Evolution of the rates of mass wasting and fluvial sediment transfer from the epicentral area of the 1999, Mw 7.6 earthquake

The 1999 Chichi earthquake (Mw=7.6) triggered more than 20,000 landslides in the epicentral area in central west Taiwan, and subsequent typhoons have caused an even larger number of slope failures. As a result, the suspended sediment load of the epi- central Choshui River has increased dramatically. Measurements of suspended sedi- ment at a downstream gauging station indicate that the unit sediment concentration increased about six times due to the earthquake, and decreased exponentially due to flushing by subsequent typhoons. The e-folding time scale of the seismic perturbation of sediment transfer in the Choshui River is 3-5 years. Based on this estimate of the de- cay of the erosional response to the earthquake, a mass balance can be calculated for the earthquake, including co-seismic uplift and subsidence, post-seismic relaxation, and erosion. This mass balance shows that the Chi-Chi earthquake has acted to build ridge topography in the hanging wall of the fault, but in the far field, some destruc- tion of topography has occurred. However, our estimate of seismically-driven erosion may be incomplete. A detailed analysis of landsliding in the Chenyoulan tributary of the Choshui River indicates that most co-and post seismic landslide debris remains on hillslopes within the catchment. Recent typhoons have continued to cause high rates of landsliding high in the landscape, but rates of mass wasting near the stream net- work have decreased. The full geomorphic response to the Chi-Chi earthquake may be much larger, and more protracted than indicated by river gauging data

Bias correction of daily precipitation simulated by a regional climate model: a comparison of methods

Quantifying the effects of future changes in the frequency of precipitation extremes is a key challenge in assessing the vulnerability of hydrological systems to climate change but is difficult as climate models do not always accurately simulate daily precipitation. This article compares the performance of four published techniques used to reduce the bias in a regional climate model precipitation output: (1) linear, (2) nonlinear, (3) γ -based quantile mapping and (4) empirical quantile mapping. Overall performance and sensitivity to the choice of calibration period were tested by calculating the errors in the first four statistical moments of generated daily precipitation time series and using a cross-validation technique. The study compared the 1961–2005 precipitation time series from the regional climate model HadRM3.0-PPE-UK (unperturbed version) with gridded daily precipitation time series derived from rain gauges for seven catchments spread throughout Great Britain. We found that while the first and second moments of the precipitation frequency distribution can be corrected robustly, correction of the third and fourth moments of the distribution is much more sensitive to the choice of bias correction procedure and to the selection of a particular calibration period. Overall, our results demonstrate that, if both precipitation data sets can be approximated by a γ -distribution, the γ -based quantilemapping technique offers the best combination of accuracy and robustness. In circumstances where precipitation data sets cannot adequately be approximated using a γ -distribution, the nonlinear method is more effective at reducing the bias, but the linear method is least sensitive to the choice of calibration period. The empirical quantile mapping method can be highly accurate, but results were very sensitive to the choice of calibration time period. However, it should be borne in mind that bias correction introduces additional uncertainties, which are greater for higher order moments

Scoping study for precipitation downscaling and bias-correction

Various methods exist for correcting biases in climate model precipitation data. This study has investigated four of these bias-correction methods, here called linear, non-linear, gamma and empirical, and extensively tested their performance and suitability for biascorrecting daily precipitation outputs from a Regional Climate Model (RCM) for use as inputs to hydrological models over six test regions spanning the Great Britain. The RCM daily precipitation data were taken from the unperturbed variant of the Met Office Hadley Centre Regional Model Perturbed Physics Ensemble (HadRM3-PPE-UK), and observed daily precipitation data were taken from the Continuous Estimation of River Flows gridded precipitation dataset. Spatial downscaling (re-gridding) and correction of the fraction of rain-days were undertaken as pre-processing steps before the bias-correction procedure, which translated the RCM data from a 0.22° grid sca le to the 1 km grid scale of the observed dataset. Re-sampling tests were used to assess the performance of the bias-correction methods in terms of the first four statistical moments, and cumulative distribution functions (cdfs) were produced to compare the distribution of the bias-corrected precipitation with respect to the observed and pre-processed RCM precipitation. We found that whilst the first and second moments of the precipitation frequency distribution can be corrected robustly, correction of the third and fourth moments of the distribution is much more sensitive to the choice of biascorrection procedure and to the selection of a particular calibration period. Overall, our results demonstrate that, if both precipitation datasets can be approximated by a gamma distribution, the gamma-based quantile-mapping technique offers the best combination of accuracy and robustness. In circumstances where precipitation datasets cannot adequately be approximated using a gamma distribution, the non-linear method is more effective at reducing the bias but the linear method is least sensitive to the choice of calibration period. The empirical quantile mapping method can be highly accurate, but results were very sensitive to the choice of calibration time period. Examination of the seasonal variation of the non-linear bias-correction factors showed that the bias-correction applied to the HadRM3 daily precipitation varied with season, location, topography and precipitation intensity, suggesting that the method is capable of reproducing many features of the complex spatial and temporal patterns of UK daily precipitation. Taking the known limitations into account this study concluded that the gamma-based quantile-mapping technique is the most suitable for bias-correcting daily HadRM3 precipitation for use in hydrological modelling in the UK

