Streamflow Response Research Articles

The directional unit hydrograph model: Connecting streamflow response to storm dynamics

Storm velocity (i.e., direction and speed) and structure (i.e., shape and intensity) play a critical role in streamflow response. These characteristics determine the timing and magnitude of precipitation fluxes across the watershed that drive runoff generation and conveyance along the river network. While previous efforts have used spatially explicit hydrologic models to assess the role of storm properties in streamflow magnitude, their computational demand significantly limits the range of scenarios that can be explored, hindering our ability to systematically identify critical conditions leading to extreme events. To address this technical gap, we introduce the Directional Unit Hydrograph (Directional-UH) model, a parsimonious approach based on the classic theory of the Unit Hydrograph. The Directional-UH extends the original theory by relaxing the assumption of spatial uniform rainfall and incorporating storm direction and speed into the unit hydrograph function. The model conceptualizes storms as rectangular structures with constant intensity moving along a linear trajectory with constant speed. We verify and validate our conceptualization by comparing with simulations, based on observations of extreme rainfall events, of the distributed hydrological model Hillslope-Link-Model (HLM) in the Turkey River basin in Iowa, USA. Then, the Turkey River basin is used as a testbed to illustrate three practical applications of the Directional-UH model. First, we identify the storm trajectory that produces the highest peak flow response. Second, we determine the storm characteristics that maximize the peak flow response by synchronizing storm motion and flood wave; we refer to this as the resonance condition. Third, we systematically explore the compounding effects of consecutive storm events with different trajectories to identify critical combinations that exacerbate the peak flow magnitude. The results on our testbed demonstrate that storm velocity has the potential to increase by a factor of two the peak flow magnitude when compared to stationary storm events. Overall, the parsimonious nature of the Directional-UH model offers a unique and valuable tool for modeling, predicting, and interpreting rainfall-runoff dynamics through the lens of storm direction, speed, and structure.

Modeling streamflow response under changing environment using a modified SWAT model with enhanced representation of CO2 effects

Study regionHaihe River Basin (HRB) in North China Plain. Study focusChanges in streamflow due to climate change and human activities are highly uncertain. An improved Soil and Water Assessment Tool (SWAT) model equipped with a dynamic CO2 input method was used to quantify the environmental change impacts on streamflow in the HRB. Streamflow changes were analyzed based on 22 bias-corrected CMIP6 GCMs under SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5 during two 30-year periods of the middle (2041–2070) and end (2071–2100) of the 21st century relative to the historical period (1971–2000). New hydrological insights for the regionLong-term simulations of annual streamflow in the HRB demonstrated substantial discrepancies between the dynamic and constant CO2 input methods under the SSP1-2.6 and SSP5-8.5 scenarios, which highlighted the importance of using the dynamic CO2 input method for streamflow simulations. Spatio-temporal analysis using the improved SWAT model revealed that streamflow generally increased under four emission scenarios compared to the historical period. In the HRB, annual streamflow in the downstream plains was higher than in the upstream mountains, and this was more evident under the highest emission scenario of SSP5-8.5. Monthly streamflow showed considerable intra-annual variability that was generally greater from July to November. The strongest increment in streamflow was projected in the SSP3-7.0 and SSP5-8.5 scenarios, indicating that flood risk would possibly increase in the late 21st century.

A comparison of annual streamflow sensitivities to vegetation change and climate variability in fourteen large watersheds along climate zones in China

