Search

Flood forecasting is crucial for flood prevention and mitigation in both watersheds and urban areas. In watersheds, the development of flood forecasting methods is relatively mature, while their application in urban areas is still in the research and exploration stage. In practice, high-precision hydrological and hydrodynamic modeling during large floods is constrained to varying degrees by power, timeliness, and transmission conditions, whereas the unit hydrograph method is simple, efficient, has fewer constraints, and plays an important role. To address the current situation, this study introduces an improved synthetic unit hydrograph method to evaluate its practical effectiveness in watershed and urban flood forecasting. Firstly, based on the Snyder's synthetic unit hydrograph method, the flooding process in Muyangxi watershed in Fujian Province was calculated and compared with the results of the Xin'anjiang model and the measured data; secondly, based on the Snyder's synthetic unit hydrograph method, the urban synthetic unit hydrograph was established by taking into account the impermeability of the sub-surface and the slope, which was then applied to the calculation of the flooding process line in the urban area of Zhengzhou, and the differences of the simulation results between this method and the mainstream SWMM model were compared. The differences between this method and the mainstream SWMM model simulation results are compared. The results show that the synthetic unit hydrograph method is close to the measured and hydrological model results in the selected watersheds, and the forecast pass rate is 91.7%, with no peak-present time difference; compared with the SWMM model results, the relative error of the peak flow of the most floods is within 10%, and the coefficient of certainty of the floods in 72.7% is above 0.9. The example application shows that compared with the hydrological and hydrodynamic modeling methods, the partial rise and fall process of the flood calculated by the synthetic unit hydrograph method is better than the model results, which are more in line with the actual process, and its calculation is concise. The synthetic unit hydrograph method requires fewer parameters, and has strong implementation and popularity. The research further verifies the superiority of the synthetic unit hydrograph method in flood forecasting and provides an effective method for flood process calculation. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change and associated increasing nutrient loading to coasts will worsen coastal eutrophication on a global scale. Basin Head is a coastal lagoon located in northeastern Prince Edward Island, Canada, with a federally protected ecosystem. Nitrate-nitrogen (NO3-N) is conveyed from agricultural fields in the watershed to the eutrophic lagoon via intertidal groundwater springs and groundwater-dominated tributaries. A field program focused on four main tributaries that discharge into the lagoon was conducted to measure year-round NO3-N loading. These measurements were used to calibrate a SWAT+ hydrologic model capable of simulating hydrologic and NO3-N loads to the lagoon. Several climate change scenarios incorporating different agricultural best management practices (BMPs) were simulated to better understand potential future NO3-N loading dynamics. Results indicate that all climate change scenarios produced increased annual NO3-N loading to the lagoon when comparing historical (1990-2020) to end of century time periods (2070-2100); however, only one climate scenario (MRI-ESM2-0 SSP5-8.5) resulted in a statistically significant (p-value <0.05) increase. Enlarged buffer strips and delayed tillage BMP simulations produced small (0%-8%) effects on loading, while changing the crop rotation from potato-barley-clover to potato-soybean-barley yielded a small reduction in NO3-N loading between the historical period and the end of the century (26%-33%). Modeling revealed changes in seasonal loading dynamics under climate change where NO3-N loads remained more consistent throughout the year as opposed to current conditions where the dominant load is in the spring. An increase in baseflow contributions to streamflow was also noted under climate change, with the largest change occurring in the winter (e.g., up to a five-fold increase in February). These findings have direct implications for coastal management in groundwater-dominated agricultural watersheds in a changing climate. [ABSTRACT FROM AUTHOR]

This article presents comparison of Artificial Neural Networks (ANNs) and Hydrologic Engineering Centre-Hydrologic Modelling System (HEC-HMS) model for rainfall-runoff (R-R) process. Aim of the present work is to forecast runoff one day ahead at Shivade station of Upper Krishna Basin, India, using 17 years of daily rainfall and discharge data. The R-R modelling can be exercised using various traditional methods which generally require exogenous data in the form of basin parameters. Unavailability of such data becomes major impediment in applying these models at many basins. In such situations, soft computing techniques like ANNs have been extensively applied to model R-R process. Though ANN is now an established tool in hydrology, compared to HEC-HMS, its results are viewed with suspicion owing to its data-driven nature rather than a model-driven nature. In this study, ANN model performed reasonably well, with a higher correlation coefficient (0.87) and the lowest Root Mean Square Error (136.28 m3/s) when compared with HEC-HMS (0.76, 139.8 m3/s) respectively. Novelty of the present work lies in model development using restricted basin data. Both models showed less accuracy in predicting extreme events. Finally, it is concluded that ANN model can be used as a supplementary technique along with HEC-HMS for this phenomenon. [ABSTRACT FROM AUTHOR]

This study aimed to enhance flood forecasting accuracy in the Liangfeng River basin, a small karst watershed in Southern China, by incorporating the Available Reservoir Capacity of Karst (ARCK) into the HEC-HMS model. This region is often threatened by floods during the rainy season, so an accurate flood forecast can help decision-makers better manage rivers. As a crucial influencing factor on karstic runoff, ARCK is often overlooked in hydrological models. The seasonal and volatile nature of ARCK makes the direct computation of its specific values challenging. In this study, a virtual reservoir for each sub-basin (total of 17) was introduced into the model to simulate the storage and release of ARCK-induced runoff phenomena. Simulations via the enhanced model for rainfall events with significant fluctuations in water levels during 2021–2022 revealed that the Nash–Sutcliffe efficiency coefficient (NSE) of the average simulation accuracy was improved by more than 34%. Normally, rainfalls (even heavy precipitations) during the dry season either do not generate runoff or cause negligible fluctuations in flow rates due to long intervals. Conversely, relatively frequent rainfall events (even light ones) during the wet season result in substantial runoff. Based on this observation, three distinct types of karstic reservoirs with different retaining/releasing capacities were defined, reflecting variations in both the frequency and volume of runoff during both seasons. As a real-time environmental variable, ARCK exhibits higher and lower values during the dry and rainy seasons, respectively, and we can better avoid the risk of flooding according to its special effects. [ABSTRACT FROM AUTHOR]

Saudi Arabia is threatened by recurrent flash floods caused by extreme precipitation events. To mitigate the risks associated with these natural disasters, we implemented an advanced nationwide flash flood forecast system, boosting disaster preparedness and response. A noteworthy feature of this system is its national-scale operational approach, providing comprehensive coverage across the entire country. Using cutting-edge technology, the setup incorporates a state-of-the-art, three-component system that couples an atmospheric model with hydrological and hydrodynamic models to enable the prediction of precipitation patterns and their potential impacts on local communities. This paper showcases the system's effectiveness during an extreme precipitation event that struck Jeddah on 24 November 2022. The event, recorded as the heaviest rainfall in the region's history, led to widespread flash floods, highlighting the critical need for accurate and timely forecasting. The flash flood forecast system proved to be an effective tool, enabling authorities to issue warnings well before the flooding, allowing residents to take precautionary measures, and allowing emergency responders to mobilize resources effectively. [ABSTRACT FROM AUTHOR]

