Runoff Sensitivity Research Articles

Uncertainty of runoff sensitivity to climate change in the Amazon River basin.

We employ the approach of Roderick and Farquhar (2011) to assess the sensitivity of runoff (R) given changes in precipitation (P), potential evapotranspiration (Ep ), and other properties that change the partitioning of P (n) by estimating coefficients that predict the weight of each variable in the relative change of R. We use this framework using different data sources and products for P, actual evapotranspiration (E), and Ep within the Amazon River basin to quantify the uncertainty of the hydrologic response at the subcatchment scale. We show that when estimating results from the different combinations of datasets for the entire river basin (at Óbidos), a 10% increase in P would increase R on average 16%, while a 10% increase in Ep would decrease R about 6%. In addition, a 10% change in the parameter n would affect the hydrological response of the entire basin around 5%. However, results change from catchment to catchment and are dependent on the combination of datasets. Finally, results suggest that enhanced estimates of E and Ep are needed to improve our understanding of the future scenarios of hydrological sensitivity with implications for the quantification of climate change impacts at the regional (subcatchment and subbasin) scale in Amazonia.

Runoff sensitivity of Indian sub-continental river basins

Understanding the sensitivity of water availability in the current and future climate in the Indian sub-continent is vital for food and water security. Using the Variable Infiltration Capacity (VIC) model and Budyko's framework with two observational datasets, we estimated water budget and mean annual runoff sensitivity to precipitation and potential evapotranspiration (PET) over 18 major river basins and 222 sub-basins in the Indian sub-continent. The river basins located in the north experienced a decline in mean annual precipitation while the basins in the south witnessed an increase in mean annual precipitation. Declined precipitation and increased PET resulted in a decrease in mean annual runoff in Brahmaputra, Ganga, and Indus basins during 1980–2014. On the other hand, mean annual runoff has increased in Sabarmati, South Coast, Subernarekha, Tapi, Mahanadi, East coast, Cauvery, and Brahmani river basins. Mean annual AET estimated using the Budyko's framework was underestimated while mean annual total runoff was overestimated for the majority of the basins in comparison to the estimates from the VIC model. Moreover, the Budyko's framework with both observational datasets underestimated runoff sensitivity to the changes in precipitation and PET in comparison to the VIC model. Runoff is more sensitive to change in precipitation than PET for the majority of the river basins highlighting the importance of changes in precipitation for water availability in the Indian sub-continent. The VIC model simulated runoff and evapotranspiration are in better agreement with the observations in comparison to the estimates from the Budyko's framework. However, a large uncertainty was found in water budget and runoff sensitivity estimated using the VIC and Budyko's models, which highlights the importance of considering multiple models for estimation of the water budget and runoff sensitivity in the sub-continental river basins.

Thermodynamic and Energetic Limits on Continental Silicate Weathering Strongly Impact the Climate and Habitability of Wet, Rocky Worlds

Abstract The “liquid water habitable zone” (HZ) concept is predicated on the ability of the silicate weathering feedback to stabilize climate across a wide range of instellations. However, representations of silicate weathering used in current estimates of the effective outer edge of the HZ account for neither the thermodynamic limit on the concentration of weathering products in runoff set by clay precipitation nor the energetic limit on precipitation set by planetary instellation. We find that when the thermodynamic limit is included in an idealized coupled climate/weathering model, the steady-state planetary climate loses sensitivity to silicate dissolution kinetics, becoming sensitive to temperature primarily through the effect of temperature on runoff and to pCO 2 through an effect on solute concentration mediated by pH. This increases sensitivity to land fraction, CO 2 outgassing, and geological factors such as soil age and lithology, all of which are found to have a profound effect on the position of the effective outer edge of the HZ. The interplay between runoff sensitivity and the energetic limit on precipitation leads to novel warm states in the outer reaches of the HZ, owing to the decoupling of temperature and precipitation. We discuss strategies for detecting the signature of the silicate weathering feedback through exoplanet observations in light of insights derived from the revised picture of weathering.

Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model

Runoff sensitivity to snow depletion curve representation within a continental scale hydrologic model

Stormflow threshold behaviour in a subtropical mountainous headwater catchment during forest recovery period

AbstractForest ecohydrological feedbacks complicate the threshold behaviour of stormflow response to precipitation or wetting conditions on a long‐term scale (e.g. several years). In this study, the threshold behaviours in an evergreen‐deciduous mixed forested headwater catchment in southern China were examined during 2009–2015, when damaged vegetation was recovering after the great 2008 Chinese ice and snowstorm. The non‐uniqueness of the thresholds and the slow and rapid responses of stormflow at the outlet of the catchment in different hydro‐climate datasets with different maximum values of gross precipitation (P) and sums of precipitation and antecedent soil moisture index (P + ASI) were assessed. The thresholds of P and P + ASI required to trigger stormflows (i.e. ‘generation thresholds’) and the transition from slow to rapid responses of stormflow (i.e. ‘rise thresholds’) were compared both seasonally and annually. The results indicated significant differences in the analysed datasets, highlighting the need to compare thresholds with care to avoid misinterpretation. Seasonal variations in threshold behaviours in the catchment suggested that vegetation canopy interception contributed to higher rise thresholds, and wetter conditions resulted in higher runoff sensitivity to precipitation during the growing and rainy seasons. Furthermore, the generation thresholds were higher in the dormant season, possibly due to drier soil moisture conditions in the near‐channel areas. During the vegetation recovery period, the annual generation thresholds increased, however the rise thresholds did not exhibit a similar trend. The rapid stormflow response above the threshold decreased, possibly due to transpiration and interception of the recovered vegetation. However, the slow stormflow response to small rainfall events below the thresholds was higher in wetter years but lower in drier years, suggesting that the total water input dominated the stormflow response during small rainfall events. In conclusion, the seasonal and annual variations in threshold behaviours highlight that vegetation recovery and hydro‐climatic conditions had a notable impact on the stormflow response.

Sensitivity of Direct Runoff to Curve Number Using the SCS-CN Method

This study explores the impact of runoff curve number (CN) on the hydrological model outputs for the Morai watershed, Sindh-Pakistan, using the Soil Conservation Service Curve Number (SCS-CN) method. The SCS-CN method is an empirical technique used to estimate rainfall-runoff volume from precipitation in small watersheds, and CN is an empirically derived parameter used to calculate direct runoff from a rainfall event. CN depends on soil type, its condition, and the land use and land cover (LULC) of an area. Precise knowledge of these factors was not available for the study area, and therefore, a range of values was selected to analyze the sensitivity of the model to the changing CN values. Sensitivity analysis involves a methodological manipulation of model parameters to understand their impacts on model outputs. A range of CN values from 40-90 was selected to determine their effects on model results at the sub-catchment level during the historic flood year of 2010. The model simulated 362 cumecs of peak discharge for CN=90; however, for CN=40, the discharge reduced substantially to 78 cumecs (a 78.46% reduction). Event-based comparison of water volumes for different groups of CN values—90-75, 80-75, 75-70, and 90-40 —showed reductions in water availability of 8.88%, 3.39%, 3.82%, and 41.81%, respectively. Although it is known that the higher the CN, the greater the discharge from direct runoff and the less initial losses, the sensitivity analysis quantifies that impact and determines the amount of associated discharges with changing CN values. The results of the case study suggest that CN is one of the most influential parameters in the simulation of direct runoff. Knowledge of accurate runoff is important in both wet (flood management) and dry periods (water availability). A wide range in the resulting water discharges highlights the importance of precise CN selection. Sensitivity analysis is an essential facet of establishing hydrological models in limited data watersheds. The range of CNs demonstrates an enormous quantitative consequence on direct runoff, the exactness of which is necessary for effective water resource planning and management. The method itself is not novel, but the way it is proposed here can justify investments in determining the accurate CN before initiating mega projects involving rainfall-runoff simulations. Even a small error in CN value may lead to serious consequences. In the current study, the sensitivity analysis challenges the strength of the results of a model in the presence of ambiguity regarding CN value.

