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The mobile ocean weather observation system, named Marine Weather Observer (MWO), developed by the Institute of Atmospheric Physics (IAP), consists of a fully solar-powered, unoccupied vehicle and meteorological and hydrological instruments. One of the MWOs completed a long-term continuous observation, actively approaching the center of Typhoon Sinlaku from 24 July to 2 August 2020, over the South China Sea. The in situ and high-temporal-resolution (1 min) observations obtained from MWO were analyzed and evaluated through comparison with the observations made by two types of buoys during the evolution of Typhoon Sinlaku. First, the air pressure and wind speed measured by MWO are in good agreement with those measured by the buoys before the typhoon, reflecting the equivalent measurement capabilities of the two methods under normal sea conditions. The sea surface temperature (SST) between MWO and the mooring buoys is highly consistent throughout the observation period, indicating the high stability and accuracy of SST measurements from MWO during the typhoon evolution. The air temperature and relative humidity measured by MWO have significant diurnal variations, generally lower than those measured by the buoys, which may be related to the mounting height and sensitivity of sensors. When actively approaching the typhoon center, the air pressure from MWO can reflect some drastic and subtle changes, such as a sudden drop to 980 hPa, which is difficult to obtain by other observation methods. As a mobile meteorological and oceanographic observation station, MWO has shown its unique advantages over traditional observation methods, and the results preliminarily demonstrate the reliable observation capability of MWO in this paper.

In the present study, a new wetland health assessment approach based on the fuzzy inference system (FIS) was developed and proposed for the first time to improve a traditional wetland classification and assessment index. One primary purpose of the study is to modify the indicators of the traditional assessment approach to suit the regional environmental conditions of the selected areas. As two of the twenty-five Iranian wetlands with international importance, Kanibarazan and Choghakhor wetlands were selected as the study areas due to their significant roles in protecting the biodiversity of their regions. The wetlands are supported by the international Ramsar Convention on Wetlands to mandate and encourage the local authorities towards their conservation and sustainable exploitation. In this regard, the Iranian Department of Environment, in cooperation with the Global Environment Facility (GEF) and the United Nations Development Programme (UNDP), selected these wetlands to demonstrate new approaches of managing the wetland areas protected by the Conservation of Iranian Wetlands Project (CIWP). A real-time wetland monitoring station with hydrological instruments, including water level, air temperature, air humidity, and water quality multi-parameter sensors recording water temperature, pH, electrical conductivity, and dissolved oxygen (DO), was implanted at the deepest part of both wetlands. The manual sampling of water quality parameters was also carried out periodically during specific intervals. The relative importance of the wetland health indicators involved in the FIS was determined via the analytic hierarchy process (AHP), utilizing the knowledge of local experts, including academic staff, environmental specialists, and natives, to localize the traditional assessment approach. In turn, the health level categories of both wetlands were assessed using the traditional and proposed wetland health assessment approaches. The efficiency of the proposed method was evaluated with the selected case studies, and it proved to be a more flexible and appropriate approach for wetland health assessment. Furthermore, the observed differences between the health level of the first case study pointed to the efficiency of the AHP-FIS method in improving the traditional index. Besides, feedforward neural network (FFNN) and support vector regression (SVR) artificial intelligence (AI) methods were used to model DO as one of the most critical water quality parameters in maintaining the biological integrity of wetland ecosystems. The obtained results indicated that FFNN performed slightly better than SVR in predicting DO of both wetlands. Consequently, the results of DO modeling were used to investigate the possibility of employing AI models in wetland health assessment in case of unavailability or failures of DO sensors, which was considered practical considering the suitable performance of AI models and the obtained wetland health level results based on the predicted DO. Highlights 1. A wetland health assessment approach based on fuzzy logic was developed. 2. Two freshwater wetlands were selected as real case study areas. 3. The fuzzy inference system and analytic hierarchy process were used to improve the process. 4. The proposed approach showed more flexibility and efficiency. 5. Two AI methods were used to model DO and investigate their applicability in health assessment.

