Antecedent Hydrological Conditions Research Articles

Antecedent Hydrologic Conditions Reflected in Stream Lithium Isotope Ratios During Storms

AbstractAntecedent hydrological conditions are recorded through the evolution of dissolved lithium isotope signatures (Li) by juxtaposing two storm events in an upland watershed subject to a Mediterranean climate. Discharge and Li are negatively correlated in both events, but mean Li ratios and associated ranges of variation are distinct between them. We apply a previously developed reactive transport model (RTM) for the site to these event‐scale flow perturbations, but observed shifts in stream Li are not reproduced. To reconcile the stability of the subsurface solute weathering profile with our observations of dynamic stream Li signatures, we couple the RTM to a distribution of fluid transit times that evolve based on storm hydrographs. The approach guides appropriate flux‐weighting of fluid from the RTM over a range of flow path lengths, or equivalently fluid residence times. This flux‐weighted RTM approach accurately reproduces dynamic storm Li‐discharge patterns distinguished by the antecedent conditions of the watershed.

Modelling antecedent soil hydrological conditions to improve the prediction of landslide susceptibility in typhoon-prone regions

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.

On the Sources of Water Supply Forecast Error in Western Colorado

Abstract Annual spring and summer runoff from western Colorado is relied upon by 40 million people, six states, and two countries. Cool season precipitation and snowpack have historically been robust predictors of seasonal runoff in western Colorado. Forecasts made with this information allow water managers to plan for the season ahead. Antecedent hydrological conditions, such as root zone soil moisture and groundwater storage, and weather conditions following peak snowpack also impact seasonal runoff. The roles of such factors were scrutinized in 2020 and 2021: seasonal runoff was much lower than expectations based on snowpack values alone. We investigate the relative importance of meteorological and hydrological conditions occurring before and after the snowpack season in predicting seasonal runoff in western Colorado. This question is critical because the most effective investment strategy for improving forecasts depends on if errors arise before or after the snowpack season. This study is conducted using observations from the Snow Telemetry Network, root zone soil moisture and groundwater data from the Western Land Data Assimilation Systems, and a random forest–based statistical forecasting framework. We find that on average, antecedent root zone soil moisture and groundwater storage values do not add significant skill to seasonal water supply forecasts in western Colorado. In contrast, using precipitation and temperature data after the time of peak snowpack improves water supply forecasts significantly. The 2020 and 2021 runoffs were hampered by dry conditions both before and after the snowpack season. Both antecedent soil moisture and spring/summer precipitation data improved water supply forecast accuracy in these years. Significance Statement Seasonal water supply forecasts in western Colorado are highly valuable because spring and summer runoff from this region helps support the water supply of 40 million people. Accurate forecasts improve the management of the region’s water. Heavy investments have been made in improving our ability to monitor antecedent hydrological conditions in western Colorado, such as root zone soil moisture and groundwater. However, results from this study indicate that the largest source of uncertainty in western Colorado runoff forecasts is future weather. Therefore, improved subseasonal-to-seasonal weather forecasts for western Colorado are what is most needed to improve regional water supply forecasts, and the ability to properly manage western Colorado water.

Stream hydrology and a pulse subsidy shape patterns of fish foraging.

Pulsed subsidy events create ephemeral fluxes of hyper-abundant resources that can shape annual patterns of consumption and growth for recipient consumers. However, environmental conditions strongly affect local resource availability for much of the year, and can heavily impact consumer foraging and growth patterns prior to pulsed subsidy events. Thus, a consumer's capacity to exploit pulse subsidy resources may be influenced by antecedent environmental conditions, but this has rarely been shown in nature and is unknown in aquatic ecosystems. Here, we sought to understand the importance of hydrologic variation and a salmon pulse subsidy on the foraging and growth patterns of two stream salmonids in a coastal southeast Alaska drainage. To do this, we sampled fish stomach contents at a high temporal frequency (daily-weekly measurements) and analyzed fish consumption rates in relation to streamflow and pulse subsidy resource availability. We then explored the influence of interannual hydrologic variation on access to pulse subsidy resources (i.e. whether fish exceeded an egg consumption gape limit) in a bioenergetic simulation. Prior to Pink Salmon spawning, Dolly Varden and Coho Salmon displayed distinct and nonlinear flow-foraging relationships, where forage for both species consisted primarily of macroinvertebrates. During this time period, consumption maxima coincided with baseflow and the highest observed flow conditions, and consumption minima were observed at severe low-water and intermediate flow values. After salmon spawning began, forage was not significantly related to flow and consisted primarily of salmon eggs. Further, consumption rates increased overall, and foraging patterns did not appear to be affected by flow in either species. Bioenergetic simulations revealed that patterns of interannual hydrologic variation may shift Coho Salmon growth trajectories among years. Together, our results suggest that access to marine pulse subsidy resources may depend on whether antecedent hydrologic conditions are suitable for juvenile salmonids to grow large enough to consume salmon eggs by the onset of spawning.