The potential to reduce uncertainty in regional runoff projections from climate models

Increasingly, climate change impact assessments rely directly on climate models. Assessments of future water security depend in part on how the land model components in climate models partition precipitation into evapotranspiration and runoff, and on the sensitivity of this partitioning to climate. Runoff sensitivities are not well constrained, with CMIP5 models displaying a large spread for the present day, which projects onto change under warming, creating uncertainty. Here we show that constraining CMIP5 model runoff sensitivities with observed estimates could reduce uncertainty in runoff projection over the western United States by up to 50%. We urge caution in the direct use of climate model runoff for applications and encourage model development to use regional-scale hydrological sensitivity metrics to improve projections for water security assessments. Model estimates of future hydroclimate are uncertain, especially at the regional scale. This Perspective argues that constraining model runoff and its sensitivity to precipitation and temperature can greatly reduce this uncertainty and improve climate model utility in water resource applications.

Will Human-Induced Vegetation Regreening Continually Decrease Runoff in the Loess Plateau of China?

China has become the largest contributing country to global vegetation regreening. However, the regreening pattern and subsequent impact on arid areas have not been comprehensively evaluated. Therefore, we selected the Loess Plateau, a representative arid region that has undergone evident vegetation restoration, to investigate the spatial patterns and temporal trends, as well as the drivers of vegetation change. This study primarily focused on 12 afforested watersheds during 2000–2018. Furthermore, both the impacts of vegetation regreening on runoff for the past two decades and the future projections were quantified based on the fraction of photosynthetically active radiation (fPAR), the Budyko model, and the global climate models (GCMs). fPAR for the last two decades indicates that vegetation in the Loess Plateau has experienced a continuous increasing trend during the growing season, primarily in response to the implementation of the Grain for Green Project (GFGP). Of the 12 watersheds, 9 experienced significant fPAR change with a change rate above 50%, and 11 exhibited a significant increase (p < 0.05) in runoff sensitivity to vegetation regreening, which indicates that vegetation regreening plays an increasingly important role in controlling runoff variation. The decline in runoff caused by vegetation regreening was particularly noticeable before 2011 or 2012; afterwards, runoff tended to vary with precipitation. In the future (2020–2049 and 2050–2099), decrease in runoff by regreening will be limited, as runoff is anticipated to decrease by 3.5% in 2020–2049 and 4.1% in 2050–2099 with a 20% increase in fPAR. These results indicate that runoff tends to be stable even with continuous vegetation regreening. While the reduction of runoff by regreening will be limited in the future, rapid human-induced vegetation regreening may aggravate water scarcity when flash droughts occur and may result in disasters in water-limited regions to the socio-economic stability and agriculture. Our study will provide an applicable theoretical foundation for water resources decision-making and ecological restoration.

Sensitivity analysis of runoff to climate variability and land-use changes in the Haihe Basin mountainous area of north China

Runoff has changed significantly in most regions in China over the past decades. Climate variability and land-use changes are considered to be the two main factors contributing to runoff variation. Investigating the mechanism of runoff variation is of great significance for the sustainable utilization of water resources. In this study, Mann-Kendall test, double cumulative curve method, and runoff sensitivity analysis method are adopted to separate and quantify the relative impacts of climate variability and land-use changes on runoff. Furthermore, the sensitivities of runoff were estimated with regard to these two factors in north China during the study period from 1957 to 2000. The results revealed a significant decreasing trend in annual runoff and indicated that land-use changes had a greater effect on runoff than climate variability since 1979. The runoff decline due to land-use changes was 68.33%, whereas the runoff decline due to climate variability was only 30.85%. Further analysis of the sensitivities of runoff to climate variability and land-use changes suggested that, with a 10% increase in precipitation, the runoff will increase by 35.1%; while a 10% increase in potential evapotranspiration or the aridity index will induce 25.1% or 27.5% decrease in runoff, respectively. The sensitivity coefficients of runoff to precipitation, potential evapotranspiration and aridity index (εP, εEP and εϕ), and runoff ratio (Q/P) and the aridity index exhibited a significant nonlinear negative correlation (Y = ax−b, R2 > 0.85), and a positive linear correlation (Y = ax + b, R2 < 0.50), respectively. All these results implied that runoff is more sensitive to the regions with water scarcity and drying climate. The average sensitivity coefficients of runoff to forest, grassland, and farmland were 1.46, 1.21 and 1.18, respectively. This means that a 10% increase in forest, grassland, and farmland coverage would induce 1.46, 1.21, and 1.18 mm decrease of runoff, respectively. This demonstrates that forests have the most effective impact on runoff reduction, followed by grassland or farmland.

