Antecedent Moisture Conditions Research Articles

Hydrological responses to rainfall events including the extratropical cyclone Gloria in two contrasting Mediterranean headwaters in Spain; the perennial font del Regàs and the intermittent Fuirosos

AbstractCatchment hydrological responses to precipitation inputs, particularly during exceptionally large storms, are complex and variable, and our understanding of the associated runoff generation processes during those events is limited. Hydrological monitoring of climatically and hydrologically distinct catchments can help to improve this understanding by shedding light on the interplay between antecedent soil moisture conditions, hydrological connectivity, and rainfall event characteristics. This knowledge is urgently needed considering that both the frequency and magnitude of extreme precipitation events are increasing worldwide as a consequence of climate change. In autumn 2018, we installed water level sensors to monitor stream water and near‐stream groundwater levels at two Mediterranean forest headwater catchments with contrasting hydrological regimes: Font del Regàs (sub‐humid climate, perennial flow regime) and Fuirosos (semi‐arid climate, intermittent flow regime). Both catchments are located in northeastern Spain, where the extratropical cyclone Gloria hit in January 2020 and left in ca. 65 h outstanding accumulated rainfalls of 424 mm in Font del Regàs and 230 mm in Fuirosos. During rainfall events of low mean intensity, hydrological responses to precipitation inputs at the semi‐arid Fuirosos were more delayed and more variable than at the sub‐humid Font del Regàs. We explain these divergences by differences in antecedent soil moisture conditions and associated differences in catchment hydrological connectivity between the two catchments, which in this case are likely driven by differences in local climate rather than by differences in local topography. In contrast, during events of moderate and high mean rainfall intensities, including the storm Gloria, precipitation inputs and hydrological responses correlated similarly in the two catchments. We explain this convergence by rapid development of hydrological connectivity independently of antecedent soil moisture conditions. The data set presented here is unique and contributes to our mechanistic understanding on how streams respond to rainfall events and exceptionally large storms in catchments with contrasting flow regimes.

Soil Moisture Influence on the Incidence of Summer Mesoscale Convective Systems in the U.S. Great Plains

Abstract Mesoscale convective systems (MCS) are known to develop under ideal conditions of temperature and humidity profiles and large-scale dynamic forcing. Recent work, however, has shown that summer MCS events can occur under weak synoptic forcing or even unfavorable large-scale environments. When baroclinic forcing is weak, convection may be triggered by anomalous conditions at the land surface. This work evaluates land surface conditions for summer MCS events forming in the U.S. Great Plains using an MCS database covering the contiguous United States east of the Rocky Mountains, in boreal summers 2004–16. After isolating MCS cases where synoptic-scale influences are not the main driver of development (i.e., only non-squall-line storms), antecedent soil moisture conditions are evaluated over two domain sizes (1.25° and 5° squares) centered on the mean position of the storm initiation. A negative correlation between soil moisture and MCS initiation is identified for the smaller domain, indicating that MCS events tend to be initiated over patches of anomalously dry soils of ∼100-km scale, but not significantly so. For the larger domain, soil moisture heterogeneity, with anomalously dry soils (anomalously wet soils) located southwest (northeast) of the initiation point, is associated with MCS initiation. This finding is similar to previous results in the Sahel and Europe that suggest that induced meso-β circulations from surface heterogeneity can drive convection initiation.

GRACE Follow-On revealed Bangladesh was flooded early in the 2020 monsoon season due to premature soil saturation

The overall size and timing of monsoon floods in Bangladesh are challenging to measure. The inundated area is extensive in low-lying Bangladesh, and observations of water storage are key to understanding floods. Laser-ranging instruments on Gravity Recovery and Climate Experiment (GRACE) Follow-On spacecraft detected the peak water storage anomaly of 75 gigatons across Bangladesh in late July 2020. This is in addition to, and three times larger than, the maximum storage anomaly in soil layers during the same period. A flood propagation model suggested that the water mass, as shown in satellite observations, is largely influenced by slow floodplain and groundwater flow processes. Independent global positioning system measurements confirmed the timing and total volume of the flood water estimates. According to land surface models, the soils were saturated a month earlier than the timing of the peak floodplain storage observed by GRACE Follow-On. The cyclone Amphan replenished soils with rainfall just before the monsoon rains started, and consequently, excessive runoff was produced and led to the early onset of the 2020 flooding. This study demonstrated how antecedent soil moisture conditions can influence the magnitude and duration of flooding. Continuous monitoring of storage change from GRACE Follow-On gravity measurements provides important information complementary to river gauges and well levels for enhancing hydrologic flood forecasting models and assisting surface water management.

