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• We proposed a new drought monitoring index; • We corroborated the applicability of this newly-proposed drought index; • We characterized spatial and temporal patterns of droughts across China. In this study, we proposed a new integrated remote sensing drought monitoring indices, i.e. Multiple Remote Sensing Drought Index integrated by Principal Component Analysis (PSDI), Multiple Remote Sensing Drought Index integrated by multiple linear regression (MRSDI) and Multiple Remote Sensing drought index integrated by gradient boosting method (GBMDI), based on the Precipitation Condition Index (PCI), Temperature Condition Index (TCI), Vegetation Condition Index (VCI), and Soil Moisture Condition Index (SMCI). The monitoring performance of PSDI, MRSDI and GBMDI was compared and verified based on the real-world observed droughts during 2002 to 2016. We also evidenced drought monitoring performance of the PSDI MRSDI and GBMDI by comparison between PSDI, MRSDI, GBMDI and SPEI, SPI and PDSI based on the in situ observed meteorological data. We found that the spatiotemporal characteristics of droughts monitored by the PSDI, MRSDI and GBMDI were generally in good agreement with those by the SPI and SPEI. The GBMDI performs better than PSDI and MRSDI in describing drought processes and spatial patterns of droughts of different drought intensities. Comparison between the real-world observed drought-affected croplands and those monitored by PSDI, MRSDI and GBMDI indicated better drought monitoring performance of GBMDI than PSDI and MRSDI in monitoring droughts across widespread drought-affected regions. Besides, the trend of GBMDI is in good agreement with standardized crop yield. Therefore GBMDI should be the first choice in drought monitoring practice. The GBMDI developed in this study can help to provide an alternative drought monitoring index for large-scale drought monitoring across China and also in other regions of the globe. [ABSTRACT FROM AUTHOR]

• Soil moisture dynamics were compared between four typical types of land use and land cover in a humid mountainous watershed. • The bamboo forest had the largest variability in soil moisture content. • The cumulative infiltration increased but the infiltration rate decreased with the rainfall grade. • The runoff coefficient during representative rainfall events varied with the LULC. Identifying soil moisture dynamics is critical for understanding watershed hydrological processes. Soil moisture responses to rainfall vary with land use and land covers (LULCs) and thus influence rainfall-runoff processes, however, the knowledge about how different LULCs affect such processes was less revealed, especially in humid areas. In this study, we investigated the characteristics of soil moisture content (SMC) and rainfall-related soil moisture responses under four typical LULCs, i.e., waxberry forest, farmland, sloping farmland, and bamboo forest, based on in-situ observations in a humid mountainous watershed. We then used the HYDRUS-1D model to analyze the patterns of the rainfall-infiltration processes and quantified corresponding runoff generation to reveal the partitioning between infiltration and runoff at the event scale. We found significant differences of the average SMC at multiple depths for four LULCs. The deep soil (80 cm) had the largest SMC for the waxberry forest, farmland, and sloping farmland, while the surface soil (10 cm) was the wettest soil layer for the bamboo forest. During the representative rainstorms, although soil moisture responses exhibited depth gradients, the earlier responses at some deep layers indicated the occurrence of preferential flow. Generally, the cumulative infiltration increased while the infiltration rate decreased with the rainfall grade. Farmland and bamboo forest showed the most and the least infiltration, respectively. These results suggested that both the rainfall properties and LULCs had a strong impact on the soil moisture responses. Meanwhile, the estimated average runoff coefficients of four typical LULCs increased with rainfall grade, and the sloping farmland generated the most runoff during the representative rainfall events. Our findings provide new insights into soil moisture dynamics and infiltration regimes under different LULCs in humid areas, which can contribute to hydrological modeling at the field-scale and regional water resource management. [ABSTRACT FROM AUTHOR]

