Evaluation of flash drought under the impact of heat wave events in southwestern Germany.

Flash droughts are a recently recognised type of extreme drought defined by the rapid onset and strong intensification of drought conditions. Our understanding of flash drought processes under the influence of heat waves needs to be improved in the context of global warming. Here, we applied a physically based hydrological model, i.e., TRAnspiration and INterception (TRAIN) model to simulate root zone soil moisture (RZSM) and evapotranspiration (ET) with daily time steps and at a 1 × 1 km resolution to identify and assess flash droughts. Two states, Baden-Württemberg (BW) and Rhineland-Palatinate (RP), located in southwestern Germany, were selected as the study areas. Three datasets, the Global Land Evaporation Amsterdam Model (GLEAM) dataset, ERA5-Land (land component of the fifth generation of European ReAnalysis) dataset, and SMAP-L4 (Soil Moisture Active Passive Level-4) dataset, were selected to evaluate the TRAIN simulated RZSM and ET from 1961 to 2016. The results show that the simulated RZSM had the highest correlation with the ERA5-Land products, followed by SMAP-L4 and GLEAM, with regional average correlation coefficients (CC) of 0.765, 0.762, and 0.746, respectively. The CC of the TRAIN simulated ET with ERA5-Land and GLEAM ET were 0.828 and 0.803, respectively. The results of the trend analyses showed a significant increase (p < 0.05) in the number of flash droughts and heat waves in both the BW and RP states. A comparative analysis revealed that the mean duration and onset speed of flash droughts in BW (RP) without heat waves were 10.42 (10.67) pentads and 19.69th percentile/pentad (17.16th percentile/pentad), respectively, while associated with heat waves they were 8.95 (9.53) pentads and 21.77th percentile/pentad (19.91th percentile/pentad), respectively. This indicates that flash droughts under the influence of heat waves are generally shorter in duration but faster in occurrence. The findings of this study have important implications for flash drought assessment, monitoring, and mitigation under the impact of heat waves.

Compound events of wet and dry extremes: Identification, variations, and risky patterns.

Compound hydrometeorological extremes have been widely examined under climate change, they have significant impacts on ecological and societal well-being. This study sheds light on a new category compound of contrasting extremes, namely compounding wet and dry extremes (CWDEs). The CWDEs are characterized as devastating dry events (EDs) accompanied by wet extremes (EWs) in a given time window. Notably, we first adopt a separate system to identify coinciding events considering the different evolving processes and impacting patterns of EDs and EWs. The peak-over-threshold and standardized index methods are used in a daily and monthly window to identify EWs and EDs respectively. Furthermore, the spatial-temporal changes and risky patterns of CWDEs are revealed by using the Mann-Kendall test, the Ordinary Least Squares, and the Global and Local Moran indices. Germany is the study case. As one major finding, the results indicate a pronounced seasonal effect and spatial clustering pattern of CWDEs. The summer is the most vulnerable period for CWDEs, and the spatial hotspots are mainly located in the southern tip of Germany, as well as in the vicinity of the capital city Berlin. Besides, robust uptrends in CWDEs across all evaluation metrics have been discovered in historical periods, and the moist climate and complex geography collectively contribute to severe CWDEs. Unexpectedly, the study finds that compounding events in dry regions are mainly driven by wet extremes, whereas they show a higher dependency on dry anomalies in wet regions. The research provides new insights into compound extremes which are composed of individual hazards with distinct features. Related findings will aid decision-makers in producing effective risk mitigation plans for prioritizing vulnerable regions. Lastly, the robust framework and open access data allow for extensive exploration of various compounding hazards in different regions.

Increasing sensitivity of dryland water use efficiency to soil water content due to rising atmospheric CO2.

