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Different scenarios of precipitation, that lead to such phenomena as droughts and floods are influenced by concurrent multiple teleconnection factors. However, the multivariate relationship between precipitation indices and teleconnection factors, including large-scale atmospheric circulations and sea surface temperature signals in China, is rarely explored. Understanding this relationship is crucial for drought early warning systems and effective response strategies. In this study, we comprehensively investigated the combined effects of multiple large-scale atmospheric circulation patterns on precipitation changes in China. Specifically, Pearson correlation analysis and Partial Wavelet Coherence (PWC) were used to identify the primary teleconnection factors influencing precipitation dynamics. Furthermore, we used the cross-wavelet method to elucidate the temporal lag and periodic relationships between multiple teleconnection factors and their interactions. Finally, the multiple wavelet coherence analysis method was used to identify the dominant two-factor and three-factor combinations shaping precipitation dynamics. This analysis facilitated the quantification and determination of interaction types and influencing pathways of teleconnection factors on precipitation dynamics, respectively. The results showed that: (1) the Atlantic Multidecadal Oscillation (AMO), EI Niño-Southern Oscillation (ENSO), East Asia Summer Monsoon (EASM), and Indian Ocean Dipole (IOD) were dominant teleconnection factors influencing Standardized Precipitation Index (SPI) dynamics; (2) significant correlation and leading or lagging relationships at different timescales generally existed for various teleconnection factors, where AMO was mainly leading the other factors with positive correlation, while ENSO and Southern Oscillation (SO) were mainly lagging behind other factors with prolonged correlations; and (3) the interactions between teleconnection factors were quantified into three types

Long-term remotely sensed soil moisture (SM) data is essential for understanding the land-atmosphere hydrological and energy interactions at both local and global scales. Passive microwave SM retrieval remains challenging at the global scale, especially in areas with complex terrain conditions, due to the difficulties in acquiring accurate land surface parameters (e.g., vegetation and surface roughness) across large extents and the uncertainties caused by the assumptions associated with current retrieval algorithms. This study addresses these challenges by providing a comprehensive evaluation of satellite SM products and introducing Soil Moisture Index (SMI)-based indicators derived from Advanced Microwave Scanning Radiometer 2 (AMSR2) brightness temperature (TB). It is noteworthy that SMI, recently proposed and showing promising results in the low-frequency L-band, lacks validation for high-frequency microwave observations. This research fills this critical gap by evaluating the emissivity-based soil moisture index (ESMI) utilizing C- and X-band TB. To mitigate potential uncertainties in land surface temperature (LST) data used in emissivity-based methods, we propose an alternative brightness temperature-based soil moisture indicator (TBSMI). Simulation experiments demonstrated the effectiveness of TBSMI for capturing SM dynamics with a strong correlation coefficient of 0.95. TBSMI was then evaluated against in situ measurements from a total of 553 ground stations in 12 dense and 4 sparse SM networks worldwide under different climatic and environmental conditions from 1 April 2015 to 31 December 2017. Inter-comparisons were also made with two widely used AMSR2 SM products [i.e., the land parameter retrieval model (LPRM) product, and the Japan Aerospace Exploration Agency (JAXA) product], as well as with the ESMI. The results suggested that TBSMI exhibited the best performance with a mean R of 0.65 against in situ observations, followed by ESMI (mean R of 0.58), whi

Study region The largest inland river basin in China (the Tarim River Basin) Study focus This study introduces a novel classification scheme for dry and warm snow droughts with focus on their duration, severity, and intensity. The Nonparametric Standardized SWE Index (NSWEI) and the Standardized Precipitation Index (SPI) were employed to assess these drought types. The optimal parameters-based geographical detector (OPGD) model and machine learning (including random forest model and Shapley-XGBoost algorithm) were applied to reveal the spatial and time dynamic driving forces of droughts. New hydrological insights for the regions Results indicated that warm-type droughts occurred more frequently and with greater intensity, exhibiting a larger spatial coverage compared to dry-type droughts, which can be attributed to warm-type droughts dominated by temperature and vapor pressure deficit, whereas dry-type are predominantly influenced by relative humidity and solar radiation. Moreover, the dynamics of snow droughts exhibited a contrasting pattern between the south (experiencing an upward trend) and the north (experiencing a descending trend). In the south, dry-type droughts were exacerbated by the strength of North Pacific Oscillation (PDO)-precipitation coupling, while warm-type droughts are influenced by the strength of North Atlantic Oscillation (NAO)-precipitation coupling and El Niño Southern Oscillation (ENSO)-precipitation.

