Hydrological Response Research Articles

Calibration and validation of DRAINMOD to predict long-term permeable pavement hydrology

The hydrologic performance of permeable pavement varies widely due to underlying soil type, drainage configuration, contributing drainage area, surface infiltration rate, and aggregate depth. A long-term hydrologic model is needed to better understand the influence of these design variables on surface runoff, drainage, exfiltration, and evaporation from permeable pavement systems. Most permeable pavement models have not been calibrated with field monitored data, are usually unable to accurately model internal water storage (IWS) zones, and do not account for evaporation from the aggregate profile. Because permeable pavement employs drainage and exfiltration as primary hydrologic mechanisms, it was hypothesized that DRAINMOD, a model shown to accurately simulate bioretention hydrology, could be calibrated to predict the hydrologic response from permeable pavements. Hydrologic data were collected from two permeable pavement applications in North Carolina (Boone and Durham) and two permeable pavement applications in Ohio (Perkins Township and Willoughby Hills) to calibrate and test the model. The specific permeable pavements applications varied widely in aggregate depth, drainage configuration, underlying soil type, and contributing drainage area. Nash-Sutcliffe Efficiencies for drainage ranged from 0.77 to 0.95 during calibration and validation of all sites. Nash-Sutcliffe Efficiencies for exfiltration/evaporation ranged from 0.55 to 0.97 for Boone and Perkins Township, but prediction of exfiltration/evaporation volumes on an event basis was poor for the remaining two sites due to low exfiltration rates (less than 0.20 mm/hr). Despite this, the cumulative volume of exfiltration/evaporation was predicted to within 1–9 % of what was measured during monitoring. Modeled and monitored surface runoff from the Willoughby Hills application (8 % of the water balance) was equivalent. The model predicted the percentage of drainage, surface runoff, and exfiltration/evaporation to within 2 % of what was monitored/estimated at each site, suggesting long-term modeling of permeable pavement hydrology with DRAINMOD is viable.

The hydrological response of melting ephemeral snowpacks compared to winter rainfall events in a mid-mountainous Pyrenean catchment

AbstractThe hydrological role of ephemeral snowpacks and their differences in stormflow and sediment transport characteristics compared to events triggered by winter rainfall conditions have received limited attention. This study aims to analyze the hydrological and sediment transport responses to rain-on-snow (ROS), melt, mixed, and rainfall events in the Araguás Catchment, situated in a mid-mountain site of the Central Spanish Pyrenees, with a climate strongly influenced by Mediterranean conditions. This catchment represents the transition from a winter ephemeral snow environment to a fully rainfall-dominated site. Results indicate that snowmelt has a modest yet measurable impact on the annual water balance, averaging 10% and rising to 30% during winter (December to February). ROS and melt events consistently exhibited higher mean and maximum discharge and elevated stormflow coefficients compared to mixed and rainfall events. The lowest water infiltration into the soil was observed during melt events, attributed to the potential for frequent freezing soils, specific poor edaphic conditions, and the rapid snowmelt in the area. Consequently, melting events displayed the shortest flood hydrographs among the four analyzed events. The study also underscores precipitation’s almost negligible erodibility capacity in the solid phase and emphasizes the protective role of snow cover in preventing soil erosion. It is important to note that the presented results are significantly influenced by the physiographic, lithological, and edaphic characteristics of the Araguás Catchment. This highlights the importance of conducting more detailed analyses of ephemeral snowpacks in experimental sites under a broader range of environmental conditions for a comprehensive understanding.

Long-term climate and hydrologic regimes shape stream invertebrate community responses to a hurricane disturbance.