Examining the intricate interplay between ecosystem carbon-water coupling and soil moisture sensitivity serves as a crucial approach to effectively assess the dilemma arising from escalating global carbon emissions and concomitant water scarcity. Using the Lund-Potsdam-Jena Dynamic Global Vegetation Model (LPJ), this study investigated the potential effects of climate change and soil water content (SWC) on terrestrial ecosystem water use efficiency (WUE) across China from 1982 to 2060. The results revealed that: (1) WUE was higher in South China and Northeast China, but lower in Northwest China and it had shown a significant upward trend in the past 40 years, especially in Northwest China where grasslands were widely distributed. The increase in WUE was mainly closely related to the greening of vegetation. In the past 40 years, the area of net primary productivity (NPP), evapotranspiration (ET), and WUE showing an upward trend accounted for 85.85 %, 63.66 %, and 83.88 % of the total area of the country, respectively. Although ET also showed an increasing trend nationwide, the increase of NPP was more obvious; (2) The control experiment showed that WUE showed a significant increase trend in arid and semi-arid areas of Northwest China with the increase of CO2 concentration, while SWC showed a significant drying trend, but both WUE and SWC showed an increasing trend in humid areas. The sensitivity of WUE to SWC was enhanced in arid and semi-arid areas, and the effect of soil drought was partially offset by the increase of WUE; (3) Future climate projections also indicated that the CO2 fertilization effect will contribute to an increase in WUE while causing drier soil moisture conditions in the arid and semi-arid regions. Especially under the SSP5-8.5 scenario, CO2 fertilization in Northwest China contributed more than 14 % to WUE from 2015 to 2060, while the impact on SWC depletion exceeded 3 %. This highlights the potential implications of rising atmospheric CO2 concentration, as it may promote a significant rise in WUE and exacerbate the drying of soil moisture in these areas. These findings emphasize the need for careful attention and consideration in managing water resources in arid and semi-arid regions in the face of future climate change.

Control of climate and physiography on runoff response behavior through use of catchment classification and machine learning.

Understanding of runoff response changes (RRC) is essential for water resource management decisions. However, there is a limited understanding of the effects of climate and landscape properties on RRC behavior. This study explored RRC behavior across controls and predictability in 1003 catchments in the contiguous United States (CONUS) using catchment classification and machine learning. Over 1000+ catchments are grouped into ten classes with similar hydrological behavior across CONUS. Indices quantifying RRC were constructed and then predicted within each class of the 10 classes and over the entire1000+ catchments using two machine learning models (random forest and CUBIST) based on 56 indicators of catchment attributes (CA) and 16 flow signatures (FS). This enabled the ranking of the important influential factors on RRC. We found that (i) CA/FS-based clusters followed the ecoregions over CONUS, and the impact of climate on RRC seemed to overlap with physiographic attributes; (ii) CUBIST outperforms the random forest model both within the cluster and over the whole domain, with a mean improvement of 39 % (depending on clusters) within clusters. Runoff sensitivity was better predicted than runoff changes; (iii) FS related to runoff ratio, average, and high flow are the most important for RRC, whereas climate (evaporation and aridity) is a secondary factor; and (iv) RRC patterns are substantial in the dominant factor space. High total changes and catchment characteristic-induced changes occurred mainly at 100°west longitude. The elasticity of climate and catchment characteristics was found to be high in spaces with high evaporation and low runoff ratios and low in spaces with low evaporation and high runoff ratios. Uncertainties existed in the number of catchments between clusters which was verified using a fuzzy clustering algorithm. We recommend that future research that clarifies the impact of uncertainty on hydrological or catchment behavior should be conducted.

A novel framework for investigating the mechanisms of climate change and anthropogenic activities on the evolution of hydrological drought.

Climate change and anthropogenic activity are the primary drivers of water cycle changes. Hydrological droughts are caused by a shortage of surface and/or groundwater resources caused by climate change and/or anthropogenic activity. Existing hydrological models have primarily focused on simulating natural water cycle processes, while limited research has investigated the influence of anthropogenic activities on water cycle processes. This study proposes a novel framework that integrates a distributed hydrological model and an attribution analysis method to assess the impacts of climate change and anthropogenic activities on hydrological drought The distributed dualistic water cycle model was applied to the Fuhe River Basin (FRB), and it generated a Nash-Sutcliffe efficiency coefficient > 0.85 with a relative error of <5 %. Excluding the year with extreme drought conditions, our analysis revealed that climate change negatively impacted the average drought duration (-105.5 %) and intensity (-23.6 %) because of increasing precipitation. However, anthropogenic activities continued to contribute positively to the drought, accounting for 5.5 % and 123.6 % of the average drought duration and intensity, respectively, because of increased water consumption. When accounting for extreme drought years, our results suggested that climate change has contributed negatively to the average duration of drought (-113.2 %) but positively to its intensity (7.8 %). Further, we found that anthropogenic activities contributed positively to both the average drought duration and intensity (13.2 % and 92.2 %, respectively). While climate change can potentially mitigate hydrological drought in the FRB by boosting precipitation levels, its overall effect may exacerbate drought through the amplification of extreme climate events resulting from global climate change. Therefore, greater attention should be paid to the effects of extreme drought.

