Baseflow Estimates Research Articles

Enhancing Sustainability in Watershed Management: Spatiotemporal Assessment of Baseflow Alpha Factor in SWAT

The increasing frequency of extreme rainfall events poses significant challenges to sustainable water resource management, leading to severe natural disasters. To mitigate these challenges, understanding the hydrological characteristics of watersheds, especially baseflow, is critical for enhancing watershed resilience and supporting sustainable water quality and resource management. However, conventional watershed models often neglect the accurate simulation of baseflow recession. This study proposes a method for calculating and applying the alpha factor for each hydrologic response unit (HRU) in the Soil and Water Assessment Tool (SWAT), considering both temporal and spatial variability in baseflow. The study watershed has undergone significant development, increasing the need for effective water management strategies that promote long-term sustainability. The alpha factor was computed using BFlow2021, and its effectiveness was evaluated by comparing recession and baseflow estimates under different methods. The results indicate that incorporating monthly HRU-specific alpha factors significantly improves model predictions of recession characteristics, highlighting the need for a more spatially and temporally detailed approach in hydrological modeling. The proposed methodology can help clarify the connection between recession and baseflow and can be applied to ungauged stations, offering a valuable tool for sustainable watershed and water quality management.

Constraining seasonal and spatial ambient and urban groundwater contributions to streamflow across a mixed land-use regional-scale Precambrian Shield watershed

Groundwater-surface water (GW-SW) interactions are complex phenomena that vary naturally over space and time and are being influenced by environmental change. The Whitson River watershed is a mixed land-use Precambrian shield watershed located in Northeastern Ontario, Canada in which groundwater is a source of municipal water supply and in which groundwater and surface waters are at risk of potential impacts from urban runoff, ongoing municipal drainage projects, periodic flooding, and climate change. This study used watershed-scale synoptic surveys of stable isotopes (δ18O and δ2H) and geochemistry, and corresponding mixing-model approaches (two-component and three-component) to quantify the seasonal and spatial variation in surface water, ambient, and urban groundwater contributions to streamflow. Multiple mixing models were generated based on isotope and geochemical source water identification, and the performance of these models was compared. Mixing-model analyses showed that groundwater contributes a critical, sustaining source to streamflow (> 50 % or more) during summer baseflow conditions, dropping to ∼ 15 % in fall when connection to shield lakes and wetlands dominate. Three-component mixing models showed similar results to the two-component models, refining groundwater contributions into ambient and urban groundwater sources. Estimated urban groundwater contributed 30 % or more to summer baseflow, dropping to ∼ 4 % in the fall. Baseflow estimates derived from graphical hydrograph separation were similar to mixing model groundwater estimates in the summer but suggest this approach overestimates groundwater contributions during the fall. These results demonstrate the complexity of source water contributions to streamflow and the challenges in their determination in mixed-land-use Precambrian Shield landscapes where communities are largely located. Assessment of source water contributions to streamflow across the Whitson River sub-watershed has generated a region-specific conceptualization (e.g. urban versus ambient groundwater) of a basin that is experiencing increased urban development, providing information that will be of value for long term management.

ИСПОЛЬЗОВАНИЕ АЛГОРИТМА СЛУЧАЙНОГО ПОИСКА ДЛЯ ОПТИМИЗАЦИИ ПРОЦЕССА ВЫДЕЛЕНИЯ БАЗИСНОГО СТОКА И ОПРЕДЕЛЕНИЯ ТРЕНДОВ ДЛЯ Р. ЕШИЛЬЫРМАК (СЕВЕРНАЯ ТУРЦИЯ)

Estimation of baseflow is a complex hydrographic task. Baseflow techniques and coefficients vary from basin to basin, stream to stream, and year to year. In this study, meta-heuristic optimization is used to automatically identify baseflow. The Particle Swarm Optimization (PSO), a meta-heuristic optimization approach, is chosen. The constraint and cost functions were determined using the PSO algorithm, Lyne and Hollick techniques, and a computer application. Over the period 1980-2015, the data were collected at the Kale station in the Yesilırmak River basin to validate the study model. The results show that the hydrographs and baseflow dividing line were separated effectively. It has also been revealed that the PSO has a high speed as well as a high level of precision. In the research, in addition to the baseflow separation, the hydrograph, baseflow, and ratio of the baseflow to the streamflow at the station No. 1402 were assessed using the Mann-Kendall test and Innovative Trend Test (ITA), and as a result, their trends have been found. By the use of both of these methods, it has been shown that all parameters have an unfavorable trend. In addition, the research came to some other significant conclusions, such as the fact that the baseflow declines in tandem with the flow values and that the baseflow rates are low in years with high peak values of the hydrograph.

