Antecedent Soil Moisture Conditions Research Articles

How Much Does Land–Atmosphere Coupling Influence Summertime Temperature Variability in the Western United States?

Abstract Interannual fluctuations in average summertime temperatures across the western United States are captured by a leading empirical orthogonal function that explains over 50% of the total observed variance. In this paper, we explain the origins of this pattern of interannual temperature variability by examining soil moisture–temperature coupling that acts across seasons in observations and climate models. We find that a characteristic pattern of coupled temperature–soil moisture climate variability accounts for 34% of the total observed variance in summertime temperature across the region. This pattern is reproduced in state-of-the-art global climate models, where experiments that eliminate soil moisture variability reduce summertime average temperature variance by a factor of 3 on average. We use an idealized model of the coupled atmospheric boundary layer and underlying land surface to demonstrate that feedbacks between soil moisture, boundary layer relative humidity, and precipitation can explain the observed relations between springtime soil moisture and summertime temperature. Our results suggest that antecedent soil moisture conditions and subsequent land–atmosphere interactions play an important role in interannual summertime temperature variability in the western United States; soil moisture variations cause distal temperature anomalies and impart predictability at time scales longer than one season. Our results indicate that 40% of the observed warming trend across the western United States since 1981 has been driven by wintertime precipitation trends in the U.S. southwest. Significance Statement Year-to-year fluctuations in summertime temperatures have a large impact on drought, wildfire, and extreme heat across the Western United States. We find strong evidence that soil moisture deficits in the preceding spring may be the primary driver of higher-than-average summer temperatures in this region. Our results suggest that memory in the water cycle may lead to greater predictability in the climate system from season to season and that trends in southwest precipitation have exerted a considerable influence on observed warming over the past 40 years.

Drivers of widespread floods in Indian river basins

Abstract Widespread floods affecting multiple subbasins in a river basin have implications for infrastructure, agriculture, environment, and groundwater recharge. However, the crucial linkage between widespread floods and their drivers remains unexplored for Indian sub-continental river basins. Here, we examine the occurrence and drivers of widespread flooding in seven Indian sub-continental river basins during the observed climate (1959-2020). The peninsular river basins have a high probability of widespread flooding, compared to the transboundary basins of Ganga and Brahmaputra. Favorable antecedent baseflow and soil moisture conditions, uniform precipitation distribution, and precipitation seasonality determine the probability of widespread floods in Indian river basins. The widespread floods are associated with large atmospheric circulations that cause precipitation in a large part of a river basin. Our findings highlight the prominent drivers and mechanisms of widespread floods with implications for flood mitigation in India.

A time-varying Copula-based approach to quantify the effects of antecedent drought on hot extremes

Hot extremes may adversely impact human health and agricultural production. Owing to anthropogenic and climate changes, the close and dynamic interaction between drought and hot extremes in most areas of China need to be revisited from the perspective of nonstationarity. This study therefore proposes a time-varying Copula-based model to describe the nonstationary dependence structure of summer extreme temperature (SET) and antecedent soil moisture condition to quantify the dynamic risk of hot extremes conditioned on dry/wet condition. A general statistical inspection procedure which was composed of maximum likelihood (ML)-based estimation, nonstationary Goodness-of-fit (GOF) tests, the log likelihood ratio (LR) tests and minimum corrected Akaike information criterion-based selection was promoted to select the best-fitted nonstationary models efficiently. This study proposed a new approach to identify the soil moisture driving law over extreme temperature from the point view of tail monotonicity and nonstationary risk assessment. Owing to the LTI-RTD (left tail increasing and right tail decreasing) tail monotonicity for dependence structure of these two extremes derived from most areas, two kinds of the driving laws of soil moisture over SET were detected. Because of the spatiotemporal divergence of sensitivity index derived from tail monotonicity (SITM), we can conclude that the spatial and temporal heterogeneity of response degree of ET over the variations of antecedent dry/wet conditions is evident. Incorporation of nonstationarity and tail monotonicity helps identify the changes of driving mechanism (laws) between soil moisture and hot extremes.

