Xiaogang He

Forecasting the Hydroclimatic Signature of the 2015/16 El Niño Event on the Western United States

AbstractDry conditions in 2013–16 in much of the western United States were responsible for severe drought and led to an exceptional fire season in the Pacific Northwest in 2015. Winter 2015/16 was forecasted to relieve drought in the southern portion of the region as a result of increased precipitation due to a very strong El Nino signal. A student forecasting challenge is summarized in which forecasts of winter hydroclimate across the western United States were made on 1 January 2016 for the winter hydroclimate using several dynamical and statistical forecast methods. They show that the precipitation forecasts had a large spread and none were skillful, while anomalously high observed temperatures were forecasted with a higher skill and precision. The poor forecast performance, particularly for precipitation, is traceable to high uncertainty in the North American Multi-Model Ensemble (NMME) forecast, which appears to be related to the inability of the models to predict an atmospheric blocking pattern ove...

Spatial downscaling of precipitation using adaptable random forests

This paper introduces Prec-DWARF (Precipitation Downscaling With Adaptable Random Forests), a novel machine-learning based method for statistical downscaling of precipitation. Prec-DWARF sets up a nonlinear relationship between precipitation at fine resolution and covariates at coarse/fine resolution, based on the advanced binary tree method known as Random Forests (RF). In addition to a single RF, we also consider a more advanced implementation based on two independent RFs which yield better results for extreme precipitation. Hourly gauge-radar precipitation data at 0.125° from NLDAS-2 are used to conduct synthetic experiments with different spatial resolutions (0.25°, 0.5° and 1°). Quantitative evaluation of these experiments demonstrates that Prec-DWARF consistently outperforms the baseline (i.e., bi-linear interpolation in this case) and can reasonably reproduce the spatial and temporal patterns, occurrence and distribution of observed precipitation fields. However, Prec-DWARF with a single RF significantly underestimates precipitation extremes and often cannot correctly recover the fine-scale spatial structure, especially for the 1° experiments. Prec-DWARF with a double RF exhibits improvement in the simulation of extreme precipitation as well as its spatial and temporal structures, but variogram analyses show that the spatial and temporal variability of the downscaled fields are still strongly underestimated. Covariate feature importance analysis shows that the most important predictors for the downscaling are the coarse scale precipitation values over adjacent grid cells as well as the distance to the closest dry grid cell (i.e., the dry drift). The encouraging results demonstrate the potential of Prec-DWARF and machine-learning based techniques in general for the statistical downscaling of precipitation. This article is protected by copyright. All rights reserved.

Intensification of hydrological drought in California by human water management

We analyze the contribution of human water management to the intensification or mitigation of hydrological drought over California using the PCR-GLOBWB hydrological model at 0.5° resolution for the period 1979–2014. We demonstrate that including water management in the modeling framework results in more accurate discharge representation. During the severe 2014 drought, water management alleviated the drought deficit by ∼50% in Southern California through reservoir operation during low-flow periods. However, human water consumption (mostly irrigation) in the Central Valley increased drought duration and deficit by 50% and 50–100%, respectively. Return level analysis indicates that there is more than 50% chance that the probability of occurrence of an extreme 2014 magnitude drought event was at least doubled under the influence of human activities compared to natural variability. This impact is most significant over the San Joaquin Drainage basin with a 50% and 75% likelihood that the return period is more than 3.5 and 1.5 times larger, respectively, because of human activities.We analyze the contribution of human water management to the intensification and mitigation of hydrological drought over California using the PCR-GLOBWB hydrological model for the period 1979-2014. We demonstrate that considering water management results in more accurate discharge representation. During the severe 2014 drought, water management alleviated the drought deficit by ?50% in Southern California through reservoir operation during low flow periods. However, human water consumption (mostly irrigation) in the Central Valley increased drought duration and deficit by 50% and 50-100%, respectively. Return level analysis indicates that there is more than 50% chance that the probability of occurrence of an extreme 2014-magnitude drought event was at least doubled under the influence of human activities compared to natural variability. This impact is most significant over the San Joaquin Drainage basin with a 50% and 75% likelihood that the return period is more than 3.5 and 1.5 times larger, respectively, because of human activities.