Disturbances can produce a spectrum of short- and long-term ecological consequences that depend on complex interactions of the characteristics of the event, antecedent environmental conditions, and the intrinsic properties of resistance and resilience of the affected biological system. We used Hurricane Harvey's impact on coastal rivers of Texas to examine the roles of storm-related changes in hydrology and long-term precipitation regime on the response of stream invertebrate communities to hurricane disturbance. We detected declines in richness, diversity and total abundance following the storm, but responses were strongly tied to direct and indirect effects of long-term aridity and short-term changes in stream hydrology. The amount of rainfall a site received drove both flood duration and flood magnitude across sites, but lower annual rainfall amounts (i.e. aridity) increased flood magnitude and decreased flood duration. Across all sites, flood duration was positively related to the time it took for invertebrate communities to return to a long-term baseline and flood magnitude drove larger invertebrate community responses (i.e. changes in diversity and total abundance). However, invertebrate response per unit flood magnitude was lower in sub-humid sites, potentially because of differences in refuge availability or ecological-evolutionary interactions. Interestingly, sub-humid streams had temporary large peaks in invertebrate total abundance and diversity following recovery period that may be indicative of the larger organic matter pulses expected in these systems because of their comparatively well-developed riparian vegetation. Our findings show that hydrology and long-term precipitation regime predictably affected invertebrate community responses and, thus, our work underscores the important influence of local climate to ecosystem sensitivity to disturbances.

Assessing rainfall-runoff models for climate change: simple and differential split-sample tests for conceptual and artificial intelligence models

ABSTRACT In this study, the performance of two popular conceptual models, a classic artificial intelligence (AI) model, and a deep learning model is compared using simple and differential split-sample tests (SSST and DSST). Finally, the two conceptual models and the classic AI model are combined employing a model ensemble technique. The SSST and DSST schemes confirmed the ability of both types of models (conceptual and AI) to capture the hydrological response to rainfall events, although the conceptual models showed a better performance in the DSST scheme. The ensemble technique enhanced the modelling performance up to 22% and 35% in the calibration and verification steps. This study highlights the importance of applying DSST using existing data to build further confidence in model projections, and suggests that the use of model ensemble techniques combining conceptual and AI models can enhance the performance of rainfall-runoff modelling in the context of climate change.

Investigating meteorological wet and dry transitions in the Dutch Meuse River basin

The Netherlands has traditionally focused on managing flood risk. However, the frequent occurrence of droughts in recent years has brought attention to managing both extremes. Transitions between these opposite extremes pose additional challenges to water management, requiring a trade-off between water storage during dry periods and flood control during wet periods. In this study, we develop a framework to define wet and dry meteorological events and study their transitions using timeseries of meteorological data namely, precipitation, temperature and potential evapotranspiration. The magnitudes of event characteristics are retained, which presents a different approach to the normalized climate indices (like the Standardized Precipitation Index) commonly used in literature. We apply this framework to the Dutch part of the Meuse River basin in northwestern Europe using climate observations between 1951 and 2022. Our analysis shows a statistically significant increase in the amount of water lost from potential evapotranspiration compared to water gained from precipitation between April and September of the water year and an increase in the length of this drying period over the past decades. Such trends in the drying period are related to variability in potential evapotranspiration caused by rising temperatures in the region, indicating the potential for increased water shortage in Spring and Summer due to future temperature increases. We also identify abrupt transitions between opposite extreme events where there is a lack of water at the end of the second event as meteorological situations that challenge water management due to overlapping impacts like flash flooding, less time for water storage, and reduced water availability. We see such conditions occur in 6% of the wet-dry transitions and 20% of the dry-wet transitions, highlighting meteorological scenarios to which the hydrological response of the catchment can be simulated to increase our understanding of the combined risk of floods and droughts.

Characterizing Seasonality and Trend From In Situ Time‐Series Observations Using Explainable Deep Learning for Ground Deformation Forecasting

AbstractGround deformation, a critical indicator of geohazard evolution, exhibits both seasonal fluctuations and long‐term trend changes. This study explores interpretable deformation forecasting using an explainable deep learning approach, utilizing field observation data to extract and characterize these critical features from time series. We demonstrate that extracting and utilizing shared, similar seasonal and trend features across multi‐point observations significantly enhances ground deformation forecasting accuracy. Notably, the extracted seasonal features reveal the dominant role of the hydrological response in deformation at seasonal timescales, providing insights into the underlying source of predictability. This proof‐of‐concept study highlights the promise of interpretable ground deformation forecasting, with applications expected to encompass landslide behavior, volcanism, or seismic activity.

