Department Systems Analysis, Integrated Assessment and Modelling

This study investigates Bayesian signature-domain inference of hydrological models usingApproximate Bayesian Computation (ABC) algorithms, and compares it to ‘‘traditional’’ time-domain inference.Our focus is on the quantification of predictive uncertainty in the streamflow time series and onunderstanding the information content of particular combinations of signatures. A combination of syntheticand real data experiments using conceptual rainfall-runoff models is employed. Synthetic experiments demonstrate:(i) the general consistency of signature and time-domain inferences, (ii) the ability to estimatestreamflow error model parameters (reliably quantify streamflow uncertainty) even when calibrating in thesignature domain, and (iii) the potential robustness of signature-domain inference when the (probabilistic)hydrological model is misspecified (e.g., by unaccounted timing errors). The experiments also suggest limitationsof the signature-domain approach in terms of information loss when general (nonsufficient) statisticsare used, and increased computational costs incurred by the ABC implementation. Real data experimentsconfirm the viability of Bayesian signature-domain inference and its general consistency with time-domaininference in terms of predictive uncertainty quantification. In addition, we demonstrate the utility of theflashiness index for the estimation of streamflow error parameters, and show that signatures based on theFlow Duration Curve alone are insufficient to calibrate parameters controlling streamflow dynamics. Overall,the study further establishes signature-domain inference (implemented using ABC) as a promising methodfor comparing the information content of hydrological signatures, for prediction under data-scarce conditions,and, under certain circumstances, for mitigating the impact of deficiencies in the formulation of thepredictive model.

Fertilization, crop uptake followed by plant harvest, runoff and erosion, and transformations ofphosphorus (P) in soil are the major factors influencing the P balance of croplands. It is important to integrateplant-soil-management interactions into consistent modeling systems to determine the effect of Pfertilization conditions on yields and to quantify P losses. Previous assessment of P losses on large scales didnot consider the interactions among these factors. Here we applied a grid-based crop model to estimateglobal P losses from three most produced crops: maize, rice, and wheat. The model was forced by detailed Pinput data sets over the period 1998–2002. According to our simulations, global P losses from the threecrops reached 1.2 Tg P/year, and about 44% of it was due to soil erosion. The global total P losses weredominated by contributions from a few hot spot regions. Reducing P fertilizer in regions experiencingexcessive P uses and hence losses, especially in China and India, could achieve the same yields as today andsave about two thirds of global total P inputs, with the cobenefits of declining global total P losses by 41%and downstream water quality improvement. Reducing soil erosion and retaining more crop residues oncroplands could further save P inputs and alleviate P losses. This study is of significance to determine themajor factors influencing P balance across regions of the world and help policy makers to propose efficientstrategies for tackling P-driven environmental problems.

Knowing the impact of land-use and land-cover (LULC) changes on the distribution of water yield (WY) is essential for water resource management. Using the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, we investigated the spatial-temporal variations of WY from 1990 to 2015 in China's northern semi-arid region of Beijing-Tianjin-Hebei (Jing-Jin-Ji). We quantified the combined effects of LULC dynamics and climatic variation on WY. Furthermore, we identified the relative contribution of main LULC types to WY. For our study region, the built-up area increased by 35.66% (5380 km²) during the study period. In the meantime, cropland, grassland, and wetland decreased continuously. The expansion of built-up area and decline of vegetated land led to an increase of 1047 million m³ (5.1%) in total WY. The impacts of LULC changes on WY were mainly determined by the biophysical characteristics of LULC composition. Vegetated land has relatively lower WY coefficients due to higher rates of evapotranspiration and water infiltration. Built-up areas and bare land have higher WY coefficients as a result of their impermeable surface. The spatial-temporal analysis of WY with specification of WY coefficients by LULC types can facilitate integrated land-use planning and water resource management.

Process-based crop models are increasingly used to assess the effects of different agricultural management practices on crop yield. However, calibration of historic crop yield is a challenging and time-consuming task due to data limitation and lack of adaptive auto-calibration tools compatible with the model to be calibrated on different spatial and temporal scales. In this study we linked the general auto-calibration procedure SUFI-2 (Sequential Uncertainty Fitting Procedure) to the crop model EPIC (Environmental Policy Integrated Climate) to calibrate maize yield in Sub-Saharan African (SSA) countries. This resulted in the creation of a user-friendly software, EPIC⁺, for crop model calibration at spatial levels of grid to continent. EPIC⁺ greatly speeds up the calibration process with quantification of parameter ranges and prediction uncertainty. In the SSA application, we calibrated three sets of parameters referred to as Planting Date (PD), Operation (e.g., fertilizer application, planting density), and Model parameters (e.g., Harvest index, biomass-energy ratio, water stress harvest index, SCS curve number) in three steps to avoid parameter interaction and identifiability problems. In the first step, by adjusting PD parameters, the simulated yield results improved in Western and Central African countries. In the next step, Operation parameters were calibrated for individual countries resulting in a better model performance by more than 40% in many countries. In the third step, Model parameters were calibrated with significant improvements in all countries by an average of 50%. We also found that countries with less socio-political volatility benefited most from the calibration. For countries where agricultural production had trends, we suggest improving the calibration results by applying linear de-trending transformations, which we will explore in more detail in a subsequent study.

Global food trade entails virtual flows of agricultural resources and pollution across countries. Here we performed a global-scale assessment of impacts of international food trade on blue water use, total water use, and nitrogen (N) inputs and on N losses in maize, rice, and wheat production. We simulated baseline conditions for the year 2000 and explored the impacts of an agricultural intensification scenario, in which low-input countries increase N and irrigation inputs to a greater extent than high-input countries. We combined a crop model with the Global Trade Analysis Project model. Results show that food exports generally occurred from regions with lower water and N use intensities, defined here as water and N uses in relation to crop yields, to regions with higher resources use intensities. Globally, food trade thus conserved a large amount of water resources and N applications, and also substantially reduced N losses. The trade-related conservation in blue water use reached 85 km3 y−1, accounting for more than half of total blue water use for producing the three crops. Food exported from the USA contributed the largest proportion of global water and N conservation as well as N loss reduction, but also led to substantial export-associated N losses in the country itself. Under the intensification scenario, the converging water and N use intensities across countries result in a more balanced world; crop trade will generally decrease, and global water resources conservation and N pollution reduction associated with the trade will reduce accordingly. The study provides useful information to understand the implications of agricultural intensification for international crop trade, crop water use and N pollution patterns in the world.