WATCH Water and Global Change. Newsletter no. 1

WATCH is an integrated project funded by the EU and is co-ordinated by CEH Wallingford. This project aims to unite researchers to evaluate the global water cycle's response to current and future drivers of climate change. In this first newsletter we describe the project objectives, the progress made in the first year and detail the outcomes of the model intercomparison workshop

Using earth observation data to evaluate a land surface model in three Siberian catchments

In this paper, we analyse the ability of the JULES (Joint UK Land Environment Simulator) model to simulate the physical conditions in the terrestrial Arctic using satellite-based earth observation data products. Catchment-average seasonal surface temperatures and snow cover are constructed over the largest river basins of the Eurasian Arctic (the Ob, Lena and Yenisei) and compared with the modelled values. The results indicate that the modelled snow cover decreases too quickly in spring in all studied areas, and that the modelled surface temperature of snow-free areas is too high. There are several causes of uncertainty in both the model outputs and the earth observation products, and care has to be taken to ensure consistent use and sampling of the data. The results indicate that earth observation products provide important information that can assist in the diagnosis of problems in a land-surface model

Trends in evapotranspiration and its drivers in Great Britain: 1961 to 2015

In a warming climate, the water budget of the land is subject to varying forces such as increasing evaporative demand, mainly through the increased temperature, and changes to the precipitation, which might go up or down. Using a verified, physically based model with 55 years of observation-based meteorological forcing, an analysis of the water budget demonstrates that Great Britain is getting warmer and wetter. Increases in precipitation (2.96.0 ± 2.03 mm yr–1 yr–1) and air temperature (0.20 ± 0.13 K decade–1) are driving increases in runoff (2.18 ± 1.84 mm yr–1 yr–1) and evapotranspiration (0.87 ± 0.55 mm yr–1 yr–1), with no significant trend in the soil moisture. The change in evapotranspiration is roughly constant across the regions, whereas runoff varies greatly between regions: the biggest change is seen in Scotland (4.56 ± 2.82 mm yr–1 yr–1), where precipitation increases were also the greatest (5.4 ± 3.0 mm yr–1 yr–1), and the smallest trend (0.33 ± 1.50 mm yr–1 yr–1, not statistically significant) is seen in the English Lowlands (East Anglia and Midlands), where the increase in rainfall is not statistically significant (1.07 ± 1.76 mm yr–1 yr–1). Relative to its contribution to the evapotranspiration budget, the increase in interception is higher than the other components. This is due to the fact that it correlates strongly with precipitation, which is seeing a greater increase than the potential evapotranspiration. This leads to a higher increase in actual evapotranspiration than the potential evapotranspiration, and a negligible increase in soil moisture or groundwater store

Using data assimilation to optimize pedotransfer functions using field-scale in situ soil moisture observations

Soil moisture predictions from land surface models are important in hydrological, ecological, and meteorological applications. In recent years, the availability of wide-area soil moisture measurements has increased, but few studies have combined model-based soil moisture predictions with in situ observations beyond the point scale. Here we show that we can markedly improve soil moisture estimates from the Joint UK Land Environment Simulator (JULES) land surface model using field-scale observations and data assimilation techniques. Rather than directly updating soil moisture estimates towards observed values, we optimize constants in the underlying pedotransfer functions, which relate soil texture to JULES soil physics parameters. In this way, we generate a single set of newly calibrated pedotransfer functions based on observations from a number of UK sites with different soil textures. We demonstrate that calibrating a pedotransfer function in this way improves the soil moisture predictions of a land surface model at 16 UK sites, leading to the potential for better flood, drought, and climate projections

Trends in atmospheric evaporative demand in Great Britain using high-resolution meteorological data