Exploring a multi-output temporal convolutional network driven encoder-decoder framework for ammonia nitrogen forecasting.

Artificial neural networks exhibit significant advantages in terms of learning capability and generalizability, and have been increasingly applied in water quality prediction. Through learning a compressed representation of the input data, the Encoder-Decoder (ED) structure not only could remove noise and redundancies, but also could efficiently capture the complex nonlinear relationships of meteorological and water quality factors. The novelty of this study lies in proposing a multi-output Temporal Convolutional Network based ED model (TCN-ED) to make ammonia nitrogen forecasts for the first time. The contribution of our study is indebted to systematically assessing the significance of combining the ED structure with advanced neural networks for making accurate and reliable water quality forecasts. The water quality gauge station located at Haihong village of an island in Shanghai City of China constituted the case study. The model input contained one hourly water quality factor and hourly meteorological factors of 32 observed stations, where each factor was traced back to the previous 24 h and each meteorological factor of 32 gauge stations was aggregated into one areal average factor. A total of 13,128 hourly water quality and meteorological data were divided into two datasets corresponding to model training and testing stages. The Long Short-Term Memory based ED (LSTM-ED), LSTM and TCN models were constructed for comparison purposes. The results demonstrated that the developed TCN-ED model can succeed in mimicking the complex dependence between ammonia nitrogen and water quality and meteorological factors, and provide more accurate ammonia nitrogen forecasts (1- up to 6-h-ahead) than the LSTM-ED, LSTM and TCN models. The TCN-ED model, in general, achieved higher accuracy, stability and reliability compared with the other models. Consequently, the improvement can facilitate river water quality forecasting and early warning, as well as benefit water pollution prevention in the interest of river environmental restoration and sustainability.

Exploring a similarity search-based data-driven framework for multi-step-ahead flood forecasting.

Due to a small proportion of observations, reliable and accurate flood forecasts for large floods present a fundamental challenge to artificial neural network models, especially when the forecast horizons exceed the flood concentration time of a river basin. This study proposed for the first time a Similarity search-based data-driven framework, and takes the advanced Temporal Convolutional Network based Encoder-Decoder model (S-TCNED) as an example for multi-step-ahead flood forecasting. A total of 5232 hourly hydrological data were divided into two datasets for model training and testing. The input sequence of the model included hourly flood flows of a hydrological station and rainfall data (traced back to the previous 32 h) of 15 gauge stations, and the output sequence stepped into 1- up to 16-hour-ahead flood forecasts. A conventional TCNED model was also built for comparison purposes. The results demonstrated that both TCNED and S-TCNED could make suitable multi-step-ahead flood forecasts, while the proposed S-TCNED model not only could effectively mimic the long-term rainfall-runoff relationship but also could provide more reliable and accurate forecasts of large floods than the TCNED model even in extreme weather conditions. There is a significant positive correlation between the mean sample label density improvement and the mean Nash-Sutcliffe Efficiency (NSE) improvement of the S-TCNED over the TCNED at the long forecast horizons (13 h up to 16 h). Based on the analysis of the sample label density, it is found that the similarity search largely improves the model performance by enabling the S-TCNED model to learn the development process of similar historical floods in a targeted manner. We conclude that the proposed S-TCNED model that converts and associates the previous rainfall-runoff sequence with the forecasting runoff sequence under a similar scenario can enhance the reliability and accuracy of flood forecasts while extending the length of forecast horizons.