BACKGROUND AND OBJECTIVES: Land use change can directly affect rainfall-runoff relationships. The change in land use is an essential factor in runoff production. This research evaluated the effect of urban development scenarios of land use change on runoff in Gonabad city of Iran. The innovation and importance of this work are to determine which land use changes have the greatest impact on the flood discharge in this urban area. Also, determine how much the minimum development of urban green space is to control and reduce peak flood discharge in this city that is located in a dry area. METHODS: The effect of urban development scenarios on runoff was evaluated by the WinTR-55 model in 5 sub-basins of Gonabad city. The main data required for inputting to the WinTR-55 model are sub-area and reach characteristics, curve number, and storm data. The storm data in TR-55 are 24-hour rainfall amounts in a return period of 2, 5, 10, 25, 50, and 100 years. The changes in the maximum flood discharge and flow hydrograph in each sub-area and return period were calculated by the WinTR-55 model under existing land use conditions and 9 scenarios of urban development. FINDINGS: The greatest increase in runoff production was related to the conversion of abandoned fallow and agricultural lands to residential. Also, the most effective increase or decrease of land cover change in peak flow discharge and total flow volume was at the 2-year return period. The decreased effect of the development of green spaces and urban gardens on peak flow discharge and total flow volume was seen only if their development was more than 50%. The average maximum decrease in peak discharge and total flow volume was 22.7% and 16.1%, respectively. While the average maximum increase in peak discharge and total flow volume was 84.4% and 53.9%, respectively. CONCLUSION: The effect of increasing green spaces and urban gardens on the reduction of peak discharge and volume of runoff was also evident in the study area. Land management and preventing the conversion of permeable land uses such as agricultural, gardening, fallow lands, and rangeland will be much simpler and less costly. Urban land use management to prevent urban floods requires the expansion of permeable surfaces, especially green spaces, and urban gardens. These are the important novelty of this research that can be beneficial for future urban developments of Gonabad city and its flood management activities. [ABSTRACT FROM AUTHOR]

Water quality and freshwater ecosystems are affected by river discharge and temperature. Models are frequently used to estimate river temperature on large spatial and temporal scales due to limited observations of discharge and temperature. In this study, we use physically based river routing and temperature models to simulate daily discharge and river temperature for rivers in 138 basins in Alaska, including the entire Yukon River basin, from 1990–2021. The river temperature model was optimized for ice free months using a surrogate‐based model optimization method, improving model performance at uncalibrated river gages. A common statistical model relating local air and water temperature was used as a benchmark. The physically based river temperature model exhibited superior performance compared to the benchmark statistical model after optimization, suggesting river temperature model optimization could become more routine. The river temperature model demonstrated high sensitivity to air temperature and model parameterization, and lower sensitivity to discharge. Validation of the models showed a Kling‐Gupta Efficiency of 0.46 for daily river discharge and a root mean square error of 2.04°C for daily river temperature, improving on the non‐optimized physical model and the benchmark statistical model, which had root mean square errors of 3.24 and 2.97°C, respectively. The simulation shows that rivers in northern Alaska have higher maximum summer temperatures and more variability than rivers in the Central and Southern regions. Furthermore, this framework can be readily adapted for use across models and regions. Plain Language Summary: Accurate data on the volume and temperature of river water are essential for understanding how changing river conditions affect water quality and freshwater ecosystems. However, direct measurements of river parameters are often lacking, leading researchers to rely on models for estimation. In this study, we utilized advanced models and techniques to compute daily water volume and temperature in 138 basins across Alaska, including the entirety of the Yukon River basin, spanning from 1990 to 2021. Our findings indicated that rivers in northern Alaska exhibited higher maximum summer water temperatures and more significant temperature fluctuations compared to those in the central and southern regions. Our analysis highlighted that adjusting air temperature and the model's internal variables were crucial in minimizing errors in river temperature prediction. We improved the accuracy of the river temperature model by applying a technique to refine the model output based on the limited available river measurements. Comparing our enhanced model to a simpler statistical approach, we observed superior performance once the necessary adjustments were implemented. Key Points: Our model system produced an accurate, high‐resolution, daily hindcast of Alaskan river temperature and discharge from 1990 to 2021We increased model performance by employing a new optimization of the River Basin Model and forcing it with a climate‐land surface modelA sensitivity analysis highlights important drivers of river temperatures in each region and the need for optimization [ABSTRACT FROM AUTHOR]

Runoff and sediment movement induced by storms have serious economic, environmental, and social impacts around the world. Hydrological models have become useful decision support tools for watershed management. The development of process-based distributed models is complex as it involves a range of disciplines and spans multiple spatial and temporal scales. In this study, a process-based and mesh-distributed model, suitable for both event-based and continuous simulations, is developed. The watershed is conceptualized in three distinct zones: a surface overland region, an unsaturated subsurface zone, and a groundwater zone. Overland flow is governed by the 2D diffusive wave equation with an optional 1D channel network solver; water in the unsaturated zone is modelled through mass conservation and vertical dominance of the processes; and saturated groundwater flow is governed by the 2D Dupuit equation. Soil erosion and transport are governed by the multi-size, non-equilibrium equations incorporating soil entrainment, transport, and deposition. The model is driven by meteorological input, land use and soil type properties. The numerical approach is a generalization of existing models to overcome some current modelling shortcomings. New contributions of the model are highlighted and model verification and validation are reported against benchmark cases. The model is then applied to the Cache Creek watershed in California, USA. Satisfactory agreement with the measured data is obtained with both the runoff flow rate and sediment load. [ABSTRACT FROM AUTHOR]

