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Blackgram, a protein-rich pulse crop (24%), is crucial for combating food insecurity, particularly in malnourished and economically weak countries. Enhancing blackgram production requires improved, input-saving management practices. Given the challenges of climate change and population growth, efficient water management is vital for increasing pulse productivity and water use efficiency with minimal investment. This study aimed to identify cost-effective irrigation methods to optimise blackgram yields. Experiments were conducted at the National Pulses Research Centre in Vamban, Pudukkottai, and the Agricultural College and Research Institute in Kumulur, Tiruchirappalli, during the kharif season of 2021 and 2022. The study compared different treatments of irrigation methods, such as check basin, raised bed, drip, sprinkler and rain hose irrigation. Results showed that the rain hose system maintained the highest soil moisture (23.93% at 10 cm depth and 19.71% at 20 cm depth). Even though drip irrigation resulted in a higher seed yield (1363 kg ha−1), the rain hose system proved to be more cost-effective, saving 27.09% in costs and achieving a 15.23% higher benefit–cost ratio. These findings suggest that the rain hose method, combined with current agronomic practices, is a viable low-cost technique for sustainable blackgram cultivation, optimising water use and maximising profits. This research provides valuable insights into water-saving irrigation methods for pulse crops. [ABSTRACT FROM AUTHOR]

Human-generated aerosol pollution gradually modifies the atmospheric chemical and physical attributes, resulting in significant changes in weather patterns and detrimental effects on agricultural yields. The current study assesses the loss in agricultural productivity due to weather and anthropogenic aerosol variations for rice and maize crops through the analysis of time series data of India spanning from 1998 to 2019. The average values of meteorological variables like maximum temperature (TMAX), minimum temperature (TMIN), rainfall, and relative humidity, as well as aerosol optical depth (AOD), have also shown an increasing tendency, while the average values of soil moisture and fraction of absorbed photosynthetically active radiation (FAPAR) have followed a decreasing trend over that period. This study's primary finding is that unusual variations in weather variables like maximum and minimum temperature, rainfall, relative humidity, soil moisture, and FAPAR resulted in a reduction in rice and maize yield of approximately (2.55%, 2.92%, 2.778%, 4.84%, 2.90%, and 2.82%) and (5.12%, 6.57%, 6.93%, 6.54%, 4.97%, and 5.84%), respectively. However, the increase in aerosol pollution is also responsible for the reduction of rice and maize yield by 7.9% and 8.8%, respectively. In summary, the study presents definitive proof of the detrimental effect of weather, FAPAR, and AOD variability on the yield of rice and maize in India during the study period. Meanwhile, a time series analysis of rice and maize yields revealed an increasing trend, with rates of 0.888 million tons/year and 0.561 million tons/year, respectively, due to the adoption of increasingly advanced agricultural techniques, the best fertilizer and irrigation, climate-resilient varieties, and other factors. Looking ahead, the ongoing challenge is to devise effective long-term strategies to combat air pollution caused by aerosols and to address its adverse effects on agricultural production and food security. [ABSTRACT FROM AUTHOR]

Unsaturated soils are the major component of both natural and artificial earth structures, and they exhibit less predictable behavior than saturated soils. Presently, the world is experiencing pronounced impacts from climate change. Sudden heavy rainfalls and droughts both result in fluctuating soil moisture levels, meaning that soil responds differently to infrastructure loads. Accordingly, a better understanding is needed of failure causes in unsaturated soils. This research was conducted to investigate the coupled behavior of unsaturated soil with varying degrees of compaction and moisture change. Samples were prepared at 81% and 88% degrees of compaction with moisture content ranging from 10 to 25% on both sides of optimum moisture content for silty soil. Different parameters such as suction, porewater pressure, and deformation were carefully measured at every stage of the triaxial test, from sample placing to the shearing phase. The results show that there is more compressive soil behavior when there is a lower degree of compaction, i.e., 81%, along with moisture content of 15–25%. Where there is a higher degree of compaction at 88%, dilative behavior is more evident, which changes to compressive behavior as the moisture content increases. Any change in moisture content (increase or decrease) during the lifecycle of any earthen structure necessitates a long time to stabilize its suction value (decrease or increase) if it compacted to a degree that still exhibits a compressive response. This slow recovery of suction, specifically from the wetting to the drying cycle, is not favorable for soil stability upon loading. [ABSTRACT FROM AUTHOR]

This study uses Multifractal Detrended Fluctuation Analysis to examine the scaling behavior and spatiotemporal variability of drought indicators. We employ two hybrid drought indicators—the GRACE Root Zone Soil Moisture Percentile (SMI) and Evaporative Demand Drought Index (EDDI)—to elucidate agricultural drought characteristics, and we quantify meteorological drought using the Standardized Precipitation Index (SPI). Our investigation was carried out in the United States in the California and Mississippi watersheds, each characterized by distinct agricultural climate conditions and planting systems. The changes in drought indicators exhibit multifractality primarily derived from the long-term memory embedded within the time series. Spatially, the increment sequence of SPI displays pronounced dependence in almost all areas. Specifically, the changes in SPI demonstrate persistence, as the Hurst exponents changed from 0.53 to 0.58 in California, revealing the possibility of a degree of trend continuation (albeit, not very strong) in the future. A regular summary cannot be obtained for Mississippi, since the Hurst exponent ranges from 0.46 to 0.51 for the increment sequence of SPI in that watershed. However, the increment sequences for the agricultural drought indicators exhibit obvious short-term persistence (anti-persistence), indicating that future drought trends may be discontinuous. The variations in the strength of anti-persistence among agricultural drought indicators can be attributed to geographical differences, diverse irrigation strategies, and varying cropping systems. No significant evolution pattern is evident for the scaling structure of drought processes due to minor variations in external controlling factors, such as rainfall and temperature, within the drought process. [ABSTRACT FROM AUTHOR]

Drought stress poses a significant risk to soybean production, as it relies on optimum rainfall under rainfed conditions. Exposure to brief dry periods during early vegetative growth impacts soybean growth and development. Choosing a genotype that can withstand stress with minimal impact on physiology and growth might help sustain biomass or yields under low rainfall conditions. Therefore, this study characterized 64 soybean genotypes for traits associated with drought tolerance during the early vegetative stage under two soil moisture treatments, 100% evapotranspiration (well-watered) and 50% evapotranspiration (drought), using the Soil–Plant–Atmosphere Research (SPAR) units. Eighteen morpho-physiological traits responses were assessed, and their relationship with the early vegetative drought tolerance was investigated. Drought stress significantly increased root weight, root volume, and root-to-shoot ratio but reduced shoot weight. Drought-stressed plants increased the canopy temperature by 3.1 °C. Shoot weight positively correlated with root surface area (r = 0.52, P < 0.001) and root weight (r = 0.65, P < 0.001). There was a strong negative correlation between shoot weight and root-to-shoot ratio (P < 0.01). Further, the combined drought response index was strongly associated with the root response index and weakly with the physiological response index. These findings suggest that genotypes (S55-Q3 and R2C4775) with high above-ground biomass with a balanced root-to-shoot ratio improves drought tolerance during the early vegetative. These genotypes could serve as valuable genetic resources to dissect the molecular networks underlying drought tolerance and ultimately be used in breeding programs to improve root ability at the early vegetative stage. [ABSTRACT FROM AUTHOR]

Most regional landslide susceptibility models do not consider the evolving soil hydrological conditions leading up to a multiple occurrence regional landslide event. This results in inaccurate predictions due to the non-linear behaviour of the terrain. To address this, we have developed a simple and efficient model that incorporates the mid-term evolution of soil hydrological conditions. The model combines a water balance model and a geotechnical model based on infinite slope theory. The analysis of 561 high-intensity rainfall events in a typhoon-prone region of the Philippines revealed that the percolation of water during the 5-month wet season is crucial in determining landslide susceptibility. Consequently, high-intensity rainfall events at the start of the wet season are less likely to trigger landslides, while later events are more hazardous. We analysed the change in landslide susceptibility during the 2018 rainy season by comparing the probability of failure (PoF) before and after three high-intensity rainfall events (July, August and September). Only the event in September caused a significant increase in the probability of failure (PoF). The model showed an accuracy of 0.63, with stable cells better represented than unstable cells. The antecedent hydrological conditions on the lower soil layers are responsible for changes in landslide susceptibility. Our findings support the hypothesis that new approaches to developing hydro-meteorological thresholds for landslide early warning systems should be evaluated, especially in regions with strong seasonality. [ABSTRACT FROM AUTHOR]