A novel framework to improve the consistency of water quality attribution from natural and anthropogenic factors.

One critical question for water security and sustainable development is how water quality responses to the changes in natural factors and human activities, especially in light of the expected exacerbation in water scarcity. Although machine learning models have shown noticeable advances in water quality attribution analysis, they have limited interpretability in explaining the feature importance with theoretical guarantees of consistency. To fill this gap, this study built a modelling framework that employed the inverse distance weighting method and the extreme gradient boosting model to simulate the water quality at grid scale, and adapted the Shapley additive explanation to interpret the contributions of the drivers to water quality over the Yangtze River basin. Different from previous studies, we calculated the contribution of features to water quality at each grid within river basin and aggregated the contribution from all the grids as the feature importance. Our analysis revealed dramatic changes in response magnitudes of water quality to drivers within river basin. Air temperature had high importance in the variability of key water quality indicators (i.e. ammonia-nitrogen, total phosphorus, and chemical oxygen demand), and dominated the changes of water quality in Yangtze River basin, especially in the upstream region. In the mid- and downstream regions, water quality was mainly affected by human activities. This study provided a modelling framework applicable to robustly identify the feature importance by explaining the contribution of features to water quality at each grid.

Oceanic climate changes threaten the sustainability of Asia's water tower.

Water resources sustainability in High Mountain Asia (HMA) surrounding the Tibetan Plateau (TP)-known as Asia's water tower-has triggered widespread concerns because HMA protects millions of people against water stress1,2. However, the mechanisms behind the heterogeneous trends observed in terrestrial water storage (TWS) over the TP remain poorly understood. Here we use a Lagrangian particle dispersion model and satellite observations to attribute about 1 Gt of monthly TWS decline in the southern TP during 2003-2016 to westerlies-carried deficit in precipitation minus evaporation (PME) from the southeast North Atlantic. We further show that HMA blocks the propagation of PME deficit into the central TP, causing a monthly TWS increase by about 0.5 Gt. Furthermore, warming-induced snow and glacial melt as well as drying-induced TWS depletion in HMA weaken the blocking of HMA's mountains, causing persistent northward expansion of the TP's TWS deficit since 2009. Future projections under two emissions scenarios verified by satellite observations during 2020-2021 indicate that, by the end of the twenty-first century, up to 84% (for scenario SSP245) and 97% (for scenario SSP585) of the TP could be afflicted by TWS deficits. Our findings indicate a trajectory towards unsustainable water systems in HMA that could exacerbate downstream water stress.

Deep learning-based neural networks for day-ahead power load probability density forecasting.

Energy efficiency is crucial to greenhouse gas (GHG) emission pathways reported by the Intergovernmental Panel on Climate Change. Electrical overload frequently occurs and causes unwanted outages in distribution networks, which reduces energy utilization efficiency and raises environmental risks endangering public safety. Electrical load, however, has a dynamically fluctuating behavior with notoriously nonlinear hourly, daily, and seasonal patterns. Accurate and reliable load forecasting plays an important role in scheduling power generation processes and preventing electrical systems from overloading; nevertheless, such forecasting is fundamentally challenging, especially under highly variable power load and climate conditions. This study proposed a deep learning-based monotone composite quantile regression neural network (D-MCQRNN) model to extract the multiple non-crossing and nonlinear quantile functions while conquering the drawbacks of error propagation and accumulation encountered in multi-step-ahead probability density forecasting. The constructed models were assessed by an hourly power load series collected at the electric grid center of Henan Province in China in two recent years, along with the corresponding meteorological data collected at 16 monitoring stations. The results demonstrated that the proposed D-MCQRNN model could significantly alleviate the time-lag and biased-prediction phenomena and noticeably improve the accuracy and reliability of multi-step-ahead probability density forecasts on power load. Consequently, the proposed model can significantly reduce the risk and impact of overload faults and effectively promote energy utilization efficiency, thereby mitigating GHG emissions and moving toward cleaner energy production.

Drying in the low-latitude Atlantic Ocean contributed to terrestrial water storage depletion across Eurasia.

