Since 2015 the SinoGerman research project SIGN supports water quality improvement in the Taihu region, China

The Taihu (Tai lake) region is one of the most economically prospering areas of China. Due to its location within this district of high anthropogenic activities, Taihu represents a drastic example of water pollution with nutrients (nitrogen, phosphate), organic contaminants and heavy metals. High nutrient levels combined with very shallow water create large eutrophication problems, threatening the drinking water supply of the surrounding cities. Within the international research project SIGN (SinoGerman Water Supply Network, www.water-sign.de), funded by the German Federal Ministry of Education and Research (BMBF), a powerful consortium of fifteen German partners is working on the overall aim of assuring good water quality from the source to the tap by taking the whole water cycle into account: The diverse research topics range from future proof strategies for urban catchment, innovative monitoring and early warning approaches for lake and drinking water, control and use of biological degradation processes, efficient water treatment technologies, adapted water distribution up to promoting sector policy by good governance. The implementation in China is warranted, since the leading Chinese research institutes as well as the most important local stakeholders, e.g. water suppliers, are involved

Spatially distributed model calibration of a highly managed hydrological system using remote sensing-derived ET data

Calibrating a spatially distributed hydrological model of a region with complex and highly diverse agricultural settings is a difficult task. It becomes even more challenging, if major parts of the hydrology are human controlled, e.g. through irrigation agriculture. In this study, a Soil and Water Assessment Tool (SWAT) was set up and calibrated for an entirely managed meso-scale irrigation scheme, where irrigation channels are the only water-courses and no natural streams exist. An automated and spatially distributed model calibration was applied, implementing the global Dynamically Dimensioned Search Algorithm (DDS), and using Kling-Gupta Efficiency (KGE) as objective function. To account for the spatio-temporal heterogeneity of water balance fluxes in the calibration process, remote sensing-derived evapotranspiration (ET) estimates with a spatial resolution of 1 km × 1 km, were used as observational data. Results showed that the automated and spatially distributed model calibration was able to increase the model ET simulation performance from 0.04 to 0.27 KGE. However, a strong land-use dependent goodness-of-fit, ranging from approx. −0.60 KGE to 0.60 KGE for individual land-use types, revealed that the information, extracted from remote sensing-derived ET estimates used in this study, is lacking the necessary spatial heterogeneity. An unsatisfactory spatial comparison between simulated ET and ET derived from remote sensing estimates showed to inhibit better calibration results for the study area, which is characterized by small-scale and diverse cropping patterns. Using a modified remote sensing-derived ET product, which accounts for land-use class dependent ET characteristics, improved the overall calibration results to 0.40 KGE. A cross-validation could indicate a successful application of this automated and spatially distributed model calibration approach, using the modified ET product

Spatially distributed model calibration of a highly managed hydrological system using remote sensing-derived ET data

Calibrating a spatially distributed hydrological model of a region with complex and highly diverse agricultural settings is a difficult task. It becomes even more challenging, if major parts of the hydrology are human controlled, e.g. through irrigation agriculture. In this study, a Soil and Water Assessment Tool (SWAT) was set up and calibrated for an entirely managed meso-scale irrigation scheme, where irrigation channels are the only water-courses and no natural streams exist. An automated and spatially distributed model calibration was applied, implementing the global Dynamically Dimensioned Search Algorithm (DDS), and using Kling-Gupta Efficiency (KGE) as objective function. To account for the spatio-temporal heterogeneity of water balance fluxes in the calibration process, remote sensing-derived evapotranspiration (ET) estimates with a spatial resolution of 1 km x 1 km, were used as observational data. Results showed that the automated and spatially distributed model calibration was able to increase the model ET simulation performance from 0.04 to 0.27 KGE. However, a strong land-use dependent goodness-of-fit, ranging from approx. -0.60 KGE to 0.60 KGE for individual land-use types, revealed that the information, extracted from remote sensing-derived ET estimates used in this study, is lacking the necessary spatial heterogeneity. An unsatisfactory spatial comparison between simulated ET and ET derived from remote sensing estimates showed to inhibit better calibration results for the study area, which is characterized by small-scale and diverse cropping patterns. Using a modified remote sensing-derived ET product, which accounts for land-use class dependent ET characteristics, improved the overall calibration results to 0.40 KGE. A cross-validation could indicate a successful application of this automated and spatially distributed model calibration approach, using the modified ET product

Estimating water balance components in irrigated agriculture using a combined approach of soil moisture and energy balance monitoring, and numerical modelling

Information on water balance components such as evapotranspiration and groundwater recharge are crucial for water management. Due to differences in physical conditions, but also due to limited budgets, there is not one universal best practice, but a wide range of different methods with specific advantages and disadvantages. In this study, we propose an approach to quantify actual evapotranspiration, groundwater recharge and water inflow, i.e. precipitation and irrigation, that considers the specific conditions of irrigated agriculture in warm, arid environments. This approach does not require direct measurements of precipitation or irrigation quantities and is therefore suitable for sites with an uncertain data basis. For this purpose, we combine soil moisture and energy balance monitoring, remote sensing data analysis and numerical modelling using Hydrus. Energy balance data and routine weather data serve to estimate ET0. Surface reflectance data from satellite images (Sentinel‐2) are used to derive leaf area indices, which help to partition ET0 into energy limited evaporation and transpiration. Subsequently, first approximations of water inflow are derived based on observed soil moisture changes. These inflow estimates are used in a series of forward simulations that produce initial estimates of drainage and ETact, which in turn help improve the estimate of water inflow. Finally, the improved inflow estimates are incorporated into the model and then a parameter optimization is performed using the observed soil moisture as the reference figure. Forward simulations with calibrated soil parameters result in final estimates for ETact and groundwater recharge. The presented method is applied to an agricultural test site with a crop rotation of cotton and wheat in Punjab, Pakistan. The final model results, with an RMSE of 2.2% in volumetric water content, suggest a cumulative ETact and groundwater recharge of 769 and 297 mm over a period of 281 days, respectively. The total estimated water inflow accounts for 946 mm, of which 77% originates from irrigation.Approach to quantify ETact, GWR and water inflow that considers the specific conditions of irrigated agriculture in warm, acid environments. It combines soil moisture and energy balance monitoring, remote sensing data analysing data analysis and numerical modelling using Hydrus. The final model results suggest that GWR accounts for one third of the total water inflow, of which 77% originates from irrigations. imageBundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/50110000234

Increased heat stress reduces future yields of three major crops in Pakistan’s Punjab region despite intensification of irrigation.

Gefördert durch den Publikationsfonds der Universität Kasse

Energy balance and soil moisture data of an agricultural test field in Faisalabad, Pakistan

In July 2018, a monitoring station was installed on a test field (31°26'21 .3 '' N, 73°04' 18 17.7 '' E) of the University of Agriculture Faisalabad (UAF) in Pakistan. The general purpose of this station is to create a database that helps to improve the estimation of water balance components. The station operated from 2018-07-03 to 2019-06-01 and is equipped with (i) one net radiometer (NR-Lite2, Kipp & Zonen), mounted at a height of 2.5 m a.g.l., (ii) one self-calibrating soil heat flux plate (HFP01SC, Hukseflux), installed in a depth of 8 cm b.g.l., and (iii) four soil moisture and temperature sensors (CS650, Campbell Scientific), installed in a depth of 10, 20, 40 and 80 cm b.g.l., respectively. The present dataset has a temporal resolution of 1 minute and comprises records of net radiation, soil heat flux, volumetric water content, electric conductivity, and temperature. Due to empty batteries or full memory storage the records show some gaps. The cumulative gap length is 51 days, and the number of full days with records is 281