Diversity mediates the responses of invertebrate density to duration and frequency of rivers' annual drying regime

Predicting the impacts of global change on highly dynamic ecosystems requires a better understanding of how communities respond to disturbance duration, frequency and timing. Intermittent rivers and ephemeral streams are dynamic ecosystems that are recognized as the most common fluvial ecosystem globally. The complexity of the drying process can give rise to different annual and antecedent hydrological conditions, but their effect on aquatic communities remains unclear. Here, using aquatic invertebrates from 33 streams across a flow‐intermittence gradient, we assessed how annual (drying duration and frequency) and recent drying characteristics (duration of the last dry period and flowing duration since the last rewetting) affect the density and diversity metrics of communities and trophic groups while controlling for other key abiotic factors (dissolved oxygen and altitude). We characterized invertebrate communities using taxonomy and functional traits to capture biological features that increase vulnerability to drying. In addition, using structural equation modelling (SEM), we evaluated pathways by which drying characteristics directly impact invertebrate density and whether diversity indirectly mediates such relationships. We show that drying frequency drove reductions in diversity at the community level and within trophic groups, whereas both the drying duration and frequency had a negative influence on density metrics. Reductions in taxonomic richness were linked to increased annual drying duration, whereas functional diversity declined in response to annual drying frequency. Filterer, predator and shredder trophic groups exhibited the strongest negative responses to drying. Recent drying characteristics had a minor effect on density and diversity metrics. Our SEM results demonstrated that diversity mediates the negative impacts of annual drying duration and frequency on invertebrate density through reductions in their taxonomic richness and functional diversity. Our results underscore the importance of considering multiple drying characteristics together with the interdependence of density and diversity to better anticipate drying responses in freshwater ecosystems.

A stacking ensemble model for hydrological post-processing to improve streamflow forecasts at medium-range timescales over South Korea

This study presents the potential of hydrological ensemble forecasts over South Korea for medium-range forecast lead times (1–7 days). To generate hydrological forecasts, this study utilizes a framework based on stacking ensemble learning, an emerging machine learning technique that includes a two-level structure: base-learner and meta-learner models. In particular, the present research contributes to hydrological post-processing techniques by: (1) introducing a penalized quantile regression-based meta-learner to generate probabilistic predictions, (2) considering modeled climate predictions and antecedent hydrologic conditions simultaneously for regional hydrological forecast development, and (3) quantifying the skill enhancements from the multi-model forecasts under the stacking generalization. The proposed model is evaluated in massive 473 grid cells along with nine additional simpler models to test the specific hypotheses introduced in this study. Results indicate that our proposed forecasts can be used for relatively short lead times. In addition, results demonstrate that utilizing a penalized probabilistic meta-learner and antecedent conditions contributes to the forecast skill improvements. Lastly, we find that base-model diversity outperforms increased ensemble size alone in enhancing the forecast abilities under the stacking ensemble generalization. We conclude this paper with a discussion of possible forecast model improvements from an adaptation of additional information from input and model structures under the stacking generalization.

HydroMet: A New Code for Automated Objective Optimization of Hydrometeorological Thresholds for Landslide Initiation