This work concerns the fabrication of a salt based form-stable phase change composite by employing halloysite nanotube (HNT) as ceramic supporting material (CSM). The HNT has been broadly reported for preparation of low-temperature organic composites but very little has been done in medium and high temperature thermal energy storage fields. Herein we demonstrate for the first time with the use of HNT as skeleton substance for the fabrication of salt composite by cold compression and hot sintering technology. The feasibility of material fabrication is confirmed by evaluating a case containing a salt of sodium nitrate as phase change material and HNT as CSM. The effect of preheating treatment of HNT on the microstructure and phase change behaviours as well as cycling performance of the composite is investigated. The results show that HNT can be wetted by the liquid nitrate salt at elevated temperatures, verifying the viability of the present strategy by using HNT as SSM for the synthesis of salt composite. A stable chemical structure is achieved in the salt-HNT composite, which confirms a preeminent chemical stability and compatibility between the HNT and nitrate salt. The hollow tubular structure of HNT could not only be able to provide extra surface for accommodating liquid salt to form a dense composite, but also strengthen the thermal stability of the nitrate salt. The preheating treatment of HNT at 300 °C and 500 °C respectively causes the occurrence of dehydration and dehydroxylation process, which in turn leads to the morphology and microstructure change of HNT, and hence affects the cycling performance of the composite. In comparison with the composite made from HNT preheated at 500 °C where a serious pulverization and deformation has been observed after 100 thermal cycles, the composite containing HNT preheated at 300 °C presents more stable structure and excellent cycling performance, revealing that the HNT is only suitable for the accommodation of inorganic

This work concerns the development of a shape-stable molten salt based composite phase change material (PCM) for low and medium temperature thermal energy storage. The composite is fabricated by using a cold compression and hot sintering method with the employment of a eutectic quinary nitrate salt of NaNO3-NaNO2-KNO3-KNO2-LiNO3 as PCM, halloysite nanotube (HNT) as skeleton supporting material and natural graphite as thermal conductivity enhancement additive. A sequence of characterizations is performed to investigate the composite microstructure, chemical and physical compatibility, thermal stability, phase change behaviour, and thermal conductivity as well as cycling performance. The results indicate that an excellent chemical compatibility has been achieved among the ingredients of quinary salt, HNT and graphite within the composite. A mass concentration of 50 wt% HNT endows the composite with the optimal formulation in which 10 wt% graphite can be successfully accommodated and a thermal conductivity around 1.31 W/m·K can be acquired. Moreover, in such a formulation, the composite presents a considerably low melting temperature of 72.4 °C and a high thermal decomposition temperature of 530 °C, which achieves the composite a relatively high energy storage density nearly 500 kJ/kg at a temperature range of 25–510 °C. The results presented in this work demonstrate that the quinary salt-HNT-graphite composite with fairly low phase transition temperature and a splendid combination of thermal properties and cycling performance could be a promising candidate to replace the conventional organic based PCMs utilized in low temperature thermal energy storage fields.

The development of drought has spatial and temporal synchronization. Previous studies usually explore the spatial and temporal evolution of drought separately. Moreover, existing approaches are based on a fixed overlapping area and do not consider the variable drought cluster area during development. This study proposes an improved and simple approach to derive dynamic overlapping area threshold for 3-dimensional droughts extraction. Based on the one monthly Nonparametric Standardized Precipitation Index (NSPI), this improved approach was applied for investigating the migration characteristics of meteorological drought events in the Loess Plateau of China. Then, Random Forest and Extreme Gradient Boosting model with Shapley additive explanation values were used to quantify the importance of driving factors on the dynamics of drought characteristics. The results showed that: (1) the improved approach has a better performance on identifying prolonged droughts than the method using a fixed overlap area threshold; (2) spatially, meteorological drought events with high severity (DS), long duration (DD), large effected area (DA) and fast migration velocity (DV) mainly occur in the central region; (3) temporally, droughts are expected to aggravate with significantly increased DS and DA which are mainly caused by increased temperature and vegetation; and (4) meteorological droughts have a preferred westward migration direction and three dominant migration paths, which are crucial for local drought prevention and control. The findings of this study provide new perspectives on drought migration characteristics, which are important for the exploration of drought-driven mechanisms, risk assessment and future prediction.

