Gridded Surface Subsurface Hydrologic Analysis Research Articles

Hydrological Effects of Spatial Harvest Patterns in a Small Catchment Covered by Fast-Growing Plantations in the Neotropics

<em>Eucalyptus</em> forests are expanding worldwide and concerns exist about their impact on water resources. There is a lack of information about the hydrological effects of spatial harvest patterns in terms of their effects on streamflow. In this paper, we examined harvest amount and hillslope position effects on flow indices (Q70; Q50 and Q10) and water yield in a small catchment covered with a fast-growing <em>Eucalyptus</em> plantation. To do that, we used the Gridded Surface Subsurface Hydrologic Analysis (GSSHA), a physical-based distributed hydrological model, to simulate harvesting scenarios with different harvest amounts (30% and 70% of the forest plantation) at two hillslope positions (downslope and upslope). We also verified the influence of the amount of rainfall on peak flows for all scenarios. The results showed that the increase in water yield is positively related to the harvest amount and that, under the same harvest amount, harvests in downslope areas caused a larger increase in water yield than harvests in upslope areas. Downslope harvests led to a greater increase in peak flow under the 30% harvest. For the 70% harvest, no substantial effects of harvest position on peak flow could be detected. Incorporating harvest amounts and spatial patterns in <em>Eucalyptus</em> plantations management practices may be useful to mitigate their effects on water resources, especially in regions where water availability is generally lower.

Simulation of Urban Areas Exposed to Hazardous Flash Flooding Scenarios in Hail City

According to the United Nations (UN), an additional 1.35 billion people will live in cities by 2030. Well-planned measures are essential for reducing the risk of flash floods. Flash floods typically inflict more damage in densely populated areas. The province of Hail encompasses 120,000 square kilometers, or approximately 6% of the total land area of the Kingdom of Saudi Arabia. Due to its innate physiographic and geologic character, Hail city is susceptible to a wide variety of geo-environmental risks such as sand drifts, flash floods, and rock falls. The aim of this work is to evaluate the rate of urban sprawl in the Hail region using remote sensing data and to identify urban areas that would be affected by simulated worst-case flash floods. From 1984 to 2022, the global urbanization rate increased from 467 to 713% in the Hail region. This is a very high rate of expansion, which means that the number of urban areas exposed to the highest level of flood risk is rising every year. With Gridded Surface Subsurface Hydrologic Analysis (GSSHA), a wide range of hydrologic scenarios can be simulated. The data sources for the soil type, infiltration, and initial moisture were utilized to create the coverage and index maps. To generate virtual floods, we ran the GSSHA model within the Watershed Modeling System (WMS) program to create the hazard map for flash flooding. This model provides a suitable method based on open access data and remote data that can help planners in developing countries to create the risk analysis for flash flooding.

Comparison of two hydro-sediment models based on energy principle of water movement in saturated/unsaturated soils

To study the impact of complex climatic conditions on runoff and sediment, physics-based distributed hydrological models are frequently employed for representation and analysis. In this work, two physics-based hydro-sediment models, namely CASC2D-SED (CASCade 2 Dimensional SEDiment) and GSSHA (Gridded Surface Subsurface Hydrologic Analysis), are used to simulate runoff and sediment in a semi-arid watershed on the Loess Plateau. Examining the degree to which the two models provide comparable or dissimilar outcomes with regard to runoff and sediment casting is an aspect of this investigation that is of utmost importance. Both the models are derived from the principle of mass and energy balance, and arrived at very similar conclusions regarding the infiltration of precipitation and the transport of silt in surface runoff. For typical flood events that occurred between 1981 and 2017, a comparison of modeled data with measured runoff and sediment is carried out. Both models achieved satisfactory results when runoff and sediment simulations were performed over three separate time periods, with average NSE (Nash–Sutcliffe coefficient) values of runoff and sediment are 0.81 and 0.70 respectively. The GSSHA model performs better in terms of the maximum and total amount of runoff and sediment, with an 6.8%, 8.6%, and 6.7% increase, respectively, in the average Rq (relative error of peak flow) Rd (relative error of runoff depth), and Rs (relative error of peak sediment values). In contrast, the CASC2D-SED model simulates better in terms of the Tq (time difference of peak discharge), and Ts (time difference of peak sediment), with the mean time difference of peak flow and sediment values being 0.27 and 1.02 higher, respectively. This shows that the CASC2D-SED model can simulate the timing of runoff and sediment more accurate.

