Explore the vast range of titles available.

Ongoing runoff declines in the Colorado River Basin have been shown to be predominately driven by decreasing albedo from warming‐driven snow‐cover loss, especially in late‐spring (hereafter snowmelt‐radiation feedback). Here, we explore the feedback's impact on annual runoff sensitivity to warming across the western U.S. (WUS) using hydrologic model simulations. For 1°C uniform warming, we show that runoff is most sensitive to warming in modestly snow‐covered, interior mountain headwaters, especially the Rocky Mountains. Runoff sensitivities are most associated with the snowmelt‐radiation feedback in basins with runoff coefficients between 0.2 and 0.6, where runoff sensitivity increases with more snow and lower winter temperature. In aggregate, ∼48% of WUS runoff sensitivity is attributable to the snowmelt‐radiation feedback and is especially pronounced in the warming‐sensitive river basins (annual runoff decreases >5%/°C). We also show that the feedback's impact decreases with increasing temperature, which has unresolved implications for streamflow declines in a less‐snow future. Plain Language Summary Regional climate warming is driving strong runoff changes in the western U.S. (WUS), especially the Upper Colorado River Basin (UCRB). Previous work showed that warming‐related snow cover reductions lead to more solar radiation absorption and evapotranspiration, which largely explain ongoing runoff declines in UCRB. Here, we assess the impact of this snowmelt‐radiation feedback on warming‐induced runoff changes across WUS. In a warmer world, we find that the largest annual runoff sensitivities are in the interior mountainous WUS with modest snow cover. The snowmelt‐radiation feedback explains over half of the warming‐induced runoff changes in warming‐sensitive WUS basins and about half of WUS' overall runoff sensitivity. In areas influenced by the snowmelt‐radiation feedback, both runoff sensitivity and the feedback's contribution become smaller with higher temperatures, suggesting a potentially slower rate of streamflow decline as temperatures rise in a warmer future. Key Points Snowmelt‐radiation feedback accounts for ∼1/2 of warming‐driven runoff decline across the Western U.S. (WUS) Runoff sensitivities are most linked to snowmelt‐radiation feedback in river basins with runoff coefficients in the range 0.2–0.6 Runoff sensitivities to warming are largest in modestly snow‐covered, interior mountainous parts of WUS, especially the Rocky Mountains

Crucial to the assessment of future water security is how the land model component of Earth System Models partition precipitation into evapotranspiration and runoff, and the sensitivity of this partitioning to climate. This sensitivity is not explicitly constrained in land models nor the model parameters important for this sensitivity identified. Here, we seek to understand parametric controls on runoff sensitivity to precipitation and temperature in a state‐of‐the‐science land model, the Community Land Model version 5 (CLM5). Process‐parameter interactions underlying these two climate sensitivities are investigated using the sophisticated variance‐based sensitivity analysis. This analysis focuses on three snow‐dominated basins in the Colorado River headwaters region, a prominent exemplar where land models display a wide disparity in runoff sensitivities. Runoff sensitivities are dominated by indirect or interaction effects between a few parameters of subsurface, snow, and plant processes. A focus on only one kind of parameters would therefore limit the ability to constrain the others. Surface runoff exhibits strong sensitivity to parameters of snow and subsurface processes. Constraining snow simulations would require explicit representation of the spatial variability across large elevation gradients. Subsurface runoff and soil evaporation exhibit very similar sensitivities. Model calibration against the subsurface runoff flux would therefore constrain soil evaporation. The push toward a mechanistic treatment of processes in CLM5 have dampened the sensitivity of parameters compared to earlier model versions. A focus on the sensitive parameters and processes identified here can help characterize and reduce uncertainty in water resource sensitivity to climate change. Key Points Parametric controls on runoff sensitivity to climate in the land component of an Earth System Model are investigated in a snowmelt runoff regime Runoff sensitivities are dominated by interactions between certain parameters of subsurface, snow, and plant processes High sensitivity is found for soil porosity and depth, saturated fraction, snow cover and densification, stomatal conductance, and photosynthetic capacity

