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• High-frequency common expansion contributed to small ratio on diameter growth. • Low-frequency dramatic expansion contributed to large ratio on diameter growth. • Diameter dramatic expansion had obvious relationships with rainfall. • Leaf wetting time directly affected sap flow and diameter change process. • Common expansion was promoted, and shrinkage was alleviated after rainfall. The dry-hot valley regions of Southwest China suffer droughts and high temperatures coexist environment. The efficient use of rainfall makes it possible for plants to safely cope with the environmental stress in the region. To reveal the driving mechanism of rainfall on typical tree secondary growth, orange tree (Citrus sinensis L. Osbeck), the commonly grown economic tree in the region was selected. Trunk diameter, sap flow, leaf humidity, meteorological conditions and soil water content were simultaneously monitored for the 2020–2022 period. Based on frequency distribution of trunk diameter changes data, trunk diameter changes were classified into positive growth (dramatic expansion, and common expansion), and negative growth (dramatic shrinkage, and common shrinkage) types. Furthermore, the relationships among the different growth patterns and rainfall or drought events were analyzed. The results showed: i) Trunk diameter maintained positive growth on rainy days, whereas both positive and negative growth occurred on rainless days. Although rainy period was not frequent, a high proportion of the total growth occurred during that time. ii) The common expansion type with a high frequency contributed to approximately 30% of the total growth, whereas the dramatic expansion type with a low frequency contributed to approximately 70% of the total growth. iii) There was a highly significant correlation between dramatic tree diameter expansion and rainfall. iv) Cumulative growth in tree diameter during rainy period was significantly driven by the duration of rainfall, the wetting time of leaf and the amount of rainfall. v) Compared with rainfall amount, the time rainfall related indexes were more closely related with tree dramatic diameter expansion. vi) While increasing soil water content and dry air mitigation due to rainfall enhanced average growth, it concurrently suppressed negative growth of tree trunk diameter after rainfall. The study confirmed that rainfall was critical for tree diameter growth in dry-hot valley regions of Southwest China. Relevant results are beneficial for improving understanding of plant precipitation utilization mechanism and could provide useful information on extreme meteorological hazards assessment in the context of climate change. [ABSTRACT FROM AUTHOR]

• Rainfall infiltration on the greening CLP is less studied in the non-growing season. • 3-year, 10-min soil moisture data were collected in paired catchments on the CLP. • Growing season favored deeper, faster rainfall percolation and preferential flow. • Non-growing season showed restrained infiltration depth and velocity. • Factors controlling infiltration varied between growing and non-growing seasons. Rainfall infiltration into hillslopes on the Chinese Loess Plateau (CLP) undergoes noticeable changes during the growing season following long-term revegetation. However, infiltration patterns during the non-growing season have been understudied. In this study, we monitored soil moisture over three years (May 2016-December 2018) at 10-minute intervals, spanning five soil depths (10, 20, 40, 60, and 100 cm). Monitoring was conducted in both a forestland catchment and an adjacent grassland catchment on the CLP, with two monitoring sites per catchment: one downhill in the gully, and the other uphill on the slope. Our findings reveal pronounced seasonal variations in meteorological conditions and vegetation index. We observed divergent rainfall infiltration patterns and their governing factors between the growing (May to October) and non-growing seasons (November to April). During the growing season, rainfall facilitated a quicker response with shorter lag time (23.34 h vs. 56.84 h in the non-growing season) and noticeable soil moisture increments (up to 35.7 % cm3/cm3 vs. only up to 6.4 % cm3/cm3). The wetting depth often extended to 40–100 cm (compared to 10–40 cm in the non-growing season), with a higher wetting front velocity (17.44 mm/h vs. 6.24 mm/h) and an increased occurrence frequency of preferential flow (19.77 % vs. 9.72 %). The impact of rainfall amount and peak intensity on infiltration was more pronounced during the non-growing season, whereas, in the growing season, infiltration was additionally influenced by antecedent moisture conditions and vegetation type. Notably, in the growing season, the enhanced soil moisture recharge due to rainfall infiltration exceeded the rise in evapotranspiration, resulting in higher soil water storage. The findings of this study shed new light on seasonal infiltration dynamics on the greening CLP, contributing valuable insights into hillslope hydropedological processes post-revegetation. [ABSTRACT FROM AUTHOR]

• DFEAT a novel tool for annual drought characterization using daily soil water balance. • DFEAT allowed the extraction of a comprehensive set of yearly drought features covering the entire drought development stages. • DFEAT captured the aridity gradient under typical Mediterranean soil desiccation scenarios from humid to semi-arid. The characterization of yearly drought events represents a key information for conducting impact assessments and forecasting future threats, and usually relies on a single index of duration or severity. Mostly based on daily soil or atmospheric water balances, the derivation of key drought facets is not yet standardized or embedded in a single tool, thus limiting intercomparisons between studies. We developed DFEAT as a fully-automated tool designed to characterize yearly drought features, based on any simulated/observed/remotely sensed soil water content time series. We provide here an application assessment performed with the Keetch-Byram Drought Index (KBDI) as a standard daily soil water model over a 60-year period and covering the Mediterranean aridity gradient in Lebanon (Middle-East) experiencing humid to semi-arid climate conditions. We computed and tested 19 drought features related to duration, severity, onset, offset, drying and wetting rates, driest peak day, and rainfall pulses across three soil water desiccation thresholds. For our study area, we revealed the uncorrelated specificities of 6 features, allowing to regionally discriminate between mountainous Mediterranean climate experiencing shorter drought duration (45 days), later onset (Day of the Year = 198), less rainfall pulses intensities (3.35 mm), and slower drying (1.68 mm/day) and wetting (−2.72 mm/day) rates, but similar offset date (Day of the Year = 356) compared to the coast. DFEAT also captured regions with prolonged Multi-Year Droughts not fully refilling field capacity under arid bioclimate reaching up to 22 years. We demonstrate here the applicability of DFEAT across water-limited bioclimates and the non-correlation between drought features with contrasted agro-ecological impacts. [ABSTRACT FROM AUTHOR]

• Changes in flood risk are investigated using a bottom-up climate change assessment. • Thermodynamic and dynamic drivers of rainfall, and losses are considered. • Increases in rainfall intensity are the key driver of increased flood risk. • Smaller flood events in drier catchments are most sensitive to changes in losses. • Uncertainty in the projected changes in flood peaks between GCMs remains large. Our understanding of the key drivers of change in flood risk due to climate change remains incomplete. Here, to understand and quantify the key drivers of change in flood risk, we present a framework to undertake a 'bottom-up' ('scenario-neutral') climate change impact study on flood risk using an event-based flood model that considers non-stationarity in rainfall extremes and catchment wetness. A key advantage of this approach is that by using an event-based model, which explicitly represents key flood drivers, we can directly understand how changes in these drivers will influence changes in flood peaks. The utility of this modelling framework is demonstrated by applying it to one temperate and one tropical catchment in Australia, focusing on the sensitivity of frequent (1 in 5 annual exceedance probability, AEP) and rare (1 in 50 AEP) flood peaks to changes in thermodynamic and dynamic drivers of rainfall extremes, and changes in catchment processes, which are quantified by changes in rainfall losses. Response surfaces of the catchment sensitivity are first produced and demonstrate that increases in rainfall intensity drive increases in the flood peak. Smaller flood peaks in drier catchments are most sensitive to changes in rainfall losses, with the modulating impact of changes in losses decreasing as the runoff ratio increases. Climate model projections of these drivers are then superimposed on these surfaces to identify plausible shifts in flood risk under climate change. Several climate model ensemble members imply a decrease in future flood peaks, most notably 14 % of ensemble members suggest future decreases for the 1 in 5 AEP event for the temperate catchment. Despite large agreement on the direction of change, we found large variability in the expected magnitude of change for both frequent and rare events with flood peaks projected to increase between 0 % and 45 % for the tropical catchment and between −10 % to more than 50 % for the temperate catchment under RCP8.5 in 2085, highlighting the deep uncertainty associated with projecting flood risk under climate change. We believe this study acts as proof of concept for how a bottom-up climate change assessment can be undertaken within an event-based flood modelling framework, and provides insights to help us better understand and communicate the key drivers of changes to flood risk into the future. [ABSTRACT FROM AUTHOR]

