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Guofeng Zhu, Zhuanxia Zhang, Huiwen Guo, Yu Zhang, Leilei Yong, Qiaozhuo Wan, Zhigang Sun, and Huiying Ma

Abstract

As raindrops fall from the cloud base to the ground, evaporation below those clouds affects the rain’s isotope ratio, reduces precipitation in arid areas, and impacts the local climate. Therefore, in arid areas with scarce water resources and fragile ecological environments, the below-cloud evaporation is an issue of great concern. Based on 406 event-based precipitation samples collected from nine stations in the Shiyang River basin (SRB) in the northwest arid area, global meteorological water line (GMWL) and local meteorological water line (LMWL) are compared, and the Stewart model is used to study the effect of spatial and temporal variation of below-cloud evaporation on isotope values in different geomorphic units at the SRB. Furthermore, factors influencing below-cloud evaporation are analyzed. The results show that 1) the change of d-excess (Δd) in precipitation at the SRB and the residual ratio of raindrop evaporation (f) vary in time and space. With regard to temporal variation, the intensity of below-cloud evaporation is described by summer < autumn < winter < spring. Regarding spatial variation, the below-cloud evaporation in mountain areas is weaker than in oases and deserts. The intensity of below-cloud evaporation in mountain areas increases with decreasing altitude, and the below-cloud evaporation in oasis and desert areas is affected by local climatic conditions. 2) Below-cloud evaporation is also affected by local transpiration evaporation, especially around reservoirs. Reservoirs increase the relative humidity of the air nearby, weakening below-cloud evaporation. This study deepens our understanding of the water cycle process in arid areas.

Open access
Zeyu Xue and Paul Ullrich

Abstract

Climate models are frequently used tools for adaptation planning in light of future uncertainty. However, not all climate models are equally trustworthy, and so model biases must be assessed to select models suitable for producing credible projections. Drought is a well-known and high-impact form of extreme weather, and knowledge of its frequency, intensity, and duration are key for regional water management plans. Droughts are also difficult to assess in climate datasets, due to the long duration per event, relative to the length of a typical simulation. Therefore, there is a growing need for a standardized suite of metrics addressing how well models capture this phenomenon. In this study, we present a widely applicable set of metrics for evaluating agreement between climate datasets and observations in the context of drought. Two notable advances are made in our evaluation system: first, statistical hypothesis testing is employed for normalization of individual scores against the threshold for statistical significance. And second, within each evaluation region and dataset, principal feature analysis is used to select the most descriptive metrics among 11 metrics that capture essential features of drought. Our metrics package is applied to three characteristically distinct regions in the conterminous United States and across several commonly employed climate datasets (CMIP5/6, LOCA, and CORDEX). As a result, insights emerge into the underlying drivers of model bias in global climate models, regional climate models, and statistically downscaled models.

Open access
Rong-Yu Gu, Min-Hui Lo, Chi-Ya Liao, Yi-Shin Jang, Jehn-Yih Juang, Cho-Ying Huang, Shih-Chieh Chang, Cheng-I Hsieh, Yi-Ying Chen, Housen Chu, and Kuang-Yu Chang

Abstract

Hydroclimate in the montane cloud forest (MCF) regions is unique for its frequent fog occurrence and abundant water interception by tree canopies. Latent heat (LH) flux, the energy flux associated with evapotranspiration (ET), plays an essential role in modulating energy and hydrological cycles. However, how LH flux is partitioned between transpiration (stomatal evaporation) and evaporation (nonstomatal evaporation) and how it impacts local hydroclimate remain unclear. In this study, we investigated how fog modulates the energy and hydrological cycles of MCF by using a combination of in situ observations and model simulations. We compared LH flux and associated micrometeorological conditions at two eddy-covariance sites—Chi-Lan (CL), an MCF, and Lien-Hua-Chih (LHC), a noncloud forest in Taiwan. The comparison between the two sites reveals an asymmetric LH flux with an early peak at 0900 local time in CL as opposed to LHC, where LH flux peaks at noon. The early peak of LH flux and its evaporative cooling dampen the increase in near-surface temperature during the morning hours in CL. The relatively small diurnal temperature range, abundant moisture brought by the valley wind, and local ET result in frequent afternoon fog formation. Fog water is then intercepted by the canopy, sustaining moist conditions throughout the night. To further illustrate this hydrological feedback, we used a land surface model to simulate how varying canopy water interception can affect surface energy and moisture budgets. Our study highlights the unique hydroclimatological cycle in the MCF and, specifically, the inseparable relationship between the canopy and near-surface meteorology during the diurnal cycle.

