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On seasonal time scales, vapor pressure deficit (VPD) is a known predictor of burned area in the southwestern United States ("the Southwest"). VPD increases with atmospheric warming due to the exponential relationship between temperature and saturation vapor pressure. Another control on VPD is specific humidity, such that increases in specific humidity can counteract temperature-driven increases in VPD. Unexpectedly, despite the increased capacity of a warmer atmosphere to hold water vapor, near-surface specific humidity decreased from 1970 to 2019 in much of the Southwest, particularly in spring, summer, and fall. Here, we identify declining near-surface humidity from 1970 to 2019 in the southwestern United States with both reanalysis and in situ station data. Focusing on the interior Southwest in the months preceding the summer forest fire season, we explain the decline in terms of changes in atmospheric circulation and moisture fluxes between the surface and the atmosphere. We find that an early spring decline in precipitation in the interior region induced a decline in soil moisture and evapotranspiration, drying the lower troposphere in summer. This prior season precipitation decline is in turn related to a trend toward a Northern Hemisphere stationary wave pattern. Finally, using fixed humidity scenarios and the observed exponential relationship between VPD and burned forest area, we estimate that with no increase in temperature at all, the humidity decline alone would still lead to nearly one-quarter of the observed VPD-induced increase in burned area over 1984–2019. Significance Statement: Burned forest area has increased significantly in the southwestern United States in recent decades, driven in part by an increase in atmospheric aridity [vapor pressure deficit (VPD)]. Increases in VPD can be caused by a combination of increasing temperature and decreasing specific humidity. As the atmosphere warms with climate change, its capacity to hold moisture increases. Despite this, there is a decrease in near-surface air humidity in the interior southwestern United States over 1970–2019, which during the summer is likely caused by a decline in early spring precipitation leading to limited soil moisture and evaporation in spring and summer. We estimate that this declining humidity alone, without an increase in temperature, would cause about one-quarter of the VPD-induced increase in burned forest area in this region over 1984–2019. [ABSTRACT FROM AUTHOR]

Currently, there is a lack of investigating moisture sources for precipitation over the upstream catchment of the Three Gorges Dam (UCTGD), the world's largest dam. Using the dynamical recycling model (DRM), trajectory frequency method (TFM), and the Climate Forecast System Reanalysis (CFSR), this study quantifies moisture sources and transport paths for UCTGD summer precipitation from 1980 to 2009 based on two categories of sources: region-specific and source-direction. Overall, the land and oceanic sources contribute roughly 63% and 37%, respectively, of the moisture to UCTGD summer precipitation. UCTGD and the Indian Ocean are the most important land and oceanic sources, respectively, in which the southern Indian Ocean with over 10% of moisture contribution was overlooked previously. Under the influence of the Asian monsoon and prevailing westerlies, the land contribution decreases to 57.3% in June, then gradually increases to 68.8%. It is found that for drought years with enhanced southwest monsoon, there is a weakening of the moisture contribution from the C-shaped belt along the Arabian Sea, South Asia, and UCTGD, and vice versa. TFM results show three main moisture transport paths and highlight the importance of moisture from the southwest. Comparison analysis indicates that, generally, sink regions are more affected by land evaporation with their locations more interior to the center of the mainland. Furthermore, correlations between moisture contributions and indices of general circulation and sea surface temperature are investigated, suggesting that these indices affect precipitation by influencing moisture contributions of the subregions. All of these are useful for comprehending the causes of summer UCTGD precipitation. Significance Statement: Quantitative research on the moisture sources of summer precipitation has been implemented for the upstream catchment of the Three Gorges Dam (UCTGD), which is of particular hydrological significance but has not been investigated previously. The dynamical recycling model (DRM)–trajectory frequency method (TFM) approach is used to quantify and interpret the results of the moisture sources both in different specific subregions and directions, which produce more meaningful results than a single method for the areal division of moisture sources. Furthermore, antecedent indices that significantly influence the following moisture contributions of the subregions and then summer UCTGD precipitation are studied in terms of large-scale general circulation indices, which would help our understanding of precipitation forecast for UCTGD. [ABSTRACT FROM AUTHOR]

Global precipitation demonstrates a substantial role in the hydrological cycle and offers tremendous implications in hydrometeorological studies. Advanced remote sensing instrumentations, such as the NASA Global Precipitation Measurement (GPM) mission Dual-Frequency Precipitation Radar (DPR), can estimate precipitation and cloud properties and have a unique capability to estimate the raindrop size information globally at snapshots in time. The present study validates the Level-2 data products of the GPM DPR with the long-term measurements of seven north Taiwan Joss–Waldvogel disdrometers from 2014 to 2022. The precipitation and drop size distribution parameters like rainfall rate (R; mm h−1), radar reflectivity factor (dBZ), mass-weighted mean drop diameter (Dm; mm), and normalized intercept parameter (Nw; m−3 mm−1) of the GPM DPR are compared with the disdrometers. Four different comparison approaches (point match, 5-km average, 10-km average, and optimal method) are used for the validation; among these four, the optimal strategy provided reasonable agreement between the GPM DPR and the disdrometers. The GPM DPR revealed superior performance in estimating the rain parameters in stratiform precipitation than the convective precipitation. Irrespective of algorithm type (dual- or single-frequency algorithm), sensitivity analysis revealed superior agreement for the mass-weighted mean diameter and inferior agreement for the normalized intercept parameter. [ABSTRACT FROM AUTHOR]