• A paired-watershed experiment provides more understanding via state-controlled test. • Runoff change after deforestation increases for higher rainfall intensity. • Higher antecedent rainfall state decreases the deforestation impact on runoff. Understanding watershed processes is critical to predicting and mitigating flood and drought risk. Over the past century, the capability to isolate the influence of individual factors on hydrologic responses has been enhanced through the introduction and application of paired-watershed experiments. However, the high variability of paired experiment outcomes has been noted to limit conclusive reasoning from the results. Thus, in an effort to resolve this issue, this study assumes that the high variability in paired-watershed experiments is due to the changes in the watershed system states (e.g., soil moisture and groundwater states). A state-controlled hypothesis test is proposed for the paired-watershed experiment to test treatment impact with the control of watershed system states. The application of the state-controlled test to one pre-existing paired-watershed experiments in southeast Australia showed that the variability in the test results can be reduced and provide further understanding about the nature of changes that occur. [ABSTRACT FROM AUTHOR]

[Display omitted] • We focused on the erosion dynamics of karst slope sunder extreme rainfall. • Surface erosion becomes the paramount concern during extreme rainfall. • Surface sediment yield is mainly driven by unit stream power. • Flow velocity is a more accurate predictor of surface erosion than runoff modulus. As global warming intensifies, frequent extreme rainfall events cause severe soil erosion. ​However, the impact of extreme rainfall on erosion on karst slopes and its hydrodynamic mechanisms are still poorly understood. This study aims to examine how extreme rainfall events and slope gradient affect soil erosion on karst slopes and its underlying dynamic mechanisms. Simulation experiments were performed in a steel tank that mimicked the dual hydrological structure of a karst slope, using various rainfall intensities (50, 105, 115, 125, and 145 mm·h−1) and slope gradients (5°, 15°, and 25°). The results show that in the event of extreme rainfall (105 – 145 mm·h−1), there was a substantial increase in the surface runoff coefficient, rising from 48.85 % to 89.79 %, representing an 83.81 % increase. Surface erosion was found to be the predominant factor on the slope, contributing to 98.74 % to 99.98 % of the overall sediment output, while underground sediment coefficient ranged from only 0.02 % to 1.26 % under same conditions. The unit stream power emerged as the most suitable hydrodynamic parameter for characterizing surface sediment yield (SS), with a critical threshold of unit water flow power for surface erosion identified at 0.006 m·s−1. Constructing a surface erosion model (R2 = 0.969) based on runoff velocity, rain intensity, and slope yields higher accuracy than the model (R2 = 0.966) based solely on runoff modulus, rain intensity, and slope. For the most precise predictions, it is recommended to combine runoff modulus and velocity with rainfall intensity and slope when constructing the surface erosion prediction model(R2 > 0.999). This study enhances understanding of extreme rainfall's effect on soil erosion in karst slopes and backs the creation of prediction models for such events. Our insights offer fresh perspectives for soil and water conservation, aiding in the formulation of precise erosion control strategies for karst slopes. [ABSTRACT FROM AUTHOR]

[Display omitted] • The replenishment showed an increase–decrease trend with increased thinning intensity. • A 35 % thinning intensity improved the replenishment of soil water by rainfall. • The leaf area index and initial soil water content controlled the replenishment. • Reasonable thinning intensity is determined by replenishment and retention. Soil water plays a key role in vegetation growth and recovery, mainly replenished by rainfall in the water-limited areas. Thinning is an important practice for forest reconstruction and soil water regulation. However, the effects of different thinning intensities on the dynamics of soil water and its replenishment by rainfall have been inadequately quantified, which could provide a guide for reasonable thinning intensity determination for in high-density forests from a hydrological perspective. In this study, one nonthinning plot and 5 plots with different thinning intensities, i.e., 35 % (light), 45 % (medium), 55 % (moderate), 65 % (heavy) and 80 % (extremely heavy) in Robinia pseudoacacia plantations, which are widely planted on the Loess Plateau, were set up. The volumetric soil water content (SWC) at 0–200 cm depths and rainfall were observed simultaneously and continuously for up to two years. The soil properties and vegetation structure in each plot were also measured regularly. The results showed that (1) the SWC in all the thinning plots was significantly greater than that in the nonthining plot (P < 0.05). (2) The replenishment increased with increasing thinning intensity, and then decreased after reaching a maximum at T 35 % under most rainfall events except for light events. (3) The variations in the soil water response characteristics among the plots with different thinning intensities were mainly significantly correlated with the initial soil water content (ISWC), leaf area index (LAI) and understorey vegetation characteristics (P < 0.05). (4) Based on the relationship between soil water replenishment and retention with thinning intensity and stand density, quantitative equations were fitted to determine the reasonable thinning intensity (22–28 %) and retained stand density (1019–1091 trees/ha), respectively. Our study concluded that thinning could effectively improve soil water, especially light thinning, which could obviously enhance the replenishment of soil water by rainfall in Robinia pseudoacacia plantations. This study can provide a reference for determining forest thinning intensity or stand retention density, which is beneficial for water and reforestation management in water-limited regions. [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]

• Rare examples of highly instrumented observatories in African drylands. • Large water surplus during monsoon not represented by annual scale of aridity index. • Overland flow is rare and root zone achieves field capacity across hillslopes. • Stable isotopes indicate recharge season timing, not a link to heavy rainfall. • Recharge is diffuse in semi-arid Africa validating soil moisture balance model use. We use two comprehensively instrumented field observatories to understand groundwater recharge processes in African drylands. The observatories are located on crystalline basement geology in semi-arid parts of Ghana and Burkina Faso, aridity indices 0.43 and 0.29, respectively, and we report 2017–2019 observations. Groundwater recharge was quantified by inverse water table fluctuation models using specific yield estimates derived from magnetic resonance soundings. Evidence for recharge drivers and mechanisms comes from high resolution meteorological observations, soil moisture (logged hourly and weekly along hillslope transects), overland flow plots, river stage, and stable isotopes of O and H in rainfall events and groundwater. Groundwater recharge varied between 87 and 175 mm/y, i.e. 7–15 % of annual rainfall. Rainfall was twice the volume of water lost via actual evapotranspiration across the four peak months (Jun-Sep) of the monsoon. This seasonal water surplus of ∼ 350 mm/y is not characterised by the annual scale of the aridity index. Overland flow was rare and soil moisture deficits were overcome at all monitoring locations. Large rainfall events only produced appreciable recharge when the antecedent soil moisture was close to field capacity, yet always produced large responses in river stage. Stable isotopes of O and H in groundwater indicate no evidence of evapotranspiration prior to infiltration and their composition is akin to depleted isotopic rainfall at the monsoon peak. Stable isotopes indicate recharge season timing and not a relationship between intense rainfall and groundwater recharge. We contend that the mechanism for groundwater recharge is predominantly diffuse in these semi-arid African settings. [ABSTRACT FROM AUTHOR]