The effects of multilayer blue-green roof on the runoff water quality

In the context of climate changes, characterized by an increase of short but intense rainfall events and rise of the average temperature, the fast population growth and consequent urbanization require the implementation of innovative solutions to mitigate pluvial floods and, at the same time, reduce the water demand. Among the different nature-based solutions, multilayer blue-green roofs have been widely recognized for their high capacity of reducing runoff generation from rooftops, and their additional storage layer enables to collect water, which could be reused for different purposes. However, the quality of the collected water in a multilayer blue-green roof and the influence that the additional storage layer has on it have not been analysed yet. Following this knowledge gap, we investigated the potential benefits of a multilayer blue-green roof installed in Cagliari, with respect to a traditional roof. The outflow triggered by artificial irrigation and natural rainfall events was analysed, both from a quantitative and qualitative perspective. Results confirm the high contribution of multilayer blue-green roofs in mitigating runoff generation, which is however influenced by antecedent soil moisture and water level conditions. The outflow from the multilayer blue-green roof presents lower suspended solids and heavy metals concentrations than from a traditional roof. On the other hand, Carbon Oxigen Demand (COD) concentrations in the multilayer blue-green roof outflow exceed the limits defined by the Italian regulations (125 mg/l) for water discharge or reuse, partially due to the high residence time in the storage layer. Specific treatments could be planned to reuse the collected water for urban purposes.

A drought monitor for Australia

The UniSQ Drought Monitor and Australian Combined Drought Indicator (CDI) are introduced. The objective of these products is to use a multi-index approach to fully capture the long- and short-term consequences of drought as an information service for the public. Since drought is a complex phenomenon relying on multiple variables such as rainfall evaporative demand, and antecedent soil moisture conditions, monitoring drought using a single index or indicator is not always appropriate.The Drought Monitor was developed using a normalized linear combination with weighting determined by PCA. It was evaluated against observed wheat yield as well as total pasture growth simulated by the AussieGRASS model. The results indicate that there was a significant positive correlation with both. The Drought Monitor was also well-received by survey respondents and has the potential to become a valuable drought monitoring tool for identifying drought impacts and related risks.

Quantifying the relative contributions of different flood generating mechanisms to floods across CONUS

The occurrence of flooding in a catchment is attributable to various mechanisms. Several researchers have used linear models to quantify the relative contributions of different flood generating mechanisms. However, the use of linear models for this quantification may not be suitable given the complex and nonlinear processes from climate to floods. In this study, we used nonlinear machine learning models with interpretability methods to quantify the contributions of multiple flood generating mechanisms to floods across the continental United States (CONUS). Four flood generating mechanisms were considered comprehensively, which varied in terms of the influence of antecedent soil moisture condition or snowmelt on flood generation. These mechanisms involved three common mechanisms (rainfall, rainfall excess, and snowmelt/rain on snow) and one complex mechanism (effective precipitation) that accounts for snowmelt/rain on snow lost to unsaturated soil zone. Consistent with previous studies, we observed that rainfall alone (i.e., without considering other hydrometeorological processes) is seldom the dominant mechanism for flood generation. Floods in most catchments are largely attributable to rainfall excess and snowmelt. Unlike previous studies, we noted that in the midwest and northeastern CONUS, snowmelt/rain on snow upon saturated soil was the greatest contributor to local flood generation. This finding highlights that both snowmelt and soil moisture condition affect flood generation in the midwest and northeastern CONUS, instead of only snowmelt as has been reported previously. Overall, this study provides new insights into flood generating mechanisms in CONUS and demonstrates the potential of applying interpretable machine learning to examine flood generating mechanisms.

The effect of antecedent soil moisture conditions on soil nitrous oxide and dinitrogen dynamics after wetting: An intact soil core study