Rainfall-induced shallow landslides and soil wetness: comparison of physically based and probabilistic predictions

Abstract. Landslides are an impacting natural hazard in alpine regions, calling for effective forecasting and warning systems. Here we compare two methods (physically based and probabilistic) for the prediction of shallow rainfall-induced landslides in an application to Switzerland, with a specific focus on the value of antecedent soil wetness. First, we show that landslide susceptibility predicted by the factor of safety in the infinite slope model is strongly dependent on soil data inputs, limiting the hydrologically active range where landslides can occur to only ∼20 % of the country with typical soil parameters and soil depth models, not accounting for uncertainty. Second, we find the soil saturation estimate provided by a conceptual hydrological model (PREVAH) to be more informative for landslide prediction than that estimated by the physically based coarse-resolution model (TerrSysMP), which we attribute to the lack of temporal variability and coarse spatial resolution in the latter. Nevertheless, combining the soil water state estimates in TerrSysMP with the infinite slope approach improves the separation between landslide triggering and non-triggering rainfall events. Third, we demonstrate the added value of antecedent soil saturation in combination with rainfall thresholds. We propose a sequential threshold approach, where events are first split into dry and wet antecedent conditions by an N d (day) antecedent soil saturation threshold, and then two different total rainfall–duration threshold curves are estimated. This, among all different approaches explored, is found to be the most successful for landslide prediction.

Dynamic Hillslope Soil Moisture in a Mediterranean Montane Watershed

AbstractVariations in hillslope soil moisture control forest hydrologic fluxes and storage pools, yet sparse observations combined with the complexity and heterogeneity of water movement and storage in the vadose zone can make temporal and spatial patterns and processes difficult to predict. We used two years of field observations of volumetric soil moisture at three depths (15, 30, and 100 cm) across five topographic positions (riparian, toeslope, sideslope, shoulder, and ridge) along three hillslope transects to better understand how soil moisture changes with hillslope position and through time. As expected, we found higher values of soil moisture at all depths at the riparian and toeslope positions. Unexpectedly, we found that ridges were particularly wet during the wet winter months and dried quickly during the summer months, indicating that topography alone cannot account for mean wet season soil moisture in our Mediterranean climate field site. The variability in soil moisture across all soil depths and topographic positions was greatest when soils were dry and decreased under wet soil conditions; this variability remained high in the deeper soil horizons, regardless of season. Lastly, event analysis suggests that the response to early season rainfall was highly variable along the hillslopes and was likely dominated by localized controls such as microtopography and vegetation as well as soil texture, antecedent moisture conditions, and rainfall characteristics. Our results suggest that the drivers of wet and dry season soil moisture dynamics can vary across topographic positions along a hillslope and do not always follow topographic controls.

Identification of the controlling factors for hydrological responses by artificial neural networks

