Streamflow Response Research Articles

Can We Use the Water Budget to Infer Upland Catchment Behavior? The Role of Data Set Error Estimation and Interbasin Groundwater Flow

AbstractWater budgets are essential for characterizing water supplies from snow‐dominated upland catchments where data are sparse, groundwater systems are complex, and measurements are prone to error (ε). One solution is imposing water budget closure (CWB) by ignoring difficult‐to‐measure variables, including inter‐basin groundwater fluxes (G) and ε. However, conventional CWB‐based analyses, which derive evapotranspiration (ET) from precipitation (P) and streamflow (Q) (e.g., the Budyko hypothesis), are limited in their ability to take advantage of recent advances in ET products, physically‐based frameworks for improving inferences about G, or tools to statistically characterize ε (Triple Collocation [TC]); all of which offer promise for improved water supply predictions via open water budgets (OWB). We clarify the value of these advances in upland settings by comparing standard land surface model, Ensemble Mean, and TC‐Merged P and ET products in 114 upland catchments. When compared against a long‐term OWB, we find that the CWB assumptions are unsupported in 75%–100% of our 114 catchments, depending on the product. We then show how applying these CWB assumptions in snowy, steep catchments where ε is large can inflate inferences about streamflow response to climate change by up 9 times more than independent (OWB) estimates of ET using TC. Finally, we demonstrate how advances in OWB analysis reveal that high, arid settings with deep permeable substrate are groundwater exporters while most other basins are groundwater importers. Our results highlight the advantages of OWB analyses that harness new products, tools, and frameworks for characterizing inter‐basin groundwater fluxes in critical upland settings.

Spatially Distributed Overstory and Understory Leaf Area Index Estimated from Forest Inventory Data

Forest change affects the relative magnitudes of hydrologic fluxes such as evapotranspiration (ET) and streamflow. However, much is unknown about the sensitivity of streamflow response to forest disturbance and recovery. Several physically based models recognize the different influences that overstory versus understory canopies exert on hydrologic processes, yet most input datasets consist of total leaf area index (LAI) rather than individual canopy strata. Here, we developed stratum-specific LAI datasets with the intent of improving the representation of vegetation for ecohydrologic modeling. We applied three pre-existing methods for estimating overstory LAI, and one new method for estimating both overstory and understory LAI, to measurements collected from a probability-based plot network established by the US Forest Service’s Forest Inventory and Analysis (FIA) program, for a modeling domain in Montana, MT, USA. We then combined plot-level LAI estimates with spatial datasets (i.e., biophysical and remote sensing predictors) in a machine learning algorithm (random forests) to produce annual gridded LAI datasets. Methods that estimate only overstory LAI tended to underestimate LAI relative to Landsat-based LAI (mean bias error ≥ 0.83), while the method that estimated both overstory and understory layers was most strongly correlated with Landsat-based LAI (r2 = 0.80 for total LAI, with mean bias error of -0.99). During 1984-2019, interannual variability of understory LAI exceeded that for overstory LAI; this variability may affect partitioning of precipitation to ET vs. runoff at annual timescales. We anticipate that distinguishing overstory and understory components of LAI will improve the ability of LAI-based models to simulate how forest change influences hydrologic processes.

Modeled Streamflow Response to Scenarios of Tundra Lake Water Withdrawal and Seasonal Climate Extremes, Arctic Coastal Plain, Alaska

AbstractOn the Arctic Coastal Plain (ACP) in northern Alaska (USA), permafrost and abundant surface‐water storage define watershed hydrological processes. In the last decades, the ACP landscape experienced extreme climate events and increased lake water withdrawal (LWW) for infrastructure construction, primarily ice roads and industrial operations. However, their potential (combined) effects on streamflow are relatively underexplored. Here, we applied the process‐based, spatially distributed hydrological and thermal Water Balance Simulation Model (10 m spatial resolution) to the 30 km2 Crea Creek watershed located on the ACP. The impacts of documented seasonal climate extremes and LWW were evaluated on seasonal runoff (May–August), including minimum 7‐day mean flow (MQ7), the recovery time of MQ7 to pre‐perturbation conditions, and the duration of streamflow conditions that prevents fish passage. Low‐rainfall scenarios (21% of normal, one to three summers in a row) caused a larger reduction in MQ7 (−56% to −69%) than LWW alone (−44% to −58%). Decadal‐long consecutive LWW under average climate conditions resulted in a new equilibrium in low flow and seasonal runoff after 3 years that included a disconnected stream network, a reduced watershed contributing area (54% of total watershed area), and limited fish passage of 20 days (vs. 6 days under control conditions) throughout summer. Our results highlight that, even under current average climatic conditions, LWW is not offset by same‐year snowmelt as currently assumed in land management regulations. Effective land management would therefore benefit from considering the combined impact of climate change and industrial LWWs.