Snow cover provides distinct seasonal controls on the exchange of energy between Earth's surface and atmosphere, and on hydrologic cycling, and holds considerable importance to communities and ecosystems worldwide. In this work, we tackle a comprehensive review of existing snow classification approaches and the development of new globally applicable snow cover–based rules for delineating snow seasonality classes. Snow classification rules are defined using machine learning approaches, which are then applied to the 22-yr record of snow cover (2000–22) from the Moderate Resolution Imaging Spectroradiometer (MODIS) on a 0.01° global grid. For the MODIS period of record, we find the global land surface can be effectively partitioned into five snow seasonality classes: no snow, ephemeral, transitional, seasonal, and perennial snow regimes which on average cover extents of approximately 76 (52% of global land areas), 19 (13%), 16 (11%), 18 (13%), and 16 million km2 (11%), respectively. Using the multidecadal dataset, we explore changes within snow regimes and find significant increases in the areal extent of no snow (approximately +70 000 km2 yr−1) as well as apparent losses in perennial (−3600 km2 yr−1) and seasonal snow regime coverage (−38 000 km2 yr−1). The resulting classification maps have strong agreement with in situ snow depth observations and present similar patterns to existing snow and climate classifications with notable discrepancies in cold arid regions. The framework's ability to accurately capture variations in snow persistence, snow accumulation, and melt cycling is shown, providing a reference to the current state of global snow seasonality. Significance Statement: Following a review of existing snow classification approaches, this study focuses on improving our understanding of snow-cover variability on Earth by using satellite-based observations of snow-covered area to derive a new snow classification. Satellite observations provide the best means of measuring snow cover at a global scale, helping to identify regions where its influence on energy, water, and climate is changing. The results are compared to existing climate and snow summaries, ground observations of snow depth, and include trends in snow cover over recent decades (2000–22). The resulting datasets are also made available to the broader scientific community. [ABSTRACT FROM AUTHOR]

Reliable quantitative precipitation estimation with a rich spatiotemporal resolution is vital for understanding the Earth's hydrological cycle. Precipitation estimation over land and coastal regions is necessary for addressing the high degree of spatial heterogeneity of water availability and demand, and for resolving the extremes that modulate and amplify hazards such as flooding and landslides. Advancements in computation power along with unique high spatiotemporal and spectral resolution data streams from passive meteorological sensors aboard geosynchronous Earth-orbiting (GEO) and low Earth-orbiting (LEO) satellites offer exciting opportunities to retrieve information about surface precipitation phenomena using data-driven machine learning techniques. In this study, the capabilities of U-Net-like architecture are investigated to map instantaneous, summertime surface precipitation intensity at the spatial resolution of 2 km. The calibrated brightness temperature products from the Global Precipitation Measurement (GPM) Microwave Imager (GMI) radiometer are combined with multispectral images (visible, near-infrared, and infrared bands) from the Advanced Baseline Imager (ABI) aboard the GOES-R satellites as main inputs to the U-Net-like precipitation algorithm. Total precipitable water and 2-m temperature from the Global Forecast System (GFS) model are also used as auxiliary inputs to the model. The results show that the U-Net-like algorithm can capture fine-scale patterns and intensity of surface precipitation at high spatial resolution over stratiform and convective precipitation regimes. The evaluations reveal the potential of extracting relevant, high spatial features over complex surface types such as mountainous regions and coastlines. The algorithm allows users to interpret the inputs' importance and can serve as a starting point for further exploration of precipitation systems within the field of hydrometeorology. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP) is a vital and vulnerable water tower that supports the livelihoods of billions of people. The use of a data-driven recharge–storage–runoff perspective enables a more comprehensive estimation of multiple aspects of the water cycle. Through an analysis of the diagnostic net water flux from ERA5, water storage changes (dS/dt) from GRACE, runoff estimations (R) from the land–atmosphere water balance, and river discharge measurements (Rd), the annual cycle of recharge–storage–runoff has been studied over the TP and its basins. The net water flux determines a recharge of 326 mm yr−1 over the TP. Recharge in coupled storages, leading to an increase in water mass (dS/dt > 0) and runoff (R > 0) during the wet season, is considered the fast response and is measured using the ratio of runoff to net water flux (r1). Conversely, the slow response determined by the water storage release (dS/dt < 0) during the dry season is quantified by the ratio of storage release to runoff (r2). The ratios of r1 and r2 are influenced by climatic and terrain drivers, indicating specific characteristics of recharge–storage–runoff at the basin scale. Small r1 values and large r2 values suggest high buffer capacity, while the basin of Amu Darya (Salween) is characterized by the highest (lowest) buffer capacity over the TP. However, measurements of river discharge at Amu Darya suggest an uncoupled recharge–storage–runoff. The imbalance between river discharge and runoff estimation was most severe in the first decade of the twenty-first century but has been mitigated since 2012. River discharge at Amu Darya is likely constrained by energy during summer. [ABSTRACT FROM AUTHOR]

