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The accurate prediction of surface soil moisture (SM) is crucial for understanding hydrological processes. Deep learning (DL) models such as the long short-term memory model (LSTM) provide a powerful method and have been widely used in SM prediction. However, few studies have notably high success rates due to lacking prior knowledge in forms such as causality. Here we present a new causality-structure-based LSTM model (CLSTM), which could learn time interdependency and causality information for hydrometeorological applications. We applied and compared LSTM and CLSTM methods for forecasting SM across 64 FLUXNET sites globally. The results showed that CLSTM dramatically increased the predictive performance compared with LSTM. The Nash–Sutcliffe efficiency (NSE) suggested that more than 67% of sites witnessed an improvement of SM simulation larger than 10%. It is highlighted that CLSTM had a much better generalization ability that can adapt to extreme soil conditions, such as SM response to drought and precipitation events. By incorporating causal relations, CLSTM increased predictive ability across different lead times compared to LSTM. We also highlighted the critical role of physical information in the form of causality structure to improve drought prediction. At the same time, CLSTM has the potential to improve predictions of other hydrometeorological variables.

The last two decades have seen a dramatic decline and strong year-to-year variability in Arctic winter sea ice, especially in the Barents-Kara Sea (BKS), changes that have been linked to extreme midlatitude weather and climate. It has been suggested that these changes in winter sea ice arise largely from a combined effect of oceanic and atmospheric processes, but the relative importance of these processes is not well established. Here, we explore the role of atmospheric circulation patterns on BKS winter sea ice variability and trends using observations and climate model simulations. We find that BKS winter sea ice variability is primarily driven by a strong anticyclonic anomaly over the region, which explains more than 50% of the interannual variability in BKS sea-ice concentration (SIC). Recent intensification of the anticyclonic anomaly has warmed and moistened the lower atmosphere in the BKS by poleward transport of moist-static energy and local processes, resulting in an increase in downwelling longwave radiation. Our results demonstrate that the observed BKS winter sea-ice variability is primarily driven by atmospheric, rather than oceanic, processes and suggest a persistent role of atmospheric forcing in future Arctic winter sea ice loss.

Long-term continuous monitoring of precipitation isotopes has great potential to advance our understanding of hydrometeorological processes that determine stable isotope variability in the monsoon regions. This study presents 4-yr daily precipitation isotopes from Yungui Plateau in southwestern China that are influenced by Indian summer monsoon and East Asian monsoon. The local meteoric water line (LMWL;δ2H = 8.12δ18O + 11.2) was first established at the Tengchong (TC) site, which was close to the global meteoric water line (GMWL;δ2H = 8δ18O + 10), indicating little secondary subcloud evaporation in the falling rain. Precipitationδ18O values exhibited significant inverse relationships with precipitation amount (r= −0.42), air temperature (r= −0.43), and relative humidity (r= −0.41) with lower correlation coefficients throughout the entire period, which indicated that precipitation isotopic variability in TC could not be well explained by the local meteorological factors but influenced by other combined factors of regional precipitation amount and upstream rainout. Precipitationδ18O values showed a clear V-shaped trend throughout the observation period, characterized by higherδ18O values during the premonsoon period whereas lower values during the postmonsoon period. This seasonal variation of precipitationδ18O values was associated with the seasonal movement of the intertropical convergence zone and seasonal changes in moisture transport. Combined with backward trajectory analysis, precipitationδ18O values were estimated by a Rayleigh distillation model showing that upstream rainout processes from the Bay of Bengal (BoB) toward land (Myanmar) and recycling moisture over land were key factors affecting the isotopic compositions of the TC precipitation. These findings could enhance our understanding of atmospheric dynamics and moisture source in the monsoon regions and will potentially facilitate the interpretation of numerous isotopic proxy records from this region.SIGNIFICANCE STATEMENTThe variability of the summer monsoon and its onset, duration, and failure directly determine the strong rainfall and drought in a given region and have great impacts on regional societies and agriculture. To better understand this variability, this study presented a 4-yr daily dataset of precipitation isotopes on the Yungui Plateau of southwestern China to explore atmospheric processes and moisture sources that drive isotopic variability in this region. Precipitationδ18O exhibited remarkably seasonal variability, with higher values in premonsoon period and lower values in the postmonsoon period. During the Indian summer monsoon period, moisture sources primarily originated from the BoB toward the TC site, experiencing rainout processes and local moisture recycling over land using a Rayleigh fractionation model. These findings shed new light on the temporal variations of precipitation stable isotopes and facilitate our understanding of hydrological cycle in the monsoon regions.

