Dr Hannah Yoon

Damage due to multi-year droughts mainly caused by climate change are increasing worldwide, and Korea was also affected by the drought that took place from 2015 to 2017. Also, traditional unilateral decision making processes to alleviate the damage from droughts have resulted in conflicts between many involved groups, the need for active participation from both stakeholders and policymakers has become more important than before. In this research, 'Shared Vision Planning,' a collaborative approach that involves participation from various groups of stakeholders, was applied in a research area by organizing a Water Policy Council. Moreover, a 'Shared Vision Model' was developed with the stakeholders by using STELLA Architect software and by applying the concept of System Dynamics. Multiple simulations that include various future climate change scenarios were selected to measure the risk from potential droughts. As a result, the source of water supply, proved to be more vulnerable than the districts in all three aspects of performance indices, reliability, resiliency, and vulnerability. Moreover, although total water deficit in the districts was not very significant, discordance between the districts where the alternatives to prevent water shortage is being developed and where water deficit is projected to happen should be managed. In the future, the model will be further modified according to the stakeholders' needs, with possible seminars to educate the stakeholders for easier use of the developed model.

The surrogate river discharge model (SRM) uses remote sensing surrogates of river discharge (SR) to estimate streamflow in ungauged basins. Integrating SR derived from L-band microwave data with climate inputs of rainfall and potential evapotranspiration, the model operates within a hydrological framework. While SR is strongly correlated with streamflow, it is unitless and requires calibration for physical coherence. Calibration translates SR into an actual discharge value using the average or mean discharge (QM) derived from the Budyko framework. A novel likelihood approach employing SR and QM eliminates reliance on direct discharge observations. Validation across three Australian catchments demonstrates satisfactory performance, with NSE >0.6 and KGE >0.6, highlighting its applicability in data-scarce regions. The SRM software includes tools for L-band microwave data acquisition, SR generation, and hydrological model calibration, enabling global application in river discharge estimation.

In recent studies, a surrogate river discharge model (SRM) has been proposed that uses remote sensing data, termed henceforth as the Surrogate River discharge (SR), instead of river discharge in hydrological model calibration. SR is calculated from the relative values of target and reference reflectance obtained from satellite observations and thus does not have a defined unit. Hence, to ensure physical consistency, the model calibration mandates the integration of a hydrological feature to translate the non-dimensional SR to a quantified river discharge estimate. In this context, an estimate of the mean catchment river discharge (QM) is proposed as a suitable signature. This variable is derived from the Budyko relationship, which only uses two climate average data: precipitation and potential evapotranspiration. This allows the SRM calibration to depend primarily on remotely sensed data and climate data. However, the model's sensitivity to the hydrologic signature approximation is generally unknown and should be characterized for improved probabilistic predictions in ungauged basins. Therefore, this study evaluates the impact of the hydrologic signature on SRM. The study first identifies the sensitivity of the SRM to the QM accuracy. Subsequently, the SRM integrated with the Budyko relationship (SRMB) was compared to the Australian Landscape Water Balance model (AWRA-L) using real catchment data on 49 selected catchments of various sizes, topographies, and climates to assess its uncertainty. Then the investigation is subsequently broadened to include 384 catchments in Australia to analyze the sensitivity of SRM with respect to classified SR quality, as assessed through a range of hydrological signature estimation methods. To summarize, while the efficacy of SRM is contingent upon SR quality and mean flow accuracy, incorporating the Budyko framework yields effective mean flow estimations, bolstering prediction reliability. Given its efficient calibration, SRM demonstrates potential for broader application in hydrological studies.

This study suggests a radical approach to hydrologic predictions in ungauged basins, addressing the long standing challenge of issuing predictions when in-situ river discharge does not exist. A simple but powerful rationale for measuring and modeling river discharge is proposed, using coupled advances in hydrologic modeling and satellite remote sensing. Our approach presents a Surrogate River discharge driven Model (SRM) that infers Surrogate River discharge (SR) from remotely sensed microwave signals with the ability to mimic river discharge in varying topographies and vegetation cover, which is then used to calibrate a hydrological model enabling physical realism in the resulting river discharge profile by adding an estimated mean of river discharge via the Budyko framework. The strength of SRM comes from the fact that it only uses remotely sensed data in prediction. The approach is demonstrated for 130 catchments in the Murray Darling Basin (MDB) in Australia, a region of high economic and environmental importance. The newly proposed SR (SRL, representing L-band microwave) boosts the Nash-Sutcliffe Efficiency (NSE) of modeled flow, showing a mean NSE of 0.54, with 70% of catchments exceeding NSE 0.4. We conclude that SRM effectively predicts high-flow and low-flow events related to flood and drought. Overall, this new approach will significantly improve catchment simulation capacity, enhancing water security and flood forecasting capability not only in the MDB but also worldwide.