The Community Land Model (CLM) is an effective tool to simulate the biophysical and biogeochemical processes and their interactions with the atmosphere. Although CLM version 5 (CLM5) constitutes various updates in these processes, its performance in simulating energy, water and carbon cycles over the contiguous United States (CONUS) at scales which land surface changes and hydrometeorological and hydroclimatological applications are more locally relevant is yet to be assessed. In this study, we conducted three simulations at 0.125° during 1979–2018 over the CONUS using different configurations of CLM, namely CLM5‐biogeochemistry (CLM5BGC), CLM4.5BGC, and CLM5‐satellite phenology (CLM5SP). We validated and compared their simulations against multiple remote‐sensed and in situ data sets. Overall, the parametric and structural updates (e.g., carbon cost for nitrogen uptake, variable soil thickness, dry surface layer) in CLM5 improve its ability in capturing terrestrial biogeochemical dynamics. The low evapotranspiration in CLM5BGC is associated with biases in simulating vegetation phenological characteristics rather than soil water limitations. The mismatch between CLM5BGC‐simulated peak leaf area index and reference data can be attributed to CLM5BGC's inability in simulating phenology of trees and grasses. The differences between CLM‐simulated irrigation and reference estimates can be attributed to differences between processes represented in models and in reality, and uncertainties in input and validation data sets. Evaluation against observations at small catchments suggest that hydrologic parameters needed to be calibrated to improve simulations of runoff, especially subsurface runoff. Additional efforts are needed to incorporate spatially distributed plant phenology and physiology parameters and regional‐specific agricultural management practices (e.g., planting and harvest).

Extreme hydrologic events are getting more frequent under a changing climate, and a reliable hydrological modeling framework is important to understand their mechanism. However, existing hydrological modeling frameworks are mostly constrained to a relatively coarse resolution, unrealistic input information, and insufficient evaluations, especially for the large domain, and they are, therefore, unable to address and reconstruct many of the water-related issues (e.g., flooding and drought). In this study, a 0.0625-degree (~6 km) resolution variable infiltration capacity (VIC) model developed for China from 1970 to 2016 was extensively evaluated against remote sensing and ground-based observations. A unique feature in this modeling framework is the incorporation of new remotely sensed vegetation and soil parameter dataset. To our knowledge, this constitutes the first application of VIC with such a long-term and fine resolution over a large domain, and more importantly, with a holistic system-evaluation leveraging the best available earth data. The evaluations using in-situ observations of streamflow, evapotranspiration (ET), and soil moisture (SM) indicate a great improvement. The simulations are also consistent with satellite remote sensing products of ET and SM, because the mean differences between the VIC ET and the remote sensing ET range from −2 to 2 mm/day, and the differences for SM of the top thin layer range from −2 to 3 mm. Therefore, this continental-scale hydrological modeling framework is reliable and accurate, which can be used for various applications including extreme hydrological event detections.

The hydrological regime in arid and semi-arid regions is quite sensitive to climate and land cover changes (LCC). The Three-North region (TNR) in China experiences diverse climate conditions, from arid to humid zones. In this region, substantial LCC has occurred over the past decades due to ecological restoration programs and urban expansion. At a regional scale, the hydrological effects of LCC have been demonstrated to be less observable than the effects of climate change, but it is unclear whether or not the effects of LCC may be intensified by future climate conditions. In this study, we employed remote sensing datasets and a macro-scale hydrological modeling to identify the dependence of the future hydrological regime of the TNR on past LCC. The hydrological effects over the period from 2020–2099 were evaluated based on a Representative Concentration Pathway climate scenario. The results indicated that the forest area increased in the northwest (11,691 km²) and the north (69 km²) of China but declined in the northeast (30,042 km²) over the past three decades. Moreover, the urban area has expanded by 1.3% in the TNR. Under the future climate condition, the hydrological regime will be influenced significantly by LCC. Those changes from 1986 to 2015 may alter the future hydrological cycle mainly by promoting runoff (3.24 mm/year) and decreasing evapotranspiration (3.23 mm/year) over the whole region. The spatial distribution of the effects may be extremely uneven: the effects in humid areas would be stronger than those in other areas. Besides, with rising temperatures and precipitation from 2020 to 2099, the LCC may heighten the risk of dryland expansion and flooding more than climate change alone. Despite uncertainties in the datasets and methods, the regional-scale hydrological model provides new insights into the extended impacts of ecological restoration and urbanization on the hydrological regime of the TNR.

