Data-Scarce Catchment

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