Runoff Estimation Research Articles

Runoff Dynamics and Soil Erosion Assessment using a SWAT Model at Upper Cauvery River Basin

The increasing availability of geospatial datasets on watershed characteristics and hydro-meteorological variables emphasises the importance of integrated hydrological models for effective catchment management. Climatic and physiographic determinants such as topography, land use, soil properties, and anthropogenic interventions substantially influence a catchment's hydrological equilibrium. The SWAT model was used to elucidate rainfall-runoff dynamics in the Upper Cauvery River Basin, Karnataka, India (36,682 km²), applying the SCS Curve Number (CN) method for runoff estimation. Runoff was estimated for 2012–2021 using rainfall data from the Indian Meteorological Department (IMD), soil data from the FAO, and land use/land cover and slope datasets.Soil erosion, exacerbated by intensified agricultural practices, was evaluated using the Revised Universal Soil Loss Equation (RUSLE) model integrated with Geographic Information Systems (GIS). The SWAT model results showed that runoff accounted for 15-20% of the total precipitation, with an annual soil loss of 2027.95 tons. The predominance of agricultural land use, covering 66.29% of the basin, significantly contributed to high runoff, whereas the forested areas (26.48%) demonstrated a low runoff potential. About 38% of the basin exhibited a very low soil loss risk, while 11% was classified as high risk and 9% as very high risk. The SWAT model is recognized for its robustness, and this research aims to leverage its capabilities to validate its effectiveness in estimating runoff and erosion, providing crucial insights for advancing sustainable catchment resource management.

Runoff Estimation in Kajurli Watershed Using SCS-CN and GIS Techniques

The study focuses on estimating surface runoff using the Soil Conservation Service Curve Number (SCS-CN) method, combined with Remote Sensing (RS) and Geographic Information System (GIS) techniques, for the Kajurli Watershed in Ratnagiri District, Maharashtra. Surface runoff is a key factor influencing agricultural productivity, soil erosion, and water management at the watershed level. The SCS-CN method, a widely used tool for runoff estimation, utilizes rainfall, land use, and soil type data. By integrating RS and GIS, the study achieves a more accurate assessment of land use and soil characteristics, which are critical inputs for runoff calculations. The Kajurli Watershed falls under hydrological soil group B, known for moderate runoff potential. The study uses 33 years of historical rainfall data (1990-2022) to compute annual runoff using the SCS-CN method. Key parameters such as Curve Numbers (CN) are derived based on land use, hydrological soil groups, and antecedent moisture conditions (AMC). The average annual rainfall in the watershed is found to be 3392.8 mm, with 39.69% of this rainfall contributing to surface runoff. The integration of RS and GIS simplifies spatial analysis, enabling accurate and efficient runoff estimation. The study highlights the sensitivity of the SCS-CN method to CN values, emphasizing the importance of precise land use and soil data for reliable hydrological modelling. These findings offer crucial insights for water resource management, flood control, and agricultural planning in the region.

A modified Soil and Water Assessment Tool Plus (M-SWAT+) model for sediment yield estimation

ABSTRACT The Soil and Water Assessment Tool Plus (SWAT+) model depends on the Modified Universal Soil Loss Equation (MUSLE), which in turn depends on the peak runoff rate for the estimation of soil loss from a catchment. The SWAT+ model generates a random number for each day in the process of peak runoff rate estimation from the catchment. On the other hand, the model provides calibration parameters like the peak rate adjustment factor for sediment routing in the tributary channels and main channels. There is uncertainty that how likely the SWAT+ model estimates the actual peak runoff rates. An improved MUSLE does not depend on the peak runoff rate, and, therefore, it minimizes uncertainty related to the peak runoff rate when estimating soil loss from the catchment in the SWAT+ environment. On the other hand, the effect of the topographic factor of the MUSLE or improved MUSLE on soil erosion and sediment transport is not explicitly explained. Therefore, we modify the source code of the SWAT+ model for sediment yield estimation by replacing the improved MUSLE and the best-fitted equations of the topographic factor in the source code. The modified SWAT+ model showed better performance than the original SWAT+ model.