Extensive studies have revealed the impacts of climate variability and vegetation change on annual streamflow. However, the response intensity of annual streamflow varies among watersheds owing to differences in climate and watershed properties, which have rarely been assessed, especially for streamflow sensitivity to forest change. This calls for studies to evaluate annual streamflow sensitivity, defined as the annual streamflow response intensity (%) per unit of external change (%), to better interpret variations in annual streamflow responses among watersheds as well as their associated driving mechanisms. In this study, we estimated and compared annual streamflow sensitivities to vegetation change (Sv) and climate variability (Sc) by applying a five-step framework in 14 large watersheds in China and disentangled the contributions of their associated key drivers. We found that Sv was significantly greater than Sc, suggesting that annual streamflow was more sensitive to vegetation change than to climate variability in 11 watersheds. Alpine watersheds have the lowest Sv because of water plays a minor supportive role in vegetation growth under low effective precipitation and leaf area index, whereas higher topsoil salinity, which largely promotes water movement, contributes to a high Sv in temperate watersheds. Temperate watersheds also have a large Sc because their climate is dry and the resilience of their topography to climate variability is low. The relative contributions of the associated key drivers showed that Sv was determined by topography (36.35%), soil (33.68%), landscape patterns (12.22%), vegetation background (9.26%), and climatic conditions (8.49%). In contrast, Sc was mostly controlled by topography (54.39%) and climatic conditions (33.08%), followed by vegetation background (12.53%). This study highlights that the management paradigm for maintaining sustainable water resources should focus on both climate and vegetation changes considering the differences in watershed properties in a changing environment.

Nonlinear control of climate, hydrology, and topography on streamflow response through the use of interpretable machine learning across the contiguous United States

Abstract Runoff has been greatly affected by climate change and human activities. Studying nonlinear controls on runoff response is of great significance for water resource management decision-making and ecological protection. However, there is limited understanding of what physical mechanisms dominate the runoff response and of their predictability over space. This study analyzed the spatial patterns of runoff response including runoff changes and its sensitivity to climate–landscape variations in 1,003 catchments of the contiguous United States (CONUS). Then, an interpretable machine learning method was used to investigate the nonlinear relationship between watershed attributes and runoff response, which enables the importance of influencing factors. Finally, the random forest model was employed to predict runoff response according to the predictors of catchment attributes. The results show that alteration of runoff is up to 56%/10 years due to climate change and human activities. Catchment attributes substantially altered runoff over CONUS (−60% to 56%/10 years). Climate, topography, and hydrology are the top three key factors which nonlinearly control runoff response patterns which cannot be captured by the linear correlation method. The random forest can predict runoff response well with the highest R2 of 0.96 over CONUS.

Estimation of Streamflow Depletion Caused by Groundwater Withdrawal in the Bokhacheon Watershed in South Korea Using the Modified SWAT Model

Understanding the effects of groundwater withdrawal on streamflow depletion is important for effectively managing water resources. The Soil and Water Assessment Tool (SWAT) model has a groundwater module to calculate the groundwater budget and groundwater discharge. However, the water pumped from the aquifer is not considered in the SWAT module that estimates groundwater discharge. Therefore, this module was modified to consider the impact of groundwater pumping on the changes in groundwater discharge in the Bokhacheon watershed, South Korea. The model’s water transfer module was improved to allow water from the aquifer to be transferred to destination locations, such as residential, industrial, and agricultural lands. Using the modified SWAT, streamflow responses to groundwater extraction were simulated for 2011–2019. The groundwater withdrawal induced decreases of 14.6 and 24.2% in low and drought flows, respectively, at the watershed’s outlet. The groundwater withdrawals decreased groundwater flow and total water yield by 23.5% and 9.8%, respectively, and increased surface flow, lateral flow, percolation, soil water, and evapotranspiration owing to the increased soil moisture resulting from the partial re-infiltration of the groundwater pumped for agricultural irrigation. The modified SWAT can effectively estimate streamflow depletion resulting from groundwater pumping without extensive hydrogeological input data and computational time.

Streamflow Response to Climate and Land-Use Changes in a Tropical Island Basin

The effects of climate change and of land use/cover change (LUCC) on streamflow as demonstrated by hydrological models are pressing issues on the frontiers of global environmental change research. The Nandu River Basin (NRB) as the largest of three river basins on the tropical Hainan Island, China, is subjected to an analysis of streamflow response to climate and to land-use change. It is based on the Soil and Water Assessment Tool (SWAT) coupled with climate change signals extracted from the global climate model data in the Coupled Model Intercomparison Project Phase 6 (CMIP6) and with land-use change scenarios modeled by Cellular Automata (CA)—Markov. The results are summarized as follows: (1) Climate change contributed more to streamflow change than land-use change in the NRB, with contributions of 97.57% and 2.43%, respectively. Precipitation and temperature were the most important climate variables, contributing 92.66% and 4.91% to streamflow change. (2) In the tropical island basin from 1990 to 2015, LUCC regulated the hydrological processes in the NRB and affected hydrological processes by increasing evapotranspiration and decreasing surface runoff and subsurface flow, which resulted in decreasing streamflow. (3) Under the climate change and land-use change scenarios of the near-term period (2021–2040), the annual streamflow decreased as during the reference period (1995–2014); particularly, it decreased most (−6.16%) on the SSP126 path. These results present a case study for understanding the hydrological cycle of tropical island basins and to provide a theoretical basis for water resources management and regional sustainable development of tropical islands.