AbstractIdentifying the controlling factors for hydrological responses is of great importance for artificial neural network‐based flood forecasting models, which are often hindered by the lack of physical mechanisms. To explore the first‐order controlling factors of hydrograph patterns, a hybrid neural network was designed to analyse the impacts of potential driving variables with different temporal and spatial resolutions on hydrograph patterns. The Jinhua River Basin in Southeast China was used as an example in this study. Flood events with different hydrograph patterns and six external factors denoting potential controlling factors were individually classified into specific clusters using self‐organizing maps (SOMs). Based on the back‐propagation neural network (BPNN) and leave‐one‐out cross‐validation methods, the controlling factors of different flood patterns were identified by comparing the performances of flood simulation models trained with datasets before and after the potential controlling factor classification. The results showed that (i) the classification of controlling factors indicating various runoff regimes significantly improved the performance of data‐driven models in flood simulation in terms of correlation coefficient, Nash‐Sutcliffe coefficient, and normalized root mean square error; (ii) the spatial distribution of antecedent soil moisture and vegetation conditions as well as the temporal distribution of rainfall dominated different hydrograph patterns; and (iii) the transition of dominant rainfall‐runoff processes could be identified in an individual flood event using the hybrid SOM–BPNN model, indicating the varying influence of potential controlling factors on streamflow. Overall, the hybrid neural network models trained with datasets classified by controlling factors provide a general analytical framework to identify the governing dynamics for different flood patterns and improve the accuracy of flood simulations. Additionally, more attention should be devoted to improving the time to peak error of hydrological models, which cannot be settled by data‐driven models trained with different data‐splitting strategies.

Characterizing the effects of dry antecedent soil moisture conditions, channel transmission losses, and variable precipitation on peak flow scaling

Peak discharge to drainage area relationships are widely applied engineering solutions for design flood estimation in ungauged basins. In this study we investigated the effects of antecedent moisture conditions (AMC), rainfall duration, and channel transmission losses on discharge-drainage area power law scaling relationships. The spatial organization of peak discharges were evaluated through coupled surface-subsurface physics based numerical modeling. We take advantage past research and the detailed flexibility of HydroGeoSphere, the numerical modeling platform, to systematically vary AMC and rainfall depths evolving the current understanding of the effects on discharge-drainage area relationships. Channel network dynamics in the Beaver Creek watershed dominated the location of a scale break, or the location where the log-log linear relationship between peak discharge and drainage area is best described by two or more slopes. Single peaked hydrographs and high-quality power law scaling relationships resulted from wet AMC conditions or high precipitation depths. As we reduced the AMC, under low rainfall depths, the hillslope response diverged from the channel response, providing an unorganized system response no longer fitting the power law scaling relationship. Additionally, when initializing the system under losing stream conditions, where the stream channel is assumed unsaturated, a single peaked response ensued whereby the channel and hillslope responded in unison. This study adds to our current understanding of the physical basis of the variability of the scaling structure of peak discharges that is observed in single rainfall-runoff events by investigating the spatial organization of peak discharges under exceedingly dry antecedent soil moisture conditions.

Integrating observations and models to determine the effect of seasonally frozen ground on hydrologic partitioning in alpine hillslopes in the Colorado Rocky Mountains, USA

AbstractThis study integrated spatially distributed field observations and soil thermal models to constrain the impact of frozen ground on snowmelt partitioning and streamflow generation in an alpine catchment within the Niwot Ridge Long‐Term Ecological Research site, Colorado, USA. The study area was comprised of two contrasting hillslopes with notable differences in topography, snow depth and plant community composition. Time‐lapse electrical resistivity surveys and soil thermal models enabled extension of discrete soil moisture and temperature measurements to incorporate landscape variability at scales and depths not possible with point measurements alone. Specifically, heterogenous snowpack thickness (~0–4 m) and soil volumetric water content between hillslopes (~0.1–0.45) strongly influenced the depths of seasonal frost, and the antecedent soil moisture available to form pore ice prior to freezing. Variable frost depths and antecedent soil moisture conditions were expected to create a patchwork of differing snowmelt infiltration rates and flowpaths. However, spikes in soil temperature and volumetric water content, as well as decreases in subsurface electrical resistivity revealed snowmelt infiltration across both hillslopes that coincided with initial decreases in snow water equivalent and early increases in streamflow. Soil temperature, soil moisture and electrical resistivity data from both wet and dry hillslopes showed that initial increases in streamflow occurred prior to deep soil water flux. Temporal lags between snowmelt infiltration and deeper percolation suggested that the lateral movement of water through the unsaturated zone was an important driver of early streamflow generation. These findings provide the type of process‐based information needed to bridge gaps in scale and populate physically based cryohydrologic models to investigate subsurface hydrology and biogeochemical transport in soils that freeze seasonally.