A Novel Approach for Identifying and Assessing MOR‐Based CMIP6 Model for Hydrological Analysis in an Ungauged Watershed

ABSTRACTThe identification of the onset and retreat dates of the monsoon season is a crucial and intricate phenomenon, given its annual spatiotemporal variability. The monsoon season contributes significantly to rainfall, replenishing water sources and hydrological systems but causes hydrological extremes, especially for the high‐altitude watersheds in Southeast Asia. Global Circulation Model (GCM)‐Coupled Model Intercomparison Project Phase 6 (CMIP6)‐based rainfall and temperature data are helpful for adequately representing present and future climate scenarios. However, the usability of uncorrected GCM‐CMIP6 datasets needs to be assessed regionally. This study focuses on identifying the best‐suited GCM‐CMIP6 based on the monsoon onset (MO) and retreat (MR) dates along with other climatological temporal parameters. A numerical definition for MO and MR has been formulated to find the best‐suited GCM‐CMIP6 (i.e., CMCC‐ESM2). In this context, runoff simulation is carried out using the meteorological inputs of the monsoon onset‐retreat (MOR)‐based best‐suited GCM to evaluate its usability. A multi‐model simulation approach has been carried out for runoff estimation based on observed datasets to find a better‐suited hydrological model. The proposed overall methodology is tested in a hydrological extreme‐prone ungauged watershed (i.e., Ranikhola). CMCC‐ESM2 and SSP2‐4.5 has been identified as best‐suited SSP based on statistical evolution (R2 [0.693], NSE [0.662] and RSR [0.581]) for future daily runoff prediction. Future hydrological analysis shows that the average monsoon peak runoff magnitude will increase from the calibrated period (2015–2020) by 18.01% in the coming years (2021–2049).

Evaluation of GPM IMERG satellite precipitation for rainfall–runoff modelling in Great Britain

ABSTRACT Reliable hydrological simulations require accurate precipitation data. However, data uncertainties due to the indirect nature of satellite estimates can propagate through hydrological models and lead to simulation errors. This study assesses the accuracy of Global Precipitation Measurement (GPM) Integrated Multi-satellite Retrievals for GPM (IMERG) products, comparing them directly with ground-based precipitation data and evaluating their performance in rainfall–runoff modelling across Great Britain. Three IMERG V06 products (IMERG-Early, IMERG-Late, and IMERG-Final) are examined. Utilizing the simple water balance model (SWBM), the analysis covers 250 basins, revealing that the SWBM performs well in over 50% of the basins. Runoff estimations show that European Observation (E-OBS) ground-based data yield the highest Nash-Sutcliffe efficiency (NSE) score (0.91), followed by IMERG-Final (0.85), IMERG-Late (0.82), and IMERG-Early (0.73). The findings underscore IMERG’s utility in hydrological modelling for ungauged or poorly gauged basins.

Estimation of Runoff at Shetrunji River Basin using SWAT Hydrological Modelling

The researchers and scientists have realized that this is the high time to act in accordance to the impact of change of climate. Because of climate change problem, various effects occur, one of them is hydrological extremes. This leads to the emergence of hot-spots and vulnerable areas causing difficult adaptation. The current analysis intends to estimate the influence of change in climate on the hydrological parameter on one of the most important tributaries of Saurashtra region of Gujarat, Western India. Out of number of methods available to perform this task, simulation (hydrological modelling) which is the most widely used techniques available at this time, has been adopted. Here in this research the SWAT (Soil and Water Assessment Tool) model which happens to be a semi-distributed model and integrates the GIS information with the attribute database of the River has been utilized. The watershed delineation has been done using AWD (Automatic Watershed Delineation) which is available as a plugin in ArcSWAT. It has classified the catchment into 28 subbasins. From Esri Sentinel-2 landuse map has been used and the soil map is extracted from FAO Raster world soil map. Moreover, we can classify the sub basins into 173 HRUs (Hydrological Response Unit) by the model. The weather files from 1982 to 2019 has been used from the global weather source as a seed to the model. The successful run of the model gives elucidate fluctuating runoff volumes over time, spanning from a lowest of 2.56 mm to a extreme of 503.78mm. Notably, the annual average runoff stands at 190.82 mm, representing approximately 28.34% of the cumulative rainfall experienced within the river basin. The estimation of runoff is helpful in allocating water for agriculture, household demands, and industrial requirements.