Issues related to large uncertainty and parameter equifinality have posed big challenges for hydrological modeling in cold regions where runoff generation processes are particularly complicated. Tracer-aided hydrological models that integrate the transportation and fractionation processes of water stable isotope are increasingly used to constrain parameter uncertainty and refine the parameterizations of specific hydrological processes in cold regions. However, the common unavailability of site sampling of spatially distributed precipitation isotopes hampers the practical applications of tracer-aided models in large-scale catchments. This study, taking the precipitation isotope data (isotopes-incorporated global spectral model – isoGSM) derived from the isotopic general circulation models (iGCMs) as an example, explored its utility in driving a tracer-aided hydrological model in the Yarlung Tsangpo River basin (YTR; around 2×105 km2, with a mean elevation of 4875 m) on the Tibetan Plateau (TP). The isoGSM product was firstly corrected based on the biases between gridded precipitation isotope estimates and the limited site sampling measurements. Model simulations driven by the corrected isoGSM data were then compared with those forced by spatially interpolated precipitation isotopes from site sampling measurements. Our results indicated that (1) spatial precipitation isotopes derived from the isoGSM data helped to reduce modeling uncertainty and improve parameter identifiability in a large mountainous catchment on the TP, compared to a calibration method using discharge and snow cover area fraction without any information on water isotopes; (2) model parameters estimated by the corrected isoGSM data presented higher transferability to nested subbasins and produced higher model performance in the validation period than that estimated by the interpolated precipitation isotope data from site sampling measurements; (3) model calibration forced by the corrected isoGSM data successfully rejected parameter sets that overestimated glacier melt contribution and gave more reliable contributions of runoff components, indicating the corrected isoGSM data served as a better choice to provide informative spatial precipitation isotope than the interpolated data from site sampling measurements at the macro scale. This work suggested plausible utility of combining isoGSM data with measurements, even from a sparse sampling network, in improving hydrological modeling in large high mountain basins.

Tracer-aided hydrological models integrating water isotope modules into the simulation of runoff generation are useful tools to reduce uncertainty of hydrological modeling in cold basins that are featured by complex runoff processes and multiple runoff components. However, there is little guidance on the strategy of field water sampling for isotope analysis to run tracer-aided hydrological models, which is especially important for large mountainous basins on the Tibetan Plateau (TP) where field water sampling work is highly costly. This study conducted a set of numerical experiments based on the THREW-T (Tsinghua Representative Elementary Watershed - Tracer-aided version) model to evaluate the reliance of the tracer-aided modeling performance on the availability of site measurements of water isotope in the Yarlung Tsangpo river (YTR) basin on the TP. Data conditions considered in the numerical experiments included the availability of glacier meltwater isotope measurement, quantity of site measurements of precipitation isotope, and the variable collecting strategies for stream water samples. Our results suggested that (1) in high-mountain basins where glacier meltwater samples for isotope analysis are not available, estimating glacier meltwater isotope by an offset parameter from the precipitation isotope is a feasible way to force the tracer-aided hydrological model. Using a set of glacier meltwater δ18O that were 2 ‰–9 ‰ lower than the mean precipitation δ18O resulted in only small changes in the model performance and the quantifications of contributions of runoff components (CRCs, smaller than 5 %) to streamflow in the YTR basin. (2) The strategy of field sampling for site precipitation to correct the global gridded isotope product of isoGSM (isotope-incorporated global spectral model) for model forcing should be carefully designed. Collecting precipitation samples at sites falling in the same altitude tends to be worse at representing the ground pattern of precipitation δ18O over the basin than collecting precipitation samples from sites in a range of altitudes. (3) Collecting weekly stream water samples at multiple sites in the wet and warm seasons is the optimal strategy for calibrating and evaluating a tracer-aided hydrological model in the YTR basin. It is highly recommended to increase the number of stream water sampling sites rather than spending resources on extensive sampling of stream water at a sole site for multiple years. These results provide important implications for collecting site measurements of water isotopes for running tracer-aided hydrological models to improve quantifications of CRCs in high-mountain basins.