Monthly Rainfall-Runoff Modeling at Watershed Scale: A Comparative Study of Data-Driven and Theory-Driven Approaches

Data-driven machine learning approaches have been rapidly developed in the past 10 to 20 years and applied to various problems in the field of hydrology. To investigate the capability of data-driven approaches in rainfall-runoff modeling in comparison to theory-driven models, we conducted a comparative study of simulated monthly surface runoff at 203 watersheds across the contiguous USA using a conceptual model, the proportionality hydrologic model, and a data-driven Gaussian process regression model. With the same input variables of precipitation and mean monthly aridity index, the two models showed similar performance. We then introduced two more input variables in the data-driven model: potential evaporation and the normalized difference vegetation index (NDVI), which were selected based on hydrologic knowledge. The modified data-driven model performed much better than either the conceptual or original data-driven model. A sensitivity analysis was conducted on all three models tested in this study, which showed that surface runoff responded positively to increased precipitation. However, a confounding effect on surface runoff sensitivity was found among mean monthly aridity index, potential evaporation, and NDVI. This confounding was caused by complex interconnections among energy supply, vegetation coverage, and climate seasonality of the watershed system. We also conducted an uncertainty analysis on the two data-driven models, which showed that both models had reasonable predictability within the 95% confidence interval. With the additional two input variables, the modified data-driven model had lower prediction uncertainty and higher prediction accuracy.

Assessing runoff sensitivities to precipitation and temperature changes under global climate-change scenarios

Abstract 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.

Contrasting warming and drought in snowmelt-dominated agricultural basins: revealing the role of elevation gradients in regional response to temperature change

For snow-dominated basins like the San Joaquin in California, warmer temperatures affect hydrologic processes and stress water resources that support valuable irrigated agriculture and urban populations. Large inter-annual swings in precipitation, seen recently in California, highlight the need to better understand the effects of potential future warming combined with such extreme multi-year precipitation variability. In this study we use an integrated hydrologic model (ParFlow-CLM) to examine the effects of mean annual warming of 2 °C and 4 °C on hydrologic response over a recent wet-dry cycle (2009–2013). Simulations are performed at a 1 km resolution over the San Joaquin basin to assess the hydrologic response that bridges the local and basin scales common to many previous studies of the region. At the basin scale, warmer temperatures reduce San Joaquin River runoff by an amount consistent with the contemporary dry years: for all but the wettest year, an increase in temperature of 4 °C reduces simulated runoff to the baseline value of the next driest year simulated. This runoff loss can be attributed to offsetting warming-induced increases in annual evapotranspiration, subsidized in dry years by subsurface storage. Locally, hydrologic response to warming manifests as variation along an elevation gradient. The basin-wide reduction in runoff is the net balance of local increases and reductions that follow a complex function of elevation: negative mean runoff sensitivity can occur at all elevations, yet a positive runoff sensitivity exists for select locations between 2000 m and 3500 m elevation. In contrast, warming increases mean ET at all elevations in the Sierra Nevada, with the highest increase between 1000 m and 3000 m. The average increase in local runoff and ET combine to reduce percolation below the root zone in locations across the Sierra Nevada while shifting event-scale increases in recharge to Central Valley river channels.