Open access
Caroline M. Wainwright, Emily Black, and Richard P. Allan

Abstract

Climate change will result in more dry days and longer dry spells; however, the resulting impacts on crop growth depend on the timing of these longer dry spells in the annual cycle. Using an ensemble of Coupled Model Intercomparison Project phase 5 and phase 6 (CMIP5 and CMIP6) simulations, and a range of emission scenarios, here we examine changes in wet and dry spell characteristics under future climate change across the extended tropics in wet and dry seasons separately. Delays in the wet seasons by up to 2 weeks are projected by 2070–99 across South America, southern Africa, West Africa, and the Sahel. An increase in both mean and maximum dry spell length during the dry season is found across Central and South America, southern Africa, and Australia, with a reduction in dry season rainfall also found in these regions. Mean dry season dry spell lengths increase by 5–10 days over northeast South America and southwest Africa. However, changes in dry spell length during the wet season are much smaller across the tropics with limited model consensus. Mean dry season maximum temperature increases are found to be up to 3°C higher than mean wet season maximum temperature increases over South America, southern Africa, and parts of Asia. Longer dry spells, fewer wet days, and higher temperatures during the dry season may lead to increasing dry season aridity and have detrimental consequences for perennial crops.

Open access
Free access
Xiangbo Feng, Wei Zhang, Zhenglei Zhu, Amulya Chevuturi, and Wenlong Chen

Abstract

Understanding water level (WL) fluctuations in river deltas is important for managing water resources and minimizing the impacts of floods and droughts. Here, we demonstrate the competing effects of atmospheric and oceanic forcing on multi-time-scale variability and changes in the Pearl River Delta (PRD) WLs in southern China, using 52 years (1961–2012) of in situ observations at 13 hydrological stations. PRD WL presents significant seasonal to decadal variations, with large amplitudes upstream related to strong variability of southern China rainfall, and with relatively small amplitudes at the coastal stations determined by sea level (SL) fluctuations of the northern South China Sea. We find that the strengths of atmospheric and oceanic forcing in PRD are not mutually independent, leading to a distinct contrast of WL–forcing relationships at upstream and coastal stations. In the transition zone, because of the counteraction of atmospheric and oceanic forcing, no robust relationships are identified between WL and either of the forcing. We further show that in the drought season of the warm ENSO and PDO epochs, the effect of atmospheric (oceanic) forcing on PRD WL is largely enhanced (weakened), due to increased southern China rainfall and negative SL anomalies. Over the observation period, WL significantly decreased at upstream stations, by up to 28–42 mm yr−1 for flood season, contrasting with the upward trends of <4.3 mm yr−1 at coastal stations across all seasons. Southern China rainfall explains little of the observed WL trends, while SL rise is mostly responsible for the WL trends at coastal stations.