Evaluation of the Streamflow Response to Agricultural Land Expansion in the Thiba River Watershed in Kenya

The increasing global population necessitates increased agricultural production, driving the expansion of agricultural lands and advancement of irrigation farming to supplement the inconsistent and insufficient rainfall patterns prevalent in many regions. This study aimed to evaluate the potential impacts of the expansion of agricultural lands on the streamflow regime of the Thiba River and its impact on the downstream users. The study involved comparing the 2004 and 2014 land uses and using the Hydrologic Engineering Centre’s Hydrologic Modelling Systems (HEC-GeoHMS and HEC-HMS) for long-term impact simulations. The results showed a considerable decline in the streamflow in the dry months compared to the wet months, with increasing water abstraction trends from 2007 to 2014. The long-term impact assessment showed an average decline in streamflow in the near (2030) and far (2060) future due to land use and population changes with minimal impact from the increasing precipitation. Based on these findings, there is a need for proper water management and adaptation mechanisms to be put in place to maintain the future water supply from the Thiba River. This study’s findings could assist policy and decision-makers in making informed water resource management decisions.

Identifying the impacts of land use landscape pattern and climate changes on streamflow from past to future

Identifying the individual and combined hydrological response of land use landscape pattern and climate changes is key to effectively managing the ecohydrological balance of regions. However, their nonlinearity, effect size, and multiple causalities limit causal investigations. Therefore, this study aimed to establish a comprehensive methodological framework to quantify changes in the landscape pattern and climate, evaluate trends in streamflow response, and analyze the attribution of streamflow events in five basins in Beijing from the past to the future. Future climate projections were based on three general circulation models (GCMs) under two shared socioeconomic pathways (SSPs). Additionally, the landscape pattern in 2035 under a natural development scenario was simulated by the patch-generating land use simulation (PLUS). The Soil and Water Assessment Tool (SWAT) was applied to evaluate the streamflow spatial and temporal dynamics over the period 2005–2035 with multiple scenarios. A bootstrapping nonlinear regression analysis and boosted regression tree (BRT) model were used to analyze the individual and combined attribution of landscape pattern and climate changes on streamflow, respectively. The results indicated that in the future, the overall streamflow in the Beijing basin would decrease, with a slightly reduced peak streamflow in most basins in the summer and a significant increase in the autumn and winter. The nonlinear quadratic regression more effectively explained the impact of landscape pattern and climate changes on streamflow. The trends in the streamflow change depended on where the relationship curve was in relation to the threshold. In addition, the impacts of landscape pattern and climate changes on streamflow were not isolated but were joint. They presented a nonlinear, non-uniform, and coupled relationship. Except for the YongDing River Basin, the annual streamflow change was influenced more by the landscape pattern. The dominant factors and the critical pair interactions varied from basin to basin. Our findings have implications for city planners and managers for optimizing ecohydrological functions and promoting sustainable development.

Rainfall‐runoff responses and hillslope moisture thresholds for an upland tropical catchment in Eastern Madagascar subject to long‐term slash‐and‐burn practices