Calculating Surface Runoff by Scs-Cn Model in the Sanjai River Basin, Jharkhand

Surface runoff prediction is a very intricate, evolving, and exponential phenomenon controlled by numerous interconnected components. Regulated by numerous types of related factors, surface runoff production is a highly complex, dynamic, and non-linear phenomena. Advancement and long-term control of water resources depend on run-off forecast. For runoff forecasts, many techniques and models are at hand; Among strategies, “Soil Conservation Services curve number(SCS- CN)” approach stands out. In order to determine potential outflow from a drainage system or region, the SCS implemented the Curve Number index. It is possible to figure out the curve number of a drainage basin by taking into account the antecedent moisture conditions(AMC), the soil, land surface use and land cover (LULC). Considered also is the Hydrological Soil Group (HSG). Four kinds of soils are offered at HSG: “A, B, C, and D”. Whereas soil type D stands for low penetration rates and greater surface flow capacity, soil category A comprises higher penetration rates below the surface and less runoff potentiality. The study's research site was the Sanjai river basin in Jharkhand. Arc Gis 10.1 and Erdas 14 software helped produce thematic layers like soil maps, LULC maps, and so on. The whole catchment was divided into sub-watersheds in order to more accurately measure runoff. The findings demonstrate that the SCS-CN simulation performs productive runoff estimates. The research demonstrates that more sustainable water use results from suitable surface runoff analysis.

Snow simulation for the rangeland hydrology and erosion model

In the western US, most rangelands receive snowfall. Yet, a commonly used tool to assess rangeland’s vulnerability to erosion, the USDA’s Rangeland Hydrology and Erosion Model (RHEM) is run using long-term simulated climate inputs that assumes that all precipitation occurs as rainfall. This can be problematic for areas that receive heavy snowfall or substantial rain-on-snow events. In this research, we have developed an efficient snow module for RHEM, called RHEM-Snow, which partitions precipitation between rainfall and snowfall, simulates snowpack accumulation and ablation, and passes net water input (consisting of rainfall, snowmelt, or both) to RHEM. In some areas, the inclusion of the snow module can reduce annual overland flow runoff and erosion estimates by more than 20 % of the total annual overland flow runoff and erosion produced without the snow module (or by as much as 10–50 mm/year for overland flow runoff or >100 kg/ha-yr for erosion). The reclassification of precipitation events from rainfall in RHEM to snowfall in RHEM-Snow tends to reduce overland flow runoff and erosion, but this reduction can be partially counterbalanced by increases from snowmelt and rain-on-snow. However, hydrologic responses to rain-on-snow events can either be enhanced or muted depending on the characteristics of the storm and the snowpack, as sometimes the snowpack can absorb the precipitation inputs, and sometimes snowmelt enhances the precipitation inputs. Because of this mixed impact, the average difference in erosion caused by rain on snow events is relatively small compared to corresponding events where only the liquid phase is considered. Further study is needed of the complex erosion processes under snowpack and frozen soil/variable saturation conditions. Overall, RHEM-Snow provides more realistic timing and magnitude of overland flow runoff and erosion in cold environments, better satisfying the conditions for RHEM applications.