• Intelligent adaptation parameter method improves the hydrological model performance. • LightGBM builds flood characteristics-parameter intelligent adaptation relationship. • The XAJ-IAP model can provide accurate and reliable flood simulation. • LightGBM analysis reveals importance of flood characteristics and parameters. Traditional hydrological modeling methods use a set of parameters to simulate flood processes with complex causes and variable intensity, which can easily lead to parameter instability. To address the problem of parameter instability, this study proposes an approach integrating the hydrological model with Intelligent Adaptation Parameters (IAP), whose intelligent adaptation relationship is established by the light gradient-boosting machine (LightGBM) based on individual calibration parameters by each flood event and flood characteristics including flood-caused rainstorm information and initial soil moisture. A widely used hydrological model, Xin 'anjiang (XAJ) model, is chosen to be integrated with IAP (XAJ-IAP) in this study, which has a relatively complex structure and a total of 15 model parameters. The obtained findings demonstrate that: (1) recalibrating the sensitive runoff concentration and separation parameters with a single flood leads to a notable enhancement in simulation accuracy, while simultaneously considering the model's physical significance; (2) the XAJ overestimates large floods and underestimates small floods. Compared with the XAJ, the XAJ-IAP has a better rain-flood response relationship and simulation accuracy for floods of different magnitudes, solving the problem of parameter instability that exists in XAJ; and (3) evaluated in terms of information gain, sensitive parameters contribute the most to the establishment of the intelligent adaptation relationship in the LightGBM compared to flood-caused rainstorm information and initial soil moisture, indicating that sensitive parameters are the most important input features of the LightGBM. It can be concluded that the intelligent adaptation system can not only solve the problem of parameter instability that exists when traditional hydrological models simulate complex and changeable floods, but also further reveal the relationship between the model and floods. [ABSTRACT FROM AUTHOR]

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. • We improved the standing PDSI. • We found less sensitivity of Modified PDSI to warming climate than PDSI. • We found improved drought monitoring performance of modified PDSI than PDSI. [ABSTRACT FROM AUTHOR]

Recent events across many regions around the world have shown that short-term droughts (i.e., daily or weekly) with sudden occurrence can lead to huge losses to a wide array of environmental and societal sectors. However, the most commonly used drought indices can only identify drought at the monthly scale. Here, we introduced a daily scale drought index, that is, the standardized antecedent precipitation evapotranspiration index (SAPEI) that utilizes precipitation and potential evapotranspiration and also considers the effect of early water balance on dry/wet conditions on the current day. The robustness of SAPEI is first assessed through comparison with two typical monthly indices [Palmer drought severity index (PDSI) and standardized precipitation evapotranspiration index (SPEI)] and soil moisture, and then applied to tracking short-term droughts during 1961–2015 for the Pearl River basin in south China. It is demonstrated that SAPEI performs as well as SPEI/self-calibrating PDSI at the monthly scale but outperforms SPEI at the weekly scale. Moreover, SAPEI is capable of revealing daily drought conditions, fairly consistent with soil moisture changes. Results also show that many of the historical short-term droughts over the Pearl River basin have multiple peaks in terms of severity, affected area, and intensity. The daily scale SAPEI provides an effective way of exploring drought initiation, development, and decay, which could be conducive for decision-makers and stakeholders to make early and timely warnings. [ABSTRACT FROM AUTHOR]

• Two-component mixture distributions (TCMD) are used to model annual maximum floods. • Usage of flood timescale (FT) to classify distinct flood generating mechanisms. • Snowmelt-induced floods and rainfall-induced floods are identified in Norway. • The FT -based TCMD reduces uncertainty of design flood by 40% than traditional TCMD. The conventional flood frequency analysis typically assumes the annual maximum flood series (AMFS) result from a homogeneous flood population. However, actually AMFS are frequently generated by distinct flood generation mechanisms (FGMs), which are controlled by the interaction between different meteorological triggers (e.g., thunderstorms, typhoon, snowmelt) and properties of underlying surface (e.g., antecedent soil moisture and land-cover types). To consider the possibility of two FGMs in flood frequency analysis, researchers often use the two-component mixture distributions (TCMD) without explicitly linking each component distribution to a particular FGM. To improve the mixture distribution modeling in seasonally snow covered regions, an index called flood timescale (FT), defined as the ratio of the flood volume to peak value and chosen to reflect the relevant FGM, is employed to classify each flood into one of two types, i.e., the snowmelt-induced long-duration floods and the rainfall-induced short-duration floods, thus identifying the weighting coefficient of each component distribution beforehand. In applying the FT -based TCMD to model the AMFS of 34 watersheds in Norway, ten types of mixture distributions are considered. The design floods and associated confidence intervals are calculated using parametric bootstrap method. The results indicate that the FT -based TCMD model reduces the uncertainty in the estimation of design floods for high return periods by up to 40% with respect to the traditional TCMD. The improved predictive ability of the FT -based TCMD model is attributed to its explicit recognition of distinct generation mechanisms of floods, thereby being able to identify the weighting coefficient and FGM of each component distribution without optimization. [ABSTRACT FROM AUTHOR]