• Rainfall classification aided the understanding of deep soil moisture (DSM) recharge. • Groundwater overwhelmed the role of direct rainfall in recharging DSM. • A four-stage relationship between DSM and groundwater table was identified. • A conceptual model for DSM recharge was proposed. Deep soil moisture (DSM) plays a critical role in vadose zone hydrological processes. However, the recharge processes and their underlying mechanisms remain unclear due to the scarcity of comprehensive deep hydrological data, especially in regions characterized by active interactions between surface and subsurface hydrological processes. To address this knowledge gap, we performed an event-based study using a four-year hydrological monitoring dataset, encompassing rainfall, profile soil moisture, and groundwater along a hillslope at the Red Soil Critical Zone Observatory (CZO) in China. A total of 226 rainfall events were classified into four distinct types (I-IV) using a k -means clustering algorithm. The changes in DSM and groundwater table (GWT) were dependent on the rainfall type. The GWT (0.57–0.97) played a greater role in contributing to the DSM than the direct rainfall (0.48–0.64) based on a structural equation model, which was more pronounced from upper slope to foot slope. DSM recharge pattern can be described by a four-stage conceptual model, as indicated by the relationship between DSM and GWT: a slow and linear increase (stage 1), a small plateau (stage 2), a rapid linear rise (stage 3), and finally a great plateau (stage 4). DSM at the small and great plateau were closely related with the field capacity and saturation, respectively. Rainfall type, vadose zone features, and soil physical properties jointly shaped DSM recharge patterns. These findings can offer valuable insights into understanding the deep hydrological processes of the red soil region as well as regions with similar hydroclimatic conditions. [ABSTRACT FROM AUTHOR]

• Critical rainfall for recharging flat sandy lands is 15 mm in hyper-arid desert. • Lateral gravity water flow and surface flow on sandy slopes were identified. • Water flow on sandy slope improves the recharge rate of deep soil from rainfall. • Local rainfall has the capacity to sustain the development of Badain Jaran Lake. • Extreme rainfall does not play a decisive role in the sustaining of desert lake. To elucidate the slope hydrology and their influence on the regional water balance and desert lake development, water migration and soil water conditions were studied in the megadunes of Badain Jaran Desert. Simulated rainfall of 15–60 mm with infiltration depths of ∼ 0.3–0.5 m, the lateral water migration ratios of ∼ 52.9 %-86.2 % and the differential distribution of tracers with depth in the flat sand lands indicate that 15 mm rainfall is the critical rainfall for effective recharge of deep soil water and that there is a shift from piston flow to preferential flow of water during infiltration. Soil micro-bedding and 4.2 % soil gravity water from locally wet surface soils and 6 mm of natural rainfall resulted in lateral migration of the tracer ∼ 1.2 m and 0.4 m in the slope surface and soil respectively, indicate surface flow and lateral gravity water flow resulting from differences in hydraulic conductivity and water-holding capacity of the soil micro-bedding. Soil water storage at 0–4 m depth showed that the bottom of the slope was ∼ 30 mm higher than the middle of the slope after the rainy season and ∼ 9 mm higher before the rainy season; in general the post-rainy season was ∼ 30–40 mm higher than the pre-rainy season. Surface flow and lateral gravity water flow in unsaturated soils increase the intensity of lateral water movement on the slope and improve the effective conversion rate of rainfall to deep soil water, especially below 15 mm. Annual recharge of deep soil water, assessed using soil water change and critical rainfall, will not be less than 8.5 mm and may even reach an upper limit of ∼ 30–40 mm. The megadune slopes respond strongly to local precipitation, which is sufficient to maintain the lake, but extreme precipitation does not play a decisive role. The methods and results provided in this work can advance our understanding of the desert hydrological cycle and water resources. [ABSTRACT FROM AUTHOR]

• The runoff-imperviousness relationship has a segmented linear pattern. • The runoff-imperviousness relationship can be described by the R-I sensitivity. • The R-I sensitivity can be expressed by a simple equation (RISE). • A framework helps for LID adaptation was established based on RISE. The urban RPA (Receiving Pervious Area)-DIA (Disconnected Impervious Area) system is the sub-catchment with a typical land distribution pattern where the connectivity between impervious area and drainage is disconnected by pervious area in the context of Low Impact Development (LID). The rainfall-runoff response of the RPA-DIA system is complex and plays an important role in LID management, which is not yet fully understood. In this study, to systematically learn the rainfall-runoff response of the RPA-DIA system, an indoor rainfall simulation experiment was applied to a laboratory RPA-DIA system, and an integrated surface-subsurface flow model was used to simulate the hydrological processes of actual-scale RPA-DIA systems with consideration of numerous rainfall and underlying surface conditions. Both the laboratory experiment and the numerical simulations show a segmented linear runoff-imperviousness relationship for the RPA-DIA system. A threshold of impervious area exists in the runoff-imperviousness relationship, and when the impervious area is less than the threshold, the surface runoff coefficient is zero, otherwise the surface runoff coefficient increases with impervious area in a generally linear trend. We define the slope of the linear trend as the R-I sensitivity (sensitivity of runoff to imperviousness), which is the key metric to determine the runoff-imperviousness relationship. We further explored how the R-I sensitivity changes between different rainfall and RPA conditions (e.g., different LID facilities and initial soil moistures) and found that both the laboratory experiment and the numerical simulations suggest a very simple equation to express the R-I sensitivity (RISE, Runoff-Imperviosness Sensitivity Equation) in terms of rainfall depth and intensity, including three parameters indicating the specific RPA conditions. Based on the segmented linear relationship and RISE, we have established a framework that vividly presents the rainfall control behavior of a specific RPA under different rainfall events, which can be a simple tool to provide cost-effective guidelines for LID facilities adapting to current or future rainfall characters. [ABSTRACT FROM AUTHOR]