Flood hazards have led to substantial fatalities and economic loss in the last five decades, making it essential to understand flood dynamics in a warming climate. This study reports the first comprehensive assessment of projected flood hazards across Vietnam. We used downscaled climate data from the CMIP6 initiative, involving a total of 20 climate models, and streamflow projection simulated using a semi-distributed hydrological model. The assessment covers seven near-natural catchments, each representing a climate zone of the country. To evaluate climate change impacts on floods, the study simultaneously analyzes changes in three indices: (i) the annual hottest day temperature, to represent temperature extremes; (ii) the maximum daily rainfall amount, to represent rainfall extremes; and (iii) the discharge value exceeding 5% in a year, to assess streamflow extremes. Changes in the selected indices (relative to the reference period from 1985 to 2014) are assessed under four emission scenarios (SSP1–2.6, SSP2–4.5, SSP3–7.0, and SSP5–8.5) and two future time slices (2036–2065 and 2070–2099). Although the robustness (as indicated by multi-model agreement) and significance (identified through the statistical test) of the changes vary substantially, depending on the selected indices and assessed time slices, an overall increase is consistently identified across all of the assessed hydro-climatic extremes (up to 4.8 °C for temperature extremes, 43 mm for rainfall extremes, and 31% for streamflow extremes). The findings suggest a potential increase in flood risk across Vietnam in a warming climate, highlighting the urgent need for improved flood preparedness and investment to reduce economic loss and mortality in an uncertain future. [ABSTRACT FROM AUTHOR]

Anthropogenic climate change and associated increasing nutrient loading to coasts will worsen coastal eutrophication on a global scale. Basin Head is a coastal lagoon located in northeastern Prince Edward Island, Canada, with a federally protected ecosystem. Nitrate-nitrogen (NO3-N) is conveyed from agricultural fields in the watershed to the eutrophic lagoon via intertidal groundwater springs and groundwater-dominated tributaries. A field program focused on four main tributaries that discharge into the lagoon was conducted to measure year-round NO3-N loading. These measurements were used to calibrate a SWAT+ hydrologic model capable of simulating hydrologic and NO3-N loads to the lagoon. Several climate change scenarios incorporating different agricultural best management practices (BMPs) were simulated to better understand potential future NO3-N loading dynamics. Results indicate that all climate change scenarios produced increased annual NO3-N loading to the lagoon when comparing historical (1990–2020) to end of century time periods (2070–2100); however, only one climate scenario (MRI-ESM2-0 SSP5-8.5) resulted in a statistically significant (p-value

A coupled hydrologic and snowpack stable water isotope model assesses controls on isotopic inputs across a mountainous basin. Annually, the most depleted isotope conditions occur in the upper subalpine where snow accumulation is high, and rainfall is low. Snowmelt isotopic evolution over time indicates fractionation processes account for

Water tanks as traditional rainwater harvesting systems for agriculture are widely distributed in South India. They have a strong impact on hydrological processes, affecting streamflow in rivers as well as evapotranspiration. This study aims at an accurate representation of water harvesting systems in a hydrologic model to improve model performance and assessment of the catchment water balance. To this end, spatio‐temporal variations of water bodies between the years 2016 and 2018 and the months of January and May 2017 were derived from Sentinel‐2 satellite data to parameterize the water tanks (reservoir) parameters in the Soil and Water Assessment Tool (SWAT+) model of the Adyar basin, Chennai, India. Approximately 16% of the basin is covered by water tanks. The initial model performance was evaluated for two model setups, with and without water tanks. The best model run was selected with a multi‐metric approach comparing observed and modelled monthly streamflow for 5000 model runs. The final model evaluation was carried out by comparing estimated water body areas by the model and remote sensing observations for January to May 2017. The results showed that representing water tanks in the hydrologic model led to an improvement in the representation of the seasonal variations of streamflow for the whole simulation period (2004–2018). The model performance was classified as good and very good for the calibration (2004–2011) and validation (2012–2018) periods as NSE varies between 0.67 and 0.85, KGE varies between 0.65 and 0.72, PBIAS varies between −24.1 and −23.6, and RSR varies between 0.57 and 0.39. The best fit was shown for the high and middle flow segments of the hydrograph where the coefficient of determination (R2) ranges from 0.81 to 0.97 and 0.75 to 0.81, respectively. The monthly variation of water body areas in 2017 estimated by the hydrologic model was consistent with changes observed in remote sensing surveys. In summary, the water tank parametrization using remote sensing techniques enhanced the hydrologic model's efficiency and applicability for future studies. [ABSTRACT FROM AUTHOR]

A combination of annual peak water demand due to seasonal population spikes along with small and shallow aquifers has prompted an assessment of the region's watersheds as operating at a net water deficit. This study uses the Soil and Water Assessment Tool (SWAT) to simulate historical drought conditions in the Chipuxet watershed in Rhode Island, USA. The calibrated and validated model uses the Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) as well as an Indicators of Hydrological Alteration (IHA) calculation to determine the frequency and severity of historical droughts and to simulate climate change conditions developed through a downscaled climate model selection. The output data for the historical and climate change scenarios were analyzed for drought frequency and severity. Results indicate that water stress will increase in both low-emission (RCP4.5) and high-emission (RCP8.5) scenarios. Additionally, the SMDI and ETDI show that RCP8.5 climate scenarios will have more severe deficits. Finally, IHA data indicate that zero-flow days and low-flow durations increase under all climate scenarios. [ABSTRACT FROM AUTHOR]

River flood routing computes changes in the shape of a flood wave over time as it travels downstream along a river. Conventional flood routing models, especially hydrodynamic models, require a high quality and quantity of input data, such as measured hydrologic time series, geometric data, hydraulic structures, and hydrological parameters. Unlike physically based models, machine learning algorithms, which are data-driven models, do not require much knowledge about underlying physical processes and can identify complex nonlinearity between inputs and outputs. Due to their higher performance, lower complexity, and low computation cost, researchers introduced novel machine learning methods as a single application or hybrid application to achieve more accurate and efficient flood routing. This paper reviews the recent application of machine learning methods in river flood routing. [ABSTRACT FROM AUTHOR]

The present study investigated the ability of Soil and Water Assessment Tool (SWAT) in simulating streamflow of the Periyar River basin in India. The model was calibrated with six years of data (1990–1995), validated with three years (1996–1998) with 3 years warm-up period for monthly time steps. SWAT-CUP's (Soil and Water Assessment Tool-Calibration and Uncertainty Program) algorithm, called Sequential Uncertainty Fitting 2 (SUFI-2), was employed for calibration and validation. The sensitivity analysis revealed that SCS runoff curve number (CN2) was the most dominant parameter affecting streamflow in the basin. The four statistical performance indices used in this study for calibration and validation showed 'good' performance of the model for the basin, with a coefficient of determination (R2) as 0.70 and 0.70; Nash-Sutcliffe Efficiency (NSE) as 0.70 and 0.68; Percent Bias (PBIAS) as 3.8 and −9.7; and the ratio of root mean square error to standard deviation of measured data (RSR) as 0.55 and 0.56, respectively. The results also showed that calibration with SUFI-2 algorithm could improve the model performance considerably. The water balance study shows that surface runoff contributes the highest with 41% of total water availability, followed by 28% actual evapotranspiration, and 24% percolation to shallow aquifers. [ABSTRACT FROM AUTHOR]