Assessment of baseflow estimates using ArcSWAT and digital filter method in Mago River Basin of Arunachal Pradesh

Assessment of baseflow estimates using ArcSWAT and digital filter method in Mago River Basin of Arunachal Pradesh

Integrated Assessment of the Land Use Change and Climate Change Impact on Baseflow by Using Hydrologic Model

Changes in land use and climate can affect both the surface runoff and baseflow of streamflow. Baseflow significantly contributes to stream function in regions where climatic characteristics are seasonally distinct. Baseflow estimation depends on the observed streamflow in gauge water sheds, but baseflow estimations in data-poor or gauged watersheds depend solely on streamflow predicted from the hydrologic model. To accurately predict base runoff through the model, it is necessary to apply proper hydrological parameters. Accordingly, the objectives of this study are to (1) improve predictions of SWAT by applying the alpha factor estimated using BFLOW for calibration, and (2) evaluate streamflow and baseflow the effects of changes in land use and climate. The results show the alpha factor estimated using BFLOW in SWAT calibration improves the prediction for streamflow and recessions in the baseflow. In this study, streamflow increased due to land use change (impervious urban and agricultural areas), while baseflow decreased. The baseflow was more significant in the dry season than in the wet season, and the baseflow fluctuation was significant from February to May. Moreover, the changes in land use in the study area lead to differences in the seasonal characteristics observed for the temporal distribution of streamflow and baseflow.

Statistical Approach for Computing Base Flow Rates in Gaged Rivers and Hydropower Effect Analysis

The calculation of base flow rates in rivers is complex since hydrogeological and hydrological studies should be performed. The estimation of base flow rates in storm hydrograph associated to various return periods is even more challenging compared to other events. This research provides a novel methodology to compute base flow rates in gaged rivers for extreme events based on statistical correlations of daily flows. The current methodology does not require complex aquifers analysis to compute base flows. Results of computed base flow rates are validated using observed storm hydrographs using a complete record. The proposed methodology was applied considering measurements of a limnigraphic station in the Sinú river located in Montería, Córdoba, Colombia. The analysis confirmed that only using series of multiannual monthly mean flows is possible to estimate base flow of flood hydrograph associated to different return periods.

Benchmarking Three Event-Based Rainfall-Runoff Routing Models on Australian Catchments

In the field of hydrology, event-based models are commonly used for flood-flow prediction in catchments, for use in flood forecasting, flood risk assessment, and infrastructure design. The models are simplistic, as they do not consider longer-term catchment processes such as evaporation and transpiration. This paper examines the relative performance of two widely used models, the American HEC-HMS model, the Australian RORB model, and a newer model, the RRR model. The evaluation is conducted on four case study catchments in Australia. The first two models, HEC-HMS and RORB, do not include baseflow, necessitating the estimation of baseflow through alternate means. By contrast, the RRR model includes baseflow, by extracting a separate loss from the rainfall, and then routing the resultant flow through the catchment, much like quickflow, but with a longer delay time. The models are calibrated and then verified with weighted mean parameter values on an independent set of events in each case study catchment. This gives an indication of the ability of the models to correctly predict flow, which is important when the models are used with design rainfalls to predict design flows. The results demonstrate that all models perform adequately on the four examined catchments, but the RRR model exhibits superior calibration, and, to a lesser extent, better validation compared to the other two models.

Improving the representation of stream water sources in surrogate nutrient models with water isotope data

An important part of meeting nutrient reduction goals in the lower Great Lakes basin and assessing the success of different land management strategies is modeling nutrient losses from agricultural land. This study aimed to improve the representation of water source contributions to streamflow in generalized additive models for predicting nutrient fluxes from three headwater agricultural streams in southern Ontario monitored during the Multi-Watershed Nutrient Study (MWNS). The previous development of these models represented baseflow contributions to streamflow using the baseflow proportion derived using an uncalibrated recursive digital filter. Recursive digital filters are commonly used to partition stream discharge into separate components from slower and faster pathways. In this study, we calibrated the recursive digital filter using stream water source information from stable isotopes of oxygen in water. Across sites, optimization of the filter parameters reduced bias in baseflow estimates by as much as 68 %. In most cases, calibrating the filter also improved agreement between filter-derived baseflow and baseflow calculated from isotope and streamflow data: the average Kling-Gupta Efficiencies using default and calibrated parameters were 0.44 and 0.82, respectively. When incorporated into the generalized additive models, the revised baseflow proportion predictor was more often statistically significant, improved model parsimony, and reduced prediction uncertainty. Moreover, this information allowed for a more rigorous interpretation of how different stream water sources influence nutrient losses from the agricultural MWNS watersheds.

