The precision of satellite-based net irrigation quantification in the Indus and Ganges basins

Even though irrigation is the largest direct anthropogenic interference in the natural terrestrial water cycle, limited knowledge of the amount of water applied for irrigation exists. Quantification of irrigation via evapotranspiration (ET) or soil moisture residuals between remote-sensing models and hydrological models, with the latter acting as baselines without the influence of irrigation, have successfully been applied in various regions. Here, we implement a novel ensemble methodology to estimate the precision of ET-based net irrigation quantification by combining different ET and precipitation products in the Indus and Ganges basins. A multi-model calibration of 15 models independently calibrated to simulate rainfed ET was conducted before the irrigation quantification. Based on the ensemble average, the 2003–2013 net irrigation amounts to 233 mm yr−1 (74 km3 yr−1) and 101 mm yr−1 (67 km3 yr−1) in the Indus and Ganges basins, respectively. Net irrigation in the Indus Basin is evenly split between dry and wet periods, whereas 70 % of net irrigation occurs during the dry period in the Ganges Basin. We found that, although annual ET from remote-sensing models varied by 91.5 mm yr−1, net irrigation precision was within 25 mm per season during the dry period for the entire study area, which emphasizes the robustness of the applied multi-model calibration approach. Net irrigation variance was found to decrease as ET uncertainty decreased, which is related to the climatic conditions, i.e., high uncertainty under arid conditions. A variance decomposition analysis showed that ET uncertainty accounted for 73 % of the overall net irrigation variance and that the influence of precipitation uncertainty was seasonally dependent, i.e., with an increase during the monsoon season. The results underline the robustness of the framework to support large-scale sustainable water resource management of irrigated land.</p

Water-table-driven greenhouse gas emission estimates guide peatland restoration at national scale

The substantial climate change mitigation potential of restoring peatlands through rewetting and intensifying agriculture to reduce greenhouse gas (GHG) emissions is largely recognized. The green deal in Denmark aims at restoring 100 000 ha of peatlands by 2030. This area corresponds to more than half of the Danish peatland, with an expected reduction in GHG emissions of almost half of the entire land use, land use change and forestry (LULUFC) emissions. Recent advances established the functional relationship between hydrological regimes, i.e., water table depth (WTD), and CO2 and CH4 emissions. This builds the basis for science-based tools to evaluate and prioritize peatland restoration projects. With this article, we lay the foundation of such a development by developing a high-resolution WTD map for Danish peatlands. Further, we define WTD response functions (CO2 and CH4) fitted to Danish flux data to derive a national GHG emission estimate for peat soils. We estimate the annual GHG emissions to be 2.6 Mt CO2-eq, which is around 15 % lower than previous estimates. Lastly, we investigate alternative restoration scenarios and identify substantial differences in the GHG reduction potential depending on the prioritization of fields in the rewetting strategy. If wet fields are prioritized, which is not unlikely in a context of a voluntary bottom-up approach, the GHG reduction potential is just 30 % for the first 10 000 ha with respect to a scenario that prioritizes drained fields. This underpins the importance of the proposed framework linking WTD and GHG emissions to guide a spatially differentiated peatland restoration. The choice of model type used to fit the CO2 WTD response function, the applied global warming potentials and uncertainties related to the WTD map are investigated by means of a scenario analysis, which suggests that the estimated GHG emissions and the reduction potential are associated with coefficients of variation of 13 % and 22 %, respectively.</p

Disaggregation of SMOS soil moisture over West Africa using the Temperature and Vegetation Dryness Index based on SEVIRI land surface parameters

The overarching objective of this study was to produce a disaggregated SMOS Soil Moisture (SM) product using land surface parameters from a geostationary satellite in a region covering a diverse range of ecosystem types. SEVIRI data at 15 minute temporal resolution were used to derive the Temperature and Vegetation Dryness Index (TVDI) that served as SM proxy within the disaggregation process. West Africa (3 N, 26 W; 28 N, 26 E) was selected as a case study as it presents both an important North-South climate gradient and a diverse range of ecosystem types. The main challenge was to set up a methodology applicable over a large area that overcomes the constraints of SMOS (low spatial resolution) and TVDI (requires similar atmospheric forcing and triangular shape formed when plotting morning rise temperature versus fraction of vegetation cover) in order to produce a 0.05 degree resolution disaggregated SMOS SM product at sub-continental scale. Consistent cloud cover appeared as one of the main constraints for deriving TVDI, especially during the rainy season and in the southern parts of the region and a large adjustment window (105x105 SEVIRI pixels) was therefore deemed necessary. Both the original and the disaggregated SMOS SM products described well the seasonal dynamics observed at six locations of in situ observations. However, there was an overestimation in both products for sites in the humid southern regions; most likely caused by the presence of forest. Both TVDI and the associated disaggregated SM product was found to be highly sensitive to algorithm input parameters; especially of conditions of high fraction of vegetation cover. Additionally, seasonal dynamics in TVDI did not follow the seasonal patters of SM. Still, its spatial heterogeneity was found to be a good proxy for disaggregating SMOS SM data; main river networks and spatial patterns of SM extremes (i.e. droughts and floods) not seen in the original SMOS SM product were revealed in the disaggregated SM product for a test case of July-September 2012. The disaggregation methodology thereby successfully increased the spatial resolution of SMOS SM, with potential application for local drought/flood monitoring of importance for the livelihood of the population of West Africa