Climate change and the response of streamflow of watersheds under the high emission scenario in Lake Tana sub-basin, upper Blue Nile basin, Ethiopia

Study regionLake Tana sub-basin, Upper Blue Nile basin, Ethiopia. Study focusesThis study evaluated the degree to which climate is changing in the region, and its impact on stream flow of watersheds simulated by Soil Water Assessment Tool (SWAT) under the Representative Concentration Pathway (RCP8.5) emission scenario using six climate models including CanESM2, EC-EARTH, CNRM-CM5, HadGEM2- ES, NORESM1-M, and CSIRO-Mk3–6–0 by comparing the last thirty years of the past century (1971–2000) and the same years of this century (2071–2100). Bias correction for maximum temperature, minimum temperature, and rainfall data obtained from all climate models have been done using CMhyd software. The SWAT model is calibrated and validated using eleven sensitive hydrological parameters. New hydrological insightsThe result revealed that the change in maximum temperature ranges from 2.93 °C (November) and 5.17 °C (March), and the change in minimum temperature also ranges from 3.08 °C to 4.79 °C on a monthly basis. Rainfall is expected to increase up to 29.75% (November) and decrease up to 9.26% (March) in different seasons. Due to the change in climate, a flow is predicted to increase up to 27.82%, 27.47%, 26.47%, and 24.97% in Ribb, Gilgel Abay, Gumara, and Megech watersheds, respectively, and it is also decreasing in winter and spring seasons. On average, the streamflow is expected to increase by 5.89%, 5.63%, 4.92%, and 4.87% in Ribb, Gumara, Megech, and Gilgel Abay watersheds, respectively.

Diel streamflow cycles suggest more sensitive snowmelt-driven streamflow to climate change than land surface modeling does

Abstract. Climate warming will cause mountain snowpacks to melt earlier, reducing summer streamflow and threatening water supplies and ecosystems. Quantifying how sensitive streamflow timing is to climate change and where it is most sensitive remain key questions. Physically based hydrological models are often used for this purpose; however, they have embedded assumptions that translate into uncertain hydrological projections that need to be quantified and constrained to provide reliable inferences. The purpose of this study is to evaluate differences in projected end-of-century changes to streamflow timing between a new empirical model based on diel (daily) streamflow cycles and regional land surface simulations across the mountainous western USA. We develop an observational technique for detecting streamflow responses to snowmelt using diel cycles of incoming solar radiation and streamflow to detect when snowmelt occurs. We measure the date of the 20th percentile of snowmelt days (DOS20) across 31 western USA watersheds affected by snow, as a proxy for the beginning of snowmelt-initiated streamflow. Historic DOS20 varies from mid-January to late May among our sites, with warmer basins having earlier snowmelt-mediated streamflow. Mean annual DOS20 strongly correlates with the dates of 25 % and 50 % annual streamflow volume (DOQ25 and DOQ50, both R2=0.85), suggesting that a 1 d earlier DOS20 corresponds with a 1 d earlier DOQ25 and 0.7 d earlier DOQ50. Empirical projections of future DOS20 based on a stepwise multiple linear regression across sites and years under the RCP8.5 scenario for the late 21st century show that DOS20 will occur on average 11±4 d earlier per 1 ∘C of warming. However, DOS20 in colder watersheds (mean November–February air temperature, TNDJF<-8 ∘C) is on average 70 % more sensitive to climate change than in warmer watersheds (TNDJF>0 ∘C). Moreover, empirical projections of DOQ25 and DOQ50 based on DOS20 are about four and two times more sensitive to climate change, respectively, than those simulated by a state-of-the-art land surface model (NoahMP-WRF) under the same scenario. Given the importance of changes in streamflow timing for water resources, and the significant discrepancies found in projected streamflow sensitivity, snowmelt detection methods such as DOS20 based on diel streamflow cycles may help to constrain model parameters, improve hydrological predictions, and inform process understanding.

