Satish Bastola

The sensitivity of fluvial flood risk in Irish catchments to the range of IPCC AR4 climate change scenarios

In the face of increased flood risk responsible authorities have set out safety margins to incorporate climate change impacts in building robust flood infrastructure. Using the case study of four catchments in Ireland, this study subjects such design allowances to a sensitivity analysis of the uncertainty inherent in estimates of future flood risk. Uncertainty in flood quantiles is quantified using regionalised climate scenarios derived from a large number of GCMs (17), forced with three SRES emissions scenarios. In terms of hydrological response uncertainty within and between hydrological models is assessed using the GLUE framework. Regionalisation is achieved using a change factor method to infer changes in the parameters of a weather generator using monthly output from the GCMs, while flood frequency analysis is conducted using the method of probability weighted moments to fit the Generalised Extreme Value distribution to ~20,000 annual maximia series. Sensitivity results show that for low frequency events, the risk of exceedence of design allowances is greater than for more frequent events, with considerable implications for critical infrastructure. Peak flows for the five return periods assessed were found to be less sensitive to temperature and subsequently to potential evaporation (PET) than to rainfall. The average width of the uncertainty range for changes in flood magnitude is greater for low frequency events than for high frequency events. In all catchments there is a progressive increase in the peak flows associated with the 5, 25, 50 and 100-year return periods when moving from the 2020s to the 2080s.

Estimating daily time series of streamflow using hydrological model calibrated based on satellite observations of river water surface width: Toward real world applications.

Lacking observation data for calibration constrains applications of hydrological models to estimate daily time series of streamflow. Recent improvements in remote sensing enable detection of river water-surface width from satellite observations, making possible the tracking of streamflow from space. In this study, a method calibrating hydrological models using river width derived from remote sensing is demonstrated through application to the ungauged Irrawaddy Basin in Myanmar. Generalized likelihood uncertainty estimation (GLUE) is selected as a tool for automatic calibration and uncertainty analysis. Of 50,000 randomly generated parameter sets, 997 are identified as behavioral, based on comparing model simulation with satellite observations. The uncertainty band of streamflow simulation can span most of 10-year average monthly observed streamflow for moderate and high flow conditions. Nash-Sutcliffe efficiency is 95.7% for the simulated streamflow at the 50% quantile. These results indicate that application to the target basin is generally successful. Beyond evaluating the method in a basin lacking streamflow data, difficulties and possible solutions for applications in the real world are addressed to promote future use of the proposed method in more ungauged basins.

Transferability of hydrological models and ensemble averaging methods between contrasting climatic periods

Understanding hydrological model predictive capabilities under contrasting climate conditions enables more robust decision making. Using Differential Split Sample Testing (DSST), we analyze the performance of six hydrological models for 37 Irish catchments under climate conditions unlike those used for model training. Additionally, we consider four ensemble averaging techniques when examining interperiod transferability. DSST is conducted using 2/3 year noncontinuous blocks of (i) the wettest/driest years on record based on precipitation totals and (ii) years with a more/less pronounced seasonal precipitation regime. Model transferability between contrasting regimes was found to vary depending on the testing scenario, catchment, and evaluation criteria considered. As expected, the ensemble average outperformed most individual ensemble members. However, averaging techniques differed considerably in the number of times they surpassed the best individual model member. Bayesian Model Averaging (BMA) and the Granger-Ramanathan Averaging (GRA) method were found to outperform the simple arithmetic mean (SAM) and Akaike Information Criteria Averaging (AICA). Here GRA performed better than the best individual model in 51%–86% of cases (according to the Nash-Sutcliffe criterion). When assessing model predictive skill under climate change conditions we recommend (i) setting up DSST to select the best available analogues of expected annual mean and seasonal climate conditions; (ii) applying multiple performance criteria; (iii) testing transferability using a diverse set of catchments; and (iv) using a multimodel ensemble in conjunction with an appropriate averaging technique. Given the computational efficiency and performance of GRA relative to BMA, the former is recommended as the preferred ensemble averaging technique for climate assessment.