• Most of the Euro-Mediterranean region affected by drought conditions during 2021/22. • Recursive high-pressure systems & a poleward jet shift explain rainfall deficits. • Most severe drought since at least 1891 when considering evaporative demand (AED) • Enhanced AED caused unprecedented low soil moisture & runoff since at least 1965. • Potential worst-case droughts if historic rainfall deficits occur with current AED. The Euro-Mediterranean region experienced a remarkable drought during the hydrological year 2021/22. Substantial and widespread impacts on water supply systems, agricultural crops, and the production of hydroelectric power were observed. This assessment characterises the drought from a long-term perspective using a multi-index approach and analyses the associated atmospheric circulation at the annual and monthly time scales. The main dynamical forcing of the drought was the unusual recurrence of high-pressure systems over western Europe, at least partly due to an anomalous southward shift in blocking activity and a remarkable occurrence of low-latitude blocks. This led to record-breaking positive geopotential height anomalies over western Europe and a poleward displacement of the North Atlantic eddy-driven jet. Although most of the region was affected by mild drought conditions, the 2021/22 event was not unprecedented in terms of precipitation deficits since other periods of the 20th century (e.g., in the 1920s, 1940s and 1970s) displayed moderate and severe drought conditions over larger areas. However, the 2021/22 drought has been the most intense since at least 1891 because of high atmospheric evaporative demand (AED) values associated with extreme temperatures, especially during the summer of 2022. This enhanced AED also contributed to depleting soil moisture and reducing runoff generation, leading to unprecedented deficits since at least 1965. Finally, we find important differences in the 2021/22 event as compared to other major historical droughts over the Euro-Mediterranean region. In particular, the contrasting effect of AED evidences its increasing role over the last decades and warns about the current risk of experiencing unprecedented droughts. [ABSTRACT FROM AUTHOR]

• In-situ soil moisture is used to characterize the spatiotemporal scales of precipitation, especially for those under 10 km. • A sliding window approach is adopted to capture the spatiotemporally varying rainfall events. • The Central TP is dominated by precipitation with relatively short durations (0–6 h) and small spatial extents (< 16 km). The spatiotemporal scale of precipitation is crucial for understanding the local hydrometeorological processes. It also plays a significant role in determining the layout of rain gauges. Convectional rainfalls are typical in the Tibetan Plateau (TP), but the spatiotemporal scales of precipitation events in the TP, particularly at scales less than 10 km, remain unclear, which is mainly due to limited in-situ precipitation observations and the relative coarse spatial resolution of satellite precipitation products. Building upon the strong response of soil moisture changes to precipitation, this study attempts to reveal the spatiotemporal scales of precipitation in the central TP during summertime (June–September) by investigating the inter-station correlation of accumulated positive soil moisture increments obtained from in-situ soil moisture measurements in a mesoscale region of the central TP. The soil moisture-implied spatiotemporal scales are further verified against precipitation from the Integrated Multi-satellitE Retrievals for GPM (IMERG) at spatial scales larger than 10 km. Results show that: 1) the variations of surface soil moisture increase and precipitation are fairly consistent, making positive increments in surface soil moisture an effective surrogate for reflecting the spatiotemporal scales of precipitation in central TP; 2) the spatial extents of precipitation as indicated by the precipitation-induced positive soil moisture increments are positively correlated with the temporal scales; 3) precipitation events with relatively short durations (0–6 h) and spatial extents of less than 16 km (and in particular, 0–3 h for scales less than 10 km) dominate the central TP. This study provides a novel approach for characterizing precipitation scales in regions with scarce in-situ precipitation observations, and is expected to inspire new way of assessing large scale soil moisture and precipitation products. [ABSTRACT FROM AUTHOR]

• Proactive drought responses are crucial in a drought vulnerable future. • Antecedent conditions need to be considered in context of anticipated conditions. • We combine rainfall, soil moisture and evapotranspiration data with forecasts for early warning. • Validation efforts found promising accuracy in the drought early warning maps. • Proof-of-concept drought early warning system can aid proactive drought management. Droughts are a cyclical feature of Australia's climate that have compounding impacts on agricultural productivity and the wellbeing of drought-affected communities. Understanding anticipated drought conditions in context of antecedent observations is critical to providing early warning of drought. In this study we paired probabilistic seasonal rainfall forecasts with precipitation, soil moisture and evapotranspiration data that were objectively combined using Principal Component Analysis (PCA). The final Drought Early Warning System (DEWS) maps overlay forecasting information with the multi-variate PCA-weighted maps at 1-, 3- and 6-month timescales over the common period of overlap between all datasets (1982–2018). In this study period, the 1982–1983 Ash Wednesday tinder drought and the 1997–2001 Millennium drought were investigated. We validated PCA-weighted maps with satellite vegetation data and found performance was strongest over the Murray Darling Basin region (R = 0.63, p = 0.009) and poorest over Central interior Australia (insignificant correlations). We also validated PCA-weighted maps using agricultural commodity data from ABARES. Significant negative correlations at 95 %, 99 % and 99.9 % confidence intervals were found between %-Area in drought category and crop cultivation area; export volume/value; crop yield; and rural debt. Our findings indicate that early warning of drought can be categorised by concern – wherein dry antecedent conditions and dry forecasted conditions are of the highest concern. The developed proof-of-concept DEWS contributes to the growing body of proactive drought management research. In a drought vulnerable future, operationalising and communicating drought early warnings will be critical to reducing the harmful impacts of drought on communities, economies, and environments. [ABSTRACT FROM AUTHOR]

Hillslopes are the fundamental building blocks of catchments, however, fully disentangling their hydrological response remains an outstanding challenge. In natural settings, hillslope hydrology is challenged by the interplay of several factors, such as, heterogeneous morphology, vegetation, and complex soils. Herein, we attempt to clarify the mechanisms that control the rainfall-runoff relationship by investigating the response of an artificial outdoor slope setup with uniform geometry, soil properties, topography, and in the absence of vegetation located in a Mediterranean climate. Based on the analysis of more than 100 storms across six consecutive hydrological years, our results demonstrate that either pre-event soil water content or rainfall conditions alone are not sufficient to explain the slope response. Seasonality played a major role through soil moisture conditions, and pre-event soil moisture, peak rainfall intensity and rainfall duration influenced the slope runoff response. Observations suggest that in fall and winter subsurface flow was the prevalent runoff mechanism, while infiltration excess overland flow mostly occurred in the dry season. Our findings show that continuous measurement of runoff could be beneficial to understand the peak rainfall-peak runoff relationship. • Rainfall-runoff studied in artificial slope through more than 100 storms. • Peak rainfall intensity, rain distribution and soil moisture influence runoff. • Occurrence of runoff before peak rainfall beneficial to predict peak runoff. [ABSTRACT FROM AUTHOR]

• MASCP considers the effect of ponding by introducing a developing saturated zone. • MASCP reveals scaling relations of infiltration for sloping and horizontal surfaces. • New numerical algorithm can model slope infiltration with non-uniform initial SWC. Slope rainfall infiltration is a critical component of the Earth's hydrologic processes. Although various infiltration models have been proposed to simulate rainfall infiltration, simple and accurate ones are still scarce. This work developed a non-integral infiltration model for sloping surfaces, called modified approximate solutions under constant pressure conditions (MASCP). New expressions were proposed to improve slope infiltration simulation by introducing a developing saturated zone in soil water content (SWC) profile to quantify the role of ponding water during infiltration. The MASCP was then extended to simulate the infiltration with uniform and non-uniform initial SWC distributions under complex rainfall patterns. The model performance was evaluated by comparing with Hydrus-1D and the sloping Green-Ampt model for four distinct textured soils. The results demonstrated that the ponding time and infiltration rate simulated by the MASCP agreed well with those by Hydrus-1D at all given conditions. Besides, the MASCP yielded higher simulation accuracy than the sloping Green-Ampt model, especially for coarse-textured soils, low rainfall intensity, and long rainfall hiatus. In conclusion, the MASCP is a simple and accurate computation tool for slope rainfall infiltration modeling. [ABSTRACT FROM AUTHOR]