Snow drought is commonly defined as the phenomenon of abnormally low snow snowpack caused by below-normal precipitation and/or above-normal temperatures for a period of time. This poses a critical knowledge gap, requiring a deeper understanding of its multivariate risk under various scenarios, dynamics, and driving mechanisms in a changing environment. In this study, we constructed a density kernel estimation for the Nonparametric Standardized SWE Index (NSWEI) to gain new insights into the implications of snow drought. We further demonstrated its ability, specifically the ability Equitable Threat Score (ETS), to anticipate warm-season hydrological droughts using a Random Forest (RF) model. In addition, we applied the concept of multivariable return periods under different scenarios for snow drought risk assessment in combination with Copula theory and its dynamics under a changing environment. This exploration was followed by a preliminary attribution analysis. Our results indicate that: (1) the NSWEI is more reasonable in characterizing snow drought than previous snowpack metrics; (2) the most probable droughts (occurring with less than a 50-year return period) belong to the moderate drought scenario; (3) the interaction between vapor pressure deficit and solar radiation significantly influences static risk, while the Digital Elevation Model (DEM) also plays undeniable roles in affecting snow drought risk; (4) a contrasting pattern is found in spatial distribution of snow drought risk dynamics, with an exacerbating snow drought risk observed in the southern part; and (5) the increasing snow drought risks in the south are generally triggered by reducing relative humidity and increasing solar radiation. Overall, this study provides new insights into snow drought risk and its dynamics, which are important for developing robust and effective management practices.

Frequent meteorological droughts can negatively impact terrestrial ecosystems by controlling the opening and closing of vegetation stomatal and altering vegetation structure to limit the ability of vegetation to sequester carbon. Due to the lagging and accumulation effects of drought on vegetation growth, drought trigger thresholds for different levels of vegetation loss are still unclear, which is very important for accurately assessing the future impacts of drought on terrestrial ecosystem. Therefore, this study proposed a framework to investigate drought trigger thresholds under various vegetation losses based on copula theory and conditional probabilities, and assessed the dynamics of drought trigger thresholds and possible causes, based on the random forest model. In addition, we used multiple GPP and soil water datasets for the analysis to ensure the robustness of relevant findings. The results show that: (1) there is a generally positive correlation between GPP and SPEI in China, and the response time of vegetation to drought is mostly on a short time scale (less than or equal to 4 months); (2) drought trigger thresholds are also higher in eastern China, with lower vegetation resistance and significantly higher risk of vegetation productivity loss than in other regions; (3) the trigger thresholds in northeastern China show a decreasing trend, with vegetation resistance gradually increasing. CO2 fertilization enhances vegetation drought resistance, but the magnitude of resistance increase is reduced due to the adverse effects of water stress and VPD on vegetation. The findings of this study may advance our comprehension of terrestrial ecosystem vulnerability and response to drought, and further provide scientific guidance for watershed water allocation, drought

Determining the threshold at which meteorological drought triggers hydrological drought is critical for early warning and proper mitigation of drought. However, drought trigger thresholds are difficult to determine owing to the nonlinearity between meteorological and hydrological drought, and their dynamics have not been explored. To this end, we introduce a precipitation-driven drought trigger threshold framework. This framework considers the multiscale characteristics of cumulative precipitation anomalies and incorporates the drought severity index of terrestrial water storage anomalies to characterize hydrological drought. The dynamics of trigger thresholds over time and the main drivers of these variations are further explored over China. The results show that hydrological drought is more sensitive to meteorological drought in south China, with some regions showing weak or negative correlations mainly determined by the differences between climate change and human activities. The risk of drought outbreak in the central, northeastern and southern regions of China is high, with trigger thresholds showing a dynamic decreasing trend over time (corresponding to lower cumulative precipitation anomalies), indicating weakened resistance to meteorological drought. Rising temperature is the main factor affecting dynamic changes in the trigger threshold. The drought trigger threshold framework proposed in this study is also applicable for assessments in other regions around the world. This study provides valuable insights and new approaches for understanding the mechanisms of hydrological drought formation. Furthermore, these results are expected to severe as a scientific basis for government departments to reduce water supply stress on human and natural systems and to develop adaptive management strategies.