Ruminant excreta urinary‑nitrogen (N) deposited onto pasture soil generates inorganic-N concentrations in excess of the pasture's immediate requirements. Consequently, nitrous oxide (N2O) and dinitrogen (N2) may be emitted from the urine-affected soil via denitrification. Soil moisture plays a key role in determining the end-product of denitrification. Although the effects of soil moisture and drying-wetting cycles on soil N2O production have been widely studied using sieved soils in a controlled environment, these experiments may not reflect the actual conditions in the field due to the lack of plant cover and the disturbed soil structure. To address this an experiment was performed using intact soil cores (7.5 cm depth), with pasture plants present, under controlled environmental conditions (20 °C, 8 h of light per day). The 15N flux method was used to investigate how antecedent soil moisture affected soil N2O production and consumption in a pasture soil after wetting with a nitrate (NO3−) solution. The results showed that the antecedent dry condition (25 % WFPS, Dp/Do = 0.1783) increased the magnitude of the N2O flux peak after the soil was wetted (75 % WFPS, Dp/Do = 0.006) when compared to antecedent wet conditions (50 % WFPS, Dp/Do = 0.0503). There were no differences in the denitrification and nitrification related functional gene abundances between the two antecedent conditions post-wetting, indicating that the effect was not regulated by microbial changes but by the intensity of the “Birch effect” induced by soil wetting. The N2O reduction related gene (nosZII) abundance decreased after wetting with the NO3− solution, indicating a suppression effect on N2O consumption from the NO3− applied that overrode the potential “Birch effect” on N2 emission. The results of the study indicate that rewetting of antecedent dry soil increase N2O emissions but not N2 emissions when the soil NO3− concentration is high.

Warm Season Extreme Flood Events in the Midwestern US—Sources of Moisture and Physical Mechanisms

AbstractA comprehensive picture of the development of warm season extreme floods in the Midwestern US is presented. We first identify the climatological moisture sources for precipitation in the Midwest using the two‐layer dynamic recycling model (2L‐DRM) with ECMWF Reanalysis v5 (ERA5) data. Terrestrial sources supply most of the moisture for Midwestern US precipitation during the warm season, while oceanic sources dominate during the cold season. The Empirical Orthogonal Function (EOF) analysis is used to select extreme flood events characterized by both positive soil moisture and precipitation anomalies. During the warm season flood events, moisture coming from oceanic sources increases by more than 45% compared to the climatology. In addition, our results show that moisture can come from remote regions due to sustained anomalous circulation. Low‐level circulation anomalies associated with wave trains that traverse the continent enhance moisture contributions from terrestrial sources along narrow paths. However, moisture budget analysis reveals that the primary flood‐producing mechanism is the convergence of moisture due to intense circulation anomalies. Moisture advection and thermodynamic terms are responsible for flood termination. Our results suggest that knowledge of antecedent wet soil moisture conditions is unlikely to improve the predictability of flood‐producing storms.

On the role of climate change in the 2018 flooding event in Kerala

The extreme precipitation during August 2018 in Kerala, India was catastrophic, triggering one of the worst floods in history. There is growing evidence of human-induced climate change in driving hydroclimatic extremes across the globe. However, whether and to what degree the 2018 flooding event in Kerala was influenced by climate change has yet to be fully understood. To this end, we present the first formal attribution analysis of the event, using the probabilistic event attribution (PEA) framework. Three methods using (i) Historical and HistoricalNat runs from CMIP6 (general circulation models-based method), (ii) observed records from 1901–2018 for two periods, split at 1950 (time-slice method) and (iii) observations that are scaled to 1901 and 2018 climates (scaling method), are considered for quantifying the risk ratio (RR) of the event. Using an objective approach, the 2018 precipitation event is defined by the return period of the 4 day cumulative precipitation over the Periyar River Basin (PRB), during 15–18 August, 2018. The subsequent flood event is characterized by the return period of the 1 day maximum streamflow at one of the outlets of the PRB, where maximum impact during the event was reported. The results from multiple methods are consistent, suggesting that the event is exceptionally less likely to have been caused by anthropogenic climate change, with RR for the precipitation and flood events ranging from 0.31 to 0.82 and 0.55 to 0.8, respectively. The role of wet antecedent soil moisture conditions, which is found to be the primary driving factor of floods in the PRB, is also found to be unchanged between simulations with and without climate change. Our results highlight the challenges in unequivocal discerning of the climate change signal on regional hydrological events and emphasize the importance of better consideration of local confounding interventions in PEA studies.