• Funneling ratio of live C. korshinskii branches was 1.8 times higher than dead ones. • Funneling ratio of dead S. psammophila branches was 1.6 times higher than live ones. • Rainfall intensity significantly affected stemflow intensity and time lags to rain. Shrub mortality due to self-thinning and climate change is a natural consequence of the long residence times of standing dead branches in unmanaged shrub plantations. Stemflow (SF) represents a key point source of input water and nutrients into the soil in shrub ecosystems. However, little information is available about how shrub mortality affects SF variables. In this study, we measured the SF volume (SFV), funneling ratio (FR), SF intensity, time lag to rain, and rainfall characteristics for live and standing dead branches of two major shrub species comprising Caragana korshinskii and Salix psammophila during the rainy season in 2022 on the Chinese Loess Plateau. The results demonstrated that the SF variables and temporal dynamic processes differed between standing live and dead branches of the two shrubs. For C. korshinskii , the mean SFV and FR were 1.9 and 1.8 times higher for live branches than dead branches, respectively, whereas for S. psammophila , the mean SFV and FR were 1.3 and 1.6 times higher for dead branches than live branches. These contradictory results can be explained by differences in the canopy structures of the species, such as the surface area that contributes to the generation of SF in the upper canopy, branch angles, and the degree of branch bark decomposition and decay. In addition, the SF variables were significantly temporally dependent on the rainfall characteristics. SFV, SFI, and SFD were positively and linearly correlated with the rainfall amount, intensity, and duration at the inter-event scale (P < 0.001). The influence of rainfall intensity on SF time lag to rain were not significant at the intra-event scale (P > 0.05). Considering the enrichment ratio due to canopy dry deposition and bark decomposition, the periodic flattening of dead S. psammophila branches to restore their growth is less significant than allowing them to die and renew naturally. These findings may serve as a reference for developing management practices for shrub species in areas with soil water and nutrient deficiencies. [ABSTRACT FROM AUTHOR]

• Rainfall amount, duration, and intensity significantly affect Ec from diurnal to seasonal timescales. • Rainfall timing has a considerable impact on the magnitude, timing, and intra-daily distribution of Ec. • The responses of Ec to rainfall vary among canopy phenology periods. • The driving mechanisms of biophysical variables to Ec are more complex in days without rainfall events. Rainfall patterns affect vegetation water use through canopy transpiration (Ec) and are becoming increasingly variable due to global warming. However, Ec and its controlling mechanisms in boreal forests have rarely been investigated under various rainfall patterns. In this study, we measured the sap flow using thermal dissipation method in a boreal larch forest (Larix gmelinii) and observed biophysical variables simultaneously (e.g., air temperature, solar radiation, relative humidity, vapor pressure deficit, wind speed, soil water content, rainfall, and leaf area index) over two consecutive growing seasons (May–September) in 2021 and 2022. The responses of Ec to rainfall patterns were evaluated among three rainfall categories (classified based on rainfall amount, duration, and intensity), five types of rainfall timing, and two phenology periods (i.e., leaf expansion and defoliation periods). The results showed that daytime Ec significantly decreased, while nighttime Ec was less variable with increasing rainfall amount, duration, and intensity. The responses of Ec to rainfall categories with different rainfall amount, duration, and intensity were inconsistent at the monthly scale. The magnitude and diurnal dynamics of Ec were greatly influenced by the rainfall timing, and these effects got more considerable with increasing rainfall amount, duration, and intensity. Surprisingly, Ec showed opposite responses to rainfall in leaf expansion and defoliation periods, suggesting an enhanced impact in the leaf expansion period while a diminished impact in the leaf defoliation period. The significantly affected biophysical factors of Ec varied dramatically among rainfall categories. The interactions between biophysical factors and Ec were more complex and diverse in days without rainfall events than days with rainfall days. These findings highlight the importance of rainfall patterns in affecting Ec, provide new insight into the interactions between rainfall patterns, Ec, and biophysical factors, and are important supports for improving evapotranspiration simulations in the context of climate change. [ABSTRACT FROM AUTHOR]

• Rainfall losses are a key input to event-based flood models widely used in practice. • Relationship between rainfall losses and antecedent soil moisture are quantified. • Climate change projections of soil moisture are used to project rainfall losses. • Both the magnitude and variance of future rainfall losses are projected to increase. • Climate change will affect rainfall losses and hence flood responses. Climate change is expected to impact the severity and frequency of floods, which has implications for flood risk management. Design floods and derived flood frequency curves obtained using event-based rainfall-runoff models are widely used in industry to assess flood risks for planning and design purposes. For these approaches it is necessary to have (a) rainfall inputs, and (b) rainfall losses specified, the latter representing the amount of rainfall that is either intercepted, stored on the surface, or infiltrated into the soil and does not contribute to the flood hydrograph. There is extensive research on changes in flooding under climate change that focus on projections of rainfall. However, there is little research into projections of rainfall losses under climate change, despite the knowledge that their changes will modulate the flood response. Here, we present one of the first studies seeking to quantify how rainfall losses, as represented by estimates of initial and continuing losses used in event-based models, are projected to change under climate change. We identify dependencies between rainfall losses and antecedent soil moisture in around half (over 200) of the largely unregulated catchments (i.e. watersheds) in Australia analysed in this study and use these relationships to project rainfall losses under climate change. Near universal increases in both the mean and variance of both initial losses and continuing losses are projected in these catchments, suggesting that increased rainfall losses could offset the impact of increased rainfalls for frequently occurring floods and result in an increased variance in flood responses. [ABSTRACT FROM AUTHOR]

• A novel algorithm named SM2RAIN-BayesOpt is proposed for estimating rainfall by integrating the SM2RAIN algorithm and Bayesian optimization. • The performance of the SM2RAIN-BayesOpt algorithm is evaluated against rain gauge data in three typical areas. • The SM2RAIN-BayesOpt rainfall estimates is compared with IMERG-V06B, and ERA5 reanalysis rainfall products. • Effects of soil freezing on SM2RAIN-BayesOpt rainfall estimation are investigated. • Effects of land cover on calibration parameters of SM2RAIN-BayesOpt are inspected. The rainfall product derived from the SM2RAIN (Soil Moisture to Rain) algorithm has been widely used. However, there is still a large uncertainty partly due to the soil moisture input and parameters estimation of the SM2RAIN algorithm, which limits the application of the model in alpine regions. Here, the SM2RAIN-BayesOpt algorithm was developed by integrating the SM2RAIN algorithm and Bayesian optimization to improve the estimation of parameters (Z, a, b, T base , T pot), subsequently incorporating SMAP Level-3 soil moisture products for rainfall estimation. The performance of the SM2RAIN-BayesOpt algorithm was evaluated based on observed rainfall data under different environmental conditions in three typical alpine regions, namely Tibetan Plateau, Heihe River Basin, and Shandian River Basin. Moreover, SM2RAIN-BayesOpt, IMERG-V06B, and ERA5 reanalysis rainfall estimates were also compared with in-situ rainfall observations. The results showed that the proposed SM2RAIN-BayesOpt algorithm can obtain more accurate rainfall estimates in all studied areas in terms of different evaluation metrics. It was also found that our proposed SM2RAIN-BayesOpt algorithm performs better in alpine meadows and grassland than in desert and forestland. SM2RAIN-BayesOpt algorithm can considerably improve the accuracy of rainfall estimation, and it is of significant value for rainfall monitoring in alpine regions where observational data are scarce. [ABSTRACT FROM AUTHOR]