Runoff sensitivity to climate change in the Nile River Basin

In data scarce basins, such as the Nile River Basin (NRB) in Africa, constraints related to data availability, quality, and access often complicate attempts to estimate runoff sensitivity using conventional methods. In this paper, we show that by integrating the concept of the aridity index (AI) (derived from the Budyko curve) and climate elasticity, we can obtain the first order response of the runoff sensitivity using minimal data input and modeling expertise or experience. The concept of runoff elasticity relies on the fact that the energy available for evapotranspiration plays a major role in determining whether the precipitation received within a drainage basin generates runoff. The approach does not account for human impacts on runoff modification and or diversions. By making use of freely available gauge-corrected satellite data for precipitation, temperature, runoff, and potential evapotranspiration, we derived the sensitivity indicator (β) to determine the runoff response to changes in precipitation and temperature for four climatic zones in the NRB, namely, tropical, subtropical, semiarid and arid zones. The proposed sensitivity indicator can be partitioned into different elasticity components i.e: precipitation (εp), potential evapotranspiration (εETp), temperature (εT) and the total elasticity (εtot). These elasticities allow robust quantification of the runoff response to the potential changes in precipitation and temperature with a high degree of accuracy. Results indicate that the tropical zone is energy-constrained with low sensitivity, (β<1.0), implying that input precipitation exceeds the amounts that can be evaporated given the available energy. The subtropical zone is subdivided into two distinct regions, the lowland (Machar and Sudd marshes), and the highland area (Blue Nile Basin), where each area has a unique sensitivity. The lowland area has high sensitivity, (β>1.0). The subtropical-highland zone moves between energy-limited to water-limited conditions during periods of wet and dry spells with varying sensitivity. The semiarid and arid zones are water limited, with high sensitivity, (β>1.0). The calculated runoff elasticities show that a 10% decrease in precipitation leads to a decrease in runoff of between 19% in the tropical zone and 30% in the arid zones. On the other hand, a 10% precipitation increase leads to a runoff increase of 14% in the tropical zone and 22% in the arid zone. The estimated runoff changes are consistent with the result obtained using other methods. Thus, the elasticity approach combines data parsimony and analytical simplicity to produce results that are practically useful for most purposes while facilitating communication with stakeholders with different levels of scientific knowledge. More research is needed to extend the application of the method to incorporate the effects of human activities, and land use change.

Sensitivity of SWAT Simulated Runoff to Temperature and Rainfall in the Upper Awash Sab-Basin, Ethiopia

This paper estimates the runoff sensitivity to change in temperature and rainfall by using Soil and Water Assessment Tool (SWAT) hydrologic model. Rainfall and temperature were perturbed to study the runoff sensitivity to these climate variables. Rainfall and Temperature play main role in the accessibility of runoff by influencing the contribution and timing of water sources. The SWAT model was run with monthly station-based temperature and rainfall data for the whole upper awash sab-basin (1980-2010). The hydrological model was calibrated for eight years (1993-2000), and the simulation results were validated with the historical stream-flow for four years (2001-2005) at outlet of Hombole gauging station. The impact of temperature and rainfall changes on the runoff in the sub-basin was estimated by using a sensitivity analysis; through the calibrated and validated SWAT hydrological model. The SWAT model has been used to study the impact of possible hypothetical scenarios of rainfall and temperature on the annual runoff of the Awash sub-basin. This was achieved through both separated and combined use of changes in the amount of rainfall (+/-20% and increase temperature (warming of up to 5°C). The results shown that runoff was sensitive to rainfall and temperature changes in the study area. It has been revealed that 1, 2, 3, 4 and 5°C increases of the annual temperature lead to reduces annual runoff by -0.085, -0.88, -1.75, -2.55 and -3.30% respectively. The runoff has a positive correlation with rainfall change; but, a negative correlation with temperature change. The results reveal that runoff was more sensitive to rainfall than that of temperature rise. Increased/decreased rainfall by 10% will result in increased/reduced annual runoff by 22% and 21% respectively. Generally, the results showed that changes in the climate variables had a significant effect on water availability.