Satellite remote sensing is a practical technique to estimate global precipitation with adequate spatiotemporal resolution in ungauged regions. However, the performance of satellite-based precipitation products is variable and uncertain for the Tibetan Plateau (TP) because of its complex terrain and climate conditions. In this study, we evaluated the abilities of nine widely used satellite-based precipitation products over the Eastern Tibetan Plateau (ETP) and quantified precipitation dynamics over the entire TP. The evaluation was carried out from three aspects, i.e., magnitude agreement, occurrence consistency, and elevation dependency, from grid-cell to regional scales. The results show that the nine satellite-based products exhibited different agreement with gauge-based reference data with median correlation coefficients ranging from 0.15 to 0.95. Three products (climate hazards group infrared precipitation with stations (CHIRPS), multi-source weighted-ensemble precipitation (MSWEP), and tropical rainfall measuring mission multi-satellite precipitation analysis (TMPA)) generally presented the best performance with the reference data, even in complex terrain regions, given their root mean square errors (RMSE) of less than 25 mm/mon. The climate prediction center merged analysis of precipitation (CMAP) product has relatively coarse spatial resolution, but it also exhibited good performance with a bias of less than 20% in watershed scale. Two other products (precipitation estimation from remotely sensed information using artificial neural networks-cloud classification system (PER-CCS) and climate prediction center morphing technique-raw (CMORPH-RAW)) overestimated precipitation with median RMSEs of 87 mm/mon and 45 mm/mon, respectively. All the precipitation products generally exhibited better agreement with the reference data for rainy season and lower-elevation regions. All of the products captured precipitation occurrence well, with hit event over 60%, and similar percentages of missed and false event. According to the evaluation, the four products (CHIRPS, MSWEP, TMPA, and CMAP) revealed that the annual precipitation over the TP fluctuated between 333 mm/yr and 488 mm/yr during the period 2003 to 2015. The study indicates the importance of integration of multiple data sources and post-processing (e.g., gauge data fusion and elevation correction) for satellite-based products and have implications for selection of suitable precipitation products for hydrological modeling and water resources assessment for the TP.

Changes in carbon dioxide (CO₂) concentration and nitrogen (N) availability can affect land surface processes by regulating physiological (e.g., stomatal opening and closure) and phenological (e.g., leaf area index) responses, which in turn influence terrestrial water cycle dynamics. In this study, we apply the Community Land Model version 5 to investigate how projected environment changes in the 21st century can affect the hydrologic budget in the Upper Columbia‐Priest Rapids watershed, a typical semiarid watershed located in the Pacific Northwest of the United States. Nine numerical experiments were performed to quantifying contributions of climate change, CO₂ concentration, and N availability to the watershed hydrologic budget under two Representative Concentration Pathways (RCPs) (i.e., RCP4.5 and RCP8.5). Our results show that compared to its historical value, evapotranspiration (ET) over the watershed increases by 15% and 12% by the end of the century under RCP4.5 and RCP8.5 in responses to changes in meteorological forcing, and further increases by 16% and 11% when elevated CO₂ concentrations are accounted for. The effect of N availability on the water balance is insignificant. Such an increase in ET is contributed by the overall increase in leaf area index of vegetation in response to increases in precipitation and CO₂ concentration. However, corn‐dominated regions can respond to these changes differently, caused by its increased stomatal resistance under an elevated CO₂ concentration with limited N. Our results demonstrate physiological and phenological responses can modulate watershed hydrologic budgets under projected future changes.