In recent decades, the Himalayas have seen increasing extreme precipitation events. Climate change has impacted the occurrence and distribution of extreme precipitation events across the Himalayas. Patterns of both western disturbances and the Indian summer monsoon are undergoing significant changes in nature due to climate change. However, the magnitude and intensity of flood in a stream are not always linearly dependent on the amount of precipitation. Other factors, such as topography, land use, soil characteristics, and antecedent hydrological conditions, play a pivotal role in modulating the response of a watershed to an extreme precipitation event. On July 07–11, 2023, several districts of Himachal Pradesh faced devastating floods resulting in loss of life, infrastructure, and environmental damage with significant economic consequences. Developing a resilient solution for managing such events and reducing damage requires an in-depth understanding of multiple causative factors of such extreme events. In this paper, we analyzed the meteorological and hydrological factors that caused the flooding situation in Himachal Pradesh during July 2023. Hydrometeorological data from several observation stations were analyzed along with reanalysis data from ERA5, SMAP-L4, and FLDAS-NOAH to understand the causative factors that lead to peak floods. The compounding of extremely heavy rainfall with near-saturation antecedent moisture content and snowmelt was found to be the leading factor in inflating and sustaining the flood peak. [ABSTRACT FROM AUTHOR]

Flash flood early warning is a very effective way to reduce casualties induced by rainstorm flash flood in mountainous area. The forecasting of flash flooding remains challenging because of the short response time and inaccurate warning threshold. So far, the flash flood disaster defenses often adopt the critical rainfall amounts inducing the peak discharge or water level to establish an early warning threshold in China. However, the runoff peak discharge depends on rainfall patterns including rainfall intensity and accumulation, result in the critical rainfall threshold has a significant uncertainty. To reduce this uncertainty, herein we present a dual-threshold method for flash flood early warning with consideration of rainfall patterns based on above two-rainfall metrics. Moreover, applying this new method in the flash flood disasters occurred in the Zhongdu river basin, Sichuan province of China to evaluate the early warning reliability. Firstly, five most likely rainfall patterns of this basin were determined according to the timing of rain peak in historical rainfall events, and then, we determined the critical rainfall thresholds under different rainfall patterns and soil moisture conditions. The result showed that the rainfall thresholds uncertainty caused by rainfall pattern is more pronounced than soil moisture. Next, using the cumulative rainfall depth and maximum rainfall intensity corresponding to disaster discharge in different flood processes to establish the dual-thresholds. We found the dual-threshold method comprehensively considers the impacts of soil moisture, rainfall temporal distribution and flood rising property, which can achieve early warning for the four protected objects along the Zhongdu River, with an average lead duration of 46.2 min. Compared with the other three single-threshold methods, the critical rainfall and the critical rainstorm curve methods frequently created false or missing warnings, making it difficult to achieve the effect of early warning. Although reliability of flood water level rising rate method is high, the lead time is relatively short and only lasts for a few minutes in some cases. As a result, the new proposed dual-threshold method, accounting for both the reliability and long lead time, can be a potential candidate for the flash flood disaster early warning. [ABSTRACT FROM AUTHOR]

Rainwater is the main water source in arid and semiarid areas of the Loess Plateau, where rainfall is generally insufficient, ineffective and underutilized during the growing season. Thus, improving rainwater utilization efficiency is essential for sustainable agricultural development. A new system composed of rainwater harvesting, an infiltrator bucket with multiple holes and mulching (RHM), was designed to maintain soil moisture at a proper level in rain-fed orchards in arid and semiarid areas of the Loess Region of China. However, there is a lack of clarity on the effectiveness of RHM. In this study, changes in the soil environment and the growth and physiology of apricot trees were monitored via two treatments: (1) Rain-harvesting irrigation system (RHM) treatment and (2) traditional orchard treatment (CK) as a baseline. The results showed that (1) RHM could effectively improve soil water storage at depths of 0–45 cm and at a horizontal distance of 40 cm from the trunk. For the 1.4 mm light rain event, the soil water content increased by 6.3–12%, and for the two moderate rains, the soil water content increased by 12–25%. The change in the soil relative water content predicted by the LSTM model is consistent with the overall trend of the measured value and gradually decreases, and the prediction accuracy is high, with an error of 0.65. (2) The average soil temperatures at 5 cm, 20 cm and 40 cm under RHM were 17.0% (2.4 °C), 13.6% (1.9 °C) and 7.5% (1 °C) greater than those under CK, respectively. (3) Compared with the control treatment, RHM improved the growth and WUEL of apricot trees. The results highlighted the efficiency of the RHM system in enhancing the soil environment and regulating the growth and physiology of apricot trees, which has greater popularization value in arid and semiarid areas. [ABSTRACT FROM AUTHOR]

Understanding the unstable evolution of railway slopes is the premise for preventing slope failure and ensuring the safe operation of trains. However, as two major factors affecting the stability of railway slopes, few scholars have explored the unstable evolution of railway slopes under the joint action of rainfall-vibration. Based on the model test of sandy soil slope, the unstable evolution process of slope under locomotive vibration, rainfall, and rainfall-vibration joint action conditions was simulated in this paper. By comparing and analyzing the variation trends of soil pressure and water content of slope under these conditions, the change laws of pore pressure under the influence of vibration and rainfall were explored. The main control factors affecting the stability of slope structure under the joint action conditions were further defined. Combined with the slope failure phenomena under these three conditions, the causes of slope instability resulting from each leading factor were clarified. Finally, according to the above conclusions, the unstable evolution of the slope under the rainfall-vibration joint action was determined. The test results show that the unstable evolution process of sandy soil slope, under the rainfall-vibration joint action, can be divided into: rainfall erosion cracking, vibration promotion penetrating, and slope instability sliding three stages. In the process of slope unstable evolution, rainfall and vibration play the roles of inducing and promoting slide respectively. In addition, the deep cracks, which are the premise for the formation of the sliding surface, and the violent irregular fluctuation of soil pressure, which reflects the near penetration of the sliding surface, constitute the instability characteristics of the railway slope together. This paper reveals the unstable evolution of sandy soil slopes under the joint action of rainfall-vibration, hoping to provide the theoretical basis for the early warning and prevention technology of railway slopes. [ABSTRACT FROM AUTHOR]

In Italy, rainfall represents the most common triggering factor for landslides; thus, many Italian Regional Departments of Civil Protection are setting up warning systems based on rainfall recordings. Common methods are mainly based on empirical relationships that provide the rainfall thresholds above which the occurrence of landslide phenomena is likely to be expected. In recent years, the use of machine learning approaches has gained popularity in landslide susceptibility analysis and prediction. To support the operational early warning system of Liguria Civil Protection Department for landslides hazard, we propose the implementation of a polynomial Kernel regularized least squares regression (KRLS) algorithm, for predicting the daily occurrence of shallow landslides in the five Alert Zones in Liguria (North Western Italy). The model provides an estimate of the number of landslides associated with the set of three different hydrological features, also used for the Hydrological Assessment procedure: the soil moisture, the accumulated precipitation over 12 h and the precipitation peak over 3 h. Results of the model are converted to an Alert Scenario of landslide occurrence, based on the magnitude of the expected event and identified according to the National and Regional legislation (Regional Civil Protection guidelines D.G.R. n. 1116, 23/12/2020). The performance of the predictive model (e.g. accuracy of 93%) is deemed satisfactory and the methodology is considered a valuable support to the operational early warning system of Liguria Civil Protection Department. The choice of predictive variables allows, in future development, the values obtained from historical data to be replaced by those obtained from meteorological forecast models, introducing the use of the developed model in the operational forecasting chain. [ABSTRACT FROM AUTHOR]

Precipitation and temperature are the two major drivers of species distribution on the earth. Change in precipitation has severe effects on the species composition of all ecosystems including grassland. In the present study, we have tried to assess the effect of precipitation on two major functional groups of tropical grassland i.e. graminoid (grasses and sedges) and forbs (herbaceous flowering plants). The study was performed in three rainout shelters with three different rain doses (16T, 11T, and 8T) and one unsheltered plot (open C) with ambient rain. Each sheltered and unsheltered plot has three 1 × 1 m randomly assigned subplots of uninvaded indigenous grassland plots (NIG). The study revealed that the aboveground net primary production (ANPP) of graminoids + forbs of the tropical grassland increase with the increase in precipitation. A significant positive correlation of ANPP was found with total inorganic – N (TIN) and soil moisture (SM) and a significant negative with microbial biomass nitrogen (MBN). Regression analysis reveals that after soil moisture, total inorganic – N and N – mineralization are the major determinants for the ANPP. However, when graminoid and forb species are studied separately, graminoids showed a positive response to increased precipitation while forbs did not show such a response. Indicating that the major contributor to the ANPP response toward precipitation increase is graminoid species in a dry tropical grassland. The study indicates the sensitivity of Indian grassland to the change in rainfall quantity, as studied forbs species decrease in both low and high precipitation. Showing that in tropics, forbs species may extinct as of their narrow range of tolerance due to precipiation change, in turn affecting the biodiversity of the area. This is the new possibility of research for researchers around the world. Moreover, to draw any conclusion a detailed study considering the nature of resource acquisition, root length, root architect, and competitive behavior among graminoids and forbs must be done separately, in relation to the precipitation. [ABSTRACT FROM AUTHOR]