Agricultural drought posing a significant threat to agricultural production is subject to the complex influence of ocean, terrestrial and meteorological multi-factors. Nevertheless, which factor dominating the dynamics of agricultural drought characteristics and their dynamic impact remain equivocal. To address this knowledge gap, we used ERA5 soil moisture to calculate the standardized soil moisture index (SSI) to characterize agricultural drought. The extreme gradient boosting model was then adopted to fully examine the influence of ocean, terrestrial and meteorological multi-factors on agricultural drought characteristics and their dynamics in China. Meanwhile, the Shapley additive explanation values were introduced to quantify the contribution of multiple drivers to drought characteristics. Our analysis reveals that the drought frequency, severity and duration in China ranged from 5–70, 2.15–35.02 and 1.76–31.20, respectively. Drought duration is increasing and drought intensity is intensifying in southeast, north and northwest China. In addition, potential evapotranspiration is the most significant driver of drought characteristics at the basin scale. Regarding the dynamic evolution of drought characteristics, the percentages of raster points for drought duration and severity with evapotranspiration as the dominant factor are 30.7 % and 32.7 %, and the percentages with precipitation are 35.3 % and 35.0 %, respectively. Precipitation in northern regions has a positive effect on decreasing drought characteristics, while in southern regions, evapotranspiration dominates the dynamics in drought characteristics due to increasing vegetation transpiration. Moreover, the drought severity is exacerbated by the Atlantic Multidecadal Oscillation in the Yangtze and Pearl River basins, while the contribution of the North Atlantic Oscillation to the drought duration evolution is increasing in the Yangtze River basin. Generally, this study sheds new insights into agricultural

Precipitation (P) and temperature (T) are key components of the hydrometeorological system, and their dependence structures exhibit significant dynamic changes, including non-stationary behavior, in response to environmental variations. These changes affect local hydrological processes and impact the predictability of the hydrometeorological system. However, the dynamics of dependence structures among meteorological factors during corresponding and adjacent seasons, as well as their underlying causes, have not been fully revealed. Therefore, this study comprehensively explored the dynamics of the precipitation-temperature dependence structure (PTDS) and temperature-temperature dependence structure (TTDS), and their possible causes. Firstly, non-stationary of PTDS was identified using a copula model. Then the main drivers of PTDS were determined by the random forest (RF) model and variable projection importance (VIP) criteria. These drivers include both conventional factors such as local meteorological factors (e.g., P, T, wind speed (WS), vapor pressure, relative humidity and sunshine duration (SD)) and teleconnection factors (e.g., Sunspots, the Arctic Oscillation, Pacific Decadal Oscillation (PDO), El Niño-Southern Oscillation (ENSO)). Additionally, the normalized difference vegetation index (NDVI) was used to investigate the response of dependence structure to vegetation dynamics. Finally, the ridge regression model was applied to construct driver models for the dynamics of dependence structures. The Loess Plateau was selected as the study area because of its high ecological sensitivity and typical human afforestation activities. The results show that: (1) non-stationarity in the PTDS occurred in different seasons and at various stations; (2) the primary drivers of PTDS and TTDS dynamics are predominantly local meteorological factors; (3) there is a strong correlation between SD and ENSO, and the impacts of PDO on local meteorological factors (WS and T) play a cr

Tremendous progress has been made in estimating soil moisture from satellite remote sensing data. Several global-scale coarse-resolution products have also been generated and released for various applications in the Earth system. However, high-resolution soil moisture estimation is still in its infancy. Currently, two main methods are used for this purpose: downscaling approaches and direct retrieval from microwave and optical/thermal data. Several studies have attempted to comprehensively evaluate the performance of these approaches and have found that each method has its own strengths and weaknesses, with no single method outperforming the others. In this study, we aim to investigate the advantages of integrating optical, thermal, and microwave data to estimate soil moisture by leveraging an intensive soil moisture network and triple collocation method. Firstly, we determined the best-performing coarse-resolution microwave soil moisture product through the triple collocation approach. Secondly, we generated 1-km soil moisture using a downscaling approach based on land surface temperature and vegetation index, utilizing the best-performing SMAP L3 descending product. Thirdly, we evaluated the high-resolution downscaled soil moisture, Sentinel 1 soil moisture, and SMAP/Sentinel 1 combined soil moisture products using soil moisture measurements from the REMEDHUS station network, ETOPO1 elevation, CHIRPS precipitation, and the ESA CCI land cover map. Finally, we investigated and demonstrated the advantages of merging these products through point-scale evaluation and large-scale spatial pattern comparison.

