Search Results (38)

This study evaluates a time-lagged ensemble averaging strategy to improve the accuracy and robustness of short-range streamflow point forecasts when hydrological simulations are driven by deterministic numerical weather prediction (NWP) forcing. We implemented WRF-Hydro in standalone mode for the Geumho River basin, South Korea, using Local Data Assimilation and Prediction System (LDAPS) forecasts initialized every 6 h with lead times up to 48 h. Time-lagged ensembles were constructed by averaging overlapping WRF-Hydro predictions from successive LDAPS initializations. Across two contrasting flood-producing storms, ensemble-mean forecasts consistently reduced lead-time-dependent skill degradation relative to single-initialization forecasts; the event-wise median Nash–Sutcliffe efficiency at the downstream gauge improved from 0.39 to 0.81 at 48 h (Event 2020) and from 0.48 to 0.85 at 24 h (Event 2022), while RMSE decreased by up to 48%. The most effective ensemble window varied with storm evolution and forecast horizon, indicating additional gains from adaptive time-lag selection. Overall, time-lagged ensemble averaging provides a practical, low-cost post-processing approach to enhance operational short-range streamflow prediction with NWP forcings. Full article

Sustainable agriculture in semi-arid regions like the Awash Basin is critically dependent on water availability, which is increasingly threatened by rapid land use and land cover (LULC) change. This study assesses the impact of multi-decadal LULC changes on water resources essential for agriculture. Using satellite-derived LULC scenarios (2001, 2010, 2020) to drive the WRF-Hydro/Noah-MP modeling framework, we provide a holistic assessment of water dynamics in Ethiopia’s Awash Basin. The model was calibrated and validated with observed streamflow (R² = 0.80–0.89). Markov analysis revealed rapid cropland expansion and urbanization (2001–2010), followed by notable woodland recovery (2010–2020) linked to national initiatives. Simulations show that early-period changes increased surface runoff, potentially enhancing reservoir storage for large-scale irrigation. In contrast, later changes promoted subsurface flow, indicating a shift towards enhanced groundwater recharge, which is critical for small-scale and well-based irrigation. Evapotranspiration (ET) trends, validated against GLEAM (monthly R² = 0.88–0.96), reflected these shifts, with urbanization suppressing water fluxes and woodland recovery fostering their resurgence. This research demonstrates that land use trajectories directly alter the partitioning of agricultural water sources. The findings provide critical evidence for designing sustainable land and water management strategies that balance crop production with forest conservation to secure irrigation water and support initiatives like Ethiopia’s Green Legacy Initiative. Full article

The impact of climate change on watershed hydrological processes has become increasingly significant, with the frequent occurrence of extreme flood events posing a severe challenge to the water resource security of the upper Yangtze River and the Three Gorges Reservoir. To enhance the understanding of runoff evolution under future climate scenarios, this study focuses on the upper Yangtze River Basin, integrating CMIP6 climate model data with the WRF-Hydro model to systematically assess the effects of climate change on runoff projections. Firstly, using CMFD data as a benchmark, the systematic biases in CMIP6 simulation results were evaluated and corrected. Precipitation and temperature data accuracy were improved through Local Intensity Correction (LOCI) and Daily Bias Correction (DBC). Secondly, a large-scale WRF-Hydro model suitable for the upper Yangtze River was developed and calibrated. Finally, based on the corrected CMIP6 data, the climate and runoff changes under the SSP2-4.5 and SSP5-8.5 scenarios for the period 2021–2050 were projected. The results show that: (1) the corrected CMIP6 data significantly improved issues of overestimated precipitation and underestimated temperature, providing a more realistic reflection of regional climate characteristics; (2) the sub-basin calibration strategy outperformed the overall calibration strategy at key control sites, with high runoff simulation accuracy during the validation period; (3) future temperatures exhibit a continuous rising trend, while precipitation changes are not significant—however, the magnitude and uncertainty of extreme events increase—and (4) under the SSP5-8.5 scenario, the inflow to the Three Gorges Reservoir during the wet season significantly increases, raising flood risk. The findings provide a scientific basis for understanding the hydrological response mechanisms in the upper Yangtze River Basin under climate change and offer decision-making support for flood control scheduling and water resource management at the Three Gorges Reservoir. Full article