Landslide detection and warning systems are important tools for mitigation of potential hazards in landslide prone areas. Traditionally, warning systems for shallow landslides have been informed by rainfall intensity-duration thresholds. More recent advances have introduced the concept of hydrometeorological thresholds that are informed not only by rainfall, but also by subsurface hydrological measurements. Previously, hydrometeorological thresholds have been shown to improve capabilities for forecasting shallow landslides, and they may ultimately be adapted to more generalized landslide forecasting. We present HydroMet, a code developed in Python by the U.S. Geological Survey, which allows users to guide the automated estimation of hydrometeorological thresholds for a site or area of interest, with the flexibility to select preferred threshold variables for the antecedent hydrologic conditions and the triggering meteorological conditions. Users can import hydrologic time-series data, including rainfall, soil-water content, and pore-water pressure, along with the times of known landslide occurrences, and then conduct objective optimization of warning thresholds using receiver operating characteristics. HydroMet presents many additional options, including selecting the threshold formula, the timescale of possible threshold variables, and the skill statistics used for optimization. Users can develop dual-stage thresholds for watch and warning alerts, with a lower, risk-averse threshold to avoid missed alarms and a less conservative threshold to minimize false alarms. Users may also choose to split their inventory data into calibration and evaluation subsets to independently evaluate the performance of optimized thresholds. We present output and applications of HydroMet using monitoring data from landslide-prone areas in the U.S. to demonstrate its utility and ability to produce thresholds with limited missed and false alarms for informing the next generation of reliable landslide warning systems.

Subalpine forest water use behaviour and evapotranspiration during two hydrologically contrasting growing seasons in the Canadian Rockies

AbstractHydrological processes in mountain headwater basins are changing as climate and vegetation change. Interactions between hydrological processes and subalpine forest ecological function are important to mountain water supplies due to their control on evapotranspiration (ET). Improved understanding of the sensitivity of these interactions to seasonal and interannual changes in snowmelt and summer rainfall is needed as these interactions can impact forest growth, succession, health, and susceptibility to wildfire. To better understand this sensitivity, this research examined ET for a sub‐alpine forest in the Canadian Rockies over two contrasting growing seasons and quantified the contribution of transpiration (T) from the younger tree population to overall stand ET. The younger population was focused on to permit examination of trees that have grown under the effect of recent climate change and will contribute to treeline migration, and subalpine forest densification and succession. Research sites were located at Fortress Mountain Research Basin, Kananaskis, Alberta, where the subalpine forest examined is composed of Abies lasiocarpa (Subalpine fir) and Picea engelmannii (Engelmann spruce). Seasonal changes in water availability from snowmelt, precipitation, soil moisture reserves yielded stark differences in T and ET between 2016 and 2017. ET was higher in the drier year (2017), which had late snowmelt and lower summer rainfall than in the wetter year (2016) that had lower snowmelt and a rainy summer, highlighting the importance of spring snowmelt recharge of soil moisture. However, stand T of the younger trees (73% of forest population) was greater (64 mm) in 2016 (275 mm summer rainfall) than 2017 (39 mm T, 147 mm summer rainfall), and appears to be sensitive to soil moisture decreases in fall, which are largely a function of summer period rainfall. Relationships between subalpine forest water use and different growing season and antecedent (snowmelt period) hydrological conditions clarify the interactions between forest water use and alpine hydrology, which can lead to better anticipation of the hydrological response of subalpine forest‐dominated basins to climate variability and change.

Hydrological processing of salinity and nitrate in the Salinas Valley agricultural watershed

Regime shifts of major salinity constituents (Ca, Mg, Na, K, SO4, Cl, HCO3, and NO3) in the lower Salinas River, an agricultural ecosystem, can have major impacts on ecosystem services central to continued agricultural production in the region. Regime shifts are large, persistent, and often abrupt changes in the structure and dynamics of social-ecological systems that occur when there is a reorganization of the dominant feedbacks in the system. Monitoring information on changes in the system state, controlling variables, and feedbacks is a crucial contributor to applying sustainability and ecosystem resilience at an operational level. To better understand the factors driving salinization of the lower Salinas River on the central coast of California, we examined a 27-year record of concentrations of major salinity constituents in the river. Although limited in providing an understanding of solute flux behavior during storm events, long-term “grab sampling” datasets with accompanying stream discharges can be used to estimate the actual history of concentrations and fluxes. We developed new concentration–discharge relationships to evaluate the dynamics of chemical weathering, hydrological processes, and agricultural practices in the watershed. Examinations of long-term records of surface water and groundwater salinity are required to provide both understanding and perspective towards managing salinity in arid and semi-arid regions while also enabling determination of the influence of external climatic variability and internal drivers in the system. We found that rock weathering is the main source of Ca, Mg, Na, HCO3, and SO4 in the river that further enables ion exchange between Ca, Mg, and Na. River concentrations of K, NO3, and Cl were associated with human activities while agricultural practices were the major source of K and NO3. A more direct anthropogenic positive trend in NO3 that has persisted since the mid-1990s is associated with the lag or memory effects of field cropping and use of flood irrigation. Event to inter-year scale patterns in the lower Salinas River salinity are further controlled by antecedent hydrologic conditions. This study underscores the importance of obtaining long-term monitoring records towards understanding watershed changes-of-state and time constants on the range of driving processes.

