Changes In Streamflow Research Articles

Physically-based distributed modelling of the hydrology and soil erosion under changes in landuse and climate of a humid tropical river basin

The effects of landuse/landcover (LULC) and climate change on hydrology and soil erosion processes are of major concern, especially in the humid tropics. In this study, an evaluation of these changes is performed in a humid tropical catchment (Vamanapuram river basin, India) using a physically based distributed model, SHETRAN. The past landuse maps and climate data from six fine resolution NASA Earth Exchange Global Daily Downscaled Projections (NEX-GDDP) are used to force the SHETRAN model. A comparison of the change in the rate of soil erosion and hydrological responses during the future climate scenarios (near: 2021–2050 and far: 2071–2100) with respect to the historical period (1980–2005) is conducted.The isolated effects of landuse, climate variability and the combined effect caused change in streamflow (1.1%, 7.9%, 9.0% respectively) and sediment load (−10.4%, 1.7%, −10.5% respectively) in the past. An ensemble mean of general circulation model (GCM) projections showed that mean temperature and average annual precipitation under representative concentration pathway RCP 4.5 (8.5) scenarios for near and far futures increased from the historic period by 0.51 °C (1.6 °C), 0.6 °C (3.2 °C) and by 9% (6%), 15% (22.8%), respectively. Under RCP4.5(8.5), the average annual streamflow increased gradually from the near to far future, by 3.2% (−1.8%) and 13.6 % (22.1%) whereas, the projected sediment load a showed change of −21.05% (−26.63%) in near future while far future indicated change by −11% (4%). The SHETRAN model has been found to be effective in evaluating climate change impacts on hydrology and sediment yield and is useful for future river basin management.

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).

Impacts of the landscape changes in the low streamflows of Pantanal headwaters—Brazil

AbstractThe Upper Paraguay Basin Plateau (UPRB) concentrates the headwaters and the largest volume of rain that supply the Pantanal biome and is essential for maintaining its water regime. Given the region's changes in land use and cover, this research sought to identify their influence on headwater flows. The use of conceptual models and time series analysis showed that the replacement of natural vegetation with pasture and the more recent activity of irrigated agriculture have produced changes in the hydrological response of the headwaters. There was a decreasing tendency of low flows during dry periods, which was correlated with anthropic changes in the use and cover of the land in the region. The research results point out changes in land use and cover as the cause of a significant decrease in streamflows during drier periods. Such behaviour at the headwaters of the Pantanal can have consequences for this biome, reducing the resilience of the system against droughts and damaging the biodiversity of the biome.

Impact of Climate Change on Water Resources in the Western Route Areas of the South-to-North Water Diversion Project

The South-to-North Water Diversion Project (SNWDP) is a national strategic project for water shortages in northern China. Climate change will affect the availability of water resources in both source and receiving areas. A grid-based RCCC-WBM model based on climate projections from nine Global Climate Models under SSP2-4.5 was used for analyzing the changes in temperature, precipitation, and streamflow in the near future (2025–2045, NF) and far future (2040–2060, FF) relative to the baseline (1956–2000). The results showed that: (1) the temperature of the western route will increase significantly in the NF and FF with an extent of 1.6 °C and 2.0 °C, respectively, (2) precipitation will very likely increase even though Global Climate Model (GCM) projections are quite dispersed and uncertain, and (3) over half of the GCMs projected that streamflow of receiving area will slightly increase with a rate of 1.68% [−8.67%, 12.3%] and 2.78% [−3.30%, 11.0%] in the NF and FF, respectively. Climate change will support the planning of the western route to a certain extent. However, water supply risk induced by the extreme situation of climate change should be paid adequate consideration when the project operates in practice due to the large dispersion and uncertainty of GCM projections.