Characterising the coincidence of soil moisture – precipitation extremes as a possible precursor to European floods

Studies around the world have shown that increases in extreme precipitation are not directly resulting in reported instances of flooding. Despite the evidence that antecedent soil moisture conditions play a significant role in regulating the runoff response during extreme storm events, fewer works have tried to understand the spatial and temporal dynamics of such coupled interactions. In the present work, we aim to improve our understanding of the dominant spatiotemporal patterns of soil moisture (SM) – precipitation (P) dependence over Europe. Using an event-based, non-parametric Event Coincidence Analysis (ECA) approach, we first quantified the probability of SM preceding P by the precursor coincidence rate. We also examined the statistical precursor relationship between SM-P joint episodes and reported annual flood events using the conditional precursor coincidence rate. The results show that seasonality is a major driving force in the spatiotemporal dynamics of SM-P coupling over Europe. The conditional precursor coincidence rates are in line with previous flood hazard studies over Europe and show the importance of considering such diverse interdependencies in flood risk mitigation. Our work has implications for understanding the occurrence of preconditioned compound events worldwide and provides a vital starting point for characterising the major processes that need to be accounted for in comprehensive state of the art flood risk assessment studies.

Interacting Effects of Watershed and Coastal Processes on the Evolution of Compound Flooding During Hurricane Irene

AbstractIn low‐lying estuarine regions, compound flooding (CF) is caused by the co‐occurrence of extreme precipitation, river flooding and storm surge. In recent decades, there has been a rise in the frequency and intensity of pluvial‐coastal CF events in different parts of the U.S. due to the increased frequency of intense precipitation and storm surge events. However, in estuarine and deltaic regions, the CF characteristics depend mainly on the storm tide and river flow interaction. Understanding how the fluvial‐coastal CF may respond to changes to watershed and estuarine characteristics is essential for future CF hazard prediction. This study examined two critical processes: (a) the interplay between antecedent soil moisture conditions and peak river flow, and (b) how the impact of sea level rise (SLR) on storm surge and river flood distribution alters the CF in complex estuaries. As the study area, we selected the Delaware Bay and River, a shallow and convergent estuary in the US Mid‐Atlantic region—where flood hazards during a CF can become more significant than the surge and river flood processes occurring in isolation. For the focal event for the study, we selected Hurricane Irene (2011) because it reportedly produced the most extreme CF over the past two decades in the same region. Ultimately, our results illustrated that the potential changes to the catchment and bay characteristics from the global temperature increase and SLR could significantly modulate the fluvial‐coastal CF variability. The potential increase in global temperature and rainfall intensity might not always exacerbate the CF.

The Pakistan Flood of August 2022: Causes and Implications

AbstractThe risk of floods has increased in South Asia due to high vulnerability and exposure. The August 2022 Pakistan flood shows a glimpse of the enormity and devastation that can further rise under the warming climate. The deluge caused by the floods in 2022, which badly hit the country’s southern provinces, is incomparable to any recent events in terms of the vast spatial and temporal scale. The flood event is ranked second in human mortality, while this was the top event that displaced about 33 million people in Pakistan. Using observations and climate projections, we examine the causes and implications of the 2022 flood in Pakistan. Multiday (∼15 days) extreme precipitation on wet antecedent soil moisture conditions was the primary driver of the flood in August 2022. The extreme precipitation in August was caused by two atmospheric rivers that passed over southern Pakistan. Streamflow simulations from the multiple hydrological models show that multiday extreme precipitation was the primary driver of floods. Several flood‐affected stations experienced anomalously higher flow than the upstream stations. The 2022 Pakistan flood highlights the adaptation challenges South Asia is facing along with the substantial need for climate mitigation to reduce the risk of such events.

On the attribution of annual maximum discharge across the conterminous United States

Floods affect many aspects of our lives, and our improved understanding of the processes driving the historical changes in this natural hazard can provide basic information to enhance our preparation and mitigation efforts. Here we analyze 3,885 streamgages across the conterminous United States and attribute the inter-annual variability in annual maximum discharge to precipitation and temperature. This is accomplished by first developing gamma regression models to describe the seasonal maximum discharge in terms of basin-averaged precipitation, temperature, and antecedent wetness (i.e., the basin-averaged precipitation for the season prior to the one of interest, and used as a proxy for antecedent soil moisture conditions). These seasonal models are then mixed through a Monte Carlo approach to obtain the annual maximum discharge distribution. Despite its simplicity, our results show that the developed statistical attribution approach can describe very well the inter-annual variability in annual maximum discharge across the conterminous United States.