We undertook a comprehensive evaluation of 22 gridded (quasi-)global (sub-)daily precipitation (P) datasets for the period 2000–2016. Thirteen non-gauge-corrected P datasets were evaluated using daily P gauge observations from 76 086 gauges worldwide. Another nine gauge-corrected datasets were evaluated using hydrological modeling, by calibrating the HBV conceptual model against streamflow records for each of 9053 small to medium-sized ( <  50 000 km2) catchments worldwide, and comparing the resulting performance. Marked differences in spatio-temporal patterns and accuracy were found among the datasets. Among the uncorrected P datasets, the satellite- and reanalysis-based MSWEP-ng V1.2 and V2.0 datasets generally showed the best temporal correlations with the gauge observations, followed by the reanalyses (ERA-Interim, JRA-55, and NCEP-CFSR) and the satellite- and reanalysis-based CHIRP V2.0 dataset, the estimates based primarily on passive microwave remote sensing of rainfall (CMORPH V1.0, GSMaP V5/6, and TMPA 3B42RT V7) or near-surface soil moisture (SM2RAIN-ASCAT), and finally, estimates based primarily on thermal infrared imagery (GridSat V1.0, PERSIANN, and PERSIANN-CCS). Two of the three reanalyses (ERA-Interim and JRA-55) unexpectedly obtained lower trend errors than the satellite datasets. Among the corrected P datasets, the ones directly incorporating daily gauge data (CPC Unified, and MSWEP V1.2 and V2.0) generally provided the best calibration scores, although the good performance of the fully gauge-based CPC Unified is unlikely to translate to sparsely or ungauged regions. Next best results were obtained with P estimates directly incorporating temporally coarser gauge data (CHIRPS V2.0, GPCP-1DD V1.2, TMPA 3B42 V7, and WFDEI-CRU), which in turn outperformed the one indirectly incorporating gauge data through another multi-source dataset (PERSIANN-CDR V1R1). Our results highlight large differences in estimation accuracy, and hence the importance of P dataset selection in both research and operational applications. The good performance of MSWEP emphasizes that careful data merging can exploit the complementary strengths of gauge-, satellite-, and reanalysis-based P estimates.

Recent theory and field observations suggest that a systematically varying weathering zone, that can be tens of meters thick, commonly develops in the bedrock underlying hillslopes. Weathering turns otherwise poorly conductive bedrock into a dynamic water storage reservoir. Infiltrating precipitation typically will pass through unsaturated weathered bedrock before reaching groundwater and running off to streams. This invisible and difficult to access unsaturated zone is virtually unexplored compared with the surface soil mantle. We have proposed the term “rock moisture” to describe the exchangeable water stored in the unsaturated zone in weathered bedrock, purposely choosing a term parallel to, but distinct from, soil moisture, because weathered bedrock is a distinctly different material that is distributed across landscapes independently of soil thickness. Here, we report a multiyear intensive campaign of quantifying rock moisture across a hillslope underlain by a thick weathered bedrock zone using repeat neutron probe measurements in a suite of boreholes. Rock moisture storage accumulates in the wet season, reaches a characteristic upper value, and rapidly passes any additional rainfall downward to groundwater. Hence, rock moisture storage mediates the initiation and magnitude of recharge and runoff. In the dry season, rock moisture storage is gradually depleted by trees for transpiration, leading to a common lower value at the end of the dry season. Up to 27% of the annual rainfall is seasonally stored as rock moisture. Significant rock moisture storage is likely common, and yet it is missing from hydrologic and land-surface models used to predict regional and global climate.

In urban areas, hydrological processes are characterized by high variability in space and time, making them sensitive to small-scale temporal and spatial rainfall variability. In the last decades new instruments, techniques, and methods have been developed to capture rainfall and hydrological processes at high resolution. Weather radars have been introduced to estimate high spatial and temporal rainfall variability. At the same time, new models have been proposed to reproduce hydrological response, based on small-scale representation of urban catchment spatial variability. Despite these efforts, interactions between rainfall variability, catchment heterogeneity, and hydrological response remain poorly understood. This paper presents a review of our current understanding of hydrological processes in urban environments as reported in the literature, focusing on their spatial and temporal variability aspects. We review recent findings on the effects of rainfall variability on hydrological response and identify gaps where knowledge needs to be further developed to improve our understanding of and capability to predict urban hydrological response.