Simulated Impact of Time‐Varying River Runoff and Greenland Freshwater Discharge on Sea Level Variability in the Beaufort Gyre Over 2005–2018

AbstractGlobal mean sea level has been rising at a rate of 3.25 ± 0.4 mm yr−1 over 1993–2018. Yet several regions are increasing at a much faster rate, such as the Beaufort Gyre in the Arctic Ocean at a rate of 9.3 ± 7.0 mm yr−1 over 2003–2014. At interannual to decadal time scales, the Beaufort Gyre sea level is controlled by salinity changes due to sea ice melt and wind‐driven lateral Ekman convergence–divergence of freshwater. This study uses recent Greenland discharge and river runoff estimates to isolate and quantify the sea level response to freshwater fluxes variability over the period 1980–2018. It relies on sensitivity experiments based on a global ocean model including sea‐ice and icebergs. These sensitivity experiments only differ by the freshwater fluxes temporal variability of Greenland and global rivers which are either seasonal climatologies or fully time varying, revealing the individual and combined impact of these freshwater sources fluctuations. Fully varying Greenland discharge and river runoff produce an opposite impact on sea level trends over 2005–2018 in the Beaufort Gyre region, the former driving an increase, while the latter, a decrease. Their combined impact leads to a fairly weak sea level trend. The sea level response is primarily driven by salinity variations in the upper 300 m, which are mainly caused by salinity advection involving complex compensations between passive, active, and nonlinear advection. This study shows that including the temporal variability of freshwater fluxes in forced global ocean models results in a better representation of regional sea level change.

Runoff simulation modeling method integrating spatial element dynamics and neural network for remote sensing precipitation data

Effective management of water resources amidst global climate change necessitates advancements in runoff simulation techniques. This study introduces a novel methodology that integrates a spatial element dynamic model with a neural network designed for satellite precipitation product (SPP) and remote sensing data to address the complex dynamics of precipitation-runoff processes. Implemented in the Songhua River basin of China, the methodology features a runoff estimation module that operates across both high-resolution and SPP grid scales. This module synergizes spatial attributes with fuzzy classification for refined confluence optimization, and yielding simulations through long short-term memory network. The results indicate that the proposed multi-scale production-confluence scheme achieved Nash-Sutcliffe efficiency (NSE) scores of 0.87 and 0.84 during calibration (2011–2015) and validation (2017 and 2018) periods, respectively, representing an 8.15% improvement in peak percentage error (PPE10%) over conventional hydrologic models. Additionally, the scheme demonstrated superior simulation performance at tributary sites set compared to main channel sites set, with differences of 0.08 and 0.07 in NSE and Kling-Gupta efficiency (KGE), respectively. The introduction of the surface Ⅱ operator for adjusting precipitation intensity effectively improved runoff estimation accuracy, reducing the average error by 25.48% across both site sets. This study provides a scientific foundation for applying SPP in hydrological modeling and offers decision support for managing water resources.

A WEAP-assisted agent-based modeling approach for watershed planning in a coupled human and nature system

ABSTRACT Coupled human and nature systems exhibit dynamic water demands due to diverse consumer socio-economic characteristics, resulting in inherent complexities. Modeling such systems effectively entails adopting an agent-based methodology. To enhance comprehension of water resource management in the Zarrineh-Roud Basin, northwest of Iran, a comprehensive hydrological-behavioral model has been utilized. This model consists of two primary components: An agent-based model (ABM) for simulating farmers’ decision-making processes and a water evaluation and planning (WEAP) model for simulating catchment area, cultivated area, and agricultural water consumption. Validation outcomes of the simulation model demonstrate precise estimation, particularly in the runoff estimation of the Nezam Abad hydrometric station, with statistically favorable correlation coefficient (R2) and Nash–Sutcliffe index (NS) values of 0.78 and 0.75, respectively. Analysis of different management scenarios indicates that with ample government funds, simultaneous implementation of ‘Education and increasing environmental awareness’ and ‘Alternative employment’ policies can significantly improve water resources conditions and income. In scenarios with limited financial resources, a combined implementation of ‘Water pricing,’ ‘Alternative employment,’ and ‘Education and raising environmental awareness’ emerges as the most effective strategy for managing water resources, albeit with a slight reduction in farmers’ income.

