Semi-distributed Hydrological Model Research Articles

Evaluation of satellite rainfall products to estimate extreme flow events over the Kulfo watershed in Ethiopia

ABSTRACT Satellite rainfall products (SRPs) with high spatial and temporal resolutions offer opportunities to monitor extreme climate event intensities and trends on spatial different scales. A critical evaluation of the satellite precipitation dataset is very important for both the end users and data developers. Meanwhile, this evaluation may provide a benchmark for the product's continued development and future improvement. The main objective of this study is to evaluate the performance of globally gridded high-resolution SRPs (Tropical Rainfall Measuring Mission (TRMM), Climate Prediction Center Morphing (CMORPH), and CHRIPS) under sparse ground-based data and the complex topography of the Kulfo watershed through the semi-distributed hydrological model (SWAT). The model is calibrated for the period of 1991–2008 and validated for the period of 2009–2015. Comparisons of the simulations to the observed stream flow at the outlet of Kulfo watershed form the basis for the conclusions of this study. The Nash–Sutcliffe efficiency and the coefficient of determination were used to benchmark the model performance. The result indicated that all models underestimate the observed rainfall. The accuracy of models is not the same in representing the rainfall of the study area with TRMM performing the best (Bias = −5.78) and CMORPH performing the worst (Bias = −9.87). Overall, the SRPs tend to overestimate inter-annual rainfall variability.

Assessment of catchment water resources allocation under climate change in Luwombwa sub-catchment, Zambia

The uncertainty in climate change and high water demand pose pressure on the natural water resources supply. Not only does this require better understanding but also a call for immediate interventions, mitigation and adaptive measures. This study evaluates catchment water resources in the Luwombwa sub-catchment in Zambia through statistical analysis in the downscaling of past, present and future climatic variables from the CMIP6 climatic model. These variables are then integrated into WEAP - a semi-distributed hydrological and water evaluation model - to perform water demand and allocation scenario modelling. Mult-site calibration and validation were conducted on five selected micro-catchments within Luwombwa sub-catchment. The model performance was assessed usng the R2, NSE and PBIAS as the objective functions. Satisfactory values of 92 % for R2, 82 % for NSE and 6.9 % for PBIAS were achieved. This allowed for scenario modelling on water demand and allocation among competing users. Three future scenarios (2022–2050) were developed from the historical to baseline (1988–2022) and included state of water resources availability under climate change, expansion of irrigation area and impact of dam construction in the sub-catchment. The study reveals a decrease of 20 % in sub-catchment's water availability resulting from 9.3 % (equivalent to 4oC) rise in maximum temperature and 4.5 % reduction in rainfall within the entire sub-catchment. This is especially under the persistence of SSP370 climate variability scenario projections downscaled from four GCM models by the year 2050. The study further revealed that the change point for anticipated future climate extremes is likely to occur between 2027 and 2030. The results are indicative of downward trends in streamflow under climate change and socioeconomic development leading to increase in water value and water scarcity. The insights from the study are critical to inform formulation of effective catchment water resources management strategies such as the development of management plans and adapation measures in the face of climate change and the needs for different stakeholders involvement.

Hybrid hydrological modeling for large alpine basins: a semi-distributed approach

Abstract. Alpine basins are important water sources for human life, and reliable hydrological modeling can enhance the water resource management in alpine basins. Recently, hybrid hydrological models, coupling process-based models and deep learning (DL), have exhibited considerable promise in hydrological simulations. However, a notable limitation of existing hybrid models lies in their failure to incorporate spatial information within the basin and describe alpine hydrological processes, which restricts their applicability in hydrological modeling in large alpine basins. To address this issue, we develop a set of hybrid semi-distributed hydrological models by employing a process-based model as the backbone and utilizing embedded neural networks (ENNs) to parameterize and replace different internal modules. The proposed models are tested on three large alpine basins on the Tibetan Plateau. A climate perturbation method is further used to test the applicability of the hybrid models to analyze the hydrological sensitivities to climate change in large alpine basins. Results indicate that proposed hybrid hydrological models can perform well in predicting runoff processes and simulating runoff component contributions in large alpine basins. The optimal hybrid model with Nash–Sutcliffe efficiencies (NSEs) higher than 0.87 shows comparable performance to state-of-the-art DL models. The hybrid model also exhibits remarkable capability in simulating hydrological processes at ungauged sites within the basin, markedly surpassing traditional distributed models. In addition, the results also show reasonable patterns in the analysis of the hydrological sensitivities to climate change. Overall, this study provides a high-performance tool enriched with explicit hydrological knowledge for hydrological prediction and improves our understanding about the hydrological sensitivities to climate change in large alpine basins.

