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 ET 0. Surface reflectance data from satellite images (Sentinel-2) are used to derive leaf area indices, which help to partition ET 0 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 ET act , 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 ET act 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 ET act 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.
Abstract. The occurrence of dry periods on the Wupper catchment has increased in the last decades in conjunction with the shifting of the precipitation regime. In the frame of the Horizon 2020 project BINGO (Bringing INnovation to onGOing water management), the effects of climate change scenarios on the water cycle in the Wupper catchment area were investigated. To quantify these effects, a set of hydrological models (NASIM and SWAT) has been set-up, calibrated, and validated for the upper part of the Dhünn River catchment area – Wupper River's main tributary. This sub-catchment corresponds to one of the inflows to the Große Dhünn Reservoir (GDR), the second largest drinking water reservoir in Germany. Both models were driven with climate data from decadal predictions, which have been selected instead of IPCC-RCP scenarios, as they provide a more realistic assumption of climate variability for the next 10 years. Ten decadal members based on the MiKlip (Mittelfristige Klimaprognose – medium-term climate prediction) framework have been prepared for the time span of 2015 to 2024. Additionally, a simulation with TALSIM-NG (a reservoir-oriented hydrological model) was carried out to obtain future reservoir storage. Special focus was given to identify observed trends and compare them to future trends. Past hydro-meteorological extreme dry periods were evaluated based on observed data. Standardized Precipitation Index (SPI), Standardized Precipitation-Evapotranspiration Index (SPEI), and Standardized Runoff Index (SRI) were estimated for different seasons to determine if they were abnormally dry or wet. SPI, SPEI, and SRI were also calculated with decadal predictions to evaluate future extreme dry periods. Uncertainties in climate data predictions are one of the greatest challenges. Observed and forecast time series were compared by means of statistical tests in order to assess uncertainties in climate data predictions. Also, the application of two hydrological models aims to determine potential uncertainties, so that predictions are more reliable. Results indicate that SRI might be more appropriate to estimate drought periods for the study area in the frame of reservoir management – where inflow rates are of crucial importance – as this index quantifies losses in runoff formation processes. In terms of inflow rates to GDR, future changes indicate a reduction in runoff for the spring season, while an increment during winter. On the other hand, a clear change in pattern for fall and summer seasons remains uncertain. Simulations of GDR reservoir volume with different climate scenarios show that water stress by the end of 2024 is not unlikely, so sustainable adaptation measures should be further considered. Effectively managing the GDR will become consequently more complex.
Water footprint evaluates impacts associated with the water use along a product’s life cycle. In order to quantify impacts resulting from water pollution in a comprehensive manner, impact categories, such as human toxicity, were developed in the context of Life Cycle Assessment (LCA). Nevertheless, methods addressing human health impacts often have a low spatial resolution and, thus, are not able to model impacts on a local scale. To address this issue, we develop a region-specific model for the human toxicity impacts for the cotton-textile industry in Punjab, Pakistan. We analysed local cause-effect chains and created a region “Punjab” in the USEtox model using local climate, landscape, and population data. Finally, we calculated human health impacts for the emissions of pesticides from the cotton cultivation and heavy metals from the textile production. The results were compared to that obtained for the region India+ (where Pakistan belongs) provided by USEtox. The overall result obtained for Punjab is higher than that for India+. In Punjab, the dominant pathway is ingestion via drinking water, which contributes to two-thirds of the total impacts. Nevertheless, the USEtox model does not reflect the local cause-effect chains completely due to absence of the groundwater compartment. Since groundwater is the main source for drinking in Punjab, a more detailed analysis of the fate of and exposure to the pollutants is needed. This study demonstrates that a region-specific assessment of the water quality aspects is essential to provide a more robust evaluation of the human health impacts within water footprinting.
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