Eurasia, home to ~70% of global population, is characterized by (semi-)arid climate. Water scarcity in the mid-latitude Eurasia (MLE) has been exacerbated by a consistent decline in terrestrial water storage (TWS), attributed primarily to human activities. However, the atmospheric mechanisms behind such TWS decline remain unclear. Here, we investigate teleconnections between drying in low-latitude North Atlantic Ocean (LNATO) and TWS depletions across MLE. We elucidate mechanistic linkages and detecte high correlations between decreased TWS in MLE and the decreased precipitation-minus-evapotranspiration (PME) in LNATO. TWS in MLE declines by ~257% during 2003-2017 due to northeastward propagation of PME deficit following two distinct seasonal landfalling routes during January-May and June-January. The same mechanism reduces TWS during 2031-2050 by ~107% and ~447% under scenarios SSP245 and SSP585, respectively. Our findings highlight the risk of increased future water scarcity across MLE caused by large-scale climatic drivers, compounding the impacts of human activities.

Droughts across China: Drought factors, prediction and impacts.

Drought is a complicated and costly natural hazard and identification of critical drought factors is critical for modeling and forecasting of droughts and hence development of drought mitigation measures (the Standardized Precipitation-Evapotranspiration Index) in both space and time. Here we quantified relationships between drought and 23 drought factors using remote sensing data during the period of 2002-2016. Based on the Gradient Boosting Algorithm (GBM), we found that precipitation and soil moisture had relatively large contributions to droughts. During the growing season, the relative importance of Normalized Difference Water Index (NDWI-7) for SPEI3, SPEI6, SPEI9, and SPEI12 reached as high as 50%. However, during the non-growing season, the Snow Cover Fraction (SCF) had larger fractional relative importance for short-term droughts in the Inner Mongolia and the Loess Plateau which can reach as high as 10%. We also compared Extremely Randomized Trees (ERT), H2O-based Deep Learning (Model developed by H2O.deep learning in R H2O.DL), and Extreme Learning Machine (ELM) for drought prediction at various time scales, and found that the ERT model had the highest prediction performance with R2 > 0.72. Based on the Meta-Gaussian model, we quantified the probability of maize yield reduction in the North China Plain under different compound dry-hot conditions. Due to extreme drought and hot conditions, Shandong Province in North China had the highest probability of >80% of the maize yield reduction; due to the extreme hot conditions, Jiangsu Province in East China had the largest probability of >86% of the maize yield reduction.

Assessing the snow cover dynamics and its relationship with different hydro-climatic characteristics in Upper Ganges river basin and its sub-basins.

Snowmelt is an important source of water in upstream part of the Ganges river basin (GRB), which provides water for different purposes to its 655 million inhabitants. However, studies assessing relationship between snow cover dynamics and changes in hydro-climatic variables are limited within this region, motivating the current research. In this study, MODIS snow cover product (MOD10A1) was used to assess the snow cover area (SCA) dynamics within the Upper Ganges river basin (UGRB) and its sub-basins for the time period of 2002-2014; available climate and hydrological data were used to assess the hydrological characteristics within three selected sub-basins in Nepal; and relationships between snow cover and different hydro-climatic variables are established for three sub-basins owing to availability of hydro-climatic data. Results show that the average annual maximum SCA is around 24.6-47.5% for UGRB and its sub-basins. Upper Yamuna river basin (UYRB) with lowest mean elevation among the sub-basins shows a single SCA peak in spring within an annual cycle, whereas UGRB and the higher sub-basins show an additional lower peak in fall mainly resulted from snow sublimation. During 2002-2014, SCA shows slight decreasing trends for UGRB (Kendall's Tau τ = -0.039) and the higher elevation zones B (3001-4500 m a.s.l.) and C (>4500 m a.s.l.) of most sub-basins, with significance in Zone C of SaRB (τ = -0.070) and KoRB (τ = -0.062). Annual discharge for Gandaki river basin (GaRB) and Koshi river basin (KoRB) shows non-significant decreasing trends (τ = -0.182, -0.303) which are resulted from decreasing discharge in different seasons in different sub-basins. Seasonal correlation analysis indicates an important water supply from rainfall in GaRB and combined water supply from rainfall and snowmelt in KoRB, along with dominant contribution of precipitation in monsoon months and snowmelt in non-monsoon months for all the three sub-basins.