A Detailed Examination of Türkiye's Projected Precipitation and Growth Season Trends under Climate Change Condition

This study presents a comprehensive analysis of Türkiye's changing precipitation patterns and growing season dynamics in the context of global warming, utilizing the Coupled Model Intercomparison Project Phase 5 (CMIP5) and various Representative Concentration Pathway (RCP) scenarios. In light of global warming, this study provides a thorough analysis of Türkiye's evolving precipitation patterns and growing season dynamics using multiple RCP scenarios and CMIP5. The research aims to fill a crucial gap in climate research by combining historical data and future projections to assess Türkiye's precipitation path under different greenhouse gas emission scenarios. The study employs linear regression for trend analysis and uses data from the Climate Change Knowledge Portal (CCKP), with a focus on precipitation data from 1986 to 2100. According to various RCP scenarios, this study’s findings show a considerable variation in precipitation trends over the 21st century. The RCP 8.5 scenario predicts a significant decrease in precipitation, which would present difficulties for the management of water resources and agricultural productivity. In contrast, the least severe RCP 2.6 pathway shows a fairly stable pattern of precipitation. Complex seasonal hydrological responses to climate change are revealed by monthly precipitation analysis; RCP8.5 predicts an increase in the frequency of periods of drought and heavy precipitation events. The impact of these changes in precipitation on Türkiye's agricultural growing seasons was further investigated in this study. In high-emission scenarios, there was an initial tendency towards longer growing seasons, which were subsequently followed by shorter ones. This suggests that although global warming might initially result in an extended growing season, it might ultimately cause a reduction in it, particularly in situations where mitigation efforts are minimal. The need for adaptable strategies that can respond to long-term climate trends as well as seasonal variability was highlighted by this research. It draws attention to the fact that to mitigate the effects of climate variability, informed policy decisions and integrated resource management are essential. The results highlight the need for quick action to lower the risks associated with water and highlight the potential advantages of intensive mitigation efforts in stabilizing and extending growing seasons.

Morphology characterization of unsaturated soils under drying-wetting cycles: crack opening and closure

The volumetric and hydrological responses of clayey soils subjected to drying-wetting (D-W) cycles are of paramount importance for the integrity of geoenvironmental infrastructures. The study aimed to investigate the cracking behavior of clayey soils under D-W cycles by using advanced 2D imaging and 3D scanning techniques to capture the initiation and propagation of desiccation cracks within a soil specimen. The temporal variation in the soil water content and the corresponding 2D digital photography and 3D morphology of cracks were simultaneously monitored, and the cracking characteristics were interpreted. It was found that the time-dependent evaporation process was independent of the D-W cycles. Both 2D and 3D characterization showed the cracking hysteresis phenomenon in the unsaturated soil, which indicates the dependency of the crack opening and closure on the degree of saturation. D-W cycles led to the formation of subcracks and the increase in the total crack length, reflecting the soil degradation. Additionally, it was demonstrated that the 3D characterization exhibited the advantage of capturing the volumetric change and the subtle change in the macroporosity of the cracked soil over the 2D visualization. The current study provides a perspective of combining 2D and 3D characterization for interpreting the volumetric change of cracked soils and enhancing the understanding of the hydromechanical responses and the soil-atmosphere interactions.