An innovative approach of GSSHA model in flood analysis of large watersheds based on accuracy of DEM, size of grids, and stream density

Distributed modeling approach may have much better performance and accuracy compared with lumped-parameter hydrologic models. The main goals of this research are: investigating the possibility of combining distributed hydrological models with an one-dimensional hydraulic model and simulating waterways in large watersheds with limited hydrological and hydraulic data. Then performing sensitivity analysis on different parameters in order to identify the parameters containing the major influences on results. In the current research, an innovative approach in Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model, the cross-sections of all 414 waterways in the 3450 km2 Karvandar watershed, used for flow routing calculations, are uniquely extracted. Then, the effect of three essential factors are evaluated. These factors are accuracy of the digital topographic model, cell size of grid network, and density of streams, on the results of GSSHA model simulations. This watershed is located in southeastern Iran, has a dry climate with limited available hydrological data. Results showed that peak discharges obtained from the GSSHA model, developed based on a DEM with a spatial resolution of 12.5 m, are slightly (< 4%) lower than the corresponding values ​​in the GSSHA model with a 30 m DEM resolution. This fact confirms that the use of the topographic model with a lower spatial resolution has no substantial effects on the accuracy of simulation. Also, the peak discharges increased significantly (44% to 57%) by increasing the density of waterways in the GSSHA model. Furthermore, results showed that peak discharge obtained from three models with grid cell sizes of 100, 150, and 200 m (base model), are close together. Comparing with two models of coarser grids (250 and 300 m), significant differences observed, which indicated that the grids larger than 200 m could induce substantial errors in results.

Flood Modeling in Coastal Cities and Flow through Vegetated BMPs: Conceptual Design

Undeniably, climate-change-induced sea-level rise has served as a powerful driving force for the occurrence of coastal storms more severe than ever before. Low-lying metropolitan coastal areas, in particular, have become increasingly vulnerable to coastal flooding as a result of the rapid expansion of urbanization along flood-prone areas. Safeguarding coastal communities from the repercussions of flooding through the utilization of best management practices (BMPs) is one of the most effective ways of alleviating the situation. However, solely relying on inshore BMPs might not be the best possible approach when the storm event’s intensity can be reduced before the storm reaches the shoreline through the installment of offshore BMPs. Moreover, many recent studies have confirmed the cost-effectiveness and usefulness of natural elements, especially when combined with engineering solutions. This paper assesses the overall suitability and effectiveness of various combinations of nature-based inshore and offshore BMPs for coastal flood mitigation of storms with different intensities with applications to the Southern Brooklyn area in New York City. Moreover, inevitable conditions stemming from climate variability and change have made the assumption of stationary recurrence of extreme events unreliable. Furthermore, historical events of remarkable impacts have not been considered differently in the attainment of the flood design values. Thus, as one of the main objectives of this study, and in order to consider both, a nonstationary partial frequency analysis is utilized, including the major historical storms jointly with the precipitation data in the study area to obtain 100-year flood design values. To achieve the goals of the study, first, the offshore simulation was carried out using the Delft3D hydrodynamic model, developed by Deltares, in order to find the water level hydrograph for the ungauged areas of interest, with and without offshore BMPs followed by the simulation of the flood events on the inland coastal area using gridded surface subsurface hydrologic analysis (GSSHA), a module of the watershed modeling system (WMS) developed by the USACE, to assess the effectiveness of inshore BMPs. The proposed scenarios are tested against Superstorm Sandy, Hurricane Irene, and the 100-year flood hydrographs. The results showed a remarkable coastal flood wave height and water level reduction when integrating offshore and inshore green BMPs.