Incorporating the Effects of Complex Soil Layering and Thickness Local Variability into Distributed Landslide Susceptibility Assessments

Incorporating the influence of soil layering and local variability into the parameterizations of physics-based numerical models for distributed landslide susceptibility assessments remains a challenge. Typical applications employ substantial simplifications including homogeneous soil units and soil-hydraulic properties assigned based only on average textural classifications; the potential impact of these assumptions is usually disregarded. We present a multi-scale approach for parameterizing the distributed Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability (TRIGRS) model that accounts for site-specific spatial variations in both soil thickness and complex layering properties by defining homogeneous soil properties that vary spatially for each model grid cell. These effective properties allow TRIGRS to accurately simulate the timing and distribution of slope failures without any modification of the model structure. We implemented this approach for the carbonate ridge of Sarno Mountains (southern Italy) whose slopes are mantled by complex layered soils of pyroclastic origin. The urbanized foot slopes enveloping these mountains are among the most landslide-prone areas of Italy and have been subjected to repeated occurrences of damaging and deadly rainfall-induced flow-type shallow landslides. At this scope, a primary local-scale application of TRIGRS was calibrated on physics-based rainfall thresholds, previously determined by a coupled VS2D (version 1.3) hydrological modeling and slope stability analysis. Subsequently, by taking into account the spatial distribution of soil thickness and vertical heterogeneity of soil hydrological and mechanical properties, a distributed assessment of landslide hazard was carried out by means of TRIGRS. The combination of these approaches led to the spatial assessment of landslide hazard under different hypothetical rainfall intensities and antecedent hydrological conditions. This approach to parameterizing TRIGRS can be adapted to other spatially variable soil layering and thickness to improve hazard assessments.

Fish community responses to antecedent hydrological conditions based on long-term data in Mediterranean river basins (Iberian Peninsula)

In recent decades many studies have proven the paramount impact of flow regimes on the structure of lotic ecosystems, both through extreme events (i.e. floods and droughts) but also during intermediate flows, which temporarily and spatially regulate the habitat availability. Human demand for water is steadily increasing and scientists are challenged to define ecosystem needs clearly enough to guide policies and management strategies. However, field studies demonstrated that a variety of interacting factors, such as, presence of barriers (e.g. dams) and temporal changes in habitat structure affect the abundance, composition and distribution of fish assemblages. This work based on quantile regression tested hypotheses to elucidate the effect of antecedent hydrological conditions on fish communities. A large monitoring database collecting and homogenizing the existing information on fish fauna in the Júcar River Basin District (Eastern Iberian Peninsula) was gathered and used to evaluate biological metrics (species richness, Capture Per Unit Effort-CPUE, and CPUE ratio over the total CPUE) related to life history strategies (i.e. periodic, opportunistic or equilibrium) and species origin (i.e. native, translocated or alien). The resulting dataset was complemented with diverse indicators of the measured daily discharge at the nearest gauging site. Most of the significant relationships confirmed the role of antecedent hydrological conditions as limiting factors, although other environmental factors likely play additional roles. In general, richness and abundance of alien species showed the higher proportion of significant associations, particularly spring flows and annual minima and maxima. These flow-ecology relationships shall be particularly useful to manage ecological responses to hydrological alteration. They also provide with clear ecological foundations for developing environmental flows assessments in Mediterranean river basins worldwide, using holistic approaches which can harmonise eco-hydrological approaches with smaller-scale and habitat-based ecohydraulics methods, especially under the current climate trends.