This study applied the fully coupled model WRF/WRF-Hydro to simulate land, air, and water cycles in the Changbai Mountain area (CMA) in Northeast China. This study evaluated the applicability of the coupled model in the region and analyzed the impact of regional climate change on the water cycle in the study area over the past half-century. The temperature in the Changbai Mountains increased significantly from 1975 to 2020. Precipitation, canopy water, and all types of evapotranspiration showed different increasing trends, whereas surface runoff showed a decreasing trend. The comparison revealed that precipitation, canopy water, canopy evaporation, and total evapotranspiration increased gradually in the low-latitude subbasins, whereas runoff decreased more rapidly. Runoff in the study area showed an annual double peak, which was due to snowmelt in spring and abundant precipitation in summer. Under the influence of climate change, the thawing time of frozen soil and snow cover in the study area will advance, leading to an increase in the spring runoff peak and earlier occurrence time. Our results provide a reference for the study of the water cycle process of the coupled model in cold mountainous areas and a scientific reference for the scientific response to climate change and the protection of regional water resource security. Full article

As climate change intensifies, extreme drought events have become more frequent, and investigating the mechanisms of watershed drought has become highly significant for basin water resource management. This study utilizes the WRF-Hydro model in conjunction with standardized drought indices, including the standardized precipitation index (SPI), standardized soil moisture index (SSMI), and Standardized Streamflow Index (SSFI), to comprehensively investigate the spatiotemporal characteristics of drought in the Huai River Basin, China, from 2012 to 2018. The simulation performance of the WRF-Hydro model was evaluated by comparing model outputs with reanalysis data at the regional scale and site observational data at the site scale, respectively. Our results demonstrate that the model showed a correlation coefficient of 0.74, a bias of −0.29, and a root mean square error of 2.66% when compared with reanalysis data in the 0–10 cm soil layer. Against the six observational sites, the model achieved a maximum correlation coefficient of 0.81, a minimum bias of −0.54, and a minimum root mean square error of 3.12%. The simulation results at both regional and site scales demonstrate that the model achieves high accuracy in simulating soil moisture in this basin. The analysis of SPI, SSMI, and SSFI from 2012 to 2018 shows that the summer months rarely experience drought, and droughts predominantly occurred in December, January, and February in the Huai River Basin. Moreover, we found that the drought characteristics in this basin have significant seasonal and interannual variability and spatial heterogeneity. On the one hand, the middle and southern parts of the basin experience more frequent and severe agricultural droughts compared to the northern regions. On the other hand, we identified a time–lag relationship among meteorological, agricultural, and hydrological droughts, uncovering interactions and propagation mechanisms across different drought types in this basin. Finally, we concluded that the WRF-Hydro model can provide highly accurate soil moisture simulation results and can be used to assess the spatiotemporal variations in regional drought events and the propagation mechanisms between different types of droughts. Full article

There is a growing need for proactive planning for natural hazards in a changing climate. Computational modeling of climate hazards provides an opportunity to inform planning, particularly in areas approaching ecosystem state changes, such as Interior Alaska, where future hazards are expected to differ significantly from historical events in frequency and severity. This paper considers improved modeling approaches from a physical process perspective and contextualizes the results within the complexities and limitations of hazard planning efforts and management concerns. Therefore, the aim is not only to improve the understanding of potential climate impacts on streamflow within this region but also to further explore the steps needed to evaluate local-scale hazards from global drivers and the potential challenges that may be present. This study used dynamically downscaled climate forcing data from ERA-Interim reanalysis datasets and projected climate scenarios from two General Circulation Models under a single Representative Concentration Pathway (RCP 8.5) to simulate an observational gage-calibrated WRF-Hydro model to assess shifts in streamflow and flooding potential in three Interior Alaska rivers over a historical period (2008–2017) and two future periods (2038–2047 and 2068–2077). Outputs were assessed for seasonality, streamflow, extreme events, and the comparison between existing flood control infrastructure in the region. The results indicate that streamflow in this region is likely to experience increases in seasonal length and baseflow, while the potential for extreme events and variable short-term streamflow behavior is likely to see greater uncertainty, based on the divergence between the models. Full article