Two physical models were used to simulate the infiltration and redistribution process of light crude oil after leakage in a heterogeneous soil layer following water level variation and rainfall. Migration fronts and redistribution characteristics of oil during gravity seepage, water level variation, and rainfall were obtained using charge-coupled device (CCD) camera shooting and cyan–magenta–yellow‒black (CMYK)-based gray analysis, which were employed efficiently and at a low cost. Then, the influencing factors and migration mechanisms were examined. Finally, the soil water and oil contents were measured to verify the simulation results. The results are as follows: (1) the geologic lens and fine–coarse interface can intercept oil, resulting in a local highly contaminated area. (2) The crude oil infiltration path and velocity varied greatly with the different soil types and initial water contents. Within a certain range, the higher the initial water content is, the higher the lateral and vertical infiltration speeds. (3) The oil redistribution process was dominated by vertical infiltration under the condition of water level variation or rainfall, but oil–water displacement and the capillary pressure caused some oil to move horizontally near the geologic lens and fine–coarse interface. (4) Water level variation resulted in a synchronous rise or fall of the oil accumulation area, but rainfall caused it to move up. (5) Water level variation and rainfall imposed a certain influence on the periodic accumulation and release of crude oil in heterogeneous soil, especially in the presence of geologic lenses and lithologic interfaces. [ABSTRACT FROM AUTHOR]

Extreme rainfall prior to a flood event is often a necessary condition for its occurrence; however, rainfall alone is not always an indicator of flood severity. Antecedent wetness condition of a catchment is another important factor which strongly influences the flood magnitudes. The key role of soil moisture in driving floods is widely recognized; however, antecedent conditions of deeper saturated zone may contribute to river floods. Here, we assess how closely the flood magnitudes are associated to extreme rainfall, soil moisture and baseflow in 70 catchments of Peninsular India for the period 1979–2018. Annual flood magnitudes have declined across most of the catchments. Effect of flow regulations is also assessed to understand the impact of human interventions on flood characteristics. Reservoir regulation has positive effect by reducing the flood peak and volume, whereas the duration of flood events has increased after the construction of dams. Baseflow exhibits similar patterns of trends as floods, whereas trends in rainfall and soil moisture extremes are weakly correlated with trends in flood magnitudes. Baseflow is found to be more strongly influencing the flood magnitudes than soil moisture at various time lags. Further analysis with event coincidence analysis confirms that baseflow has stronger triggering effect on river floods in Peninsular India. [ABSTRACT FROM AUTHOR]

Purpose: Understanding the characteristics of soil infiltration response to rainfall is critical for soil water transport processes and hydrologic modeling. However, little is known about how they vary with forest conversion at different temporal stages (< 20 years) below the rooting zone. Therefore, this study aims to investigate soil infiltration response patterns in different subtropical forest conversion hillslopes, including mature original forest (thick root), young secondary forest (middle root), and very young secondary grassland (fine root), and analyzed the effects of environmental factors on the dynamics of soil infiltration. Materials and methods: Several metrics were evaluated to characterize and quantify the nature of these responses by estimating changes in the soil water content, the response time difference between two adjacent soil depths, and infiltration velocities for 1144 infiltration events at 6 locations on the three hillslopes. Results and discussion: Soil infiltration responses were similar on both forestland hillslopes, yet significantly different from those on the grassland hillslope. The preferential flow was more evident in the profile of thick-rooted vegetation, and the velocity of the wetting front was faster in the profiles of middle- and fine-rooted vegetation. Topography and root characteristics interact to influence soil infiltration response at the hillslope scale. Conclusions: Conversion from thick-rooted forests into fine-rooted grasslands altered the rainfall-related soil infiltration dynamics below the rooting zone. In particular, the occurrence of preferential flow and infiltration rates varied, which helps enhance our understanding of ecohydrological processes in the context of changing land use and hydroclimatic conditions. [ABSTRACT FROM AUTHOR]

The purpose of this study is to experimentally estimate the criteria for shallow landslide occurrence using hydrological indicators such as matric suction and volumetric water content for representative soils with the different geological conditions in which landslides frequently occur in South Korea. To investigate the detection criteria for shallow landslides, a series of landslide model tests are conducted using weathered soils obtained from regions of granite, gneiss, and mudstone where landslides occur. A landslide model test device, which includes a rainfall simulator, a slope model flume, and a measurement system with sensors, is developed to simulate shallow landslides that generally occur on natural slopes during rainfall. Based on the results of the model test, an infinite slope stability analysis considering the suction stress of unsaturated soil is applied to analyze changes in the safety factor of the slopes according to rainfall. Using the domestic standard of slope design used in South Korea, landslide detection criteria based on the safety factor of slopes are recommended as 1.3 for attention-level alerts and 1.0 for warning-level alerts. The matric suction corresponding to the attention and warning levels is defined as the critical matric suction, and the volumetric water content corresponding to the critical matric suction on the soil‒water characteristic curve (SWCC) is defined as the critical volumetric water content. The proposed critical matric suctions and critical volumetric water contents can potentially be used as basic data to detect the time of shallow landslide occurrence and issue a landslide early warning. [ABSTRACT FROM AUTHOR]

Latent heat released during orographic precipitation over the Western Ghat (WG) Mountain drives the Indian Summer Monsoon. Instrumental data record a considerable spatiotemporal variation of rainfall amount along the mountain. Although Indian teak (Tectona grandis LF) samples from WG have been used earlier for reconstructing past monsoon rainfall, the effect of rainfall variation on the annual ring growth pattern along the mountain has not been extensively studied yet. To address this issue, tree-ring width data series from three locations—Dahanu (1763 -1985), Nagerhole (1703–2016), and Tekkedy (1785–2003) are presented in this study. The locations are situated along WG from north to south. Monthly mean rainfall data obtained from the nearest observatories show a decreasing (increasing) rainfall amount from north to south during summer monsoon (pre and post-monsoon). We obtain a significant positive correlation between ring width indices and monsoon rainfall and a negative correlation with summer (especially May) temperature suggesting a contrasting response of summer temperature and monsoon rain on teak growth. The correlation between rainfall and ring width varies from southern to northern WG (both in significance level and the number of months with significant correlation). While the southernmost location exhibits positive (significant) correlations for both pre and summer monsoon months, the correlations are found for only two summer monsoonal months in the northernmost location. This spatial correlation trend reflects the variation of the pre-monsoon to monsoon rainfall ratio observed in our study locations. This observation is further substantiated by soil moisture-ring width relationship. Furthermore, our study shows that ring width indices respond to a variation of western equatorial Pacific sea surface temperature and vapor pressure deficit. Our study, therefore, suggests that the Indian teak samples can be used for understanding regional and seasonal scale rainfall/soil moisture variation along the WG and teleconnection studies. [ABSTRACT FROM AUTHOR]

Soil moisture plays a vital role in slope stability. As water infiltrates into the soil, shear strength decreases eventually leading to failure. However, most of the existing regional-scale landslide early warning systems (LEWS) rely solely on rainfall information and use rainfall thresholds to determine if the landslide triggering conditions are met. The original version of the Catalonia region LEWS combines real-time rainfall observations and susceptibility to compute warnings. The LEWS applies a set of rainfall intensity-duration thresholds to determine if the rainfall conditions have the potential to trigger a landslide. This work explores the potential of using modelled soil moisture data in the Catalonia region LEWS. Volumetric water content (VWC) from the LISFLOOD hydrological simulations of the European Flood Awareness System and rainfall estimates have been analysed at the location of recent landslide events. Based on this data, a set of empirical hydrometeorological thresholds combining rainfall and soil moisture information has been obtained for their application into the Catalonia region LEWS. The LEWS has been run for nine months (April–December 2020) using two approaches: (i) combining susceptibility and rainfall intensity-duration (I-D) thresholds and (ii) combining susceptibility and the new hydrometeorological thresholds including soil moisture information. Generally, both LEWS approaches issued moderate or high warnings in the areas where significant rainfall accumulations were recorded. The outputs have been compared at specific locations where landslides were reported during the analysed period. Results show that at the analysed locations false positives are generally reduced when employing the hydrometeorological thresholds in the LEWS. Therefore, this approach is promising and could help improve regional scale LEWS in Catalonia. [ABSTRACT FROM AUTHOR]