Evaluation of hydrological responses to climate change in the Gibe Gojeb catchment, southwestern Ethiopia

ABSTRACT The expected influences of climate alteration on hydrological responses in the Gibe Gojeb catchment, southwestern Ethiopia, were evaluated using the Soil and Water Assessment Tool (SWAT) model and regional climate models (RCMs). This study emphasizes the evaluation of the climate change impact on hydrological responses. The RCM data were downloaded from CORDEX-Africa. Power transformation and delta change methods were used for bias correction of precipitation and temperature, respectively. The calibration and validation of the SWAT model showed good agreement, and climate change impact was simulated. Accordingly, precipitation, soil water content, percolation, water yield, and groundwater are projected to decrease by 28.18, 7.5, 9.1, 5.4, and 9.2%, respectively, under RCP4.5 in the future (2021–2050). Besides, surface runoff and potential and actual evapotranspiration are projected to increase by 17.4, 11.32, and 5.06%, respectively. Soil water content, percolation, water yield, and precipitation are projected to decrease by 10.51, 5.63, 2.76, and 21.57%, respectively, under RCP8.5 in the future. Rain-fed crop production and hydropower generation in the catchment would be vulnerable to the effects of climate change. The results help to develop effective adaptation measures to reduce the impacts of climate change and draft long-term water resource management plans in the Gibe Gojeb catchment.

The Effects of Climate Change on Streamflow, Nitrogen Loads, and Crop Yields in the Gordes Dam Basin, Turkey

The Mediterranean region is highly vulnerable to climate change. Longer and more intense heatwaves and droughts are expected. The Gordes Dam in Turkey provides drinking water for Izmir city and irrigation water for a wide range of crops grown in the basin. Using the Soil and Water Assessment Tool (SWAT), this study examined the effects of projected climate change (RCP 4.5 and RCP 8.5) on the simulated streamflow, nitrogen loads, and crop yields in the basin for the period of 2031–2060. A hierarchical approach to define the hydrological response units (HRUs) of SWAT and the Fast Automatic Calibration Tool (FACT) were used to reduce computational time and improve model performance. The simulations showed that the average annual discharge into the reservoir is projected to increase by between 0.7 m3/s and 4 m3/s under RCP 4.5 and RCP 8.5 climate change scenarios. The steep slopes and changes in precipitation in the study area may lead to higher simulated streamflow. In addition, the rising temperatures predicted in the projections could lead to earlier spring snowmelt. This could also lead to increased streamflow. Projected nitrogen loads increased by between 8.8 and 25.1 t/year. The results for agricultural production were more variable. While the yields of poppy, tobacco, winter barley, and winter wheat will increase to some extent because of climate change, the yields of maize, cucumbers, and potatoes are all predicted to be negatively affected. Non-continuous and limited data on water quality and crop yields lead to uncertainties, so that the accuracy of the model is affected by these limitations and inconsistencies. However, the results of this study provide a basis for developing sustainable water and land management practices at the catchment scale in response to climate change. The changes in water quality and quantity and the ecological balance resulting from changes in land use and management patterns for economic benefit could not be fully demonstrated in this study. To explore the most appropriate management strategies for sustainable crop production, the SWAT model developed in this study should be further used in a multi-criteria land use optimization analysis that considers not only crop yields but also water quantity and quality targets.

Magnesium Isotopes of Carbonate Reveal Seasonal Climate Variation in the Central East Asia During the Middle Eocene

AbstractIt is debated whether there was strong climate seasonality during the Eocene, which provides a close geological analogy for near‐future scenarios of greenhouse gas emissions. Lithological data suggest the existence of a broad arid zone centered around 30°N paleo‐latitude, while a humid climate was supported by palaeobotanic assemblages in East Asia. Here, we report the occurrence of massive primary lacustrine dolomite and magnesite in the central East Asia during the middle Eocene. We provide a novel perspective from magnesium isotopes to link the formation of Mg‐carbonates to seasonal dry‐wet cycles. Rapid magnesium input during the rainy season and intense evaporation in the dry season likely caused the formation of magnesium carbonates in an enclosed lake. These findings provide insights into hydroclimatic seasonality during the Eocene, contributing to our understanding of the hydrological cycle response to a greenhouse climate.

