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Variability in soil moisture has implications for regional terrestrial environments under a warming climate. This paper focuses on the spatiotemporal variability in the intra-annual persistence of soil moisture in China using the fifthgeneration reanalysis dataset by the European Centre for Medium-Range Weather Forecasts for the period 1979-2018. The results show that in China, the mean intra-annual persistence in the humid to arid zones increased from 60 to 115 days in the lower layer but decreased from 19 to 13 days and from 25 to 14 days in the upper and root layers, respectively. However, these changes were strongly attenuated in extremely dry and wet regions due to the scarcity of soil moisture anomalies. Large changes in persistence occurred in the lower soil layer in dryland areas, with a mean difference of up to 40 days between the 2010s and the 1980s. Overall increasing trends dominated the large-scale spatial features, despite regional decreases in the eastern arid zone and the North and Northeast China plains. In the root layer, the two plains experienced an expanded decrease while on the Tibetan Plateau it was dominated by decadal variability. These contrasting changes between the lower and root layers along the periphery of the transition zone was a reflection of the enhanced soil hydrological cycle in the root layer. The enhanced persistence in drylands lower layer is an indication of the intensified impacts of soil moisture anomalies (e.g., droughts) on terrestrial water cycle. These findings may help the understanding of climate change impacts on terrestrial environments. [ABSTRACT FROM AUTHOR]

The systematic bias of the estimated average temperature using daily Tmax and Tmin records relative to the standard average temperature of four time-equidistant observations and its effect on the estimated trend of long-term temperature change have not been well understood. This paper attempts to evaluate the systematic bias across mainland China using the daily data of national observational stations. The results revealed that the positive bias of annual mean temperature was large, reaching 0.58°C nationally on average; regional average bias was lowest in the northwest arid region and highest in the Qinghai–Tibetan Plateau; the bias was low in spring and summer and high in autumn and winter, reaching its lowest point in mid- and late May and highest point in early November. Furthermore, the bias showed a significant upward trend in the past 50 years, with a rising rate of 0.021°C (10 yr)−1, accounting for about 12% of the overall warming as estimated from the data of the observational network; the largest positive trend bias was found in the northwest arid region, while the east monsoon region experienced the smallest change; the most remarkable increase of the bias occurred after early 1990s. These results indicate that the customarily applied method to calculate daily and monthly mean temperature using Tmax and Tmin significantly overestimates the climatological mean and the long-term trend of surface air temperature in mainland China. [ABSTRACT FROM AUTHOR]

Hydrological processes in most semiarid regions on Earth have been changing under the impacts of climate change, human activities, or combinations of the two. This paper first presents a trend analysis of the spatiotemporal changes in water resources and then diagnoses their underlying atmospheric and socio-economic causes over 10 catchments in the Laoha basin, a typical semiarid zone of northeast China. The impacts of climate variability and human activities on streamflow change were quantitatively evaluated by the VIC (Variable Infiltration Capacity) model. First, results indicate that six out of the 10 studied catch-ments have statistically significant downward trends in annual streamflow; however, there is no significant change of annual precipitation for all catchments. Two abrupt changes of annual streamflow at 1979 and 1998 are identified for the four largest catchments. Second, the Laoha basin generally experienced three evident dry-wet pattern switches during the past 50 years. Furthermore, this basin is currently suffering from unprecedented water shortages. Large-scale climate variability has affected the local natural hy-drologie system. Third, quantitative evaluation shows human activities were the main driving factors for the streamflow reduction with contributions of approximately 90% for the whole basin. A significant in-crease in irrigated area, which inevitably resulted in tremendous agricultural water consumption, is the foremost culprit contributing to the dramatic runoff reduction, especially at midstream and downstream of the Laoha basin. This study is expected to enable policymakers and stakeholders to make well-informed, short-term practice decisions and better plan long-term water resource and ecoenvironment management strategies. [ABSTRACT FROM AUTHOR]

