Distributed Hydrological Model Research Articles

Impact of Rain Gauge Density on Flood Forecasting Performance: A PBDHM’s Perspective

The structures and parameters of physically-based distributed hydrological models (PBDHMs) can now be established and derived from remote-sensing data with relative ease. When engineers apply PBDHMs for flood forecasting in mesoscale catchments, they encounter varying rain gauge infrastructure conditions. Understanding model performance expectations under varying rain gauge density conditions is crucial for wide PDBHM construction. This study presents a case study of a PBDHM called the Liuxihe Model and examines six rain gauge density scenarios designed based on real-world data to assess the impact of rain gauge density on model flood forecasting performance. The study focuses on a mesoscale catchment in Jiangxi Province, China, covering an area of 2364 km2 with 62 rain gauges. The results indicate that models optimized under an adequate rain gauge density condition are less affected by gauge density changes, maintaining accuracy within a range of change. Compared to Kling–Gupta Efficiency (KGE) and Nash–Sutcliffe Efficiency (NSE), the indicators absolute peak time error (APTE) and peak relative error (PRE) are less sensitive to variation in rain gauge density. The study further discusses how rain gauge density changes related to the interpolated rainfall surfaces and parameter optimization, hoping to facilitate the broader application of PBDHMs and offer insights for future practices.

A unified runoff generation scheme for applicability across different hydrometeorological zones

Runoff generation in humid and semi-arid regions are usually dominated by saturation-excess mechanism and infiltration-excess mechanism, respectively. However, both mechanisms can co-exist in semi-humid regions. Therefore, we proposed a unified runoff generation scheme to represent the single and mixed runoff generation processes, making it applicable for different hydrometeorological conditions. The saturation-excess runoff generation scheme of the Xin'anjiang model was integrated with a modified Horton infiltration scheme at the grid cell scale. By substituting the original runoff generation scheme of the grid-Xin'anjiang (GXAJ) with the integrated scheme, we developed a new distributed hydrological model called grid-Xin'anjiang-infiltration-excess (GXAJ-IE) model. GXAJ-IE was tested in four watersheds and compared with two benchmark models with single runoff generation mechanism. The results indicate that GXAJ-IE model has higher flexibility and robustness in reproducing flood hydrographs under different rainfall conditions in semi-humid watersheds and has comparable performances with the benchmark models in humid and semi-arid regions.

Evaluating the significance of wetland representation in isotope-enabled distributed hydrologic modeling in mesoscale Precambrian shield watersheds

Evaluation of regional-scale models against both streamflow and stable isotopes of water is a relatively new and evolving area of distributed hydrological modeling. While applications of isoWATFLOOD, an isotope-enabled distributed hydrological model are increasing, evaluation of the impacts of model structural features, such as wetland connectivity, on simulation of runoff components is still limited. Wetlands are important water bodies that impact streamflow generation and its spatial variation in Precambrian shield watersheds in parts of Canada and elsewhere. This study investigates the efficiency of isoWATFLOOD in simulating streamflow and instream δ18O and the effects of model percent connected wetlands (%CW) in a mesoscale (∼12,000 km2) Precambrian Shield watershed in Northeastern Ontario, Canada. Model calibration and validation are performed using daily streamflow for five hydrometric gauging stations and biweekly to monthly instream δ18O from 11 river sampling locations from across the watershed. Five model versions with different %CW are generated, calibrated, and validated separately. Results show all models can simulate the timing and pattern of streamflow and δ18O. %CW impacts simulated streamflow during both calibration and validation periods, calibrated parameter sets and simulated internal hydrological processes. Model performance varies spatially with no single value of %CW providing best performance across sub-catchments with 40 % CW providing best mean streamflow simulations in calibration. Increasing %CW (10 % to 40 %) results in higher contribution of lower soil zone water to streamflow that markedly improves baseflow simulation. This study builds on recent isotope-enabled hydrologic simulation capacity of isoWATFLOOD, contributing new examination of model representation of wetland connectivity, a critical component of Precambrian Shield watersheds and evidence of how stable isotopes can contribute to addressing the challenge of equifinality. Results suggest the need to develop model capability for representation of spatial variation in wetland connectivity to recognize this important aspect of heterogeneity in watersheds influencing hydrologic function.