Alpine foreland running drier? Sensitivity of a drought vulnerable catchment to changes in climate, land use, and water management

Southeastern Austria as part of the southeastern Alpine forelands experiences an increase of temperature and a tendency of decreasing precipitation. Especially in summer, the temperature strongly increased by about 0.7 °C per decade since the 1970s. Drought vulnerability under climate change is therefore a key question in this region. Here, we address this question by exploring the hydrological sensitivity of the Raab catchment in Austria (area 987 km2), a typical catchment in these Alpine forelands. Using the process-oriented Water Flow and Balance Simulation Model (WaSiM) over 1982–2011, we focus on low-flow conditions during extended summer (May–September) and analyze the catchment’s runoff sensitivity to climate change, but also land use and water management change. We find that climate change drivers dominate the summertime runoff response (decrease > 40/> 70%), based on moderate and strong climate change cases in the region (temperature + 2/+ 4 K, precipitation − 15/− 30%). Land use changes towards more dry and sealed areas enhance surface runoff and thus may lead to somewhat increased flood peaks. In contrast, water withdrawal for irrigation reduces runoff during low-flow periods in the summer when the irrigation demand is high. Although the impact of these non-climatic drivers on runoff generally is lower than that of the climate change considered, their interactive effects may reinforce the catchment’s tendency of running drier during summer. While more detailed scenario-based assessments are needed to further assess drought risks, this initial study provides clear evidence for the vulnerability of Alpine foreland catchments to increasing summer dryness under climate change.

A Global Assessment of Runoff Sensitivity to Changes in Precipitation, Potential Evaporation, and Other Factors

AbstractPrecipitation (P) and potential evaporation (Ep) are commonly studied drivers of changing freshwater availability, as aridity (Ep/P) explains ∼90% of the spatial differences in mean runoff across the globe. However, it is unclear if changes in aridity over time are also the most important cause for temporal changes in mean runoff and how this degree of importance varies regionally. We show that previous global assessments that address these questions do not properly account for changes due to precipitation, and thereby strongly underestimate the effects of precipitation on runoff. To resolve this shortcoming, we provide an improved Budyko‐based global assessment of the relative and absolute sensitivity of precipitation, potential evaporation, and other factors to changes in mean‐annual runoff. The absolute elasticity of runoff to potential evaporation changes is always lower than the elasticity to precipitation changes. The global pattern indicates that for 83% of the land grid cells runoff is most sensitive to precipitation changes, while other factors dominate for the remaining 17%. This dominant role of precipitation contradicts previous global assessments, which considered the impacts of aridity changes as a ratio. We highlight that dryland regions generally display high absolute sensitivities of runoff to changes in precipitation, however within dryland regions the relative sensitivity of runoff to changes in other factors (e.g., changing climatic variability, CO2‐vegetation feedbacks, and anthropogenic modifications to the landscape) is often far higher. Nonetheless, at the global scale, surface water resources are most sensitive to temporal changes in precipitation.

Use of a scenario-neutral approach to identify the key hydro-meteorological attributes that impact runoff from a natural catchment

Scenario-neutral approaches are being used increasingly for assessing the potential impact of climate change on water resource systems, as these approaches allow the performance of these systems to be evaluated independently of climate change projections. However, practical implementations of these approaches are still scarce, with a key limitation being the difficulty of generating a range of plausible future time series of hydro-meteorological data. In this study we apply a recently developed inverse stochastic generation approach to support the scenario-neutral analysis, and thus identify the key hydro-meteorological variables to which the system is most sensitive. The stochastic generator simulates synthetic hydro-meteorological time series that represent plausible future changes in (1) the average, extremes and seasonal patterns of rainfall; and (2) the average values of temperature (Ta), relative humidity (RH) and wind speed (uz) as variables that drive PET. These hydro-meteorological time series are then fed through a conceptual rainfall-runoff model to simulate the potential changes in runoff as a function of changes in the hydro-meteorological variables, and runoff sensitivity is assessed with both correlation and Sobol’ sensitivity analyses. The method was applied to a case study catchment in South Australia, and the results showed that the most important hydro-meteorological attributes for runoff were winter rainfall followed by the annual average rainfall, while the PET-related meteorological variables had comparatively little impact. The high importance of winter rainfall can be related to the winter-dominated nature of both the rainfall and runoff regimes in this catchment. The approach illustrated in this study can greatly enhance our understanding of the key hydro-meteorological attributes and processes that are likely to drive catchment runoff under a changing climate, thus enabling the design of tailored climate impact assessments to specific water resource systems.