Attribution of runoff variation to climate and human-driven changes in the transition zone between the Qinling Mountains and the Loess Plateau under vegetation greening

AbstractThe need for water safety management has increased in the transition zone between the Qinling Mountains and the Loess Plateau, China due to streamflow decline over the past 30 years. Vegetation greening, largely due to the result of the ‘Grain for Green’ program implemented in the Loess Plateau, is affecting regional streamflow together with climate change and direct human impacts. There is thus an urgent need to evaluate the relative importance of causes of streamflow variation in this region. A Hydrological Model of L′École de Technologie Supérieure (HMETS)-based segment identification analysis framework was presented to quantify the impacts of climate and human-driven changes on runoff under regional vegetation greening. Results showed that climate change and human interference were alternately dominant in the hydrological cycle from 1976 to 2015. Climate change played a major role in affecting runoff variation before 2000, and then human interference dominated. It is worth noting that temperature increases resulted in runoff reduction and induced more changes in streamflow when precipitation was high. Vegetation greening contributed highly to streamflow attenuation, and its impact on runoff variation was more significant after 2007. Generally, understanding the effects of temperature increases and vegetation greening on streamflow is important for the development of appropriate adaptation strategies for the planning and management of regional water resources.

Statistical Attribution of the Influence of Urban and Tree Cover Change on Streamflow: A Comparison of Large Sample Statistical Approaches

AbstractThe strengths and weaknesses of different statistical methodologies for attributing changes in streamflow to land cover are still poorly understood. We examine the relationships between high (Q99), mean (Qmean), and low (Q01) streamflow and urbanization or tree cover change in 729 catchments in the United States between 1992 and 2018. We apply two statistical modeling approaches and compare their performance. Panel regression models estimate the average effect of land cover changes on streamflow across all sites, and show that on average, a 1%‐point increase in catchment urban area results in a small (0.6%–0.7%), but highly significant increase in mean and high flows. Meanwhile, a 1%‐point increase in tree cover does not correspond to strongly significant changes in flow. We also fit a generalized linear model to each individual site, which results in highly varied model coefficients. The medians of the single‐site coefficients show no significant relationships between either urbanization or tree cover change and any streamflow quantile (although at individual sites, the coefficients may be statistically significant and positive or negative). On the other hand, the GLM coefficients may provide greater nuance in catchments with specific attributes. This variation is not well represented through the panel model estimates of average effect, unless moderators are carefully considered. We highlight the value of statistical approaches for large‐sample attribution of hydrological change, while cautioning that considerable variability exists.

Implication of bias correction on climate change impact projection of surface water resources in the Gidabo sub-basin, Southern Ethiopia

AbstractClimate change impact studies that evaluated the biases of climate models' simulations showed the presence of large systematic errors in their outputs. However, many studies continue to arbitrarily select bias correction methods for error reduction. This work evaluated the implications of bias correction methods on the projections of climate change impact on streamflow of the Gidabo sub-basin, Ethiopia. Climate outputs from four global climate model and regional climate model (GCM–RCM) combinations for the representative concentration pathway (RCP4.5) scenario were used. Five bias correction methods were used to reduce the systematic errors of the simulated rainfall data. The future changes in rainfall pattern, evapotranspiration, and streamflow were analyzed by using their relative percentage difference between the projected and the baseline period. The distribution mapping method provided better results in mean and extreme rainfall cases. This is also reflected in streamflow projections, as the daily interquartile range value indicates the lowest variability of the projected streamflow. The wet season streamflow will likely decrease in the future, whereas the short rainy season streamflow will increase. Our findings show that climate models and bias correction methods considerably limit the magnitude of future projections of streamflow. However, similar research should be conducted in other catchments to extend the conclusions of this study.

Influence of climate and land-use changes on the sensitivity of SWAT model parameters and water availability in a semi-arid river basin