Wflow_sbm v0.7.3, a spatially distributed hydrological model: from global data to local applications

Abstract. The wflow_sbm hydrological model, recently released by Deltares, as part of the Wflow.jl (v0.7.3) modelling framework, is being used to better understand and potentially address multiple operational and water resource planning challenges from a catchment scale to national scale to continental and global scale. Wflow.jl is a free and open-source distributed hydrological modelling framework written in the Julia programming language. The development of wflow_sbm, the model structure, equations and functionalities are described in detail, including example applications of wflow_sbm. The wflow_sbm model aims to strike a balance between low-resolution, low-complexity and high-resolution, high-complexity hydrological models. Most wflow_sbm parameters are based on physical characteristics or processes, and at the same time wflow_sbm has a runtime performance well suited for large-scale high-resolution model applications. Wflow_sbm models can be set a priori for any catchment with the Python tool HydroMT-Wflow based on globally available datasets and through the use of point-scale (pedo)transfer functions and suitable upscaling rules and generally result in a satisfactory (0.4 ≥ Kling–Gupta efficiency (KGE) < 0.7) to good (KGE ≥ 0.7) performance for discharge a priori (without further tuning). Wflow_sbm includes relevant hydrological processes such as glacier and snow processes, evapotranspiration processes, unsaturated zone dynamics, (shallow) groundwater, and surface flow routing including lakes and reservoirs. Further planned developments include improvements on the computational efficiency and flexibility of the routing scheme, implementation of a water demand and allocation module for water resource modelling, the addition of a deep groundwater concept, and computational efficiency improvements through for example distributed computing and graphics processing unit (GPU) acceleration.

Evaluation of satellite precipitation products for real-time extreme river flow modeling in data scarce regions

Abstract. Inadequacy of spatio-temporal hydro-climatic data limits the efficacy of hazard monitoring and disaster risk reduction activities in disaster-prone areas. Various satellite missions are recently providing climate data, but prior evaluation and enhancement of these data are necessary for a reliable application. In this study, we conducted performance evaluation and enhancement of three real-time satellite precipitation products (SPPs) (GSMaP, GPM-IMERG, and PERSIANN) for flood modeling in the Blue Nile basin. The bias correction improved the original SPPs, with the largest improvement being for factors generated from 10 d mean data. Flood event hydrograph indicated satisfactory results of error metrics on the devastating flood event of 2012. Employing reliable physical–based distributed hydrologic models provided longer lead time and high-accuracy flood simulation. Furthermore, the results indicate that integrating available initial observed precipitation data improved the efficiency of SPPs simulation, and hence are applicable in operational flood monitoring.

Distributed Hydrological Modeling With Physics‐Encoded Deep Learning: A General Framework and Its Application in the Amazon

AbstractWhile deep learning (DL) models exhibit superior simulation accuracy over traditional distributed hydrological models (DHMs), their main limitations lie in opacity and the absence of underlying physical mechanisms. The pursuit of synergies between DL and DHMs is an engaging research domain, yet a definitive roadmap remains elusive. In this study, a novel framework that seamlessly integrates a process‐based hydrological model encoded as a neural network (NN), an additional NN for mapping spatially distributed and physically meaningful parameters from watershed attributes, and NN‐based replacement models representing inadequately understood processes is developed. Multi‐source observations are used as training data, and the framework is fully differentiable, enabling fast parameter tuning by backpropagation. A hybrid DL model of the Amazon Basin (∼6 × 106 km2) was established based on the framework, and HydroPy, a global‐scale DHM, was encoded as its physical backbone. Trained simultaneously with streamflow observations and Gravity Recovery and Climate Experiment satellite data, the hybrid model yielded median Nash‐Sutcliffe efficiencies of 0.83 and 0.77 for dynamic and distributed simulations of streamflow and total water storage, respectively, 41% and 35% higher than those of the original HydroPy model. Replacing the original Penman‒Monteith formulation in HydroPy with a replacement NN produces more plausible potential evapotranspiration (PET) estimates, and unravels the spatial pattern of PET in this giant basin. The NN used for parameterization was interpreted to identify the factors controlling the spatial variability in key parameters. Overall, this study lays out a feasible technical roadmap for distributed hydrological modeling in the big data era.