Comparative analysis of lumped and semi-distributed hydrological models in humid Mediterranean environments

ABSTRACT This study conducts a comparative analysis of four hydrological models—GR4J, HBV-light, HEC-HMS, and WEAP—to evaluate their effectiveness in simulating surface water responses in humid Mediterranean climates. The Longaví basin, located in the Maule River basin of central Chile, was selected for its characteristic climate. The models used lumped and semi-distributed approaches to simulate the basin’s hydrological cycle. The study area was divided into three zones, considering climate, soil properties, and altitude. Results showed that GR4J, HEC-HMS, and WEAP performed well during the winter months (June to October), while HBV-light was more effective in summer (November to May). WEAP model, with its extensive parameterization, effectively captured streamflow variability during the rainy season. The Differential Split Sample Test indicated that GR4J and WEAP offered the best fit for analysing climatic variability. These models are highly recommended for climate change impact studies, given their strong performance in capturing seasonal hydrologic responses across similar regions.

Coupling WRF with HEC-HMS and WRF-Hydro for flood forecasting in typical mountainous catchments of northern China

Abstract. The atmospheric–hydrological coupling systems are essential to flood forecasting because they allow for more improved and comprehensive prediction of flood events with an extended forecast lead time. Achieving this goal requires a reliable hydrological model system that enhances both rainfall predictions and hydrological forecasts. This study evaluates the potential of coupling the mesoscale numerical weather prediction model, i.e., the weather research and forecasting (WRF) model, with different hydrological modeling systems to improve the accuracy of flood simulation. The fully distributed WRF-Hydro modeling system and the semi-distributed Hydrological Engineering Center Hydrological Modeling System (HEC-HMS) were coupled with the WRF model, and the lumped HEC-HMS model was also adopted using the observed gauge precipitation as a benchmark to test the model uncertainty. Four distinct storm events from two mountainous catchments in northern China characterized by varying spatial and temporal rainfall patterns were selected as case studies. Comparative analyses of the simulated flooding processes were carried out to evaluate and compare the performance of the coupled systems with different complexities. The coupled WRF–HEC-HMS system performed better for long-duration storm events and obtained optimal performance for storm events uniformly distributed both temporally and spatially, as it adapted to more rapid recession processes of floods. However, the coupled WRF–HEC-HMS system did not adequately capture the magnitude of the storm events as it had a larger flow peak error. On the other hand, the fully distributed WRF–WRF-Hydro system performed better for shorter-duration floods with higher flow peaks as it can adapt to the simulation of flash floods. However, the performance of the system became poor as uniformity decreased. The performance of the lumped HEC-HMS indicates some source of uncertainty in the hydrological model when compared with the coupled WRF–HEC-HMS system, but a larger magnitude error was found in the WRF output rainfall. The results of this study can help establish an adaptive atmospheric–hydrologic coupling system to improve flood forecasting for different watersheds and climatic characteristics.

Comparative analysis of decision tree, time series, and semi-distributed hydrological models for runoff prediction in snowmelt-influenced and human-activity-intensive basins: a case study of the Shiribeshi-Toshibetsu basin, Japan

ABSTRACT This study evaluates the performance of decision tree model (XGBoost), time series model (LSTM), and semi-distributed hydrological model (HBV) in simulating daily runoff in a watershed with significant snowmelt contributions and intensive human activity. Daily runoff data from the Shiribeshi-Toshibetsu basin in Japan, spanning the period from 1998 to 2015, were used for model training, with data from 2016 to 2019 for validation. Comparative analysis reveals that the XGBoost model outperforms the LSTM and HBV models,achieving a Nash–Sutcliffe Efficiency (NSE) of 0.94, compared to 0.85 and 0.84 for the LSTM and HBV models, respectively. Additionally, Partial Dependence Plots (PDPs) were utilized to investigate the nonlinear impacts of climatic factors on runoff, identifying precipitation, average temperature, and wind speed as the most significant drivers. Specifically, the relationship between temperature and runoff follows a unique pattern: runoff initially increases with rising temperatures, then decreases, and subsequently increases again. These findings underscore the robustness of decision tree models in handling complex hydrological data and highlight the importance of understanding the intricate interactions between climatic variables and runoff. This study provides scientific support for water resource management in similar watersheds under changing climatic conditions.