The Qinghai–Tibet Plateau, known as the Asian Water Tower, has a significant area of water bodies that provide a wide range of valuable ecosystem services. In the context of climate change, the formation condition of surface water and water extent is changing fast. Thus, there is a critical need for monthly detection algorithms at high spatial resolution (∼30 m) with good accuracy. Multiple sensors' observations are available, but producing reliable long time series surface water mapping at a subannual temporal frequency still remains a challenge, mainly due to data limitations. In this study, we proposed a neural-network-based monthly surface water classification framework relying on Landsat 5/7/8 images in 2000–20 and topographic indices, and retrieved monthly water mask for the year 2020. The surface water was mainly distributed in the central and western parts of the plateau and the maximum area of permanent surface water (water frequency > 60%) was 26.66 × 103 km2 in 2020. The overall, producer, and user accuracies of our surface water map were 0.96, 0.94, and 0.98, respectively, and the kappa coefficient reached 0.90, demonstrating a better performance than existing products [i.e., Joint Research Centre (JRC) Monthly Water History with overall accuracy 0.94, producer accuracy 0.89, user accuracy 0.99, and kappa coefficient 0.89]. Our framework efficiently solved the problem of missing data in Landsat images referring to the JRC and a priori information and performed well in dealing with ice/snow cover issues. We showed that higher uncertainties exist on wetlands and recommended exploring relationships between water and wetlands in the future. Significance Statement: In this paper, we present a new methodology to estimate surface water and its intra-annual changes using Landsat data. Missing data and retrieval errors in the winter are major issues in the existing products (i.e., JRC dataset). This motivated us to develop a new machine learning algorithm to better improve the retrieval scheme. We show that our approach, based on a neural network classifier, delivers a significant improvement compared to the previous estimates. As shown in the literature, JRC data can hardly be used at the monthly level, whereas our retrieval appears to be exploitable at the monthly scale. This is essential to understand the trend in surface water, one of the key elements of the water cycle. [ABSTRACT FROM AUTHOR]

Atmospheric simulation-based gridded precipitation datasets have been widely used in hydrological and land surface modeling, but may contain larger uncertainties in mountainous regions. This study compared the performance of the fifth European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) precipitation data with two fused precipitation datasets [China Meteorological Administration Land Data Assimilation System version 2.0 (CLDAS2.0) and China Meteorological Forcing Dataset (CMFD)] in the Yarlung Zangbo River basin (YZRB), which has a complex terrain and climate. Compared to in situ observations, ERA5 could capture the spatial--temporal pattern of precipitation but showed high precipitation, especially in the downstream region (lower Nuxia discharge station). In terms of the correlation coefficient, the overall performance of the ERA5 data was slightly worse than that for CMFD data at both the monthly and yearly scales, and was comparable to that of the CLDAS2.0 data. Given that the spatial mismatch between the gridded and in situ data may influence the evaluation, we also employed the water balance method to constrain basinwide precipitation amounts. We found that CLDAS2.0 and CMFD precipitation data tended to cause long-term water imbalance, and ERA5, with a much larger multiyear average annual precipitation, could better close the water budget. Further analysis showed that the differences in multiyear average annual precipitation between ERA5 and in situ observations were closely related to the slope and standard deviation of the subgrid-scale orography, indicating the substantial influence of subgrid topography on precipitation simulation. These findings highlight that ERA5 could be a potential reference dataset for hydrological modeling of the YZRB. [ABSTRACT FROM AUTHOR]

Extreme droughts and floods in the Amazon have great implications for ecosystems and societies. Over the last decade, the region has undergone major extreme events with no equivalent in the previous 100 years. Wetlands have been greatly impacted by these events. This study aims at presenting new indicators for wetlands based on water surface extent (WSE): duration of the flooded and nonflooded season, number of days of extreme events, delay of the start of the flooded season, and severity for each season. These indicators are more adapted for monitoring of wetlands than those based on precipitation, discharge, or groundwater information. They are computed for seven major Amazon subbasins for flooded and nonflooded seasons. These indicators improve our knowledge of the temporal behavior of the water surface during different extreme events, such as the 2015/16 drought and the 2014 flood that occurred in the Madeira basin. For the Negro basin and from the point of view of wetlands, the 2015 nonflooded season was 55% more severe than the average of the nonflooded season during the 2011-18 period. For the Paru, Trombetas, Negro, and Solimões basins, we found that a delay in the arrival of the flooded season led to a weak flood season in terms of severity. No correlation between the onset of the flooded season and its severity was found for the Madeira, Xingu, and Tapajós basins. Future hydrometeorological monitoring systems would benefit from including, in addition to variables such as river discharge and water elevation, precipitation, and vegetation dynamics, a severity index based on water surfaces as proposed in this study. [ABSTRACT FROM AUTHOR]

Eurasian spring snowmelt plays an important role in the subsequent climate and hydrological cycle, however, the understanding of snowmelt itself and its causes remains insufficient. This study explored the basic characteristics of spring snowmelt in the eastern Europe–western Siberia (EEWS) region by classifying snowmelt anomalies into two categories based on the different factors that dominate spring snowmelt, and then investigated the associated atmospheric circulation anomalies and local physical processes. The first category of anomalous snowmelt (category 1) is controlled by both the initial snow mass and the later snowmelt process, while the second category of anomalous snowmelt (category 2) is mainly linked to the later snowmelt process. Specifically, category 1 is characterized by an anomalous trough in EEWS in winter, where water vapor transported and converged, accompanied by anomalous upward motion, which promotes snowfall and snow accumulation, providing initial conditions conducive to snowmelt. In April, this region is controlled by an anomalous ridge, with significant warm advection anomalies and subsidence promoting surface warming, thereby accelerating snow melting. In contrast, the winter circulation anomalies are insignificant in category 2, while the anomalous ridge in April is stronger than in category 1, accompanied by more intense snowmelt processes. In addition, from the surface energy balance perspective, atmospheric downward sensible heat transport is an important factor influencing the anomalous snowmelt in category 1, while shortwave radiation plays a secondary role. Conversely, the snowmelt in category 2 is dominated by shortwave radiation forcing, but the sensible heat effect is slightly weaker. Significance Statement: Eurasian spring snowmelt significantly impacts the subsequent climate and hydrological cycle, but the understanding of snowmelt itself and its causes is still inadequate. The purpose of this study is to explore the monthly evolution of atmospheric circulation associated with anomalous snowmelt and its local physical processes associated by categorizing them based on snowmelt characteristics. Category 1 is jointly affected by winter snow accumulation and later warming, while category 2 is dominated by strong snowmelt process in late spring. These two categories are accompanied by different winter and spring circulation configurations. Our results provide a basis for further investigation of snowmelt precursor signals. [ABSTRACT FROM AUTHOR]