Assessing environmental flow alterations induced by dams and climate change using a distributed hydrological model at catchment scale

Abstract Hydrological alterations by dams and climate change can reduce aquatic biodiversity by disrupting the life cycles of organisms. Here, we aimed to evaluate and compare the hydrological alterations caused by dams and climate change throughout the Omaru River catchment, Japan, using a distributed hydrological model (DHM). First, to assess the impacts of dam and climate change independently, we performed runoff analyses using either dam discharge or future climatic data (two future periods, 2031–2050 and 2081–2100 × three representative concentration pathways). Subsequently, we derived indicators of hydrologic alterations (IHA) to quantify changes in flow alterations by comparing them to IHA under natural conditions (i.e., without dam or climate change data). We found that dams altered IHAs more than climate change. However, on a catchment-scale standpoint, climate change induced wider ranges of flow alterations, such as a further decrease in low flow metrics along the tributaries and uppermost main stem, suggesting a catchment-level shrinkage in important corridors of aquatic organisms. We also observed that the altered flow by water withdrawals was ameliorated by the confluence of tributaries and downstream hydropower outflows. Our approach using a DHM captured the various patterns of flow alterations by dams and climate change.

Development of a cellular automata-based distributed hydrological model for simulating urban surface runoff

The present study developed a distributed urban surface runoff model based on the cellular automata framework (CA-DUSRM) to simulate two-dimensional urban surface runoff processes. CA-DUSRM uses an improved nonlinear reservoir algorithm to simulate the generation of surface runoff outflow at grid cell scale and flow interactions among cells. The CA-DUSRM simulation results (total surface runoff and peak discharge) for an asphalt road sub-catchment during different rainfall events were in good agreement with actual measurements of both surface runoff and rainfall processes. The performance of CA-DUSRM was comprehensively and systematically analyzed under different rainfall–terrain scenarios. The results showed that increasing the temporal grain resulted in uncertain simulation results in relation to the rainfall intensity, slope, and terrain undulation employed; and significant spatial grain effects were mainly caused by large terrain undulation changes. Compared with the storm water management model (SWMM), CA-DUSRM performed better overall and was more sensitive to variation in terrain undulation and fluctuations in rainfall. Based on model structure and simulation mechanism perspectives, CA-DUSRM can present the influence of spatially heterogeneous underlying surface characteristics on the runoff processes and describe the lateral water exchanges, which makes the simulated processes closer to the actual processes when compared with SWMM. CA-DUSRM is potentially suitable for application in urban areas that have complex underlying surfaces.

Temporal and spatial changes in hydrological wet extremes of the largest river basin on the Tibetan Plateau