The role of climate conditions and groundwater on baseflow separation in Urmia Lake Basin, Iran

Study regionUrmia Lake basin, Iran. Study focusAn accurate estimation of baseflow provides useful information for various aspects of water resources management. Baseflow estimation is subject to many uncertainties because it cannot be measured directly. This study aims to quantify the influence of uncertainties on the baseflow separation in the Urmia Lake basin, Iran. Herein, long-term observational data was used to evaluate the stability and reliability of baseflow estimation. Seven baseflow separation methods were compared with the mass balance filter (MBF) as a reference method. Here, two indicators including the average annual baseflow coefficient (BFC) and the baseflow index (BFI) were used to assess the impact of climate condition on baseflow separation. In addition, we investigated two sources of uncertainty in the digital filter methods: the recession constant (α) value and the approximation of groundwater recharge. New hydrological insights for the regionEckhardt's method exhibited a better estimate of the baseflow during both wet and dry years, and exhibited acceptable accuracy during peak flows, even during multiple events. The results of uncertainties among the filter methods revealed that the Lynie & Holick (LH) algorithm and Eckhardt’s method were more sensitive to the amount of α. Moreover, Eckhardt’s method demonstrated a more reliable estimation of the ratio of groundwater recharge to streamflow.

Baseflow estimation based on a self-adaptive non-linear reservoir algorithm in a rainy watershed of eastern China

Accurate baseflow estimation is critical for water resources evaluation and management, and non-point source pollution quantification. Nonlinear reservoir algorithm (NRA) has been increasingly applied to baseflow separation because of its good approximation to the real groundwater discharge (commonly dominated by the unconfined aquifer) in most watersheds. However, in the rainy regions, large uncertainties may remain in the traditional NRA-separated baseflow sequences due to its empirical transition function for the rising limb of discharge process, and the evident variations of baseflow recession in the initial period of the falling limb caused by the disturbance from surface flow or rainfall events. To improve the reliability of baseflow separation, a self-adaptive non-linear reservoir algorithm (SA-NRA) was developed in this study based on the NRA, a self-adaptive groundwater discharge modified parameter, and the Particle Swarm Optimization algorithm (PSO). The validation of SA-NRA in a rainy watershed of eastern China showed that SA-NRA could be the approach to provide a goodness-of-fit for baseflow recession behaviors in the rainy regions. The traditional NRA and Eckhardt's two-parameter recursive digital filter (ERDF), calibrated (or validated) only with the pure baseflow recession data, can hardly provide reliable baseflow predictions for the non-pure baseflow recession periods (including the rising limb and the falling limb with surface flow or rainfall disturbance) due to the apparent variations of baseflow recession behavior. Therefore, more attentions should be paid to the uncertainties of baseflow separation for the non-pure baseflow recession periods in the rainy regions.

Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake Bay

Estimation of base and surface flow using deep neural networks and a hydrologic model in two watersheds of the Chesapeake Bay

Evaluation of baseflow separation methods with real and synthetic streamflow data from a watershed

• Performance of graphical & digital filter baseflow separation methods are compared. • Methods are compared using real data with a subjective concept of hydrologic plausibility. • Synthetic data from an integrated hydrologic model used for more rigorous assessments. • Estimated baseflow hydrographs reveal seasonal variability. • Filtered UKIH approach works best for most study locations but depends spatially. Baseflow originating primarily from groundwater is a critical streamflow component, although its accurate estimation is fraught with significant difficulties. This study estimates baseflow through existing graphical and digital filter methods, using actual streamflow data from a gauging station at the Alder Creek Watershed (ACW) and synthetic streamflow data at ten study locations within the same watershed simulated with HydroGeoSphere (HGS) (Aquanty Inc., 2018). There are four widely used graphical (Institute for Hydrology, 1980; Sloto and Crouse, 1996; Aksoy et al., 2008) and six digital filtering (Lyne and Hollick, 1979; Chapman and Maxwell, 1996; Furey and Gupta, 2001; Eckhardt, 2005; Tularam and Ilahee, 2008; Aksoy et al., 2009) baseflow separation approaches compared in this study. To determine the most optimal approach, baseflow estimates from real data are assessed based on the subjective concept of hydrologic plausibility, while baseflow estimates obtained from a HGS streamflow record with graphical and digital filtering methods are compared to those computed directly by HGS. Overall, results from this study indicate that baseflow hydrographs reveal a seasonal pattern at the ACW. During wintertime, streamflow is composed almost entirely of baseflow, whereas during summertime, baseflow only consists approximately 20% to 60% of streamflow. After comparing baseflow estimates with those computed by HGS, the most optimal approaches at the ten study locations are assessed. Results show that the best approach at six study locations is the FUKIH (Aksoy et al., 2009) approach, while at three locations, the Chapman and Maxwell (1996) approach and for one location, the Eckhardt (2005) approach performed the best. In conclusion, it is inferred that the most optimal approach within the ACW varies spatially.