Developing a flood monitoring system by utilizing real-time satellite rainfall estimates and water energy budget-based rainfall-runoff inundation model in West Africa

Abstract. The African regions have been affected by frequent flood-related disasters. To support decision-makers in flood disaster management, a real-time flood monitoring system was established in the Niger and Volta basins under the Water disaster platform to enhance climate resilience in Africa (WADiRE-Africa). To bridge a gap between data unavailability and inaccessibility, the near-real-time satellite rainfall data (GSMaP-NRT) was evaluated and found to have a very low correlation (<0.4) with gauge data at daily scales and a higher correlation (>0.7) at monthly scales. The statistically bias-corrected GSMaP-NRT data was used in the Water and Energy Budget-based Rainfall-Runoff-Inundation (WEB-RRI) model to generate hydrological responses of the basins. As the simulated river discharges were overestimated, an additional correction method was employed for the GSMaP-NRT data to match well with observed discharges. The prototype system provides real-time flood information in both basins.

Rainfall-runoff Modelling Using MIKE11 NAM Model for Ravishankar Sagar Catchment, Chhattisgarh, India

Estimating rainfall-runoff within a catchment is inherently intricate and crucial for water resource planning via hydrological evaluations. The study focuses on utilizing the MIKE 11 NAM model to simulate rainfall-runoff dynamics within the Ravishankar Sagar Reservoir catchment in the Chhattisgarh state. In order to ensure accurate estimation, data on stream flows from 2004 to 2015 was used for calibration, and from 2016 to 2020 was used for validation. The MIKE 11 NAM model accurately predicted daily runoff and adequately reproduced the hydrological response of the Ravishankar Sagar watershed to rainfall. The calibrated model outputs were good to employ in the water resources management model, specifically for MIKE BASIN. During calibration, the optimal values of the nine NAM model parameters were determined and subsequently employed in the simulation. The reliability of the MIKE 11 NAM model was assessed for the study area using the Coefficient of Determination (R2) and the Nash–Sutcliffe Efficiency Index (EI). The sensitivity analysis helped to determine the most important model parameters. R2 values of 0.730 and 0.704 were obtained from the model's calibration and validation, respectively. With an Efficiency Index of 81%, the model demonstrated its efficiency and ability to forecast runoff for Ravishankar Sagar Reservoir over an extended period. This study helps to manage the water resource and to improve the reservoir operation policy for the reservoir.

Insights to the water balance of a Boreal watershed using a SWAT model

The hydrological characteristics of a watershed play a crucial role in shaping ecosystems within the Boreal zone and have a significant impact on regional environments. Knowing these characteristics, such as the distinctive topography, vegetation, soil composition, and climatic conditions in the Canadian Boreal ecozone, is essential for implementing sustainable water management. This study focuses on assessing the hydrological dynamics of the Upper Humber River Watershed (UHRW) in western Newfoundland, Canada, using the Soil and Water Assessment Tool (SWAT) model. The UHRW includes sub-basins and hydrological response units (HRUs), with diverse land uses, soil types, and slope characteristics. Key parameters influencing streamflow simulation were identified through sensitivity analysis, including the runoff curve number, the effective hydraulic conductivity, the temperature lapse rate, the soil evaporation compensation factor, and the available water capacity of the soil layer. The SWAT model, using data from the Reidville hydrometric station, shows favorable performance metrics, with R2 values of 0.79 and 0.83 during the calibration and evaluation periods, respectively. The model effectively captures seasonal and monthly flow patterns, displaying right-skewed distributions and seasonal variations. The analyzed hydrological parameters, such as precipitation, evaporation, transpiration, surface runoff, and groundwater flow, reveal their significant contributions to the water balance. The flow duration curve analysis indicates the model’s capability to estimate peak and low flows, with slight under-prediction during the recession phase. Seasonal analysis further supports the model’s performance, with positive Nash-Sutcliffe Efficiency (NSE) values ranging from 0.65 to 0.91. The study concludes that the SWAT model is suitable for simulating the hydrological processes in the studied watershed providing valuable insights for sustainable water resource management and decision-making in the UHRW. The results can be useful for other Boreal ecozone watersheds.