Quantifying relations between altered hydrology and fish community responses for streams in Minnesota

BackgroundAltered hydrology is a stressor on aquatic life, but quantitative relations between specific aspects of streamflow alteration and biological responses have not been developed on a statewide scale in Minnesota. Best subsets regression analysis was used to develop linear regression models that quantify relations among five categories of hydrologic metrics (i.e., duration, frequency, magnitude, rate-of-change, and timing) computed from streamgage records and six categories of biological metrics (i.e., composition, habitat, life history, reproductive, tolerance, trophic) computed from fish-community samples, as well as fish-based indices of biotic integrity (FIBI) scores and FIBI scores normalized to an impairment threshold of the corresponding stream class (FIBI_BCG4). Relations between hydrology and fish community responses were examined using three hydrologic datasets that represented periods of record, long-term changes, and short-term changes to flow regimes in streams of Minnesota.ResultsRegression models demonstrated significant relations between hydrologic explanatory metrics and fish-based biological response metrics, and the five regression models with the strongest linear relations explained over 70% of the variability in the biological metric using three hydrologic metrics as explanatory variables. Tolerance-based biological metrics demonstrated the strongest linear relations to hydrologic metrics. The most commonly used hydrologic metrics were related to bankfull flows and aspects of flow variability.ConclusionsFinal regression models represent paired streamgage records and biological samples throughout the State of Minnesota and encompass differences in stream orders, hydrologic landscape units, and watershed sizes. Presented methods can support evaluations of stream fish communities and facilitate targeted efforts to improve the health of fish communities. Methods also can be applied to locations outside of Minnesota with continuous streamgage data and fish-community samples.

Variable Streamflow Response to Forest Disturbance in the Western US: A Large‐Sample Hydrology Approach

AbstractForest cover and streamflow are generally expected to vary inversely because reduced forest cover typically leads to less transpiration and interception. However, recent studies in the western U.S. have found no change or even decreased streamflow following forest disturbance due to drought and insect epidemics. We investigated streamflow response to forest cover change using hydrologic, climatic, and forest data for 159 watersheds in the western U.S. from the CAMELS data set for the period 2000–2019. Forest change and disturbance were quantified in terms of net tree growth (total growth volume minus mortality volume) and mean annual mortality rates, respectively, from the U.S. Forest Service's Forest Inventory and Analysis database. Annual streamflow was analyzed using multiple methods: Mann‐Kendall trend analysis, time trend analysis to quantify change not attributable to annual precipitation and temperature, and multiple regression to quantify contributions of climate, mortality, and aridity. Many watersheds exhibited decreased annual streamflow even as forest cover decreased. Time trend analysis identified decreased streamflow not attributable to precipitation and temperature changes in many disturbed watersheds, yet streamflow change was not consistently related to disturbance, suggesting drivers other than disturbance, precipitation, and temperature. Multiple regression analysis indicated that although change in streamflow is significantly related to tree mortality, the direction of this effect depends on aridity. Specifically, forest disturbances in wet, energy‐limited watersheds (i.e., where annual potential evapotranspiration [PET] is less than annual precipitation) tended to increase streamflow, while post‐disturbance streamflow more frequently decreased in dry water‐limited watersheds (where the PET to precipitation ratio exceeds 2.35).

