River systems represent important drivers of carbon loading, utilization and storage. However, underlying controls of hydrological transport and biological degradation on fluvial dissolved organic carbon (DOC) have yet to be revealed. Here, we explored spatiotemporal variability of DOC concentrations, components and sources, as well as its biodegradability in a headwater tributary of the Yangtze. We found that temporal rainfall stimulated terrestrial inputs and increased terrigenous DOC abundance. Hydrological transport was accompanied by biological generation and utilization of DOC, resulting in reduced labile components and accumulated recalcitrant components from tributaries to the main stem. Biodegradable DOC (BDOC) notably responded to temperature gradients over a 56-day laboratory incubation. Riverine DOC component, molecular weight and source highly predicted its biodegradation. Particularly, partial refractory (ultraviolet humic-like) fractions contributed to biological degradation of DOC, which was incompletely degraded from high-molecular to low-molecular weight compounds. The findings hope to supplement a new understanding of carbon fate under global change.

The National Water Model (NWM) currently requires the post-processing of forecast discharges to produce forecast flood inundation maps (FIM) that support the National Weather Service’s mission of protecting life and property. Height Above Nearest Drainage (HAND) is a means of detrending digital elevation models (DEM) by normalizing elevations to the nearest, relevant drainage line (creeks, rivers, etc). It’s worthy of producing high-resolution FIMs at large spatial scales and frequent time steps using reach-averaged synthetic rating curves. Current operational capabilities support 10 meter (1/3 arc-second) spatial resolution DEMs sourced from the National Elevation Dataset (NED). The 3D Elevation Program (3DEP) managed by the United States Geological Survey (USGS) publishes a variety of gridded elevation datasets at 1 m, 3 m (1/3 arc-second), 5 m, and 10 m (1/9 arc-second) among others. While the 1/3 arc-second product provides seamless coverage across CONUS, the remaining products lack full spatial support with respect to that of the NWM. However, 3DEP is actively publishing additional data with national coverage scheduled for 2023. We seek to investigate the efficacy of assimilating higher resolution 1 m and 3 m (1/3 arc-second) data derived from light detection and ranging sensors (Lidar). These Lidar derived datasets not only represent higher horizontal resolution but also have improved vertical accuracy when compared to the NED. We seek to utilize Py3DEP from the HyRiver ecosystem of tools to retrieve 3DEP data. HAND derived FIMs will be evaluated against high-fidelity HEC-RAS 1D inundation maps for 100 year and 500 year events. Possible skill enhancements can be observed from having terrain information that better agrees with those of the benchmark HEC-RAS datasets. Lidar terrain data can better resolve fine scale features that flood inundation extents may be very sensitive to. Additionally, we would investigate mosaicing techniques to deal with processing units (hydrologic unit codes) of heterogeneous data availability. This can involve resampling DEM’s to create seamless rasters within units. Lastly, we can investigate the effect of Lidar data on synthetic rating curves as well as consider the latest hydro-conditioning techniques from GeoFlood for stream line delineation on Lidar data.

Forest management can enhance forest resiliency against natural disturbances such as fire, drought, or disease. Mechanical thinning, followed by a prescribed burn, is a useful technique to achieve a desired forest structure, usually maximizing large tree basal area or decreasing fuel loads, meant to protect against wildfire or reduce water stress in the western US. Changing forest structure can alter ecosystem function by reducing competition and exposing soil, modifying microclimates and creating suitable conditions for shrubs and grasses to encroach. Typically, forest treatments are expected to make the remaining trees more productive through competitive release, and an open canopy helps the understory to thrive. This enhanced plant water use often contradicts the expected result of increased streamflow following thinning. In mountainous terrain, water yield is further complicated by hillslope-scale processes of subsurface lateral flow and groundwater recharge. This research seeks to understand how management-derived forest structure influences hillslope-scale forest regrowth and water yield. We apply a spatially-distributed ecohydrologic model (RHESSys) to an experimental hillslope in the Sierra Nevada, CA. We incorporate multi-temporal Lidar observations and U.S. Forest Service Forest Inventory & Analysis (FIA) survey data to estimate post-thinning regrowth in treated plots in the watershed, which is used to verify RHESSys accuracy of vegetation regrowth. Then, we run long-term virtual thinning experiments to understand how the combination of thinning and prescribed burns in upslope and riparian sites separately and concurrently influences regrowth and water fluxes in these sites. We expect that an intermediate forest density will yield the most co-benefits in terms of carbon sequestration and water yield. However, these patterns will likely be modified along a hillslope, such that riparian forest stands will be less sensitive to the competitive release that thinning provides, whereas dense upslope forests will be highly sensitive to treatment since they are more water-limited. Water yield is likely to be confounded by multiple factors, including topography, whether a burn follows thinning to remove understory fluxes, and interactions between upslope thinning and processes of lateral flow and groundwater recharge when increased riparian water use compensates for additional upslope subsidies.