In the context of hydrological model calibration, observational data play a central role in refining and evaluating model performance and uncertainty. Among the critical factors, the length of the data records and the associated climatic conditions are paramount. While there is ample research on data record length selection, the same cannot be said for the selection of data types, particularly when it comes to choosing the climatic conditions for calibration. Conceptual hydrological models inherently simplify the representation of hydrological processes, which can lead to structural limitations, which is particularly evident under specific climatic conditions. In this study, we explore the impact of climatic conditions during the calibration period on model predictive performance and uncertainty. We categorize the inflow data from AnDong Dam and HapCheon Dam in southeastern South Korea from 2001 to 2021 into four climatic conditions (dry years, normal years, wet years, and mixed years) based on the Budyko dryness index. We then use data from periods within the same climatic category to calibrate the hydrological model. Subsequently, we analyze the model's performance and posterior distribution under various climatic conditions during validation periods. Our findings underscore the substantial influence of the climatic conditions during the calibration period on model performance and uncertainty. We discover that when calibrating the hydrological model using data from periods with wet climatic conditions, achieving comparable predictive performance in validation periods with different climatic conditions remains challenging, even when the calibration period exhibits excellent model performance. Furthermore, when considering model parameters and predicted streamflow uncertainty, it is advantageous to calibrate the hydrological model under dry climatic conditions to achieve more robust results. [ABSTRACT FROM AUTHOR]

Study region: This study focuses on the Chao Phraya (CPY) Basin, Thailand. Study focus: This study aims to improve the skill of the CPY flood forecasting system, developed by Hydro-Informatics Institute (HII) and DHI A/S since 2012. It introduces two pre-processing workflows that are applied to the raw numerical weather prediction (NWP) provided by Weather Research and Forecasting model (WRF): quantile mapping bias correction (QM) and random forest regression (RF). Rainfall forecasts, updated with two pre-processing methods, WRF-QM and WRF-RF, were evaluated against daily rainfall measurements from HII’s stations in each subcatchment. Six hydrological re-forecasting experiments were conducted using a hydrological model to compare runoff forecasts with and without preprocessing method as well as with in-situ rainfall, climatology and persistence benchmark. We assessed rainfall and runoff predictions during training (2016–2019) and testing periods (2020–2021). New Hydrological Insights for the Region: Utilizing pre-processing methods in rainfall prediction enhances the accuracy for raw rainfall and runoff predictions. The WRF-QM and WRF-RF methods improved rainfall prediction by 12% and 18% in RMSE’s terms during testing period, respectively. Overall performance results indicate runoff forecasting with WRF-QM and WRF-RF pre-processing reduces RMSE by 34% and 40%, respectively, compared to Raw WRF. Wilcoxon signed-test confirmed significant improvement with pre-processing methods. Our study demonstrates the potential of pre-processed NWP to enhance the skill of hydrologic forecasting systems. Pre-processing methods boost flood forecasting reliability, addressing challenges caused by more frequent and severe hydrologic extremes.

The current global climate has shown a significant change, mostly resulting from human-induced activities. Frequent experiences of extreme rainstorms, deadly landslides, and floods followed by the destruction of roads, bridges, drainage, buildings, agriculture, and other infrastructures have been appearing across the globe along with extensive socio-economic effects including human lives losses whereby tropical Africa is among the greatly affected regions. Several studies in the region acclaim the increase of climate-related extremes due to a gradual climate variation. Hence, this study aimed to evaluate how existing water structures might respond to the future climate, which is getting more severe and frequent in the region. The study was conducted on the Nyabugogo River catchment (NRC), covering a huge part of the Kigali metropolitan area. It was carried out through a downscaled global climate model (CMIP6 GCM) projection coupled with a joint SWAT + hydrological model and HEC-RAS hydrodynamic simulation. The study showed that the annual precipitation in Kigali might keep increasing, resulting in increased risks of extreme weather events. The study identified up to 38% (+514.9 mm) annual precipitation increment, which resulted in more than a doubled flow rate (+28.0 m3/s) increment by the end of the century under a high greenhouse gas emission scenario (ssp585). As a result, hydrodynamic simulations revealed that the Bridge-1 in NRC might fail to accommodate the 50-year return peak storm under ssp585. Henceforth, there is a need to adopt high GHG emission scenarios in critical infrastructure development. Further, enforcing green-grey infrastructures in flood risk-low resilient areas is recommended to improve climate resilience. Thus, the results of this study might prove useful in climate-resilient infrastructure development and other pre-emptive adaptation practices, most importantly building anticipated resilience against climate-related hazards.

This research introduces a novel nonlinear Muskingum model for river flood routing, aiming to enhance accuracy in modeling. It integrates lateral inflows using the Whale Optimization Algorithm (WOA) and employs a distributed Muskingum model, dividing river reaches into smaller intervals for precise calculations. The primary goal is to minimize the Sum of Square Errors (SSE) between the observed and modeled outflows. Our methodology is applied to six distinct flood hydrographs, revealing its versatility and efficacy. For Lawler's and Dinavar's flood data, the single-reach Muskingum model outperforms multi-reach versions, demonstrating its effectiveness in handling lateral inflows. For Lawler's data, the single-reach model (NR = 1) yields optimal parameters of K = 0.392, x = 0.027, m = 1.511, and β = 0.010, delivering superior results. Conversely, when fitting flood data from Wilson, Wye, Linsley, and Viessman and Lewis, the multi-reach Muskingum model exhibits better overall performance. Remarkably, the model excels with the Viessman and Lewis flood data, especially with two reaches (NR = 2), achieving a 21.6% SSE improvement while employing the same parameter set. This research represents a significant advancement in flood modeling, offering heightened accuracy and adaptability in river flood routing. HIGHLIGHTS Flooding can have a significant impact on people and environment.; Distributed Muskingum model was introduced to improve the accuracy and efficiency of the model's calculations.; This study suggests WOA to improve the local optimum.; Considering lateral flow is the major feature developed for the new approach in our paper.; The results will be used in Red River of the North to anticipate floods in ND and MN in the US.;