The elevation of the Tibetan Plateau has a major impact on climate, affecting the monsoons and regional weather patterns. Although some isotope proxies have suggested a roughly equivalent height for the plateau as far back as the Eocene (∼40 million years ago), other lines of evidence suggest a lower elevation in the distant past. Botsyun et al. used a model to show that several previously overlooked factors contribute to the isotopic record from the Eocene (see the Perspective by van Hinsbergen and Boschman). The results harmonize the isotopic record with other proxies and argue for a Tibetan Plateau that was about 1000 meters lower than it is today. Science , this issue p. eaaq1436 ; see also p. 928 Climate models for the Tibetan Plateau in the Eocene support a lower overall elevation. Paleotopographic reconstructions of the Tibetan Plateau based on stable isotope paleoaltimetry methods conclude that most of the Plateau’s current elevation was already reached by the Eocene, ~40 million years ago. However, changes in atmospheric and hydrological dynamics affect oxygen stable isotopes in precipitation and may thus bias such reconstructions. We used an isotope-equipped general circulation model to assess the influence of changing Eocene paleogeography and climate on paleoelevation estimates. Our simulations indicate that stable isotope paleoaltimetry methods are not applicable in Eocene Asia because of a combination of increased convective precipitation, mixture of air masses, and widespread aridity. Rather, a model-data comparison suggests that the Tibetan Plateau only reached low to moderate (less than 3000 meters) elevations during the Eocene, reconciling oxygen isotope data with other proxies.

Streamflow variability and trends in Australia were investigated for 222 high-quality stream gauging stations having 30 years or more continuous unregulated streamflow records. Trend analysis identified seasonal, inter-annual and decadal variability, long-term monotonic trends and step changes in streamflow. Trends were determined for annual total flow, baseflow, seasonal flows, daily maximum flow and three quantiles of daily flow. A distinct pattern of spatial and temporal variation in streamflow was evident across different hydroclimatic regions in Australia. Most of the stations in southeastern Australia spread across New South Wales and Victoria showed a significant decreasing trend in annual streamflow, while increasing trends were retained within the northern part of the continent. No strong evidence of significant trend was observed for stations in the central region of Australia and northern Queensland. The findings from step change analysis demonstrated evidence of changes in hydrologic responses consistent with observed changes in climate over the past decades. For example, in the Murray–Darling Basin, 51 out of 75 stations were identified with step changes of significant reduction in annual streamflow during the middle to late 1990s, when relatively dry years were recorded across the area. Overall, the hydrologic reference stations (HRSs) serve as critically important gauges for streamflow monitoring and changes in long-term water availability inferred from observed datasets. A wealth of freely downloadable hydrologic data is provided at the HRS web portal including annual, seasonal, monthly and daily streamflow data, as well as trend analysis products and relevant site information.