Evidence of shorter more extreme rainfalls and increased flood variability under climate change

Increases in extreme rainfall intensities as a result of climate change pose a great risk due to the possibility of increases in pluvial flooding, particularly in urban and developed areas. But evidence is emerging that the observed increases in extreme rainfall are not resulting in universal increases in flooding. Indeed, on a global scale, studies consistently find more gauge stations with decreasing rather than increasing trends in the annual maxima flood magnitude. Here, we aim to improve our understanding of how rainfall and streamflow extremes are changing and why floods are not always observed to increase despite increases in rainfall extremes. To do so, we examine trends in streamflow events using 2776 stations from the Global Runoff Data Centre, with events chosen to isolate the impact of changes to their respective rainfall and antecedent soil moisture. The analysis is limited to stations with 30 years or more of active record with the majority of stations in North America, Europe, Brazil, Oceania, and southern Africa.Consistent with physical reasoning, for more frequent events such as the annual maxima, it is found the peak 1-day rainfall event intensity is increasing approximately a rate of 6–7%/°C, with rarer event intensities increasing at a rate exceeding the Clausius-Clapeyron relation. We find that storm volumes are not increasing as greatly as the peak rainfall and storm durations are decreasing, pointing to an intensification of rainfall events and a peakier temporal pattern. While rainfall is intensifying, the magnitude of frequent floods (those expected to occur on average once per year) are in general decreasing, particularly in tropical and arid regions of the world. We find that this is likely due to a dominance of drying antecedent soil moisture conditions. However, the magnitude of rarer floods has, on average been increasing. We suggest this is because, for these rarer events, the increase in rainfall outweighs the decrease in soil moisture. Our results point to a worst of both world’s scenario where small floods, responsible for filling our water supplies, are decreasing, while the large flood events which pose a risk to life and infrastructure, are increasing.

Low hydrological connectivity after summer drought inhibits DOC export in a forested headwater catchment

Abstract. Understanding the controls on event-driven dissolved organic carbon (DOC) export is crucial as DOC is an important link between the terrestrial and the aquatic carbon cycles. We hypothesized that topography is a key driver of DOC export in headwater catchments because it influences hydrological connectivity, which can inhibit or facilitate DOC mobilization. To test this hypothesis, we studied the mechanisms controlling DOC mobilization and export in the Große Ohe catchment, a forested headwater in a mid-elevation mountainous region in southeastern Germany. Discharge and stream DOC concentrations were measured at an interval of 15 min using in situ UV-Vis (ultraviolet–visible) spectrometry from June 2018 until October 2020 at two topographically contrasting subcatchments of the same stream. At the upper location (888 m above sea level, a.s.l.), the stream drains steep hillslopes, whereas, at the lower location (771 m a.s.l.), it drains a larger area, including a flat and wide riparian zone. We focus on four events with contrasting antecedent wetness conditions and event size. During the events, in-stream DOC concentrations increased up to 19 mg L−1 in comparison to 2–3 mg L−1 during baseflow. The concentration–discharge relationships exhibited pronounced but almost exclusively counterclockwise hysteresis loops which were generally wider in the lower catchment than in the upper catchment due to a delayed DOC mobilization in the flat riparian zone. The riparian zone released considerable amounts of DOC, which led to a DOC load up to 7.4 kg h−1. The DOC load increased with the total catchment wetness. We found a disproportionally high contribution to the total DOC export of the upper catchment during events following a long dry period. We attribute this to the low hydrological connectivity in the lower catchment during drought, which inhibited DOC mobilization, especially at the beginning of the events. Our data show that not only event size but also antecedent wetness conditions strongly influence the hydrological connectivity during events, leading to a varying contribution to DOC export of subcatchments, depending on topography. As the frequency of prolonged drought periods is predicted to increase, the relative contribution of different subcatchments to DOC export may change in the future when hydrological connectivity will be reduced more often.

