Impact analysis of climate data aggregation at different spatial scales on simulated net primary productivity for croplands

Abstract

a b s t r a c tFor spatial crop and agro-systems modelling, there is often a discrepancy between the scale of measured driving data and the target resolution. Spatial data aggregation is often necessary, which can introduce additional uncertainty into the simulation results. Previous studies have shown that climate data aggregation has little effect on simulation of phenological stages, but effects on net primary production (NPP) might still be expected through changing the length of the growing season and the period of grain filling. This study investigates the impact of spatial climate data aggregation on NPP simulation results, applying eleven different models for the same study region (∼34,000 km 2 ), situated in Western Germany. To isolate effects of climate, soil data and management were assumed to be constant over the entire study area and over the entire study period of 29 years. Two crops, winter wheat and silage maize, were tested as monocultures. Compared to the impact of climate data aggregation on yield, the effect on NPP is in a similar range, but is slightly lower, with only small impacts on averages over the entire simulation period and study region. Maximum differences between the five scales in the range of 1-100 km grid cells show changes of 0.4-7.8% and 0.0-4.8% for wheat and maize, respectively, whereas the simulated potential NPP averages of the models show a wide range (1.9-4.2 g C m −2 d −1 and 2.7-6.1 g C m −2 d −1 for wheat and maize, respectively). The impact of the spatial aggregation was also tested for shorter time periods, to see if impacts over shorter periods attenuate over longer periods. The results show larger impacts for single years (up to 9.4% for wheat and up to 13.6% for maize). An analysis of extreme weather conditions shows an aggregation effect in vulnerability up to 12.8% and 15.5% between the different resolutions for wheat and maize, respectively. Simulations of NPP averages over larger areas (e.g. regional scale) and longer time periods (several years) are relatively insensitive to climate data

Pedotransfer Functions in Earth System Science: Challenges and Perspectives

“…Soil parameters in these models cannot be measured, and the efficiency of PTFs can be evaluated only in terms of their utility (Gutmann & Small, 2007;Shen et al, 2014). The general problem of change in spatial resolution of the input data by aggregating small-scale information and the resulting output uncertainty for various model states was reported, for example, by Cale et al (1983), Rastetter et al (1992), Hoffmann et al (2016), and Kuhnert et al (2016). Besides presenting spatially dependent variability, PTFs may also have scale dependence (Pringle et al, 2007).…”

A global data set of soil hydraulic properties and sub-grid variability of soil water retention and hydraulic conductivity curves

“…State-of-the-art LSMs, e.g. NOAH (Niu et al, 2011), CLM (Oleson et al, 2008), VIC (Liang et al, 1994), JULES (Best et al, 2011) and ORCHIDEE (Ngo-Duc et al, 2007), are key components of regional and global climate models (RCMs/GCMs) and numerical weather prediction models (NWPMs). They form important components of reanalyses (e.g.…”

“…Consequently, the fine-scale soil information, available from state-of-the-art soil maps such as the European LUCAS (Land Use/Land Cover Area Frame Survey) (Toth et al, 2013;Ballabio et al, 2016) at 500 m resolution or the global SoilGrid database at 1 km resolution (Hengl et al, 2014), has to be up-scaled to the scale at which the LSMs are being employed. The general problem of up-scaling, or change in spatial resolution of the input data by aggregating small-scale input data, and the resulting output uncertainty for various model states was reported, for example, by Cale et al (1983), Rastetter et al (1992), Pierce and Running (1995), Hoffmann et al (2016), and Kuhnert et al (2016). A practical example is GLDAS2-NOAH, where the porosity and the percentages of sand, silt and clay at the original scale of the input data from Reynolds et al (2000) were horizontally resampled, i.e.…”

Analysis of Changes and Potential Characteristics of Cultivated Land Productivity Based on MODIS EVI: A Case Study of Jiangsu Province, China

“…During recent years, progress has been made in the research of cultivated land productivity [13][14][15][16]. Internationally, research on cultivated land productivity has mainly focused on the potential of land productivity.…”