The majority of the world's population growth to 2050 is projected to occur in the tropics. Hence, there is a serious need for robust methods for undertaking water resource assessments to underpin the sustainable management of water in tropical regions. This paper describes the largest and most comprehensive assessment of the future impacts of runoff undertaken in a tropical region using conceptual rainfall-runoff models (RRMs). Five conceptual RRMs were calibrated using data from 115 streamflow gauging stations, and model parameters were regionalized using a combination of spatial proximity and catchment similarity. Future rainfall and evapotranspiration projections (denoted here as GCMES) were transformed to catchment-scale variables by empirically scaling (ES) the historical climate series, informed by 15 global climate models (GCMs), to reflect a 1°C increase in global average surface air temperature. Using the best-performing RRM ensemble, approximately half the GCMES used resulted in a spatially averaged increase in mean annual runoff (by up to 29%%) and half resulted in a decrease (by up to 26%%). However, ~70%% of the GCMES resulted in a difference of within ±5%% of the historical rainfall (1930-2007). The range in modeled impact on runoff, as estimated by five RRMs (for individual GCMES), was compared to the range in modeled runoff using 15 GCMES (for individual RRMs). For mid- to high runoff metrics, better predictions will come from improved GCMES projections. A new finding of this study is that in the wet-dry tropics, for extremely large runoff events and low flows, improvements are needed in both GCMES and rainfall-runoff modeling. [ABSTRACT FROM AUTHOR]

Because land surface emissivity (ℇ) has not been reliably measured, global climate model (GCM) land surface schemes conventionally set this parameter as simply constant, for example, 1 as in the National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Prediction (NCEP) model, and 0.96 for bare soil as in the National Center for Atmospheric Research (NCAR) Community Land Model version 2 (CLM2). This is the so-called constant-emissivity assumption. Accurate broadband emissivity data are needed as model inputs to better simulate the land surface climate. It is demonstrated in this paper that the assumption of the constant emissivity induces errors in modeling the surface energy budget, especially over large arid and semiarid areas where ℇ is far smaller than unity. One feasible solution to this problem is to apply the satellite-based broadband emissivity into land surface models. The Moderate Resolution Imaging Spectroradiometer (MODIS) instrument has routinely measured spectral emissivities (ℇλ) in six thermal infrared bands. The empirical regression equations have been developed in this study to convert these spectral emissivities to broadband emissivity (ℇ) required by land surface models. The observed emissivity data show strong seasonality and land-cover dependence. Specifically, emissivity depends on surface-cover type, soil moisture content, soil organic composition, vegetation density, and structure. For example, broadband ℇ is usually around 0.96–0.98 for densely vegetated areas [(leaf area index) LAI > 2], but it can be lower than 0.90 for bare soils (e.g., desert). To examine the impact of variable surface broadband emissivity, sensitivity studies were conducted using offline CLM2 and coupled NCAR Community Atmosphere Models, CAM2–CLM2. These sensitivity studies illustrate that large impacts of surface ℇ occur over deserts, with changes up to 1°–2°C in ground temperature, surface skin temperature, and 2-m surface air temperature, as well as evident changes in sensible and latent heat fluxes. [ABSTRACT FROM AUTHOR]

The area–time integral (ATI) method has previously been successfully used to estimate the area-averaged rain-rate distribution and the rainfall volume over an area from radar or from satellite infrared (IR) data. In most cases, the method was implemented over regions or test areas with an assumed homogeneous climatic character, that is, without a strong spatial variation of the rain regime throughout the test area. In the present paper, the behavior of the ATI method is discussed for a test area displaying two strong gradients of the cumulative annual rainfall: one meridional, at the transition between regions having, respectively, a desertic and an equatorial climate and the other zonal, at the transition between land and sea. The studied area is divided into four subtest areas (north, south, land, and sea) over which the ATI computation is applied separately. The linear coefficient relating the radar-observed area-averaged rain rate and the fractional area where the rain is higher than a threshold calculated over the four subtest areas is found to be almost constant, in agreement with the ergodic character of the rain-rate distribution observed in this region. Similarly, the linear coefficient relating the rain volume over the subtest areas to the IR satellite-derived ATI, a parameter analogous to the Geostationary Operational Environmental Satellite (GOES) Precipitation Index (GPI), is found to be very steady, with a mean value of 3.02 mm h-1 and a coefficient of variation of only 8%. These coefficients, as well as the underlying dynamic and microphysical processes, do not seem significantly influenced by the climatic character, even at a short space scale, in the studied area. The ratio of radar rain areas to cloud areas is, notably, almost constant. For a brightness temperature of 235 K, the ratio of the cloud area to rain area is around 1.68. [ABSTRACT FROM AUTHOR]