Impacts and socioeconomic exposures of global extreme precipitation events in 1.5 and 2.0 °C warmer climates.

The rise of global mean temperature has aroused wide attention in scientific communities. To reduce the negative climate change impact, the United Union's Intergovernmental Panel on Climate Change (IPCC) set a goal to limit global warming to 1.5 °C relative to pre-industrial levels based on the previous 2.0 °C target in October 2018. To understand the necessity of more stringent emission reduction, this study investigates the impacts of additional 0.5 °C global warming from 1.5 to 2.0 °C on global extreme precipitation, and especially its socioeconomic consequences. The extreme precipitation is represented by extreme precipitation frequency (R95pF), extreme precipitation percentage (R95pT), and maximum one-day precipitation (RX1day) as indicators, calculated based on daily precipitation data extracted from 29 Coupled Model Inter-comparison Project Phase 5 (CMIP5) global climate models (GCMs) under two representative concentration pathways: RCP4.5 and RCP8.5. The exposures of economy and population to extreme precipitation events are also computed and compared for two warming levels by using the Shared Socioeconomic Pathways (SSPs). The results show that most regions in the world are likely to suffer from increasing extreme precipitation hazards in a warming climate, with ascending gross domestic product (GDP) and population being exposed to extreme dangers with an additional 0.5 °C warming. R95pT and RX1day are projected to increase overwhelmingly throughout all continents, directly leading to intensified precipitation extremes and flash floods. In middle and low latitudes, the annual total wet-day precipitation (PRCPTOT) shows a rich-get-richer trend and R95pF decreases, which will reinforce the intensified trend of the magnitude of extreme precipitation. The exposures of GDP and population in regions with extensive exposure to extreme precipitation events at the 1.5 °C warming increase more remarkably with the additional 0.5 °C warming. In particular, Asia and Africa show lager sensitivity to global warming than other regions. These findings could provide information for mitigation and adaptation policymaking.

Multivariate framework for the assessment of key forcing to Lake Malawi level variations in non-stationary frequency analysis.

Lake Malawi in south eastern Africa is a very important freshwater system for the socio-economic development of the riparian countries and communities. The lake has however experienced considerable recession in the levels in recent years. Consequently, frequency analyses of the lake levels premised on time-invariance (or stationarity) in the parameters of the underlying probability distribution functions (pdfs) can no longer be assumed. In this study, the role of hydroclimate forcing factors (rainfall, lake evaporation, and inflowing discharge) and low frequency climate variability indicators (e.g., El Nino Southern Oscillation-ENSO and the Indian Ocean Dipole Mode-IODM) on lake level variations is investigated using a monthly mean lake level dataset from 1899 to 2017. Non-stationarity in the lake levels was tested and confirmed using the Mann-Kendall trend test (α = 0.05 level) for the first moment and the F test for the second moment (α = 0.05 level). Change points in the series were identified using the Mann-Whitney-Pettit test. The study also compared stationary and non-stationary lake level frequency during 1961 to 2004, the common period where data were available for all the forcing factors considered. Annual maximum series (AMS) and peak over threshold (POT) analysis were conducted by fitting various candidate extreme value distributions (EVD) and parameter fitting methods. The Akaike information criteria (AIC), Bayesian information criteria (BIC), deviance information criteria (DIC), and likelihood ratios (RL) served as model evaluation criteria. Under stationary conditions, the AMS when fitted to the generalized extreme value (GEV) distribution with maximum likelihood estimation (MLE) was found to be superior to POT analysis. For the non-stationary models, open water evaporation as a covariate of the lake levels with the GEV and MLE was found to have the most influence on the lake level variations as compared with rainfall, discharge, and the low frequency climatic forcing. The results are very critical in flood zoning especially with various planned infrastructural developments around the lakeshore.

A new statistical downscaling approach for global evaluation of the CMIP5 precipitation outputs: Model development and application.