Seasonal Hydrological Forecasts for Watersheds over the Southeastern United States for the Boreal Summer and Fall Seasons

AbstractThe authors evaluate the skill of a suite of seasonal hydrological prediction experiments over 28 watersheds throughout the southeastern United States (SEUS), including Florida, Georgia, Alabama, South Carolina, and North Carolina. The seasonal climate retrospective forecasts [the Florida Climate Institute–Florida State University Seasonal Hindcasts at 50-km resolution (FISH50)] is initialized in June and integrated through November of each year from 1982 through 2001. Each seasonal climate forecast has six ensemble members. An earlier study showed that FISH50 represents state-of-the-art seasonal climate prediction skill for the summer and fall seasons, especially in the subtropical and higher latitudes. The retrospective prediction of streamflow is based on multiple calibrated rainfall–runoff models. The hydrological models are forced with rainfall from FISH50, (quantile based) bias-corrected FISH50 rainfall (FISH50_BC), and resampled historical rainfall observations based on matching observed an...

Topographic variability and the influence of soil erosion on the carbon cycle

Soil erosion, particularly that caused by agriculture, is closely linked to the global carbon (C) cycle. There is a wide range of contrasting global estimates of how erosion alters soil-atmosphere C exchange. This can be partly attributed to limited understanding of how geomorphology, topography, and management practices affect erosion and oxidation of soil organic C (SOC). This work presents a physically based approach that stresses the heterogeneity at fine spatial scales of SOC erosion, SOC burial, and associated soil-atmosphere C fluxes. The Holcombes Branch watershed, part of the Calhoun Critical Zone Observatory in South Carolina, USA, is the case study used. The site has experienced some of the most serious agricultural soil erosion in North America. We use SOC content measurements from contrasting soil profiles and estimates of SOC oxidation rates at multiple soil depths. The methodology was implemented in the tRIBS-ECO (Triangulated Irregular Network-based Real-time Integrated Basin Simulator-Erosion and Carbon Oxidation), a spatially and depth-explicit model of SOC dynamics built within an existing coupled physically based hydro-geomorphic model. According to observations from multiple soil profiles, about 32% of the original SOC content has been eroded in the study area. The results indicate that C erosion and its replacement exhibit significant topographic variation at relatively small scales (tens of meters). The episodic representation of SOC erosion reproduces the history of SOC erosion better than models that use an assumption of constant erosion in space and time. The net atmospheric C exchange at the study site is estimated to range from a maximum source of 14.5 g m−2 yr−1 to a maximum sink of −18.2 g m−2 yr−1. The small-scale complexity of C erosion and burial driven by topography exerts a strong control on the landscapes capacity to serve as a C source or a sink.

Sensitivity of Hydrological Simulations of Southeastern United States Watersheds to Temporal Aggregation of Rainfall

AbstractThis study investigates the sensitivity of the performance of hydrological models to certain temporal variations of precipitation over the southeastern United States (SEUS). Because of observational uncertainty in the estimates of rainfall variability at subdaily scales, the analysis is conducted with two independent rainfall datasets that resolve the diurnal variations. In addition, three hydrological models are used to account for model uncertainty. Results show that the temporal aggregation of subdaily rainfall can translate into a markedly higher volume error in flow simulated by the hydrological models. For the selected watersheds in the SEUS, the volume error is found to be high (~35%) for a 30-day aggregation in some of the selected watersheds. Hydrological models tend to underestimate flow in these watersheds with a decrease in temporal variability in precipitation. Furthermore, diminishing diurnal amplitude by removing subdaily rainfall corresponding to times of climatological daily maxim...

Hydrologic impacts of future climate change on Southeast US watersheds

The hydrological impact of climate change is assessed for 28 watersheds located within the Southeast United States using output from global climate models (GCMs) from the Climate Model Intercomparison Project phase 3 (CMIP3) and phase 5 (CMIP5) run. Subsequently, the impact of projected change in seasonal streamflow is derived by propagating projected scenarios, generated using changes derived from GCMs and weather generators, through a suite of conceptual hydrological models. Analysis shows that the spread in the magnitude of change in temperature and rainfall for CMIP3 is wider than that for CMIP5. The reduction in the spread among many factors may be attributed to improved physics, model number and resolution, and emission scenarios. The spread in projected change in temperature (precipitation) increases (decreases) from southernmost to northernmost latitude. Hydrological projection with CMIP3 output for the 2070s shows that streamflow decreases for most of the watersheds in spring and summer and increased in fall. In contrast, CMIP5 results show an increase in flow for all seasons except with the high-end scenarios in spring. However, the uncertainty in projections in streamflow is high with model uncertainty dominating emission scenario. The variability in prediction uncertainty among watersheds is partly explained by the variability in wetness index. The probability distribution function for projected seasonal flow for each scenario is markedly wide and therefore reflects that the uncertainty associated with using multiple GCMs from both CMIP3 and CMIP5 experiment is high which makes design and implementation of adaption measure a difficult task.