Flash droughts have recently gained significant attention due to their severe economic and ecological impacts. Despite extensive and growing research on flash drought processes, predictability, and trends, there is still no standard quantitative definition that encompasses all flash drought characteristics and pathways. This has motivated efforts to define, inventory, monitor, and forecast flash drought events. In our recent studies of flash droughts over the United States, we have introduced the Soil Moisture Volatility Index definition (SMVI) to inventory the onset dates and severity of flash across the Contiguous United States (CONUS) for the period 1979-2018. Post to an extended evaluation and comparison to other flash drought definitions and independent vegetation and crop datasets for seminal flash drought events, the SMVI has proved effectiveness in capturing flash drought onset in both humid and semi-arid regions. Using our SMVI inventory of flash droughts, we examine and classify flash droughts events based on multiple land surface and atmospheric conditions that may represent predictable drivers using a K-means-based clustering methodology. We found that there are three distinct classes of flash drought that can be diagnosed in our inventory. The first defined class of events are the “dry and demanding” droughts, showing high anomalies of evaporative demand and low soil moisture levels; The second are “evaporative” events, which develop under conditions of high demand and when elevated evapotranspiration accelerates soil drying, and a third class that we refer to as “stealth” events, which may be challenging to predict based on precursor atmospheric conditions due to the lack of a clear atmospheric signal with the observed modest anomalies. The contrasting meteorological and surface process signatures of the three classes do, however, indicate that events identified as “flash drought” using a reasonable definition, including events that have been widely reported as seminal flash droughts, represent a diversity of onset and intensification processes. Our results suggest that recognizing this diversity is critical to advance our understanding and ability to predict these events.

Land evapotranspiration (ET) and lake evaporation are important water budget components, representing the main processes of energy and water exchange between the earth and the atmosphere, and thus can influence the regional-scale hydrological cycles. Based on several long-term and comprehensive land-atmosphere interaction measurements over the Tibetan Plateau, the total amounts of land ET and lake evaporation are estimated by a combination of satellite products and meteorological data, the results show that: (1) the total ET amount has an average annual value of 1.238±0.058×103 km3. The trends of annual ET amount show high variable in spatial distributions, with an increasing trend in the east plateau and a decreasing trend in the west plateau. (2) As for the lake surface, lake ice phenology are clearly presented by MODIS 8-day snow cover products, and they show large spatial variability in the duration of ice-free season. The estimated Bowen ratio and evaporation amounts show acceptable accuracies, and display opposite spatial distributions, with the latter being higher in the southern part than in the northern part. On the TP, a lake with a higher elevation, a smaller area and a larger latitude mostly corresponds to a shorter ice-free season (a lower total net radiation), a larger Bowen ratio and finally a lower evaporation amount. The multi-year average evaporation amounts are listed, with the total water evaporated from lake surface being approximately 29.4±1.2 km3 year-1 for the studied 75 lakes and 51.7±2.1 km3 year-1 for all Plateau lakes included. (3) To further explore the land/lake-atmosphere interaction processes in detail over data-limited regions of the TP and supported by the “Third Pole Environment (TPE) program, 16 comprehensive observation and research stations have been constructed over all kinds of landscapes and in different regions of the TP in 2021. These data have provided significance for future research on plateau- and regional-scale water budget, hydrological cycle and water resources management.