The present study assesses the impact of climate change (CC) and land use land cover change (LULCC) on model parameter variability and alterations in streamflow and water balance components of a semi-arid river basin in western India. The Dharoi catchment in the Sabarmati River basin is a water-scarce region with India's lowest per-capita water availability. The water scarcity conditions are likely to aggravate due to changing climate and landscape modifications through human interventions. A hydrological modelling framework using a semi-distributed Soil and Water Assessment Tool (SWAT) is adopted in this study, considering two approaches: a variable-period calibration and a fixing-changing approach. Several model scenarios considered in these approaches are simulated through both single-site (SSC) and simultaneous multi-site calibration (SMSC) techniques. The variable-period calibration is adopted to assess the individual and combined influences of CC and LULCC on SWAT model parameter variability and change in water balance ratios. The fixing-changing approach evaluates the relative contribution of CC and LULCC to streamflow changes. The temporal changes in the hydroclimatic variables are analysed using non-parametric Pettitt, Mann-Whitney, Modified Mann-Kendall, and Spearman’s rho tests. The results indicated an abrupt increase in the streamflow in 2004, attributed to a significant increase in total and extreme rainfall magnitude and frequency in the catchment. The SSC and SMSC techniques yielded satisfactory and comparable performance for all model scenarios. The results showed that CC has a more significant impact on SWAT model parameter variability than LULCC. Thus, CC is the primary driver of streamflow changes in the Dharoi catchment, wherein a four-fold increase in the streamflow is noticed in the period 2005–2014 vis-à-vis 1995–2004. Thus, a transition from drier to wetter hydroclimatic conditions is evident in the Dharoi catchment, which would enhance water sufficiency in the region if appropriately managed.

Projection of Streamflow Changes Under CMIP6 Scenarios in the Urumqi River Head Watershed, Tianshan Mountain, China

Analyzing climate change impacts on hydrology and future water supply projections is essential for effective water resource management and planning in the large river basins of Asia. In these regions, streamflow and glacier melt remain subject to significant uncertainties due to the lack of confidence in climate change projections and modeling methods. In this study, a glacier dynamics model (the Open Global Glacier Model was coupled with a glacio-hydrological model [the Glacio-hydrological Degree-day Model (GDM)] to predict possible hydrological changes in the head watershed of the Urumqi River under three shared socioeconomic pathways SSP2-4.5, SSP3-7.0, and SSP5-8.5. The GDM was calibrated and validated against in situ observed discharge data for the 2007–2011 and 2012–2018 periods. The resulting Nash–Sutcliffe efficiency (NSE) values were 0.82 and 0.81, respectively. The GDM was driven with an ensemble of five downscaled CMIP6 datasets to examine the potential impacts of climate change on hydrologic processes in the basin. Four runoff components were simulated with the GDM: base flow, rainfall, ice melt, and snow melt. It was determined that rainfall constituted the predominant source of runoff, followed by baseflow and ice melt. During the calibration and validation periods, snow and ice melt contributed 25.14 and 25.62%, respectively, to the total runoff. Under all SSP scenarios, the projected runoff decline indicated that the peak runoff time had passed. It was revealed that a 2°C increase in the monthly average temperature could result in a 37.7% increase in the total discharge of the basin. Moreover, the GDM was more responsive to changes in air temperature than to changes in glacier extent.

The Complex and Spatially Diverse Patterns of Hydrological Droughts Across Europe

AbstractThis study presents a new data set of gauged streamflow (N = 3,224) for Europe spanning the period 1962–2017. The Monthly Streamflow of Europe Dataset (MSED) is freely available at http://msed.csic.es/. Based on this data set, changes in the characteristics of hydrological drought (i.e., frequency, duration, and severity) were assessed for different regions of Europe. Due to the density of the database, it is possible to delimit spatial patterns in hydrological droughts trend with the greatest detail available to date. Results reveal bidirectional changes in monthly streamflow, with negative changes predominating over central and southern Europe, while positive trends dominate over northern Europe. Temporally, two dominant patterns were noted. The first pattern corresponds to a consistent downward trend in all months, evident for southern Europe. A second pattern was noted over central and northern Europe and western France, with a predominant negative trend during warm months and a positive trend in cold months. For hydrological drought events, results suggest a positive trend toward more frequent and severe droughts in southern and central Europe and conversely a negative trend over northern Europe. This study emphasizes that hydrological droughts show complex spatial patterns across Europe over the past six decades, implying that hydrological drought behavior in Europe has a regional character. Accordingly it is challenging to adopt “efficient” strategies and policies to monitor and mitigate drought impacts at the continental level.