Spatial access and resource limitations control carbon mineralization in soils

Core-scale soil carbon fluxes are ultimately regulated by pore-scale dynamics of substrate availability and microbial access. These are constrained by physicochemical and biochemical phenomena (e.g. spatial access and hydrologic connectivity, physical occlusion, adsorption-desorption with mineral surfaces, nutrient and resource limitations). We conducted an experiment to determine how spatial access and resource limitations influence core-scale water-soluble SOM mineralization, and how these are regulated by antecedent moisture conditions. Intact soil cores were incubated at field-moist vs. drought conditions, after which they were saturated from above (to simulate precipitation) or below (to simulate groundwater recharge). Soluble C (acetate) and N (nitrate) forms were added to some cores during the rewetting process to alleviate potential nutrient limitations. Soil respiration was measured during the incubation, after which pore water was extracted from the saturated soils and analyzed for water soluble organic carbon concentrations and characterization. Our results showed that C amendments increased the cumulative CO2 evolved from the soil cores, suggesting that the soils were C-limited. Drought and rewetting increased soil respiration, and there was a greater abundance of complex aromatic molecules in pore waters sampled from these soils. This newly available substrate appeared to alleviate nutrient limitations on respiration, because there were no further respiration increases with subsequent C and N amendments. We had hypothesized that respiration would be influenced by wetting direction, as simulated precipitation would mobilize C from the surface. However, as a main effect, this response was seen only in the C-amended soils, indicating that surface-C may not have been bioavailable. At the pore scale (pore water samples), drought and the C, N amendments caused a net loss of identified molecules when the soils were rewet from below, whereas wetting from above caused a net increase in identified molecules, suggesting that fresh inputs stimulated the C-and N-limited microbial populations present deeper in the soil profile. Our experiment highlights the complex and interactive role of antecedent moisture conditions, wetting direction, and resource limitations in driving core-scale C fluxes.

A global perspective on the probability of propagation of drought: From meteorological to soil moisture

With a copula-based probabilistic model, this study presents a global view on the propagation of meteorological to soil moisture drought. Meta-Gaussian and three-dimensional vine copulas were employed to construct the bivariate and trivariate conditional joint drought distributions, respectively, using the time series of standardized precipitation evapotranspiration index (SPEI) and standardized soil moisture index (SMI) derived from the Global Land Data Assimilation System (GLDAS) simulations. Three different cases, including the probability and magnitude of soil moisture drought (SMI) conditioned on 1-month meteorological drought (SPEI1), seasonal meteorological drought (SPEI3), and the joint effects of SPEI1 and antecedent soil moisture conditions (SMIlag-1), were analyzed. Then, the duration of soil moisture drought in response to meteorological drought, and potentially affected cropland area were analyzed to assess potential agricultural impacts. Results showed that the likelihood of soil moisture drought conditioned on SPEI1 in the Amazon rainforest, southern and eastern China, Southeast Asia, southeastern United States, and west Africa was generally higher than in other regions. Under the impact of seasonal meteorological drought, except for the Qinghai-Tibet Plateau in China, northern Russia, and North Africa, the probability of soil moisture drought occurrence overall increased with significant increments in Europe and North America. The antecedent soil moisture condition also influenced the probability of drought propagation, and this effect was particularly significant for Northern China, Russia, the U.S. Midwest, Canada, and Australia. Spatially, the western U.S., northern Canada, South Africa, eastern Europe, North China, northern Russia, and most parts of Australia present longer duration of soil moisture drought in response to meteorological drought. Comprehensive considerations of the conditional probability, duration, and potentially affected area of soil moisture drought for croplands revealed that among five continents, South America was more vulnerable to agricultural drought under the condition of meteorological drought.