Global warming accelerates the rate of inter-regional hydrological cycles, leading to a significant increase in the frequency and intensity of hydrological wet extremes. The Tibetan Plateau (TP) has been experiencing a rapid warming and wetting trend for decades. This trend is especially strong for the upper Brahmaputra basin (UBB) in the southern TP. The UBB is the largest river on the TP, and these changes are likely to impact the water security of local and downstream inhabitants. This study explores the spatial-temporal variability of wet extremes in the UBB from 1981–2019 using a water- and energy-budget distributed hydrological model (WEB-DHM) to simulate river discharge. The simulated results were validated against observed discharge from the Ministry of Water Resources at a mid-stream location and our observations downstream. The major findings are as follows: (1) the WEB-DHM model adequately describes land-atmosphere interactions (slight underestimation of −0.26 K in simulated annual mean land surface temperature) and can accurately reproduce daily and monthly discharge (Nash-Sutcliffe efficiency is 0.662 and 0.796 respectively for Nuxia station); (2) although extreme discharge generally occurs in July and is concentrated in the southeastern TP, extreme wet events in the UBB are becoming increasingly frequent (after 1998, the number of extreme days per year increased by 13% compared to before) and intense (maximum daily discharge increased with a significant trend of 444 (m3s−1) yr−1), and are occurring across a wider region; (3) Precipitation is more likely to affect the intensity and spatial distribution of wet extremes, while the air temperature is more correlated with the frequency. Our wet extreme analysis in the UBB provides valuable insight into strategies to manage regional water resources and prevent hydrological disasters.

Simulation of runoff process based on the 3-D river network

Many rivers originating from mountainous areas cannot be adequately measured in the field to obtain sufficient basic data due to the challenging terrain, which poses difficulties for conducting relevant research such as hydrodynamic and sediment transport simulations, fluvial material fluxes. This study utilized a combination of 71 river cross sections extracted from multisource remote sensing data and 34 cross sections measured by hydrological stations between 2010 and 2018 to generate generalized cross sections for all river orders in the middle reaches of the Yellow River. By integrating the generalized cross sections, river surfaces, and traditional DEM river network, a comprehensive 3-D river network has been established to provide a more accurate representation of the morphological changes along the river channel. To tackle the challenge of simulating runoff in data-deficient areas, the Digital Yellow River Integrated Model (DYRIM), a potent distributed hydrological model, was enhanced by incorporating geometric parameters derived from a 3-D river network into almost all input parameters that can be obtained from multi-source remote sensing. This enhancement enabled the model to accurately simulate runoff in representative data-deficient rivers of the Loess Plateau. The Nash Efficiency coefficients for the simulated runoff exhibited improvement of 23% compared to previous studies, surpassing 0.8 for the period between 2010 and 2018. Additionally, the simulated errors for flow velocity and water depth were 7% and 9% respectively. This approach can provide essential data for the simulation of hydrological processes and improving hydrological models, as well as technical methodologies for related research such as sediment transport simulations and river carbon emissions.

The quantitative attribution of climate change to runoff increase over the Qinghai-Tibetan Plateau

Runoff from the Qinghai-Tibetan Plateau, a major global water tower, is crucial to regional hydrological processes and the availability of water for a large population living downstream. Climate change, especially changes in precipitation and temperature, directly impacts hydrological processes and exacerbates shifts in the cryosphere, such as glacier and snow melt, leading to changes in runoff. Although there is a consensus on increased runoff due to climate change, it is still unclear to what extent precipitation and temperature contribute to runoff variations. This lack of understanding is one of the primary sources of uncertainty when assessing the hydrological impacts of climate change. In this study, a large-scale, high-resolution, and well-calibrated distributed hydrological model was employed to quantify the long-term runoff of the Qinghai-Tibetan Plateau, and the changes in runoff and runoff coefficient were analyzed. Furthermore, the impacts of precipitation and temperature on runoff variation were quantitatively estimated. The results found that runoff and runoff coefficient decreased from southeast to northwest, with mean values of 184.77 mm and 0.37, respectively. Notably, the runoff coefficient exhibited a significant increasing trend of 1.27 %/10 yr (P < 0.001), while the southeastern and northern regions of the plateau showed a declining tendency. We further showed that the warming and humidification of the Qinghai-Tibetan Plateau led to an increase in the runoff by 9.13 mm/10 yr (P < 0.001). And precipitation is a more important contributor than temperature across the plateau, contributing 72.08 % and 27.92 % to the runoff increase, respectively. At the basin scale, the influence of precipitation and temperature on runoff varies among basins, with the Daduhe basin and the Inner basin being the most and least influenced by precipitation, respectively. This research analyses historical runoff changes on the Qinghai-Tibetan Plateau and provides insights into the contributions of climate change to runoff.

