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The land surface model is extensively used to simulate turbulence fluxes and hydrological and momentum variables at the land–atmosphere interface. In this study, the Community Land Model, version 5 (CLM5), driven by the 0.1° × 0.1° Chinese Meteorological Forcing Dataset (CMFD) and the field-surveyed soil parameters, is used to simulate land surface processes during 1979–2018. Various high-quality land surface datasets are adopted to assess the model simulations. In general, the CLM5 well captures the monthly variations of 0–10-cm soil moisture in subregions, particularly in the Tibetan Plateau, with an anomaly correlation coefficient between 0.56 and 0.88. However, the simulated soil moisture shows overall wet biases in the whole country, resulting from several reasons. The model simulation is skillful in replicating both the magnitude and spatial pattern when they are compared with the MODIS snow cover dataset. Compared with in situ measured soil temperature in multiple soil layers within 320-cm soil depth from 1980 to 2018, the simulations accurately capture spatial patterns, vertical profiles, and long-term warming trends. For land surface energy components, the simulations have a highly temporal correlation with the observation of Chinese Flux Observation and Research Network (ChinaFLUX) cropland and grassland sites, except for four forest sites, where biases exist in both atmospheric forcing variables and surface vegetation phenology in the model default input dataset. In summary, this study reveals the overall capability of CLM5 in reproducing land surface energy fluxes and hydrological variables over conterminous China, and the validation results may also provide some references for future model improvement and application. Significance Statement: The offline Community Land Model, version 5 (CLM5), driven by a 0.1° × 0.1° (∼10 km) horizontal resolution atmospheric forcing dataset and a set of field-surveyed soil parameters, are used to simulate the land surface hydrological and heat fluxes in continental China for 1980–2018. The simulated hydrological variables and energy fluxes are validated with various sources of high-quality observation-based datasets. From our systematic evaluations, the current CLM5 high–resolution simulation accurately captures the spatial patterns and temporal variations in most of the water and energy balance components, although biases exist in some simulated variables. Overall, this study reveals the capability of the offline CLM5 simulation in conterminous China and provides the reference for future model improvement and application. [ABSTRACT FROM AUTHOR]

This study assesses the performance of temperature extremes over China in two regional climate models (RCMs), RegCM4 and WRF, driven by the ECMWF's 20th century reanalysis. Based on the advice of the Expert Team on Climate Change Detection and Indices (ETCCDI), 12 extreme temperature indices (i.e., TXx, TXn, TNx, TNn, TX90p, TN90p, TX10p, TN10p WSDI, ID, FD, and CSDI) are derived from the simulations of two RCMs and compared with those from the daily station-based observational data for the period 1981-2010. Overall, the two RCMs demonstrate satisfactory capability in representing the spatiotemporal distribution of the extreme indices over most regions. RegCM performs better than WRF in reproducing the mean temperature extremes, especially over the Tibetan Plateau (TP). Moreover, both models capture well the decreasing trends in ID, FD, CSDI, TX10p, and TN10p, and the increasing trends in TXx, TXn, TNx, TNn, WSDI, TX90p, and TN90p, over China. Compared with observation, RegCM tends to underestimate the trends of temperature extremes, while WRF tends to overestimate them over the TP. For instance, the linear trends of TXx over the TP from observation, RegCM, and WRF are 0.53°C (10 yr)−1, 0.44°C (10 yr)−1, and 0.75°C (10 yr)−1, respectively. However, WRF performs better than RegCM in reproducing the interannual variability of the extreme-temperature indices. Our findings are helpful towards improving our understanding of the physical realism of RCMs in terms of different time scales, thus enabling us in future work to address the sources of model biases. [ABSTRACT FROM AUTHOR]

We introduce a Rainfall, Snow and Glacier melt (RSG) standardized anomaly (SA) index to reflect water availability in cold river basins by taking into account snow and glacier melt that influence seasonal water availability. The study takes advantage of a high-resolution Water and Energy Budget-Based Hydrological Distributed Model with improved snow physics (WEB-DHM-S) at a grid size of 5 km to quantify hydrological regimes in a typical cold river basin in the Tibetan Plateau (Lhasa River basin as a demonstration site) from 1983 to 2012. Standardized anomaly index was utilized as drought Indicator whereby each meteo-hydrological parameter involved in drought quantification was fitted to a distribution pattern on a monthly basis. Akaike Information Criterion and Bayesian Information Criterion were used as selection criteria. Drought indices were computed from the model inputs and outputs, which included RSG for meteorological drought, soil moisture (surface and root-zone) for agricultural drought and discharge and groundwater level for hydrological drought. From spatial and temporal analyses, drought occurred in 1984, 1988, 1995, 1997, 2009 and 2010, with the highest severity in August, September, July, August, June and June, respectively. This study addresses the glacierized cold river basin’s dryness by considering the contribution of snow and glacier in drought quantification, an integration of meteorological, agricultural and hydrological was performed to highlight drought hotspots in the Lhasa River Basin. To the best of our knowledge, this is the first drought study in Lhasa River Basin. [ABSTRACT FROM AUTHOR]