Downward shortwave radiation Rsd determines the surface energy balance, alters evapotranspiration and hydrological conditions, and feeds back to the regional and global climate. Large-scale Rsd estimates are usually retrieved from satellite-based top-of-atmosphere radiation and cloud parameters. These estimates are subject to biases and temporal inhomogeneity due to errors in atmospheric parameters, algorithms, and sensor changes. We found that three satellite products overestimate Rsd by 8%–10% over Asia for 1984–2006, particularly in high latitudes. We used the model tree ensemble (MTE) machine-learning algorithm and commonly used ensemble averaging methods to integrate ground observations and satellite products. Validations based on test stations and independent networks showed that the MTE approach reduces the median relative biases from 8%–10% to 2%, which is more effective than the ensemble averaging methods. We further evaluated the impacts of uncertainty in radiation forcing on surface energy and water balances using the land surface model Noah-MP. The uncertainty of radiation data affects the prediction of sensible heat the most, and also largely affects latent heat prediction in humid regions. Holding the other variables constant, a 10% positive bias in Rsd can lead to a 20%–60% positive bias in the monthly median sensible heat. The simulated hydrological responses to changing radiation forcing are nonlinear as a result of the interactions among evapotranspiration, snowpack, and soil moisture. Our analysis concludes that reducing uncertainty of radiation data is beneficial for predicting regional energy and water balances, which requires more high-quality ground observations and improved satellite retrieval algorithms.

Accurate spatiotemporal predictions of surface soil moisture (SM) are important for many critical applications. Machine learning models provide a powerful method for building an accurate and reliable predictive model of SM. However, the models used in recent studies have some limitations, including lack of spatial autocorrelation (SAC), vague representation of important features, and primarily focused on the one-step forecast. Thus, we proposed an attention based convolutional long-short term memory model (AttConvLSTM) for multistep forecasting. The model includes three layers; spatial compression, axial attention, and encoder-decoder prediction, which are used for compressing spatial information, feature extraction, and multistep prediction, respectively. The model was trained using surface SM from Soil Moisture Active Passive L4 product at 18km spatial resolution over the United States. The results show that AttConvLSTM predicts 24 hours ahead SM with mean R2 and RMSE is equal to 0.82 and 0.02, respectively. Compared with LSTM, AttConvLSTM improves the model performance over 73.6% of regions, with an improvement of 8.4% and 17.4% in R2 and RMSE, respectively. The performance of the model is mainly influenced by temporal autocorrelation (TAC). Moreover, we also highlight the importance of SAC on model performance, especially over regions with high SAC and low TAC. Moreover, our model is also competent for SM predictions from several hours to several days, which could be a useful tool for predicting all meteorological variables and forecasting extremes.

Hydrological extremes, in the form of droughts and floods, have impacts on a wide range of sectors including water availability, food security, and energy production. Given continuing large impacts of droughts and floods and the expectation for significant regional changes projected in the future, there is an urgent need to provide estimates of past events and their future risk, globally. However, current estimates of hydrological extremes are not robust and accurate enough, due to lack of long-term data records, standardized methods for event identification, geographical inconsistencies, and data uncertainties. To tackle these challenges, this article presents the development of the first Global Drought and Flood Catalogue (GDFC) for 1950–2016 by merging the latest in situ and remote sensing datasets with state-of-the-art land surface and hydrodynamic modeling to provide a continuous and consistent estimate of the terrestrial water cycle and its extremes. This GDFC also includes an unprecedented level of detailed analysis of drought and large-scale flood events using univariate and multivariate risk assessment frameworks, which incorporates regional spatial–temporal characteristics (i.e., duration, spatial extent, severity) and global hazard maps for different return periods. This Catalogue forms a basis for analyzing the changing risk of droughts and floods and can underscore national and international climate change assessments and provide a key reference for climate change studies and climate model evaluations. It also contributes to the growing interests in multivariate and compounding risk analysis.