Altitude characteristics in the response of rain-on-snow flood risk to future climate change in a high-latitude water tower

Global warming is profoundly impacting snowmelt runoff processes in seasonal freeze-thaw zones, thereby altering the risk of rain-on-snow (ROS) floods. These changes not only affect the frequency of floods but also alter the allocation of water resources, which has implications for agriculture and other key economic sectors. While these risks present a significant threat to our lives and economies, the risk of ROS floods triggered by climate change has not received the attention it deserves. Therefore, we chose Changbai Mountain, a water tower in a high-latitude cold zone, as a typical study area. The semi-distributed hydrological model SWAT is coupled with CMIP6 meteorological data, and four shared socioeconomic pathways (SSP126, SSP245, SSP370, and SSP585) are selected after bias correction, thus quantifying the impacts of climate change on hydrological processes in the Changbai Mountain region as well as future evolution of the ROS flood risk. The results indicate that: (1) Under future climate change scenarios, snowmelt in most areas of the Changbai Mountains decreases. The annual average snowmelt under SSP126, SSP245, SSP370, and SSP585 is projected to be 148.65 mm, 135.63 mm, 123.44 mm, and 116.5 mm, respectively. The onset of snowmelt is projected to advance in the future. Specifically, in the Songhua River (SR) and Yalu River (YR) regions, the start of snowmelt is expected to advance by 1–11 days. Spatially, significant reductions in snowmelt were observed in both the central part of the watershed and the lower reaches of the river under SSP585 scenario. (2) In 2021–2060, the frequency of ROS floods decreases sequentially for different scenarios, with SSP 126 > SSP 245 > SSP 370 > SSP 585. The frequency increments of ROS floods in the source area for the four scenarios were 0.12 days/year, 0.1 d/yr, 0.13 days/year, and 0.15 days/year, respectively. The frequency of high-elevation ROS events increases in the YR in the low emission scenario. Conversely, in high emission scenarios, YR high-elevation ROS events will only increase in 2061–2100. This phenomenon is more pronounced in the Tumen River (TR), where floods become more frequent with increasing elevation.

Evaluation of distributed and semi-distributed hydrological models in complex River Basin system, Nepal

Hydrological information is essential for planning water resource projects and for simulating hydrological models to calibrate and generate streamflow data. This study evaluates the performance of distributed and semi-distributed hydrological models in the Tamakoshi River Basin (TRB), Nepal. The models assessed include the Spatial Process in Hydrology (SPHY), Hydrologiska Byråns Vattenbalansavdelning (HBV), and Hydrologic Engineering Centre Hydrologic Modeling System (HEC-HMS). The models were evaluated using metrics such as Nash-Sutcliffe Efficiency (NSE) ranging from 0.62 to 0.77, Coefficient of Determination (R2) from 0.76 to 0.77, and Percentage Bias (P Bias) from 26.16 % to −3.54 % during daily calibration. All three models were successfully calibrated and validated on a daily and monthly basis using observed discharge data at the Busti station in the TRB. The SPHY model performed better during the calibration period compared to the validation period, while the HBV and HEC-HMS models showed better performance during validation than calibration. Among the statistical indicators, the coefficient of determination closely matched, but NSE and P Bias showed some deviations. This comparative analysis of calibrated and validated models is valuable for filling gaps in hydrological data records and for selecting appropriate hydrological models for planning, managing, and developing water resource projects in the TRB.