AbstractSlash‐and‐burn agriculture is an important driver of tropical forest loss and typically results in a mosaic of land uses. However, there is little quantitative information about the hydrological effects of long‐term slash‐and‐burn agriculture and how such mosaics affect the rainfall‐runoff response at the catchment scale. We monitored streamflow responses at two points along a perennial stream in a 31.7 ha catchment in eastern Madagascar that was monitored previously between 1963 and 1972. Land cover in 2015 consisted of degraded grasslands, shrub and tree fallows at various stages of regrowth (64%), eucalypt stands for charcoal production (30%), and rice paddies and wetlands in the valley‐bottom (3%). For the majority (60%) of the events during the study period, the ratio between the total amount of stormflow and rainfall was <3%, suggesting that for these events runoff was generated in the valley‐bottom only. Events for which an antecedent soil moisture storage plus rainfall (ASI + P) threshold was exceeded had much higher stormflow ratios (up to 50%), indicating that a certain wetness was required for the hillslopes to contribute to stormflow. Stable isotope sampling for four small to moderate events indicated that stormflow was dominated by pre‐event water. Total stormflow and annual water yield in 2015 were higher than in the 1960s, despite much lower rainfall in 2015. We attribute these differences to changes in soil physical properties caused by the repeated burning and loss of top‐soil, which has resulted in a reduction in the depth to the impeding layer. The changed runoff‐processes (less infiltration, more saturation‐excess overland flow) thus affect local water resources.

A climate stress testing method for changes in spatially variable rainfall

With emerging evidence that anthropogenic climate change is affecting the spatial pattern of rainfall, it is important to understand how such changes affect a range of climate-sensitive systems. A method for generating multi-site rainfall sequences is presented that enables the exploration of changes in spatial rainfall patterns separately from various other potential changes (e.g. to the averages, extremes, seasonality), thereby enabling the separation of key drivers of change as part of climate stress tests. The proposed method is based on the ‘inverse approach’ to stochastic weather generation, and is evaluated through a case study with rainfall at 12 sites in the Barossa Valley, South Australia, using a multi-site latent variable stochastic rainfall model. The new method accurately captures (1) observed marginal attributes at each site (i.e. annual totals, seasonality, extreme rainfall, number of dry days, and intermittency), (2) spatial correlation between sites, and (3) observed attributes for areal average rainfall. We then apply the new method to stress test the response of streamflow in the region to changes in spatial rainfall patterns, quantifying how reductions in spatial correlation (while holding all other attributes constant) will lead to reduced mean annual flow and extreme flows, and increased number of low-flow days. The results show that not only can a broad range of rainfall properties (including both marginal and spatial attributes) be simulated using the inverse approach, but also that changes to the spatial properties of rainfall can have large implications on water resources systems, with the potential to amplify or dampen other changes to rainfall attributes.

Spatiotemporal differences in dominant drivers of streamflow evolution on the Loess Plateau

Previous studies have found that climate change and underlying surface change are the most direct and important drivers of the streamflow change in the Yellow River Basin. However, there are significant spatial differences in climate and underlying surface in the Yellow River Basin, especially in the Loess Plateau, attribution analysis of runoff evolution in the Loess Plateau from the perspective of spatial heterogeneity was rarely reported. Therefore, in order to further reveal the causes of runoff evolution in the Loess Plateau, this study have quantitatively revealed the spatial differences of climate and underlying surface changes driving streamflow changes in 34 main Loess Plateau basins from 1956 to 2020 basing on the Budyko equation with fixed parameters. Results show that the streamflow on the Loess Plateau decreased significantly from 1956 to 2020, and streamflow decreased by 17.19 mm (26.95%) after abrupt change in 1990. The sensitivity coefficients of streamflow change on the Loess Plateau to precipitation, potential evapotranspiration and underlying surface parameters are 0.30, −0.08 and −69.26 respectively. The contributions of precipitation, potential evapotranspiration and underlying surface change to streamflow change were 26.24%, 2.15% and 73.01%, respectively, and showed significant spatiotemporal differences. The spatial variability of streamflow is mainly controlled by underlying surface. For 29 of the whole 34 tributaries, underlying surface change was the dominant driver of streamflow change with the contribution of 52.50%–98.22%. The temporal variability of streamflow on the Loess Plateau is mainly controlled by precipitation. The wetter the climate is, the more sensitive of the streamflow changes are to climate change. And the smaller the underlying surface parameters are, the more sensitive the streamflow changes are to the underlying surface change. The results further deepen our understanding for the response of streamflow to climate and underlying surface changes in the Loess Plateau and can provide theoretical support for systematic regulation of basin water resources in the future.