This study examines potential changes in the summer rainy season of a watershed, using a cumulative rainy deficit curve to define the onset and demise of the rainy season. Historical daily rainfall data from 41 gauge stations within the Xiang River Basin in 1960–2017 were analyzed. Results indicate that the climatology of the onset date is Julian day 72 and the climatology of the demise date is Julian day 236. There is, however, a difference between the two segments, e.g., 1960–1989 and 1990–2017. The median rainy season duration in 1990–2017 is 138 days. This is larger than that of 1960–1989, which is 120 days. There is a statistically significant correlation between the onset date of the rainy season and the duration of the rainy season. Daily soil moisture estimates were derived from satellite remote sensing data. The averaged soil moisture during the rainy season is much higher than that of the non-rainy season. To examine the difference in quick flow between rainy and non-rainy season, baseflow separation was applied to daily streamflow time series. Result reveals the median of weekly quick flow in rainy season is 1316 cms, which is 5.7 times that of the weekly quick flow in non-rainy season. Different mechanisms that drive spring and summer rainfall, as well as potential use of the results in water management, are discussed. [ABSTRACT FROM AUTHOR]

Rainfall-induced landslides represent a major threat to human activities, and thus an improved understanding of their triggering mechanisms is needed. The paper reports some preliminary inferences on this topic, based on the data recorded over a 2-year period by a multi-parametric monitoring station located on one of the slopes of the Monterosso catchment (Cinque Terre, north-western Italy). This catchment has experienced multiple, concurrent shallow landslides after intense rainfall events. After defining a soil hydraulic model through data interpretation and numerical simulations, slope stability analyses were performed to elucidate several aspects related to shallow landslide occurrence. Both long-term climate conditions and single rainfall events were simulated via physically based approaches. The findings from these simulations enabled us to assume the pattern of infiltration and quantify the impact of soil hydraulic behavior on landslide triggering conditions. In this regard, various analyses were carried out on the same triggering event both at local scale and in the overall catchment, with a view to highlighting the role of initial soil moisture and soil hysteretic behavior in slope stability. [ABSTRACT FROM AUTHOR]

This study was performed in order to understand the effect of climatological variables on the malaria situation in the north-east region of India, which is prolonged by the disease. Time-series analysis of major climate parameters like rainfall, maximum temperature, minimum temperature, mean temperature, relative humidity, and soil moisture distributions is carried out, and their correlation with the malaria incidence is quantified state-wise, which is the unique part of the study. The correlation analysis reveals that malaria is significantly related with the maximum temperature and soil moisture in three out of eight states in NE India. To assess the climate variability, the inter-dependency between the meteorological parameters is obtained and the state wise correlation matrix for all states are reported. The analysis shows that maximum and mean temperature has highest positive correlation whereas minimum temperature and relative humidity has negative correlation. The climate-malaria relation is being carried out in the study region using the regression analysis and the results revealed that the regional climate has the most impact for the malaria incidence in the state of Arunachal Pradesh, Meghalaya, Tripura and Nagaland and in other states the impact is moderate. Analysis of variance modelling in the regions also indicates the degree of the fitment of both the data sets with the regression model and it is observed that the relation is also significant in the same 4 states. As a case study the impact of large scale oscillations like El Niño-Southern Oscillation on the malaria load is also assessed which can be a good indicator in the prediction of the climate and in turn the malaria incidences over the region. [ABSTRACT FROM AUTHOR]

Early-season agricultural drought is frequent over South Asian region due to delayed or deficient monsoon rainfall. These drought events often cause delay in sowing and can even result in crop failure. The present study focuses on monitoring early-season agricultural drought in a semi-arid region of India over 5-year period (2016–2020). It utilizes hydro-climatic and biophysical variables to develop a combined drought index (CDI), which integrates anomalies in soil moisture conditions, rainfall, and crop-sown area progression. Synthetic aperture radar (SAR)–based soil moisture index (SMI) represents in situ measured soil moisture with reasonable accuracy (r=0.68). Based on the highest F1-score, SAR backscatter in VH (vertical transmit-horizontal receive) polarization with specific values for parameter threshold (−18.63 dB) and slope threshold (−0.072) is selected to determine the start of season (SoS) with a validation accuracy of 73.53%. The CDI approach is used to monitor early-season agricultural drought and identified drought conditions during June–July in 2019 and during July in 2018. Conversely, 2020 experienced consistently wet conditions, while 2016 and 2017 had near-normal conditions. Overall, the study highlights the use of SAR data for early-season agricultural drought monitoring, which is mainly governed by soil moisture–driven crop-sowing progression. The proposed methodology holds potential for effective monitoring, management, and decision-making in early-season agricultural drought scenarios. [ABSTRACT FROM AUTHOR]

The Nepal Himalayas is one of the world's most active mountain belts and home to widespread natural hazards of various types, including landslides, which claim numerous lives and result in massive property damage in the country. Landslides occur due to the combined effects of seismic activity, monsoon rainfall, and improper land-use practices. The prevention and mitigation of landslides are challenging for countries such as Nepal. However, low-cost techniques such as bioengineering combined with affordable early warning systems have been implemented in recent decades. The Methum landslide in Lalitpur district, central Nepal, was selected as a case study to investigate the landslide geomorphology along with triggering factors and to evaluate the effectiveness of a landslide early warning system (LEWS). Multiple field visits were conducted to learn the patterns of landslide evolution, assess landslide risk, and identify potential triggers. This study analyzed aerial photos, satellite images, and precipitation records. Heavy rainfall and past earthquake events have been major landslide triggers, while sloped terrace farming has acted as a preparatory factor. LEWS, installed, measures rainfall, soil moisture, and displacement activity and generates an alarm to alert nearby inhabitants if any of these parameters exceed the threshold set. This monitoring system is a cost-effective technique and exemplifies the reduction of landslide risk at the community level in the landslide-prone mid-hills of Nepal. [ABSTRACT FROM AUTHOR]

The water retention properties of soils present in formation layers of roads and railways continuously vary due to repeated traffic loads and periodic rainfall events. This is important because the accumulated deformation and resilient modulus of such soils under cyclic loading are profoundly affected by water retention properties. This paper discusses the cyclic and water retention response of a clayey sand subjected to repetitive cyclic loading and wetting stages. The results show that the accumulated permanent strains and resilient modulus of the tested soil are dependent on the suction level while the main wetting water retention curve of the soil dictates the variation of the suction measured during cyclic loading and wetting. This bounding effect of the water retention curve is found to be dependent on the void ratio where the suction can even increase due to the accumulation of strains under cyclic loading while the degree of saturation increases. This contradicts the suction reduction typically observed with an increase in the degree of saturation. A void ratio dependent water retention model is developed accounting for the observed bounding effect and employed to predict the measured suction during repetitive cyclic loading and wetting. The suction values predicted by the void ratio dependent water retention model are in good agreement with the experimental data. The predicted suctions are then used in semi-empirical formulations to obtain the accumulated permanent strains and resilient modulus. A better correlation between model predictions and experimental data is achieved where the suction values predicted by the void ratio dependent water retention model are used. The results imply that predictive frameworks proposed for the cyclic behaviour of road and railway formation layers require water retention counterparts that incorporate the bounding effect of void ratio on soil water retention curves. [ABSTRACT FROM AUTHOR]

Earthquake and rainfall may be coupled, which will lead to landslides easily, but the studies about this problem are at the initial stage. In this paper, the failure behavior and mechanism of a slope under the action of earthquake after rainfall were investigated with a shaking table test, based on similarity law. The experimental results showed that the failure process of the slope during earthquake after rainfall undergoes four characteristic stages. There exist velocity zoning in the slope during failure. The moisture content of soil in the slope has different growth rates at different positions during rainfall. Transient and cumulative pore water pressure occurs in the slope during earthquake after rainfall. However, due to the short period of earthquake action, the soil moisture content is not very sensitive to this action, but the pore water pressure changes sharply during earthquake. Under the combined action of rainfall and earthquake, there is a multi-physical field effect in the slope. Rainfall weakens the strength of the soil, mainly causes local instability of the slope. More extensive damage of the slope is caused under the combined action of seismic inertia force. [ABSTRACT FROM AUTHOR]