Assessment of the Impact of Climate Change on Stream Flow: The Case of Little Ruaha Catchment, Rufiji Basin, Tanzania

Little Ruaha catchment has been recognized for its potential to support multi-projects including irrigated schemes, urban water supply and providing significant inflow to Mtera reservoir for hydropower generation and ultimately Julius Nyerere Hydropower Plant (JNHPP). Despite the potential, the catchment has experienced declining flows in the recent years. This study assessed the likely changes in streamflow due to future climate change in the Little Ruaha catchment for the period 2025-2060. General Circulation Model (GCM) datasets from ACCESS1.0, CNRM-CM5 and BCC-CSM1 models and RCP4.5 and RCP8.5 greenhouse gas concentration scenarios were selected as the representative scenarios. Impact of climate change on stream flows was assessed using the calibrated NAM hydrological model. The impact assessment results show that under the climate change scenario (2025–2060), the monthly maximum and minimum temperatures will increase in the range of 0.8 °C to 2 °C for both RCP4.5 and RCP8.5 scenarios. For the case of rainfall, average annual rainfall is expected to increase by about 10% compared to the baseline. However, the inter-annual variability of rainfall for the period between 2025 and 2060 shows the decreasing trend for RCP 8.5. The simulation results show that streamflow will decrease by about 30% and 6% for RCP4.5 and RCP8.5, respectively.
 Keywords: Hydrological modelling; climate change scenarios; hydrological impacts; Little Ruaha.

Hybrid Data-Driven Models for Hydrological Simulation and Projection on the Catchment Scale

Changes in streamflow within catchments can have a significant impact on agricultural production, as soil moisture loss, as well as frequent drying and wetting, may have an effect on the nutrient availability of many soils. In order to predict future changes and explore the impact of different scenarios, machine learning techniques have been used recently in the hydrological sector for simulation streamflow. This paper compares the use of four different models, namely artificial neural networks (ANNs), support vector machine regression (SVR), wavelet-ANN, and wavelet-SVR as surrogate models for a geophysical hydrological model to simulate the long-term daily water level and water flow in the River Shannon hydrological system in Ireland. The performance of the models has been tested for multi-lag values and for forecasting both short- and long-term time scales. For simulating the water flow of the catchment hydrological system, the SVR-based surrogate model performs best overall. Regarding modeling the water level on the catchment scale, the hybrid model wavelet-ANN performs the best among all the constructed models. It is shown that the data-driven methods are useful for exploring hydrological changes in a large multi-station catchment, with low computational cost.

Crop improvement influences on water quantity and quality processes in an agricultural watershed

Field crop traits have and are experiencing significant changes due to genetic and agronomic improvements. How these changes affect regional water quantity and quality processes has not been clarified. The St. Joseph River Watershed (SJRW) located in the U.S. Corn Belt was selected as a case study area. Crop (corn and soybean) trait improvements in the past decades were reviewed and summarized and include changes of growing degree days (GDD), leaf area index (LAI), light utilization (LU), drought tolerance (DT), nutrient content (NC), and harvest index (HI). Based on a calibrated 9-year (from 2011 to 2019) SWAT (Soil and Water Assessment Tool) simulation in SJRW, sensitivities of the above crop traits to yield, ETa, stream flow, tile flow, surface runoff, and nutrient loads (NO3N, TN, soluble-P, and TP) were analyzed. Crop traits and their corresponding SWAT parameters for the 2010s were obtained from model calibration and used as the baseline/current scenario; for the 1980s, they were summarized from literature review and used as an historical scenario, while those for the 2040s were determined by assuming crop traits are changing linearly with time and projected as the future scenario. Water quantity and quality changes under the historical and future crop scenarios were compared with the baseline/current simulation. Results showed LU and DT were the most sensitive crop traits to water quantity (i.e., ETa, stream flow, tile flow, and surface runoff), while HI was the most sensitive to nutrient loads. The impacts of crop improvements on nutrient loads were more significant than on water budgets. Compared with the baseline, the historical and future scenarios resulted in 1.5 − 2.0% changes of stream flow, 6.8 − 18.6% changes of nitrogen loads (NO3N and TN) and 2.6 − 3.9% changes of phosphorus loads (soluble-P, and TP) in the stream flow, annually. Moreover, in certain months, these changes can reach about 12% for stream flow, 42% for nitrogen loads, and 12% for phosphorus loads. Nitrogen losses by tile drainage and percolation, and phosphorus losses by surface runoff and tile drainage were most significantly affected by the crop improvements. Future work should consider expected crop improvements when studying long-term hydrology and nutrient cycles in agricultural watersheds.