Modeling wildfire effects on streamflow in the Cascade Mountains, Oregon, USA

Increasing occurrence of large and severe wildfires represents a growing threat to forested watersheds and the many ecosystem services they provide. Past research has shown that wildfires can cause substantial increases in peak flows and annual water yields, leading to potential water quality concerns and land and water management challenges. However, responses have been variable, and there have been few studies at large basin scales, leading to uncertainties about post-fire hydrologic responses. To address these uncertainties, we projected the effect of three large wildfires (>70,000 ha) on streamflow in two important forested watersheds in the Cascade Range of Oregon, US. We modeled the streamflow response using the Soil and Water Assessment Tool (SWAT) model, calibrated on data from prior to the wildfires. We modified model parameters to represent the impacts of the wildfires based on burn severity maps. Burned and unburned scenarios were compared using random forest models to identify drivers of increased annual water yields and peak flows. Post-fire annual water yield changes were controlled by burn severity, annual precipitation, area burned, aridity, and vegetation type, while post-fire peak flow changes were controlled by burn severity, area burned, aridity, soil type, and geologic province. We also found that post-fire increases in annual water yields, peak flows, and low flows were greatest at the headwater scale but were more muted at the downstream basin scale. Our work provides valuable insights into the range of potential post-fire streamflow changes at the headwater and larger basin scale, which is becoming increasingly critical for effective forest and water management decisions.

Multi-objective operation-decision-making-risk propagation analysis for cascade reservoirs affected by future streamflow process variations

Significant climate variations have decreased the stability of water resource systems, leading to multiple uncertainties in streamflow response, reservoir operation optimization, decision-making, and adaptive adjustments for water resource scheduling. Understanding the impact of climate change on reginal streamflow is necessary and crucial to identifying reservoir operation strategies and decision-making responses. In this study, we created an integrated systematic “uncertain streamflow responses”– “reservoir operation”– “optimization”– “decision-making risk analysis” chain. Three bias-corrected and downscaled general circulation models (GCMs) were used to analyze the inter-model uncertainties under three representative concentration pathways (RCPs). The streamflow responses and uncertainty in the future were determined using a distributed hydrological model and the fuzzy extension principle under predefined scenarios and uncertainty levels. Then, a stochastic simulation model and modified stochastic multi-criteria decision-making model were applied to identify the effects of climate change projections and streamflow responses on reservoir multi-objective operation and decision-making. Moreover, risk quantification indices were used to determine the uncertainty propagation and potential risks accumulated in the chain. We applied this framework to cascade reservoirs in the Qing River Basin. The results indicate that the mean annual streamflow projected using selected GCMs will increase, enhancing the hydropower response and weakening the ecological benefit response. The Pareto non-dominated solutions optimized based on the streamflow projections obtained using the GCMs (under the same RCP) and hydrological model are more distinct than those based on different RCPs and the same GCM. Moreover, a high emission scenario may increase the uncertainty of the streamflow projections and reservoir operation responses, which is consistent with the finding that the decision-making process becomes more variable and sensitive with increasing streamflow uncertainty. Finally, we identified the preferred solutions for reservoir operation under different uncertainties, the respective expected values, and the 95% confidence interval bands to enhance the adaptability of future reservoir operation.

Effects of landscape attributes and climate variables on catchment hydrology

Catchments characteristics, such as geomorphology, geology, soil, land use, and climatic variables, play an important role in total stream flow responses, a critical resource for people and the environment. Most of the previous literatures were applied a conventional statistical regression model to assess the relationship between landscape-climate descriptors, and streamflow and PET. However, a conventional statistical regression model didn’t consider dependence of explanatory variables that were collected or extracted across both space and time. This paper investigated the impacts of landscape attributes and climate variables on catchment scale temporal variation of total streamflow and spatio-temporal variation of potential evapotranspiration (PET) in the Mille catchment using multiple linear regression techniques, and the importance of this study was to test spatial autocorrelation in the spatial regression model which is required to properly assess and quantify the relationship between hydrological regime response components and Landscape-climate descriptors in a catchment with topographically complex, and high spatio-temporal climatic variation like in our case study area, the Mille catchment. Statistical regression analysis revealed significant relationships between streamflow and climate variables, especially with rainfall. Mean maximum temperature is the most dominant factor controlling temporal variation of potential evapotranspiration at a monthly scale, whereas NDVI is the most significant factor that controls the spatial variability of PET. The multiple regression model shows that 91.1% of temporal variation in streamflow was accounted for rainfall, whereas, 96.6% and 78.4% of temporal and spatial variation in potential evapotranspiration was accounted for in maximum temperature and NDVI, respectively. Methods also can be applied to catchments with similar landscape attributes and climate variables.