Outputs of the Coupled Model Intercomparison Project Phase 5 (CMIP5) models have been widely used in studies of climate changes related to scenarios at global and regional scales. However, CMIP5 outputs cannot be used directly in analysis of climate changes due to coarse spatial resolution. Here, we proposed a new statistical downscaling method for the downscaling practice of the CMIP5 outputs, i.e. Bias-corrected and station-based Non-linear Regression Downscaling method based on Randomly-Moving Points (BNRD). And up to now, there are only two global downscaled CMIP5 precipitation datasets, i.e. NASA daily downscaled CMIP5 precipitation product and BCSD-based (Bias Correction Spatial Disaggregation) monthly downscaled CMIP5 precipitation product available online, which are both based on BCSD downscaling method. Hence, we evaluated downscaling performance of BNRD by comparing it with the downscaled CMIP5 outputs using the BCSD method in this current study. The results indicate that: (1) during the period for development of the model (1964-2005), the error between downscaled CMIP5 precipitation and GPCC ranges between -50 mm-50 mm at monthly scale. When compared to BCSD-downscaled CMIP5 precipitation, BNRD-downscaled CMIP5 precipitation well reduces errors and avoids underestimation and overestimation of GPCC by BCSD-downscaled CMIP5 precipitation; (2) during period for verification of the downscaling models (2006-2013), the maximum (182 mm), minimum (15 mm) and average (68 mm) RMSEs between BNRD-downscaled CMIP5 precipitation and GPCC are all lower than those between BCSD-downscaled CMIP5 precipitation and GPCC at continental scales. Besides, from the average precipitation viewpoint, BNRD-downscaled CMIP5 precipitation is in higher correlation (around 0.75) with GPCC than BCSD-downscaled CMIP5 precipitation under RCP4.5 and RCP8.5 scenarios at continental scales; (3) BNRD resolved the negative relation to GPCC in the areas near equator, including north part of the South America, southern Africa, northern Australia. In all, BNRD downscaling method developed in this study performs better in describing GPCC changes in both space and time when compared to BCSD and can be used for downscaling practice of CMIP5 and even potentially CMIP6 precipitation outputs over the globe.

Modified Palmer Drought Severity Index: Model improvement and application.

Monitoring of droughts is the first step into human adaptation and related management of drought hazards. Therefore, drought index is critical in drought monitoring practice. However, the standing drought indices include no information about agricultural irrigation. In this case, based on the Palmer Drought Severity Index (PDSI) and the Self-Calibrating Palmer Drought Severity Index (sc-PDSI), here we proposed the Modified Palmer Drought Severity Index (MPDSI) by considering agricultural irrigation such as irrigation quotas and soil water deficits. We compared changes of droughts monitored by MPDSI and other drought indices considered in this study, and found that MPDSI can well monitor drought conditions in irrigated regions. In this sense, MPDSI can monitor the actual drought conditions under human influences such as irrigation. Besides, we also found that MPDSI can well lessen overestimation of drought conditions by PDSI in terms of drought duration and drought intensity. Therefore, we can conclude that MPDSI can be accepted in drought monitoring practice across China. It should be noted here that the idea behind development of MPDSI and also the MDPSI proposed in this study can be well referenced in drought monitoring in other regions of the globe.

The contribution of internal climate variability to climate change impacts on droughts.

The assessment of climate change impacts is usually done by calculating the change in drought conditions between future and historical periods by using multiple climate model simulations. However, this approach usually focuses on anthropogenic climate changes (ACCs) while ignoring the internal climate variability (ICV) caused by the chaotic nature of the climate system. Recent studies have shown that ICV plays an important role in the projected future climate change. To evaluate that role, this study quantifies the contribution of ICV to climate change impacts on regional droughts by using the signal-to-noise ratio (SNR) and the fraction of standard deviation (FOSD) as metrics for China. The internal climate variability or noise (i.e. ICV) is estimated as the inter-member variability of two climate models' large-member ensembles; the signal (i.e. ACC) and the climate model uncertainty (or inter-model uncertainty, IMU) are estimated as the ensemble mean and inter-model variability of 29 global climate models, respectively. The drought conditions are characterized by drought frequency, duration and severity, which are quantified by using the theory of run based on the standardized precipitation evapotranspiration index (SPEI). The results show that deteriorated drought conditions induced by ACCs are projected to occur over China. From the perspective of the SNR, the ICV impacts are less significant compared to the ACC impacts for drought metrics. Remarkable spatial variations of SNRs for future drought metrics are found, with values varying from 0.001 to exceeding 10. In terms of the FOSD, ICV contributions relative to the IMU are large, as FOSDs are >1 for around 22% grids. These results imply the significance of taking into account the impacts of ICV in drought assessment, any study ignores the influence of ICV may be biased.