Event controls on intermittent streamflow in a temperate climate

Abstract. Intermittent streams represent a substantial part of the total stream network, and their occurrence is expected to increase due to climate change. Thus, it is of high relevance to provide detailed information on the temporal and spatial controls of streamflow intermittency to support management decisions. This study presents an event-based analysis of streamflow responses in intermittent streams in a mesoscale catchment with a temperate climate. Based on the streamflow responses, precipitation events were classified into flow or no-flow classes. Response controls like precipitation, soil moisture, and temperature were used as predictors in a random forest model to identify the temporally changing factors that explain streamflow intermittency at the event scale. Soil moisture was the most important predictor, but the predictor importance varied with the geology in the catchment. Streamflow responses in the slate geology were controlled by soil moisture in the shallow and deep soil layers, while streamflow in the marl geology was primarily controlled by soil moisture in the upper soil layer. Streamflow responses in catchments underlain by both marl and sandstone were dependent on soil moisture, whereas streamflow in the only catchment with a pure sandstone geology depended on precipitation characteristics. In all slate and marl catchments, streamflow intermittency also varied with soil temperature, which is probably a proxy for seasonal changes in evapotranspiration and an indicator of freezing conditions. Our findings underline the importance of using high temporal resolution data and tailored event definitions that account for the fast changes between flow/no flow in intermittent streams to identify streamflow controls at the event scale.

Snowpack driven streamflow predictability under future climate: contrasting changes across two western Canadian river basins

Abstract Anthropogenic climate change induced snowpack loss is affecting streamflow predictability, as it becomes less dependent on the initial snowpack conditions and more dependent on meteorological forecasts. We assess future changes to seasonal streamflow predictability over two large river basins, Liard and Athabasca in western Canada, by approximating streamflow response from the variable infiltration capacity (VIC) hydrologic model with the Bayesian regularized neutral network (BRNN) machine learning emulator. We employ the BRNN emulator in a test-bed ensemble streamflow prediction system by treating VIC simulated snow water equivalent (SWE) as a known predictor, and precipitation and temperature from GCMs as ensemble forecasts, thereby isolating the effect of SWE on streamflow predictability. We assess warm-season mean and maximum flow predictability over 2041-2070 and 2071-2100 future periods against 1981-2010 historical period. The results indicate contrasting patterns of change, with the predictive skills for mean flow generally declining for the two basins, and marginally increasing or decreasing for the headwater subbasins. The predictive skill for maximum flow declines for the relatively warmer Athabasca basin, and improves for the colder Liard basin and headwater subbasins. While the decreasing skill for the Athabasca is attributable to substantial loss in SWE, the improvement for the Liard and headwaters can be attributed to an earlier maximum flow timing that reduces the forecast horizon and offsets the effect of SWE loss. Overall, while the future change in SWE does affect the streamflow prediction skill, the loss of SWE alone is not a sufficient condition for the reduction in streamflow predictability.

Spruce Beetle Outbreak Increases Streamflow From Snow‐Dominated Basins in Southwest Colorado, USA

AbstractBark beetles have impacted over 58 million acres of coniferous forest in the Western US since 2000. Most beetle impacted forests are in snow dominated, water limited headwater basins, which generate a disproportionate fraction of water supplies. Previous studies show mixed impacts of bark beetle outbreaks on streamflow with the potential to cause increased or decreased flows, but these studies either predate long‐term snowpack data, are model‐based, or examine only mountain pine beetle outbreaks. Ours is the first study to use an empirical, climate‐normalized paired catchment approach to quantify streamflow response to spruce beetle kill. Using 27 years of climate and streamflow observations from southwest Colorado, we show that in three of the six beetle impacted study basins, annual climate‐normalized streamflow increased by 22%–37% for at least three to 6 years after the beetle outbreak. Impacted basins exhibited no decreased flows and flows in unimpacted control basins remained unchanged. Among impacted basins, no single basin characteristic clearly explained variation of streamflow response. Higher runoff ratios during snowmelt contribute anywhere from 9% to 64% of streamflow increases, implying the importance of both snow and growing season processes in driving streamflow increases. These findings show variable, sometimes substantial streamflow increases in critical water supply basins following beetle kill in subalpine spruce forests, and contrast with evidence of unchanged or decreased streamflow following beetle kill in lower elevation pine forests in colder northern Colorado basins, highlighting the importance of climate and forest composition in refining hydrologic predictions following mountain forest disturbances.