The present study is aimed at examining the rainfall changes and their relationship to vegetation dynamics in the central-coastal region of Bangladesh. Different statistical tests were used in the study to investigate the rainfall changes, such as the Mann-Kendall test and Sen's slope estimation. The normalized difference vegetation index (NDVI) time series were collected from Google Earth Engine and processed in R programming to examine the level and extent of vegetation coverage. A correlation analysis was used to determine the association between the total rainfall (June–September) and mean NDVI (October–November). Results depict that annual rainfall decreased in all the stations, with the highest values in Senbag (Z = -5.16 and Q = 55.24). The post-monsoon variability exhibited an increasing trend in the majority of the stations, whereas other seasons showed a decreasing trend with the highest values in Feni (pre-monsoon Z = -2.37 and monsoon Z = -2.49). The relationship between rainfall and vegetation dynamics (NDVI) was observed, which was a moderate to strong positive lag correlation throughout the region. It was also noticed that the locations with higher rainfall fluctuations have a weaker association with the NDVI. Reduced rainfall can reduce vegetation productivity by disrupting growth seasons, lowering soil water content, and increasing salinity in the area. It is expected that the findings will contribute to a better understanding of the impacts of climate change induced rainfall variability and the response of the vegetative environment to it. [ABSTRACT FROM AUTHOR]

With its predisposition to adapt to different environments, Cucurbita moschata grows well in various ecosystems. It is not a very exigent plant and has an inherent capacity for plasticity that underlies its large variability. An assessment of the accessions of C. moschata in Cote d'Ivoire shows that the plant exhibits large morphological and phenological variability for all the 28 measured traits. There are outliers among most measured traits. Further analysis indicates the emergence of three ecotypes in congruence with the three distinct ecosystems and their respective bioclimatic characteristics. In the savannah region, characterized by a short rainy season followed by a long dry season, a total yearly rainfall of 900 mm, an elevated daily temperature of 29 °C, and a high relative humidity of 80%, the cline of C. moschata is long and thin, with small leaves, small peduncles and small fruits. It has a high growth rate and accelerated phenology. The mountain region has a very long rainy season followed by a short dry season, a total pluviometry of 1400 mm, an average daily temperature of 27 °C and a relative humidity of 69%. The cline of C. moschata in the mountain region is characterized by a delayed flowering and a delayed fruit maturity, large number of small seeds and large fruits. The forest region has a favorable climate for the growth of C. moschata in Cote d'Ivoire. It has two rainy seasons that alternate with two dry seasons of unequal durations, an annual rainfall of 1200 mm, an average daily temperature of 27 °C and a relative humidity of 70%. The cline of C. moschata in that region has a large girth size, large dimensions of the leaves, long peduncles and larger and heavier fruits. The seeds are also large but in small number. It appears that the anatomy and physiology of the clines are differentiated primarily in response to soil water content and availability for the ontogeny of the plant. [ABSTRACT FROM AUTHOR]

Since climate change is a major challenge that affects the environment, especially the hydrological flow, this research has used the Soil and Water Assessment Tool (SWAT) to study the climate change effects on the runoff in Alvand mountain basin in Iran. The SWAT model calibration was done based on the SUFI-2 algorithm and the validation results for a 20-year prediction period (2020–2040) showed that the model had high accuracy in simulating the runoffs. CMIP5 models were used to predict the basin's temperature and rainfall (the best scenario was Miroc5) and the results showed that the basin's future climate conditions increase in temperature and decrease in rainfall were appropriate. According to Miroc5 (RCP8.5), the annual runoff in the mentioned period would decrease by 8.36% compared to the past. In a seasonal scale, the largest runoff reduction would be 75% in summer (under the Miroc5 (RCP4.5)), and in a monthly scale, it would be 79% in April increasing dramatically to reach 335% in September (under Miroc5 (RCP8.5)). The trend of the future runoff was investigated using the Mann–Kendall model and the Gresson estimation method. Results showed that there would be no significant trend in the mentioned period and the trend of the future drought intensity would be increasing under all studied scenarios. [ABSTRACT FROM AUTHOR]

Purpose: Soil moisture (i.e., the changes in the gravimetric soil water content) redistribution is closely linked with root distribution and preferential flow in soils. This study aimed at exploring the soil water content distribution in the presence of root-enhanced preferential flow in deciduous (Quercus variabilis BI.) and coniferous forests (Platycladus orientalis (L.)). Methods: Dual-tracer experiments (Brilliant Blue FCF and Bromide [Br−]) were conducted in both forests under large (40 mm) and extreme (70 mm) rainfall amount. The distribution of soil water contents was investigated in vertical soil profiles after dual-tracer experiments. Additionally, the correlations among soil water contents, root traits (diameter ≤ 5 mm), and preferential flow features (preferential flow paths and root-solute interaction) were analyzed. Results: Abundant root-induced soil moisture accumulation was found in the upper soil layer (0–20 cm), which was more apparent in deciduous forest than in coniferous forest. Preferential flow strongly influenced the variation distribution of soil water content. The greater degree of preferential flows was, the higher soil water content was. In both forests, the effect of root traits on soil water distribution was more evident under large rainfall amount as compared to extreme rainfall amount. In addition, positive significant correlation between root-solute interaction and soil water content was found at both sites. Conclusions: These results indicated that tree species with different roots distribution patterns enhance different degree of preferential flow, and have varied impacts on the soil moisture redistribution in forest soils. The effects of root traits on soil moisture are more apparent under lower rainfall amount. [ABSTRACT FROM AUTHOR]

Torrential flows (debris flows and debris floods) are mainly triggered by precipitation and soil hydrological processes. Most early warning systems in torrential catchments are rainfall-based. However, this approach can result in frequent false positives, due to its pure black-box nature, in which soil water conditions are neglected. We aim to contribute to the understanding of the conditions required for triggering torrential flows by considering also in situ measurements of soil water content. Herein, monitoring data of 12 years of rainfall and torrential flow occurrence (2009–2020) and 8 years of soil hydrologic conditions (2013–2020) in the Rebaixader catchment (Central Pyrenees, Spain) are analyzed. The dataset includes more than 1000 rainfall events and 37 torrential flows. First, rainfall thresholds using maximum rainfall intensity (Imax) and mean intensity (Imean) are defined. For the 2013–2020 dataset, which includes 15 torrential events, the Imean threshold predicted 2 false negatives and 73 false positives (positive predictive value, PPV, of 15.1%) and the best Imax threshold predicted also 2 false negatives but only 11 false positives (PPV of 54.2%). However, our observations confirmed quantitatively that the lower is the soil moisture the higher is the rainfall intensity to trigger torrential flows. Then, we combined Imax and volumetric water content at 15 and 30 cm depth to define a hydro-meteorological threshold. This latter threshold reduced false negatives to 1 and false positives to 8 and increased the PPV to 63.6%. These results confirm that soil hydrological conditions are key factors for torrential flow triggering and may improve early-warning predictions. [ABSTRACT FROM AUTHOR]

Climate change severely affects sub-Saharan African economies in several ways: increased temperatures, erratic rainfall variability patterns, and recurrent droughts and floods. Such adverse climate change effects may result in a greater incidence of crop pests, loss of soil moisture content, rapid soil nutrient depletion and substantial decreases in crop productivity and yields. These effects in combination with lack of access to improved, high-yielding crop varieties, limited agricultural extension services and poor access to irrigation infrastructure could further threaten access to food, limit export earnings and markedly lower net crop revenue. Several definitions, assessment approaches and methods for the concepts related to "vulnerability" and "adaptation" exist due to complex nature of climate impact and strategies used to deal with them. Based on the context of such studies, this paper reviews climate change risks, people's vulnerability to such risks as well as synthesize different approaches, methods and models used to assess vulnerability and adaptation in the area of agriculture in sub-Saharan Africa. As climate change effects are complex and site-specific, the understanding of several concepts, approaches and methods provides detailed information on farmers' vulnerability and adaptation process. Such information would be useful to inform adaptation interventions and agricultural policies that build farmers' resilience. [ABSTRACT FROM AUTHOR]