Climate changes significantly impact the hydrological cycle. Precipitation is one of the most important atmospheric inputs to the terrestrial hydrologic system, and its variability considerably influences environmental and socioeconomic development. Atmospheric warming intensifies the hydrological cycle, increasing both atmospheric water vapor concentration and global precipitation. The relationship between heavy precipitation and temperature has been extensively investigated in literature. However, the relationship in different percentile ranges has not been thoroughly analyzed. Moreover, a percentile-based regression provides a simple but effective framework for investigation into other factors (precipitation type) affecting this relationship. Herein, a comprehensive investigation is presented on the temperature dependence of daily precipitation in various percentile ranges over the Qinghai–Tibet Plateau. The results show that 1) most stations exhibit a peaklike scaling structure, while the northeast part and south margin of the plateau exhibit monotonic positive and negative scaling structures, respectively. The scaling structure is associated with the precipitation type, and 2) the positive and negative scaling rates exhibit similar spatial patterns, with stronger (weaker) sensitivity in the south (north) part of the plateau. The overall increase rate of daily precipitation with temperature is scaled by Clausius–Clapeyron relationship. 3) The higher percentile of daily precipitation shows a larger positive scaling rate than the lower percentile. 4) The peak-point temperature is closely related to the local temperature, and the regional peak-point temperature is roughly around 10°C. Significance Statement: This study aims to better understand the relationship between precipitation and surface air temperature in various percentile ranges over the Qinghai–Tibet Plateau. This is important because percentile-based regression not only accurately describes the response of precipitation to warming temperature but also provides a simple but effective framework for investigating other factors (precipitation type) that may be affecting this relationship. Furthermore, the sensitivity and peak-point temperature are evaluated and compared among different regions and percentile ranges; this study also attempts to outline their influencing factors. To our knowledge, this study is the first integration of percentile-based analysis of the dependence of daily precipitation on surface air temperature. [ABSTRACT FROM AUTHOR]

Previous studies show that some soil moisture products have a good agreement with in situ measurements on the Tibetan Plateau (TP). However, the soil moisture response to precipitation variability in different products is yet to be assessed. In this study, we focus on the soil moisture response to precipitation variability across weekly to decadal time scales in satellite observations and reanalyses. The response of soil moisture to precipitation variability differs between products, with large uncertainties observed for variations in weekly accumulated precipitation. Using June 2009 as an example, weekly mean anomalous soil moisture varies by up to 25%between products. Across decadal time scales, soil moisture trends vary spatially and across different products. In light of the soil moisture response to precipitation at different time scales, we conclude that remote sensing products developed as part of the European Space Agency’s (ESA) Water Cycle Multimission Observation Strategy and Soil Moisture Climate Change Initiative (CCI) projects are the most reliable, followed by the Global Land Evaporation Amsterdam Model (GLEAM) dataset. Even products that strongly agree with in situ observations on daily time scales, such as the Global Land Data Assimilation System (GLDAS), show inconsistent soil moisture responses to decadal precipitation trends. European Centre for Medium-Range Weather Forecasts (ECWMF) reanalysis products have a relatively poor agreement with in situ observations compared to satellite observations and land-only reanalysis datasets. Unsurprisingly, products which show a consistent soil moisture response to precipitation variability are those mostly aligned to observations or describe the physical relationship between soil moisture and precipitation well. SIGNIFICANCE STATEMENT: We focus on soil moisture responses to precipitation across weekly to decadal time scales by using multiple satellite observations and reanalysis products. Several soil moisture products illustrate good consistency with in situ measurements in different biomes on the Tibetan Plateau, while the response to precipitation variability differs between products, with large uncertainties observed for variations in weekly accumulated precipitation. The response of soil moisture to decadal trends in boreal summer precipitation varies spatially and temporally across products. Based on the assessments of the soil moisture response to precipitation variability across different time scales, we conclude that remote sensing products developed as part of the European Space Agency’s Water Cycle Multimission Observation Strategy and Soil Moisture Climate Change Initiative (CCI) projects are the most reliable, followed by the Global Land Evaporation Amsterdam Model (GLEAM) dataset. Reanalysis products from ECWMF show inconsistent soil moisture responses to precipitation. The results highlight the importance of using multiple soil moisture products to understand the surface response to precipitation variability and to inform developments in soil moisture modeling and satellite retrievals. [ABSTRACT FROM AUTHOR]