Sensitivity analysis of SWAT streamflow and water quality to the uncertainty in soil properties generated by the SoLIM model

Hydrological models are essential tools for understanding natural processes, yet they often encounter with uncertainties due to limited input data and simplified assumptions. Soil data is a crucial input parameter for hydrological models, but it faces cartographic challenges due to lack of standardized soil mapping methods. To explore the applicability of soil datasets derived from the Third Law of Geography in hydrological models, the Soil Landscape Inference Model (SoLIM) based on this principle is used to generate a soil dataset. This dataset is then utilized as input data for a semi-distributed hydrological model (Soil and Water Assessment Tool, SWAT) to assess the impact of soil dataset uncertainty on performance of the hydrological model. This study, utilized the Fengyu River Watershed in Yunnan Province, China as an example, showed that: 1) Although the integration of SoLIM-generated soil data into the SWAT model introduces sensitivity in sediment yield, the overall results remain reliable. This suggests that soil datasets based on the Third Law of Geography can serve as input for semi-distributed hydrological models. 2) Uncertainty introduced by SoLIM-generated soil data affects parameters such as evaporation, total porosity of the soil layer (Φd), and groundwater infiltration coefficient (Ksat). These variations influence the sensitivity of SWAT’s predictions regarding streamflow and water quality. 3) Factors influencing model outcomes include steeper slopes in certain areas, which enhance streamflow velocity and flow rates, resulting in more dynamic and non-linear responses. Additionally, soil water dynamics exhibit higher temporal and spatial variability in forests compared to pasture and rice paddy fields, where vegetation tends to be more uniform, maintaining stable soil water loss pathways and reducing uncertainty in streamflow predictions. The study provides a foundation for studies addressing the difficulties in acquiring high-precision soil datasets for such models.

Hydrologic applicability of satellite-based precipitation estimates for irrigation water management in the data-scarce region

Reliable precipitation estimates are crucial for planning and managing water resources, monitoring hydrologic extremes, and fulfilling irrigation water requirements. Accurate precipitation estimates are particularly challenging in complex mountain terrains, where monitoring gauges are often sparsely distributed due to their remote locations, and high installation and long-term operation costs. Recent advances in satellite-based precipitation estimates offer promising opportunities to improve our understanding of hydrologic processes and their applications for irrigation water management. Several datasets are available varying considerably in terms of their data sources, quality control methods, estimation procedure, and spatiotemporal resolutions. Choosing the most suitable dataset for a particular application is a complex task. In this study, we (1) evaluate the performance of six satellite-based precipitation estimates (SPEs): i) CHIRPS v2.0, ii) CMORPH v1.0, iii) ERA5, iv) IMERG v6, v) MSWEP v2.8, and vi) PERSIANN-CDR against the gauge precipitation using continuous statistical and categorical indices, (2) integrate SPEs with a calibrated semi-distributed hydrologic model to predict streamflow, and (3) demonstrate practical implications of improved streamflow prediction for irrigation water management in the central Himalayan region, Nepal. Our results illustrate that satellite-based precipitation estimates have competitive performance in capturing a wide range of rainfall characteristics, with demonstrated variability across river basins and time scales. There are no significant discrepancies observed in satellite-based precipitation estimates for estimating irrigation water requirements for the three major crops (maize, wheat, and paddy) during the cropping period across the selected river basins, showing a greater promise for irrigation water management planning and decision making.

Extension of a Monolayer Energy-Budget Degree-Day Model to a Multilayer One

This paper presents the extension of the monolayer snow model of a semi-distributed hydrological model (HYDROTEL) to a multilayer model that considers snow to be a combination of ice and air, while accounting for freezing rain. For two stations in Yukon and one station in northern Quebec, Canada, the multilayer model achieves high performances during calibration periods yet similar to the those of the monolayer model, with KGEs of up to 0.9. However, it increases the KGE values by up to 0.2 during the validation periods. The multilayer model provides more accurate estimations of maximum SWE and total spring snowmelt dates. This is due to its increased sensitivity to thermal atmospheric conditions. Although the multilayer model improves the estimation of snow heights overall, it exhibits excessive snow densities during spring snowmelt. Future research should aim to refine the representation of snow densities to enhance the accuracy of the multilayer model. Nevertheless, this model has the potential to improve the simulation of spring snowmelt, addressing a common limitation of the monolayer model.