Estimation of Probable Maximum Precipitation (PMP) and Probable Maximum Flood (PMF) Using GSSHA Model (Case Study Area Upper Citarum Watershed)

Probable Maximum Flood (PMF) used in the design of hydrological structures reliabilities and safety which its value is obtained from the Probable Maximum Precipitation (PMP). The objectives of this study are to estimate PMP and PMF value in Upper Citarum Watershed and understand the impact from different PMP value to PMF value with two scenarios those are Scenario A and B. Scenario A will calculate the PMP value from each Global Satellite Mapping of Precipitation (GSMaP) rainfall data grid and Scenario B calculate the PMP value from the mean area rainfall. PMP value will be obtained by the statistical Hershfield method, and the PMF will be obtained by employed the PMP value as the input data in Gridded Surface Subsurface Hydrologic Analysis (GSSHA) hydrologic model. Model simulation results for PMF hydrographs from both scenarios show that spatial distribution of rainfall in the Upper Citarum watershed will affect the calculated discharge and whether Scenario A or B can be applied in the study area for PMP duration equal or higher than 72 hours. PMF peak discharge for Scenario A is averagely 13,12% larger than Scenario B.

The impact of the spatiotemporal structure of rainfall on flood frequency over a small urban watershed: an approach coupling stochastic storm transposition and hydrologic modeling

Abstract. The role of rainfall space–time structure, as well as its complex interactions with land surface properties, in flood response remains an open research issue. This study contributes to this understanding, specifically for small (&lt;15 km2) urban watersheds. Using a flood frequency analysis framework that combines stochastic storm transposition (SST)-based rainfall scenarios with the physically based distributed Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model, we examine the role of rainfall spatial and temporal variability in flood frequency across drainage basin scales in the highly urbanized Dead Run watershed (14.3 km2), Maryland, USA. The results show the complexities of flood response within several subwatersheds for both short (&lt;50 years) and long (&gt;100 years) rainfall return periods. The impact of impervious area on flood response decreases with increasing rainfall return period. For extreme storms, the maximum discharge is closely linked to the spatial structure of rainfall, especially storm core spatial coverage. The spatial heterogeneity of rainfall increases flood peak magnitudes by 50 % on average at the watershed outlet and its subwatersheds for both small and large return periods. The framework of SST–GSSHA-coupled frequency analysis also highlights the fact that spatially distributed rainfall scenarios are needed in quick-response flood frequency, even for relatively small basin scales.

Flood Impacts on Critical Infrastructure in a Coastal Floodplain in Western Puerto Rico during Hurricane María

Flooding during extreme weather events damages critical infrastructure, property, and threatens lives. Hurricane María devastated Puerto Rico (PR) on 20 September 2017. Sixty-four deaths were directly attributable to the flooding. This paper describes the development of a hydrologic model using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA), capable of simulating flood depth and extent for the Añasco coastal flood plain in Western PR. The purpose of the study was to develop a numerical model to simulate flooding from extreme weather events and to evaluate the impacts on critical infrastructure and communities; Hurricane María is used as a case study. GSSHA was calibrated for Irma, a Category 3 hurricane, which struck the northeastern corner of the island on 7 September 2017, two weeks before Hurricane María. The upper Añasco watershed was calibrated using United States Geological Survey (USGS) stream discharge data. The model was validated using a storm of similar magnitude on 11–13 December 2007. Owing to the damage sustained by PR’s WSR-88D weather radar during Hurricane María, rainfall was estimated in this study using the Weather Research Forecast (WRF) model. Flooding in the coastal floodplain during Hurricane María was simulated using three methods: (1) Use of observed discharge hydrograph from the upper watershed as an inflow boundary condition for the coastal floodplain area, along with the WRF rainfall in the coastal flood plain; (2) Use of WRF rainfall to simulate runoff in the upper watershed and coastal flood plain; and (3) Similar to approach (2), except the use of bias-corrected WRF rainfall. Flooding results were compared with forty-two values of flood depth obtained during face-to-face interviews with residents of the affected communities. Impacts on critical infrastructure (water, electric, and public schools) were evaluated, assuming any structure exposed to 20 cm or more of flooding would sustain damage. Calibration equations were also used to improve flood depth estimates. Our model included the influence of storm surge, which we found to have a minimal effect on flood depths within the study area. Water infrastructure was more severely impacted by flooding than electrical infrastructure. From these findings, we conclude that the model developed in this study can be used with sufficient accuracy to identify infrastructure affected by future flooding events.