Nutrient delivery by groundwater discharge to headwater streams in agricultural catchments

AbstractNutrient concentrations in riparian groundwater are variable from site to site, making it challenging to use these data to quantify nutrient fluxes contributed by groundwater discharge to headwater streams. Instead, we define event‐excluded baseflow (BFEE), and use stream discharge data and stream samples collected during BFEEto estimate groundwater discharge and loading of nutrients to the streams. Based on this method, in two study catchments (Thames River basin, Ontario, Canada) groundwater discharge during BFEEcontributed about 30% of the total streamflow and 20%–30% of the total loading of soluble reactive phosphorus (SRP) and nitrate. Previous estimates of baseflow using hydrographic separation techniques indicate that groundwater discharge likely contributes even larger fractions, ~50%–60% of flow in these catchments. We infer that groundwater likely contributes ~40% of the annual SRP load and ~ 50%–60% of the nitrate‐N load. These results indicate that groundwater discharge plays an important role in the nutrient loading to headwater streams. This reinforces the findings of earlier studies that have inferred a dominant influence of legacy reservoirs of nutrients in the subsurface of similar agricultural catchments on the loads of these nutrients in headwater streams.

Impact of Watershed Management on Stream Flow and Build-up Water Storage in Parasai-Sindh Watershed of SAT Region, India

Aims: The study of runoff and base flow estimates and their impact on groundwater status in the semi-arid region of India is typically limited. We are focused on runoff and base flow in the Parasai-Sindh watershed, and impact of rain water harvesting structure on harvesting of stream flow and change ground water status.
 Place and Duration of Study: The study site is located in Jhansi district (Bundelkhand region) of Uttar Pradesh and started from 2012 and completed at end of 2013.
 Methodology: Total six rainwater harvesting structure (RHS) or checkdams were constructed on the drainage line from ridge to final outlet of watershed. Daily rainfall data was observed with automatic rain gauge during the study period. For discharge estimation, the runoff gauging station was installed at the outlet of watershed. The runoff and base flow of watershed were estimated by subtracting barometric pressure from the stage recoded by Baro and Diver install at outlet to get actual stage of runoff passing over it. The water pressure head of 200 randomly selected open wells were monitored on monthly interval.
 Results: The geological area of watershed is 12.46 km2 and it’s main stream is found to be of 4th order. The results indicate that out of 49.2 mm rainfall event, runoff and base flow at outlet of watershed were found as 20.98% and 4.19%, respectively. Whereas, runoff and base flow were recorded 14.12% and 11.18%, respectively against 86.2 mm rainfall. Total runoff and base flow produced from outlet was recorded as 16 and 7%, respectively. The water head pressure of open wells was improved 92.45% after the rainy season as compared to before the rainy season.
 Conclusion: The findings of the study are useful and show relationship between rainfall: runoff and estimation of base flow separation from the total discharge at the outlet of watershed. The estimation of volume of runoff which was harvested as surface runoff during rainy season and it's indirectly effected groundwater recharging through construction of RHS in ephemeral drain of Bundelkhand region.

Estimating Gridded Monthly Baseflow From 1981 to 2020 for the Contiguous US Using Long Short‐Term Memory (LSTM) Networks

AbstractAccurate baseflow estimation is essential for ecological protection and water resources management. Past studies have used environmental predictors to extend baseflow from gauged basins to ungauged basins, publishing several regional or global datasets on mean annual baseflow. However, time series datasets of baseflow are still lacking due to the complexity of baseflow generation processes. Here, we developed a monthly baseflow data set using a Deep learning model called the long short‐term memory (LSTM) networks. To better train the networks across basins, we compared the standard LSTM architecture using 8 time series as inputs with four variant architectures using 16 additional static properties as inputs. Dividing the contiguous United States into nine ecoregions, we used baseflow calculated from 1,604 gauged basins as training targets to calibrate the five LSTM architectures for each ecoregion separately. Results show that three variant architectures (Joint, Front, and Entity‐Aware‐LSTM) perform better than the standard LSTM, with median Kling‐Gupta Efficiencies across basins greater than 0.85. Based on Front LSTM, the monthly baseflow data set with 0.25° spatial resolution across the contiguous United States from 1981 to 2020 was obtained. Potential applications of the data set include analyzing baseflow trends under global change and estimating large‐scale groundwater recharge.