• Extreme rainfall events that lead to floods in large rivers are expected to decrease. • Antecedent soil moisture is expected to be reduced in most of South America. • Soil moisture seems more impactful than precipitation regarding ordinary floods. A warmer atmosphere is able to hold more water which consequently intensifies the hydrological cycle. The projected increase in extreme precipitation has been associated with greater floods; however, most recent studies have argued that the reduced soil moisture could be causing the opposite effect. We aim to understand how the hydrometeorological variables affect flood discharge and what the projections are for South America, a vulnerable continent that has been barely studied regarding flood trends. We used climate data from Eta simulations nested in 4 global climate models (BESM, CanESM2, HadGEM2-ES, MIROC5) as input for the MGB-SA hydrological model to yield flood discharge estimates. Then we were able to project the climate impacts on extreme precipitation, antecedent soil moisture, and flood discharge for large rivers (>1,000 km2) and understand how these variables are related. Our results showed a strong sign that antecedent soil moisture is expected to be reduced in most of the continent except in Southeastern South America (SESA). On the other hand, there are mixed signs for rarer precipitation and a clear spatial pattern for 2-year precipitations (RP2), which is expected to increase on the SESA and west Amazon and decrease on Central South America (CSA). For basins>100,000 km2, results indicate a negative change sign for 2-year precipitations, meaning that rainfall events that generate ordinary floods in large South American rivers are expected to decrease in the XXI century due to climate change. The change signs for flood discharge and extreme precipitation are spatially similar but more basins show a decrease for flooding than for rainfall. While only half of the South American basins are expected to present reduced 2-year precipitations, nearly 70 % of the rivers present a negative sign for 2-year floods, which can be attributed to the reduced antecedent soil moisture. [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel regionalization-based sediment-response similarity (SRS) method was proposed. • The physical similarity and SRS methods were compared using the SOM and SWAT model. • Results showed that SRS was superior to the conventional method. • The SRS method showed the potential to estimate catchment-wide sediment load. The development and application of reliable hydrological models are challenging in ungauged or poorly gauged catchments. Although several regionalization methods have been developed for predicting hydrological variables in ungauged catchments, a large uncertainty usually exists in the prediction because previous methods typically depended on several static catchment attributes or long-term mean climatic descriptors, thereby neglecting the spatiotemporal variability of the climate. In this study, we propose a new regionalization method called sediment-response similarity (SRS) using the soil and water assessment tool model and self-organizing maps to estimate sediment load in ungauged catchments, considering spatiotemporal variability and the sediment load relation to rainfall characteristics of individual catchments. The performance of SRS was evaluated by comparing it against the conventional regionalization method (i.e., physical similarity) and calibration model results, in four gauged catchments of the lower Mekong River basin. The SRS method showed improved performance over the conventional approach in estimating sediment loads, with an average Nash-Sutcliffe efficiency (NSE) and coefficient of determination (R2) of 0.75 and 0.76, respectively, which were close to the respective values of NSE and R2 = 0.77, of the calibration model. Additionally, the SRS approach showed an error reduction of up to 7 %, compared with that of the physical similarity method, indicating the potential of the proposed regionalization method in estimating catchment-wide sediment load in ungauged catchments. The findings indicate that the SRS method ideally determines the similarity of sediment loads between gauged and ungauged catchments. The SRS approach effectively tackled the main challenge of selecting catchment attributes in the conventional regionalization method and minimizing the uncertainty of sediment prediction in ungauged catchments. This novel SRS method is a promising method for estimating, not only sediment load, but also other hydrological variables and rainfall-driven phenomena in the ungauged catchments globally. [ABSTRACT FROM AUTHOR]

• Meadow tends to retain infiltration at the surface soil due to mattic epipedon. • Hillslope sites affect infiltration partitioning by influencing soil properties. • The quantitative indices improve our understanding of infiltration partitioning. Infiltration process controls how rainfall is stored in soil and subsequently used by plants, thus, plays a critical role in eco-hydrological processes. However, the vertical pattern of infiltration partitioning within a soil profile and the factors that control it remain poorly understood, especially in data-scarce mountainous areas. Based on a large-scale long-term soil moisture (SM) network in the Qilian Mountains, Northwest China, we explored infiltration partitioning and its control factors using a new metric, percentage of soil water storage increment in each layer to the soil profile during a rainfall infiltration event (PSTI). We found that both vegetations with deep roots and wet SM conditions facilitated infiltration into the deeper soil, although meadow favored infiltration within the surface layer. Hillslope stations showed a higher degree of infiltration partitioning in subsurface layers than the flatland stations due to the influence of topography on soil properties. Meanwhile, the increasing slope gradient will reduce the partition of infiltration at the subsurface layers. Among the soil properties, sand content, soil organic carbon, α (parameter of soil retention curve), and soil saturated hydraulic conductivity promoted infiltration into greater depth, while clay content and bulk density had the opposite effect. The major factors controlling the pattern of infiltration partitioning were n (parameter of soil retention curve) for 0–10 cm depth, α for 10–20 cm and 20–30 cm depths, and SM for PSTI below 30 cm. This quantitative analysis method for the infiltration partitioning improves our understanding of infiltration regimes, and supports water resources management in dryland mountains. [ABSTRACT FROM AUTHOR]

• Root water uptake of winter wheat across distinct rainfall years was quantified by using stable isotope. • Precipitation years led to significant differences in the isotopic signatures. • The appropriate irrigation wetting depth should be set as<60 cm for all kinds of rainfall years. Quantifying water availability in different soil layers to crop root water uptake (RWU) in different rainfall years helps deliver strategies to combat water scarcity and improve water productivity. To investigate the responses of RWU to soil water availability across distinct rainfall years, a field experiment of winter wheat was conducted in 2017–2018 (wet year), 2018–2019 (dry year), and 2019–2020 (ordinary year) in the North China Plain. Stable isotopes (δ18O and δD) in precipitation, irrigation water, xylem water, and soil water, as well as soil water content (SWC), were investigated during the three wheat growth seasons and were used to determine the soil water evaporation line (SWEL: the linear relationship between δD and δ18O in soil water), the line-conditioned excess (lc-excess: the deviation between δD and the local meteoric water line in the dual-isotope space) and the proportion of RWU from different soil layers with the MixSIAR model. The results indicated that there were significant differences in δ18O and δD in soil water and xylem water across different rainfall years. The slopes of SWEL and lc-excess of the 0–100 cm soil layer in the dry year were significantly smaller than those in the ordinary and wet years, which suggests that more substantial soil evaporation occurred in the dry year. The MixSIAR results revealed that the three-year wheat RWU exhibited different patterns, which were related to soil water availability. Wheat mainly utilized soil water from the 0–20 cm and 20–60 cm layers during the greening to filling stage under the dry year, while it mainly utilized the 0–20 cm soil layer under the wet and ordinary years. However, at the harvest stage, wheat under all precipitation years could transfer its main RWU zone to the 20–60 cm soil layer. There were significantly different proportions of RWU from 0 to 60 cm under different rainfall years, shown as 75.3 %, 67.5 %, and 69.5 % in the wet year, dry year, and ordinary year, respectively. These findings indicated that the appropriate irrigation wetting depth should be set as<60 cm for all kinds of rainfall years. [ABSTRACT FROM AUTHOR]