Assessment of combined sewer overflows impacts under flooding in coastal cities

Abstract Wastewater treatment plants (WWTPs) are among the most important infrastructures, especially in coastal cities with a risk of flooding. During intense floods, runoff volume may exceed the capacity of a WWTP causing plant failures. This paper investigates the impacts of flooding on combined sewer overflows (CSOs) in a WWTP in New York City. The impacts of CSOs after flooding are classified into four categories of health, economic, social, and environmental factors. Different factors are defined to evaluate the impacts of CSOs using multi-criteria decision-making of Preference Ranking Organization Method For Enrichment Evaluation and fuzzy technique for order performance by similarity to ideal solution. Since volume and depth were found to be the most significant factors for the CSO impact assessment, the Gridded Surface Subsurface Hydrologic Analysis model was run to compute flood depth and CSO volume under three treatment plant failure scenarios considering the Hurricane Sandy information. Sensitivity analysis revealed that the Total Suspended Solids (TSS), Biochemical Oxygen Demand (BOD), and dissolved oxygen have the highest impacts on CSO. Uncertainty analysis was applied to investigate CSO impact variation. Results show that evaluating the impacts of CSOs in different aspects can help improve the efficiency of flood planning and management during storms.

High temporal resolution rainfall–runoff modeling using long-short-term-memory (LSTM) networks

Accurate and efficient models for rainfall runoff (RR) simulations are crucial for flood risk management. Most rainfall models in use today are process-driven; i.e. they solve either simplified empirical formulas or some variation of the St. Venant (shallow water) equations. With the development of machine-learning techniques, we may now be able to emulate rainfall models using, for example, neural networks. In this study, a data-driven RR model using a sequence-to-sequence Long-short-Term-Memory (LSTM) network was constructed. The model was tested for a watershed in Houston, TX, known for severe flood events. The LSTM network's capability in learning long-term dependencies between the input and output of the network allowed modeling RR with high resolution in time (15 minutes). Using 10-years precipitation from 153 rainfall gages and river channel discharge data (more than 5.3 million data points), and by designing several numerical tests the developed model performance in predicting river discharge was tested. The model results were also compared with the output of a process-driven model Gridded Surface Subsurface Hydrologic Analysis (GSSHA). Moreover, physical consistency of the LSTM model was explored. The model results showed that the LSTM model was able to efficiently predict discharge and achieve good model performance. When compared to GSSHA, the data-driven model was more efficient and robust in terms of prediction and calibration. Interestingly, the performance of the LSTM model improved (test Nash-Sutcliffe model efficiency from 0.666 to 0.942) when a selected subset of rainfall gages based on the model performance, were used as input instead of all rainfall gages.

Using shallow groundwater modeling to frame the restoration of a wet prairie in the Oak Openings Region, Ohio, USA: GSSHA model implementation

The hydrologic characteristics of the Oak Openings Region in northwest Ohio, USA, a globally rare ecosystem are not well understood. Currently, the Oak Openings supports globally rare oak savanna and wet prairie habitats. The wet prairies in the region have been drained by ditches and encroached by invasive plants, which have altered the natural flow making it an unusually variable and artificial system. A shallow groundwater model was implemented using the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) software to simulate continuous, long-term groundwater and surface water interaction in a small subwatershed in the Oak Openings Region. The implemented GSSHA model simulates physical processes such as infiltration, evapotranspiration, snowmelt, overland flow, and interaction of groundwater with ditches. The model was calibrated using a time series of water table elevations collected in the field. Although the model tends to slightly underestimate water table elevations (mean and standard deviation percent differences of less than 1%), statistical analysis indicate a good fit between observed and simulated water table elevations (Nash Sutcliffe Efficiency Index of 0.78). The model would be a useful tool for the preservation of natural areas such as wet prairies.

Using a Distributed Hydrologic Model to Improve the Green Infrastructure Parameterization Used in a Lumped Model

Stormwater represents a complex and dynamic component of the urban water cycle. Hydrologic models have been used to study pre- and post-development hydrology, including green infrastructure. However, many of these models are applied in urban environments with very little formal verification and/or benchmarking. Here we present the results of an intercomparison study between a distributed model (Gridded Surface Subsurface Hydrologic Analysis, GSSHA) and a lumped parameter model (the US Environmental Protection Agency (EPA) Storm Water Management Model, EPA-SWMM) for an urban system. The distributed model scales to higher resolutions, allows for rainfall to be spatially and temporally variable, and solves the shallow water equations. The lumped model uses a non-linear reservoir method to determine runoff rates and volumes. Each model accounts for infiltration, initial abstraction losses, but solves the watershed flow equations in a different way. We use an urban case study with representation of green infrastructure to test the behavior of both models. Results from this case study show that when calibrated, the lumped model is able to represent green infrastructure for small storm events at lower implementation levels. However, as both storm intensity and amount of green infrastructure implementation increase, the lumped model diverges from the distributed model, overpredicting the benefits of green infrastructure on the system. We performed benchmark test cases to evaluate and understand key processes within each model. The results show similarities between the models for the standard cases for simple infiltration. However, as the domain increased in complexity the lumped model diverged from the distributed model. This indicates differences in how the models represent the physical processes and numerical solution approaches used between each. When the distributed model results were used to modify the representation of impermeable surface connections within the lumped model, the results were improved. These results demonstrate how complex, distributed models can be used to improve the formulation of lumped models.