Reconstruction of high spatial resolution surface air temperature data across China: A new geo-intelligent multisource data-based machine learning technique.

Good knowledge of the surface air temperature (SAT) is critical for scientific understanding of ecological environment changes and land-atmosphere thermodynamic interactions. However, sparse and uneven spatial distribution of the temperature gauging stations introduces remarkable uncertainties into analysis of the SAT pattern. From a geo-intelligent perspective, here we proposed a new SAT reconstruction method based on the multisource data and machine learning technique which was developed by considering autocorrelation of the in situ observed SAT in both space and time, or simply STAML, i.e. Geoi-SVM (Geo-Intelligent Support Vector Machine), Geoi-BPNN (Geo-Intelligent Back Propagation Neural Network) and Geoi-RF (Geo-Intelligent Random Forest). The multisource data used in this study include the in situ observed SAT and multisource remotely sensed data such as MODIS land surface temperature, NDVI (Normalized Difference Vegetation Index) data. Intermodel comparisons amidst reconstructed SAT data were done to evaluate reconstructing performance of abovementioned models. Besides, the SAT reconstructed by CART (Classification and Regression Tree) was also included to evaluate the reconstructing performance of the models considered in this study when compared to SAT data by CART algorithm. We found that the estimation error of the reconstructed SAT by the STAML is smaller than 0.5K (Kelvin). In addition, it is interesting to note that the Geoi-RF performs better with Mean Absolute Error (MAE) of lower than 0.25K, and Root Mean Squared Error (RMSE) and Standard Deviation (SD) of lower than 0.5K respectively. Correlation coefficients between the reconstructed SAT by Geoi-RF and the observed SAT are close to 1. Besides, the estimation accuracy of the SAT by the Geoi-RF technique is 18.51-63.17% higher than that by the other techniques considered in this study. This study provides a new idea and technique for reconstruction of SAT over large spatial extent at regional and even global scale.

Net primary productivity dynamics and associated hydrological driving factors in the floodplain wetland of China's largest freshwater lake.

Wetlands are thought to be the most unique ecosystem in the world which plays an important role in water and material circulation. However, investigation of ecosystem dynamics in those lake floodplain wetlands that suffering rapid and significant short-term water level fluctuation is quite a challenge. In this study, the short- and long-term characteristics of vegetation NPP (net primary productivity) and their driving mechanism were investigated in the Poyang Lake floodplain wetland, an important international wetland that listed in the Global Eco-region by the World Wildlife Fund (WWF). Attempts were achieved through validating the Carnegie-Ames-Stanford Approach (CASA) model based on observed biomasses of different vegetation types and reconstructed continuous high spatiotemporal resolution (30 m and 16 days) of NDVI data during 2000-2015 according to the fused Landsat and MODIS data. Major result indicates that the intra-annual variation of NPP of most vegetation types shows two peaks in a year due to combined effects of vegetation growth rhythm and seasonal exposure of the lake floodplain. Annual NPP of the lake floodplain ranges in 360.09-735.94 gC/m2 and shows an increasing trend during the study period. The change of NPP in space indicates that the distribution elevation of the major vegetation types decreased and evoluted toward the center lake floodplain. Different from the terrestrial ecosystem, inundation duration is the dominant factor that controls NPP dynamics in the lake floodplain, while the influences of other meteorological factors are much weakened. Recent decline of lake water level was the major reason for the spatio-temporal evolution of annual and seasonal vegetation NPP in the lake floodplain.