Europe witnessed a strong increase in climate variability and enhanced climate-induced extreme events, such as hot drought periods, mega heat waves, and persistent flooding and flash floods. Intensified land degradation, land use, and landcover changes further amplified the pressure on the environmental system functionalities and fuelled climate change feedbacks. On the other hand, global satellite observations detected a positive spectral greening trend—most likely as a response to rising atmospheric CO2 concentrations and global warming. But which are the engines behind such shifts in surface reflectance patterns, vegetation response to global climate changes, or anomalies in the environmental control mechanisms? This article compares long-term environmental variables (1948–2021) to recent vegetation index data (Normalized Difference Vegetation Index (NDVI), 2001–2021) and presents regional trends in climate variability and vegetation response across Europe. Results show that positive trends in vegetation response, temperature, rainfall, and soil moisture are accompanied by a strong increase in climate anomalies over large parts of Europe. Vegetation dynamics are strongly coupled to increased temperature and enhanced soil moisture during winter and the early growing season in the northern latitudes. Simultaneously, temperature, precipitation, and soil moisture anomalies are strongly increasing. Such a strong amplification in climate variability across Europe further enhances the vulnerability of vegetation cover during extreme events. [ABSTRACT FROM AUTHOR]

India receives more than 70% of its annual rainfall in the summer monsoon from June to September. The rainfall is scanty and scattered for the rest of the year. Combining satellite data and model simulations, we show that the soil-vegetation continuum works as a natural capacitor of water, storing the monsoon pulse and releasing the moisture to the atmosphere through evapotranspiration over approximately 135 days when the moisture supply from precipitation is less than the evapotranspiration losses. The total Gross Primary Productivity of vegetation in India during the capacitor period accounts for almost 35% of the total annual GPP value. It primarily depends on the soil moisture at the beginning of the period, a measure of moisture capacitance of soil, with a correlation of 0.6. Given that India is the second largest contributor to recent global greening, its soil-vegetation water capacitance plays a significant role in the global carbon balance. [ABSTRACT FROM AUTHOR]

Rainfall-induced landslides are currently one of the most frequent disasters in China. Compared with rainfall, the increase of soil moisture and its continuous infiltration of soil are the direct factors leading to landslides. However, few researches have studied landslide forecasting taking the soil moisture into consideration. In addition, soil moisture data have an important depth attribute. Soil moisture in root zone is difficult to obtain, and the separate comparison of the impact of the root zone and shallow soil moisture on landslide is more scarce. After comparing the commonly used satellite data, this article chose the CLDAS-V2.0 data set as the source of soil moisture with the depths of 0–10 cm and 100–200 cm. One hundred and sixty-six rainfall-induced landslides that occurred in Tongzi and Xishui counties from February to July 2020 were studied. This paper first obtains the effective rainfall that has the strongest correlation with the landslide and then uses the effective rainfall to explore the best combination of rainfall and soil moisture, including separate modeling and joint modeling of rainfall and soil moisture. Then support vector machine, logistic regression and three decision tree models are developed to predict the landslides. The results show that the combined model of rainfall and soil moisture is better than the model that only considers rainfall or soil moisture, and the landslide forecasting accuracy is improved by more than 5%, which is about 30% higher than the traditional ED rainfall threshold method. Landslide prediction model is proposed to be used as a help for urban planning and government decision-making. [ABSTRACT FROM AUTHOR]

The effects of inoculations with a N2-fixing cyanobacterium (Anabaena cylindrica) on nutrient and microbial dynamics of an arable Mollisol were studied under simulated high intensity rainfall. Inoculated and non-inoculated pots (controls) were incubated for 14 days and then subjected to three rain events in 1 week, followed by three weekly events for a total of six events. Soil samples at three depth layers (0–1, 5–6, and 10–11 cm) were taken at four time points: 1 day after inoculation, 14 days after inoculation, after rain 1, and after rain 6. Soil chlorophyll a (Chla), soluble fractions of N (SolN) and organic C (SolC), microbial biomass C (MBC) and N (MBN), microbial activity (FDA hydrolysis), and soil respiration (CO2 efflux) were determined. Results revealed depth-related changes in Chla, SolC, SolN, MBC, MBN, and FDA hydrolysis with greater effects at the surface layer (0–1 cm). Soil respiration varied with soil moisture and increased in inoculated treatments. After all rain simulations (49 days after inoculation), higher SolC, MBC, MBN, and FDA hydrolysis in surface soil of inoculated treatments indicated positive changes that persisted after consecutive rains. These findings suggest that cyanobacterial soil amendments might enhance soil quality and resiliency in agricultural soils exposed to high intensity rainfall. [ABSTRACT FROM AUTHOR]

Soil water is a critical factor closely related to hydrological and ecological processes. Owing to the complex surface conditions with heterogeneous soil thickness and abundant underlying fissures, soil water in the karst region has been a complicated issue. In this study, the dynamic changes of soil water in the vertical profile of selected grassland, farmland and bare land on a karst yellow soil hillslope in southwest China were monitored at five depths including 20, 40, 60, 80 and 100 cm with an interval of 15 min. Results showed that (1) there were spatial differences in the response to rainfall of the soil water content at different depths. When the rainfall amount was similar, the soil water replenishment amount and migration depth under the three land uses decreased with the rainfall intensity. In the case of light rainfall, the soil water content at 20 cm was the most sensitive to rainfall, and the response of soil water to rainfall mainly occurred at 0–40 cm. In the case of moderate and heavy rainfall, soil water could migrate down to 100 cm on grassland but less than 100 cm on farmland and bare land under heavy intensity rainfall. (2) The variation in the soil water content had interlayer differences over time. The response of soil water to rainfall in different soil layers showed multipeak fluctuations. In general, when the rainfall intensity was the same, the soil water fluctuation on grassland and farmland at the same depth was larger than that on bare land; however, the peak value of soil water decreased with soil depth. (3) Land use and the antecedent soil water content had important effects on soil water loss during the dry period. Soil water loss was faster at the beginning and before slowing down. The soil water loss rate on grassland and farmland increased with the length of the dry period, but decreased gradually on bare land. These results can support the utilization and protection of soil and water resources on karst yellow soil slopes and help to understand the temporal and spatial dynamics of soil water under natural rainfall. [ABSTRACT FROM AUTHOR]

This paper addresses the nexus of climate change and variability, soil moisture and surface runoff over the Lake Baikal catchment. Water level and distribution of dissolved and suspended matter over Lake Baikal are strongly affected by river inflow during rain-driven floods. In this study, we evaluate river flow changes at 44 streamflow gauges as well as related precipitation, evaporation, potential evaporation and soil moisture obtained from the ERA5-Land dataset. Based on Sen's slope trend estimator, Mann–Kendall non-parametric test, and using dominance analysis, we estimated the influence of meteorological parameters on river flow during 1979–2019. We found a significant correlation between the precipitation elasticity of river flow and catchment characteristics. Half of the gauges in the eastern part of the Selenga River basin showed a significant decreasing trend of average and maximum river flow (up to −2.9%/year). No changes in the central volume date of flood flow have been found. The reduction in rainfall amount explains more than 60% of runoff decrease. A decrease in evaporation is observed in areas where precipitation decrease is higher than 0.8%/year. Catchments, where the precipitation trends are not as substantial, are associated with increasing evaporation as a result of the increasing potential evaporation. Negative precipitation trends are accompanied by negative trends of soil moisture. Finally, the study reveals the sensitivity of catchments with steep slopes located in humid areas to precipitation change. [ABSTRACT FROM AUTHOR]

Evaporative loss of interception (Ei) is the first process occurring during rainfall, yet its role in large-scale surface water balance has been largely underexplored. Here we show that Ei can be inferred from flux tower evapotranspiration measurements using physics-informed hybrid machine learning models built under wet versus dry conditions. Forced by satellite and reanalysis data, this framework provides an observationally constrained estimate of Ei, which is on average 84.1 ± 1.8 mm per year and accounts for 8.6 ± 0.2% of total rainfall globally during 2000–2020. Rainfall frequency regulates long-term average Ei changes, and rainfall intensity, rather than vegetation attributes, determines the fraction of Ei in gross precipitation (Ei/P). Rain events have become less frequent and more intense since 2000, driving a global decline in Ei (and Ei/P) by 4.9% (6.7%). This suggests that ongoing rainfall changes favor a partitioning towards more soil moisture and runoff, benefiting ecosystem functions but simultaneously increasing flood risks. Canopy rainfall interception (Ei) is a key component of global water cycle. Here, the authors quantify Ei using flux tower data and machine learning, and find that rainfall gets less partitioned into Ei as it gets more intense and less frequent. [ABSTRACT FROM AUTHOR]