Seasonal Variations in Fjord Sediment Grain Size: A Pre‐requisite for Hydrological and Climate Reconstructions in Partially Glacierized Watersheds (Baker River, Patagonia)

AbstractFjord sediments are increasingly recognized as high‐resolution archives of past hydrological and climate variability. Using them as such, however, requires a comprehensive understanding of the variables that affect their accumulation rates and properties. Here, we conduct a spatial and temporal study of sediment samples collected at the head of Martínez Channel (Chilean Patagonia, 48°S), to understand how the fjord's sediments register changes in the hydrology of Baker River, Chile's largest river in terms of mean annual discharge. We apply end‐member modeling to particle‐size distributions of: (a) river suspended sediments, (b) surface sediments collected along a proximal‐distal transect at the fjord head, and (c) fjord sediments collected in a sequential sediment trap at 15‐day resolution during two consecutive years. We then validate the use of the grain‐size end members for hydrological and climate reconstructions, using a sediment core that covers the last 35 years. Results show that the river suspended sediments and fjord sediments are consistently composed of two grain‐size subpopulations. The finest end member (EM1; mode 4.03 μm) reflects the meltwater contribution, which dominates in all but the winter season. The coarser end member (EM2; mode 18.7 μm) dominates in winter, when meltwater contribution is reduced, and is associated with rainfall. We show that the fluxes of EM1and EM2provide quantitative estimates of baseflow (r = 0.87,p < 0.001) and quickflow (r = 0.86,p < 0.001), respectively. Additionally, we propose that log (EM1/EM2) can be used to reconstruct meltwater production (r = 0.67,p < 0.001) and temperature (r = 0.81,p < 0.001) in the lower Baker River watershed. These results support the use of fjord sediments for quantitative reconstructions of hydrological and climate variability in partially glacierized watersheds.

Implications of variations in stream specific conductivity for estimating baseflow using chemical mass balance and calibrated hydrograph techniques

Abstract. Baseflow to rivers comprises regional groundwater and lower-salinity intermediate water stores such as interflow, soil water, and bank return flows. Chemical mass balance (CMB) calculations based on the specific conductivity (SC) of rivers potentially estimate the groundwater contribution to baseflow. This study discusses the application of the CMB approach in rivers from south-eastern Australia and assesses the feasibility of calibrating recursive digital filters (RDFs) and sliding minima (SM) techniques based on streamflow data to estimate groundwater inflows. The common strategy of assigning the SC of groundwater inflows based on the highest annual river SC may not always be valid due to the persistent presence of lower-salinity intermediate waters. Rather, using the river SC from low-flow periods during drought years may be more realistic. If that is the case, the estimated groundwater inflows may be lower than expected, which has implications for assessing contaminant transport and the impacts of near-river groundwater extraction. Probably due to long-term variations in the proportion of groundwater in baseflow, the RDF and SM techniques cannot generally be calibrated using the CMB results to estimate annual baseflow proportions. Thus, it is not possible to extend the estimates of groundwater inflows using those methods, although in some catchments reasonable estimates of groundwater inflows can be made from annual streamflows. Short-term variations in the composition of baseflow also lead to baseflow estimates made using the CMB method being far more irregular than expected. This study illustrates that estimating baseflow, especially groundwater inflows, is not straightforward.