Impact of hydrologically driven hillslope erosion and landslide occurrence on soil organic carbon dynamics in tropical watersheds

The dynamics of soil organic carbon (SOC) in tropical forests play an important role in the global carbon (C) cycle. Past attempts to quantify the net C exchange with the atmosphere in regional and global budgets do not systematically account for dynamic feedbacks among linked hydrological, geomorphological, and biogeochemical processes, which control the fate of SOC. Here we quantify effects of geomorphic perturbations on SOC oxidation and accumulation in two adjacent wet tropical forest watersheds underlain by contrasting lithology (volcaniclastic rock and quartz diorite) in the Luquillo Critical Zone Observatory. This study uses the spatially-explicit and physically-based model of SOC dynamics tRIBS-ECO (Triangulated Irregular Network-based Real-time Integrated Basin Simulator-Erosion and Carbon Oxidation) and measurements of SOC profiles and oxidation rates. Our results suggest that hillslope erosion at the two watersheds may drive C sequestration or CO2 release to the atmosphere, depending on the forest type and land use. The net erosion-induced C exchange with the atmosphere was controlled by the spatial distribution of forest types. The two watersheds were characterized by significant erosion and dynamic replacement of upland SOC stocks. Results suggest that the landscape underlain by volcaniclastic rock has reached a state close to geomorphic equilibrium, and the landscape underlain by quartz diorite is characterized by greater rates of denudation. These findings highlight the importance of the spatially-explicit and physical representation of C erosion driven by local variation in lithological and geomorphological characteristics and in forest cover. This article is protected by copyright. All rights reserved.

Reconciling droughts and landfalling tropical cyclones in the Southeastern United States

A popular perception is that landfalling tropical cyclones help to mitigate droughts in the Southeastern United States (SeUS). However intriguing paradigms on the role of large scale SST variations on continental US including SeUS droughts and seasonal Atlantic tropical cyclone activity confronts us. These paradigms suggest that in the presence of warm (cold) eastern tropical Pacific and cold (warm) Atlantic Ocean sea surface temperature anomaly (SSTA) lead to the increased likelihood of wetter (drier) conditions over the continental US including the SeUS. Juxtaposing this understanding with the fact that landfalling tropical cyclones contribute significantly to the annual mean total rainfall in the SeUS and in El Niño (La Niña) years with cold (warm) tropical Atlantic SSTA lead to reduced (increased) Atlantic tropical cyclone activity raises a conflict on the role of the large-scale SST variations in SeUS hydroclimate. This study attempts to investigate the apparent dichotomous role of the large scale SST variations on the SeUS hydrology by examining the role of rainfall from landfalling tropical cyclones in the SeUS to local seasonal droughts. Our study finds that the contribution of the rainfall from landfalling tropical cyclone on the mitigation of monthly drought in the 28 SeUS watersheds is relatively insignificant. So much so that the hydrological model uncertainty in estimating the drought index over the 28 SeUS watersheds is larger than the sensitivity exhibited by the drought index to the inclusion of rain from landfalling tropical cyclone. The conclusions of this study are justified by the fact that the timing of the landfalling tropical cyclone in relation to overall soil moisture conditions of the watershed does not coincide with a drought like situation in the 1948–2006 time period analyzed in this study. This largely stems from the fact that the large-scale flow pattern resulting in abundant (lack of) advection of moisture for anomalously wet (dry) summer and fall seasons in the SeUS emanating from the source region of the Caribbean Sea and the northwestern tropical Atlantic Ocean coincides with the steering flow of the Atlantic tropical cyclones bound to make landfall in the SeUS (recurving away from the SeUS).

Against a ‘wait and see’ approach in adapting to climate change

Simulations of future climate change impacts are highly uncertain, particularly for catchment hydrology, where output from models of complex dynamic systems (global climate) are used as inputs to models of complex dynamic systems (hydrology models). This is problematic where decision-making for adaptation is underpinned by future climate predictions, and where policy-makers have opted to delay adaptation until either uncertainties are reduced, or climate change signals emerge from observations. This paper, using the Boyne catchment in the east of Ireland as a case study, discusses the uncertainties involved in climate change impact assessment for catchment hydrology and highlights why uncertainties are unlikely to be constrained or reduced in the time-scale required for adaptation. In addition, by calculating the time required for climate change signals to emerge from the observational record and the magnitude of change required for detection, it is highlighted that waiting for climate signals to be statistically detectable is not an option for effective adaptation. The paper concludes by considering how a paradigm shift in how we use the output from climate impact assessments can progress the adaptation agenda given the limits to prediction identified.