Runoff sensitivity over Asia: Role of climate variables and initial soil conditions

AbstractWe applied statistical and numerical modeling approach to evaluate the sensitivity of runoff (ROF) to climate variables using Global Land Data Assimilation System (GLDAS) data and regional climate model (RegCM4). It was observed that ROF is more sensitive to precipitation (P) compared to other analyzed hydroclimatic variables (potential evapotranspiration (PET), 2 m air temperature (T2m), solar radiation (Rn), specific humidity (SSH), and wind speed (U), especially over India, Indochina, and south‐north‐northeast China semihumid‐humid climate transition zones based on the higher correlation coefficient (&gt;0.7) and elasticity (&gt;2). The abnormal positive T2m‐ROF observed over Tibetan Plateau region (TP) may be due to its high topography and cold weather regime, while positive PET‐ROF over India and north China‐southeast Mongolia regions can be attributed to the stronger influence of local land‐atmosphere interactions. Soil moisture (SM) reflects high correlation with runoff, especially over the climate transition zones (i.e., India and Indochina‐southeast China). The initial wet (dry) soil moisture (SM) anomalies lead to an increase (decrease) of ROF in each season with the hot spots mainly located in middle to high latitudes (spring), TP and northeast (summer and autumn), and Indochina (autumn) regions. Such influence can persist almost 4 months in spring while only about 1 month in autumn during dry and wet conditions. The wet condition has stronger influence at beginning but dissipates quickly, while the dry condition can last longer within the same season. The impact of initial soil temperature anomalies on ROF is weaker than SM, with the only obvious ROF changes located over south China (spring and summer) and north India (autumn).

Impact of evapotranspiration process representation on runoff projections from conceptual rainfall‐runoff models

AbstractConceptual rainfall‐runoff models are commonly used to estimate potential changes in runoff due to climate change. The development of these models has generally focused on reproducing runoff characteristics, with less scrutiny on other important processes such as the conversion from potential evapotranspiration (PET) to actual evapotranspiration (AET). This study uses three conceptual rainfall‐runoff models (GR4J, AWBM, and IHACRES_CMD) and five catchments in climatologically different regions of Australia to explore the role of ET process representation on the sensitivity of runoff to plausible future changes in PET. The changes in PET were simulated using the Penman‐Monteith model and by perturbing each of the driving variables (temperature, solar radiation, humidity, and wind) separately. Surprisingly, the results showed the potential of a more than sevenfold difference in runoff sensitivity per unit change in annual average PET, depending on both the rainfall‐runoff model and the climate variable used to perturb PET. These differences were largely due to different ways used to convert PET to AET in the conceptual rainfall‐runoff models, with particular dependencies on the daily wet/dry status, as well as the seasonal variations in store levels. By comparing the temporal patterns in simulated AET with eddy‐covariance‐based observations at two of the study locations, we highlighted some unrealistic behavior in the simulated AET from AWBM. Such process‐based evaluations are useful for scrutinizing the representation of physical processes in alternative conceptual rainfall‐runoff models, which can be particularly useful for selecting models for projecting runoff under a changing climate.

Relative contribution of land use change and climate variability on discharge of upper Mara River, Kenya

Abstract Study region Nyangores River watershed, headwater catchment of Mara River basin in Kenya. Study focus Climate variability and human activities are the main drivers of change of watershed hydrology. The contribution of climate variability and land use change to change in streamflow of Nyangores River, was investigated. Mann Kendall and sequential Mann Kendall tests were used to investigate the presence and breakpoint of a trend in discharge data (1965–2007) respectively. The Budyko framework was used to separate the respective contribution of drivers to change in discharge. Future response of the watershed to climate change was predicted using the runoff sensitivity equation developed. New hydrological insights for the region There was a significant increasing trend in the discharge with a breakpoint in 1977. Land use change was found to be the main driver of change in discharge accounting for 97.5% of the change. Climate variability only caused a net increase of the remaining 2.5% of the change; which was caused by counter impacts on discharge of increase in rainfall (increased discharge by 24%) and increase in potential evapotranspiration (decreased discharge by 21.5%). Climate change was predicted to cause a moderate 16% and 15% increase in streamflow in the next 20 and 50 years respectively. Change in discharge was specifically attributed to deforestation at the headwaters of the watershed.