We use the Budyko framework to calculate catchment‐scale evapotranspiration (E) and runoff (Q) as a function of two climatic factors, precipitation (P) and evaporative demand (Eo = 0.75 times the pan evaporation rate), and a third parameter that encodes the catchment properties (n) and modifies how P is partitioned between E and Q. This simple theory accurately predicted the long‐term evapotranspiration (E) and runoff (Q) for the Murray‐Darling Basin (MDB) in southeast Australia. We extend the theory by developing a simple and novel analytical expression for the effects on E and Q of small perturbations in P, Eo, and n. The theory predicts that a 10% change in P, with all else constant, would result in a 26% change in Q in the MDB. Future climate scenarios (2070–2099) derived using Intergovernmental Panel on Climate Change AR4 climate model output highlight the diversity of projections for P (±30%) with a correspondingly large range in projections for Q (±80%) in the MDB. We conclude with a qualitative description about the impact of changes in catchment properties on water availability and focus on the interaction between vegetation change, increasing atmospheric [CO2], and fire frequency. We conclude that the modern version of the Budyko framework is a useful tool for making simple and transparent estimates of changes in water availability. Key Points A new simple approach to estimating the sensitivity to runoff change

River runoff is a key index of renewable water resources which affect almost all human and natural systems. Any substantial change in runoff will therefore have serious social, environmental, and ecological consequences. We estimate the runoff response to global mean temperature change implied by the climate change experiments generated for the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (IPCC AR4). In contrast to previous studies, we estimate the runoff sensitivity using global mean temperature change as an index of anthropogenic climate changes in temperature and precipitation, with the rationale that this removes the dependence on emissions scenarios. Our results show that the runoff sensitivity implied by the IPCC experiments is relatively stable across emissions scenarios and global mean temperature increments, but varies substantially across models with the exception of the high‐latitudes and currently arid or semi‐arid areas. The runoff sensitivities are slightly higher at 0.5°C warming than for larger amounts of warming. The estimated ratio of runoff change to (local) precipitation change (runoff elasticity) ranges from about one to three, and the runoff temperature sensitivity (change in runoff per degree C of local temperature increase) ranges from decreases of about 2 to 6% over most basins in North America and the middle and high latitudes of Eurasia. Large river runoff sensitivity to global warming is independent of emissions pathway Runoff sensitivity to global warming is slightly larger for small temperature changes Runoff sensitivity to local forcing changes is generally consistent with observations

Climate change and human activities exert significant impact on the mechanism of runoff generation and confluence. Comprehending the reasons of runoff change is crucial for the sustainable development of water resources. Taking the Upper Shule River as the research area, the M-K test and the moving t test were used to diagnose the runoff mutation time. Furthermore, the slope changing ratio of cumulative quantity method (SCRCQ), climate elasticity method, and Budyko equation were utilized to quantitatively evaluate the impacts and contribution rates of climate change and human activities. The following results were obtained: (1) The Upper Shule River experienced a significant increase in runoff from 1972 to 2021, with 1998 marking the year of abrupt change. (2) The runoff sensitivity showed a downward trend from 1972 to 2021. The main factor affecting the decrease in runoff sensitivity was the characteristic parameters of underlying surface (n), followed by precipitation (P), while the influence of potential evapotranspiration (ET0) was the weakest. (3) The response of runoff changes to runoff sensitivity and influencing factors were 90.32% and 9.68%, respectively. (4) The results of three attribution methods indicated that climate change was the primary factor causing the alteration of runoff in the Upper Shule River. The research results supplement the hydrological change mechanisms of the Upper Shule River and provide a scientific basis for future water resources management and flood control measures.

The Kolyma is the largest river, the basin of which lies entirely in the zone of continuous permafrost and is subject to considerable climate changes, which affects its hydrological regime. The current dynamics of water flow and runoff-formation factors over 1979–2020 are analyzed, and the sensitivity of water regime characteristics of the Kolyma River to changes in climate parameters is studied. The comparison of the mean values of meteorological characteristics in the Kolyma basin over 2000–2020 with the mean values over 1979–1999 showed an increase in the normal annual air temperature by 1.3°C, the total annual precipitation increased by 8.3% with the most abrupt increase in September and March. According to observation data, the annual river runoff at the Kolymskoe gage (283 km from the Kolyma mouth), increased by 5.2%. Physical-mathematical models of river runoff formation have gained in popularity as a tool for studying the features of the water regime and its dynamics. In this study, the ECOMAG model is applied to the Kolyma R. basin, where its calibration and verification for two gages showed a good reproducibility of the actual water discharges. To better understand the mechanisms of the response of Kolyma water regime to changes in the climate characteristics based on ECOMAG model, the sensitivity of runoff characteristics to air temperature and precipitation was analyzed.