Recent proof-of-concept experiments have demonstrated the potential utility of Signals of Opportunity (SoOp) in remote sensing. SoOp methods involve the re-use of existing satellite transmissions as sources in bistatic radar, applying fundamental physical principles to estimate surface and scattered medium properties from reflectivity and phase observables in the reflected signal. Through utilizing signals intended for communications, SoOp methods can make these observables using frequencies that are not allocated or protected for scientific use. Two promising applications in hydrology have been studied: Sub-canopy root-zone soil moisture (RZSM) using satellite communications signals below 500 MHz and snow water equivalent (SWE) retrieval from the observed phase different through propagation through the snow layer. Signals of Opportunity P-band Investigation (SNOOPI) is a NASA Cubesat technology demonstration mission to test forward scattering models and validate a prototype instrument for SoOp reflectometry in 250-380 MHz range. Contribution to the panel discussion will focus on the expected contributions of the SoOp techniques validated in the SNOOPI mission and the existing challenges in the full utilization of SoOp methods for both RZSM and SWE remote sensing. Multiple frequencies are required in order to solve the inverse problem and estimating a sub-surface profile. In the case of SoOp, this may require combining observations with diverse geometry due to the different orbits of the potential sources. This presents new challenges in the development of retrieval algorithms and may possibly require the integration of additional data sources. Another important challenge for SWE retrieval is the need for repetitive coverage to extract phase differences between subsequent passes, coupled with orbit determination for the non-cooperative sources. In contrast to GNSS reflectometry (in which high-precision orbits are publicly available for use in positioning), communication satellite orbits are not known to the required meter-level accuracy. Even geostationary sources frequently have a small inclination which results in motion relative to the surface of the Earth. Finally, antenna calibration is a substantial contribution to the error budget.

We present a high-resolution continental-scale compound flood modeling system. It aims to quantify inland flooding resulting from the composite effects of riverine discharge and surface runoff and storm surge, in the inland-coastal zone during significant riverine and coastal storm events. This is achieved by coupling three continental models: the National Water Model (NWM) for the hydrology component, the Advanced Circulation Ocean Model for the coastal storm surge component, and the WAVEWATCH III model for the surface wave component with a detailed inland-coastal inundation model as the mediator between coastal and inland hydrology module. The inundation model, Delft3D FM, D-Flow Flexible Mesh (D-Flow FM), uses a high quality 2D unstructured grid with high-resolution (~100 m) near coastal features and lower-resolution in other areas to resolve the geometry of the study area. The coastal features are collected from NWM streamlines, National Hydrography Dataset, US medium shorelines and bathymetric features from the United States Army Corps of Engineers . The D-Flow FM model is forced by time-varying water levels and riverine discharges applied at its offshore and inland boundaries, respectively, by spatially- and time-varying wind and pressure fields and incorporates the contributions of surface and subsurface runoff to the total discharge in rivers, channels and streams. We conducted model validations for the following four major flooding events across the US coast: Hurricanes Ike (2008), Sandy (2012), Irma (2017), and Florence (2018). The results highlight the importance of including composite effects of compound flooding to accurately predict water levels during combined river flooding and extreme storm surge events.