Forecasting 100-year changes of streamflow in the Mun River Basin (NE Thailand) under the HAPPI experiment using the SWAT model

Abstract The Lower Mekong River is one of the significant rivers nurturing people on the Southeast Asian mainland. Its tributaries include the Mun River (NE Thailand), which often experiences extreme water events. In this study, the streamflow change in the year 2115 was simulated by relying on the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) experiment and the Model for Interdisciplinary Research on Climate, version 5 (MIROC5) climate model for average global warming of 1.5 °C (Plus1.5) and 2.0 °C (Plus2.0) above pre-industrial levels and compared with the base year in 2015 for the Mun River Basin. The Soil–Water Assessment Tool (SWAT) was used for the streamflow simulation. The results showed an increasing air temperature against lowering rainfall and relative humidity (except for the post-monsoon months), suggesting overall rain suppression in response to the warming climate. The median projected annual streamflow to the Mekong River in 2115 decreased for both ‘Plus1.5’ (−32.5%, median) and ‘Plus2.0’ (−23.1%, median). However, increasing annual streamflow could be found only in the middle part. Seasonal streamflow changes revealed a different spatiotemporal response to climate change resulting in inconsistent streamflow changes across the basin. The adaptive measures for the middle part should be focused on flooding control, whereas the upper and the lower parts should be against drought.

Evaluation of Future Streamflow in the Upper Part of the Nilwala River Basin (Sri Lanka) under Climate Change

Climate change is a serious and complex crisis that impacts humankind in different ways. It affects the availability of water resources, especially in the tropical regions of South Asia to a greater extent. However, the impact of climate change on water resources in Sri Lanka has been the least explored. Noteworthy, this is the first study in Sri Lanka that attempts to evaluate the impact of climate change in streamflow in a watershed located in the southern coastal belt of the island. The objective of this paper is to evaluate the climate change impact on streamflow of the Upper Nilwala River Basin (UNRB), Sri Lanka. In this study, the bias-corrected rainfall data from three Regional Climate Models (RCMs) under two Representative Concentration Pathways (RCPs): RCP4.5 and RCP8.5 were fed into the Hydrologic Engineering Center-Hydrologic Modeling System (HEC-HMS) model to obtain future streamflow. Bias correction of future rainfall data in the Nilwala River Basin (NRB) was conducted using the Linear Scaling Method (LSM). Future precipitation was projected under three timelines: 2020s (2021–2047), 2050s (2048–2073), and 2080s (2074–2099) and was compared against the baseline period from 1980 to 2020. The ensemble mean annual precipitation in the NRB is expected to rise by 3.63%, 16.49%, and 12.82% under the RCP 4.5 emission scenario during the 2020s, 2050s, and 2080s, and 4.26%, 8.94%, and 18.04% under RCP 8.5 emission scenario during 2020s, 2050s and 2080s, respectively. The future annual streamflow of the UNRB is projected to increase by 59.30% and 65.79% under the ensemble RCP4.5 and RCP8.5 climate scenarios, respectively, when compared to the baseline scenario. In addition, the seasonal flows are also expected to increase for both RCPs for all seasons with an exception during the southwest monsoon season in the 2015–2042 period under the RCP4.5 emission scenario. In general, the results of the present study demonstrate that climate and streamflow of the NRB are expected to experience changes when compared to current climatic conditions. The results of the present study will be of major importance for river basin planners and government agencies to develop sustainable water management strategies and adaptation options to offset the negative impacts of future changes in climate.