Nutrient capture in an Iowa farm pond: Insights from high-frequency observations

Shallow constructed ponds are abundant landscape features in the midwestern United States, suggested as an edge of field best management practice (BMP) in voluntary nutrient reduction strategies. The efficacy of such features is highly uncertain, however, and previous studies have lacked sufficient temporal resolution to determine N and P removals during critical periods of transport. We utilized high-frequency in-situ measurements and flow-weighted grab sampling to determine water and nutrient budgets for a typical constructed “farm pond” in central Iowa situated within the Iowa Southern Drift Plain. Our monitoring approach yielded insight into in-stream nitrogen processing and the relative importance of transport-vs. supply-limited N delivery. Diel patterns in NO3–N observed during early Spring, prior to canopy closure, revealed that in-stream primary production and NO3–N assimilation can influence downstream N delivery in a stream with nitrate pollution (mean annual NO3–N of nearly 5 mg/L). Analysis of discharge-concentration hysteresis for NO3–N showed a shift from transport to supply limitation for NO3–N delivery over the growing season, influenced by antecedent moisture, with wet antecedent conditions leading to supply limitation.Significant NO3–N removal (64% of 19.8 kg/ha inputs) occurred within the 4.2 ha pond (230 ha watershed), but total N removal was much lower (36% removal of 22.3 kg/ha inputs). The lower total N removal highlights the importance of both particulate N and dissolved organic N and ammonia export to the N budgets of hypereutrophic small ponds. Total P removal in the pond was only 8% of 2.3 kg/ha inputs, likely due to internal loading of recent and legacy sedimentary P within the pond. High-flow events dominated N and P inputs, during which removal efficacy of the pond was significantly diminished. Poor process performance during critical moments may partially explain lower than expected water quality improvements post-BMP implementation. Accordingly, shifting hydroclimatic regimes (e.g., frequency of intense rainfall events) will impact the efficacy of small ponds and other edge of field BMPs for nutrient reduction.

Performance evaluation of spatially distributed, CN-based rainfall-runoff model configurations for implementation in spatial land use optimization analyses

Due to their large computational burden, spatially distributed hydrological models are unsuitable for iterative spatial optimization analyses, which need to take into account location-specific runoff water accumulation and re-infiltration processes. Therefore, a computationally efficient, raster-based and spatially-explicit RR-model was conceptualized. For this purpose, the SCS-CN approach was implemented in a spatially distributed manner, adjusting CN values to antecedent soil moisture conditions (AMC) and routing runoff to the catchment outlet. To take into account spatial interaction along the overland flow paths, the SCS-CN approach was extended with a re-infiltration algorithm. Several model configurations were evaluated, based on combinations of two AMC correction methods and three re-infiltration algorithms. These model configurations were applied in three catchments in Flanders, Belgium. The results indicate a poor performance of the default SCS-CN method, which was, however, considerably improved by the implementation of AMC correction and re-infiltration. Though calibration of the RR-model configurations was limited, several model configurations achieved model performances adequate for use in spatial optimization analyses. The most performant RR-model configuration (NSE of 0.57, 0.56 and 0.64 for the three catchments) implements a λ of 0.05, the AMC correction method proposed by Neitsch et al. (2011), and the re-infiltration method of Van Loo (2018), calculating overland flow velocity with Manning’s n adjusted to seasonal vegetation cover variations. As this model allows for spatial interaction through re-infiltration, but with limited computation times, it is well applicable for iterative spatial optimization analyses, although a more extensive calibration and validation exercise on catchments with a wider range of characteristics is required to provide a general assessment of its applicability and accuracy.

Semi‐Automated Characterization of Streamwater Specific Conductivity Response to Storms

AbstractSpecific electrical conductivity (SC) is a basic, effective indicator of water quality. The recent increase in SC data collected with high‐frequency sensors has created a strong need for algorithms that can aid interpretation of these data. This study presents an algorithm that finds and quantifies SC temporal patterns and applies that algorithm to a dataset from a 7.5 km2 forested catchment in central New Hampshire. During and after rain events, we show three patterns that emerge in SC time series: A solute flush, resulting in an initial increase in SC, followed by a dilution, followed by the SC’s recovery toward pre‐rain conditions. We compared these SC patterns to precipitation amount and intensity, antecedent wetness, and seasonality. Our results indicate that the magnitude of the flush was driven primarily by precipitation intensity and total rainfall during a storm, and secondarily by antecedent moisture conditions. The magnitude of the dilution was driven mainly by the precipitation amount. The rate of SC recovery was driven by precipitation amount and was correlated with dilution. Overall, the algorithm successfully extracted event‐driven characteristics in the SC time series, allowing the development of functional relationships with hydrologic drivers. Applying similar methodologies to more catchments in the future will help identify functional relationships at more sites and use these relationships to identify catchments most sensitive to future precipitation changes.