The processes underlying heavy rainfall in the higher elevations of the Himalayas are still not well known despite their importance. Here, we examine the detailed process causing a heavy rainfall event, observed by our rain gauge network in the Rolwaling valley, eastern Nepal Himalayas, using ERA5 and a regional cloud-resolving numerical simulation. Heavy precipitation (112 mm day−1) was observed on 8 July 2019 at Dongang (2790 m above sea level). Most of the precipitation (81 mm) occurred during 1900–2300 local time (LT). The synoptic-scale environment is characterized by a monsoon low pressure system (LPS) over northeastern India. The LPS lifted moisture upward from the lower troposphere and then horizontally transported it into the eastern Nepal Himalayas within the middle troposphere, increasing the content of the water vapor around Dongang. A mesoscale convective system passed over Dongang around the time of the intense precipitation. The numerical simulation showed that surface heat fluxes prevailed under the middle tropospheric (∼500 hPa) southeasterly flow associated with the LPS around a mountain ridge on the upwind side of Dongang until 1900 LT, enhancing convective instability. Topographic lifting led to the release of the enhanced instability, which triggered the development of a mesoscale precipitation system. The southeasterly flow pushed the precipitation system northward, which then passed over Dongang during 2000–2200 LT, resulting in heavy precipitation. Thus, we conclude that the heavy precipitation came from the multiscale processes such as three-dimensional moisture transport driven by the LPS and the diurnal variation in heat fluxes from the land surface. SIGNIFICANCE STATEMENT: Precipitation in the Himalayas is closely related to the hydrological cycle, floods, and landslide disasters in South Asia. Thus, elucidating the features of precipitation in the Himalayas is important. This study explored multiscale processes leading to a heavy precipitation event that was observed on 8 July 2019 at Dongang in the Rolwaling valley of the eastern Nepal Himalayas. We identified new processes producing heavy precipitation in the Himalayas: the three-dimensional synoptic-scale moisture transport driven by a monsoon low pressure system and the effect of the diurnal variation in heat fluxes from the land surface on the development and movement of a mesoscale precipitation system causing heavy precipitation. These findings broaden our understanding of heavy precipitation in the Himalayas. [ABSTRACT FROM AUTHOR]

The Surface Water and Ocean Topography (SWOT) mission will allow the estimation of discharge in rivers wider than 100 m, filling important gaps in the network of in situ measurements. This novel source of discharge observations has the potential to enable significant progress toward closing Earth's water budget and creating new understanding of the water cycle. Quantifying the uncertainty in the SWOT estimates of discharge, mapping the error sources, and understanding their relative importance is essential to the fulfillment of this potential. Here, we break the SWOT discharge production process into its essential parts: 1) retrieval of river width and water surface heights and slopes, 2) estimation of unobservable parameters, and 3) computation of discharge with the selected flow law, and through a Monte Carlo simulation study, we assess the sensitivity of the overall discharge error to each of these parts. We analyze the discharge error characteristics in terms of bias, error standard deviation, and the correlation between true and retrieved discharges and map the contribution of the essential discharge production elements to each of the error metrics. Our study revealed that biases in parameters are the most important source of discharge biases, yet we found that a larger than expected fraction of the discharge biases can be attributed to observation errors. Surprisingly, we found that parameter biases are also the most important contributor to discharge error standard deviation and to the deterioration of the correlation between truth and retrieved discharges, which were previously thought of as being controlled by observation errors. [ABSTRACT FROM AUTHOR]

Moisture transport and changes in the source–sink relationship play a vital role in the atmospheric branch of the hydrological cycle. Lagrangian approaches have emerged as the dominant tool to account for estimations of moisture sources and sinks; those that use the FLEXPART model fed by ERA-Interim reanalysis are most commonly used. With the release of the higher spatial resolution ERA5, it is crucial to compare the representation of moisture sources and sinks using the FLEXPART Lagrangian model with different resolutions in the input data, as well as its version for WRF-ARW input data, the FLEXPART-WRF. In this study, we compare this model for 2014 and moisture sources for the Iberian Peninsula and moisture sinks of North Atlantic and Mediterranean. For comparison criteria, we considered FLEXPARTv9.0 outputs forced by ERA-Interim reanalysis as "control" values. It is concluded that FLEXPARTv10.3 forced with ERA5 data at various horizontal resolutions (0.5° and 1°) represents moisture source and sink zones as represented forced by ERA-Interim (1°). In addition, the version fed with the dynamic downscaling WRF-ARW outputs (∼20 km), previously forced with ERA5, also represents these patterns accurately, allowing this tool to be used in future investigations at higher resolutions and for regional domains. Significance Statement: The FLEXPART dispersion model forced with ERA5 reanalysis data at various resolutions represents moisture source and sink zones compared to when it is forced by ERA-Interim. When the Weather Research and Forecasting Model is used to dynamically downscale ERA5, FLEXPART-WRF can also represent moisture sources and sinks, allowing this tool to be used in future investigations requiring higher resolution and regional domains and on regions with a predominance of complex orography due to its ability to represent local moisture transport. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change is affecting rivers worldwide, threatening water availability and altering the risk of natural hazards. Understanding the pattern of regional streamflow trends can help to inform region-specific policies to mitigate and adapt to any negative impacts on society and the environment. We present a benchmark dataset of long, near-natural streamflow records across Aotearoa New Zealand (NZ) and the first nationwide analysis of observed spatiotemporal streamflow trends. Individual records rarely have significant trends, but when aggregated within homogenous hydrologic regions (determined through cluster analyses), significant regional trends emerge. A multitemporal approach that uses all available data for each region and considers trend significance over time reveals the influence of decadal variability in some seasons and regions, and consistent trends in others. Over the last 50+ years, winter streamflow has significantly increased in the west South Island and has significantly decreased in the north North Island; summer streamflow has significantly decreased for most of the North Island; autumn streamflow has generally dried nationwide; and spring streamflow has increased along the west coast and decreased along the east coast. Correlations between streamflow and dynamic and thermodynamic climate indices reveal the dominant drivers of hydrologic behavior across NZ. Consistencies between the observed near-natural streamflow trends and observed changes in circulation and thermodynamic processes suggest possible climate change impacts on NZ hydrology. [ABSTRACT FROM AUTHOR]