The Impact of Climate Change on Hydrological Processes of the Glacierized Watershed and Projections

Under the influence of climate change, the hydrological processes of glaciers have undergone significant changes, a fact which is seriously affecting agricultural production in the downstream region of the Tianshan Mountains, China. In order to explore the intrinsic relationship between climate change and hydrological elements, we proposed an “evaluation-driving-prediction” system to study it. First, we constructed a glacier-enhanced soil and water assessment tool model (GE-SWAT) and used a two-stage calibration method to optimize the model parameters. Next, a scenario analysis was used to evaluate the driving factors of historical runoff changes. Finally, we projected future runoff changes using bias-corrected regional climate model (RCM) outputs. The results of the case study on the Jinghe River Basin in the Tianshan Mountains show that from 1963 to 2016, total runoff increased by 13.3%, 17.7% of which was due to increasing precipitation and 1.8% of which was negated by rising temperatures. The glacier runoff increased by 14.5%, mainly due to the rising temperatures. A 3.4% reduction in snowmelt was caused by a lower snowfall/precipitation ratio, which significantly reduced the snowfall from June to August. The RCM projection indicated that the warming and humidification phenomenon in the study area will continue at least through to the mid-21st century. A consistent increase in glacier runoff and total runoff is projected, but the contribution rate of the glacier runoff will have little to no change under the RCP4.5 and RCP8.5 emission scenarios. Our research demonstrates the simulation performance of the GE-SWAT model in a basin with moderate glacier cover. This method is shown to be efficient in quantifying the impact of climate change on glacier hydrological processes and predicting future streamflow changes, providing a good research reference for similar regions.

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.

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.

Probabilistic Budyko-based Separation of Climate and Catchment Effects on Streamflow

Streamflow variability is affected by both climate change and direct human impacts within the catchment. Quantification of the role of these two factors have significant implications for water resources. In recent years, Budyko-based analytical methods have been widely used for such quantification due to their simplicity and efficacy. This paper presents a comparison of such methods to attribute changes in streamflow in four Model Parameter Estimation Experiment (MOPEX) catchments in the contiguous United States. Further, a new, improved Two-stage Decomposition method to separate the role of climate and catchment changes on streamflow is proposed, considering the uncertainty in the catchment parameter that further leads to a range of evaporative index values within a catchment. We find that climate and catchment effects are sensitive to the choice of Budyko equation, disaggregation algorithm and approximation technique. Further, the catchment parameter can be expressed probabilistically, to account for the uncertainty because of limited hydro-meteorological records. The proposed approach employs a bootstrap analysis to obtain the stochastic range of the catchment parameter, considering different choices of Budyko equations. We argue that rather than relying on a single Budyko-based approach for separation of climate and catchment effects on streamflow, the proposed method offers a more robust way by considering the entire range of uncertainty of the catchment parameter.

An approach to identify the best climate models for the assessment of climate change impacts on meteorological and hydrological droughts

Abstract. This paper describes the benefits of using more reliable local climate scenarios to analyse hydrological responses. It assumes that Regional Climate Model (RCM) simulations are more reliable when they provide better approximations to the historical basic and drought statistics after applying a bias correction to them. We have investigated whether the best solutions in terms of their approximation to the local meteorology may also provide the best hydrological assessments. We have carried out a classification of the corrected RCM simulations used for both approximations. This has been applied in the Cenajo basin (south-eastern Spain), where we show that the best approximations of the historical meteorological statistics also provide the best approximations for the hydrological statistics. The selected RCMs were used to generate future (2071–2100) local scenarios under the RCP8.5 emission scenario. The two selected RCMs predict significant changes in mean precipitation (−31.6 % and −44.0 %) and mean temperature (+26.0 % and +32.2 %). They also predict higher frequency (from 5 events in the historical period to 20 and 22 in the future), length (4.8 to 7.4 and 10.5 months), magnitude (2.53 to 6.56 and 9.62 SPI) and intensity (0.48 to 1.00 and 0.94 SPI) of extreme meteorological droughts. These two RCMs also predict higher changes in mean streamflow (−43.5 % and −57.2 %) and hydrological droughts. The two RCMs also predict worrying changes in streamflow (−43.5 % and −57.2 %) and hydrologically extreme droughts: frequency (from 3 to 11 for the first model and 8 events for the second model), length (8.3 to 15.4 and 29.6 months), magnitude (from 3.98 to 11.84 and 31.72 SSI), and intensity (0.63 to 0.90 and 1.52 SSI).