Forest Disturbance Thresholds and Cumulative Hydrological Impacts

AbstractForest disturbance threshold is defined as a critical disturbance level (e.g., % of forest cover change) in forested landscapes above which significant hydrological impacts are detected. Determining disturbance thresholds is critically important for supporting forest management to ensure the sustaining of ecological and hydrological functions. However, there are very few quantitative evaluations of forest disturbance thresholds globally. In this study, we applied a well‐tested methodology (the modified double mass curve) to derive the long‐term, continuous hydrological response curves and then to quantify forest disturbance thresholds on annual streamflow in 42 forested watersheds in British Columbia, Canada. The results show that forest disturbance thresholds for significant and cumulative hydrological impacts vary from 7% to 52% of cumulative equivalent clear‐cut area with an average of 17% or from 8% to 52% of disturbed area with an average of 19%. Climate (inter‐annual and intra‐annual) and watershed properties exert critical controls on forest disturbance thresholds. Watersheds with greater snowfall proportions (more annual precipitation falling as snow), more desynchronizations (temporal mismatching) of energy demand and water supply at the intra‐annual scale, less diverse ecosystems, and larger watershed sizes have lower forest disturbance thresholds. Given the present forest disturbance levels in the central interior of British Columbia, about half (53%) of the forested watersheds have already crossed the average disturbance threshold. These results highlight that watershed planning and management using forest disturbance thresholds must carefully consider local climate and watershed properties. The methodology can be effectively and robustly extended elsewhere around the globe.

Spatiotemporal estimation of groundwater and surface water conditions by integrating deep learning and physics-based watershed models

The impacts of climate change on hydrology underscore the urgency of understanding watershed hydrological patterns for sustainable water resource management. The conventional physics-based fully distributed hydrological models are limited due to computational demands, particularly in the case of large-scale watersheds. Deep learning (DL) offers a promising solution for handling large datasets and extracting intricate data relationships. Here, we propose a DL modeling framework, incorporating convolutional neural networks (CNNs) to efficiently replicate physics-based model outputs at high spatial resolution. The goal was to estimate groundwater head and surface water depth in the Sabgyo Stream Watershed, South Korea. The model datasets consisted of input variables, including elevation, land cover, soil type, evapotranspiration, rainfall, and initial hydrological conditions. The initial conditions and target data were obtained from the fully distributed hydrological model HydroGeoSphere (HGS), whereas the other inputs were actual measurements in the field. By optimizing the training sample size, input design, CNN structure, and hyperparameters, we found that CNNs with residual architectures (ResNets) yielded superior performance. The optimal DL model reduces computation time by 45 times compared to the HGS model for monthly hydrological estimations over five years (RMSE 2.35 and 0.29 m for groundwater and surface water, respectively). In addition, our DL framework explored the predictive capabilities of hydrological responses to future climate scenarios. Although the proposed model is cost-effective for hydrological simulations, further enhancements are needed to improve the accuracy of long-term predictions. Ultimately, the proposed DL framework has the potential to facilitate decision-making, particularly in large-scale and complex watersheds.

Holocene Temperature and Water Stress in the Peruvian Andes: Insights From Lake Carbonate Clumped and Triple Oxygen Isotopes

AbstractGlobal climate during the Holocene was relatively stable compared to the late Pleistocene. However, evidence from lacustrine records in South America suggests that tropical latitudes experienced significant water balance variability during the Holocene, rather than quiescence. For example, a tight coupling between insolation and carbonate δ18O records from central Andean lakes (e.g., Lakes Junín, Pumacocha) suggest water balance is tied directly to South American summer monsoon (SASM) strength. However, lake carbonate δ18O records also incorporate information about temperature and evaporation. To overcome this ambiguity, clumped and triple oxygen isotope records can provide independent constraints on temperature and evaporation. Here, we use clumped and triple oxygen isotopes to develop Holocene temperature and evaporation records from three central Andean lakes, Lakes Junín, Pumacocha, and Mehcocha, to build a more complete picture of regional water balance (P–E). We find that Holocene water temperatures at all three lakes were stable and slightly warmer than during the latest Pleistocene. These results are consistent with global data assimilations and records from the foothills and Amazon basin. In contrast, evaporation was highly variable and tracks SASM intensity. The hydrologic response of each lake to SASM depends greatly on the physical characteristics of the lake basin, but they all record peak evaporation in the early to mid‐Holocene (11,700 to 4,200 years BP) when regional insolation was relatively low and the SASM was weak. These results corroborate other central Andean records and suggest synchronous, widespread water stress tracks insolation‐paced variability in SASM strength.