Vegetation and rainfall pattern are two important factors affecting the infiltration-runoff processes in slopes. Rainfall events in the eastern margin of the Tibetan Plateau, monitored from June 2019 to October 2020, were classified into four patterns using the self-organizing mapping method. To determine the relationship between infiltration potential, runoff generation, soil erosion, and rainfall parameters, the four rainfall patterns were simulated on the bare and vegetated slopes, and soil moisture content, surface runoff, subsurface flow, and sediment yield were measured. The results showed that rainfall characteristics and meteorological parameters significantly affected soil moisture supply. Rainfall intensity and soil moisture content mainly affected the initial infiltration rate, soil properties and root characteristics determined the stable infiltration rate. Vegetation could accelerate the rate and amplitude of soil moisture absorption and dehumidification. Soil erosion intensity and rainfall duration were found to follow a power function (R2 > 0.84) in the bare slope and an exponential function (R2 > 0.64) in the vegetated slope. Vegetation could significantly mitigate soil erosion by altering the hydrodynamic parameters of overland flow, and the mitigating effect was found to be more pronounced for light rainfall events. Moreover, the existence of an optimal initial moisture content for minimizing soil erodibility could be ascertained. These results provide insight into the dynamic responses of infiltration potential and erosion process in bare and vegetated slopes to different rainfall patterns and could guide the prevention of slope instability and soil erosion in response to complex climate change in the next two decades. [ABSTRACT FROM AUTHOR]

The aim of this study was to convert a resistivity field into a local factor of safety (LFS) field in order to capture the characteristics of the water infiltration and slope failure processes in loess landslides. The generalized earth pressure coefficient and the LFS were introduced, and a method based on the monitoring of the resistivity field was proposed. An improved electrical resistivity tomography (ERT) method was then adopted. The water infiltration and the distribution of water inside the slope were analyzed using the slope sequence resistivity field for two rainfall scenarios. The validity and the physical justification of the proposed method were then verified. The results were as follows: (i) the proposed method could effectively capture the failure location and the instability process of the slope. (ii) Although the proposed method ignored the heterogeneity of the slope body, when the migration process and the distribution characteristics of the water within the slope under two rainfall scenarios were clearly presented, the conversion from the slope resistivity field to the soil moisture content (SMC) field was theoretically feasible. (iii) The uniform infiltration pattern induced the shallow landslides or mudflows with long-term light rainfall. Middle and deep loess landslides were easily induced by the significant preferential infiltration phenomenon with short-term heavy rainfall. The proposed method is very important for the early identification and risk mitigation of water-inducing landslides in loess areas, and it is helpful for developing a deeper understanding of the preferential flow within soil. [ABSTRACT FROM AUTHOR]

This study introduces the soil water index (SWI) approach, the sum of water depths in the three-layer tank model, with rainfall data to understand landslide occurrence in Taiwan. A total of 236 historical instance data were compiled to investigate the characteristics of different types of landslides: debris flows (DF), shallow landslides (SL), and large-scale landslides (LL). The data shows that most DF occurred near the time of maximum rainfall intensity. However, several SL and LL events indicate that the occurrence of SL and LL has correlations with more than just rainfall intensity or rainfall amount. The SWI and the water depth in the tanks (from top to bottom: H1, H2, H3) provide an intriguing approach to correlate soil moisture content at occurrence time with the landslide types. DF events show the highest H1/SWI value, which indicates the correlation with water content in the surficial soil. LL events after rainfall have the highest H3/SWI ratio, indicating that prolonged rainfall events cause rainwater to infiltrate a deeper soil layer and trigger these LL. We developed a ternary diagram of the water depth-to-SWI ratios to show characteristics of the three landslide types. This study found that the H2/SWI value is typically in the range of 0.2–0.4. In this range, the landslide type gradually shifts from DF to SL, and then to LL, along the H3/SWI axis. Therefore, the ternary diagram can explain landslide type changes with the water depth-to-SWI ratios and better understand landslide occurrence. [ABSTRACT FROM AUTHOR]

Resistivity is used to evaluate soil water content (SWC), which has the advantages of not causing soil disturbance and in low price. It is an effective way to assess the SWC variability. This paper aims to evaluate the variability of loess slope SWC through the change of resistivity. It provides a simple way for long term SWC monitoring to solve the expensive cost of deploying moisture sensors. In this context, geoelectric and environmental factors such as soil temperature and SWC were monitored for three years. The prediction model of apparent resistivity and SWC was calibrated. The post processing of geoelectric data was introduced. In addition, the SWC collected by Time-Domain Reflectometry (TDR) was used to verify the feasibility of electrical resistivity tomography (ERT) data. The SWC variability in the process of rainfall, the evolution of four seasons, and the alternation of drying and wetting were evaluated. The research results show that: i) the SWC monitored by ERT and TDR can reflect the response and hysteretic effect of water content at 0.5–3.0 m depth. ii) The moisture content monitored by ERT reflects that the soil is relatively wet in summer and autumn and dry in winter and spring. iii) From 2017 to 2020, the SWC increased in August, and the soil became dry in January. iv) Two areas with high SWC and three areas with low SWC on loess slope are reflected by resistivity. The outcome can provide the change information of SWC to a great extent without excavating boreholes. [ABSTRACT FROM AUTHOR]

Most land surface system models and observational assessments ignore detailed soil characteristics while describing the drought attributes such as growth, duration, recovery, and the termination rate of the event. With the national-scale digital soil maps available for India, we assessed the climate-catchment-soil nexus using daily observed streamflow records from 98 sites in tropical rain-dominated catchments of peninsular India (8–25° N, 72–86° E). Results indicated that climate-catchment-soil properties may control hydrological drought attributes to the tune of 14–70%. While terrain features are dominant drivers for drought growth, contributing around 50% variability, soil attributes contribute ~ 71.5% variability in drought duration. Finally, soil and climatic factors together control the resilience and termination rate. The most relevant climate characteristics are potential evapotranspiration, soil moisture, rainfall, and temperature; temperature and soil moisture are dominant controls for streamflow drought resilience. Among different soil properties, soil organic carbon (SOC) stock could resist drought propagation, despite low-carbon soils across the Indian subcontinent. The findings highlight the need for accounting feedback among climate, soil, and topographical properties in catchment-scale drought propagations. [ABSTRACT FROM AUTHOR]

River flooding has large societal and economic impacts across Africa. Despite the importance of this topic, little is known about the main flood generating mechanisms in Africa. This study is based on 13,815 flood events that occurred between 1981 and 2018 in 529 catchments. These flood events are classified to identify the different flood drivers: excess rains, long rains and short rains. Out of them, excess rains on saturated soils in Western Africa, and long rains for catchments in Northern and Southern Africa, are the two dominant mechanisms, contributing to more than 75% of all flood events. The aridity index is strongly related to the spatial repartition of the different flood generating processes showing the climatic controls on floods. Few significant changes were detected in the relative importance of these drivers over time, but the rather short time series available prevent a robust assessment of flood driver changes in most catchments. The major implication of these results is to underline the importance of soil moisture dynamics, in addition to rainfall, to analyze the evolution of flood hazards in Africa. [ABSTRACT FROM AUTHOR]

The objective of this study is to assess the groundwater recharge in the Makutupora basin, Central Tanzania using the soil moisture balance (SMB) and chloride mass balance (CMB) methods. The SMB method considered the estimation of both evapotranspiration in CROPWAT 8.0 software and surface runoff by the curve number (CN) method. The CN of the basin was determined based on hydrological soil group (HSG), antecedent moisture condition (AMC) and land use type using Arc GIS 10.2. Distributed land-use maps, slope and soil texture maps, daily climatological data (e.g., precipitation, temperature, wind speed, etc.), chloride concentration in rainfall and groundwater are the basic data used in this study. Input data were prepared in the form of digital maps using GIS and remote sensing tools and finally excel models were created to compute recharge using a set of given conditions. The results indicate that the average chloride concentration measured in the groundwater samples of the study area is 282.88 mg/L; much higher than the average concentration in rainwater samples which is 3.47mg/L while the average potential evapotranspiration is 2280 mm/year with an average rainfall of 680mm/year. Runoff computed using the CN method is ranging from 7.6–246mm/year or 0.68–22%. The final recharge from SMB is ranging from 0.025–117.86mm/year or 0.006–10% of annual rainfall equivalent to an average annual volume of 41.58Mm3 while recharge from CMB ranges from 2.57–44.6mm/year or 0.38–6.56% of annual rainfall equivalent to an average annual volume of 23.85Mm3. The results show that CMB underestimated the recharge compared to the SMB method. The reason for this is thought to be caused by the high chloride concentration analysed in groundwater compared to rainfall samples which may be caused by increased chloride concentration as water passes through the rock formations (unsaturated zone) to the saturated zone. Further lack of atmospheric chloride deposition data may be another contributing factor. [ABSTRACT FROM AUTHOR]