Large-sample study of uncertainty of hydrological model components over North America

The impacts of hydrological model components on performance and uncertainty of streamflow simulations are investigated using a multi-model approach over 698 catchments in North America. An ensemble of 180 different hydrological model structures is obtained by combining five snow models (SM), four potential evapotranspiration formulations (PET), three vertical (VFS) and three horizontal (HFS) flow rounting schemes. This study aims to first compare the multiple component combinations of SM, PET, VFS and HFS in terms of their ability at simulating streamflows for ten Köppen climate zones. A secondary goal is to evaluate the contribution of each model component to the total variance of the streamflow response according to the model structure for three streamflow metrics representing mean, high and low flows. Results indicate that there is no clear relationship between the structural and parametric complexity of the hydrological components and model performance for all three streamflow metrics. There is no single model structure which performs best in all climate zones and for all streamflow metrics. Streamflow uncertainty generated by the 180 synthetic hydrological model structures depends primarily on the metrics under study, but also on the climate zone. The evapotranspiration formulation is only critical for the mean flow metric. For the low-flow criterion, the VFS generates the most uncertainty and is therefore the most critical, whereas for the high-flow metric, performance is mostly related to the HFS component and SM for Nordic catchments. A better understanding of how hydrological model components generate uncertainty for a given climate and streamflow metric will lead to more robust model structures and therefore reduce the uncertainty of hydrological simulations.

A novel Budyko-based approach to quantify post-forest-fire streamflow response and recovery timescales

Recent increases in the incidences of wildfires have necessitated the development of methodologies to quantify the effect of these fires on streamflows. Climate variability has been cited as a major challenge in revealing the true contribution of disturbance to streamflow changes. To address this, we developed an annual Budyko “decomposition” method for (1) statistical change detection of hydrologic signatures post-fire, (2) separating climate-driven and fire-driven changes in streamflow, and (3) estimating hydrologic recovery timescales after fire. We demonstrate the use of this methodology for 17 watersheds in Southern California with high interannual variability in precipitation. We show that while traditional metrics like changes in flow or runoff ratio might not detect a disturbance effect due to confounding climate signals, the Budyko framework can be used successfully for statistical change detection. The Budyko approach was also found to be robust in detecting changes in 5 highly burned catchments (>40% burned area ratio), while changes in less burned (2) and unburned catchments (10) were insignificant. We further used the Budyko approach to quantify the contribution of fire-driven versus climate driven changes in streamflow and found that fire contributed to an average increase in streamflow on the order of 80 mm yr−1, though the effect varied greatly between years. Finally, we estimated hydrologic recovery timescales that varied between 5 and 45 years for four burned catchments. We found a significant linear relationship between recovery time and burned area at medium and high severity for our study catchments, with about 4 years of recovery time per 10% of the watershed burned.

Hybrid Physically Based and Deep Learning Modeling of a Snow Dominated, Mountainous, Karst Watershed

AbstractSnow dominated mountainous karst watersheds are the primary source of water supply in many areas in the western U.S. and worldwide. These watersheds are typically characterized by complex terrain, spatiotemporally varying snow accumulation and melt processes, and duality of flow and storage dynamics because of the juxtaposition of matrix (micropores and small fissures) and karst conduits. As a result, predicting streamflow from meteorological inputs has been challenging due to the inability of physically based or conceptual hydrologic models to represent these unique characteristics. We present a hybrid modeling approach that integrates a physically based, spatially distributed, snow model with a deep learning karst model. More specifically, the high‐resolution snow model captures spatiotemporal variability in snowmelt, and the deep learning model simulates the corresponding response of streamflow as influenced by complex surface and subsurface properties. The deep learning model is based on the Convolutional Long Short‐Term Memory (ConvLSTM) architecture capable of handling spatiotemporal recharge patterns and watershed storage dynamics. The hybrid modeling approach is tested on a watershed in northern Utah with seasonal snow cover and variably karstified carbonate bedrock. The hybrid models were able to simulate streamflow at the watershed outlet with high accuracy. The spatial and temporal recharge and discharge patterns learned by the ConvLSTM model were then examined and compared with known hydrogeologic information. Results suggest that ConvLSTM simulates streamflow with higher accuracy than reference models for the study area and provides insight into spatially influenced hydrologic responses that are unavailable within lumped modeling approaches.