Interpretable machine learning on large samples for supporting runoff estimation in ungauged basins

The distribution of flowmeter data and basin characteristic information exhibits substantial disparities, with most flow observations being recorded at a limited number of well-monitored locations. The perennial challenge of achieving reliable and robust hydrological modeling in ungauged catchments through regionalization has persisted. The increasing availability of large-scale hydrological datasets, coupled with recent advancements in machine learning techniques, offers new opportunities to explore patterns of association between basin attributes and hydrological parameters to enhance streamflow predictions. A novel parameter cross-regional transfer approach based on interpretable machine learning (XGBoost) is proposed to accurately predict runoff processes in ungauged regions by leveraging well-trained models across numerous basins within climate zones. We validate the effectiveness of this framework across 5,764 basins in a large sample dataset (Caravan), employing Nash-Sutcliffe Efficiency (NSE), RMSE and bias to assess performance. And a comparison is made with deep transfer learning based on LSTM and Transformer. Results indicate that the proposed method achieves NSE values exceeding 0.2 for 75 % of the ungauged basins, demonstrating superior performance and more stable accuracy compared to pure deep learning models, owing to its incorporation of physical constraints. Furthermore, the response of parameters to basin attributes within different climatic zones in the large-sample context is elucidated through SHAP values, enriching the understanding of hydrological features through data-driven inverse inference. These findings underscore the capability of interpretable machine learning to leverage hydro-physical regularities extracted from abundant basin features, thereby enhancing the accuracy of runoff predictions in ungauged regions.

Uncertainty analysis on long-term runoff projection from the Budyko framework and a conceptual hydrological model

ABSTRACT Runoff projections are subject to uncertainties related to model structure and parameters. This study aims to analyze uncertainties in long-term runoff estimations from an empirical (Budyko framework) and a conceptual hydrological model (MHD-INPE). Results indicate that both MHD-INPE and Budyko estimations tend to overestimate long-term runoff during years of recurring droughts. Pareto front solutions in MHD-INPE exhibited small uncertainties in long-term runoff estimations regarding parameter calibration (bias between 5 and 7%); differences were observed in low (bellow 5% variation) and high (bellow 10% variation) daily runoff. Related to model structure uncertainties, both models follow similar patterns and performance for a qualitative analysis. Budyko's future projections tend to exceed MHD-INPE's during high precipitation estimates, where at 2000 mm yearly precipitation the estimated runoff from Budyko tends to be 100 mm greater than the hydrological model. Under arid conditions Budyko tends to estimate smaller runoff than MHD-INPE due to variations in soil moisture and water storage not properly represented in Budyko's parameter. Although uncertainties were identified related to model complexity and calibrated parameters, higher uncertainties were identified as related to the climate models. Therefore, the Budyko method is a viable alternative for first-order analysis of long-term impacts.

Influence of SMAP soil moisture retrieval assimilation on runoff estimation across South Asia

This study was designed to characterize and quantify the influence of surface soil moisture assimilation on estimated runoff (surface flow and baseflow) and hydraulically-routed streamflow across three large river basins in South Asia that are at risk of impending water stress. Soil Moisture Active Passive (SMAP) surface soil moisture retrievals were assimilated into the Noah-MP land surface model. The gridded runoff was hydraulically routed to obtain volumetric streamflow values for a river network using a runoff routing module (Hydrological Modeling and Analysis Platform, HyMAP). The open loop (OL, model-only) and data assimilated (DA, includes soil moisture retrieval assimilation) Noah-MP runoff estimates highlighted the improvements in estimated total runoff across irrigated areas. Soil moisture assimilation impacted baseflow more relative to surface runoff. The HyMAP-based OL and DA streamflow generally underestimated the streamflow at upstream stations and overestimated the streamflow at downstream stations within the Indus basin due to missing physics related to reservoir operations. For stations located in the Ganges–Brahmaputra basins, the OL and DA estimation performance varied. The OL and DA results showed that the assimilation of soil moisture retrievals improves gridded runoff and volumetric streamflow across irrigated areas. Considerable relative change in streamflow (>70% increase in magnitude relative to OL) is noted after assimilation across the highly irrigated lower Indus basin and high precipitation regions in Bangladesh. Improving the modeling system’s representativeness of ground conditions via inclusion of water management information could improve large-scale streamflow modeling across South Asia.