Impact of rainfall characteristics on the NO3 – N concentration in a tailwater recovery ditch

Rainfall characteristics can be a major factor influencing the ability of best management practices to reduce nutrient loss from agricultural lands to receiving waterbodies. The goal of this study was to examine how rainfall characteristics impacted NO3 – N concentrations in a tailwater recovery (TWR) ditch implemented at a farm within the Porter Bayou watershed in Mississippi, USA. We used a methodology that correlates rainfall characteristics (i.e., a combination of variables such as depth, intensity, frequency, duration, and antecedent hydrological conditions before water sampling) and NO3 – N levels measured in the ditch. Subsequently, a hierarchical clustering approach was implemented to classify rainfall events in the context of the NO3 – N concentrations. Results indicate that NO3 – N concentrations observed in the ditch were strongly dependent on antecedent hydrological conditions within the study area, specifically on the (1) duration of rainfall events before sampling and (2) characteristics of next-to-last rainfall events that occurred prior to sampling. Combined variables of total rainfall depth and frequency showed that rainfall classes I and III were likely to have the most impact on in-ditch NO3 – N concentration. Effects of class I rainfall events on NO3 – N levels appear to be magnified under higher depth, intensity, duration, and a shorter time before next-to-last rainfall events. Also, the influence of class III rainfall events on NO3 – N concentrations in the ditch was driven mainly by high-frequency, low magnitude, and dry antecedent conditions. Results suggest that during periods with frequent, consecutive rainfall events, the nutrient reduction potential of TWR ditches may be reduced. Data can be used to improve management of TWR ditches with more frequent pumping when possible, to increase retention times, particularly during the spring and late fall periods.

Nitrous Oxide and Methane Dynamics in Woodchip Bioreactors: Effects of Water Level Fluctuations on Partitioning into Trapped Gas Phases.

Woodchip bioreactors (WBRs) are low-cost, passive systems for nonpoint source nitrogen removal at terrestrial-aquatic interfaces. The greenhouse gases nitrous oxide (N2O) and methane (CH4) can be produced within WBRs, and efforts to reduce N2O and CH4 emissions from WBR systems require improved understanding of the biogeochemical and physical-chemical mechanisms regulating their production, transport, and release. This study evaluates the impact of trapped gas-filled void volumes as sinks of dissolved gases from water and as sources of episodic fluxes when water levels fall. Dissolved gas tracer experiments in a laboratory bioreactor were used to parameterize nonequilibrium advection-dispersion-gas transfer models and quantify trapping of gas-filled voids as a function of antecedent hydrological conditions. Experiments following a water-level rise revealed that up to 24% of the WBR pore volume was occupied by trapped gas phases, which were primarily located in pore spaces inside woodchips. This finding was confirmed with X-ray-computed microtomography. N2O (3.3-10%) and CH4 (4.3-14%) injected into the reactor following a water table rise partitioned into gas-filled voids and were released when water tables fell. In the case of N2O, partitioning into trapped gas phases makes N2O unavailable for enzymatic reduction, potentially enhancing N2O fluxes under fluctuating water levels.

The Role of Hydrological Initial Conditions on Atmospheric River Floods in the Russian River Basin

Abstract A body of work over the last decade or so has demonstrated that most major floods along the U.S. West Coast are attributable to atmospheric rivers (ARs). Recent studies suggest that observed changes in extreme precipitation associated with a general warming of the western United States have not necessarily led to corresponding changes in floods, and changes in antecedent hydrological conditions could be a primary missing link. Here we examine the role of antecedent soil moisture (ASM) conditions on historical AR flooding on California’s Russian River basin, a coastal watershed whose winter precipitation extremes are dominated by ARs. We examined the effect of observed warming on ASM for the period 1950–2017. We first constructed an hourly precipitation product at 1/32° spatial resolution. We used the Distributed Hydrology Soil Vegetation Model (DHSVM) to estimate storm total runoff volumes and soil moisture. We found that up to 95% of peaks-over-threshold (POT) extreme discharge events were associated with ARs. The storm runoff–precipitation ratio generally increased with wetter prestorm conditions, and the relationship was stronger as drainage area increased. We found no trends in extreme precipitation but weak downward trends in extreme discharge. The latter were mostly consistent with weak downward trends in the first 2-day storm precipitation. We found no trends in ASM; however, ASM was significantly correlated with peak flow. The ASM was affected more by antecedent precipitation than evapotranspiration, and hence temperature increases had weak effects on ASM.