Evaluation of the Offsets of Artificial Recharge on the Extra Run-Off Induced by Urbanization and Extreme Storms Based on an Enhanced Semi-Distributed Hydrologic Model with an Infiltration Basin Module

Urbanization and climate change exacerbate groundwater overexploitation and urban flooding. The infiltration basin plays a significant role in protecting groundwater resources because it is a prevalent technology of managed aquifer recharge. It could also be utilized as a retention pond to mitigate city waterlogging. The goal of this study was to explore the offsets of artificial recharge on the extra runoff induced by urbanization and extreme storms via infiltration basins. To achieve this objective, a lumped infiltration basin module was developed and integrated into a semi-distributed hydrologic model. Then, the enhanced model was applied to an agriculture watershed with urban areas. Finally, the functionalities of the infiltration basins were evaluated under the scenarios of the predicted urbanization and extreme storms. The results demonstrated the capability of the infiltration basins to influence both artificial recharge and flood mitigation. To mitigate floods, especially peak flows, larger areas are needed for infiltration basins than for artificial recharge purposes only. Based on different demands, the intermittent regulation of infiltration basins according to different hydrologic periods is recommended. The offsets of artificial recharge on the extra surface runoff provide insight into the comprehensive preservation and management of surface water resources and groundwater resources.

Modelling Floodplain Vegetation Response to Climate Change, Using the Soil and Water Assessment Tool (SWAT) Model Simulated LAI, Applying Different GCM’s Future Climate Data and MODIS LAI Data

Scientists widely agree that anthropogenically driven climate change significantly impacts vegetation growth, particularly in floodplain areas, by altering river flow and flood regimes. This impact will accelerate in the future, according to climate change projections. For example, in Australia, climate change has been attributed to a decrease in winter precipitation in the range of 56% to 72.9% and an increase in summer from 11% to 27%, according to different climate scenarios. This research attempts to understand vegetation responses to climate change variability at the floodplain level. Further, this study is an effort to enlighten our understanding of temporal climate change impacts under different climate scenarios. To achieve these aims, a semi-distributed hydrological model was applied at a sub-catchment level to simulate the Leaf Area Index (LAI). The model was simulated against future time series of climate data according to Global Climate Model (GCM) projections. The time series data underwent a non-parametric Mann–Kendall test to detect trends and assess the magnitude of change. To quantify the model’s performance, calibration and validation were conducted against the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI. The calibration and validation results show Nash–Sutcliffe efficiency (NSE) values of 0.85 and 0.78, respectively, suggesting the model’s performance is very good. The modeling results reveal that the rainfall pattern fluctuates under climate projections within the study site, in which vegetation tends to be more vibrant during the warmer seasons. Moreover, the modeling results highlighted increases in the average projected future winter temperatures, which can help vegetation growth during winter. The results of this study may be employed for sustainable floodplain management, restoration, land-use planning, and policymaking, and help floodplain communities better prepare for and respond to changing flood patterns and related challenges under a future changing climate.

Effect of water movement in frozen soil on long-term hydrological simulation of Soil and Water Assessment Tool in cold climate watershed in Hokkaido, Japan

ABSTRACT Despite the unique hydrological properties exhibited by frozen soil, its characteristics are often oversimplified in most distributed hydrological models, resulting in major errors in material cycle predictions in cold climate regions. This study aims to modify the Soil and Water Assessment Tool (SWAT), a semi-distributed hydrological model that originally assumes no water movement in frozen soil layers, by incorporating the dynamic change of soil permeability based on the degree of soil freezing, and to understand the effect of water movement in frozen soil on watershed hydrology. SWAT with frozen soil permeability (SWAT-FSP) was developed by adding two modules to the original SWAT: a heat conduction equation that considers the phase change of ice and liquid water and a frozen-soil percolation model that considers dynamic decrease in soil permeability due to soil freezing. The original SWAT and SWAT-FSP were set up in the Tokoro River watershed in northern Japan. The river flow and soil water dynamics during the snowmelt period (March–May) simulated using the original SWAT and SWAT-FSP were compared. The modified model significantly alters the soil water dynamics simulation to generate earlier soil water drainage and reduces the soil water content during the snowmelt period in years featuring weak soil freezing. Consequently, the SWAT-FSP estimated an earlier increase in river flow at the beginning of the snowmelt period and a smaller river flow in the middle of the snowmelt period as compared with the original SWAT. The modified model improved the overall performance in river flow simulation for the snowmelt period (March–May) of 2012–2019. The Nash-Sutcliffe efficiency obtained during the calibration period by the original SWAT was 0.602, and during the validation period it was 0.367. For the SWAT-FSP, it was 0.659 during calibration, and 0.639 during validation. Meanwhile, the SWAT-FSP showed unsatisfactory performance in 2014 and 2015, with underestimated snow water equivalent, indicating that improvements in the snowmelt/accumulation module may further enhance its accuracy. Our results showed that the dynamic change of soil permeability governed by the degree of soil freezing is an important factor affecting the water cycle of cold climate watersheds with seasonally frozen soil.