Shallow groundwater evaporation (Eg) is a major component of the hydrological cycle, especially in semiarid and arid locations. Empirical methods are commonly used to estimate Eg. However, most of these methods can only weakly represent Eg variations along the soil depth and do not consider the energy driver. In this paper, a temperature coefficient was proposed and incorporated into two preferred empirical models to characterize the impacts of soil temperature and air temperature lags on Eg. The method was evaluated using in situ daily data obtained from nonweighing bare soil lysimeters. The results indicated that the models that considered the temperature gradient variable (T) conformed to the changes in the actual Eg values with depth more appropriately than the original models, accompanied by 4.3%–8.8% accuracy improvements overall. Shallow groundwater evaporation Eg was found to be influenced by the water table depth (H), T, and pan evaporation (E0) in descending order, and strong interactions were found between H and T. Moreover, the impact of precipitation on Eg was investigated; measurements from dry days without precipitation revealed the actual Eg process, the relative errors in the cumulative Eg values derived at different depths demonstrated a positive relationship with infiltration recharge, and the errors related to precipitation induced 6.7%–8.3% Eg underestimations. These results contribute to a better understanding of evaporative losses from shallow groundwater and the typical Eg situation that occurs simultaneously with recharge, and they provide promising perspectives for corresponding integrated hydrologic modeling research. [ABSTRACT FROM AUTHOR]

Dominant hydrological processes of a catchment could shift due to a changing climate. This climate-induced hydrological nonstationarity could affect the reliability of future runoff projection developed using a hydrological model calibrated for the historical period as the model or parameters may no longer be suitable under a different future hydroclimate. This paper explores whether competing parameterization approaches proposed to account for hydrological nonstationarity could improve the robustness of future runoff projection compared to the traditional approach where the model is calibrated targeting overall model performance over the entire historical period. The modeling experiments are carried out using climate and streamflow datasets from southeastern Australia, which has experienced a long drought and exhibited noticeable hydrological nonstationarity. The results show that robust multicriteria calibration based on the Pareto front can provide a more consistent model performance over contrasting hydroclimate conditions, but at a slight expense of increased bias over the entire historical period compared to the traditional approach. However, the robust calibration does not necessarily result in a more reliable projection of future runoff. This is because the systematic bias in any parameterization approach would propagate from the historical period to the future period and would largely be cancelled out when estimating the relative runoff change. Ensemble simulations combining results from different parameterization considerations could produce a more inclusive range of future runoff projection as it covers the uncertainties due to model parameterization. [ABSTRACT FROM AUTHOR]

Precipitation is one of the most important atmospheric inputs to hydrological models. However, existing precipitation datasets for the Third Pole (TP) basins show large discrepancies in precipitation magnitudes and spatiotemporal patterns, which poses a great challenge to hydrological simulations in the TP basins. In this study, a gridded (10 km × 10 km) daily precipitation dataset is constructed through a random-forest-based machine learning algorithm (RF algorithm) correction of the ERA5 precipitation estimates based on 940 gauges in 11 upper basins of TP for 1951–2020. The dataset is evaluated by gauge observations at point scale and is inversely evaluated by the Variable Infiltration Capacity (VIC) hydrological model linked with a glacier melt algorithm (VIC-Glacier). The corrected ERA5 (ERA5_cor) agrees well with gauge observations after eliminating the severe overestimation in the original ERA5 precipitation. The corrections greatly reduce the original ERA5 precipitation estimates by 10%–50% in 11 basins of the TP and present more details on precipitation spatial variability. The inverse hydrological model evaluation demonstrates the accuracy and rationality, and we provide an updated estimate of runoff components contribution to total runoff in seven upper basins in the TP based on the VIC-Glacier model simulations with the ERA5_cor precipitation. This study provides good precipitation estimates with high spatiotemporal resolution for 11 upper basins in the TP, which are expected to facilitate the hydrological modeling and prediction studies in this high mountainous region. Significance Statement: The Third Pole (TP) is the source of water to the people living in the areas downstream. Precipitation is the key driver of the terrestrial hydrological cycle and the most important atmospheric input to land surface hydrological models. However, none of the current precipitation data are equally good for all the TP basins because of high variabilities in their magnitudes and spatiotemporal patterns, posing a great challenge to the hydrological simulation. Therefore, in this study, a gridded daily precipitation dataset (10 km × 10 km) is reconstructed through a random-forest-based machine learning algorithm correction of ERA5 precipitation estimates based on 940 gauges in 11 TP basins for 1951–2020. The data eliminate the severe overestimation of original ERA5 precipitation estimates and present more reasonable spatial variability, and also exhibit a high potential for hydrological application in the TP basins. This study provides long-term precipitation data for climate and hydrological studies and a reference for deriving precipitation in high mountainous regions with complex terrain and limited observations. [ABSTRACT FROM AUTHOR]