Integrated experiments on field monitoring and hydro-mechanical modeling for determination of a triggering threshold of rainfall-induced shallow landslides. A case study in Ren River catchment, China

Understanding the hydrological mechanism of rainfall-induced shallow landslides is critical to make reliable predictive assessments and to reduce risks of economic loss and fatalities. An integrated approach was identified by combining the field-based monitored hydrological data with a hydro-mechanical analysis, which can be utilized to better understand seasonal hydrological features in shallow soils and the corresponding triggering mechanism of rainfall induced shallow landslides, with the aim of setting up an improved landslide early warning system. A hydrological monitoring system on a representative slope that was experiencing frequent local shallow failures in the Ren River catchment was constructed to provide available seasonal hydrological features with regard to time trends in volumetric water content and pore water pressure in shallow soils. A transition of vertical downward flow from unsaturated to near-saturated conditions has been fully detected since the significant saturation degree and associated pore pressure varies with vertical depths. Consequently, the triggering mechanism of shallow landslides should be associated with the combined effects of dissipation of matrix suction in the upper unsaturated zone and increased positive pore pressure due to the generation of a temporal perched water table in the deeper saturated zone. This process was confirmed through the field evidence and a back analysis on the long-term hydrological evolution of slope soil using a calibrated one-dimensional hydrological model. Antecedent hydrological conditions were also found to have an effect on rainfall intensity–duration thresholds in the landslide early warning system based on a hydro-mechanical analysis, with the aim of improving the accuracy of the model’s predictive ability. The results indicated that this research promoted the effectiveness of a landslide early warning system through a hydro-mechanical analysis based on capturing the real hydrological features of a landslide.

Ensemble-based flood vulnerability assessment for probable maximum flood in a changing environment

The magnitude and frequency of hydro-meteorological extremes are expected to increase in a changing environment in ways that threaten the security of US energy-water assets. These include probable maximum precipitation (PMP) and probable maximum flood (PMF), which are used as hydraulic design standards for highly sensitive infrastructures such as nuclear power plants and main dams. To assess the flood vulnerability due to PMP/PMF, an integrated high-resolution process-based hydro-meteorologic modeling framework was used to develop ensemble-based probabilistic flood maps based on best-available historic observations and future climate projections. A graphics processing unit–accelerated 2-dimensional hydrodynamic model was used to simulate the surface inundation areas corresponding to a total of 120 PMF hydrographs. These ensemble-based PMF maps were compared with flood maps obtained from the conventional deterministic PMP/PMF approach, revealing added information about conditional probability of flooding. Further, a relative sensitivity test was conducted to explore the effects of various factors in the framework, such as meteorological forcings, antecedent hydrologic conditions, reservoir storage, and flood model input resolution and parameters. The proposed framework better illustrates the uncertainties associated with model inputs, parameterization, and hydro-meteorological factors, allowing more informed decision-making for future emergency preparation.

Hydroclimatological Perspective of the Kerala Flood of 2018

Flood is among the deadliest disasters in India, and the frequency of floods and extreme precipitation events is projected to increase under the warming climate. The frequency of floods in India varies geographically as some regions are more prone to floods than the others. The Kerala flood of 2018 caused enormous economic damage, affected millions of people, and resulted in the death of more than 400 people. Here we provide a hydroclimatological perspective on the Kerala flood of 2018. Using the observations and model simulations from the Variable Infiltration Capacity (VIC) model, we show that the 2018 extreme precipitation and runoff conditions that caused flooding were unprecedented in the record of the past 66 years (1951–2017). Our results show that mean monsoon precipitation has significantly declined while air temperature has significantly increased during 1951–2017 in Kerala. The drying and warming trends during the monsoon season resulted in a declined total runoff in large part of the state in the last 66 years. Apart from the mean hydroclimatic conditions, extreme precipitation, and extreme total runoff have also declined from 1951 to 2017. However, 1 and 2-day extreme precipitation and extreme runoff conditions in August 2018 exceeded substantially from the long-term 95th percentiles recorded during 1951–2017. Since there is no increase in mean and extreme precipitation in Kerala over the last six decades, the extreme event during August 2018 is likely to be driven by anomalous atmospheric conditions due to climate variability rather anthropogenic climate warming. The severity of the Kerala flood of 2018 and the damage caused might be affected by several factors including land use/land cover change, antecedent hydrologic conditions, reservoir storage and operations, encroachment of flood plains, and other natural factors. The impacts of key drivers (anthropogenic and natural) on flood severity need to be established to improve our understanding of floods and associated damage.