Extreme hydrometeorological events induce abrupt and widespread freshwater temperature changes across the Pacific Northwest of North America

AbstractThe Pacific Northwest of North America experienced four extreme hydrometeorological events during 2021 including intense cold waves in mid-February and late December, the record-setting June heat dome, and catastrophic floods caused by two November atmospheric rivers. While the synoptic-scale patterns and terrestrial hydrological responses to these extreme events are well documented, scant information has been published on corresponding freshwater temperature responses. Here, we apply an observational database of hourly freshwater temperatures at 554 sites across the region to characterize their evolution during these four extreme hydrometeorological events. The two cold snaps and summer heat dome induced a general 1 °C decline and 2.7 °C increase, respectively, in water temperatures with subdued changes (+0.4 °C) during the mid-November floods. For 193 sites with long-term records, 478 daily maximum water temperatures were exceeded during the heat dome and 94 were surpassed during the flooding event, suggesting deleterious effects for water quality and aquatic species.

Hydropedology of South African soil forms and families

Hydropedology is an interdisciplinary field that studies the interactions between soil and water, recognizing that soils influence hydrological processes through their hydraulic properties, and serve as indicators of hydrological behaviour through their morphological properties that are shaped by water regimes. Given the practical implications of hydropedology and its integration into South Africa's latest soil classification system, an updated categorization of soil forms and 1 657 (1 629 + 28) families was necessary, organizing them into three overarching response groups based on their predominant hydrological responses: recharge, interflow, and responsive. Within these groups, recharge soils are further classified into deep, shallow, and slow subgroups, interflow soils encompass soil/bedrock, shallow, and slow categories, while responsive soils are subdivided into responsive shallow and responsive wet. This paper aims to enhance the reader's comprehension of hydrological responses and simplify the intricacies integrated into South Africa's official soil classification system.

Assessing the Spatiotemporal Variations in the Hydrological Response of the Qin River Basin in Loess Plateau, China

In recent decades, the hydrological processes on the Loess Plateau have undergone significant changes due to the combined effects of climate change and human activities. This study investigates the Qin River basin, the core area of the Loess Plateau. Between 1971 and 2000, the hydrometeorology of the flood season in the Qin basin on the Loess Plateau in China changed abruptly in 1982 and 1992. This study uses distributed hydrologic modeling and statistical analysis to distinguish the spatial and temporal impacts of climate change and human activities. The fastest dynamic changes in land use occurred in grassland and construction land, accounting for 0.36% and −0.1%, respectively, from 1982 to 1991. From 1992 to 2000, it was 0.29% and 0.3%, respectively. The increase in basin area where the Normalized Difference Vegetation Index increased was 95%. However, except for significant changes in runoff from 1981 to 1992, there were no significant changes in precipitation, runoff, and temperature during other flood seasons. During the two studied variation periods, from 1982 to 1991 and from 1992 to 2000, the contribution rate of climate change to flood season runoff (CClimate) decreased from 83.26% to 74.47%, while the contribution rate of human activity disturbance to flood season runoff (Chuman) increased from 16.74% to 25.53%. In terms of spatial distribution, from 1982 to 1991, areas with a Chuman > 50% were primarily concentrated in the upper reaches of the basin, while from 1992–2000, these areas shifted to the lower reaches. This study offers valuable insights for water resource planning and soil conservation measures in the Loess Plateau and similar arid regions worldwide, aiming to ensure water resource safety and sustainability in response to climate change and human interference.