A distributed hydrological model for semi-humid watersheds with a thick unsaturated zone under strong anthropogenic impacts: A case study in Haihe River Basin

In the past decades, human activities, including hydraulic structure construction and operation, and groundwater overexploitation have largely changed the land surface conditions and correspondingly altered the natural rainfall-runoff processes. These changes make the traditional operational flood forecasting models without considering these anthropogenic impacts fail to capture these changes, leading to a downgraded forecasting capability. To improve the accuracy of flood forecasting in semi-humid watersheds under strong aboveground and underground anthropogenic impacts, the widely used operational flood forecasting model, the Xin’anjiang model, was chosen as a basis to build a new distributed hydrological model accounting for these anthropogenic impacts. To characterize the runoff generation process under the condition of a thick unsaturated zone caused by groundwater overexploitation, this study developed a runoff generation algorithm based on an idea that conceptualizes the soil free-water storage into two virtual reservoirs. To characterize the impacts of manmade reservoirs on the runoff concentration process, we modified the Muskingum method. In addition, we further developed a priori estimation method for deriving the spatial distribution of the tension water storage capacity by considering the anthropogenic impacts and geographical conditions. Based on the above work, a Grid Xin’anjiang-Haihe model (GXH) was developed. In this study, the Qingshui River, which is a tributary of the Haihe River Basin and a typical semi-humid medium-sized watershed in China with poor flood forecasting accuracy, was selected as the study area to test the effectiveness of the model. The results show that the GXH model can simulate the flood peak flow and peak time with higher accuracy than the current operational flood forecasting models and several other models. In addition, the parameter estimation method developed in this study has also proven to be superior in theory and practice.

IHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction

Abstract. Forecasting flood–landslide cascading disasters in flood- and landslide-prone regions is an important topic within the scientific community. Existing hydrological–geotechnical models mainly employ infinite or static 3D stability models, and very few models have incorporated the 3D landslide model into a distributed hydrological model. In this work, we modified a 3D landslide model to account for slope stability under various soil wetness states and then coupled it with the Coupled Routing and Excess STorage (CREST) distributed hydrology model, forming a new modeling system called iHydroSlide3D v1.0. Through embedding a soil moisture downscaling method, this model is able to model hydrological and slope-stability submodules even at different resolutions. For a large-scale application, we paralleled the code and elaborated several computational strategies. The model produces a relatively comprehensive and reliable diagnosis for flood–landslide events, including (i) complete hydrological components (e.g., soil moisture and streamflow), (ii) a landslide susceptibility assessment (factor of safety and probability of occurrence), and (iii) a landslide hazard analysis (geometric properties of potential failures). We evaluated the plausibility of the model by testing it in a large and complex geographical area, the Yuehe River basin, China, where we attempted to reproduce cascading flood–landslide events. The results are well verified at both hydrological and geotechnical levels. iHydroSlide3D v1.0 is therefore appropriately used as an innovative tool for assessing and predicting cascading flood–landslide events once the model is well calibrated.

Recognition of the Interaction Mechanisms between Water and Land Resources Based on an Improved Distributed Hydrological Model

