Parameter identification and global sensitivity analysis of Xin'anjiang model using meta-modeling approach

Parameter identification, model calibration, and uncertainty quantification are important steps in the model-building process, and are necessary for obtaining credible results and valuable information. Sensitivity analysis of hydrological model is a key step in model uncertainty quantification, which can identify the dominant parameters, reduce the model calibration uncertainty, and enhance the model optimization efficiency. There are, however, some shortcomings in classical approaches, including the long duration of time and high computation cost required to quantitatively assess the sensitivity of a multiple-parameter hydrological model. For this reason, a two-step statistical evaluation framework using global techniques is presented. It is based on (1) a screening method (Morris) for qualitative ranking of parameters, and (2) a variance-based method integrated with a meta-model for quantitative sensitivity analysis, i.e., the Sobol method integrated with the response surface model (RSMSobol). First, the Morris screening method was used to qualitatively identify the parameters' sensitivity, and then ten parameters were selected to quantify the sensitivity indices. Subsequently, the RSMSobol method was used to quantify the sensitivity, i.e., the first-order and total sensitivity indices based on the response surface model (RSM) were calculated. The RSMSobol method can not only quantify the sensitivity, but also reduce the computational cost, with good accuracy compared to the classical approaches. This approach will be effective and reliable in the global sensitivity analysis of a complex large-scale distributed hydrological model.

A two-step approach to investigate the effect of rating curve uncertainty in the Elbe decision support system

For river basin management, the reliability of the rating curves mainly depends on the accuracy and time period of the observed discharge and water level data. In the Elbe decision support system (DSS), the rating curves are combined with the HEC-6 model to investigate the effects of river engineering measures on the Elbe River system. In such situations, the uncertainty originating from the HEC-6 model is of significant importance for the reliability of the rating curves and the corresponding DSS results. This paper proposes a two-step approach to analyze the uncertainty in the rating curves and propagate it into the Elbe DSS: analytic method and Latin Hypercube simulation. Via this approach the uncertainty and sensitivity of model outputs to input parameters are successfully investigated. The results show that the proposed approach is very efficient in investigating the effect of uncertainty and can play an important role in improving decision-making under uncertainty.

Determination of the effective utilization coefficient of irrigation water based on geographically weighted regression

This study uses geographically weighted regression to determine the spatial distribution of the effective utilization coefficient of irrigation water in Zhejiang Province,China,owing to the influences of spatial attributes on the irrigation efficiency.The sample set of this study comprised 165 agricultural test sites.A multivariate linear regression model and a geographically weighted regression model were established using the effective utilization coefficient of agricultural irrigation water as the dependent variable in addition to a suite of independent variables,including the actual irrigation area,the percentage of farmland using water-saving irrigation,the type of irrigation area,the net water consumption per mu,the water intake method,the terrain slope,and the soil field capacity.Results revealed a positive spatial correlation and noticeable agglomeration features in the effective utilization coefficient of irrigation water in Zhejiang Province.The geographically weighted regression model performed better in terms of fit and prediction accuracy than the multivariate linear regression model.The obtained findings confirm the suitability of the geographically weighted regression model for determining the spatial distribution of the effective utilization coefficient of irrigation water in Zhejiang,and offer a new approach on a regional scale.

京津冀地区不同土地利用类型对潜在蒸散发的影响（英文）

Potential evapotranspiration(ET_0) is vital for hydrologic cycle and water resource assessments as well as crop water requirement and irrigation demand assessments. The Beijing-Tianjin-Hebei region(Jing-Jin-Ji)–an important, large, regional, economic community in China has experienced tremendous land use and land cover changes because of urbanisation and ecological restoration, affecting the hydrologic cycle and water resources of this region. Therefore, we analysed ET_0 in this region using climate data from 22 meteorological stations for the period 1991–2015 to understand this effect. Our findings show that ET_0 increased significantly at a rate of 7.40 mm per decade for the region. Based on the major land use type surrounding them, the meteorological stations were classified as urban, farmland, and natural stations using the 2015 land use dataset. The natural stations in the northern mountainous area showed a significant increase in ET_0, whereas most urban and farmland stations in the plain area showed a decrease in ET_0, with only a few of the stations showing an increase. Based on the different ET_0 trends for different land use types, these stations can be ranked as follows: urban stations(trend value:-4.663 to-1.439) > natural stations(trend value: 2.58 to 3.373) > farmland stations(trend value:-2.927 to-0.248). Our results indicate that land use changes affect meteorological parameters, such as wind speed and sunshine duration, which then lead to changes in ET_0. We noted that wind speed was the dominant parameter affecting ET_0 at all the natural stations, and wind speed and sunshine duration were the dominant parameters affecting ET_0 at most of the urban stations. However, the main controlling parameters affecting ET_0 at the farmland stations varied. These results present a scope for understanding land use impact on ET_0, which can then be applied to studies on sustainable land use planning and water resource management.