Reconstructions of river discharge and other hydrologic variables often exploit large available networks of tree-ring chronologies from multiple species and hydrologic settings. A common early step in such studies is screening to reduce the predictor data set and focus on chronologies with a strong hydrologic signal. A stepwise regression approach to screening is proposed and illustrated for reconstruction of April 1 snow-water equivalent (SWE) at three snow courses in the northern Sierra Nevada and Lake Tahoe region from a multi-species tree-ring network. SWE is regressed separately on each chronology lagged t-2 to t+2 years from the year of SWE. A chronology is accepted based on specified criteria for temporal stability of signal and skill of the lagged model in predicting SWE outside the calibration space. A cross-validation stepwise cutoff rule is applied to guard against over-fitting the lagged model. Illustration for a network of 23 chronologies of five snow-adapted species (Juniperus occidentalis, Pinus jeffreyi, Pinus ponderosa, Abies magnifica, and Tsuga mertensiana) underscores the critical importance of lags in the tree-ring response to SWE. For Abies and Tsuga, in particular, chronologies passing screening are characterized by lagged models with a positive coefficient on the year following the hydrologic anomaly (deep snowpack this year, wide ring the following year) and no dependence or a negative coefficient on the current year (deep snowpack, narrow current ring). The SWE signal is strongest for one particular Juniperus chronology whose regression explains 39% of the SWE variance at two snow courses. The strength of SWE signal varies greatly over sites within species. More than half of the Juniperus and Pinus chronologies were rejected by screening because of either weak or temporally unstable signal. A repeat of the regression screening using water-year precipitation (Global Historical Climate Network) instead of SWE suggests that different subsets of chronologies are optimal depending on the target hydologic variable. Few chronologies have a significant signal for the residual of SWE regressed on water-year precipitation. This suggests little snow-specific information on the moisture signal in annual ring width. Sub-annual ring measurements and quantitative wood anatomy are suggested as possible ways to help discriminate the rain and snow signals in tree rings from the region.

Since first diagnosed in the early 1990s, chronic kidney disease of unknown etiology (CKDu) has markedly increased in the North Central Province in the dry zone of Sri Lanka. CKDu has been identified as a global health issue in more than a dozen countries in Asia, South America, and the Middle East. It has been reported that out of these countries, Sri Lanka is the most affected, with the highest cases of CKDu patients and mortality rates. In Sri Lanka, the disease primarily affects male paddy (rice) farmers from low socioeconomic levels. A major river diversion scheme completed in the 70s feeds water from wet zones to ancient tanks that rely on rainwater only. The drinking water for the CKDu affected farming communities comes from the irrigation canals, shallow regolith water table aquifers recharged by canal seepage and precipitation, and deep-bored wells. Many contributing factors and hypotheses have been presented and discussed in the literature. Out of these multiple factors, the suspected environmental exposure pathways are through water (potable water and food) and air (unprotected pesticide spraying). Extensive data on water quality have been collected to develop, test, and support hypotheses on the role of water on the disease. However, no systematic investigations have been conducted to identify, study and analyze how pathways develop through the water storage and distribution systems from sources to the receptors where human exposure occurs. This study proposes a systems-based framework to conduct such analysis using numerical models of the intergraded surface and subsurface system. The models will simulate the fate and transport of naturally occurring toxins and agrichemicals and their geo-bio-chemicals transformation products. These models should incorporate characterization parameters of the surface water storage and distribution system and hydrogeologic data for shallow and deep aquifers, water quality data, epidemiological data, and climate drivers. Innovations methods to use the downscaled climate and regional hydrological model simulations to evaluate exposure pathways at local scales (e.g., villages) under different climate scenarios.