Study on the early warning and forecasting of flash floods in small watersheds based on the rainfall pattern of risk probability combination

Flash floods cause great harm to people’s lives and property safety. Rainfall is one of the main causes of flash floods in small watersheds. The uncertainty of rainfall events results in inconsistency between the traditional single rainfall pattern and the actual rainfall process, which poses a great challenge for the early warning and forecasting of flash floods. To carry out the effective flash flood early warning and forecasting, this paper proposes a novel rainfall pattern by coupling total rainfall and peak rainfall intensity based on copula functions, i.e., the rainfall pattern of risk probability combination (RPRPC). On this basis, the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) hydrological model is used to simulate the rainfall-runoff process, the trial algorithm is used to calculate the critical rainfall (CR), and the optimistic-general-pessimistic (O–G-P) early warning mode considering the decision maker's risk preference is proposed. The small watershed of Xinxian in Henan province, China, is taken as a case study for calculation. The results show that the RPRPC is feasible and closer to the actual rainfall process than the traditional rainfall pattern, Frank copula function is the best for determining the joint distribution function of total rainfall and peak rainfall intensity, and the HEC-HMS model can be applied to small watersheds in hilly areas. Additionally, both RPRPC and antecedent soil moisture condition (ASMC) have influence on CR, and the variation of RPRPC will change the influence of ASMC on CR. Finally, the effectiveness of O–G-P early warning mode is verified.

The importance of antecedent vegetation and drought conditions as global drivers of burnt area

Abstract. The seasonal and longer-term dynamics of fuel accumulation affect fire seasonality and the occurrence of extreme wildfires. Failure to account for their influence may help to explain why state-of-the-art fire models do not simulate the length and timing of the fire season or interannual variability in burnt area well. We investigated the impact of accounting for different timescales of fuel production and accumulation on burnt area using a suite of random forest regression models that included the immediate impact of climate, vegetation, and human influences in a given month and tested the impact of various combinations of antecedent conditions in four productivity-related vegetation indices and in antecedent moisture conditions. Analyses were conducted for the period from 2010 to 2015 inclusive. Inclusion of antecedent vegetation conditions representing fuel build-up led to an improvement of the global, climatological out-of-sample R2 from 0.579 to 0.701, but the inclusion of antecedent vegetation conditions on timescales ≥ 1 year had no impact on simulated burnt area. Current moisture levels were the dominant influence on fuel drying. Additionally, antecedent moisture levels were important for fuel build-up. The models also enabled the visualisation of interactions between variables, such as the importance of antecedent productivity coupled with instantaneous drying. The length of the period which needs to be considered varies across biomes; fuel-limited regions are sensitive to antecedent conditions that determine fuel build-up over longer time periods (∼ 4 months), while moisture-limited regions are more sensitive to current conditions that regulate fuel drying.

Role of underlying surface, rainstorm and antecedent wetness condition on flood responses in small and medium sized watersheds in the Yangtze River Delta region, China

Floods are becoming more frequent and are hard to control due to the shortage of water conservancy projects in small and medium sized basins, especially in developing regions. Understanding the hydrologic responses and estimated flood characteristics to storm events can help to predict flood disasters and form better effective mitigation and adaptation strategies. Using observations of several representative watersheds and Artificial Neural Networks (ANN) and Principal Component Regression (PCR) models, we conducted data-driven analyses to examine the flood responding characteristics. We quantified the relative contributions of the influencing factors to the variation of each flood characteristic. Statistical analysis of the observations shows that the drainage area plays a key role in determining the distribution of lag time and peak discharge. Rainstorm variability has direct influence on floods, and typhoon-induced rainstorms with high total rainfall and rainfall intensity generate higher lag time, flood peak, unit discharge and runoff depth, but lower runoff coefficient. The ANN and PCR models accurately predicted the variations of flood features using the driving factors including physical geographical characteristics, rainstorm features, and antecedent condition. Physical geographic characteristics are key influential factors of lag time, flood peak and runoff coefficient, while the rainstorm features control the magnitude of unit discharge and runoff depth. These results indicate that floods are mainly affected by rainstorm features and physical geographic characteristics in the Yangtze River delta, and it might become more damaging with the increasing rainfall extremes and sprawling impervious surfaces in a changing environment.