Streamflow Response to Wildfire Differs With Season and Elevation in Adjacent Headwaters of the Lower Colorado River Basin

AbstractFires increasingly impact forested watersheds, with uncertain water resources impacts. While research has revealed higher peak flows, longer‐term yields may increase or decrease following fire, and the mechanisms regulating post‐fire streamflow are little explored. Hydrologic response to disturbance is poorly understood in the Lower Colorado River Basin (LCRB), where snowmelt often occurs before the growing season. Here, we quantify annual streamflow changes following what have been, before 2020, two of the largest wildfires in the modern history of the contiguous United States. We evaluate nine nested watersheds with >50 years records within the Salt River Basin to evaluate fire impact over ranges of elevation, climate, vegetation, burned area, and spatial scale. We employ double‐mass comparison of paired watersheds, pre‐ and post‐fire runoff ratio comparison, multiple linear regression of climate and fire, and time‐trend analysis. Precipitation and streamflow are decoupled during dry periods; therefore we conduct separate change detection for wet and dry periods. Post‐fire summer streamflow increased by 24%–38% at all elevations. While winter/spring streamflow remained constant in the highest, coldest headwaters, winter flows declined in lower‐elevation headwaters. As a result, basin annual streamflow declined. These results support emerging understanding that warm semiarid watersheds respond differently to disturbance than well‐studied, colder watersheds. Asynchrony between winter snowmelt and summer evaporative demand is likely important when considering long‐term impacts of forest management and disturbance on water supply in the LCRB.

Growing impact of wildfire on western US water supply

Streamflow often increases after fire, but the persistence of this effect and its importance to present and future regional water resources are unclear. This paper addresses these knowledge gaps for the western United States (WUS), where annual forest fire area increased by more than 1,100% during 1984 to 2020. Among 72 forested basins across the WUS that burned between 1984 and 2019, the multibasin mean streamflow was significantly elevated by 0.19 SDs (P < 0.01) for an average of 6 water years postfire, compared to the range of results expected from climate alone. Significance is assessed by comparing prefire and postfire streamflow responses to climate and also to streamflow among 107 control basins that experienced little to no wildfire during the study period. The streamflow response scales with fire extent: among the 29 basins where >20% of forest area burned in a year, streamflow over the first 6 water years postfire increased by a multibasin average of 0.38 SDs, or 30%. Postfire streamflow increases were significant in all four seasons. Historical fire-climate relationships combined with climate model projections suggest that 2021 to 2050 will see repeated years when climate is more fire-conducive than in 2020, the year currently holding the modern record for WUS forest area burned. These findings center on relatively small, minimally managed basins, but our results suggest that burned areas will grow enough over the next 3 decades to enhance streamflow at regional scales. Wildfire is an emerging driver of runoff change that will increasingly alter climate impacts on water supplies and runoff-related risks.

Asymmetry of Western U.S. River Basin Sensitivity to Seasonally Varying Climate Warming

AbstractFuture climate warming over the Western U.S. (WUS) is projected to be greater in summer than winter. Previous model‐based studies of large river basins in the WUS showed much different annual streamflow responses to warming in warm versus cool seasons. However, it remains unclear how the annual streamflow relative responses to seasonal warming (asymmetry) and drivers of the response asymmetry vary across the entire WUS at the catchment‐scale, and how the simulated results compare with observations. Here, we investigate the asymmetry of annual streamflow responses to warm versus cool season warming at the HUC‐8 level across the entire WUS using model simulations and observations. We also examine the asymmetries' relationship with land surface and hydroclimate characteristics, and the primary contributor to the response asymmetry for each HUC‐8 basin. The HUC‐8 level results reveal more complexity than do earlier analyses of much larger river basins. Over 25% of WUS area has annual streamflow increases in response to warming in at least one season (mostly cool season). Annual streamflow is most sensitive to warm season warming in cool, inland basins, especially the northern Columbia River basin and most of the Upper Colorado River Basin, and most sensitive to cool season warming in warm, coastal basins. This bi‐directional pattern is enhanced by vegetation coverage but weakened by long‐term snowpack decline. In coldest basins with short snow‐free seasons, net radiation changes dominate the streamflow response asymmetry. For basins with cold‐to‐intermediate temperatures, vapor pressure deficit changes dominate. For warmest basins, surface resistance changes dominate.