Observations of climate are often available on very different spatial scales from observations of the natural environments and resources that are affected by climate change. In order to help bridge the gap between these scales using modelling, a new dataset of daily meteorological variables was created at 1 km resolution over Great Britain for the years 1961–2012, by interpolating coarser resolution climate data and including the effects of local topography. These variables were used to calculate atmospheric evaporative demand (AED) at the same spatial and temporal resolution. Two functions that represent AED were chosen: one is a standard form of potential evapotranspiration (PET) and the other is a derived PET measure used by hydrologists that includes the effect of water intercepted by the canopy (PETI). Temporal trends in these functions were calculated, with PET found to be increasing in all regions, and at an overall rate of 0.021 ± 0.021 mm day−1 decade−1 in Great Britain. PETI was found to be increasing at a rate of 0.019 ± 0.020 mm day−1 decade−1 in Great Britain, but this was not statistically significant. However, there was a trend in PETI in England of 0.023 ± 0.023 mm day−1 decade−1. The trends were found to vary by season, with spring PET increasing by 0.043 ± 0.019 mm day−1 decade−1 (0.038 ± 0.018 mm day−1 decade−1 when the interception correction is included) in Great Britain, while there is no statistically significant trend in other seasons. The trends were attributed analytically to trends in the climate variables; the overall positive trend was predominantly driven by rising air temperature, although rising specific humidity had a negative effect on the trend. Recasting the analysis in terms of relative humidity revealed that the overall effect is that falling relative humidity causes the PET to rise. Increasing downward short- and longwave radiation made an overall positive contribution to the PET trend, while decreasing wind speed made a negative contribution to the trend in PET. The trend in spring PET was particularly strong due to a strong decrease in relative humidity and increase in downward shortwave radiation in the spring

A global-scale evaluation of extreme event uncertainty in the eartH2Observe project

Knowledge of how uncertainty propagates through a hydrological land surface modelling sequence is of crucial importance in the identification and characterisation of system weaknesses in the prediction of droughts and floods at global scale. We evaluated the performance of five state-of-the-art global hydrological and land surface models in the context of modelling extreme conditions (drought and flood). Uncertainty was apportioned between the model used (model skill) and also the satellite-based precipitation products used to drive the simulations (forcing data variability) for extreme values of precipitation, surface runoff and evaporation. We found in general that model simulations acted to augment uncertainty rather than reduce it. In percentage terms, the increase in uncertainty was most often less than the magnitude of the input data uncertainty, but of comparable magnitude in many environments. Uncertainty in predictions of evapotranspiration lows (drought) in dry environments was especially high, indicating that these circumstances are a weak point in current modelling system approaches. We also found that high data and model uncertainty points for both ET lows and runoff lows were disproportionately concentrated in the equatorial and southern tropics. Our results are important for highlighting the relative robustness of satellite products in the context of land surface simulations of extreme events and identifying areas where improvements may be made in the consistency of simulation models

Sensitivity of an ecosystem model to hydrology and temperature

We tested the sensitivity of a dynamic ecosystem model (LPJ-GUESS) to the representation of soil moisture and soil temperature and to uncertainties in the prediction of precipitation and air temperature. We linked the ecosystem model with an advanced hydrological model (JULES) and used its soil moisture and soil temperature as input into the ecosystem model. We analysed these sensitivities along a latitudinal gradient in northern Russia. Differences in soil temperature and soil moisture had only little influence on the vegetation carbon fluxes, whereas the soil carbon fluxes were very sensitive to the JULES soil estimations. The sensitivity changed with latitude, showing stronger influence in the more northern grid cell. The sensitivity of modelled responses of both soil carbon fluxes and vegetation carbon fluxes to uncertainties in soil temperature were high, as both soil and vegetation carbon fluxes were strongly impacted. In contrast, uncertainties in the estimation of the amount of precipitation had little influence on the soil or vegetation carbon fluxes. The high sensitivity of soil respiration to soil temperature and moisture suggests that we should strive for a better understanding and representation of soil processes in ecosystem models to improve the reliability of predictions of future ecosystem change

A clustering approach to reduce computational expense in land surface models: a case study using JULES vn5.9

Land surface models such as JULES (the Joint UK Land Environment Simulator) are usually run on a regular, rectilinear grid, resulting in gridded outputs for variables such as soil moisture and water fluxes. Here we investigate a method of clustering grid cells with similar characteristics together in JULES. Clustering grid cells has the potential to reduce computational expense as well as providing an alternative to tiling approaches for capturing sub-grid heterogeneity. In this study, we cluster grid cells exclusively in the land surface part of modelling, i.e., separate from river routing. We compare gridded and clustered soil moisture outputs from JULES with measurements from the UK Centre for Ecology and Hydrology (UKCEH) COSMOS-UK network and show that the clustering approach can model soil moisture well while reducing computational expense. However, soil moisture results are dependent on the characteristics used to create the clusters. We investigate the effect of using clusters on predicted river flows, and compare routed JULES outputs with NRFA gauge data in the catchment. We show that less expensive JULES clustered outputs give similar river flow results to standard gridded outputs when routed at the grid resolution, and are able to match observed river flow better than gridded outputs when routed at higher resolution