Flow measurement is critical in hydraulic engineering for developing and managing water resources. The mean flow velocity and cross-sectional flow area at the measurement location are two major metrics required for discharge estimation. River bathymetric surveys or advanced technologies such as the Acoustic Doppler Current Profiler (ADCP) might be used to determine the cross-sectional area. The mean flow velocity can be measured using different techniques depending on whether the measurements are taken from a distance (non-contact) or a contact method (traditional approach). Non-contact velocity measuring techniques are becoming increasingly common in recent years since they are less time-consuming and easier to utilize while dealing with heavy flows and inclement weather. One such modern approach is the entropy-based concepts (such as Shannon entropy, Tsallis entropy and Renyi entropy) used to calculate the discharge from non-contact observations, yielding superior results to classic approaches such as the velocity area method. Entropy-based velocity distribution depends on the crucial parameter called entropy parameter (a function of the ratio of the mean and maximum velocity), which is linked to the channel and flow characteristics. Its value is surmised to be constant for a particular river reach. Due to this fact, the entropy-based approach was used in this study to evaluate the discharge in case of the adverse bed slope condition that may arise due to several reasons, and one among them is the excessive mining in the particular river reach. This study collected the experimental velocity data for the mild, horizontal and adverse bed slope conditions from a rectangular flume fitted with a mechanical apparatus to change the bed slope. Results concluded that the mild and horizontal slope conditions depicted only a slight variation in entropy parameter value, i.e., almost constant. The same was adopted for finding the mean velocity for the adverse bed slope condition to calculate the discharge. Furthermore, the discharge error analysis presented a substantial justification for the utilized single constant value of the entropy parameter for the whole cross-section, and the same can be employed for future explorations on the same channel stretch.

Human pressures on the coastal zones and oceans have increased considerably in the last decades. Human activities constitute the greatest threat to the coastal and marine environment, generating considerable quantities of plastic waste. Currently, it is widely recognized that the increase of marine-related activities has adversely affected the coastal environment as well as the associated ecosystems. Our study focuses on marine litter and specifically on the floating part of it which is frequently composed of plastic materials. Floating litter tends to accumulate on beach-dune ecosystems, already characterized by multiple anthropogenic pressures and environmental factors. In addition, litter items may be trapped by coastal dune vegetation or saltmarsh. Successively, the degradation of marine litter will cause the entering of secondary microplastics. Most of the previous studies are based on monitoring activities and aim to identify the origin and destination of litter in order to manage the fate and transport issues. Therefore, it is important to develop modeling and monitoring tools to detect and prevent marine debris dispersal in coastal environments. We applied field sampling and UAVs (Unmanned Aerial Vehicles) survey over a complex geomorphic set up in the Po River Delta (Italy). Our field data are implemented into a high-resolution hydro-morphodynamic numerical model for validation. Then, we are able to project into different scenarios of plastic debris accumulation in the coastal zone. Our preliminary results show an accumulation of floating debris in coastal dunes vegetation mainly driven by alongshore currents and wave set up in the nearshore area. Then, wind-dominated directions and magnitude disperse plastic debris in embryo dunes and back-barrier marshes. Specific cleaning operations are therefore needed. Considering that coastal management scenarios and decisions rely on numerical models that can predict best practices for coastal sustainability, our results might help local agencies and stakeholders to manage coastal environments.

The Maranhão reservoir (~180 hm3) is mainly used for energy production and irrigation of 15360 ha in the Sorraia Valley, in southern Portugal. The necessity to save water in the Autumn/Winter rainfall season to be used for irrigation of Spring/Summer crops creates a sensible management situation regarding the control of floods and their impact on downstream areas in years with extreme precipitation events. Streamflow forecast is then essential to improve the reservoir’s management regarding water storage. This study addresses the estimation of the daily streamflow in the watershed draining to the Maranhão reservoir (2311 km3) following two different approaches. Firstly, the physically based hydrological model, MOHID-Land, was calibrated/validated for estimating daily streamflow in the study area following physically processes and using a finite volume approach, which require considerable amount of input data. Secondly, a data-driven model composed of an artificial neural network (ANN) was used with the same purpose. This ANN model was selected from a previous work where different models (multi-layer perceptron, convolutional neural network, and long short-term memory model) were tested with different scenarios for the combination of input variables. Both models were optimized considering the observed streamflow in the Ponte Vila Formosa station, which drains 30% of the Maranhão watershed, being after applied to the entire domain. The results for the Maranhão watershed were then validated considering a mass balance considering the reservoir’s outflow, level, and water consumed values. The ANN model had a better performance predicting streamflow than the physically based model, and with less calibration effort. However, the physically based model can give much more information about the entire system. Lastly, it is expected that a physically based model can correctly estimate the streamflow for extreme events not considered in the calibration and validation datasets, but for the ANN models this question should be carefully addressed since data-driven models are event-based.