Pre-monsoon season (PMNS) over India is characterized by a number of hazardous meteorological conditions, including sudden strong rains, violent thunderstorms, and squalls. Farmers can choose from a variety of crops for the following months with the help of rain that fell in PMNS. The months of March, April, and May are referred to as the pre-monsoon season. Over the Indian subcontinent, the meteorological variables such as wind patterns, pressure, rainfall, humidity, and temperature vary substantially by region. Over the course of the year, the weather changes. The PMNS precipitation patterns are crucial because they have a significant impact on a variety of crops. During PMNS, four different locations over India are examined for the traditionally calculated total totals index (TTT) and K index (TKI), humidity index (HI), enhanced k index (EKI), enhanced total totals index (ETT), excessive precipitation with enhanced convection (EPEC), Showalter index (SWI), and convective available potential energy (CAPE). In PMNS, these factors are examined both on rainy days (RD) and on days without rain (NRD). These four places—Kapurthala (KAP), Purnia (PUR), Sonipat (SON), and Haridwar (HAR)—were the ones we chose for our study. For the PMNS from 1981 to 2021, daily ERA5 reanalysis data were used to calculate all indicators related to rainfall. All measurements used during PMNS clearly distinguished between the RD and the NRD. [ABSTRACT FROM AUTHOR]

Climate change is altering precipitation regimes globally, with expectations of intensified precipitation patterns (for example, larger but fewer rainfall events) and more frequent and extreme drought. Both aspects of precipitation change can impact ecosystem function individually, but it is more likely that they will occur in combination. In a central US mesic grassland, we imposed an extreme 2-year drought (growing season precipitation reduced by 66%) on plots with a long-term (16-year) history of exposure to either ambient or intensified precipitation patterns (average threefold increase in event size and threefold decrease in event number during the growing season). While this intensified pattern did not alter total precipitation amount, it generally led to ecosystem responses consistent with a drier environment (for example, reduced soil moisture, aboveground net primary production (ANPP), and soil CO2 flux, but little evidence for altered root biomass). Surprisingly, this history of intensified precipitation patterns did not affect the response of ANPP to the subsequent extreme drought. In contrast, previous exposure to intensified precipitation patterns reduced root production and muted soil CO2 flux responses to rainfall events during drought. Reduced root production in plots experiencing compounded precipitation extremes was driven not by the dominant C4 grass species, Andropogon gerardii, but collectively by the subdominant species in the plant community. Overall, our results reveal that compound changes in precipitation patterns and amount affected this grassland in ways that were less apparent (that is, belowground) than responses to either change individually and significantly reduced ecosystem carbon uptake. [ABSTRACT FROM AUTHOR]

It has been widely recognised that the degree of soil wetness before precipitation events can be decisive for whether or not shallow rainfall-induced landslides occur. While there are methods to measure and/or model soil wetness in complex topography, they often exhibit limitations in spatial or temporal resolution, hindering their application in regional landside initiation modelling. In this study, we address the need for high-resolution predictions of initial saturation before rainfall events by employing data-driven linear regression models. The models were trained using in-situ soil moisture data collected from six measurement stations located in a landslide-prone region in Switzerland. Various topographic attributes, along with multiple antecedent rainfall and evapotranspiration variables were tested as input for the models. The final model consisted of five measurable variables, including cumulative antecedent rainfall, cumulative evapotranspiration, and the topographic wetness index (TWI). The model effectively reproduced the observed spatial and temporal variability of the in-situ measurements with a coefficient of determination R2 = 0.62 and a root mean square error RMSE = 0.07. Subsequently, we applied the regression model to predict the spatial soil saturation at the onset of actual landslide triggering rainfall events and integrated these patterns into the hydromechanical model STEP-TRAMM. The results demonstrate improvements in predicting observed landslide occurrences compared to simulations assuming spatially uniform initial saturation conditions, highlighting the importance of in-situ measurements and a realistic extrapolation of such data in space and time for accurate modelling of shallow landslide initiation. [ABSTRACT FROM AUTHOR]

Climate change leads to changes in climatic variables, with rainfall being one of them. Changes in rainfall influence rainfall erosivity and subsequently erosion rates. This study analysed rainfall data from 1901 to 2017 in Odisha, focusing on different agro-climatic zones to discern annual rainfall pattern, its spatial variation, and trend, particularly concerning the rainfall erosivity factor and its impact on soil erosion and agricultural productivity. Notably, the Eastern Ghats Highland region received the highest average annual rainfall of 1578.5 mm, while the Western Undulating Zone received the lowest (1308.4 mm). The rainfall distribution showed spatial variability largely influenced by topography, with areas experiencing orographic lifting receiving higher rainfall. The study observed significant trend in annual rainfall, noting a maximum decline of 1.2 mm yr−1 in the North Western Plateau, Western Central Table Land, and Western Undulating Zone, whereas the East and South Eastern Plain, Mid Central Table Land, North Eastern Coastal Plain, North Eastern Ghats, and South Eastern Ghats exhibited a noteworthy increase in annual rainfall (0 to 3.9 mm yr−1). The decline in rainfall can result in the drying up of water bodies and reduced soil water availability to crop, thereby influencing agricultural production. On the other hand, areas with increased rainfall, may face extreme events which can aggravate soil erosion and thereby loss of soil fertility. Considering the scarcity of pluviographic data in countries like India, Modified Fournier Index (MFI) may be considered as one of the useful methods to capture rainfall’s aggressiveness towards soil erosion through rainfall erosivity (R-factor). Therefore, to evaluate potential soil erosion levels, the Modified Fournier Index method was employed, revealing varying degrees of soil erosiveness across different regions. The Eastern Ghats Highlands exhibited the highest erosion potential. The R-factor, aligned with these spatial patterns, with the Eastern Ghats Highland (12,965.4 MJ mm ha−1 h−1 yr−1) and South Eastern Ghats (12,242.3 MJ mm ha−1 h−1 yr−1) regions displaying the highest R-factor values. Furthermore, the research identified areas prone to soil erosion by overlaying R-factor, slope, and land use maps, highlighting vulnerable regions such as Eastern Ghats Highlands, North Eastern Ghats, South Eastern Ghats, and Western Undulating Zone. This comprehensive analysis allows for informed prioritization of conservation efforts and the implementation of appropriate measures like strip cropping of finger millet with groundnut, intercropping finger millet with hedgerows of Gliricidia and Leucaena, bio-engineering measures such as earthen or stone bunds with broom grass in arable land and growing of aromatic grasses like lemon and citronella grass, construction of staggered trenches in non-arable land to mitigate erosion in these susceptible areas. [ABSTRACT FROM AUTHOR]

The present study assesses the ability of Monsoon Mission CFSv2-T382 initialized at 4-month and 2-month leads (Feb IC and Apr IC hindcast) to simulate the short-term drought during the Indian Summer Monsoon (June to September). The rainfall-based standardized precipitation index (SPI) averaged over the Indian land region showed significant skills for the 1981–2017 hindcast period for both the ICs. However, the spatial distribution of the skill is moderate and is limited to some regions, and varies within the season. Category-wise forecasts indicate that even with strong rainfall bias and moderate false alarm ratio, model hindcasts have a better detection ratio, hit rate, etc., demonstrating the model’s usefulness for predicting the all-India drought. The observed SPI over the entire India during the month of June has a strong positive (negative) relationship with soil moisture (surface temperature) over India, along with warm (cold) SST anomalies in the northern Indian Ocean (north tropical Atlantic Ocean). The SST anomalies associated with El Nino Southern Oscillation (ENSO) dominate the drought towards the end of the monsoon season. However, SPI simulated by model hindcasts has a stronger relationship with ENSO throughout the season. The homogenous region-wise skill analysis and teleconnections of SPI explain its scattered skill over Indian land regions. The observed regional surface temperature and soil moisture have a strong 1–2-month lead relationship with SPI in all homogenous regions, and forcing from ENSO is evident for WCI and SPInd only. The model overestimates the simultaneous ENSO relationships for all the homogenous regions, while the lead role of moisture, surface temperature, and north tropical Atlantic SST is under-predicted. WCI and SPInd regions have a moderate lead relationship with soil moisture and surface temperature along with ENSO teleconnection, resulting in improved skill compared to other subdivisions of India. The study shows that further improvement of the regional scale skill of CFSv2 for capturing SPI is possible mainly with the regional scale improvement of land surface processes and its lead teleconnection with SPI in the model. [ABSTRACT FROM AUTHOR]