Analysis of Flood Inundation in Ungauged Mountainous River Basins: A Case Study of an Extreme Rain Event on 5–6 July 2017 in Northern Kyushu, Japan

The heavy rainfall event that occurred on 5–6 July 2017 in Northern Kyushu, Japan, caused extensive flooding across several mountainous river basins and resulted in fatalities and extensive damage to infrastructure along those rivers. For the periods before and during the extreme event, there are no hydrological observations for many of the flooded river basins, most of which are small and located in mountainous regions. We used the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model, a physically based model, to acquire more detailed information about the hydrological processes in the flood-affected ungauged mountain basins. We calibrated the GSSHA model using data from an adjacent gauged river basin, and then applied it to several small ungauged basins without changing the parameters of the model. We simulated the gridded flow and generated a map of the possible maximum flood depth across the basins. By comparing the extent of flood-affected areas from the model with data of the Japanese Geospatial Information Authority (GSI), we found that the maximum flood inundation areas of the river networks estimated by the GSSHA model are sometimes less than those estimated by the GSI, as the influence of landslides and erosion was not considered in the modeling. The model accuracy could be improved by taking these factors into account, although this task would be challenging. The results indicated that simulations of flood inundation in ungauged mountain river basins could contribute to disaster management during extreme rain events.

Dynamic Modeling of Surface Runoff and Storm Surge during Hurricane and Tropical Storm Events

Hurricane events combine ocean storm surge penetration with inland runoff flooding. This article presents a new methodology to determine coastal flood levels caused by the combination of storm surge and surface runoff. The proposed approach couples the Simulating Waves Nearshore model and the Advanced Circulation (ADCIRC) model with the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) two-dimensional hydrologic model. Radar precipitation data in a 2D hydrologic model with a circulation model allows simulation of time and spatially varied conditions. The method was applied to study flooding scenarios occurring during the passage of Hurricane Georges (1998) on the east coast of Puerto Rico. The combination of storm surge and surface runoff produced a critical scenario, in terms of flood depth, at this location. The paper describes the data collection process, circulation and hydrologic models, their assemblage and simulation scenarios. Results show that peak flow from inland runoff and peak flow due to storm surge did not coincide in the coastal zone; however, the interaction of both discharges causes an aggravated hazardous condition by increasing flood levels beyond those obtained with storm surge penetration only. Linking of storm surge and hydrologic models are necessary when storm surge conditions occur simultaneously with high precipitation over steep and small coastal watersheds.

An analysis of the unit hydrograph peaking factor: A case study in Goose Creek Watershed, Virginia

Study regionGoose Creek Watershed, Loudoun County, Virginia, USA. Study focusExisting U.S. Army Corps of Engineers (USACE) policy suggests that the unit hydrograph peaking factor (UHPF) – taken as the unit hydrograph peak of any flood runoff, including the probable maximum flood, over unit hydrograph peak of the inflow design flood (IDF) – range between 1.25 and 1.50 to ensure dam safety. It is pertinent to investigate the impact of extreme flood events on the validity of this range through physically based rainfall-runoff models not available during the planning and design of most USACE dams. The UHPF range was analyzed by deploying the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model. New hydrological insightsThis study concludes that design events with return periods greater than 5-years are required for the UHPF to fall within the guidance range and that UHPF becomes less sensitive to rainfall intensity with increasing accumulation time. An effective rainfall factor (ERF) is introduced to validate existing UHPF guidance as well as provide a nonlinear UHPF scaling relation when effective rainfall does not match that of the UH design event. Finally, a method for quantifying the effect of hydrologic parameter and precipitation magnitude uncertainty on UHPF are demonstrated. The Goose Creek facility is shown to maintain dam safety given current UHPF guidance as it was designed using 25-year return-period rainfall.