Long-term continuous monitoring of precipitation isotopes has great potential to advance our understanding of mechanisms that determine stable isotope variability in hydrological processes of monsoon regions. This study presents a 4-year daily data set of precipitation isotopes from Yungui Plateau of southwest China, influenced by southwest monsoon and East Asian monsoon. The local meteoric water line [LMWL, δ2H=8.12 (±0.04) δ18O+11.2(±0.4)] was established at the Tengchong (TC) site, which was similar to the global meteoric water line (GMWL, δ2H=8 δ18O+10) indicating little secondary sub-cloud evaporation in the falling rain. Precipitation δ18O exhibited significant inverse relationships with precipitation amount (r = -0.42) and air temperature (r = -0.43) throughout the entire period, which indicated that precipitation isotopic variability largely depended on the local meteorological conditions. Precipitation δ18O values are characterized by remarkably seasonal variability: In the summer monsoon period, moisture sources primarily originated from BoB source towards TC site experiencing local moisture recycling over land. The air masses were derived from the northern region of Africa and East Asia with the longest transporting distance and cyclonic activity over the source region in the Fall-winter period characterized with the depleted δ18O values. Precipitation δ18O at the TC site was estimated by a Rayleigh fractionation model considering rainout over BoB and land (Myanmar) during the vapor advection, and local recycling processes consistent with the observed precipitation δ18O values. These findings enhance our understanding of hydrological cycle in the Southwest monsoon regions and will potentially facilitate the interpretation of numerous isotopic proxy records from this region.

We develop a novel climate-informed, policy-relevant framework to guide future managed aquifer recharge under deep uncertainties., Harvesting floodwaters to recharge depleted groundwater aquifers can simultaneously reduce flood and drought risks and enhance groundwater sustainability. However, deployment of this multibeneficial adaptation option is fundamentally constrained by how much water is available for recharge (WAFR) at present and under future climate change. Here, we develop a climate-informed and policy-relevant framework to quantify WAFR, its uncertainty, and associated policy actions. Despite robust and widespread increases in future projected WAFR in our case study of California (for 56/80% of subbasins in 2070–2099 under RCP4.5/RCP8.5), strong nonlinear interactions between diversion infrastructure and policy uncertainties constrain how much WAFR can be captured. To tap future elevated recharge potential through infrastructure expansion under deep uncertainties, we outline a novel robustness-based policy typology to identify priority areas of investment needs. Our WAFR analysis can inform effective investment decisions to adapt to future climate-fueled drought and flood risk over depleted aquifers, in California and beyond.

Mulching is a common and effective practice to artificially reduce evaporation for the purposes of water conservation in arid/semiarid ecosystems. Few studies have quantified the effects of plastic mulch on evapotranspiration (ET) partitioning impacted by different biophysical processes between bare and mulch-covered soil within a soil-mulch–plant-atmosphere continuum. Here, we partitioned ET flux into bare soil evaporation (Ebs), mulch-covered soil evaporation (Ems) and transpiration (T) in an oasis cropland ecosystem that was partially covered by plastic film, using an improved multisource energy balance model. The modeled water/energy fluxes agreed well with those measured by the eddy covariance. The modeled surface temperatures of both bare soil and mulch-covered soil were also consistent with measured fluxes, with a high R2 (0.81). During the growing season, the mean fractional contribution to total ET was 21 ± 12%, 6 ± 4%, and 73 ± 14% for Ebs, Ems and T, respectively. Comparisons of modeled output with and without considering mulching effects indicate that mulching increases the T/ET by decreasing E (0.02 ± 0.02 mm h−1), with a mean value of 7.2% and a range of 0–16% during the growing season. Mulching also increases soil temperature, while decreasing available energy at the land surface. These results show that the effects of mulching on ET partitioning varied with leaf development and were more sensitive during periods characterized by a lower leaf area index (e.g., the initial growing period and the period after clear cutting). Without considering mulch effects, the ET (E/ET) flux would be an overestimate, mainly because soil resistance and the availability of soil water would be underestimated. This study highlights the potential of plastic film mulch as a viable management option for soil water conservation and improving soil temperature, which is crucial for plant growth in arid and semiarid ecosystems.