Solar cycle modulation of the Pacific–North American teleconnection influence on North American winter climate

We investigate the role of the 11-year solar cycle in modulating the Pacific‐North American (PNA) influence on North American winter climate. The PNA appears to play an important conduit between solar forcing and surface climate. The low solar (LS) activity may induce an atmospheric circulation pattern that resembles the positive phase of the PNA, resulting in a significant warming over northwestern North America and significant dry conditions in the Pacific Northwest, Canadian Prairies and the Ohio-Tennessee-lower Mississippi River Valley. The solar-induced changes in surface climate share more than 67% and 14% of spatial variances in the PNA-induced temperature and precipitation changes for 1950‐2010 and 1901‐2010 periods, respectively. These distinct solar signatures in North American climate may contribute to deconvolving modern and past continental-scale climate changes and improve our ability to interpret paleoclimate records in the region.

The Diurnal Cycle of Precipitation in Regional Spectral Model Simulations over West Africa: Sensitivities to Resolution and Cumulus Schemes

AbstractAs a basic form of climate patterns, the diurnal cycle of precipitation (DCP) can provide a key test bed for model reliability and development. In this study, the DCP over West Africa was simulated by the National Centers for Environmental Prediction (NCEP) Regional Spectral Model (RSM) during the monsoon season (April–September) of 2005. Three convective parameterization schemes (CPSs), single-layer simplified Arakawa–Schubert (SAS), multilayer relaxed Arakawa–Schubert (RAS), and new Kain–Fritsch (KF2), were evaluated at two horizontal resolutions (20 and 10 km). The Benin mesoscale site was singled out for additional investigation of resolution effects. Harmonic analysis was used to characterize the phase and amplitude of the DCP. Compared to satellite observations, the overall spatial distributions of amplitude were well captured at regional scales. The RSM properly reproduced the observed late afternoon peak over land and the early morning peak over ocean. Nevertheless, the peak time was early...

Predictability of a Physically Based Model for Rainfall-induced Shallow Landslides: Model Development and Case Studies

A cost-effective physical model (SLope-Infiltration-distributed Equilibrium—SLIDE) has been developed to identify the spatial and temporal occurrences of rainfall-induced landslides, employing a range of remotely sensed and in situ data. The main feature of SLIDE is that it takes into account of some simplified hypotheses on water infiltration and defines a direct relationship between the factor of safety and the rainfall depth on an infinite slope model. This prototype has been applied to two case studies in Indonesia and Honduras during heavy rainfall events brought by typhoon and hurricane, respectively. Simulation results from SLIDE demonstrated good skills in predicting rainfall-induced shallow landslides by assimilating the most important dynamic triggering factor (i.e., rainfall) quantitatively. The model’s prediction performance also suggested that SLIDE could serve as a potential tool for the future landslide early-warning system. Despite positive model performance, the SLIDE model is limited by several assumptions including using general parameter calibration rather than in situ tests and neglecting geotechnical information and some of the hydrological processes in deep soil layers. Advantages and limitations of this physically based model are also discussed with respect to future applications of landslide assessment and prediction over large scales.

Responses of land evapotranspiration to Earth's greening in CMIP5 Earth System Models

Satellite-observed Earths greening has been reproduced by the latest generation of Earth System Models (ESMs) participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5). Land evapotranspiration (ET) is expected to rise with increasing leaf area index (LAI, Earths greening). The responses of ET play a key role in the land–climate interaction, but they have not been evaluated previously. Here, we assessed the responses of ET to Earths greening in these CMIP5 ESMs. We verified a significant and positive response of ET to the modeled greening in each model. However, the responses were not comparable across the ESMs because of an inherent bias in the sensitivity of ET to LAI

Landslides Susceptibility Mapping in Oklahoma State Using GIS-Based Weighted Linear Combination Method

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Impacts of climate change on peanut yield in China simulated by CMIP5 multi-model ensemble projections

in the models:

Development of a coupled hydrological-geotechnical framework for rainfall-induced landslides prediction

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