Use of expert elicitation to assign weights to climate and hydrological models in climate impact studies

Various methods are available for assessing uncertainties in climate impact studies. Among such methods, model weighting by expert elicitation is a practical way to provide a weighted ensemble of models for specific real-world impacts. The aim is to decrease the influence of improbable models in the results and easing the decision-making process. In this study both climate and hydrological models are analysed, and the result of a research experiment is presented using model weighting with the participation of six climate model experts and six hydrological model experts. For the experiment, seven climate models are a priori selected from a larger EURO-CORDEX (Coordinated Regional Downscaling Experiment - European Domain) ensemble of climate models, and three different hydrological models are chosen for each of the three European river basins. The model weighting is based on qualitative evaluation by the experts for each of the selected models based on a training material that describes the overall model structure and literature about climate models and the performance of hydrological models for the present period. The expert elicitation process follows a three-stage approach, with two individual rounds of elicitation of probabilities and a final group consensus, where the experts are separated into two different community groups: a climate and a hydrological modeller group. The dialogue reveals that under the conditions of the study, most climate modellers prefer the equal weighting of ensemble members, whereas hydrological-impact modellers in general are more open for assigning weights to different models in a multi-model ensemble, based on model performance and model structure. Climate experts are more open to exclude models, if obviously flawed, than to put weights on selected models in a relatively small ensemble. The study shows that expert elicitation can be an efficient way to assign weights to different hydrological models and thereby reduce the uncertainty in climate impact. However, for the climate model ensemble, comprising seven models, the elicitation in the format of this study could only re-establish a uniform weight between climate models

Inter-comparison of energy balance and hydrological models for land surface energy flux estimation over a whole river catchment

Evapotranspiration (ET) is the main link between the natural water cycle and the land surface energy budget. Therefore water-balance and energy-balance approaches are two of the main methodologies for modelling this process. The water-balance approach is usually implemented as a complex, distributed hydrological model, while the energy-balance approach is often used with remotely sensed observations of, for example, the land surface temperature (LST) and the state of the vegetation. In this study we compare the catchment-scale output of two remote sensing models based on the two-source energy-balance (TSEB) scheme, against a hydrological model, MIKE SHE, calibrated over the Skjern river catchment in western Denmark. The three models utilize different primary inputs to estimate ET (LST from different satellites in the case of remote sensing models and modelled soil moisture and heat flux in the case of the MIKE SHE ET module). However, all three of them use the same ancillary data (meteorological measurements, land cover type and leaf area index, etc.) and produce output at similar spatial resolution (1 km for the TSEB models, 500 m for MIKE SHE). The comparison is performed on the spatial patterns of the fluxes present within the catchment area as well as on temporal patterns on the whole catchment scale in 8-year long time series. The results show that the spatial patterns of latent heat flux produced by the remote sensing models are more similar to each other than to the fluxes produced by MIKE SHE. The temporal patterns produced by the remote sensing and hydrological models are quite highly correlated (r ≈ 0.8). This indicates potential benefits to the hydrological modelling community of integrating spatial information derived through remote sensing methodology (contained in the ET maps derived with the energy-balance models, satellite based LST or another source) into the hydrological models. How this could be achieved and how to evaluate the improvements, or lack of thereof, is still an open research question.The work has been carried out under the HOBE project funded by the VILLUM FOUNDATIONPeer reviewe

Spatial pattern evaluation of a calibrated national hydrological model – a remote-sensing-based diagnostic approach

Distributed hydrological models are traditionally evaluated against discharge stations, emphasizing the temporal and neglecting the spatial component of a model. The present study widens the traditional paradigm by highlighting spatial patterns of evapotranspiration (ET), a key variable at the land–atmosphere interface, obtained from two different approaches at the national scale of Denmark. The first approach is based on a national water resources model (DK-model), using the MIKE-SHE model code, and the second approach utilizes a two-source energy balance model (TSEB) driven mainly by satellite remote sensing data. Ideally, the hydrological model simulation and remote-sensing-based approach should present similar spatial patterns and driving mechanisms of ET. However, the spatial comparison showed that the differences are significant and indicate insufficient spatial pattern performance of the hydrological model.The differences in spatial patterns can partly be explained by the fact that the hydrological model is configured to run in six domains that are calibrated independently from each other, as it is often the case for large-scale multi-basin calibrations. Furthermore, the model incorporates predefined temporal dynamics of leaf area index (LAI), root depth (RD) and crop coefficient (Kc) for each land cover type. This zonal approach of model parameterization ignores the spatiotemporal complexity of the natural system. To overcome this limitation, this study features a modified version of the DK-model in which LAI, RD and Kc are empirically derived using remote sensing data and detailed soil property maps in order to generate a higher degree of spatiotemporal variability and spatial consistency between the six domains. The effects of these changes are analyzed by using empirical orthogonal function (EOF) analysis to evaluate spatial patterns. The EOF analysis shows that including remote-sensing-derived LAI, RD and Kc in the distributed hydrological model adds spatial features found in the spatial pattern of remote-sensing-based ET