The Source Region of the Yellow River (SRYR), renowned as the “Water Tower of the Yellow River”, serves as an important water conservation domain in the upper reaches of the Yellow River, significantly influencing water resources within the basin. Based on the Weather Research and Forecasting (WRF) Model Hydrological modeling system (WRF-Hydro), the key variables of the atmosphere–land–hydrology coupling processes over the SRYR during the 2013 rainy season are analyzed. The investigation involves a comparative analysis between the coupled WRF-Hydro and the standalone WRF simulations, focusing on the hydrological response to the atmosphere. The results reveal the WRF-Hydro model’s proficiency in depicting streamflow variations over the SRYR, yielding Nash Efficiency Coefficient (NSE) values of 0.44 and 0.61 during the calibration and validation periods, respectively. Compared to the standalone WRF simulations, the coupled WRF-Hydro model demonstrates enhanced performance in soil heat flux simulations, reducing the Root Mean Square Error (RMSE) of surface soil temperature by 0.96 K and of soil moisture by 0.01 m³/m³. Furthermore, the coupled model adeptly captures the streamflow variation characteristics with an NSE of 0.33. This underscores the significant potential of the coupled WRF-Hydro model for describing atmosphere–land–hydrology coupling processes in regions characterized by cold climates and intricate topography. Full article

In this study, we analyzed the impact of model spatial resolution on streamflow predictions, focusing on high-resolution scenarios (<1 km) and flooding conditions at catchment scale. Simulation experiments were implemented for the Geumho River catchment in South Korea using Weather Research and the Forecasting Hydrological Modeling System (WRF-Hydro) with spatial resolutions of 100 m, 250 m, and 500 m. For the estimation of parameters, an automatic calibration tool based on the Model-Independent Parameter Estimation and Uncertainty Analysis (PEST) method was utilized. We assessed the hydrological predictions across different spatial resolutions considering calibrated parameters, calibration runtime, and accuracy of streamflow before and after calibration. For both Rainfall Events 1 and 2, significant improvements were observed after event-specific calibration in all resolutions. Particularly for 250 m resolution, NSE values of 0.8 or higher were demonstrated at lower gauging locations. Also, at a 250 m resolution, the changes in the calibrated parameter values ($REFKDT$) were minimized between Rainfall Events 1 and 2, implicating more effective calibration compared to the other resolutions. At resolutions of 100 m and 500 m, the optimal parameter values for the two events were distinctively different while more computational resources were required for calibration in Event 2 with drier antecedent conditions. Full article

Ensemble weather forecasting involves the integration of multiple simulations to improve the accuracy of predictions by introducing a probabilistic approach. It is difficult to accurately predict heavy rainfall events that cause flash floods and, thus, ensemble forecasting could be useful to reduce uncertainty in the forecast, thus improving emergency response. In this framework, this study presents the efforts to develop and assess a flash flood forecasting system that combines meteorological, hydrological, and hydraulic modeling, adopting an ensemble approach. The integration of ensemble weather forecasting and, subsequently, ensemble hydrological-hydraulic modeling can improve the accuracy of flash flood predictions, providing useful probabilistic information. The flash flood that occurred on 26 January 2023 in the Evrotas river basin (Greece) is used as a case study. The meteorological model, using 33 different initial and boundary condition datasets, simulated heavy rainfall, the hydrological model, using weather inputs, simulated discharge, and the hydraulic model, using discharge data, estimated water level at a bridge. The results show that the ensemble modeling system results in timely forecasts, while also providing valuable flooding probability information for 1 to 5 days prior, thus facilitating bridge flood warning. The continued refinement of such ensemble multi-model systems will further enhance the effectiveness of flash flood predictions and ultimately save lives and property. Full article