A hybrid approach for integrated surface and subsurface hydrologic simulation of baseflow with Iterative Ensemble Smoother

Integrated surface and subsurface hydrologic models are particularly well suited for the simulation of baseflow as exchange fluxes does not rely on boundary conditions. Regional scale hydrologic models are of particular interest as they can provide guidance to stakeholders toward sustainable water resources management. However, integrated hydrologic models are rarely considered at the regional scale because their computational costs usually prevent efficient model calibration and predictive uncertainty analysis. Moreover, estimation of baseflow and each water budget component usually requires additional post-processing steps. In this paper, we thus aim here to provide a hybrid approach for the application of an integrated hydrologic model at the regional scale when low-flow processes are of primary concern. A surface water mass balance module has been developed to solve the hydrological mass balance from precipitations and temperature datasets. This module calculates potential infiltration fluxes that are used as input to the integrated modeling platform HydroGeoSphere (HGS). This approach provides a computationally tractable integrated model where low-flow processes are explicitly considered, baseflows generated in an integrated fashion, and each water budget component accessible. The model is applied to a region covering 36 900 km2 in the Southeastern part of the province of Quebec, Canada. Thanks to the computational efficiency of the approach, a rigorous mathematical parameter estimation, the Levenberg–Marquardt form of the Iterative Ensemble Smoother, can be considered to calibrate the model to baseflow of eight main rivers, snow water equivalent and evapotranspiration. An ensemble of 187 equally-probable realizations was used for history matching of observations and for the non-linear uncertainty analysis. An improved representation of baseflow can be linked to an appropriate non-linear uncertainty quantification through an efficient integrated surface and subsurface hydrologic model to support managing water resources at the regional scale.

Simulation of long-term spatiotemporal variations in regional-scale groundwater recharge: contributions of a water budget approach in cold and humid climates

Abstract. Groundwater recharge (GWR) is a strategic hydrologic variable, and its estimate is necessary to implement sustainable groundwater management. This is especially true in a global warming context that highly impacts key winter conditions in cold and humid climates. For this reason, long-term simulations are particularly useful for understanding past changes in GWR associated with changing climatic conditions. However, GWR simulation at the regional scale and for long-term conditions is challenging, especially due to the limited availability of spatially distributed calibration data and due to generally short observed time series. The objective of this study is to demonstrate the relevance of using a water budget model to understand long-term transient and regional-scale GWR in cold and humid climates where groundwater observations are scarce. The HydroBudget model was specifically developed for regional-scale simulations in cold and humid climate conditions. The model uses commonly available data such as runoff curve numbers to describe the study area, precipitation and temperature time series to run the model, and river flow rates and baseflow estimates for its automatic calibration. A typical case study is presented for the southern portion of the Province of Quebec (Canada, 36 000 km2). With the model simultaneously calibrated on 51 gauging stations, the first GWR estimate for the region was simulated between 1961 and 2017 with very little uncertainty (≤ 10 mm/yr). The simulated water budget was divided into 41 % runoff (444 mm/yr), 47 % evapotranspiration (501 mm/yr), and 12 % GWR (139 mm/yr), with preferential GWR periods during spring and winter (44 % and 32 % of the annual GWR, respectively), values that are typical of other cold and humid climates. Snowpack evolution and soil frost were shown to be a key feature for GWR simulation in these environments. One of the contributions of the study was to show that the model sensitivity to its parameters was correlated with the average air temperature, with colder watersheds more sensitive to snow-related parameters than warmer watersheds. Interestingly, the results showed that the significant increase in precipitation and temperature since the early 1960s did not lead to significant changes in the annual GWR but resulted in increased runoff and evapotranspiration. In contrast to previous studies of past GWR trends in cold and humid climates, this work has shown that changes in past climatic conditions have not yet produced significant changes in annual GWR. Because of their relative ease of use, water budget models are a useful approach for scientists, modelers, and stakeholders alike to understand regional-scale groundwater renewal rates in cold and humid climates, especially if they can be easily adapted to specific study needs and environments.

A Study to Suggest Monthly Baseflow Estimation Approach for the Long-Term Hydrologic Impact Analysis Models: A Case Study in South Korea

Changes in both land use and rainfall patterns can lead to changes in the hydrologic behavior of the watershed. The long-term hydrologic impact analysis (L-THIA) model has been used to predict such changes and analyze the changes in mitigation scenarios. The model is simple as only a small amount of input data are required, but it can predict only the direct runoff and cannot determine the streamflow. This study, therefore, aimed to propose a method for predicting the monthly baseflow while maintaining the simplicity of the model. The monthly baseflows for 20 watersheds in South Korea were estimated under different land use conditions. Calibration of the monthly baseflow prediction method produced values for R2 and the Nash–Sutcliffe efficiency (NSE) within the ranges of 0.600–0.817 and 0.504–0.677, respectively; during validation, these values were in the ranges of 0.618–0.786 and 0.567–0.727, respectively. This indicates that the proposed method can reliably predict the monthly baseflow while maintaining the simplicity of the L-THIA model. The proposed model is expected to be applicable to all the various forms of the model.