• Reduced natural rainfall led to a downgrade of soil moisture and groundwater. • Soil water movements were affected by land use and rainfall events. • Shallow groundwater variations depicted ecological water utilization in check dams. • Processes from rainfall to shallow groundwater were constructed in check dams. Stable regional water resource distribution is essential for ecosystems, yet soil water dynamics and groundwater evolutions of check dams remain many uncertainties under natural rainfall reduction in semi-arid areas. To explore these changeable hydrologic processes, continuous fixed-point observation and water balance models were introduced in nearly-four consecutive hydrological years (2018–2021). The results showed that soil water storage presented a downward fluctuation in each soil layer of all land uses. Moreover, the grassland profile moisture descended slowly and had deeper infiltration depths than others, which might be an ideal land-use type for the reservoir's sustainable utilization in check dams. The results also indicated that surface soil moisture (<50 cm) could rapidly respond to a single rainfall event > 10 mm d−1, whereas deep soil moisture (>50 cm) and shallow groundwater fluctuations were mainly affected by annual cumulative precipitation. Furthermore, a conceptual hydrological model of rainwater falling to the ground, percolating through soil profiles, and reaching groundwater through different vegetation covers in check dams has been proposed. Soil infiltration rates varied under different land use types and depths, ranging from 0.82 to 3.47 cm h−1. The groundwater recharge time from accumulated annual rainfall was 69–250 days, whereas the discharge time due to vegetation water consumption and groundwater lateral movement was 66–774 days. With a decrease in annual rainfall, the recharge time gradually increased, whereas the discharge time gradually decreased. These results inaugurate new perspectives for a deeper understanding of ecohydrological equilibrium in check dams and provide theoretical insights into ecological restoration in semi-arid areas. [ABSTRACT FROM AUTHOR]

• Developed computationally efficient three new variants of the CN model. • Introduced flexible initial abstraction to avoid sudden jumps in runoff estimation. • New weighted score-based statistical indicators for the models' evaluation. • The proposed models outperformed in almost all watersheds. The globally adopted and computationally efficient curve number (CN) model fills an active hydrological professional niche and has a well-documented history. However, it is structurally inconsistent and fails to reliably estimate runoff from rainfall. This is mainly due to the much-debated fixed initial abstraction (λ) and associated sudden jumps in runoff based on CN obtained from the documented tables. In this study, three new variants (M4, M5, M6) of the CN model are proposed that consider the hydrological imbalance between pre-storm soil moisture and initial abstraction after a rainfall event. A total of 1837 rainfall-runoff events were analyzed from 41 steep-slope watersheds in South Korea to test the robustness of the proposed CN models. The results were compared to the recently updated original CN model (M1) and two other recent variants (M2, M3) of this model. These models were evaluated using root mean squared error (RMSE), Nash-Sutcliffe efficiency (NSE), percent bias (PB), Kling-Gupta efficiency (KGE), a proposed overall weighted score, 1:1 line graph, and the FITEVAL tool. Using data from 41 watersheds, the lowest mean (median) RMSE of M5, M6, M4 [15.54(16.56), 15.84(11.45), 18.00(16.56)], PB for M4, M6, M5 [−1.28(−1.03), 1.29(0.03), −7.06(−7.27)]; the highest mean (median) NSE for M5, M6, M4 [0.86(0.87), 0.85(0.87), 0.81(0.83)], KGE for M6, M5, M4 [0.81(0.84), 0.79(0.81), 0.79(0.81)] and other graphical assessments show a better agreement between the observed and the runoff estimated by the proposed models. The corresponding mean (median) of RMSE, PB, NSE, and KGE statistics for M2 [19.26(17.69), 9.50(10.34), 0.78(0.80), 0.77(0.79)], and M3 [19.99(18.47), 05.56(7.17), 0.76(0.79), 0.77(0.79)] models show comparatively inferior results. Based on the same statistics, the M1 [24.60(22.48), 33.62(32.77), 0.63(0.68), 0.60(0.64)] model yields unrealistic results. It is inferred that both λ and CN should be kept flexible for a systematic and region-specific revision of the CN model to improve runoff estimation. In addition, a structurally consistent model with a stable soil moisture accounting (SMA) procedure is vital to get more reliable runoff estimates without compromising the simplicity and applicability to ungauged watersheds. [ABSTRACT FROM AUTHOR]

• Global temperature change vs moisture deficit studied for number of hot days (NHD). • The area where deficient moisture impacts NHD more than rising temperature is larger. • Temperature-sensitive regions are located in mountain ranges, plateaus, and deserts. • Soil moisture deficit-sensitive regions have strong land–atmosphere coupling. • Wavelet decomposition improves GRACE TWS for examining moisture-temperature coupling. This study compares the relative importance of global temperature change and local moisture deficit in influencing the frequency of hot extremes on a global scale. For the first time, the wavelet decomposed GRACE terrestrial water storage is applied in examining the relationship between soil moisture (θ) and the number of hot days in the hottest month (NHD). It reveals stronger θ-NHD relationships over larger areas than the selected commonly used soil moisture proxies (i.e., standardised precipitation index and a GLDAS model-derived product). During 1985–2015, local moisture deficit played a more important role in influencing hot extreme occurrences in the regions with relatively flat topography, thick soil and where inter-annual rainfall variability is large. They cover an area 1.6 times larger than the areas (e.g., mountain ranges, deserts) where the global temperature change has posed a stronger influence. Under a continuing increase of greenhouse gas forcing, global actions in reducing emissions will support combating the expansion of hot extremes. However, this study shows that necessary attention should also be directed to mitigating the moisture deficit exacerbating hot extremes, e. g., through regional adaptive land management. [ABSTRACT FROM AUTHOR]