Quantifying the propagation time (PT) and trigger threshold (TR) from meteorological to soil moisture drought is critically important for drought early warning and precise defense. Nevertheless, existing propagation characteristics usually have a low temporal-spatial resolution, and their dynamics and related driving mechanisms (such as global warming and greening) are still incompletely understood. To fill the knowledge gap, this study proposes a drought propagation model based on the Bayesian causal analysis framework for quantifying the PT and TR with a high resolution. Taking Northeast China (NEC) as a case study, we further explore the dynamics of drought propagation characteristics in recent decades and possible driving mechanisms using the sliding window and Random Forest model. Results showed that: (1) the drought PT varies spatially and temporally in the study area, with long PT in the central plain and western high-altitude areas in the early growing season (typically over 200 days), while short in the middle and late growing season in most regions (less than one month in July and August), The TR is generally lower than 80 mm in the western regions and do not change significantly with time; (2) the PT and TR in the vast central and western regions exhibit a downward trend in the late growing season, resulting in a strikingly increased risk of drought propagation; (3) Increasing vapor pressure deficit (VPD) due to warming, along with decreasing aridity index (AI) due to precipitation shortage are the main drivers on the accelerated drought propagation. Moreover, local greening has also played a critical role in accelerating propagation via transpiration that consumes soil water, which contributes more than 20% to propagation dynamics. Overall, this study sheds new insights into drought propagation dynamics and mechanisms in a changing environment, providing a promising avenue for drought early warning and mitigation.

In the propagation from meteorological to hydrological drought, there are time-lag and step-abrupt effects, quantified in terms of propagation time and threshold, which play an important role in hydrological drought early warning. However, seasonal drought propagation time and threshold and their dynamics as well as the corresponding driving mechanism remain unknown in a changing environment. To this end, the standardized precipitation index (SPI) and standardized runoff index (SRI) were used respectively to characterize meteorological and hydrological droughts and to determine the optimal propagation time. Then, a seasonal drought propagation framework based on Bayesian network was proposed for calculating the drought propagation threshold with SPI. Finally, the seasonal dynamics and preliminary attribution of propagation characteristics were investigated based on the random forest model and correlation analysis. The results show that 1) relatively short propagation time (less than 9 months) and large propagation threshold (from −3.18 to −1.19) can be observed in the Toxkan River basins (subbasin II), especially for spring, showing low drought resistance; 2) drought propagation time shows an extended trend in most seasons, while the drought propagation threshold displays an increasing trend in autumn and winter in the Aksu River basin (subbasins I–II), and the opposite characteristics in the Hotan and Yarkant River basins (subbasins III–V); and 3) the impacts of precipitation, temperature, potential evapotranspiration, and soil moisture on drought propagation dynamics are inconsistent across subbasins and seasons, noting that reservoirs serve as a buffer to regulate the propagation from meteorological to hydrological droughts. The findings of this study can provide scientific guidelines for watershed hydrological drought early warning and risk management.

Quantifying changes in the vulnerability of vegetation to various drought stresses in different seasons is important for rational and effective ecological conservation and restoration. However, the vulnerability of vegetation and its dynamics in a changing environment are still unknown, and quantitative attribution analysis of vulnerability changes has been rarely studied. To this end, this study explored the changes of vegetation vulnerability characteristics under various drought stresses in Xinjiang and conducted quantitative attribution analysis using the random forest method. In addition, the effects of ecological water transport and increased irrigation areas on vegetation vulnerability dynamics were examined. The standardized precipitation index (SPI), standardized precipitation-evapotranspiration index (SPEI), and standardized soil moisture index (SSMI) represent atmospheric water supply stress, water and heat supply stress, and soil water supply stress, respectively. The results showed that: (1) different vegetation types responded differently to water stress, with grasslands being more sensitive than forests and croplands in summer; (2) increased vegetation vulnerability under drought stresses dominated in Xinjiang after 2003, with vegetation growth and near-surface temperature being the main drivers, while increased soil moisture in the root zone was the main driver of decreased vegetation vulnerability; (3) vulnerability of cropland to SPI/SPEI/SSMI-related water stress increased due to the rapid expansion of irrigation areas, which led to increasing water demand in autumn that was difficult to meet; and (4) after ecological water transport of the Tarim River Basin, the vulnerability of its downstream vegetation to drought was reduced.