High-quality, freely accessible, long-term precipitation estimates with fine spatiotemporal resolution play essential roles in hydrologic, climatic, and numerical modeling applications. However, the existing daily gridded precipitation datasets over China are either constructed with insufficient gauge observations or neglect topographic effects and boundary effects on interpolation. Using daily observations from 2839 gauges located across China and nearby regions from 1961 to the present, this study compared eight different interpolation schemes that adjusted the climatology based on a monthly precipitation constraint and topographic characteristic correction, using an algorithm that combined the daily climatology field with a precipitation ratio field. Results from these eight interpolation schemes were validated using 45 992 high-density daily gauge observations from 2015 to 2019 across China. Of these eight schemes, the one with the best performance merges the Parameter-elevation Regression on Independent Slopes Model (PRISM) in the daily climatology field and interpolates station observations into the ratio field using an inverse-distance weighting method. This scheme had median values of 0.78 for the correlation coefficient, 8.8 mm d−1 for the root-mean-square deviation, and 0.69 for the Kling–Gupta efficiency for comparisons between the 45 992 high-density gauge observations and the best interpolation scheme for the 0.1∘ latitude × longitude grid cells from 2015 to 2019. This scheme had the best overall performance, as it fully considers topographic effects in the daily climatology field and it balances local data fidelity and global fitting smoothness in the interpolation of the precipitation ratio field. Therefore, this scheme was used to construct a new long-term, gauge-based gridded precipitation dataset for the Chinese mainland (called CHM_PRE, as a member of the China Hydro-Meteorology dataset) with spatial resolutions of 0.5, 0.25, and 0.1∘ from 1961 to the present. This precipitation dataset is expected to facilitate the advancement of drought monitoring, flood forecasting, and hydrological modeling. Free access to the dataset can be found at https://doi.org/10.6084/m9.figshare.21432123.v4 (Han and Miao, 2022).

Coupled atmospheric–hydrologic systems are increasingly used as instruments for flood forecasting and water management purposes, making the performance of the hydrologic routines a key indicator of the model functionality. This study's objectives were (i) to calibrate the one-way-coupled WRF-Hydro model for simulating extreme events in Cyprus with observed precipitation and (ii) to evaluate the model performance when forced with WRF-downscaled (1×1 km2) re-analysis precipitation data (ERA-Interim). This set-up resembles a realistic modelling chain for forecasting applications and climate projections. Streamflow was modelled during extreme rainfall events that occurred in January 1989 (calibration) and November 1994 (validation) over 22 mountain watersheds. In six watersheds, Nash–Sutcliffe efficiencies (NSEs) larger than 0.5 were obtained for both events. The WRF-modelled rainfall showed an average NSE of 0.83 for January 1989 and 0.49 for November 1994. Nevertheless, hydrologic simulations of the two events with the WRF-modelled rainfall and the calibrated WRF-Hydro returned negative streamflow NSE for 13 watersheds in January 1989 and for 18 watersheds in November 1994. These results indicate that small differences in amounts or shifts in time or space of modelled rainfall, in comparison with observed precipitation, can strongly modify the hydrologic response of small watersheds to extreme events. Thus, the calibration of WRF-Hydro for small watersheds depends on the availability of observed rainfall with high temporal and spatial resolution. However, the use of modelled precipitation input data will remain important for studying the effect of future extremes on flooding and water resources.

The alpine region is important in riverine and watershed ecosystems as a contributor of freshwater, providing and stimulating specific habitats for biodiversity. In parallel, recent climate change, human activities and other perturbations may disturb hydrological processes and eco-functions, creating the need for next-generation observational and modeling approaches to advance a predictive understanding of such processes in the alpine region. However, several formidable challenges, including the cold and harsh climate, high altitude and complex topography, inhibit complete and consistent data collection where and when it is needed, which hinders the development of remote-sensing technologies and alpine hydrological models. The current study presents a suite of datasets consisting of long-term hydrometeorological, snow cover and frozen-ground data for investigating watershed science and functions from an integrated, distributed and multiscale observation network in the upper reaches of the Heihe River Basin (HRB) in China. Meteorological and hydrological data were monitored from an observation network connecting a group of automatic meteorological stations (AMSs). In addition, to capture snow accumulation and ablation processes, snow cover properties were collected from a snow observation superstation using state-of-the-art techniques and instruments. High-resolution soil physics datasets were also obtained to capture the freeze–thaw processes from a frozen-ground observation superstation. The updated datasets were released to scientists with multidisciplinary backgrounds (i.e., cryospheric science, hydrology and meteorology), and they are expected to serve as a testing platform to provide accurate forcing data and validate and evaluate remote-sensing products and hydrological models for a broader community. The datasets are available from the Cold and Arid Regions Science Data Center at Lanzhou (https://doi.org/10.3972/hiwater.001.2019.db, Li, 2019).