Temporal dynamics of groundwater-surface water interaction under the effects of climate change: A case study in the Kiskatinaw River Watershed, Canada

Groundwater-surface water (GW-SW) interaction plays a vital role in the functioning of riparian ecosystem, as well as sustainable water resources management. In this study, temporal dynamics of GW-SW interaction were investigated under climate change. A case study was chosen for a study area along the Kiskatinaw River in Mainstem sub-watershed of the Kiskatinaw River Watershed, British Columbia, Canada. A physically based and distributed GW-SW interaction model, Gridded Surface Subsurface Hydrologic Analysis (GSSHA), was used. Two different greenhouse gas (GHG) emission scenarios (i.e., A2: heterogeneous world with self-reliance and preservation of local identities, and B1: more integrated and environmental friendly world) of SRES (Special Report on Emissions Scenarios) from Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) were used for climate change study for 2020–2040. The simulation results showed that climate change influences significantly the temporal patterns of GW-SW interaction by generating variable temporal mean groundwater contributions to streamflow. Due to precipitation variability, these contributions varied monthly, seasonally, and annually. The mean annual groundwater contribution to streamflow during 2020–2040 under the A2 and B1 scenarios is expected to be 74.5% (σ=2%) and 75.6% (σ=3%), respectively. As compared to that during the base modeling period (2007–2011), the mean annual groundwater contribution to streamflow during 2020–2040 under the A2 and B1 scenarios is expected to decrease by 5.5% and 4.4%, respectively, due to the increased precipitation (on average 6.7% in the A2 and 4.8% in the B1 scenarios) and temperature (on average 0.83°C in the A2 and 0.64°C in the B1 scenarios). The results obtained from this study will provide useful information in the long-term seasonal and annual water extractions from the river for future water supply, as well as for evaluating the ecological conditions of the stream, which will be beneficial to aquatic ecosystems.

Understanding the Effects of Parameter Uncertainty on Temporal Dynamics of Groundwater-Surface Water Interaction

This study presents the understanding of temporal dynamics of groundwater-surface water (GW-SW) interaction due to parameter uncertainty by using a physically-based and distributed gridded surface subsurface hydrologic analysis (GSSHA) model combined with a Monte Carlo simulation. A study area along the main stem of the Kiskatinaw River of the Kiskatinaw River watershed, Northeast British Columbia, Canada, was used as a case study. Two different greenhouse gas (GHG) emission scenarios (i.e., A2: heterogeneous world with self-reliance and preservation of local identities, and B1: a more integrated and environmental-friendly world) of the Special Report on Emissions Scenarios (SRES) from the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC) for 2013 were used as case scenarios. Before conducting uncertainty analysis, a sensitivity analysis was performed to find the most sensitive parameters to the model output (i.e., mean monthly groundwater contribution to stream flow). Then, a Monte Carlo simulation was used to conduct the uncertainty analysis. The uncertainty analysis results under both case scenarios revealed that the pattern of the cumulative relative frequency distribution of the mean monthly and annual groundwater contributions to stream flow varied monthly and annually, respectively, due to the uncertainties of the sensitive model parameters. In addition, the pattern of the cumulative relative frequency distribution of a particular month’s groundwater contribution to the stream flow differed significantly between both scenarios. These results indicated the complexities and uncertainties in the GW-SW interaction system. Therefore, it is of necessity to use such uncertainty analysis results rather than the point estimates for better water resources management decision-making.

Comparison of SWAT and GSSHA for High Time Resolution Prediction of Stream Flow and Sediment Concentration in a Small Agricultural Watershed

In this study, two hydrologic models, the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) and the Soil and Water Assessment Tool (SWAT), were applied to predict stream flow and suspended sediment concentration (SSC) in a small agricultural watershed in Ishigaki Island, Japan, in which the typical time scale of flood event was several hours. The performances of these two models were compared in order to select the right model for the study watershed. Both models were calibrated and validated against hourly stream flow and SSC for half-month periods of 15 to 31 May 2011 and 17 March to 7 April 2013, respectively. The results showed that both models successfully estimated hourly stream flow and SSC in a satisfactory way. For the short-term simulations, the GSSHA model performed slightly better in simulating stream flow as compared to SWAT during both calibration and validation periods. GSSHA only gave better accuracy when predicting SSC during calibration, while SWAT performed slightly better during validation. For long-term simulations, both models yielded comparable results for long-term stream flow and SSC with acceptable agreement. However, SWAT predicted the overall variation of long-term SSC better than GSSHA.

