How human activities have altered hydrological droughts (streamflow deficits) in China during the past five decades (1961–2016) is investigated using the latest version (v2.0) of PCR‐GLOBWB model at ...high spatial resolution (~10 km). Although both human activities and climate variability have significant effects on river flows over China, there are large regional north‐south contrasts. Over northern China, human activities generally intensify hydrological droughts. We find that human activities exacerbated drought deficit by about 70–200% from 2004 to 2015. In contrast, droughts over southern China are generally alleviated by human activities. For instance, irrigation and water management (such as reservoir operation and water ion) increase drought StDef (standardized drought deficit volume) by about 80% in the Yellow River (north) but reduce it by about 20% in the Yangtze River (south). Human activities slightly reduce drought deficit in the Yangtze River due to the combination of large reservoir storage and low ratio of agriculture consumption to ed irrigation water. In contrast, hydrological drought is aggravated in the semiarid Yellow River basin because of high water consumption from agricultural sectors. This study suggests that human activities have contrasting influences on hydrological drought characteristics in the northern (intensification) and southern (mitigation) parts of China. Therefore, it is critical to consider the variable roles of human activities on hydrological drought in China when developing mitigation and adaptation strategies.
Plain Language Summary
China faces unprecedented challenges for water resources management under a changing climate, which is expected to lead to more frequent and severe droughts in the future. Of particular importance is streamflow drought, which jeopardizes regional water supply and local ecosystem services. On one hand, human activities through reservoir operation can effectively alleviate drought by releasing water during the low flow period. But on the other hand, water ion to meet sectoral water demand (such as irrigation) could exacerbate the streamflow deficit. To what extent such human activities differ across regions is not clear. In this study, we use a physically based hydrological and water resources model to investigate how human activities have altered streamflow droughts in China during the past five decades (1961–2016). We find that human activities generally alleviate streamflow droughts in the southern region (e.g., Yangtze River) but intensify them in the northern part of China (e.g., Yellow River). Our research highlights the contrasting geographical differences of human influences on hydrological drought across China, which can be useful for making more effective drought adaptation strategies.
Key Points
We used the PCR‐GLOBWB model at high spatial resolution to investigate the effects of human activities on hydrological drought over China
Influences of human activities on hydrological drought characteristics have a strong and contrasting north‐south gradient
Reservoir operation, water ion, and irrigation increase drought deficit in the Yellow River but reduce it in the Yangtze River
Correct quantification of mass and energy exchange processes between land surface and atmosphere requires an accurate description of unsaturated soil hydraulic properties. Soil pedotransfer functions ...(PTFs) have been widely used to predict soil hydraulic parameters. Here, 13 PTFs were grouped according to input data requirements and evaluated against a well‐documented database (National Cooperative Soil Survey Characterization NCSS) covering the continental United States (87.7% of data) and other regions of the globe (12.3% of data). Weighted ensembles were shown to have improved performance over individual PTFs in terms of evaluation criteria. Validation of moisture content estimated from the ensemble models against observations showed promising results. Global maps of soil water retention data from the ensemble models as well as their uncertainty were provided. Our full 13‐member ensemble model provides more accurate estimates than PTFs that are currently being used in Earth system models, which may, therefore, provide improved water fluxes and reduce uncertainty of the estimations.
Plain Language Summary
The availability of soil water retention data is essential for quantifying mass and energy exchange processes at the interface between land surface and atmosphere. In large‐scale applications, soil water retention characteristics usually are estimated with empirical models that, unfortunately, use nonuniform predictors and were developed on subsets of the global distribution of soils. Their reliability for global estimates is often unknown. Using a global database, we developed an ensemble of up to 13 previously published models allowing estimates of soil water retention data under data‐poor to data‐rich conditions. High‐resolution global maps of key points in soil water retention characteristics (and their uncertainties) were produced. These maps suggest that middle and high latitudes in the Northern Hemisphere have larger variability of the estimates. The new model provides more accurate estimates than models currently being used in Earth system models.
Key Points
The performance of 13 popular models for estimating soil water retention was quantified using a data set with global coverage
Relative to individual models, weighted multimodel ensembles had improved performance with the best obtained with the full ensemble
High‐resolution global maps of soil water retention properties suitable for Earth system modeling were produced
The complementary principle, which was first proposed by Bouchet (1963), illustrates a complementary relationship among the actual evaporation, the potential evaporation, and the apparent potential ...evaporation. It has generated increasing attention for estimating evaporation by using only routinely observed meteorological variables (radiation, wind speed, air temperature, and humidity) without complex surface property parameters. However, this principle still poses great challenges because of the underlying uncertainties in estimating its critical parameter, namely, asymmetric parameter b. In this study, we adopted a sigmoid generalized complementary function and utilized the eddy covariance (EC) data from 217 sites around the world to determine b values in different ecosystems and their correlation with environmental factors. We found b has a mean value of 6.01 ± 0.08. The asymmetric parameter b is small in dry regions (i.e., the desert ecosystem, 0.42 ± 0.02) and increases as the land surface wetness improves. The ecosystem mean air temperature and vapor pressure deficit have negative correlations with b (Pearson correlation coefficients are −0.57 and −0.52, respectively), and the mean soil water content has a positive correlation with b (0.69). Besides, the sigmoid function has a favorable capability in estimating evaporation no matter based on the site‐specific b values or the ecosystem mean b values. The ecosystem mean b values given in the current study also perform acceptably in the independent verifications, indicating these values can be applied extendedly for regional and global studies.