Hydrological Analysis of Extreme Rain Events in a Medium-Sized Basin

The hydrological response of a medium-sized watershed with both rural and urban characteristics was investigated through event-based modeling. Different meteorological event conditions were examined, such as events of high precipitation intensity, double hydrological peak, and mainly normal to wet antecedent moisture conditions. Analysis of the hydrometric features of the precipitation events was conducted by comparing the different rainfall time intervals, the total volume of water, and the precedent soil moisture. Parameter model calibration and validation were performed for rainfall events under similar conditions, examined in pairs, in order to verify two hydrological models, the lumped HEC-HMS (Hydrologic Engineering Center’s Hydrologic Modeling System model) and the semi-distributed HBV-light (a recent version of Hydrologiska Byråns Vattenbalansavdelning model), at the exit of six individual gauged sub-basins. Model verification was achieved by using the Nash–Sutcliffe efficiency and volume error index. Different time of concentration (Tc) formulas are better applied to the sub-watersheds with respect to the dominant land uses, classifying the Tc among the most sensitive parameters that influence the time of appearance and the magnitude of the peak modeled flow through the HEC-HMS model. The maximum water content of the soil box (FC) affects most the peak flow via the HBV-light model, whereas the MAXBAS parameter has the greatest effect on the displayed time of peak discharge. The modeling results show that the HBV-light performed better in the events that had less precipitation volume compared to their pairs. The event with the higher total precipitated water produced better results with the HEC-HMS model, whereas the rest of the two high precipitation events performed satisfactorily with both models. April to July is a flood hazard period that will be worsened with the effect of climate change. The suggested calibrated parameters for severe precipitation events can be used for the prediction of future events with similar features. The above results can be used in the water resources management of the basin.

Influence of Agricultural Managed Aquifer Recharge (AgMAR) and Stratigraphic Heterogeneities on Nitrate Reduction in the Deep Subsurface

AbstractAgricultural managed aquifer recharge (AgMAR) is a strategy whereby surface water is used to intentionally flood croplands to recharge underlying aquifers. However, nitrate (NO3−) contamination in agriculturally intensive regions poses a threat to groundwater resources under AgMAR. We use a reactive transport model to understand the effects of AgMAR management strategies (i.e., by varying the frequency, duration between flooding events, and amount of water) on NO3− leaching to groundwater under different stratigraphic configurations and antecedent moisture conditions. We examine the potential of denitrification and nitrogen retention in deep vadose zone sediments (∼15 m) using variable AgMAR application rates on two‐dimensional representations of differently textured soils, soils with discontinuous bands/channels, and with preferential flow paths characteristic of agricultural fields. Simulations indicate finer textured sediments, alone or embedded within/adjacent to high flow regions, are important reducing zones providing conditions needed for denitrification. Simulation results suggest that applying water all‐at‐once rather than in increments transports higher concentrations of NO3− deeper into the profile, which may exacerbate groundwater quality. This transport into deeper depths can be aggravated by wetter antecedent soil moisture conditions. However, applying water all‐at‐once also increases denitrification within the vadose zone by promoting anoxic conditions. We conclude that AgMAR can be designed to enhance denitrification in the subsurface and reduce NO3− leaching to groundwater, while specifically accounting for lithologic heterogeneity, antecedent soil moisture conditions, and depth to the water table. Our findings are potentially relevant to other systems that experience flooding inundation such as floodplains and dedicated recharge basins.