Flood early warning systems are crucial for disaster risk reduction strategies, enabling communities to take timely action against threats. However, the effectiveness of these systems depends on accurate and timely hydrological data, particularly river discharge and water level measurements. Unfortunately, many regions face significant challenges in obtaining hydrological data, especially discharge data, due to outdated rating curves, high equipment costs, and logistical constraints. In contrast, water-level measurements offer reduced uncertainty and are often more accessible, providing an alternative to hydrological modeling in data-scarce regions. To address these limitations, we developed and validated the Discharge-to-Water Level Transformation (DWLT) method, which uses the monthly duration curves to transform discharge simulations from the GEOGLOWS ECMWF Global Hydrological Model into water level predictions using data from over 19,000 ground- and satellite-based river gauge stations. The results indicate that the water levels generated by DWLT are closely aligned with the observed water levels, especially when using satellite measurements, which offer a valuable alternative when ground-based data are scarce. Despite quality issues such as spikes and zero-level inconsistencies in ground-based data and temporal limitations such as short monitoring periods and infrequent measurements in satellite-based data, the methodology shows promising potential for large-scale and local hydrological applications. This work supports future flood forecasting and water resource management efforts, highlighting water level as an effective variable in hydrological modeling.

Water managers face the daunting task of managing freshwater resources in the face of industrialization and population growth. As surface water resources become fully allocated, increased groundwater use can fill the void, particularly during periods of drought. Improper groundwater management can result in reduced water quality, land subsidence, increased pumping costs, and in some cases, the complete exhaustion of an aquifer and the loss of groundwater as a buffer during times of drought. Assessing the long-term impact of various groundwater management decisions can be difficult and costly, and therefore many decisions are made without sufficient analysis. Advancements in the acquisition and dissemination of Earth observations, coupled with advances in cloud computing, web apps, online mapping, and visualization provide a unique opportunity to deliver tools and actionable information to groundwater managers to assist them in addressing global and regional challenges and opportunities. We have developed a web-based tool that ingests in situ groundwater level measurements for specific aquifers and generates time series plots, maps, and raster animations showing groundwater depletion over time and short-term projections into the future. This process involves both temporal and spatial interpolation algorithms. In some aquifers, the observation wells are sparse and/or the historical observations have large gaps, leading to greater uncertainty in the interpolation and the resulting groundwater depletion estimates. To address this, we utilize Earth observations (GRACE, SMAP, etc.) and a co-kriging algorithm to enhance the interpolation process. The utility of the Earth observations in improving the estimates is evaluated using a jackknifing process. We present case studies for application of the system in the states of Utah and Texas.

Groundwater development will provide a more stable water source and enhance food security. Sustainable groundwater development requires collecting and analyzing data produced at global and national levels and disseminating that data and knowledge to end users such as States, NGOs, municipalities, businesses, and agropastoralists in a format that is useful for planning and decision-making. In developing countries, analyzing in situ measurements to de can be challenging due to sparsity of data and lack of tools and expertise. To address these problems we have developed a web-based geospatial tool that ingests in situ water level measurements and performs temporal and spatial interpolation to build interactive animated maps, time series plots, and long-term aquifer depletion curves. We use machine learning to find correlations among Earth observation data, such as precipitation or soil moisture, with water level data and perform more accurate interpolation. This approach ensures that scarce in situ data are used as effectively and accurately as possible. This tool helps water managers gain a better understanding of groundwater resources and determine how aquifers are responding to groundwater development, droughts, and climate change.