Water is one of the most critical factors affecting environmental health. Therefore, it is essential to be able to predict water behavior in nature and prevent water pollution to avoid environmental health problems. In order to predict the behavior of water, the hydrological cycle needs to be evaluated at the basin level. To this aim, hydrological models can be used to obtain mathematical representations of hydrological processes. These models allow the anticipation and monitoring of issues regarding water quality, pollution, sediment transport, and proliferation of oil, and petroleum derivatives, among others, which can affect environmental health. In this study, a 2D surface water model was created using the soil and water assessment tool (SWAT) to simulate the lotic ecosystem and present water quality in the Tatlıçay Basin and to propose solutions for improving environmental health in the Cankiri provincial center in Türkiye. The accuracy of the input data and the validity of the model were tested with calibration and validation studies by using monthly or trimonthly observation data obtained from the flow observation and water quality stations of the General Directorate of State Hydraulic Works from 2016 to 2020. The aim was to create a model able to provide fast, accurate, and practical solutions in the face of water-related and environmental issues. The calibration and validation of this model were successfully carried out with very few observation data. Since surface water models are dynamic, long-term daily or monthly flow and water quality measurements should increase the accuracy of their predictions. Additionally, in the presence of pollution sources that may affect environmental health, monitoring and analyses of their possible effects should be carried out. As one of the few studies from the Middle East describing a hydrological model, this research makes a significant contribution to the literature on environmental health. [ABSTRACT FROM AUTHOR]

Flooding is a major threat that presents a significant risk to human survival and development worldwide. Regarding flood risk management, flood modeling enables understanding, assessing, and forecasting flood conditions and their impact. This paper gives an overview of prevailing flood simulation models given their potentials and limitations for reflecting pluvial floods in watershed settings. The existing models are categorized into hydrologic, hydrodynamic, and coupled hydrologic-hydrodynamic models. The coupled hydrologic-hydrodynamic model can be further classified into full, external, and internal coupling models. The definitions, advantages, and limitations of each coupling model are discussed. It is found that the existing coupling types cannot accurately reflect the flood evolution processes. A dynamic bidirectional coupled hydrologic-hydrodynamic model is then detailed, where the watershed is spatially divided into inundation and non-inundation regions. These two regions are connected by a coupling moving interface. Only 2D hydrodynamic models are applied to the local inundation regions to ensure numerical accuracy, whereas the fully distributed hydrologic model is applied to non-inundation regions to improve computational efficiency. Future investigation should focus on the development of a dynamic bidirectional coupling procedure that can accurately represent the complex physical interactions between upstream rainfall-runoff and the local inundation process. This paper would help flood managers and potential users undertake effective flood modeling tasks, balancing their needs, model complexity, and requirements of input data and time. [ABSTRACT FROM AUTHOR]

Green roofs are among the important green infrastructures which decrease the negative impacts of urbanization. In this study, the green roof modeling capability of U.S. Environmental Protection Agency Storm Water Management Model (EPA SWMM) is evaluated and the modeling methods used in EPA SWMM, which need improvements, are determined. In particular, the drainage flow, which is originally calculated with Manning's equation in SWMM, is improved by introducing the drainage mat void thickness and adapting a new value for the exponential coefficient of depth in the equation. In addition, the capability of the percolation equation is further investigated, i.e., the value of the coefficient of the exponential function of soil moisture content that considers the percolation through the soil layer is determined for different substrates using the existing unsaturated hydraulic conductivity and soil moisture content data from steady-state tests. A comparison of the improved equation of drainage flow results with the existing experimental data shows that the improved equation represents the drainage flow more accurately than the one used in EPA SWMM. HIGHLIGHTS Green roof modeling capability of EPA SWMM is investigated.; An improved drainage flow equation for the green roof is obtained.; Decay constant HCO in the percolation equation is determined for different green roof substrates.;

Deeper flows through bedrock in mountain watersheds could be important, but lack of data to characterize bedrock properties limits understanding. To address data scarcity, we combine a previously published integrated hydrologic model of a snow-dominated, headwater basin of the Colorado River with a new method for dating baseflow age using dissolved gas tracers SF6, CFC-113, N2, and Ar. The original flow model predicts the majority of groundwater flow through shallow alluvium (

Parameters in hydrologic models used in mixed rain-snow regions are often uncertain to calibrate and overfitted on streamflow. To contribute addressing these challenges, we used an algorithm that assesses modeling performances through time (Dynamic Identifiability Analysis) to quantify the information content of spatially distributed ground-based measurements for identifying optimal parameter values in the Precipitation Runoff Modeling System (PRMS) model. Including spatially distributed ground-based measurements in Identifiability Analysis allowed us to unambiguously estimate more parameter values than only using streamflow (seven parameters instead of two out of a pool of thirty-three). Peaks in information gain were obtained when using dew-point temperature to identify precipitation phase-partitioning parameters. Multi-attribute identifiability analysis also yielded optimal parameter values that were temporally less variable than those estimated using streamflow alone. Overall, identifying parameter values using ground-based measurements improved the simulation of key drivers of the surface-water budget, such as air temperature and precipitation-phase partitioning. However, parameters simulating surface-to-subsurface mass fluxes like snow accumulation and melt or evapotranspiration were poorly identified by any attribute and so emerged as key sources of predictive uncertainty for this distributed-parameter hydrologic model. This work demonstrates the value of expanded ground-based measurements for identifying parameters in distributed-parameter hydrologic models and so diagnosing their conceptual uncertainty across the water budget.

Parameters in hydrologic models used in mixed rain-snow regions are often uncertain to calibrate and overfitted on streamflow. To contribute addressing these challenges, we used an algorithm that assesses modeling performances through time (Dynamic Identifiability Analysis) to quantify the information content of spatially distributed ground-based measurements for identifying optimal parameter values in the Precipitation Runoff Modeling System (PRMS) model. Including spatially distributed ground-based measurements in Identifiability Analysis allowed us to unambiguously estimate more parameter values than only using streamflow (seven parameters instead of two out of a pool of thirty-three). Peaks in information gain were obtained when using dew-point temperature to identify precipitation phase-partitioning parameters. Multi-attribute identifiability analysis also yielded optimal parameter values that were temporally less variable than those estimated using streamflow alone. Overall, identifying parameter values using ground-based measurements improved the simulation of key drivers of the surface-water budget, such as air temperature and precipitation-phase partitioning. However, parameters simulating surface-to-subsurface mass fluxes like snow accumulation and melt or evapotranspiration were poorly identified by any attribute and so emerged as key sources of predictive uncertainty for this distributed-parameter hydrologic model. This work demonstrates the value of expanded ground-based measurements for identifying parameters in distributed-parameter hydrologic models and so diagnosing their conceptual uncertainty across the water budget.