Conflicts between humans and land use in the process of using water and conflicts between humans and water resources in the process of using land have led to an imbalance between natural ecosystems and socio-economic systems. It is difficult to understand the impact of the processes of water production and consumption on land patches and their ecological effects. A grid-type, basin-distributed hydrological model was established in this study, which was based on land-use units and coupled with groundwater modules to simulate the water production and consumption processes in different units. By combining land use and net primary productivity, the runoff coefficient and the water use efficiency (NPP/ET) of different land units were used as indicators to characterize the interaction between water and land resources. The results showed that the average runoff coefficients of cultivated land, forest land and grassland were 0.7, 0.5 and 0.9, respectively. Moreover, the average runoff coefficients of hills, plains and basins were 0.7, 0.7 and 0.8, respectively. The NPP produced by the average unit, evapotranspiration, in cultivated land, forest land and grassland was 7 (gC/(m2•a))/mm, 0.7 (gC/(m2•a))/mm and 0.2 (gC/(m2•a))/mm, respectively. These results provide quantitative scientific and technological support in favor of the comprehensive ecological management of river basins.

Evaluation of Temperature-Index and Energy-Balance Snow Models for Hydrological Applications in Operational Water Supply Forecasts

In the western United States, snow accumulation, storage, and ablation affect seasonal runoff. Thus, the prediction of snowmelt is essential to improve the reliability of water supply forecasts to guide water allocation and operational decisions. The current method used at the Colorado Basin River Forecast Center (CBRFC) couples the SNOW-17 temperature-index snow model and the Sacramento Soil Moisture Accounting (SAC-SMA) runoff model in a lumped approach. Limitations in parameter transferability and calibration requirements for changing conditions with the temperature-index model motivated this research, in which new avenues were investigated to assess and prototype the application of an energy-balance snow model in a distributed modeling approach. The Utah Energy Balance (UEB) model was chosen to compare with the SNOW-17 model because it is simple and parsimonious, making it suitable for distributed application with the potential to improve water supply forecasts. Each model was coupled with the SAC-SMA model and the Rutpix7 routing model to simulate basin snowmelt and discharge. All the models were applied on grids over watersheds using the Research Distributed Hydrologic Model (RDHM) framework. Case studies were implemented for two study sites in the Colorado River basin over a period of two decades. The model performance was evaluated by comparing the model output with observed daily discharge and snow-covered area data obtained from remote sensing sources. Simulated evaporative components of sublimation and evapotranspiration were also evaluated. The results showed that the UEB model, requiring calibration of only a snow drift factor, achieves a comparable performance to the calibrated SNOW-17 model, and both provided reasonable basin snow and discharge simulations in the two study sites. The UEB model had the additional advantage of being able to explicitly simulate sublimation for different land types and thus better quantify evaporative water balance components and their sensitivity to land cover change. UEB also has a better transferability potential because it requires calibration of fewer parameters than SNOW-17. The majority of the parameters for UEB are physically based and regarded as constants characterizing spatially invariant properties of snow processes. Thus, the model remains valid for different climate and terrain conditions for multiple watersheds.

GEE can prominently reduce uncertainties from input data and parameters of the remote sensing-driven distributed hydrological model

The coupling of multisource remote sensing data and the lack of measured runoff introduce input data and model parameters uncertainties to the remote sensing-driven distributed hydrological model (RS-DHM). The PB satellite remote sensing datasets of the Google Earth Engine (GEE) are widely used in RS-DHM and remote sensing runoff inversion research, but whether GEE can reduce the two abovementioned uncertainties is still unknown. To answer this question, twelve remote sensing data sources provided by GEE were used in this study to drive a typical RS-DHM called the remote sensing-driven distributed time-variant gain model (RS-DTVGM) and the remote sensing runoff inversion technology called remote sensing hydrological station (RSHS), and the contribution of GEE to the improving hydrological model uncertainties was quantitatively analyzed from 2001 to 2020. The results showed that (1) the GEE-based improved data preparation not only effectively reduced the uncertainty in the input data with better spatial-temporal continuity and a 6.20 % reduction in the total area occupied by invalid grids, but also enhanced the operational efficiency by reducing the image number, memory size and data processing time of the satellite remote sensing data by 83.63 %, 99.53 %, and 98.73 %, respectively; (2) the GEE-based RSHS technology provided sufficient data support for parameter adjustment and accuracy validation of the RS-DTVGM, which effectively reduced the uncertainty in the model parameters and increased the Nash efficiency coefficient (NSE) in the calibration and validation period from 0.67 to 0.87 and 0.75, respectively; and (3) the calibrated RS-DTVGM was more reliable and robust, and its runoff and evapotranspiration were consistent with the actual statistical data. In the future, GEE and RSHS technology should be widely adopted to drive the RS-DHM to more quickly and easily provide reliable hydrological processes simulation results for integrated water resource management, therefore achieving win–win results in terms of efficiency and accuracy.