Abstract The Himalayan-Tibetan Plateau (HTP), often known as the "Third Pole" and the "Asian Water Tower", is the source of water resources for many Asian rivers and in turn for hundreds of millions of people living downstream. The HTP has direct impacts on the establishment and maintenance of Asian monsoon, and therefore on the climate of its surrounding areas. Besides, soil moisture plays a critical role in the hydrological cycle and is a critical link between land surface and atmosphere. Hence, soil moisture was greatly emphasized by Global Climate Observing System Programme as an Essential Climate Variable. However, little is known about soil moisture changes on the HTP from a long-term perspective. By comparing remotely sensed and modelled soil moisture datasets against in-situ observations from 100 observation stations, here we find that Noah performed better than other soil moisture datasets. In past years, soil moisture first decreased and then increased obviously. In most regions on HTP, precipitation changes can be taken as the major cause behind soil moisture variations. In future, there is persistently decreasing soil moisture trend since ~2010 with a decreasing rate of −0.044 kg/m2/10a, −0.031 kg/m2/10a and −0.0p 88 kg/m2/10a under RCP2.6, RCP4.5 and RCP8.5 scenarios, respectively, in CMIP5 (Coupled Model Intercomparision Project Phase 5). Specifically, a sudden decrease of soil moisture with a rate of −0.372 kg/m2/10a can be expected after ~2080 under RCP8.5 scenario. Amplifying terrestrial aridity due to increasing precipitation but more significant increasing potential evapotranspiration potentially results in drying HTP. Potential water deficiency for Asian rivers due to drying HTP should arouse considerable concerns. Graphical abstract Unlabelled Image Highlights • New finding about impacts of soil moisture on near-surface air temperature via evaporation fraction. • Identification of different response regimes via relations between soil moisture and evaporation fraction. • Vegetation coverage is the major influencing factor between coupling between soil moisture and temperature. [ABSTRACT FROM AUTHOR]

• The path analysis method is proposed to explain the interplay of drivers in soil moisture drought. • Precipitation deficits dominates the interannual variation of soil moisture drought while potential evapotranspiration impacts are magnified by drought deterioration. • Atmospheric movement and temperate indirectly affects soil moisture drought through precipitation and potential evapotranspiration. • Proportion of drought explanation decreased with increasing soil depth. Soil moisture drought, as one of the most important drought categories, is determined by both water supply (e.g., precipitation) and demand (e.g., potential evapotranspiration). To shed light on the underlying mechanisms driving soil moisture drought, the statistical multiple linear regression, machine learning, and modeling experiments methods have been pervasively used in early studies. However, these methods neglect the collinearity and interactions of climate variables, and thus cannot reflect the direct and indirect interaction of factors leading to soil moisture drought. To reveal the synergistic effects of water supply and demand on soil moisture drought, this study quantified the contributions of key drivers to the change of soil moisture drought by a path analysis method to exhibit the relationships between atmospheric movement state and soil moisture drought. Prior to applying the systematic path analysis model, we identified the spatial patterns of soil moisture droughts at different depths by using a state-of-art three-dimensional drought recognition method in China. Our results showed that precipitation deficits dominated the interannual variation of soil moisture drought while increasing potential evapotranspiration only had marginal intensification in drought. The response of soil moisture drought to potential evapotranspiration was magnified by drought deterioration, especially in basically severe drought conditions. The total column water vapor and the horizontal divergence of the vapor flux, as well as temperature, directly affected precipitation and potential evapotranspiration and led to soil moisture drought through various direct and indirect processes. This study highlighted that the interactions among precipitation, potential evapotranspiration, and atmospheric vapor movement state in space and time were important for understanding the drought development mechanisms. [ABSTRACT FROM AUTHOR]