Process representation of conifer tree-ring growth is improved by incorporation of climate memory effects

Process modeling of tree growth responses to climate can improve our understanding of the functioning of tree growth under climate change, but these models do not consider so-called memory effects. Further, memory effects are unstudied in temperate forests of China, which is showing high vulnerability to climate warming. Here, we integrate a process-based Vaganov–Shashkin–Lite (VS-Lite) model of tree ring formation with the stochastic antecedent modeling (SAM) approach, to quantify the current and antecedent climatic drivers of the tree-ring width chronologies of 109 conifers in temperate China. Based on climate-response patterns, tree-ring width series were clustered into “warm-dry,” “cold-dry,” and “cold-wet.” The VS-Lite results indicated growth in the “warm-dry” and “cold-dry” sites were primarily constrained by current soil moisture, while growth in the “cold-wet” sites was mainly constrained by cotemporaneous temperature. Compared to VS-Lite alone, we found significant improvements in model fit for the SAM framework in all forest types. Notably, antecedent drivers explained 14%, 11%, and 21% of the growth variations in “warm-dry,” “cold-dry,” and “cold-wet” sites, respectively, challenging the assertion that long memory is only important in dry forests. Commonality analysis revealed that growth in “cold-dry” and “cold-wet” sites was determined by antecedent climate while growth in the “cold-dry” and “warm-dry” clusters were determined by the endogenous (autoregressive) memory. In “warm-dry” and “cold-wet” sites, growth was significantly affected by antecedent temperature in opposite directions, whereas in the “cold-dry” sites it was affected by antecedent soil moisture conditions and soil moisture-temperature interactions. Our combined process-empirical model demonstrates that memory is prevalent across temperate forests in China and also improves representation of tree growth in diverse forests under novel climate conditions of the future.

Hydrology and riparian forests drive carbon and nitrogen supply and DOC : NO3−stoichiometry along a headwater Mediterranean stream

Abstract. In forest headwater streams, metabolic processes are predominately heterotrophic and depend on both the availability of carbon (C) and nitrogen (N) and a favourable C:N stoichiometry. In this context, hydrological conditions and the presence of riparian forests adjacent to streams can play an important, yet understudied role in determining dissolved organic carbon (DOC) and nitrate (NO3-) concentrations and DOC:NO3- molar ratios. Here, we aimed to investigate how the interplay between hydrological conditions and riparian forest coverage drives DOC and NO3- supply and DOC:NO3- stoichiometry in an oligotrophic headwater Mediterranean stream. We analysed DOC and NO3- concentrations and DOC:NO3- molar ratios during both base flow and storm flow conditions at three stream locations along a longitudinal gradient of increased riparian forest coverage. Further, we performed an event analysis to examine the hydroclimatic conditions that favour the transfer of DOC and NO3- from riparian soils to the stream during storms. Stream DOC and NO3- concentrations were generally low (overall averages ± SD were 1.0±0.6 mg C L−1 and 0.20±0.09 mg N L−1), although significantly higher during storm flow compared to base flow conditions in all three stream sites. Optimal DOC:NO3- stoichiometry for stream heterotrophic microorganisms (corresponding to DOC:NO3- molar ratios between 4.8 and 11.7) was prevalent at the midstream and downstream sites under both flow conditions, whereas C-limited conditions were prevalent at the upstream site, which had no surrounding riparian forest. The hydroclimatic analysis of storms suggested that large and medium storm events display a distinct mechanism of DOC and NO3- mobilization. In comparison to large storms, medium storm events showed limited hydrological responses that led to significantly lower increases in stream DOC and NO3- concentrations. During large storm events, different patterns of DOC and NO3- mobilization arise, depending on antecedent soil moisture conditions: drier antecedent conditions promoted rapid elevations of the riparian groundwater table, hydrologically activating a wider and shallower soil layer, and leading to relatively higher increases in stream DOC and NO3- concentrations compared to large storm events preceded by wet conditions. Our results suggest that (i) increased supply of limited resources during storms can potentially sustain in-stream heterotrophic activity during high flows, especially during large storm events preceded by dry conditions, and (ii) C-limited conditions upstream were overcome downstream, likely due to higher C inputs from riparian forests present at lower elevations. The contrasting spatiotemporal patterns in DOC and NO3- availability and DOC:NO3- stoichiometry observed at the studied stream suggest that groundwater inputs from riparian forests are essential for maintaining in-stream heterotrophic activity in oligotrophic, forest headwater catchments.