As Earth's ecological landscape continues to change, it will become increasingly important to understand how it has changed, and how it may change in the future. Freely available multispectral remote sensing datasets, such as the Landsat-8 dataset accessed through the USGS EarthExplorer tool, provide large scale, high resolution satellite imagery that can be leveraged by researchers across scientific disciplines for timeseries index analysis. LINDEX provides an extensible framework that automates Landsat-8 timeseries index analysis, resulting in an average ninety-four percent reduction in overall processing time compared to hands-on methods. Traditionally, in order to make use of this historical data, researchers must acquire the data, uncompress each scene, crop each scene to their region of interest, sort each cropped scene by date and cloud cover, and then either use GIS tools to run index analyses or code the index analyses themselves. This process is time consuming, requires extensive computational knowledge, and is prone to human error. In order to address these challenges, we developed LINDEX, a fully containerized end-to-end extensible processing framework for Landsat-8 timeseries index analysis. LINDEX is open source and leverages open source python packages to automate decompression, cropping, cloud cover detection, sorting by date, and index analysis for Landsat-8 data while also providing a customisable and growing library of eleven ecologically useful indices including NDWI, NDMI, NDSI and NDGI. LINDEX is designed to work synergistically with the EarthExplorer Bulk Download Application as well as QGIS, providing a bridge from download through analysis. The LINDEX framework uses a well defined methodology for incorporating custom indices into your workflow, making LINDEX a useful tool for researchers interested in exploring any Landsat-8 multispectral index while saving time, and reducing error.

Year-to-year variability of precipitation and temperature has significant consequences for water management decision making. “Whiplash” is a term which describes this variability at its most severe, referring to events at various timescales in which the hydroclimate switches between extremes. Tree-rings in semi-arid environments like the Truckee-Carson River Basin (California/Nevada watersheds with headwaters in the Sierra Nevada) can provide proxy records of hydroclimate as their annual growth is tied directly to limitations in water-year rainfall and temperature, but traditional metrics of reporting explained variance do not distinguish a reconstruction’s sensitivity to whiplash events. In this study, a pool of total ring width indices from five snow-adapted conifer species (Abies magnifica, Juniperus occidentalis, Pinus ponderosa, Pinus jeffreyi, Tsuga mertensiana) were used to develop a series of standardized reconstructions of water-year PRISM precipitation (P12) using stepwise linear regression. A nonparametric analysis approach was then used to determine positive and negative whiplash events in reconstructed and instrumental precipitation records. Hypergeometric distribution of the resulting timeseries datasets illustrates relationships between reconstructions and recorded whiplash events and allows for determination of patterns in tree-ring growth response. The results of this study suggest that ring-width indices from the assessed conifer species in the snow-belt of the Sierra Nevada are often able to record consecutive years of opposing extreme precipitation and report such events through derived models. Negative WL events are tracked more consistently across species in site-specific reconstructions of P12 than positive ones. It appears that residual effects of a preceding year’s drought or pluvial do not necessarily suppress records of WL, though sensitivity to precursor conditions in tracking of WL events may differ across species, and the absolute WL events captured in a reconstruction vary.

Percolation theory addresses the spatial and temporal scaling properties of disordered networks, both for topological and transport-related processes. The critical zone is, effectively, a random network. The ability to predict the dimensions of the critical zone as a function of time, as well as its carbon storage properties, are both critical to the understanding of its dynamics. In addition, combining the scaling relationships with the principal that represented ecosystems will tend to optimize net primary productivity, generates a basic understanding of the water cycle. When the future of hydrology is discussed, these facts should be of central importance. Furthermore, although the definition of complex systems is not necessarily generally agreed on, it is usually understood that phase transitions, such as described by percolation theory, are emergent phenomena within the range of topics encompassed. Thus, this topic is of fundamental relevance to the present conference.