Flash drought occurs in a short timescale and has more adverse impacts on society and ecosystems than regular drought. Considering flash drought as a special type of regular drought and identifying it by the rate of the first intensification period might ignore posterior intensification periods. To this end, we introduced the combinatorial decomposable flash drought identification framework (CDFDI) to identify flash drought from the perspective of regarding it as the subphase of drought and explore the characteristics and vegetation response of flash drought in the Loess Plateau (LP), China, where is prone to drought and its ecological environment is very fragile. The effectiveness of the framework is evaluated through the comparison analysis with a pre-existing identification method proposed by Liu et al., (2020a). Results indicate that: (1) the CDFDI framework can better capture flash drought events than the Liu’s method in terms of time series and spatial patterns, its rationality was further demonstrated by the obvious latitudinal pattern of correlation between flash drought and NDVI; (2) the correlation shows an southeast (positive) and northwest (negative) contrasting pattern in the LP; (3) such contrasting pattern was also found in the response time of GPP, which suggests a more rapid impact on vegetation of flash drought in the arid northwestern LP; and (4) flash drought shows an exacerbating trend across the LP under the background of climate warming and mainly concentrated in the southern part. In general, this study provides new insights on flash drought connotations in a changing environment.

Although global- and catchment-scale hydrological models are often shown to accurately simulate long-term runoff time-series, far less is known about their suitability for capturing hydrological extremes, such as droughts. Here we evaluated simulations of hydrological droughts from nine catchment scale hydrological models (CHMs) and eight global scale hydrological models (GHMs) for eight large catchments: Upper Amazon, Lena, Upper Mississippi, Upper Niger, Rhine, Tagus, Upper Yangtze and Upper Yellow. The simulations were conducted within the framework of phase 2a of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a). We evaluated the ability of the CHMs, GHMs and their respective ensemble means (Ens-CHM and Ens-GHM) to simulate observed hydrological droughts of at least one month duration, over 31 years (1971–2001). Hydrological drought events were identified from runoff-deficits and the Standardised Runoff Index (SRI). In all catchments, the CHMs performed relatively better than the GHMs, for simulating monthly runoff-deficits. The number of drought events identified under different drought categories (i.e. SRI values of -1 to -1.49, -1.5 to -1.99, and ≤-2) varied significantly between models. All the models, as well as the two ensemble means, have limited abilities to accurately simulate drought events in all eight catchments, in terms of their occurrence and magnitude. Overall, there are opportunities to improve both CHMs and GHMs for better characterisation of hydrological droughts.

Global water models (GWMs) simulate the terrestrial water cycle on the global scale and are used to assess the impacts of climate change on freshwater systems. GWMs are developed within different modelling frameworks and consider different underlying hydrological processes, leading to varied model structures. Furthermore, the equations used to describe various processes take different forms and are generally accessible only from within the individual model codes. These factors have hindered a holistic and detailed understanding of how different models operate, yet such an understanding is crucial for explaining the results of model evaluation studies, understanding inter-model differences in their simulations, and identifying areas for future model development. This study provides a comprehensive overview of how 16 state-of-the-art GWMs are designed. We analyse water storage compartments, water flows, and human water use sectors included in models that provide simulations for the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b). We develop a standard writing style for the model equations to enhance model intercomparison, improvement, and communication. In this study, WaterGAP2 used the highest number of water storage compartments, 11, and CWatM used 10 compartments. Six models used six compartments, while four models (DBH, JULES-W1, Mac-PDM.20, and VIC) used the lowest number, three compartments. WaterGAP2 simulates five human water use sectors, while four models (CLM4.5, CLM5.0, LPJmL, and MPI-HM) simulate only water for the irrigation sector. We conclude that, even though hydrological processes are often based on similar equations for various processes, in the end these equations have been adjusted or models have used different values for specific parameters or specific variables. The similarities and differences found among the models analysed in this study are expected to enable us to reduce the uncertainty in multi-model ensembles, improv