The impacts of wildfires of different burn severities on vegetation structure across the western United States rangelands

Large wildfires have increased in western US rangelands over the last three decades. There is limited information on the impacts of wildfires with different severities on the vegetation in these rangelands. This study assessed the impacts of large wildfires on rangeland fractional cover including annual forbs and grasses (AFG), perennial forbs and grasses (PFG), shrubs (SHR) and trees (TREE) across the western US, and explored relationships between changes in fractional cover and prefire soil moisture conditions. The Expectation Maximization (EM) algorithm was used to group wildfires into nine clusters based on the prefire rangeland fractional cover extracted from the Rangeland Analysis Platform. The Standardized Precipitation Evapotranspiration Index (SPEI) with various lag scales from the Gridded Surface Meteorological (GRIDMET) dataset was used to represent antecedent soil moisture conditions. The results showed generally that fractional cover decreased most for AFG and PFG during the fire year, one year postfire for SHR, and two years postfire for TREE. High severity wildfires led to the greatest decrease in cover for all plant functional types, while low severity wildfires caused the least decrease in the functional type cover in most cases, though some variations existed. Furthermore, the impacts of wildfires on vegetation cover were greater in woody (SHR and TREE) types than in herbaceous (AFG and PFG) types. Significant negative correlation existed between percent changes in AFG and PFG cover and SPEI indicating higher prefire soil moisture conditions likely increased fine fuel loads and led to a larger decrease in AFG and PFG cover following wildfires. Significant positive correlation existed between percent changes in SHR and TREE cover and SPEI indicating drier prefire conditions resulted in larger decreases in SHR and TREE cover following wildfires. These findings help better understand the impacts of wildfires on rangelands and provide insights for rangeland management.

Combined effects of rainfall types and antecedent soil moisture on runoff generation at a hillslope of red soil region

AbstractThe runoff threshold is one of the important parameters in hillslope hydrology. Information on the combined roles of rainfall types and antecedent soil moisture in the runoff generation threshold is still scarce. Therefore, we linked the runoff generation behaviours of a 150‐m hillslope with rainfall types and antecedent soil moisture conditions during a 4‐year observation period between January 2017 and December 2020 in the red soil region of China. We found that the surface runoff of a hillslope could be modelled with a relatively high accuracy using HYDRUS 1D. Rainfall event types, classified into four types (I, II, III and IV) using a machine learning based k‐means clustering algorithm, significantly affected the modelling accuracy of runoff. The prediction accuracy decreased as the level of rainfall type increased, with decreasing from 0.93 for type I to 0.41 for type IV and RMSE increasing from 0.0082 to 0.025 accordingly. The scope of the runoff threshold, represented by antecedent soil water storage of 0–30 cm and event rainfall amount (ASW+P), ranged between 97 and 139 mm. That is, when ASW+P was lower than the lower limit, no runoff occurred in all of the rainfall events, and runoff would be generated without considering the hydrological conditions if ASW+P was higher than 139 mm. The driving factors of runoff generation were strongly dependent on the classified rainfall types. Hourly maximum rainfall intensity was the main contributor to runoff generation for the relatively slight rainfall events (i.e., types I and II). The rainfall amount, however, played a dominant role for the rainfall types with larger amounts and intensities (i.e., types III and IV). These findings provide insights into the method of determining runoff thresholds as well as the inner mechanisms of runoff generation behaviours at the hillslope scale under regions with similar climatic and hydrological conditions to the present study.Highlights Heavy rainfall events were the main error sources of runoff modelling. The threshold of runoff generation was identified by the combination of antecedent soil moisture and rainfall. Threshold scope of runoff generation ranged between 97 and 139 mm. Rainfall classification is valuable in understanding runoff generation behaviours. Runoff driving factors depended on rainfall types.

