Evaluating the benefits of merging near-real-time satellite precipitation products: a case study in the Kinu basin region, Japan

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

Groundwater discharge to the western Antarctic coastal ocean

Submarine groundwater discharge (SGD) measurements have been limited along the Antarctic coast, although groundwater discharge is becoming recognized as an important process in the Antarctic. Quantifying this meltwater path-way is important for hydrologic budgets, ice mass balances and solute delivery to the coastal ocean. Here, we estimate the combined discharge of subglacial and submarine groundwater to the Antarctic coastal ocean. SGD, including subglacial and submarine groundwater, is quantified along the WAP at the Marr Glacier terminus using the activities of naturally occurring radium isotopes (223Ra, 224Ra). Estimated SGD fluxes from a 224Ra mass balance ranged from (0.41 ± 0.14)×104 and (8.2 ± 2.3)×104m3 d−1. Using a salinity mass balance, we estimate SGD contributes up to 32% of the total freshwater to the coastal environment near Palmer Station. This study suggests that a large portion of the melting glacier may be infiltrating into the bedrock and being discharged to coastal waters along the WAP. Meltwater infiltrating as groundwater at glacier termini is an import-ant solute delivery mechanism to the nearshore environment that can influence biological productivity. More importantly, quantifying this meltwater pathway may be worthy of attention when predicting future impacts of climate change on retreat of tidewater glaciers

Modelling water and solute flows at land-sea and land-atmosphere interfaces under data limitations

Water and vapour flows from land to sea and the atmosphere are important for water resources, coastal ecosystems and climate. This thesis investigates possible methods for modelling these flows under often encountered unmonitored hydrological conditions and data limitations. Two contrasting types of drainage basin and associated data limitation/availability cases are considered: the Swedish unmonitored near-coastal catchment areas Forsmark and Simpevarp, for which detailed spatial but not much temporal variability data is available; and the much larger Aral Sea Drainage Basin (ASDB), for which spatial hydrological information is limited, while there is relatively well-known temporal change occurring in the Aral Sea itself and in the land and water use of the region over the last 50 years. The hydrologic modelling for the Forsmark and Simpevarp catchment areas showed that the relatively large focused stream flows, and the mean values and total sums of the diffuse small stream-groundwater flow fields in between the large stream flows from land to sea are largely constrained by the catchment hydrological balances and relatively robust and certain to estimate. The ASDB hydrologic modelling indicated an evapotranspiration return flow to the atmosphere from the irrigation water input on irrigated land that is much higher than previous estimates in atmospheric modelling, implying possible considerably larger than previously estimated non-local water and climate effects of the world’s irrigated areas. The more detailed groundwater-seawater dynamics modelling carried out for the coastal parts of the ASDB showed that regional topography and bathymetry largely influence coastal water fluxes during sea level lowering, with the Aral Sea shrinkage decreasing the seawater intrusion risk into the coastal groundwater considerably more for steeper than for flatter coastal topography parts of the region

Modelling water and solute flows at land-sea and land-atmosphere interfaces under data limitations

Water and vapour flows from land to sea and the atmosphere are important for water resources, coastal ecosystems and climate. This thesis investigates possible methods for modelling these flows under often encountered unmonitored hydrological conditions and data limitations. Two contrasting types of drainage basin and associated data limitation/availability cases are considered: the Swedish unmonitored near-coastal catchment areas Forsmark and Simpevarp, for which detailed spatial but not much temporal variability data is available; and the much larger Aral Sea Drainage Basin (ASDB), for which spatial hydrological information is limited, while there is relatively well-known temporal change occurring in the Aral Sea itself and in the land and water use of the region over the last 50 years. The hydrologic modelling for the Forsmark and Simpevarp catchment areas showed that the relatively large focused stream flows, and the mean values and total sums of the diffuse small stream-groundwater flow fields in between the large stream flows from land to sea are largely constrained by the catchment hydrological balances and relatively robust and certain to estimate. The ASDB hydrologic modelling indicated an evapotranspiration return flow to the atmosphere from the irrigation water input on irrigated land that is much higher than previous estimates in atmospheric modelling, implying possible considerably larger than previously estimated non-local water and climate effects of the world’s irrigated areas. The more detailed groundwater-seawater dynamics modelling carried out for the coastal parts of the ASDB showed that regional topography and bathymetry largely influence coastal water fluxes during sea level lowering, with the Aral Sea shrinkage decreasing the seawater intrusion risk into the coastal groundwater considerably more for steeper than for flatter coastal topography parts of the region

Design and Implementation of a Training Course on Big Data Use in Water Management

Big Data has great potential to be applied to research in the field of geosciences. Motivated by the opportunity provided by the Data Integration and Analysis System (DIAS) of Japan, we organized an intensive two-week course that aims to educate participants on Big Data and its exploitation to solve water management problems. When developing and implementing the Program, we identified two main challenges: (1) assuring that the training has a lasting effect and (2) developing an interdisciplinary curriculum suitable for participants of diverse professional backgrounds. To address these challenges, we introduced several distinctive features. The Program was based on experiential learning – the participants were required to solve real problems and worked in international and multidisciplinary teams. The lectures were strictly relevant to the case-study problems. Significant time was devoted to hands-on exercises, and participants received immediate feedback on individual assignments to ensure skills development. Our evaluation of the two occasions of the Program in 2015 and 2016 indicates significant positive outcomes. The successful completion of the individual assignments confirmed that the participants gained key skills related to the usage of DIAS and other tools. The final solutions to the case-study problems showed that the participants were able to integrate and apply the obtained knowledge, indicating that the Program’s format and curriculum were effective. We found that participants used DIAS in subsequent studies and work, thus suggesting that the Program had long-lasting effects. Our experience indicates that despite time constraints, short courses can effectively encourage researchers and practitioners to explore opportunities provided by Big Data

Evaluating the benefits of merging near-real-time satellite precipitation products: A case study in the Kinu basin region, Japan

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