A widely used approach for estimating actual evapotranspiration (AET) in hydrological and earth system models is to constrain potential evapotranspiration (PET) with a single empirical stress factor (Ω = AET/PET). Ω represents the water availability and is fundamentally linked to canopy-atmosphere coupling. However, the mean and seasonal variability of Ω in the models have rarely been evaluated against observations, and the model performances for different climates and biomes remain unclear. In this study, we first derived the observed Ω from 28 FLUXNET sites over North America during 2000-2007, which was then used to evaluate Ω in six large-scale model-based datasets. Our results confirm the importance of incorporating canopy height in the formulation of aerodynamic conductance in the case of forests. Furthermore, leaf area index (LAI) is central to the prediction of Ω and can be quantitatively linked to the partitioning between transpiration and soil evaporation (R2 = 0.43). The substantial differences between observed and model-based Ω in forests (range: 0.2~0.9) are highly related to the way these models estimated PET and the way they represented the responses of Ω to the environmental drivers, especially wind speed and LAI. This is the first assessment of Ω in models based on in situ observations. Our findings demonstrate that the observed Ω is useful for evaluating, validating, and optimizing the modeling of AET and thus of water and energy balances.

Terrestrial evapotranspiration (ET) consists of evaporation (E) from canopy-intercepted water, evaporation from soil and open water, and transpiration (T) from plants. Determining the contribution of T to ET (hereafter T/ET) is challenging but necessary for improving water resource management and understanding the response of ecosystem water/energy budgets to climate change. Water stable isotopes provide unique information on ecosystem processes and can be used to partition evapotranspiration at the ecosystem scale. In this paper, the aim is to review the state of the science on the isotope method for ecosystem ET partitioning, with a focus on uncertainties related to estimating the three isotopic end members (isotopic compositions of ET, T and E). The published results show larger T/ET variations during the growing season in croplands due to water management and rapid leaf area index (LAI) changes compared to in other natural ecosystems. Another robust result is that on average, grasslands have lower T/ET than woodlands. The isotopic composition of ET is provided by measurements, while the isotopic compositions of T and E are generally obtained using the Craig-Gordon model with appropriate modifications. Significant advances have been made in the techniques for estimating the isotopic composition of ET, largely due to the availability of fast-responding instruments for in situ measurements of water vapor isotopic composition. The largest source of uncertainty in the T/ET estimation comes from uncertainties in the isotopic composition of ET. Based on published results of the uncertainties in the three end members, we estimate that a typical uncertainty range for T/ET is ±21% (one standard deviation). This review provides background information and theoretical references for studies on isotopic hydrology, ecosystem processes and climate change.

Stable isotopes of water (H2O, HDO, and H218O) are tracers that provide powerful constraints on water transport processes in the atmosphere. This paper presents a description of an atmospheric boundary layer (ABL) simulation system called ISOLESC that couples water isotope fractionation processes with a land surface model, a large eddy simulation model, and a two‐moment cloud microphysics parameterization. Results from two model configurations—one with shallow precipitating cumulus and the other for a cloud‐free ABL—are presented to evaluate the model performance and determine its sensitivity to isotopic parameterizations. The coupled model successfully reproduces important ABL statistics (ABL height, cloud fraction, and cloud liquid water content), the expected effects of mixing and fractionation on the time evolution of ABL vapor isotopic composition, and observed diurnal variations of near‐surface water vapor isotopic composition. For the current configuration, nondiscriminating entrainment contributes 17% to the subdaily time variation of near‐surface vapor deuterium excess, while surface evapotranspiration contributes 83%. The isotopic compositions of water vapor and cloud water are insensitive to mesh resolution, but the profiles of cloud water specific humidity, rainwater specific humidity, and its isotopic ratios show moderate response to changes in grid size. Since ISOLESC resolves the energy containing scales of turbulent motions in the ABL and incorporates microphysical processes, it can be used for constraining ABL parameterizations. We find that a further improvement of raindrop reevaporation in the current cloud microphysical scheme is required in order to produce realistic near‐surface raindrop deuterium excess for the case simulated here. We suggest that ISOLESC provides a quantitative framework for utilizing vapor‐phase isotopic measurements to study local hydrological processes.