The effective management of water resources in regions with a high potential for water resources, such as the Tocantins/Araguaia Basin in Brazil, is crucial in the face of current climate change and urban and agricultural expansion. In this context, this study evaluates the WRF-Hydro hydrological model to simulate the flow of the Manuel Alves Pequeno, Vermelho, and Manuel Alves Grande rivers in the MATOPIBA region (encompassing areas from the states of Maranhão, Tocantins, Piauí, and Bahia), an agricultural frontier and the most key area in terms of grain production in Brazil. The aim is to analyze the hydrological parameters of soil infiltration, surface retention depth, land surface roughness, and Manning’s channel roughness. The simulations are conducted at a spatial resolution of 3 km with a channel network of 100 m, covering a period of heavy rainfall from 13 March to 1 June 2018. For model validation, observational data from three river gauge stations of the National Water and Sanitation Agency are used, with assessments based on the Nash-Sutcliffe efficiency index, standard deviation of observations, root mean square error, percentage bias, and correlation coefficient, resulting in values of 0.69, 0.56, 4.99, and 0.83, respectively. In particular, the adjustment of the infiltration factor and surface roughness parameter has a greater contribution to improving the statistical results than the adjustment of the other two hydrological parameters. Additionally, the quality of discharge simulation at each river gauge station is correlated with the temporal distribution of simulated precipitation compared to observed data in the drainage network. Highlighting WRF-Hydro’s potential as a fine-scale model easily coupled with numerical weather prediction, this study significantly advances regional river dynamics evaluation, crucial for strategic water resource management. Full article

Flash floods are increasingly frequent worldwide. Recent flooding in eastern Ethiopia resulted in casualties, destruction of property and interruptions of service. National flash flood forecasts made today primarily consider precipitation, putting less emphasis on surface processes. Enhancing accurate flash flood forecasts by accounting for surface processes and hydrological models together with a deeper understanding of heavy precipitation mechanisms is of paramount importance. To this end, an uncoupled WRF-Hydro model was calibrated for eastern Ethiopia to simulate extreme floods. Sensitivity analysis for August 2006 showed that infiltration runoff, hydraulic soil conductivity and saturated volumetric soil moisture with parameter values of 0.1, 1.5 and 1.0 produced realistic streamflow distribution. Extreme floods in March 2005 and April 2007 were further studied. The results showed that WRF-Hydro replicates temporal and spatial patterns well. Analysis using observational/reanalysis data revealed associated physical processes. Precipitation during these events exceeded long-term climatology and spanned wider areas in eastern Ethiopia. These heavy precipitation events are associated with strong upper-level westerly jet streams and rainfall-conducive circulation anomalies at lower levels. Positive outcomes from WRF-Hydro suggest operational implementation for flood monitoring and early warning systems in forecasting centers. Full article

This study aims at assessing meteorological, hydrological, and hydraulic modeling to develop a flash flood forecasting tool. The flash flood that occurred in the Evrotas River Basin (ERB) on 26 January 2023 is used as a case study. Precipitation over 150 mm and water depths exceeding 2.5 m were recorded. The meteorological model initialized one day before flooding and simulated precipitation; the hydrological model, using meteorological input data, simulated discharge; and the hydraulic model, using discharge, estimated water depth at a bridge. The results indicate that the system can provide skillful and timely flash flood forecasts, thereby facilitating flood warnings. Full article