Temporal response of mountain drainage basins in Taiwan to earthquake and typhoon perturbation.

In tectonically-active mountain belts, earthquake-triggered landslides deliver large amounts of sediment to rivers. In previous work, we have quantified the geomorphic impact of the 1999 Mw 7.6 Chi-Chi earthquake in Taiwan, which triggered >20,000 landslides and elevated suspended sediment loads in rivers by up to a factor of four. At the time, many coseismic landslides remained confined to hillslopes and, on the basis of four years of hydrometric data, we predicted that downslope transport of sediment would continue to occur during later storms. During the seven years since the Chi- Chi earthquake, several major typhoons storms have hit Taiwan (e.g., Typhoons Bilis, Toraji, Nari, Mindulle, Aere) and the Water Resources Agency of Taiwan has contin- ued to monitor water discharge and suspended sediment concentration. Here we use these new data to refine the spatial and temporal pattern of the decaying geomorphic response to the Chi-Chi earthquake in the face of several large typhoons. Our results indicate that the broad pattern of exponential decay in sediment concentration for a given river discharge (prevalent in winter seasons without typhoons) is punctuated by markedly elevated periods associated with typhoon storms. However, our analyses show that the change in unit sediment concentration (i.e., suspended sediment concen- tration for a unit water discharge) associated with each storm depends more strongly on the length of time elapsed since the earthquake than it does on the magnitude of the storm itself

Elasticity curves describe streamflow sensitivity to precipitation across the entire flow distribution

Streamflow elasticity is the ratio of the expected percentage change in streamflow for a 1% change in precipitation; a simple approximation of how responsive a river is to precipitation. Typically estimated for the annual average streamflow, we propose a new concept in which streamflow elasticity is estimated for multiple percentiles across the full range of the streamflow. This “elasticity curve” can then be used to develop a more complete depiction of how streamflow responds to climate. Representing elasticity as a curve which reflects the range of responses across the distribution of streamflow within a given time period, instead of as a single point estimate, provides a novel lens through which we can interpret hydrological behaviour. As an example, we calculate elasticity curves for 805 catchments in the United States and then cluster them according to their shape. This results in three distinct elasticity curve types which characterize the streamflow-precipitation relationship at the annual and seasonal timescales. Through this, we demonstrate that elasticity estimated from the central summary of streamflow, e.g. the annual median, does not provide a complete picture of streamflow sensitivity. Further, we show that elasticity curve shape, i.e. the response of different flow percentiles relative to one another in one catchment, can be interpreted separately from between-catchment variation in the average magnitude of streamflow change associated with a one percent change in precipitation. Finally, we find that available water storage is likely the key control which determines curve shape

Modelling the future impacts of climate and land-use change on suspended sediment transport in the River Thames (UK)