Key Points
The asymmetric parameters (b) of the different ecosystems were determined and the averaged value is 6.01 ± 0.08
The b values of the different ecosystems increase as their land surface wetness condition improves
The sigmoid function has a favorable capability in estimating evaporation in different ecosystems
Global land surface models use spatially distributed soil information for the parameterization of soil hydraulic properties (SHP). Parameters of measured SHP are correlated with easy‐to‐measure soil ...properties to construct general pedotransfer functions (PTFs) used to predict SHP from spatial soil information. Global PTFs are based on a limited number of samples yielding highly variable and poorly constrained SHP. The study implements a physical constraint, soil‐specific capillary length, to reduce unphysical combinations of SHP. The procedure fits concurrently soil water retention and capillary length using the same parameters. Results suggest that meeting the capillary length constraint has minor effects on the goodness of fit to soil water retention data. Constrained SHP were applied to represent 4 years of lysimeter fluxes yielding evapotranspiration values in close agreement with measurements relative to slight overestimation by unconstrained SHP. The procedure was applied for testing constraint SHP at a regional scale in New Zealand using the surface evaporation capacitance model and Noah‐MP for detailed simulations of land surface processes. The use of constrained SHP in both models yields higher surface runoff in agreement with observations (unconstrained SHP severely underestimated runoff generation). The concept of constrained SHP could be extended to include other physical constraints to improve PTFs, for example, by consideration of vegetation cover and soil structure effects on infiltration.
Key Points
Land surface models use spatially distributed soil hydraulic properties that may not honor constraints of capillary flow length
Physically constrained soil hydraulic properties were obtained by fitting soil water characteristics and capillary length simultaneously
Simulation results with physically constrained parameter values can improve predictive power of hydraulic and climatic models
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.
Even though knowing the contributions of transpiration (T), soil and open water evaporation (E), and interception (I) to terrestrial evapotranspiration (ET = T + E + I) is crucial for understanding ...the hydrological cycle and its connection to ecological processes, the fraction of T is unattainable by traditional measurement techniques over large scales. Previously reported global mean T/(E + T + I) from multiple independent sources, including satellite‐based estimations, reanalysis, land surface models, and isotopic measurements, varies substantially from 24% to 90%. Here we develop a new ET partitioning algorithm, which combines global evapotranspiration estimates and relationships between leaf area index (LAI) and T/(E + T) for different vegetation types, to upscale a wide range of published site‐scale measurements. We show that transpiration accounts for about 57.2% (with standard deviation ± 6.8%) of global terrestrial ET. Our approach bridges the scale gap between site measurements and global model simulations,and can be simply implemented into current global climate models to improve biological CO2 flux simulations.
Key Points
We develop an ET partitioning method, by combining remote sensing, land surface model, and LAI regression obtained from in situ measurements
We show that transpiration accounts for about 57.2% (with standard deviation ± 6.8%) of global terrestrial ET
Uncertainty in canopy interception loss estimation is the largest source of bias in ET partitioning
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.
Quantifying the transpiration fraction of evapotranspiration (T/ET) is crucial for understanding plant functionality in ecosystem water cycles, land‐atmosphere interactions, and the global water ...budget. However, the controls and mechanisms underlying the temporal change of T/ET remain poorly understood in arid and semiarid areas, especially for remote regions with sparse observations such as the Tibetan Plateau (TP). In this study, we used combined high‐frequency laser spectroscopy and chamber methods to constrain estimates of T/ET for an alpine meadow ecosystem in the central TP. The three isotopic end members in ET (δET), soil evaporation (δE), and plant transpiration (δT) were directly determined by three newly customized chambers. Results showed that the seasonal variations of δET, δE, and δT were strongly affected by the precipitation isotope (R2 = 0.53). The δ18O‐based T/ET agreed with that of δ2H. Isotope‐based T/ET ranged from 0.15 to 0.73 during the periods of observation, with an average of 0.43. This mean result was supported by T/ET derived from a two‐source model and eddy covariance observations. Our overarching finding is that at the seasonal timescale, surface soil water content (θ) dominated the change of T/ET, with leaf area index playing only a secondary role. Our study confirms the critical impact of soil water on the temporal change of T/ET in water‐limited regions such as the TP. This knowledge sheds light on diverse land‐surface processes, global hydrological cycles, and their modeling.
Key Points
Laser spectroscopy and the chamber method were used to estimate T/ET in the central Tibetan Plateau
Near‐surface soil water content dominated the temporal change of T/ET, with leaf area index playing a secondary role
Our study highlights the critical impact of environmental conditions on the temporal change of T/ET in water‐limited regions
Abstract
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
R
2
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
R
2
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.
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.
This study provides the first assessment of seasonality of the actual to potential evapotranspiration (AET/PET) ratio in models based on the FLUXNET observations over North America. The AET/PET ratio is closely related to green leaf area and evapotranspiration partitioning, and is strongly linked to canopy height in forests. Models fail to capture the mean and variability of the ratio mainly mainly due to the way they formulate PET and represent the sensitivities of the ratio to wind speed and leaf area index.
PDF