Physically, Fully-Distributed Hydrologic Simulations Driven by GPM Satellite Rainfall over an Urbanizing Arid Catchment in Saudi Arabia

A physically-based, distributed-parameter hydrologic model was used to simulate a recent flood event in the city of Hafr Al Batin, Saudi Arabia to gain a better understanding of the runoff generation and spatial distribution of flooding. The city is located in a very arid catchment. Flooding of the city is influenced by the presence of three major tributaries that join the main channel in and around the heavily urbanized area. The Integrated Multi-satellite Retrievals for Global Precipitation Measurement Mission (IMERG) rainfall product was used due to lack of detailed ground observations. To overcome the heavy computational demand, the catchment was divided into three sub-catchments with a variable model grid resolution. The model was run on three subcatchments separately, without losing hydrologic connectivity among the sub-catchments. Uncalibrated and calibrated satellite products were used producing different estimates of the predicted runoff. The runoff simulations demonstrated that 85% of the flooding was generated in the urbanized portion of the catchments for the simulated flood. Additional model simulations were performed to understand the roles of the unique channel network in the city flooding. The simulations provided insights into the best options for flood mitigation efforts. The variable model grid size approach allowed using physically-based, distributed models—such as the Gridded Surface Subsurface Hydrologic Analysis (GSSHA) model used in this study—on large basins that include urban centers that need to be modeled at very high resolutions.

Evidence of equilibrium peak runoff rates in steep tropical terrain on the island of Dominica during Tropical Storm Erika, August 27, 2015

Tropical Storm Erika was a weakly organized tropical storm when its center of circulation passed more than 150km north of the island of Dominica on August 27, 2015. Hurricane hunter flights had difficulty finding the center of circulation as the storm encountered a high shear environment. Satellite and radar observations showed gyres imbedded within the broader circulation. Radar observations from Guadeloupe show that one of these gyres formed in convergent mid-level flow triggered by orographic convection over the island of Dominica. Gauge-adjusted radar rainfall data indicated between 300 and 750mm of rainfall on Dominica, most of it over a four hour period. The result was widespread flooding, destruction of property, and loss of life. The extremity of the rainfall on steep watersheds covered with shallow soils was hypothesized to result in near-equilibrium runoff conditions where peak runoff rates equal the watershed-average peak rainfall rate minus a small constant loss rate. Rain gauge adjusted radar rainfall estimates and indirect peak discharge (IPD) measurements from 16 rivers at watershed areas ranging from 0.9 to 31.4km2 using the USGS Slope-Area method allowed testing of this hypothesis. IPD measurements were compared against the global envelope of maximum observed flood peaks versus drainage area and against simulations using the U.S. Army Corps of Engineers Gridded Surface/Subsurface Hydrologic Analysis (GSSHA) model to detect landslide-affected peak flows. Model parameter values were estimated from the literature. Reasonable agreement was found between GSSHA simulated peak flows and IPD measurements in some watersheds. Results showed that landslide dam failure affected peak flows in 5 of the 16 rivers, with peak flows significantly greater than the envelope curve values for the flood of record for like-sized watersheds on the planet. GSSHA simulated peak discharges showed that the remaining 11 peak flow values were plausible. Simulations of an additional 24 watersheds ranging in size from 2.2 to 75.4km2 provided confirmation that the IPD measurements varied from 40 to nearly 100% of the envelope curve value depending on storm-total rainfall. Results presented in this paper support the hypothesis that on average, the peak discharges scaled linearly with drainage area, and the constant of proportionality was equivalent to 134mmh−1, or a unit discharge of 37.22m3s−1km−2. The results also indicate that after the available watershed storage was filled after approximately 450–500mm of rain fell, runoff efficiencies exceeded 50–60%, and peak runoff rates were more than 80% of the peak rainfall rate minus a small constant loss rate of 20mmh−1. These findings have important implications for design of resilient infrastructure, and means that rainfall rate was the primary determinant of peak flows once the available storage was filled in the absences of landslide dam failure.