Abstract This paper illustrates the intimate coupling of a hydrologic model with an economic input–output model. A realistic watershed and a simple hypothetical economy are used to illustrate the trade-off between water use and water availability. This approach provides two key benefits for water management. First, it directly links the supply side (the hydrologic model is used to estimate water availability) to the demand side (the economic model is used to estimate water use by sector) using a common framework that accounts for the interdependence of the two models. This link allows us to analyze water allocation and calculate the intensity of water scarcity. Second, it enables us to consider the effect of spatial distribution of economic activity on the hydrologic model and prevents either under or over estimating water scarcity. Without this spatial disaggregation, a shortfall in one sub-watershed may be offset by an abundance in another sub-watershed. The framework is sufficiently flexible to assess more complex situations, including varied spatial disaggregation and feedbacks. The coupled model is much faster and can be applied to watersheds with different characteristics. We use system dynamics to develop the integrated hydrologic-economic modelling framework and analyze three scenarios: a baseline situation, a spatially-resolved coupled model, and a temporally-resolved coupled model. The paper concludes with recommendations for implementation and future research.

Physically-based hydrologic models can accurately simulate flow discharge in natural environment, but they cannot precisely consider the anthropogenic disturbance caused by the operation of large-scale dams in a watershed. This study tried to overcome this issue by developing a hybrid modeling framework, consisting of physically-based models for simulating upstream natural watersheds and deep-learning-based models for simulating dam operation. The model was developed for the Paldang Dam watershed, a major water source for Seoul metropolitan area, where the importance of stable water supply has increased due to the increase of population and water use per capita. The prediction performance of the hybrid model was compared with that of models built based only on the physically-based hydrologic model, namely the Variable Infiltration Capacity model (VIC) model, with single and cascaded structure. For the validation period, Nash–Sutcliffe Efficiency from the developed hybrid model, the single model, and the cascaded model were 0.6410, − 0.1054, and 0.2564, respectively, suggesting that the consideration of dam operation aided by the machine learning algorithm is essential for accurate assessment of river flow discharge and the subsequent water resources vulnerability. In order to evaluate the impact of climate change, future meteorological data under RCP4.5 scenario was used as an input for the hybrid model simulation, of which result revealed that the drought flow value (the 10th lowest daily flow over a year) with the return period of 10-year, 20-year, 50-year, 100-year, and 200-year in the far future (2071–2100) were projected to decrease by 22%, 28%, 37%, 43%, and 50%, respectively, compared to the near future (2021–2040), which calls for a proper drought mitigation measures. [ABSTRACT FROM AUTHOR]

This paper illustrates the intimate coupling of a hydrologic model with an economic input–output model. A realistic watershed and a simple hypothetical economy are used to illustrate the trade-off between water use and water availability. This approach provides two key benefits for water management. First, it directly links the supply side (the hydrologic model is used to estimate water availability) to the demand side (the economic model is used to estimate water use by sector) using a common framework that accounts for the interdependence of the two models. This link allows us to analyze water allocation and calculate the intensity of water scarcity. Second, it enables us to consider the effect of spatial distribution of economic activity on the hydrologic model and prevents either under or over estimating water scarcity. Without this spatial disaggregation, a shortfall in one sub-watershed may be offset by an abundance in another sub-watershed. The framework is sufficiently flexible to assess more complex situations, including varied spatial disaggregation and feedbacks. The coupled model is much faster and can be applied to watersheds with different characteristics. We use system dynamics to develop the integrated hydrologic-economic modelling framework and analyze three scenarios: a baseline situation, a spatially-resolved coupled model, and a temporally-resolved coupled model. The paper concludes with recommendations for implementation and future research. [ABSTRACT FROM AUTHOR]

Hydrologic models are a powerful tool to predict water-related natural hazards. Of all hydrologic models, CREST (Coupled Routing and Excess STorage) was developed to facilitate hydrologic sciences and applications across various spatial and temporal scales. The CREST model was the earliest implementation of a quasi-global flood model integrating remote-sensing data and is the first operational deployment of a real-time model in the National Weather Service functioning at flash flood scales across a continent. Since being published in 2011, the CREST model has been evolving to empower flood predictions and to inform water resources management practices. Moreover, the CREST model is convenient to couple with other models/schemes (e.g., weather forecast model, snowmelt model, land surface model, hydrodynamic model, groundwater model, landslide model, vector-based routing) for border practices of investigating water-related natural hazards. To date its 10th anniversary, more than 80 peer-reviewed journal articles that have used the CREST model are curated and reviewed from the aspects of model development, worldwide applications, and outreach to emerging regions. Finally, the future directions for the CREST model family are outlined in the hope of stimulating new research endeavors. A digital collection of CREST model family is archived online at https://crest-family.readthedocs.io/en/latest/.

A combination of annual peak water demand due to seasonal population spikes along with small and shallow aquifers has prompted an assessment of the region’s watersheds as operating at a net water deficit. This study uses the Soil and Water Assessment Tool (SWAT) to simulate historical drought conditions in the Chipuxet watershed in Rhode Island, USA. The calibrated and validated model uses the Soil Moisture Deficit Index (SMDI) and Evapotranspiration Deficit Index (ETDI) as well as an Indicators of Hydrological Alteration (IHA) calculation to determine the frequency and severity of historical droughts and to simulate climate change conditions developed through a downscaled climate model selection. The output data for the historical and climate change scenarios were analyzed for drought frequency and severity. Results indicate that water stress will increase in both low-emission (RCP4.5) and high-emission (RCP8.5) scenarios. Additionally, the SMDI and ETDI show that RCP8.5 climate scenarios will have more severe deficits. Finally, IHA data indicate that zero-flow days and low-flow durations increase under all climate scenarios.

In order to understand the hydrological impacts of the nature-based solutions in the Cantareira Water Supply System, this study evaluates six different land cover and land use change scenarios. The first and second consider the restoration of native vegetation in riparian areas, the third prioritizes restoration sites using biophysical characteristics (optimized restoration scenario derived from Resource Investment Optimization System—RIOS), the fourth considers best management practices and the fifth and sixth are hypothetical extreme scenarios converting all pasture to forest and vice versa. Two hydrological models were developed to represent the distributions of water and yields in the study watershed: HEC-HMS and SWAT. Simulation results indicate that when nature-based solutions are implemented, surface runoff is reduced and ambient storage increases during the rainy season (December–March); while the overall flow increases during the dry season (June–September). The combination of specific hydrologic components of RIOS-customized intervention scenario simulation outputs—namely surface flows and groundwater contribution to stream flows—indicate on average 33% increase in the overall water yield, or 206 hm3/year, across the study watershed when comparing against the baseline conditions. In the same modeling scenario, the water storage in the sub-watersheds adjacent to the reservoirs showed an increase of 58% (or 341 hm3/year). The results indicate that adopting NbS in the source watershed can mitigate the impacts of extreme drought conditions and contribute toward building long-term water security. [ABSTRACT FROM AUTHOR]