Droughts and associated near-surface temperature anomalies can be attributed to amplified vertical subsidence and anomalous anticyclonic circulations from dynamic perspectives. However, two open and interesting issues remain unknown: 1) whether hydrometeorological situations under droughts can be reproduced directly utilizing variability of atmospheric dynamics and 2) what specific roles atmospheric dynamics play in drought reconstruction. To explore these questions, this study employs three kinds of dynamic features (i.e., vertical velocity, relative vorticity, and horizontal divergence) for hydrometeorological reconstruction (e.g., precipitation and near-surface air temperature) under drought situations through a so-called XGBoost (extreme gradient boosting) ensemble learning method. The study adopts two different reconstruction schemes (i.e., statistically preexisting dynamic–hydrometeorological relationships and interannual variability) and finds dynamically based reconstruction feasible. The three main achievements are as follows. 1) Regarding different hydrometeorological situations reconstructed with preexisting dynamic–hydrometeorological relationships, good reconstruction performance can be captured with the same or different lead times, depending on whether the evolution of dynamic anomalies (e.g., vertical motion and relative vorticity) is temporally asynchronous. 2) Reconstruction on the interannual scale performs relatively well, seemingly regardless of seasonality and drought-inducing mechanisms. 3) More importantly, from interpretable perspectives, global-scale analysis of dynamic contributions helps discover unexpected dynamic drought-inducing roles and associated latitudinal modulation. That is, low-level cyclonic/anticyclonic anomalies contribute to drought development in the northern middle and high latitudes, while upper-level vertical subsidence contributes significantly to tropical near-surface temperature anomalies concurrent with droughts. These achievements could provide guidance for dynamically based drought monitoring and prediction in different geographic regions. Significance Statement: It is common sense that severe drought events are physically attributable to amplified vertical subsidence and anomalous anticyclonic circulations. However, the specific contributions of atmospheric dynamics, together with the feasibility of dynamically based drought reconstruction, are crucial components that are seldom investigated. To our knowledge, this manuscript is the first to reconstruct drought utilizing atmospheric dynamics and to interpret quantified dynamic contributions; it also represents a new interdisciplinary attempt to reproduce hydrological variability based on routine atmospheric dynamic variables. [ABSTRACT FROM AUTHOR]

Precipitation estimates are highly uncertain in complex regions such as High Mountain Asia (HMA), where ground measurements are very difficult to obtain and atmospheric dynamics poorly understood. Though gridded products derived from satellite-based observations and/or reanalysis can provide temporally and spatially distributed estimates of precipitation, there are significant inconsistencies in these products. As such, to date, there is little agreement in the community on the best and most accurate gridded precipitation product in HMA, which is likely area dependent because of HMA's strong heterogeneities and complex orography. Targeting these gaps, this article presents the development of a consensus ensemble precipitation product using three gridded precipitation datasets [the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG), the Climate Hazards Group Infrared Precipitation with Station data (CHIRPS), and the ECMWF reanalysis ERA5] with a localized probability matched mean (LPM) approach. We evaluate the performance of the LPM estimate along with a simple ensemble mean (EM) estimate to overcome the differences and disparities of the three selected constituent products on long-term averages and trends in HMA. Our analysis demonstrates that LPM reduces the high biases embedded in the ensemble members and provides more realistic spatial patterns compared to EM. LPM is also a good alternative for merging data products with different spatiotemporal resolutions. By filtering disparities among the individual ensemble members, LPM overcomes the problem of a certain product performing well only in a particular area and provides a consensus estimate with plausible temporal trends. [ABSTRACT FROM AUTHOR]

Precipitation is one of the most important meteorological factors affecting the water cycle and ecological system over the Source Region of the Three Rivers (SRTR), where the Yangtze River, Yellow River, and Lantsang River originate. The characteristics of annual and summer water vapor transport and budget over the SRTR are analyzed using monthly observational and reanalysis datasets during 1980–2019. The linkage between water vapor transport and summer precipitation is also explored in this study. The results show that the Global Precipitation Climatology Project (GPCP) data are in agreement with the measured precipitation. The SRTR is a sink region for water vapor, where the water vapor content shows an increasing trend with a rate of 0.2 mm (10 yr)−1 annually and 0.3 mm (10 yr)−1 in the summer. The water vapor mainly flows into the SRTR from the lower (521.2 × 106 kg s−1) and the middle (195.7 × 106 kg s−1) layers of the southern boundary in summer, while it exports from the middle (208.1 × 106 kg s−1) layer of the eastern boundary. The abnormal wind convergence and the low pressure system, combined with the effects of the western Pacific subtropical high and the Mongolian high, provide conditions for the transport of water vapor and precipitation over the SRTR. A close relationship is found between water vapor flux and precipitation from the singular value decomposition (SVD) analysis. The Brahmaputra River basin is the key region of water vapor transport over the SRTR, which contributes to further understanding the mechanisms of water vapor transport and the regional water cycle. Significance Statement: Under the background of global warming, the Tibetan Plateau has an obvious trend of warming and humidification. The purpose of this study was to investigate the characteristics of water vapor transport and its linkage with summer precipitation over Source Region of the Three Rivers, which is located in the hinterland of the Tibetan Plateau. We found that the Brahmaputra River basin is the key region affecting the precipitation. These findings contribute to the understanding of the regional water cycle characteristics and the mechanism of the synergistic effect of westerly wind and monsoon on the change of "Water Tower of Asia." [ABSTRACT FROM AUTHOR]