Divergent runoff impacts of permafrost and seasonally frozen ground at a large river basin of Tibetan Plateau during 1960–2019

Since the 20th century, due to global warming, permafrost areas have undergone significant changes. The degradation of permafrost has complicated water cycle processes. Taking the upper Yellow River basin (UYRB) as a demonstration, this study discusses the long-term (1960–2019) changes in frozen ground and their hydrological effects with a cryosphere-hydrology model, in particular a permafrost version of the water and energy budget-based distributed hydrological model. The permafrost at the UYRB, with thickening active layer and lengthening thawing duration, has degraded by 10.8%. The seasonally frozen ground has a more pronounced intra-annual regulation that replenishes surface runoff in the warm season, while the degradation of permafrost leads to a runoff increase. The occurrence of extreme events at the UYRB has gradually decreased with the degradation of frozen ground, but spring droughts and autumn floods become more serious. The results may help better understand the hydrological impacts of permafrost degradation in the Tibetan Plateau.

Applying the NWS’s Distributed Hydrologic Model to Short-Range Forecasting of Quickflow in the Mahantango Creek Watershed

Abstract Accurate and reliable forecasts of quickflow, including interflow and overland flow, are essential for predicting rainfall–runoff events that can wash off recently applied agricultural nutrients. In this study, we examined whether a gridded version of the Sacramento Soil Moisture Accounting model with Heat Transfer (SAC-HT) could simulate and forecast quickflow in two agricultural watersheds in east-central Pennsylvania. Specifically, we used the Hydrology Laboratory–Research Distributed Hydrologic Model (HL-RDHM) software, which incorporates SAC-HT, to conduct a 15-yr (2003–17) simulation of quickflow in the 420-km2 Mahantango Creek watershed and in WE-38, a 7.3-km2 headwater interior basin. We directly calibrated HL-RDHM using hydrologic observations at the Mahantango Creek outlet, while all grid cells within Mahantango Creek, including WE-38, were calibrated indirectly using scalar multipliers derived from the basin outlet calibration. Using the calibrated model, we then assessed the quality of short-range (24–72 h) deterministic forecasts of daily quickflow in both watersheds over a 2-yr period (July 2017–October 2019). At the basin outlet, HL-RDHM quickflow simulations showed low biases (PBIAS = 10.5%) and strong agreement (KGE″ = 0.81) with observations. At the headwater scale, HL-RDHM overestimated quickflow (PBIAS = 69.0%) to a greater degree, but quickflow simulations remained satisfactory (KGE″ = 0.65). When applied to quickflow forecasting, HL-RDHM produced skillful forecasts (&gt;90% of Peirce and Gerrity skill scores above 0.5) at all lead times and significantly outperformed persistence forecasts, although skill gains in Mahantango Creek were slightly lower. Accordingly, short-range quickflow forecasts by HL-RDHM show promise for informing operational decision-making in agriculture. Significance Statement Daily runoff forecasts can alert farmers to rainfall–runoff events that have the potential to wash off recently applied fertilizers and manures. To gauge whether daily runoff forecasts are accurate and reliable, we used runoff monitoring data from a large agricultural watershed and one of its headwater tributaries to evaluate the quality of short-term runoff forecasts (1–3 days ahead) that were generated by a National Weather Service watershed model. Results showed that the accuracy and reliability of daily runoff forecasts generally improved in both watersheds as lead times increased from 1 to 3 days. Study findings highlight the potential for National Weather Service models to provide useful short-term runoff forecasts that can inform operational decision-making in agriculture.