Wildfire is a growing concern as climate shifts. The hydrologic effects of wildfire, which include elevated hazards and changes in water quantity and quality, are increasingly assessed using numerical models. Post‐wildfire application of physically based distributed models provides unique insight into the underlying processes that affect water resources after wildfire. This work reviews and synthesizes post‐wildfire applications of physically based distributed models by examining the scales and geographic/ecohydrologic distribution of model applications, hydrologic response process representation, model parameterization, and model performance metrics. Highlighted gaps and opportunities for advancing physically based distributed hydrologic response modeling after wildfire include the following: (a) applying models in under‐represented geographic (S. America, Africa, Asia) and ecohydrologic regions (arid or dry subhumid climates), (b) incorporating all four major streamflow generation mechanisms (infiltration excess, saturation excess, subsurface storm flow, and groundwater flow), (c) representing integrated vadose zone and saturated zone processes to better capture subsurface streamflow generation, (d) building new remotely sensed model parameterization methods for precipitation interception, infiltration, and overland flow that account for burn severity and recovery, (e) incorporating distributed state variables (e.g., soil moisture, groundwater levels) in model performance assessment, (f) designing model intercomparison studies, including field datasets specifically for post‐wildfire model development and validation, (g) linking mechanistic vegetation regrowth models with hydrologic models to improve simulation of process shifts as ecosystems recover, and (h) creating a new community modeling framework to integrate modeling advances across the wildfire science community. Plain Language Summary: Wildfire can lead to flooding, debris flows, and water supply issues. Computer models of the post‐fire landscape response to rainfall are often used to predict the magnitude and timing of hydrologic concerns. In this work, we review and synthesize post‐fire applications of a type of computer model, termed physically based distributed models that use equations based on physical principles to predict where and when post‐fire problems may be most substantial. This work examines where models have been applied, how the models are set up, and how closely the models match measured responses to identify gaps and opportunities to improve hydrology modeling after fire. Key Points: Including subsurface processes and distributed hydrologic states in model performance metrics could improve post wildfire hydrologic modelsRemotely sensed model parameterization methods incorporating burn severity and temporal recovery are opportunities for model progressWildfire‐specific model intercomparisons and supporting data collection will advance predictive capability [ABSTRACT FROM AUTHOR]

Parameters of hydrologic models are typically calibrated using in situ runoff observations. This calibration strategy cannot be implemented when runoff observations are not available. Additionally, the high accuracy of runoff simulation does not guarantee high simulation accuracy of other hydrologic variables, such as evaporation (ET) and terrestrial water storage change (TWSC), leading to uncertainties in hydrologic predictions. To overcome the shortcomings of the runoff‐based calibration strategy, ET and TWSC products from remote sensing were used to design parameter calibration schemes in 59 large basins worldwide. Runoff observations were used to evaluate the effects of different calibration schemes. Results show that single‐objective calibration schemes using the ET or TWSC products for parameter calibrations cannot obtain acceptable runoff simulations. Multi‐objective calibration schemes using the combination of ET and TWSC products for parameter calibrations had better runoff simulation accuracy. The multi‐objective calibration scheme using the combination of ET product adjusted by the Budyko formula and TWSC product as the calibration objective had the best runoff simulation accuracy. It is necessary to adjust ET products before using them for parameter calibrations of hydrologic models. The Budyko formula can be used to reduce the bias in remote sensing ET products, thereby providing better constraints on hydrologic simulations. We also find that different precipitation products used as inputs to hydrologic models can significantly affect the accuracy of runoff simulations. The results are expected to promote the application of remote sensing products in hydrologic simulations and predictions. Plain Language Summary: Traditionally, when we use a hydrologic model to simulate runoff in a basin, we typically use the historical runoff observations to optimize the parameters of the hydrologic model. This traditional strategy cannot be conducted when runoff observations are not available in some basins, especially in less developed countries. Over the past two decades, with the development of satellite observations, a large number of hydrologic variable products based on remote sensing have been generated. Parameter optimizations of hydrologic models based on these remote sensing products is a promising solution to overcome the shortcomings of the runoff‐based calibration strategy. In this study, we compared different combinations of remote sensing products to optimize the parameters of the hydrologic model in 59 global large basins. We found that hydrologic models optimized by the combination of evaporation and terrestrial water storage change products can simulate runoff well. This study gives us confidence that remote sensing products can greatly aid in hydrologic modeling and forecasting. Key Points: Evaporation (ET) and terrestrial water storage change (TWSC) products from remote sensing were used to calibrate the parameters of hydrologic models in 59 global large basinsMulti‐objective calibrations based on ET and TWSC had better runoff simulations than single‐objective calibrations based on ET or TWSCAdjusting ET products based on the Budyko formula can significantly improve the accuracy of runoff simulations [ABSTRACT FROM AUTHOR]

Identification and prioritization of critical sub-watershed is essential to sediment control at watershed scale. Many studies have been carried out on the topic of identifying and prioritizing the critical source areas (CSAs); however, only few researches have been conducted to prioritize sub-watersheds in terms of their contributions to the sediment yield of the main watershed outlet. In fact, observations alone are not enough for CSAs prioritization; hence, hydrological models may be applied for prioritization of sub-watershed. Therefore, in the present study, prioritization of sub-watershed was considered based on (1) on-site effects of soil erosion: specific sediment yield at sub-watershed scale (sediment yield/area), (2) offsite effect of soil erosion: sub-watershed contribution to the sediment yield of the main watershed outlet. In this study, Soil and Water Assessment Tool (SWAT) was applied for prediction of runoff and sediment load at sub-watershed scale and the main watershed outlet. The Unit Response Approach (URA) has also been applied to prioritize sub-watersheds in terms of their contributions to the sediment yield at the watershed outlet. The proposed model was applied in Mehran watershed, in West of Tehran (capital of Iran). The study watershed was divided into 37 sub-watersheds. The results showed that the contribution of sub-watershed No.14 and No. 17 had the largest sediment contribution into the main outlet of watershed. The results of this ranking can differ entirely from those based on single sub-watershed sediment production at the sub-watershed outlet. Targeting the placement of Best Management Practices, based on the CSAs concept, will assist sediment control in watersheds.