Experimental analysis of soil moisture response to rainfall in a typical grassland hillslope under different vegetation treatments

The responses of soil moisture to rainfall are of great significance for watershed hydrological modeling. However, few studies have been done to investigate these responds on hillslope in a typical semi-arid grassland region. This study used high temporal resolution soil moisture data to explore the soil moisture dynamics, response conditions and its controls of 0–40 cm soil profile in the upslope (14°), midslope (9°), and downslope (4°) of a typical grassland inland river basin under bare ground (BG), stubble (SG), and natural grassland (CK) treatments. The results showed that soil water content and water storage increased in the downslope direction, and all showed as BG > SG > CK. The dry and wet changes in fast-changing layer (5 cm) and active layer (10 cm) were rapid, while soil moisture below 20 cm was relatively stable and fluctuated only in heavy or continuous rainfalls. The soil moisture response process varied greatly under different rainfall, rainfall intensity and antecedent soil moisture conditions, which explained 41.1% of the total difference. The rainfall replenishment threshold and the required initial soil profile water content of soil moisture response in 5 cm, 10 cm and 20 cm soil layers were 5.8 mm, 8.0 mm, 11.4 mm and 8.7 vol%, 9.4 vol%, 10.8 vol%, respectively. Soil properties, vegetation characteristics and topography could explain 38.8%, 14.5% and 5.6% of the soil moisture variation on the hillslope. In addition, under the comprehensive influence of environmental factors, changes in soil moisture of the upslope were significantly affected by soil sand content, the differences in the midslope were mainly due to soil clay content and belowground biomass, whereas the vegetation characteristics were the main factors in the downslope. This study can contribute to the further understanding of slope-scale ecohydrological processes and hydrological simulation of semi-arid grassland watersheds.

Hydraulic performance of mole drains and validation of steady-state drainage spacing equations for Mollisols

Central Santa Fe (Argentina) is a low-gradient region with impaired natural drainage. Extreme rainfall events are increasingly common and cause significant economic losses to agriculture. There is a growing interest in the use of mole drainage as a cost-effective strategy to mitigate such impacts. However, there is no information available that assists with mole drainage design, installation and maintenance, or the field assessment of a system’s hydraulic performance. This work was conducted with the dual objective of evaluating the hydraulic performance of an existing mole drains system and validating Hooghoudt’s steady-state drainage spacing equation for Mollisols. Three treatments representing different hydrologic conditions were tested; namely: (T1) rainfall of 360-min duration and intensity of 23 mm h-1, (T2) surface water ponding achieved through rainfall intensity of 50 mm h-1 and water depths between 50 and 100 mm, and (T3) similar surface water ponding conditions to treatment T2, but achieved through a rainfall intensity of 65 mm h-1 and either one (T3A) or two (T3B) sequential rainfall events. Following application of water, drainage commenced at 270 and 21 min for T1 and T2, and at 40 and 30 min for T3A and T3B, respectively. Mean drained depths were 1 and 3 mm for T1 and T2, respectively. For T3A, the mean drained depth was about one-third that measured for T3B (2.5 vs. 8.5 mm). Under a dry antecedent soil moisture condition, and rainfall intensity of 23 mm h-1, downward water movement was better described by piston flow. As surface water depth and soil water content increased, water flowed preferentially through the fissures and leg slots created by the mole plough implement at installation. Soil fissuring increased field hydraulic conductivity by factors between approximately three and ten. A 4 m mole drains spacing system was considered to be appropriate for managing excess water in the studied soil type. If the contribution of soil fissures to the field hydraulic conductivity can be accounted for, the Hooghoudt’s equation applied to two-layered soils may be used with confidence to help optimise the design of mole drainage systems for Mollisols. Application of this numerical approach, however, will require that the resultant spacing between mole drains be increased by about 30–40 %. Such an allowance will reduce installation costs and will not compromise the hydraulic efficiency of the drainage system.

Estimation of rainfall-induced surface runoff for the Assam region, India, using the GIS-based NRCS-CN method

ABSTRACT The NRCS-CN method, integrated with GIS and remote sensing, can be used for estimating curve numbers (CN) and surface runoff in geohydrological systems. The study area is divided into 63 sub-basins, and the land use land cover (LULC)-hydrologic soil group (HSG) complex is identified for each sub-basin. The CN values for three antecedent soil moisture (AMC) conditions are calculated and corrected for surface slope variations. The surface runoff depth is determined using the rainfall data for 16 years (2005–2020). The average runoff depth and mean annual precipitation ranges from 444.50 to 1960.55 mm and 936.99 to 3520.55 mm, respectively. For all sub-basins, strong correlations between runoff depth and rainfall (R2 ≥ 0.8) as well as between simulated runoff and measured runoff (R2 ≥ 0.8) are observed. The Nash–Sutcliffe model efficiency coefficient (NSE) values suggest that the model's efficiency is good to satisfactory.