Changing pathways of soil moisture loss, either directly from soil (evaporation) or indirectly through vegetation (transpiration), are an indicator of ecosystem and land hydrological cycle responses to the changing climate. Based on the ratio of transpiration to evaporation, this paper investigates soil moisture loss pathway changes across China using five reanalysis-type datasets for the past and Coupled Model Intercomparison Project Phase 6 (CMIP6) climate projections for the future. The results show that across China, the ratio of vegetation transpiration to soil evaporation has generally increased across vegetated land areas, except in grasslands and croplands in north China. During 1981–2014, there was an increase by 51.4 percentage points (pps, p < 0.01) on average according to the reanalyses and by 42.7 pps according to 13 CMIP6 models. The CMIP6 projections suggest that the holistic increasing trend will continue into the twenty-first century at a rate of 40.8 pps for SSP585, 30.6 pps for SSP245, and −1.0 pps for SSP126 shared socioeconomic pathway scenarios for the period 2015–2100 relative to 1981–2014. Major contributions come from the increases in vegetation transpiration over the semiarid and subhumid grasslands, croplands, and forestlands under the influence of increasing temperatures and prolonged growing seasons (with twin peaks in May and October). The future increasing vegetation transpiration ratio in soil moisture loss implies the potential of regional greening across China under global warming and the risks of intensifying land surface dryness and altering the coupling between soil moisture and climate in regions with water-limited ecosystems. [ABSTRACT FROM AUTHOR]

As more global satellite-derived precipitation products become available, it is imperative to evaluate them more carefully for providing guidance as to how well precipitation space–time features are captured for use in hydrologic modeling, climate studies, and other applications. Here we propose a space–time Fourier spectral analysis and define a suite of metrics that evaluate the spatial organization of storm systems, the propagation speed and direction of precipitation features, and the space–time scales at which a satellite product reproduces the variability of a reference "ground-truth" product ("effective resolution"). We demonstrate how the methodology relates to our physical intuition using the case study of a storm system with rich space–time structure. We then evaluate five high-resolution multisatellite products (CMORPH, GSMaP, IMERG-Early, IMERG-Final, and PERSIANN-CCS) over a period of 2 years over the southeastern United States. All five satellite products show generally consistent space–time power spectral density when compared to a reference ground gauge–radar dataset (GV-MRMS), revealing agreement in terms of average morphology and dynamics of precipitation systems. However, a deficit of spectral power at wavelengths shorter than 200 km and periods shorter than 4 h reveals that all satellite products are excessively "smooth." The products also show low levels of spectral coherence with the gauge–radar reference at these fine scales, revealing discrepancies in capturing the location and timing of precipitation features. From the space–time spectral coherence, the IMERG-Final product shows superior ability in resolving the space–time dynamics of precipitation down to 200-km and 4-h scales compared to the other products. Significance Statement: Precipitation estimation products are essential for understanding water cycle dynamics and climate change, and for decision support in regions lacking ground observations. Several global products exist from multiple satellites orbiting Earth, but the challenge remains that of evaluating these products for accuracy and for improving the retrieval algorithms. Here we posit that the classical "pixel-to-pixel" comparison is not adequate and propose an approach that focuses on comparing space–time dynamics through a Fourier spectral analysis, which provides information about the size, shape, and orientation of precipitation systems, as well as their motion speed and direction. We evaluate five state-of-the-art multisatellite products and identify shortcomings, in particular in their ability to capture the submesoscale variability of precipitation. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Precipitation recycling, as represented by the precipitation contributed by locally evaporated water vapor, is a key indicator of regional changes in the water cycle. The quasi-isentropic back-trajectory method, combined with a global climate model [Community Climate System Model (CCSM)] and regional climate model [Weather Research and Forecasting (WRF) Model simulation forced by CCSM (WRF-CCSM)], was used to analyze historical (1982–2005) and future (2090–99) precipitation recycling over the Tibetan Plateau (TP). The study focuses on the differences in the projection of precipitation recycling ratio (PRR) changes and relevant mechanisms between the fine-resolution (30 km) WRF-CCSM and coarse-resolution (~110 km) CCSM simulations. Compared with CCSM, the biases and root-mean-square errors of the historical evapotranspiration and precipitation over the TP were greatly reduced in the WRF-CCSM simulation, particularly in precipitation. Using WRF-CCSM outputs, higher PRRs in all elevation bands than CCSM, as well as the opposite seasonal pattern and linear trend of PRR for the river basins in the northern TP, were revealed. Unlike the CCSM projections, WRF-CCSM projects increasing trends of PRR changes with elevation under the RCP4.5 and RCP8.5 scenarios, with the largest increase at an elevation of about 5000 m. WRF-CCSM projects a diverse spatial and seasonal pattern of PRR changes, in contrast to the uniform decrease projected by CCSM. The larger fractional increases of future evapotranspiration contribution (precipitation contributed by local evapotranspiration) per unit warming than precipitation changes in WRF-CCSM suggests an enhanced contribution of locally evaporated moisture to total precipitation in the future under the RCP4.5 and RCP8.5 scenarios. Significance Statement: Rapid warming over the Tibetan Plateau has caused changes in the levels of wetness and dryness, meaning there is considerable concern surrounding the consequent environmental changes under continued warming in the future. The aim of this study was to quantify the historical (1982–2005) and future (2090–99) contributions to precipitation from external moisture transportation and local evapotranspiration over the Tibetan Plateau. At a fine resolution of 30 km, our results showed higher precipitation recycling ratios and different linear trend signs compared with that at the coarse resolution over 100 km at all elevations. In the future period, a contrasting contribution of local evapotranspiration to precipitation over the Tibetan Plateau is projected, with an 8% increase in the northwestern TP and a 3% decrease in the southeastern TP. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]