Hydroclimate extreme events, especially precipitation and streamflow extremes during wet seasons, pose severe threats to life, livelihoods, and infrastructure. Therefore, timely and skillful projections of attributes of seasonal streamflow extremes are imperative to plan mitigation strategies. In particular, the number of ‘events’ – i.e., exceedances of flow thresholds that result in flooding and the magnitude of such extremes during the season, will be of immense use to policymakers for early planning and implementation of flood risk mitigation and adaptation strategies. However, predicting seasonal extremes is challenging, particularly under spatial and temporal non-stationarity. To address this need, we develop a space-time model to project seasonal flow risk attributes using a Bayesian hierarchical modeling (BHM) framework in this study. In this model, the number of events exceeding a threshold during a season at a suite of gauge locations on a river network are modeled as Poisson margins. The seasonal daily maximum flows are modeled as a generalized extreme value (GEV). The rate parameters of the Poisson distribution and scale and shape parameters of the GEV are modeled as a linear function of suitable covariates. Gaussian Elliptical Copulas are applied to capture the spatial dependence. The best set of covariates is selected using the leave-one-out cross-validation information criteria (LOOIC). The modeling framework results in the posterior distribution of the risk attributes for each season and, thus, the uncertainties. We demonstrate the utility of this modeling framework to project the flood risk attributes during the summer peak monsoon season (July-August) at five gauges in the Narmada River basin of West-Central India. As potential covariates, we consider climate indices such as El Niño–Southern Oscillation (ENSO), the Indian Ocean Dipole (IOD), and the Pacific Warm Pool Region (PWPR) from the precedent season, which have shown strong teleconnections with the Indian monsoon. This spatiotemporal modeling framework helps in the planning of seasonal adaptation and preparedness measures as predictions of monsoon high flow risk occurrence become available up to 3 months before actual flood occurrence.

Grid-based spatially distributed hydrological modeling became feasible along with advances in watershed routing scheme, remote sensing technology, and computing resources. Such modeling is expected to be common in routine hydrological analysis and watershed management planning as it can maximize the use of spatial information and provide the detailed picture of transport processes. However, the heavy computational requirement and resulting long running time are still barriers that prevent the spatially detailed modeling practices from being employed widely, particularly in a fine-resolution large-scale study. Parallelizing computational tasks has been successful in mitigating the difficulty. We propose a noble way to improve the simulation efficiency of direct runoff transport processes by carefully grouping watershed areas based on the time-area routing scheme. The proposed parallelization method was applied to simulating the runoff routing processes of three watersheds draining the areas of 3.79 km2, 133.59 km2, and 2,800 km2 respectively at a 30-m resolution. Results demonstrated that the new method could substantially improve the computational efficiency of the time-area routing method with common computing resources. The efficiency of the parallelization scheme was not limited by the hierarchical relationship between upstream and downstream catchments along flow paths, which could be possible with the Lagrangian flow tracking strategy of the time-area routing method.

Compound event research has gained a lot of momentum over the past few years. Traditionally risk assessment studies used to consider only one climatic driver/ process at a time. However, it was then recognized that it is the combination of multiple drivers and their statistical dependencies that lead to aggravated, non-linear impacts. The present study investigated and quantified the preconditioning of precipitation extremes (P) by existing soil moisture (SM) anomalies. Event coincidence analysis (ECA) was employed to investigate the coupling nature between SM & P event series. The datasets used include GLDAS-2.2 CLSM model products for soil moisture and GPM IMERG V06 for gridded rainfall data. Using SM and P data from 2004-2020, we identified hot-spots of SM-P coupling over India. A statistical significance test (α = 0.05) was carried out to ensure that the observed coincidences are not by chance. Our observed results agree with the widely regarded hypothesis of stronger SM-P coupling in transitional regions between wet and dry climates. The temporal evolution of SM-P dependence over the past two decades is also investigated. Results obtained provides critical insights on the complex dynamical relationship between soil moisture and precipitation. The dependence nature unraveled has vast implications for directing future research on coupled hydro-meteorological phenomena.