• Hydrologic functions of vegetation in karst regions still need to be enhanced. • Thinning alone increased SSWC and released water limitation on woody species. • We provided a testable path for enhancing hydrologic functions of karst vegetation. The use of plantations and natural restoration enhance vegetation coverage and contribute significantly to the hydrologic functions of degraded terrestrial ecosystems. Researchers have investigated the development and use of plantations, however, few studies looked at naturally-restored vegetation, especially for early successional areas. We applied a series of treatments and their impacts on water status and the dominant woody species were monitored. Our goal is to enhance the hydrologic functions of naturally-restored karst vegetation that stagnated as early successional shrubby-grassland. Our results show that vegetation on both the upper and lower slopes intercepted approximately 50 % of the rainfall because of the dense community structure. With more than 60 % of the community-level root biomass concentrated in the upper 20 cm soil depth, shallow soil water content (SSWC) decreased continuously even when there was scattered light rain events. On the lower slopes, competition-reducing oriented removal of non-woody species significantly reduced canopy interception of water, which contributed to the increase of SSWC and water availability for the remaining dominant woody species. Moreover, dominant woody species in the treated plot exhibited more negative leaf carbon isotope values than the same species in the control plot, indicating released water limitation on plant growth because of the treatment. On the contrary, this treatment exhibited negligible effects on SSWC on the upper slope, environmental water status could only be improved by improving soil water storage capacity. Our results highlight strong competition on SSWC for naturally-restored vegetation on karst hillslopes that stagnated during the early successional stages. In lower slope position where soil is relatively deep, competition-reducing oriented treatment alone can release water limitations on plant growth and provide opportunities for the settlement of species with different hydrologic niches, thus providing a path for enhancing hydrologic functions of naturally-restored karst vegetation. [ABSTRACT FROM AUTHOR]

• The PF contributed to the total infiltration was >50% • The control mechanisms of temporal and small-scale spatial characteristics on different types of preferential flow processes were clarified. • Environmental characteristics differed between two types of preferential flow. • The two types of preferential flow affected soil moisture parameters differently. The control mechanisms of temporal and small-scale spatial characteristics on different types of preferential flow (PF) processes during infiltration are still lacking. Two-year high-resolution in situ soil moisture profiles were used to investigate the controls of PF processes and its interaction with moisture and vegetation dynamics, which covers three hillslope positions (upslope–14˚, midslope–9˚, and downslope–4˚) and three vegetation treatments (bare ground, stubble grassland and natural grassland) in a typical semi-arid grassland of Eurasia. Two types of PF were identified. Non-sequential response (NSR) was taken as one indication of PF using the disordered response of soil moisture profiles to rainfall. Fast preferential flow in sequence response (PF-rate) was taken as another indicator when a measured maximum pore water rate higher than saturated hydraulic conductivity. The conditions for the occurrence of the two PFs were inconsistent and different from the studies which showed the higher probability of PFs under dryer conditions and with higher rainfall intensities. Small rainfall events with low rainfall intensity and relatively low soil moisture at most locations showed higher NSR frequency, which further highlight the strong dependence of NSR on hydrophobicity and macropore structure of the topsoil. Whereas a higher rainfall intensity increased PF-rate under wet conditions. Meanwhile, the occurrence of PF-rate was also significantly controlled by vegetation coverage, soil texture and saturated hydraulic conductivity of 5–10 cm soil layer. Furthermore, the frequency of PF was 15.9%–44.5%, and its contribution to the total infiltration was >50%. The water recession rate of NSR and PF was slower and faster than that of matrix flow, respectively. This demonstrates the importance of PF to water replenishment in scarce rain areas and the influence of combinations of spatial–temporal factors on different flow processes. [ABSTRACT FROM AUTHOR]

[Display omitted] • SM2RAIN-NWF is developed through substituting the net water flux model. • Performance of SM2RAIN-NWF algorithm was compared against SM2RAIN. • Results showed the better performance of SM2RAIN-NWF algorithm compared to SM2RAIN. • The quality of soil moisture data impact on the performance of the algorithm. Sparse distribution of rain gauge networks challenges the estimation of rainfall variability over space and time. The SM2RAIN algorithm was developed to estimate rainfall from the knowledge of soil moisture (SM) by inverting the soil‐water balance equation. The algorithm was simplified by neglecting the contribution of evapotranspiration and surface runoff rate during the rainfall event. A recent study developed an analytical model to estimate the net water flux (NWF) from SM data via inversion of analytical Warrick's equation. In this study, the SM2RAIN-NWF algorithm was developed by integrating the SM2RAIN algorithm and the NWF model to improve the accuracy of rainfall estimation. The applicability of the SM2RAIN-NWF algorithm was evaluated based on observed rainfall data in the Lake Urmia basin, Iran. Satellite SM data was obtained from the Advanced Microwave Scanning Radiometer 2 (AMSR2). The algorithm calibrated based on the data from July 3, 2012, to December 31, 2017, was then used to estimate rainfall for two years extending from January 2018 to December 2019. Estimated rainfall through SM2RAIN-NWF algorithm improved compared to SM2RAIN by 14% and 37.4% increase in the average values of correlation coefficient (R) and Nash–Sutcliffe (NS), and 11.5% decrease in the Percentage Root Mean Square Error (PRMSE) over the calibration period. Validating the estimated rainfall showed a considerable improvement in the performance of the SM2RAIN-NWF algorithm compared to the SM2RAIN algorithm by 8.6% and 30.4% increase in the average values of R and NS, and 13.4% decrease in the PRMSE. It was also found that the SM2RAIN-NWF algorithm contributes to the improvement of error indices in rainfall estimation and simulates the rainfall variation trend in a better fashion than the SM2RAIN algorithm. [ABSTRACT FROM AUTHOR]

• New procedure for estimating SM2RAIN parameters values without a reference. • The new self-calibrated SM2RAIN-ASCAT product is applied on a global scale. • Comparison with previous version and other satellite products provides good performance. Rainfall monitoring is fundamental in many hydrological applications such as flood and landslide forecasting and water resources management. In-situ measurements are the traditional data source of rainfall, but the worldwide declining number of stations, their low spatial representativeness and the data access problem limit their use. Satellite products are being widely used as an alternative data source. Among them, SM2RAIN-based products, which exploit the inversion of the water balance equation to derive rainfall from soil moisture observations, have shown relatively good skills for hydrological applications. However, the need of calibrating the SM2RAIN parameter values against a reference represents one important limitation, particularly over data scarce regions. In this study, we explore the possibility to self-calibrate SM2RAIN and thus to obtain rainfall estimates from the Advanced SCATterometer (ASCAT) soil moisture independently from any reference rainfall dataset. Four parametric relationships relating SM2RAIN parameter values to static descriptors (average rainfall, topography, soil moisture noise) are developed. To develop such relationships, a sample of 1009 points uniformly distributed over the areas covered by rain gauges in Australia, India, Italy and the United States is selected. A global validation of the methodology is conducted by comparing the performances of the parameterized product with the classical product in which the parameter values are estimated by calibration against a reference rainfall dataset. The Final Run of the Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) precipitation dataset is used for performance assessment, together with the triple collocation techniques by using the Gauge-based Global Precipitation Climatology Center (GPCC) product and the Late Run of IMERG. The aim of the analysis is to obtain an uncalibrated SM2RAIN methodology to retrieve rainfall whose performance are similar to those obtained with calibration. The results at 1009 points show that the performances of the parameterized SM2RAIN product are in line with those of the calibrated one, with an increased capability in the detection of intense rainfall events and an acceptable reduction of the performance according to both Pearson Correlation and Root Mean Square Error indexes. The application of triple collocation confirms these findings on a global scale, showing that the SM2RAIN product outperforms both GPCC and IMERG –– Late run estimations in areas characterized by low density of rain gauges and good quality of ASCAT soil moisture retrievals (i.e., Africa and South America). [ABSTRACT FROM AUTHOR]