The effects of climate change and variability on river flows have been widely studied. However the impacts of such changes on sediment transport have received comparatively little attention. In part this is because modelling sediment production and transport processes introduces additional uncertainty, but it also results from the fact that, alongside the climate change signal, there have been and are projected to be significant changes in land cover which strongly affect sediment-related processes. Here we assess the impact of a range of climatic variations and land covers on the River Thames catchment (UK). We first calculate a response of the system to climatic stressors (average precipitation, average temperature and increase in extreme precipitation) and land-cover stressors (change in the extent of arable land). To do this we use an ensemble of INCA hydrological and sediment behavioural models. The resulting system response, which reveals the nature of interactions between the driving factors, is then compared with climate projections originating from the UKCP09 assessment (UK Climate Projections 2009) to evaluate the likelihood of the range of projected outcomes. The results show that climate and land cover each exert an individual control on sediment transport. Their effects vary depending on the land use and on the level of projected climate change. The suspended sediment yield of the River Thames in its lowermost reach is expected to change by −4% (−16% to +13%, confidence interval, p = 0.95) under the A1FI emission scenario for the 2030s, although these figures could be substantially altered by an increase in extreme precipitation, which could raise the suspended sediment yield up to an additional +10%. A 70% increase in the extension of the arable land is projected to increase sediment yield by around 12% in the lowland reaches. A 50% reduction is projected to decrease sediment yield by around 13%

Broadleaf afforestation impacts on terrestrial hydrology insignificant compared to climate change in Great Britain

Widespread afforestation has been proposed internationally to reduce atmospheric carbon dioxide; however, the specific hydrological consequences and benefits of such large-scale afforestation (e.g. natural flood management) are poorly understood. We use a high-resolution land surface model, the Joint UK Land Environment Simulator (JULES), with realistic potential afforestation scenarios to quantify possible hydrological change across Great Britain in both present and projected climate. We assess whether proposed afforestation produces significantly different regional responses across regions; whether hydrological fluxes, stores and events are significantly altered by afforestation relative to climate; and how future hydrological processes may be altered up to 2050. Additionally, this enables determination of the relative sensitivity of land surface process representation in JULES compared to climate changes. For these three aims we run simulations using (i) past climate with proposed land cover changes and known floods and drought events; (ii) past climate with independent changes in precipitation, temperature, and CO2; and (iii) a potential future climate (2020–2050). We find the proposed scale of afforestation is unlikely to significantly alter regional hydrology; however, it can noticeably decrease low flows whilst not reducing high flows. The afforestation levels minimally impact hydrological processes compared to changes in precipitation, temperature, and CO2. Warming average temperatures (+3 °C) decreases streamflow, while rising precipitation (130 %) and CO2 (600 ppm) increase streamflow. Changes in high flow are generated because of evaporative parameterizations, whereas low flows are controlled by runoff model parameterizations. In this study, land surface parameters within a land surface model do not substantially alter hydrological processes when compared to climate

Hydrometeorological response to afforestation in the UK: findings from a kilometer-scale climate model

Afforestation is of international interest for its positive benefits on carbon storage, ecology, and society, but its impacts on terrestrial and atmospheric processes are still poorly understood. This study presents the first use of a coupled land surface and convection permitting atmospheric model (CPM) to quantify hydrometeorological effects of afforestation across the United Kingdom, focusing on atmospheric processes often missing in hydrological models. Generating a scenario of 93 000 km2 (40%) additional woodland across the UK, the periods of 2042–2052 and 2062–2072 are analysed. Simulated afforestation alters seasonal and regional UK hydrometeorology. Countrywide runoff increases in all seasons (between 5.4–11 mm and 4.3–8.6% per season) due to elevated subsurface flows from greater soil moisture. Evaporation decreases in summer (−20.6 mm, −10%) but increases in winter (8.1 mm, 15%) whereas rainfall increases throughout all seasons (between 2.2–6.86 mm and 0.9%–2.2% per season). Greater winter rainfall is detected along Great Britain’s west coastline as increased surface roughness produces prolonged and heavier rainfall. In the summer, lower albedo increases potential evapotranspiration and reduces near surface specific humidity: water is locked in deeper soil layers as transpiration diminishes and the topsoil dries out. However, the magnitude of hydrometeorological change due to altered land cover is smaller than the uncertainty in local climate change projections. This work sets a precedent in illustrating the impacts of afforestation on hydrology using a high-resolution CPM and highlights the importance of coupled hydrometeorological processes when investigating land cover impacts on hydrological processes