EVALUATION OF THE HYDROLOGICAL RESPONSE TO CLIMATE AND LAND USE CHANGE IN A WATERSHED

Climate change and land-use change influence hydrological processes in a basin, thereby affecting water production. The Cañar River basin originates in the Andean moors and flows into the Pacific Ocean, covering areas of great importance for conservation and agriculture. The basin exhibits a high degree of climatic and ecosystem variability. Two scenarios were proposed and studied using a semi-distributed hydrological model: a) a future climate scenario based on the projection of historical precipitation and temperature trends observed from 1985 to 2010 at 9 stations distributed throughout the basin, and b) a future soil use scenario derived from a multi-temporal analysis of land use, explanatory variables, and transition models. The analysis revealed an increase in precipitation during the rainy season and a decrease in precipitation during the dry season, resulting in a slight reduction in annual precipitation. During the study period, an increase in temperatures is observed in the low and high zones, while in the middle zone, there is a slight reduction in temperatures. The most significant land use changes were observed in natural vegetation, which experienced a significant reduction in area. If the observed climatic trends persist, precipitation would be expected to be concentrated mainly in the rainy season, leading to increased water availability during that period and a significant decrease during the dry season.

Future Changes in Hydro-Climatic Extremes across Vietnam: Evidence from a Semi-Distributed Hydrological Model Forced by Downscaled CMIP6 Climate Data

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.

Potential of snow data to improve the consistency and robustness of a semi-distributed hydrological model using the SAFRAN input dataset

This paper compares different calibration strategies for using snow data combined with streamflow records to constrain model optimisation in mountain catchments. In particular, it assesses to what extent the use of snow observations makes it possible to improve the consistency, identifiability and robustness of the calibrated parameters. To answer this question, a semi-distributed snow and ice model was used on top of a rainfall-runoff model using the SAFRAN meteorological reanalysis as input dataset on several catchments in the Alps and Pyrenees. Model calibration and control were based on streamflow observations, remotely-sensed snow-covered areas and in-situ snow water equivalent (SWE) measurements. The snow and rainfall-runoff parameters were calibrated sequentially and simultaneously against objective functions integrating different combinations of runoff, snow cover and SWE criteria. Statistical assessment of model performances in independent evaluation periods showed that sequential calibration of the snow parameters gives too much weight to snow compared to runoff. Instead, incorporating snow data in the simultaneous calibration of the parameters improves snow simulations without impairing runoff performance. This can be achieved by limiting the weight of the snow criteria to 25% in the objective function. Although local SWE measurements were found to be more useful than satellite observations for identifying more consistent parameters, it is advisable to include both in the calibration process through a compromise objective function. However, the improved model consistency was not accompanied by a significant reduction in equifinality and optimisation times. On the other hand, improving internal consistency made it possible to reduce the interdependence between the parameters of the snow model and those of the rainfall-runoff model. This also made it possible to identify the least sensitive snow parameters in order to fix them at general values without impairing model performance while reducing equifinality with a more parsimonious model. Finally, there was no evidence that using snow observations in the calibration process improves model robustness with respect to climate variability.