This research presents a blended system implemented by the Vietnam National Center for Hydro-Meteorological Forecasting to enhance the nowcasting and forecasting services of quantitative precipitation forecasts (QPFs) of tropical cyclone (TC) landfalls on Vietnam’s coast. Firstly, the extrapolations of rain/convective systems from multiple radars in Vietnam in ranges up to 6 h were carried out using Short-Range Warning of Intense Rainstorms in Localized Systems (SWIRLS) developed by the Hong Kong Observatory. Secondly, the forecast from the numerical weather prediction (NWP) system, based on the WRF-ARW model running at 3 km horizontal resolution, was blended with radar-based quantitative precipitation estimates and nowcasts of SWIRLS. The analysis showed that the application of the nowcast system to TC-related cloud forms is complicated, which is related to the TC’s evolution and the different types and multiple layers of storm clouds that can affect the accuracy of the derived motion fields in nowcast systems. With hourly accumulated rainfall observation, skill score validation conducted for several TCs that landed in the center of Vietnam demonstrated that the blending of nowcasting and NWP improve the quality of the QPFs of TCs in forecast ranges up to 3 h compared to the pure NWP forecasts. Full article

This study established a WRF/WRF-Hydro coupled forecasting system for precipitation–runoff forecasting in the Daqing River basin in northern China. To fully enhance the forecasting skill of the coupled system, real-time updating was performed for both the WRF precipitation forecast and WRF-Hydro forecasted runoff. Three-dimensional variational (3Dvar) multi-source data assimilation was implemented using the WRF model by incorporating hourly weather radar reflectivity and conventional meteorological observations to improve the accuracy of the forecasted precipitation. A deep learning approach, i.e., long short-term memory (LSTM) networks, was adopted to improve the accuracy of the WRF-Hydro forecasted flow. The results showed that hourly data assimilation had a positive impact on the range and trends of the WRF precipitation forecasts. The quality of the WRF precipitation outputs had a significant impact on the performance of WRF-Hydro in forecasting the flow at the catchment outlet. With the runoff driven by precipitation forecasts being updated by 3Dvar data assimilation, the error of flood peak flow was decreased by 3.02–57.42%, the error of flood volume was decreased by 6.34–39.30%, and the Nash efficiency coefficient was increased by 0.15–0.52. The implementation of LSTM can effectively reduce the forecasting errors of the coupled system, particularly those of the time-to-peak and peak flow volumes. Full article

Streamflow modelling is one of the most important elements for the management of water resources and flood control in the context of future climate change. With the advancement of numerical weather prediction and modern detection technologies, more and more high-resolution hydro-meteorological data can be obtained, while traditional physical hydrological models cannot make full use of them. In this study, a hybrid deep learning approach is proposed for the simulation of daily streamflow in two mountainous catchments of the Daqing River Basin, northern China. Two-dimensional high-resolution (1 km) output data from a WRF model were used as the model input, a convolutional neural network (CNN) model was used to extract the physical and meteorological characteristics of the catchment at a certain time, and the long short-term memory (LSTM) model was applied to simulate the streamflow using the time-series data extracted by the CNN model. To reduce model input noise and avoid overfitting, the Gamma test method was adopted and the correlations between the input variables were checked to select the optimal combination of input variables. The performance of the CNN-LSTM models was acceptable without using the Gamma test (i.e., with all WRF input variables included), with NSE and RMSE values of 0.9298 and 9.0047 m³/s, respectively, in the Fuping catchment, and 0.8330 and 1.1806 m³/s, respectively, in the Zijingguan catchment. However, it was found that the performance of the model could be significantly improved by the use of the Gamma test. Using the best combination of input variables selected by the Gamma test, the NSE of the Fuping catchment increased to 0.9618 and the RMSE decreased to 6.6366 m³/s, and the NSE of the Zijingguan catchment increased to 0.9515 and the RMSE decreased to 0.6366 m³/s. These results demonstrate the feasibility of the CNN-LSTM approach for flood streamflow simulation using WRF-downscaled high-resolution data. By using this approach to assess the potential impacts of climate change on streamflow with the abundant high-resolution meteorological data generated by different climate scenarios, water managers can develop more effective strategies for managing water resources and reducing the risks associated with droughts and floods. Full article