This study evaluated the performance of the early, late and final runs of IMERG version 06 precipitation products at various spatial and temporal scales in China from 2008 to 2017, against observations from 696 rain gauges. The results suggest that the three IMERG products can well reproduce the spatial patterns of precipitation, but exhibit a gradual decrease in the accuracy from the southeast to the northwest of China. Overall, the three runs show better performances in the eastern humid basins than the western arid basins. Compared to the early and late runs, the final run shows an improvement in the performance of precipitation estimation in terms of correlation coefficient, Kling–Gupta Efficiency and root mean square error at both daily and monthly scales. The three runs show similar daily precipitation detection capability over China. The biases of the three runs show a significantly positive (p < 0.01) correlation with elevation, with higher accuracy observed with an increase in elevation. However, the categorical metrics exhibit low levels of dependency on elevation, except for the probability of detection. Over China and major river basins, the three products underestimate the frequency of no/tiny rain events (P < 0.1 mm/day) but overestimate the frequency of light rain events (0.1 ≤ P < 10 mm/day). The three products converge with ground-based observation with regard to the frequency of rainstorm (P ≥ 50 mm/day) in the southern part of China. The revealed uncertainties associated with the IMERG products suggests that sustaining efforts are needed to improve their retrieval algorithms in the future.

In the context of global warming and human interventions, the impact of climate on crop yield may change over time. Therefore, assessing the dynamics of drought thresholds that trigger various maize yield losses is critical for food security under climate change. To this end, this study aims to assess the vulnerability of maize to drought stress in three provinces of Northeast China (Heilongjiang, Jilin and Liaoning provinces) and to quantify the drought thresholds that cause different levels of maize yield loss. A Copula-Bayesian conditional probability bivariate model is constructed to combine drought conditions and maize yield. Results indicate that: (1) for the whole study period 1980–2018, drought thresholds that induced different levels of maize yield reduction were significantly different in the three northeastern provinces of China; on average, the drought thresholds that induced 30%, 40% and 50% maize yield losses were −1.06, −1.53 and −2.23 in the three provinces of Northeast China; (2) during the transition from mild to moderate and severe drought, maize vulnerability in Liaoning province gradually exceeded that of Jilin and Heilongjiang province; (3) from 1980 to 1999–2000–2018, the drought thresholds that triggered the same percentage of maize yield reduction increased in all three provinces, suggesting a dramatically increasing trend in the vulnerability of maize yields to drought; (4) the changes in precipitation and evapotranspiration leading to increased drought severity were the main factors inducing drought threshold dynamics in both sub-study periods. The probabilistic assessment of the impact of drought on maize yield is expected to provide useful insights into the mitigation of drought and its effects under climate change.

Hydrological models have been developed in response to the need to understand the complex water cycle of the Earth and to assess its interaction with historical and future climate scenarios. In the global water sector of the Inter-Sectoral Impact Model Intercomparison Project phase 2b (ISIMIP2b), six land surface models (LSMs), eight hydrological models (GHMs), and one dynamic vegetation model (DGVM) are contributing with transient simulations spanning from 1660 to 2300. The model simulations follow a common protocol and are driven by common bias adjusted climate model outputs combined with plausible socio-economic scenarios and representative concentration pathways. The main goal of this study is to highlight similarities and differences among these models in simulating the vertical water balance. The main similarity of these models consists in the water cycle simulation, even if the models have been developed for different purposes such as energy cycle (LSMs), water cycle (GHMs), or vegetation cycle (DGVM) simulation. In particular, we address the following research question: 1) what equations are used to compute water storages and water fluxes; 2) how different are the equations among the models; 3) how the equations were adjusted; 4) how many parameters are used by the models; 5) how often the parameters are used; 6) how similar or different are the parameters among the models. To this end, we apply a standard writing style of the water storages and water fluxes included in the models, to easily identify the similarities and differences among them. Most of the models include in their structure the canopy, soil, and snow storages, and almost half of them include the groundwater storage. Furthermore, we find that: 1) a model needs a very good documentation, this would help to easily identify and understand the equations in the code; 2) some modelers teams use common approaches resulting in similar equations of water storages or water fluxes, but different models s