An accurate grasp of the influence of precipitation and temperature changes on the variation in both the magnitude and temporal patterns of runoff is crucial to the prevention of floods and droughts. However, there is a general lack of understanding of the ways in which runoff sensitivities to precipitation and temperature changes are associated with the CMIP5 scenarios. This paper investigates the hydrological response to future climate change under CMIP5 RCP scenarios by using the Variable Infiltration Capacity (VIC) model and then quantitatively assesses runoff sensitivities to precipitation and temperature changes under different scenarios by using a set of simulations with the control variable method. The source region of the Yellow River (SRYR) is an ideal area to study this problem. The results demonstrated that the precipitation effect was the dominant element influencing runoff change (the degree of influence approaching 23%), followed by maximum temperature (approaching 12%). The weakest element was minimum temperature (approaching 3%), despite the fact that the increases in minimum temperature were higher than the increases in maximum temperature. The results also indicated that the degree of runoff sensitivity to precipitation and temperature changes was subject to changing external climatic conditions.

Several simulations of the surface climate and energy balance of Vatnajökull ice cap, Iceland, are used to estimate the glacier runoff for the period 1980–2015 and the sensitivity of runoff to the spring conditions (e.g., snow thickness). The simulations are calculated using the snow pack scheme from the regional climate model HIRHAM5, forced with incoming mass and energy fluxes from the numerical weather prediction model HARMONIE-AROME. The modeled runoff is compared to available observations from two outlet glaciers to assess the quality of the simulations. To test the sensitivity of the runoff to spring conditions, simulations are repeated for the spring conditions of each of the years 1980–2015, followed by the weather of all summers in the same period. We find that for the whole ice cap, the variability in runoff as a function of varying spring conditions was on average 31% of the variability due to changing summer weather. However, some outlet glaciers are very sensitive to the amount of snow in the spring, as e.g., the variation in runoff from Brúarjökull due to changing spring conditions was on average 50% of the variability due to varying summer weather.

An understanding of the role of hydro-climatic and geographic regimes on regional actual evapotranspiration (AET) change is essential to improving our knowledge on predicting water availability in a changing climate. This study investigates the relationship between AET change for a 60 year period (1951-2010) and the runoff sensitivity in 255 undisturbed catchments over the US. The runoff sensitivity to climate change is simply defined as the relative magnitude between runoff and precipitation changes with time. Runoff sensitivity can readily explain the conflicting directions of AET changes under similar precipitation change. Under increasing precipitation, AET decreases when runoff is increasing more rapidly than precipitation based on the water balance. Conversely, AET increases when runoff is decreasing more rapidly than precipitation. This result indicates that runoff sensitivity to climate change is a key factor for understanding regional water availability change at the catchment scale. In addition, a stepwise multiple regression analysis and a geographically weighted regression analysis show that the portion of evergreen forest and the mean elevation of a catchment may play a secondary role in the spatial pattern of the AET change, and the relative importance of such explanatory variables may change over space.

Land and water are the most necessary natural resources because the entire life system depends on them. It requires proper management to achieve maximum utilization. When used in conjunction with Arc GIS, the Soil and Water Assessment Tool (SWAT) is a promising model for simulating the agricultural watershed since it can forecast runoff, sediment and nutrient transport, and erosion under various management scenarios. Furthermore, the model is better at evaluating both the spatial and non-spatial variation of hydrological methods under a very large watershed. This study uses the methodology employed by the SWAT model for the estimation of surface runoff and sediment yield and discusses in detail the setup of the model computer file needed by the model sensitivity analysis parameter and validation area unit. SWAT is a well-known hydrological modeling method used in many hydrologic and environmental simulations. Over 17 years (2005-2021), 212 studies were found from various peer-reviewed scientific publications listed on the SWAT online database (CARD). Applicability studies were divided into five categories: water resources, streamflow, erosion, land-use planning and agricultural settings, climate change scenarios, and model parameterization. Hydrologic phenomena and adaptations in various river basins have been investigated. They mostly examined environmental impacts and preventive techniques to ensure an understanding of effective environmental regulation. Streamflow susceptibility to climatic changes was shown in climate change studies. Modeling streamflow parameters, model modifications, and basin-scale calibrations were investigated. Future simulation aspects such as data sharing and the opportunity for improved future analysis are also discussed. A multimodal approach to future simulations, as well as more efforts to make local data available, are both very good ideas.