Open access
Nergui Nanding, Huan Wu, Jing Tao, Viviana Maggioni, Hylke E. Beck, Naijun Zhou, Maoyi Huang, and Zhijun Huang

Abstract

This study characterizes precipitation error propagation through a distributed hydrological model based on the river basins across the contiguous United States (CONUS), to better understand the relationship between errors in precipitation inputs and simulated discharge (i.e., PQ error relationship). The NLDAS-2 precipitation and its simulated discharge are used as the reference to compare with TMPA-3B42 V7, TMPA-3B42RT V7, Stage IV, CPC-U, MERRA-2, and MSWEP V2.2 for 1548 well-gauged river basins. The relative errors in multiple conventional precipitation products and their corresponding discharges are analyzed for the period of 2002–13. The results reveal positive linear PQ error relationships at annual and monthly time scales, and the stronger linearity for larger temporal accumulations. Precipitation errors can be doubled in simulated annual accumulated discharge. Moreover, precipitation errors are strongly dampened in basins characterized by temperate and continental climate regimes, particularly for peak discharges, showing highly nonlinear relationships. Radar-based precipitation product consistently shows dampening effects on error propagation through discharge simulations at different accumulation time scales compared to the other precipitation products. Although basin size and topography also influence the PQ error relationship and propagation of precipitation errors, their roles depend largely on precipitation products, seasons, and climate regimes.

Open access
Omar V. Müller, Pier Luigi Vidale, Benoît Vannière, Reinhard Schiemann, and Patrick C. McGuire

Abstract

Previous studies showed that high-resolution GCMs overestimate land precipitation when compared against observation-based data. Particularly, high-resolution HadGEM3-GC3.1 shows a significant precipitation increase in mountainous regions, where the scarcity of gauge stations increases the uncertainty of gridded observations and reanalyses. This work evaluates such precipitation uncertainties indirectly through the assessment of river discharge, considering that an increase of ~10% in land precipitation produces ~28% more runoff when the resolution is enhanced from 1° to 0.25°, and ~50% of the global runoff is produced in 27% of global land dominated by mountains. We diagnosed the river flow by routing the runoff generated by HadGEM3-GC3.1 low- and high-resolution simulations. The river flow is evaluated using a set of 344 monitored catchments distributed around the world. We also infer the global discharge by constraining the simulations with observations following a novel approach that implies bias correction in monitored rivers with two methods, and extension of the correction to the river mouth, and along the coast. Our global discharge estimate is 47.4 ± 1.6 × 103 km3 yr−1, which is closer to the original high-resolution estimate (50.5 × 103 km3 yr−1) than to the low-resolution (39.6 × 103 km3 yr−1). The assessment suggests that high-resolution simulations perform better in mountainous regions, either because the better-defined orography favors the placement of precipitation in the correct catchment, leading to a more accurate distribution of runoff, or the orographic precipitation increases, reducing the dry runoff bias of coarse-resolution simulations. However, high-resolution slightly increases wet biases in catchments dominated by flat terrain. The improvement of model parameterizations and tuning may reduce the remaining errors in high-resolution simulations.

Open access
Free access
Thomas C. van Leth, Hidde Leijnse, Aart Overeem, and Remko Uijlenhoet

Abstract

We investigate the spatiotemporal structure of rainfall at spatial scales from 7 m to over 200 km in the Netherlands. We used data from two networks of laser disdrometers with complementary interstation distances in two Dutch cities (comprising five and six disdrometers, respectively) and a Dutch nationwide network of 31 automatic rain gauges. The smallest aggregation interval for which raindrop size distributions were collected by the disdrometers was 30 s, while the automatic rain gauges provided 10-min rainfall sums. This study aims to supplement other micro-γ investigations (usually performed in the context of spatial rainfall variability within a weather radar pixel) with new data, while characterizing the correlation structure across an extended range of scales. To quantify the spatiotemporal variability, we employ a two-parameter exponential model fitted to the spatial correlograms and characterize the parameters of the model as a function of the temporal aggregation interval. This widely used method allows for a meaningful comparison with seven other studies across contrasting climatic settings all around the world. We also separately analyzed the intermittency of the rainfall observations. We show that a single parameterization, consisting of a two-parameter exponential spatial model as a function of interstation distance combined with a power-law model for decorrelation distance as a function of aggregation interval, can coherently describe rainfall variability (both spatial correlation and intermittency) across a wide range of scales. Limiting the range of scales to those typically found in micro-γ variability studies (including four of the seven studies to which we compare our results) skews the parameterization and reduces its applicability to larger scales.

Open access