Modeling the influence of small reservoirs on hydrological drought propagation in space and time

To increase drought preparedness in semi-arid regions across the world many small and medium reservoirs have been built in recent decades. Together these reservoirs form a Dense Reservoir Network (DRN) and its presence generates numerous challenges for water management. Most of the reservoirs that constitute the network are unmonitored and unregistered, posing questions on their cumulative effects on strategic reservoirs and water distribution at watershed scale. Their influence on hydrological drought propagation is thus largely unexplored. The objective of this study is then to assess DRN effects on droughts both in time and space. A modeling-analytical framework is proposed to achieve this goal. A mesoscale semi-distributed hydrological model was utilized to simulate both a network of large strategic reservoirs and a DRN in a large-scale tropical semiarid watershed. To investigate the effects in time and space generated by the network’s presence, the differences between multiple network scenarios were analyzed. Results show that the presence of the DRN accelerates the transition from meteorological to hydrological drought phases by 20% on average and slows down the recharge in strategic reservoirs by 25%, leading to a 12% increase of periods in hydrological drought conditions in a highly strategic basin and 26% without strategic reservoirs. This is because the DRN increases the hydrological dysconnectivity at catchment scale, reducing inflow to strategic reservoirs. In space, the DRN shifts upstream the basin’s water storage capacity by 8%, but when both large and small reservoirs are present the stored volume distribution behavior is not straightforward. The findings confirm the need to consider the effect of small reservoirs when developing and implementing drought management policies and reservoir-management approaches at regional scale.

Construction of a semi-distributed hydrological model considering the combination of saturation-excess and infiltration-excess runoff space under complex substratum

Study regionTypical basin in humid areas in the Huaihe River Study focusAccurate flood forecasting is essential for making timely decisions regarding flood control and disaster reduction. The theory of watershed runoff generation and convergence serves as a crucial foundation for flood forecasting, while the calculation of runoff is necessary to simulate flood discharge. Identifying watershed runoff generation mechanisms has been a challenging task, particularly under complex underlying surface conditions. To improve the accuracy of flood simulation, this study examines the underlying surface information in the watershed, such as particle composition and content, soil bulk density, geological slope, land use, and other spatial attributes, aiming to analyze the mechanisms of runoff generation. In the study of sub-watersheds, various combinations of runoff generation mechanisms are identified to determine the patterns of runoff. Subsequently, a semi-distributed hydrological model is developed, which incorporates both saturation-excess and infiltration-excess runoff, utilizing the information obtained from the underlying surface. The model is validated using rainfall-runoff data from 14 events at the Xiagushan watershed. New hydrological insights for the regionThe analysis of the fundamental physical conditions of the underlying surface of the watershed revealed that 69.70% of the area is prone to saturation-excess runoff, with an additional 30.30% of the area being susceptible to infiltration-excess runoff. The model considers the spatial distribution of runoff patterns by incorporating complex underlying surface information and demonstrates high accuracy in simulating flood events (NSE= 0.87, Epeak = 12.08%, Wpeak = 13.16%, Tpeak = 0.14 h, R2 = 0.90). The model is straightforward, practical, and exhibits promising potential in terms of timeliness and applicability, thus lending itself well to further application in other watersheds, contributing to the scientific foundation of flood warning and forecasting efforts.

Impact of Climate Change on the Water Balance of the Akaki Catchment

Climate change has an impact on water resources. Estimations of the variations in water balance under climate change variables are essential for managing and developing the water resource of a catchment. The current investigation evaluated the magnitude of the change in the water balance component of the Akaki catchment, Ethiopia, using the semi-distributed hydrological model, the Soil and Water Assessment Tool (SWAT), with the integration of the Coordinated Regional Downscaling Experiment of Africa under RCP4.5 and 8.5. The SWAT model was developed using spatial and temporal data; it was calibrated (1991–2001) and validated (2002–2004) using SWAT-CUP. The statistical monthly SWAT model performance values of the NSE, PBIAS (%), and R2 showed good agreement between calibration and validation. On an annual basis, projected rainfall is expected to increase by 14.96%, 4.13%, 8.39%, and 10.39% in the 2040s under RCP4.5 and 8.5 and in the 2060s under RCP4.5 and 8.5, respectively, with inconsistent change on a monthly projections basis for each scenario. The projected monthly and yearly temperatures are expected to increase under different climate change scenarios. Annual evapotranspiration and potential evapotranspiration increased under both RCPs, whereas surface runoff, lateral flow, and water yield declined under the climate scenarios of each RCP. Monthly projected water yield showed a non-uniform change in the first 30 years and in the second years under the RCP4.5 and RCP8.5 scenarios. These results show that the catchment is highly vulnerable to hydrological and agricultural drought due to water availability. These research findings provide valuable evidence on the role of climate change in water balance, which will help decision makers to achieve better water resource management.