Abstract Wildfire is a growing concern as climate shifts. The hydrologic effects of wildfire, which include elevated hazards and changes in water quantity and quality, are increasingly assessed using numerical models. Post‐wildfire application of physically based distributed models provides unique insight into the underlying processes that affect water resources after wildfire. This work reviews and synthesizes post‐wildfire applications of physically based distributed models by examining the scales and geographic/ecohydrologic distribution of model applications, hydrologic response process representation, model parameterization, and model performance metrics. Highlighted gaps and opportunities for advancing physically based distributed hydrologic response modeling after wildfire include the following: (a) applying models in under‐represented geographic (S. America, Africa, Asia) and ecohydrologic regions (arid or dry subhumid climates), (b) incorporating all four major streamflow generation mechanisms (infiltration excess, saturation excess, subsurface storm flow, and groundwater flow), (c) representing integrated vadose zone and saturated zone processes to better capture subsurface streamflow generation, (d) building new remotely sensed model parameterization methods for precipitation interception, infiltration, and overland flow that account for burn severity and recovery, (e) incorporating distributed state variables (e.g., soil moisture, groundwater levels) in model performance assessment, (f) designing model intercomparison studies, including field datasets specifically for post‐wildfire model development and validation, (g) linking mechanistic vegetation regrowth models with hydrologic models to improve simulation of process shifts as ecosystems recover, and (h) creating a new community modeling framework to integrate modeling advances across the wildfire science community.

Study region: Rio Verde Grande Basin, Brazil. Study Focus: Extensive regional development, mainly expansion of irrigated areas and urban population, has resulted in low water availability and caused water conflicts since the 1980s. Therefore, it was necessary to enact the Water Resources Plan of the Rio Verde Grande in 2011. The plan provided actions to improve water availability and meet increased water demand but there have been no studies on the sustainability of these policies. It was evaluated the future of the water management and calculate the sustainability index for water resources in the Rio Verde Grande Basin (RVGB), Brazil. New hydrological insights for the region: The water demand and available water have been compared and evaluated for activities in the Water Resources Plan (WRP) based on three different improvement of available water scenarios. These scenarios include water imports and the construction of channels and reservoirs. A sustainability index was used to evaluate and compare alternative plans for future water availability and water supply in these scenarios, considering measures of reliability, resilience, vulnerability and maximum deficit. The SI has identified the best scenario foreseen in the WRP for the RVGB that will improve the availability of water through 2030 having positive impact to water users of the basin. The results also indicate that the increase in available water will not result in significant improvements of sustainability of water resources by the implementation of the policies proposed in the WRP for the RVGB.

Study region: Rio Verde Grande Basin, Brazil. Study Focus: Extensive regional development, mainly expansion of irrigated areas and urban population, has resulted in low water availability and caused water conflicts since the 1980s. Therefore, it was necessary to enact the Water Resources Plan of the Rio Verde Grande in 2011. The plan provided actions to improve water availability and meet increased water demand but there have been no studies on the sustainability of these policies. It was evaluated the future of the water management and calculate the sustainability index for water resources in the Rio Verde Grande Basin (RVGB), Brazil. New hydrological insights for the region: The water demand and available water have been compared and evaluated for activities in the Water Resources Plan (WRP) based on three different improvement of available water scenarios. These scenarios include water imports and the construction of channels and reservoirs. A sustainability index was used to evaluate and compare alternative plans for future water availability and water supply in these scenarios, considering measures of reliability, resilience, vulnerability and maximum deficit. The SI has identified the best scenario foreseen in the WRP for the RVGB that will improve the availability of water through 2030 having positive impact to water users of the basin. The results also indicate that the increase in available water will not result in significant improvements of sustainability of water resources by the implementation of the policies proposed in the WRP for the RVGB.

Changing of precipitation regime and intensification of extreme storms in semi-arid regions because of climate change requires the use of numerical models to forecast the outlet hydrographs. In this paper, HEC-HMS software was applied using a loss method the curve number CN to estimate the precipitation excess and a parametric unit hydrograph model to compute the transformation of precipitation excess into direct runoff over the watershed. The Muskingum-Cunge routing model was used to simulate the propagation of direct runoff through the main streams of Koudiet Rosfa watershed. The curve number CN and lag time parameters were used to calibrate the model towards several storms. The Nash–Sutcliffe efficiency coefficient (NSE) was adopted to assess the performance of the model to reproduce the observed hydrographs. Volume of the storms, peak discharges, times of peak and times of center of mass between the simulated and observed discharges were used to validate the model. The simulated discharges reproduce the hydrological events. The calibrated model was used to simulate the different hypothetical storms that could occur in the future in order to ensure the safety of Koudiet Rosfa dam towards extreme rainfall-runoff events.

Model invalidation is a good thing. It means that we are forced to reconsider either model structures or the available data more closely, that is to challenge our fundamental understanding of the problem at hand. It is not easy, however, to decide when a model should be invalidated, when we expect that the sources of uncertainty in environmental modelling will often be epistemic rather than simply aleatory in nature. In particular, epistemic errors in model inputs may well exert a very strong control over how accurate we might expect model predictions to be when compared against evaluation data that might also be subject to epistemic uncertainties. We suggest that both modellers and referees should treat model validation as a form of Turing‐like Test, whilst being more explicit about how the uncertainties in observed data and their impacts are assessed. Eight principles in formulating such tests are presented. Being explicit about the decisions made in framing an analysis is one important way to facilitate communication with users of model outputs, especially when it is intended to use a model simulator as a 'model of everywhere' or 'digital twin' of a catchment system. An example application of the concepts is provided in Part 2. [ABSTRACT FROM AUTHOR]

Part 1 of this study discussed the concept of using a form of Turing‐like test for model evaluation, together with eight principles for implementing such an approach. In this part, the framing of fitness‐for‐purpose as a Turing‐like test is discussed, together with an example application of trying to assess whether a rainfall‐runoff model might be an adequate representation of the discharge response in a catchment for predicting future natural flood management scenarios. It is shown that the variation between event runoff coefficients in the record can be used to create some limits of acceptability that implicitly take some account of the epistemic uncertainties arising from lack of knowledge about errors in rainfall and discharge observations. In the case study it is demonstrated that the model used cannot be validated in this way across all the range of observed discharges, but that behavioural models can be found for the peak flows that are the subject of interest in the application. Thinking in terms of the Turing‐like test focusses attention on the critical observations needed to test whether streamflow is being produced in the right way so that a model is considered as fit‐for‐purpose in predicting the impacts of future change scenarios. As is the case for uncertainty estimation in general, it is argued that the assumptions made in setting behavioural limits of acceptability should be stated explicitly to leave an audit trail in any application that can be reviewed by users of the model outputs. [ABSTRACT FROM AUTHOR]

This communication introduces HLM-Tetis, which is a model structure coupled to the Hillslope Link Model (HLM) framework developed by the Iowa Flood Center. The model was designed to improve some limitations of previous HLM model structures. The following changes have been made: (1) modules to simulate snow processes; (2) better flexibility to simulate infiltration and percolation; (3) more flexibility to derive total runoff from the partitioning of overland flow, interflow and baseflow components. We show applications of the model in flood events at five basins in Iowa where previous model structures had performance limitations. [ABSTRACT FROM AUTHOR]