• We developed a modified drought severity index (MDSI) with a constrained optimization technique. • MDSI improves drought monitoring for forests and farmland. • Ecological restoration and cultivated land reclamation can help mitigate droughts, while urbanization can potentially intensify droughts. With advancement of remote sensing techniques, remote-sensing drought indices have been widely used for drought monitoring. However, the monitoring accuracy of a specific drought index regionally varies. Considering the deficiency of existing drought indices in reflecting vegetation growth, here we propose a Modified Drought Severity Index (MDSI) with local optimization method constrained by the inclusion of vegetation greenness, crop water shortage, canopy temperature, vegetation structure, and physiological status. We evaluated drought monitoring performance of MDSI across China, and detected high correlations between MDSI and soil moisture (SM), Standardized Precipitation Index at a 3-month scale (SPI-3), actual drought-affected areas (ADA), evidencing higher drought performance of MDSI when compared to 8 widely-used drought indices. Besides, MDSI performed better in monitoring agricultural drought. We found amplifying short-term drought intensity in the future. Ecological restoration and cultivated land reclamation can alleviate drought effects. However, urbanization can potentially intensify droughts. How to adapt human behavior to droughts is a challenging task. [ABSTRACT FROM AUTHOR]

• We evaluated soil moisture droughts with duration, magnitude and extremum in monsoon and non-monsoon regions. • We identified more evident impacts of anthropogenic forcing on soil moisture drought in monsoon region than in non-monsoon region. • We found larger impacts of anthropogenic forcing on drought magnitude, relative to drought duration and drought extremum. The spatio-temporal patterns of drought changes and relevant forcings are still open for debate, especially under global warming, even though agricultural drought has long been receiving increasing concern for food security and sustainable development. In this study, we depicted global spatiotemporal patterns of agricultural drought using the Soil Water Deficit Index (SWDI) and reflected on the underlying forcings using the optimal fingerprint method. Three aspects of droughts were analyzed, i.e. drought duration (DD), drought magnitude (DM) and drought extremum (DE) over three regions, i.e. global, monsoon and non-monsoon regions. We found distinct spatial heterogeneity of DD, DM and DE. However, DM (DE) had mainly a decreasing (increasing) tendency. Anthropogenic forcing ([ANT] including greenhouse gas, anthropogenic aerosol, and ozone) and greenhouse gas forcing (GHG) played a prominent role in driving drought changes and were followed by the combination of anthropogenic and natural forcing (ALL). Soil moisture drought (DD, DM and DE) responses to external forcing of ANT and GHG were detected more easily in the monsoon region than in the non-monsoon region. Specifically, DM changes due to ANT (2.58 per century) contributed 39.88% of the DM changes by ALL (6.47 per century) in the monsoon regions, comparatively, the GHG and ANT induced changes of DM in the non-monsoon regions were quite slight. This study further clarified the impacts of anthropogenic warming on agricultural drought over the globe. [ABSTRACT FROM AUTHOR]

The traditional calibration objective of hydrological models is to optimize streamflow simulations. To identify the value of satellite soil moisture data in calibrating hydrological models, a new objective of optimizing soil moisture simulations has been added to bring in satellite data. However, it leads to problems: (i) how to consider the trade-off between various objectives; (ii) how to consider the uncertainty these satellite data bring in. Among existing methods, the multi-objective Bayesian calibration framework has the potential to solve both problems but is more suitable for lumped models since it can only deal with constant variances (in time and space) of model residuals. In this study, to investigate the utilization of a soil moisture product from the Soil Moisture Active Passive (SMAP) satellite in calibrating a distributed hydrological model, the DEM (Digital Elevation Model) -based Distributed Rainfall-Runoff Model (DDRM), a multi-objective Bayesian hierarchical framework is employed in two humid catchments of southwestern China. This hierarchical framework is superior to the non-hierarchical framework when applied to distributed models since it considers the spatial and temporal residual heteroscedasticity of distributed model simulations. Taking the streamflow-based single objective calibration as the benchmark, results of adding satellite soil moisture data in calibration show that (i) there is less uncertainty in streamflow simulations and better performance of soil moisture simulations either in time and space; (ii) streamflow simulations are largely affected, while soil moisture simulations are slightly affected by weights of objectives. Overall, the introduction of satellite soil moisture data in addition to observed streamflow in calibration and putting more weights on the streamflow calibration objective lead to better hydrological performance. The multi-objective Bayesian hierarchical framework implemented here successfully provides insights into the value of satellite soil moisture data in distributed model calibration. [ABSTRACT FROM AUTHOR]