Understanding the 2011 Upper Missouri River Basin floods in the context of a changing climate

Study RegionUpper Missouri River Basin. Study FocusThe semi-arid Upper Missouri River Basin (UMRB) has experienced notable volatility in high and low streamflow extremes in recent decades, punctuated by the record 2011 flood. This study provides a new perspective into the relative importance of precipitation and antecedent moisture conditions in driving extreme streamflow. Ensemble streamflow simulations demonstrate that precipitation is largely the dominant driver for high streamflows. Applying the observed atmospheric forcing in 2011 with initial conditions of antecedent hydrologic conditions from 64 historic years consistently produces large streamflow events exceeding the 85th percentile of historical peak flows. This study attributes the individual roles of atmospheric conditions and antecedent soil moisture on extreme streamflow production. It uses a novel modeling framework that provides a greater understanding for the role that heterogeneity in basin-scale hydrologic features have in extreme streamflow generation. New hydrologic insights for the regionA detailed analysis of the record 2011 flood event shows that streamflow generated over the region’s easternmost sub-basin is acutely sensitive to antecedent moisture. Yet, the 2011 record streamflow cannot be explained by a single factor or as the result of long-term trends, with the basin responding to several independent factors; significantly high (p < 0.05) antecedent moisture and significant cold-season precipitation. Perhaps most importantly was the record-setting May precipitation, which limited the ability of ensemble streamflow simulations initialized on 1-March from reliably predicting the record June streamflows. The recent volatility of UMRB streamflow may be a harbinger of future decades based on our analysis of climate projections that indicate increased hydroclimate variability by the latter half of the 21st century.

Hurricanes and tropical storms: A necessary evil to ensure water supply?

AbstractSeveral parts of the globe including Southeast North America, the Caribbean, Southeast Asia, Australia, and China are often hit by hurricanes and tropical storms (HTSs), which can deliver a large amount of rainfall within a period of a few days. Although HTSs are mostly studied as disaster agents, considering that they occur during the period when water supply systems are generally depleted, it is important to ascertain their potential contributions toward sustaining water supply. Using the Lake Michie–Little River reservoir system that supplies water to the city of Durham (North Carolina) as a representative test case, we implemented an integrated watershed and reservoir management model, supported by publicly available observations, to evaluate the extent to which HTSs impact water storages. Results indicate that HTSs can have a significant impact on reservoir water storage, with their effects being felt for more than a year for some storms. The impact on reservoir water storage is identified to be primarily controlled by 3 factors, namely, streamflow response size from HTSs, storage in the reservoir right before the event, and streamflow succeeding the event response to HTS (henceforth referred as postevent streamflow). Although the impact of streamflow response size on water storage is generally proportional to its magnitude, initial water storage in the reservoir and postevent streamflow have a nonmonotonic influence on water storage. As all the 3 identified controls are a function of antecedent hydrologic conditions and meteorological forcings, these 2 factors indirectly influence the impact of HTS on water storage in a reservoir. The identification of controlling factors and assessment of their influence on reservoir response will further facilitate implementation of more accurate estimation and prediction frameworks for within‐year reservoir operations.

Importance of antecedent environmental conditions in modeling species distributions

Although species distributions can change in an unexpectedly short period of time, most species distribution models (SDMs) use only long‐term averaged environmental conditions to explain species distributions. We aimed to demonstrate the importance of incorporating antecedent environmental conditions into SDMs in comparison to long‐term averaged environmental conditions. We modeled the presence/absence of 18 fish species captured across 108 sampling events along a 50‐km length of the Sagami River in Japan throughout the 1990s (one to four times per site at 45 sites). We constructed and compared the two types of SDMs: 1) a conventional model that uses only long‐term averaged (10‐yr) environmental conditions; and 2) a proposed model that incorporates environmental conditions 2 yr prior to a sampling event (antecedent conditions) together with long‐term averages linked to life‐history stages. These models both included geomorphological, hydrological, and sampling conditions as predictors. A random forest algorithm was applied for modeling and quantifying the relative importance of the predictors. For seven species, antecedent hydrological conditions were more important than the long‐term averaged hydrological conditions. Furthermore, the distributions of two species with low prevalence could not be predicted using long‐term averaged hydrological conditions but only using antecedent hydrological conditions. In conclusion, incorporating antecedent environmental factors linked with life‐history stages at appropriate time scales can better explain changes in species distribution through time.