Tracer‐aided modelling reveals quick runoff generation and young streamflow ages in a tropical rainforest catchment

AbstractThere is still limited understanding of where stream water originates, their flow paths, how water sources mix, and for how long water transits montane tropical catchments. Here, we used a simple gamma convolution integral model (GM), ensemble hydrograph separation (EHS) and a tracer‐aided model (TAM) to assess runoff generation, mixing processes and water ages in the pristine tropical rainforest Quebrada Grande catchment in Costa Rica. Model simulations are based on a four‐year record (2016–2019) of continuous hydrometric and stable isotope observations. Comparative model tests included multi‐objective calibration (2017–2019) and validation (2016) using stream discharge and isotope data as well as an independent model evaluation using groundwater and soil water isotope data. GM and TAM agreed on the dominance of young water in streamflow that was less than 95 days old for 75% of the study period. The EHS suggested a young water fraction threshold of 12 ± 2 days with a transit time distribution that approximates the best‐fit GM. These short water ages are the result of high annual rainfall even during drier years such as 2019 with 4300 mm/a and consistent quick near‐surface runoff generation with limited mixing. A supra‐regional loss (~55%) of likely older groundwater was detected. The TAM‐based hydrograph separation (streamflow KGE &gt; 0.78, δ2H KGE &gt; 0.90) suggested an average near‐surface water contribution of more than 60% to streamflow emphasizing the dominance of quick flow paths. This tropical rainforest represents one of the quickest streamflow responses of mostly young water of pristine catchments globally.

Multi-site calibration of hydrological model and the response of water balance components to land use land cover change in a rift valley Lake Basin in Ethiopia

This study aims to perform multi-site calibration and validation of SWAT model using streamflow data, and investigating the responses of water balance components to land use land cover (LULC) change in the Gidabo river sub-basin of rift valley lake basin in southern part of Ethiopia. SUFI-2 algorithm embedded in the SWAT-CUP was employed for sensitivity analysis, calibration, and validation on a monthly basis. The 17 years (1990–2006) streamflow data for three (Aposto, Bedessa, and Measso) gauging stations were used for calibrating the model while 8 years (2007–2014) streamflow data was used for validating the model. The calibrated and validated SWAT model was then used to investigate the response of water balance components to LULC change for three periods (1990, 2005, and 2019) which were performed using ERDAS Imagine 2014 with a maximum likelihood classifier. The most common statistical model performance evaluation indices namely Coefficient of Determination (R2), Nash-Sutcliffe Efficiency (NSE), and Percent Bias (PBIAS) were used to evaluate performance of the SWAT model in simulating sub-basin hydrology; in addition to physical inspection of observed and simulated streamflow hydrograph. The findings of the multi-site model performance evaluation indicated that the values of R2 ranged from 0.80 to 0.64 and 0.74 to 0.72 during calibration and validation periods respectively. The values of NSE ranged from 0.74 to 0.61 and 0.71 to 0.65 during calibration and validation periods respectively whereas PBIAS ranged from 19.70 to -3.20 and 18.10 to 0.80 during calibration and validation periods respectively. The calibration and validation results indicated that the SWAT model would simulate fairly well the historical streamflow at three gauging stations. The mean annual streamflow response to LULC change for the periods 1990–2005 and 2005–2019 was observed to increase by 2.13% (1.16m3/s) and 3.62% (2.04m3/s), respectively and the mean seasonal streamflow was obtained to increase during wet season (April-September) while decreasing trend was observed during dry season (October-March) in all three gauged stations. Results also revealed that there were significant spatiotemporal variations of surface runoff, groundwater flow, lateral flow, and evapotranspiration in the sub-basin. The multi-site calibration and validation together with uncertainty analysis detects spatial variability and simulates the water balance components under changing LULC, which is paramount importance for planning and formulating appropriate integrated land and water resources management and development strategies in the rift valley lake basin of Gidabo river sub-basin in Ethiopia.