Investigating impact of CORDEX-based predicted climatic and LCM-based LULC scenarios on hydrologic response of a semi-gauged Indian catchment.

The present study aims at documenting the impact of different climate and land use change scenarios on runoff in the Kangsabati River basin. While the study relies on India Meteorological Department (IMD), National Oceanic and Atmospheric Administration's Physical Sciences Laboratory (NOAA-PSL), and a multi-model ensemble of six driving models from Coordinated Regional Downscaling Experiment-Regional Climate Models (CORDEX RCM) for climate data input, it depends on IDRISI Selva's Land Change Modeller (LCM) and Soil and Water Assessment Tool (SWAT) model to generate projected land use land change maps and simulate its streamflow response, respectively. A total of four land use and land cover (LULC) scenarios, representing four projected land use change, were modelled across three climatic scenarios, called Representative Concentration Pathways (RCPs). With runoff being predominantly impacted more by climate change than LULC, volumetric runoff is expected to be 12-46% higher than the baseline period of 1982-2017. Conversely, while surface runoff is expected to decrease by 4-28% in lower parts of the basin, it will increase by 2-39% in the rest of it, depending on the subtle alterations in land use and climatic variability.

Bridging the gap from hydrological to biogeochemical processes using tracer-aided hydrological models in a tropical montane ecosystem

How simple or complex catchment models need to be is still unclear particularly for tracer-aided models that go beyond hydrograph fitting. Here, we take advantage of a sub-daily hydrometric and tracer data set from a tropical montane (páramo) experimental catchment to fill this knowledge gap. We evaluated six conceptual rainfall-runoff model structures with different complexity that represent the perceptual understanding of the catchment's hydrological functioning. The models solved conservative and reactive tracer mass balances to simulate streamflow, stable isotopes, and DOC concentrations and were assessed for performance and parsimony using three calibration targets (discharge only, discharge and stable isotopes, and discharge and DOC), resulting in 18 model configurations. Although all models satisfied the discharge calibration (KGE > 0.8), differences arose when considering tracer transport. The more complex models outperformed the simpler ones in terms of goodness-of-fit and parsimony, indicating that the catchment streamflow response consisted of quick near-surface flow and tracer contributions, more mixed flow through the two main soil types (Andosols and Histosols), as well as flow from the well-mixed shallow fractured rock (up to 20 m depth). The bedrock flow pathway contributed up to ∼25% of total flow during baseflow conditions. This study provides a benchmark experiment to identify hydrological and biogeochemical behavior in tropical montane catchments using relatively parsimonious tracer-aided hydrological models.

Effects of meteorological forcing uncertainty on high-resolution snow modeling and streamflow prediction in a mountainous karst watershed

In the mountainous Western U.S., a considerable portion of water supply originates as snowmelt passing through karst watersheds. Accurately simulating streamflow in snow-dominated, karst basins is important for water resources management. However, this has been challenging due to high spatiotemporal variability of meteorological and hydrogeological processes in these watersheds and scarcity of climate stations. To overcome these challenges, a physically based snow model is used to simulate snow processes at 100 m resolution, and the calculated snowmelt and potential evapotranspiration rates are fed into a deep learning model to simulate streamflow. The snow model was driven by meteorological variables from a regional scale Weather Research and Forecasting (WRF) model or from the North American Land Data Assimilation System (NLDAS-2). The two datasets were used both at the original resolution and downscaled to 100 m resolution based on orographic adjustments, leading to four sets of forcings. Snow model simulation results from the four sets of forcings showed large differences in simulated snow water equivalent (SWE) and snowmelt rate and timing. However, the differences were damped in simulated streamflow, as the deep learning model is partially immune to input bias and picked up different streamflow responses to snowmelt and rainfall when trained using snow model results. While the meteorological datasets considered yielded close streamflow simulation accuracy, averaging simulated streamflow from the four sets of forcings consistently achieved better performance, suggesting the value of including multiple meteorological datasets for modeling streamflow in mountainous watersheds.

