[1] Hydrological model interpretation of the water flow development in the Aral Sea Drainage Basin (ASDB) indicates that the water diversion and irrigation schemes in this region have considerably increased evapotranspiration and thereby decreased net water flux (precipitation minus evapotranspiration) from the atmosphere to the surface of the ASDB. Increased evapotranspiration cools the irrigated areas, and the decrease of net atmospheric water influx to the ASDB may also have non-local effects outside the basin. Such effects have previously been estimated by atmospheric modeling, assuming a global average evapotranspiration return flow to the atmosphere of about 40% of irrigation. Our results indicate larger return flows, of nearly 100% of the applied irrigation water in the ASDB, which may also imply considerably larger than previously estimated non-local water and climate effects of the world's irrigated areas.
[1] Continental freshwater transports and loads excess nutrients and pollutants from various land surface sources into downstream inland and coastal water environments. This study shows that even small, hydrologically unmonitored near-coastal catchment areas may generate large nutrient and pollutant mass loading to the sea of a magnitude similar to or greater than monitored river loads. Systematic near-coastal gaps in the monitoring of freshwater discharges to the sea may therefore mislead the quantification of coastal mass loading significantly. A methodology is presented for quantifying the mass load contributions of all the different unmonitored pathways of hydrological mass transport to the coast, including unmonitored river parts, whole unmonitored streams, and submarine groundwater discharge. This can be used for guiding future efforts to improve monitoring so that it includes the essential hydrological pathways of nutrient and pollutant loading to the sea.
Beyond the monitoring of main river flows, the discharges of freshwater from land to the sea are typically left unmonitored along long coastline stretches. This study uses uniquely fine‐resolved data and determines the spatial variability and statistics of the freshwater fluxes to the sea along two Swedish coastlines. The flux statistics depend greatly on subjective investigation choices of the support (or aggregation) scale of flux measurement, H, and the coastline length resolution, G. For common H and G values and relations, the flux coefficient of variation ranges from 1.5 to 22.5 and there is around 90–95% probability that locally measured or modelled fluxes miss the high‐end fluxes that are greater than the arithmetic mean flux and carry most of the total freshwater discharge across the coastline. Quantification of the inland hydrological balance and its distribution over the whole coastal catchment area is needed for objective guidance of coastal discharge interpretations.
After the launch of the Global Precipitation Measurement (GPM) mission in 2014, many satellite precipitation products (SPPs) are available at finer spatiotemporal resolution and/or with reduced latency, potentially increasing the applicability of SPPs for near-real-time (NRT) applications. Therefore, there is a need to evaluate the NRT SPPs in the GPM era and investigate whether bias-correction techniques or merging of the individual products can increase the accuracy of these SPPs for NRT applications. This study utilizes five commonly used NRT SPPs, namely, CMOPRH RT, GSMaP NRT, IMERG EARLY, IMERG LATE, and PERSIANN-CCS. The evaluation is done for the Kinu basin region in Japan, an area that provides observed rainfall data with high accuracy in space and time. The selected bias correction techniques are the ratio bias correction and cumulative distribution function matching, while the merged products are derived with the error variance, inverse error variance weighting, and simple average merging techniques. Based on the results, all SPPs perform best for lower-intensity rainfall events and have challenges in providing accurate estimates for typhoon-induced rainfall (generally more than 50% underestimation) and at very fine temporal scales. Although the bias correction techniques successfully reduce the bias and improve the performance of the SPPs for coarse temporal scales, it is found that for shorter than 6-hourly temporal resolutions, both techniques are in general unable to bring improvements. Finally, the merging results in increased accuracy for all temporal scales, giving new perspectives in utilizing SPPs for NRT applications, such as flood and drought monitoring and early warning systems.
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