Recent rapid Arctic sea-ice reduction has been well documented in observations, reconstructions and model simulations. However, the rate of sea ice loss is highly variable in both time and space. The western Arctic has seen the fastest sea-ice decline, with substantial interannual and decadal variability, but the underlying mechanism remains unclear. Here we demonstrate, through both observations and model simulations, that the Pacific North American (PNA) pattern is an important driver of western Arctic sea-ice variability, accounting for more than 25% of the interannual variance. Our results suggest that the recent persistent positive PNA pattern has led to increased heat and moisture fluxes from local processes and from advection of North Pacific airmasses into the western Arctic. These changes have increased lower-tropospheric temperature, humidity and downwelling longwave radiation in the western Arctic, accelerating sea-ice decline. Our results indicate that the PNA pattern is important for projections of Arctic climate changes, and that greenhouse warming and the resultant persistent positive PNA trend is likely to increase Arctic sea-ice loss., The fastest sea-ice decline has been observed in the western Arctic, but the underlying mechanisms are still unclear. Here, the authors show that the Pacific North American pattern plays an important role in western Arctic sea-ice variability.

The isotopic composition of surface water vapor flux (δE) is a quantity frequently used to investigate the local and regional water cycle. This study reports the results of a comparative evaluation of δE determined with the Keeling plot and the flux‐gradient methods using high‐frequency data collected at a cropland site and a lake site. Three regression models, ordinary least squares (OLS), York's solution (YS), and geometric mean regression, were tested with the Keeling plot method. Results show that concentration‐dependent field characterization of measurement errors can improve the estimation of the YS regression. For both sites, broad agreement was achieved among the Keeling plot method with YS regression, the Keeling plot method with OLS regression and the flux‐gradient method. For the lake site, OLS was the least biased of the three regression models in reference to the δE calculated by the Craig‐Gordon (CG) model of isotopic evaporation of open water. Good agreement was aslo achieved between the flux‐gradient method and the CG model at the lake site under open‐fetch conditions. A footprint analysis suggests that the Keeling method with OLS regression may be less sensitive to fetch than the flux‐gradient method.

Earth system models use soil information to parameterize hard-to-measure soil hydraulic properties based on pedotransfer functions. However, current parameterizations rely on sample-scale information which often does not account for biologically-promoted soil structure and heterogeneities in natural landscapes, which may significantly alter infiltration-runoff and other exchange processes at larger scales. Here we propose a systematic framework to incorporate soil structure corrections into pedotransfer functions, informed by remote-sensing vegetation metrics and local soil texture, and use numerical simulations to investigate their effects on spatially distributed and areal averaged infiltration-runoff partitioning. We demonstrate that small scale soil structure features prominently alter the hydrologic response emerging at larger scales and that upscaled parameterizations must consider spatial correlations between vegetation and soil texture. The proposed framework allows the incorporation of hydrological effects of soil structure with appropriate scale considerations into contemporary pedotransfer functions used for land surface parameterization., Communications Earth & Environment, 2 (1), ISSN:2662-4435

Agriculture is expanding in tropical mountainous areas, yet its climatic effect is poorly understood. Here, we investigate how elevation regulates the biophysical climate impacts of deforestation over tropical mountainous areas by integrating satellite-observed forest cover changes into a high-resolution land–atmosphere coupled model. We show that recent forest conversion between 2000 and 2014 increased the regional warming by 0.022 ± 0.002 °C in the Southeast Asian Massif, 0.010 ± 0.007 °C in the Barisan Mountains (Maritime Southeast Asia), 0.042 ± 0.010 °C in the Serra da Espinhaco (South America) and 0.047 ± 0.008 °C in the Albertine Rift mountains (Africa) during the local dry season. The deforestation-driven local temperature anomaly can reach up to 2 °C where forest conversion is extensive. The warming from mountain deforestation depends on elevation, through the intertwined and opposing effects of increased albedo causing cooling and decreased evapotranspiration causing warming. As the elevation increases, the albedo effect increases in importance and the warming effect decreases, analogous to previously highlighted decreases of deforestation-induced warming with increasing latitude. As most new croplands are encroaching lands at low to moderate elevations, deforestation produces higher warming from suppressed evapotranspiration. Impacts of this additional warming on crop yields, land degradation and biodiversity of nearby intact ecosystems should be incorporated into future assessments. Deforestation causes elevation-dependent warming over tropical mountain regions, according to high-resolution climate simulations.