Application of geomorphometric characteristics to prioritize watersheds for soil and water conservation practices in the Lake Tana Basin, Ethiopia

This study employed geomorphometric analysis to characterize the four major watersheds (Gilgel Abay, Gumara, Rib, and Megech) of Lake Tana sub-basin, Ethiopia, and prioritize the watersheds for the implementation of SWC practices using GIS and remote sensing techniques. Also, the study analyzed streamflow and sediment data recorded at the outlets of each watershed to associate the geomorphometric prioritization result with recordings of the watersheds. Geomorphometric analysis results indicated that the Rib watershed is the most susceptible watershed for soil erosion and should be prioritized for the implementation of SWC practices. The analysis of streamflow and suspended sediment concentration suggest that the Rib watershed had the second maximum sediment yield (14.3 t ha−1 yr−1) compared to the other three watersheds. This is because of the low streamflow response of the watershed compared with the Gumara watershed which had the highest sediment yield (18.9 t ha−1 yr−1).

Wildfire-induced shifts in groundwater discharge to streams identified with paired air and stream water temperature analyses

Within the western United States, increasingly severe and frequent wildfires may alter the magnitude, timing, and quality of water exported from burned areas by streams. Post-fire hydrologic studies often focus on peak stream flow responses to shifts in runoff generation or on annual streamflow yield response to changes in evapotranspiration following fire. However, the magnitude and duration of wildfire effects on groundwater recharge, changes in subsurface routing, and consequences for stream low flows sourced predominately by baseflow are poorly understood. Here, we demonstrate an approach using the amplitude and phase of paired annual air and stream water temperature signals to broadly identify changes in watershed subsurface flow contributions after fire. Watersheds were classified using pre-fire temperature data, as having air-coupled (i.e., reduced apparent groundwater signature), deep groundwater, or shallow groundwater stream temperature signals. Changes in pre- and post-fire paired air and stream water temperature metrics were compared for locations (n = 17) spanning a large range of physiographic and climatic conditions across the western United States. Pre- and post-fire comparisons were computed by quantile using bootstrapped confidence intervals (ci = 95), as well as in aggregate using Kruskal-Wallis and post-hoc Dunn tests. Statistical comparisons of pre- and post-fire temperature metrics suggest that overall, watersheds classified as having minimal groundwater influence are the most likely to experience fire-induced subsurface hydrologic change. More specifically, watersheds classified as having air-coupled or shallow groundwater signals experienced increases in the magnitude of groundwater discharge, with more stable annual thermal regimes post-fire that are less-coupled to ambient air temperature. These findings form the basis of a conceptual framework for watershed resistance to subsurface hydrologic change following fire that can be broadly applied as a first approximation for water management, impacts on aquatic habitat, and post-wildfire response planning.

Rainfall–streamflow response times for diverse upland UK micro-basins: quantifying hydrographs to identify the nonlinearity of storm response

AbstractWhile it is known that antecedent conditions and rainfall profiles contribute to the nonlinearity of streamflow response and that hydrograph shape can be dependent on the nature of rainfall inputs, how antecedent conditions (with similar rainfall inputs) impact hydrograph shape is less known. Here, a data-based mechanistic (DBM) approach is applied to quantify hydrograph shape, in terms of timing and volume, for the purposes of comparing hydrographs across 17 micro-basins at selected localities in upland UK over a 4-year period. The analysis demonstrates the nonlinearity of storm response for small catchments and revealed that with low antecedent conditions and/or small rainfall inputs there was a high variance in hydrograph shape quantifiers and that these variances decrease (at rates micro-basin dependent) as the micro-basins became wetter or as the storms increased in size, potentially converging to a more stable response.