A Comparative Evaluation of Using Rain Gauge and NEXRAD Radar-Estimated Rainfall Data for Simulating Streamflow

Ascertaining the spatiotemporal accuracy of precipitation is a challenge for hydrologists and planners for flood protection measures. The objective of this study was to compare streamflow simulations using rain gauge and radar data from a watershed in Southern Ontario, Canada, using the Hydrologic Engineering Center’s event-based distributed Hydrologic Modeling System (HEC-HMS). The model was run using the curve number (CN) and the Green and Ampt infiltration methods. The results show that the streamflow simulated with rain gauge data compared better with the observed streamflow than the streamflow simulated using radar data. However, when the Mean Field Bias (MFB) corrections were applied, the quality of the streamflow results obtained from radar rainfall data improved. The results showed no significant difference between the simulated streamflow using the SCS and the Green and Ampt infiltration approach. However, the SCS method is reasonably more appropriate for modeling the runoff at the sub-basin-scale than the Green and Ampt infiltration approach. With the SCS method, the simulated and observed runoff amount obtained using rain gauge rainfall showed an R2 value of 0.88 and 0.78 for MFB-corrected radar and 0.75 for radar only. For the Green and Ampt modeling option, the R2 value for the simulated and observed runoff amounts were 0.87 with rain gauge, 0.66 with radar only, and 0.68 with MFB-corrected radar rainfall inputs. The NSE values for rain gauge input ranged from 0.65 to 0.35. Overall, three values were less than 0.5 for streamflow for both the methods. For seven radar rainfall events, the NSE was greater than 0.5, with a range of very good to satisfactory. The analysis of RSR showed a very good comparison of stream flow using the SCS curve number method and Green and Ampt method using different rainfall inputs. Only one value, the 2 November 2003 event, was above 0.7 for rain gauge-based streamflow. The other RSR values were in the range of “very good”. Overall, the study showed better results for the simulated runoff with the MFB-corrected radar rainfall when compared with the simulations obtained using radar rainfall only. Therefore, MFB-corrected radar could be explored as a substitute rainfall source.

Analysis on the Disaster Mechanism of “8.12” Flash Flood in Liulin River Basin

Hubei province is located in the center of China with 56% total area characterized with mountainous area. Thus, flash flood caused by extreme rainfall has become one of the significant obstacles that highly affect the social and economic development of the province. In order to scientifically understand the mechanism of flash flood disasters and provide technological support to the local flood prevention and control work, the IWHR designed and developed a new distributed hydrological model named China-FFMS that can simulate the evolution of natural disasters and make an assessment by setting the flood water sources in line with the flow discharge. The FFMS was further applied to simulate the 8.12 flash flood disaster that occurred in the Liulin county of Hubei province on 12 August (“8.12”) and fed by the data collected from the national flash flood disaster investigation and assessment. The calculated peak flow was 666.22 m3/s with an error of +13% compared with postdisaster investigation data (589 m3/s). The results showed that using a multisourced modelling approach, e.g., mixing spatiotemporal variables and sources, to simulate the flash flood process was able to accurately reproduce the flood process and the consistence of the flow discharge, thereby explaining the underlying reason of the disaster formation and evolution. Regarding the case of the Liulin county, the main factor leading to the disaster was the overlapped peak flow where the Dunne flood peak of three different tributaries from the upper reach met together at the same time. Moreover, the peak flow of the Lianhua river at the downstream of Liulin County also arrived at the same time as the upstream peak, which obstructed the flood progress and increased the damage of the disaster. According to the analysis, several suggestions and recommendations are proposed such as the improvement of the forecast and early warning system of the upstream areas, the optimization of the current flood defense plan, and the enhancement of the residents’ awareness of flash flood disasters.