An artificial neural network and SCS–CN-based model for runoff estimation: a case study of the Peddavagu watershed

ABSTRACT The accurate prediction of runoff from rainfall events is crucial for effective water resource management, especially in regions with diverse climatic patterns like India. This study proposes a novel approach by integrating the soil conservation service (SCS)–curve number (CN) method with artificial neural networks (ANNs) to model rainfall–runoff relationships. In this research, an SCS–CN method is utilized to estimate initial runoff volumes, accounting for local soil and land-cover characteristics. Subsequently, an ANN-based model is developed to capture the complex nonlinear relationships between rainfall inputs and runoff outputs. Taking the rainfall of the watershed as the inputs, an ANN model was developed in MATLAB for runoff simulation at the main outlet of the Peddavagu watershed. A feed-forward back-propagation method is employed in the ANN model. The architecture with a 1-10-1 configuration based on tan–sig transfer function performed well in terms of MSE 0.019. The model efficiency was satisfactory with the coefficient of correlation (R), 0.96 for training, 0.98 for validation, and 0.98 for testing period. The overall value of R (0.96) indicates the utility of this ANN–SCS-based coupled model for rainfall–runoff simulation.

Rainfall-Runoff Estimation in the North-Eastern Region of Bangladesh Using SCS-CN Method

Measuring discharge in a developing nation like Bangladesh is very important for flood prediction, land management, and sustainable development. Rainfall-induced runoff is a part of the cycle of hydrology and is required for efficient water resource planning. The most significant debatable procedure in hydrology is calculating and determining catchment surface runoff. The study aims to estimate runoff using the GIS-based Soil Conservation Service-Curve Number method (SCS – CN) method, where this hydrological model has a physical foundation and spatial distribution, and the curve number plays a key role in this model's runoff computation. The Hydrologic Soil Group (HSG) present in the study area and the land use pattern are used to find the Curve Number (CN). The calculated weighted CN for Antecedent Moisture Condition (AMC) I, II, and III for the study area was roughly 74.78, 87.12, and 94.06 respectively with the incorporation of Land use and land cover (LULC) and HSG. For total average rainfall of 35163.8mm, the SCS-CN approach determined the total average runoff 16077.55 mm for the period of 2009-2018. With a correlation coefficient of 0.966, the average rainfall and SCS-CN discharge have a significant linear relationship. The runoff regulates the amount of water that enters stream systems and returns extra precipitation to the ocean benefitting the hydrological cycle. With this runoff estimation, the quality and amount of water resources can be better understood, managed, and tracked. The Dhaka University Journal of Earth and Environmental Sciences, Vol. 12(2), 2023, P 83-96

Assessing the Impact of Modified Initial Abstraction Ratios and Slope Adjusted Curve Number on Runoff Prediction in the Watersheds of Sulaimani Province.

A popular way for describing the link between storm rainfall depth and direct runoff is the curve number (CN) method. It is a straightforward approach that has been extensively studied and widely adopted. However, there has been less focus on the impact of slope and the initial abstraction ratio, which is a crucial factor for accurately estimating direct runoff when utilizing the soil conservation service- Curve Number (SCS-CN) method. The initial abstraction ratio is typically assumed to be 0.20, as initially proposed by the method's developers. In this study, we analyzed daily rainfall data from seventeen watersheds in different physiographic locations in the Kurdistan region of Iraq, recorded between 2022 and 2023. Our aim was to assess the effect of slope adjusted curve number and modified the initial abstraction ratio (0.1) on estimation of direct runoff. The results demonstrated that adjusting the CN for slope and using a modified initial abstraction ratio increased the estimated runoff compared to the original method (without adjustment for slope and initial abstraction ratio=0.2). Therefore, when applying the SCS-CN method, it is crucial to correct the CN for slope in steeper areas and consider the initial abstraction ratio rather than relying on the suggested value of 0.2. this study highlights the importance of considering local conditions and estimating the initial abstraction ratio based on specific watershed characteristics to enhance the accuracy of direct runoff estimation using the CN method.