Untangling harvest‐streamflow responses in foothills conifer forests: Nexus of teleconnections, summer‐dominated precipitation, and storage

AbstractThis study re‐evaluated data from the historical Tri‐Creeks Experimental Watershed (1967–1988) in Alberta, Canada to address the initial question of forest harvest effects on streamflow and investigate the potential influence of teleconnections, summer‐dominated precipitation, and watershed storage on runoff generation. Tri‐Creeks has deep (up to 21 m) glacial deposits underlain by folded and faulted sedimentary bedrock with considerable potential for subsurface water storage. Timing of the conifer forest harvest experiment in two sub‐watersheds (&gt;50% harvested) and one reference occurred near the 1976–77 Pacific Decadal Oscillation (PDO) phase change that led to less snowfall, but little difference in annual precipitation or runoff between phases after harvest. Established statistical and hydrological modelling methods that used regression techniques of observed and simulated streamflow to separately analyse sub‐watersheds did not detect change in average daily or annual runoff due to harvest. The interannual hydroclimatic variability influenced by the climate shift, attenuation of summer precipitation by the drier antecedent conditions in the warm period following harvest, and large potential for subsurface water storage contributed to shifts in runoff and uncertain detection of streamflow response. However, a hydrological modelling approach using calibrated parameters separately in the pre‐ and post‐harvest periods indicate significant change in rainfall‐generated peak runoff events and summer runoff following harvest, which was not detected in the reference watershed. Model calibration required less soil storage capacity in the treated watersheds in the post‐harvest period compared to the reference likely due to reduced transpiration that increased the likelihood of storm runoff during larger summer rainfall events. Within the context of streamflow responses to harvest in conifer dominated forest landscapes with seasonal snow cover, this study illustrates how complexity of climate variability and interaction with watershed storage and continental summer‐dominated precipitation may confound and mask the interpretation of harvest effects in paired‐watershed studies.

Hydrologic Effects of Fire in a Sub-Alpine Watershed: AgES Outperforms Previous PRMS Simulations

HighlightsAgES and PRMS accurately simulated the dramatic change in streamflow response to precipitation after fire.Measured annual streamflow increased by 170% in 4-year postfire period with similar precipitation.Postfire AgES parameters were drastically reduced for snow and rain interception, canopy density, and ET.Decreased soil depression storage and field capacity also contributed to increased streamflow in AgES.AgES shows promise for simulating hydrologic impacts of sub-alpine forest resource management and fire response.Abstract. Forest fires alter hydrologic responses to precipitation, and streamflow changes can be challenging to simulate. Streamflow data before and after wildfire in a 14 km2 mixed-conifer, sub-alpine watershed in south-central New Mexico were used to calibrate a watershed model (Agricultural Ecosystems Services, AgES) using four years of prefire and postfire conditions. Streamflow results and parameter values from AgES and a prior Precipitation Runoff Modeling System (PRMS) modeling study in the same watershed were compared. Both AgES and PRMS simulated the dramatic change in streamflow response to precipitation after a fire, including a smaller precipitation threshold to induce streamflow. Still, AgES had substantially less bias and better performance when comparing monthly metrics. Only AgES was assessed at the daily scale. AgES simulated daily streamflow accurately throughout the study period, with slight overestimation in the prefire period (Oct 2007 to Oct 2011, 1.2% bias, 0.90 Nash-Sutcliffe efficiency [NSE]) and in the postfire period (Oct 2013 to Oct 2017, 4.4% bias, 0.54 NSE). Although each 4-year model period had nearly identical cumulative precipitation, annual streamflow increased by 170% in the postfire period compared to the prefire period. Appropriate soil and vegetation parameters were modified by the stepwise calibration process to represent the effects of fire in AgES: interception storage was decreased for rain (-99.8%) and snow (-26%), two factors influencing ET were decreased, soil depression storage was reduced (-97%), and soil field capacity was reduced (-50%). AgES demonstrated an improved streamflow simulation compared with PRMS, calibrated parameter shifts that were more consistent to interpret, and particular skill in simulating daily streamflow response to fire. Knowledge gained from this study will guide future simulations of hydrologic responses to fires in the western USA using AgES. Keywords: Agricultural Ecosystems Services model, Forest, Landscape change, Precipitation-Runoff Modeling System, Streamflow change, Surface hydrology, Wildfire.