Surface Runoff Estimation Using SCS-CN Method for Kurumballi Sub-watershed in Shivamogga District, Karnataka, India

SCS-curve number (CN) is one of the most well-liked and commonly applied methods for estimating surface runoff. The present study aims to calculate surface runoff using SCS-CN watershed-based calculation and geospatial technology in the Kurumballi sub-watershed Shivamogga District of Karnataka, India. The study area covers about an area of 47.67 sq. km. The union of land use/land cover classification with hydrological soil groups (HSG) yields the runoff estimation by the SCS-CN curve approach. This method calculates the runoff volume from the land surface flows into the river or streams. Moreover, the study area’s delineation of runoff potential zones was done using the thematic integration method. Different thematic layers were used, including lithology, geomorphology, soil, slope, land use and land cover, drainage, surface water bodies, groundwater contour, and isohyetal maps. Furthermore, associating it with the SCS-CN technique, the total surface runoff volume of the study area was estimated. The total surface runoff volume in the study area is 21065849.7 m3. To this study, thematic integration with the SCS-CN approach to estimate runoff for watersheds is valuable for improving water management and soil conservation.

DEVELOPMENT AND ANALYSIS OF A GIS-BASED CURVE NUMBER MAP OF THE PHILIPPINES

Stormwater management requires quantitative methods to determine objective flood risk by estimating how much rainfall becomes runoff. With the absence of a locally generated runoff coefficient database in the Philippines, the standard model for surface runoff estimation is yet to be implemented using homegrown datasets. Traditionally, the empirical method is adapted from the National Resources Conservation Service (NRCS). The method quantifies rainfall-runoff relationships underscoring the combined effects of ground cover, soil hydraulic conductivity, and antecedent runoff condition (ARC) on runoff potential via a hydrologic parameter called the Curve Number (CN). Using geographic information system (GIS) tools, the Philippine CN map is developed by preprocessing and intersecting land use and land cover (LULC) and hydrologic soil group (HSG) with the CN look-up table. It was revealed that Group C soils dominate the Philippines. New data products—three raster CN maps at 25m spatial resolution—indicated the prevalence of medium to high runoff potential. National curve numbers were 61, 78, and 89 for dry, average, and wet ARCs, respectively. The outputs of this study provide access to spatially varied local runoff potential data in GIS format thus allowing direct GIS use for the swift simulation of flood damage mitigation models, among others.

ERA5-Land overestimates runoff coefficient but underestimates runoff recession rate in the central Tibetan permafrost region

Study regionThe source region of Yangtze River (SRYR) Study focusBetter understanding the influence of soil freeze/thaw (F/T) activity on runoff production is vital for cold regions like the Qinghai-Tibet Plateau. While global reanalysis products offer full spatial coverage and fine temporal resolution for runoff estimates, their accuracy in depicting runoff dynamics within permafrost-dominated regions remains uncertain. To address this gap, a thorough assessment of the ERA5-Land runoff products within the SRYR was undertaken to determine their effectiveness in representing specific permafrost hydrological mechanisms. New hydrological insights for the regionOur analysis reveals that ERA5-Land adequately captures the intra-annual variability of runoff, but tends to overestimate runoff coefficients while underestimate runoff recession rates. The monthly runoff coefficient fluctuates during the thaw season, showing decreasing trends from May to increasing trends after July. The runoff recession rate generally shifts from acceleration to retardation during the freezing period (October-March), influenced significantly by snow cover and soil F/T dynamics. There's a discernible delay in the observed transition time (0.3 days/a) of runoff recession from 1980 to 2020, closely linked (R>0.35, p<0.05) to soil F/T indices. Notably, the simulated transition time lacks significant trends, and displays contrasting correlations with soil F/T indices. This highlights a critical need for the ERA5-Land model to refine its representation of soil F/T dynamics, particularly snow physics and soil thermal schemes.