Accurate flow prediction is a primary goal of hydrological modeling studies, which can be affected by the use of varying rainfall datasets, autocalibration methods, and performance indices. The combined effect of three rainfall datasets — Fifth generation of European ReAnalysis (ERA-5), Gridded meteorological data (gridMET), Global Precipitation Measurement Integrated Multi-satellitE Retrievals (GPM IMERG) — and three autocalibration techniques — Dynamically Dimensioned Search (DDS), Generalized Likelihood Uncertainty Estimation (GLUE), Latin Hypercube Sampling (LHS) — on SWAT+ river flow prediction was measured using three evaluation metrics — Nash Sutcliffe Efficiency (NSE), Kling Gupta Efficiency (KGE) and coefficient of determination (R 2) — for two watersheds in North Carolina (Cape Fear, Tar Pamlico) using the Soil Water Assessment Tool Plus (SWAT+) model. Five parameters in the SWAT+ model, cn2, revap_co, flo_min, revap_min, and awc, were found to be significantly sensitive under all combinations for both watersheds. Simulated flow varied more with the change in rainfall than the calibration technique used. We discovered that GPM IMERG gave the best results of the rainfall datasets, followed by ERA-5 and gridMET. We observed that the NSE score is more sensitive to different combinations of rainfall datasets and calibration techniques than the KGE scores. SWAT+ underperformed in the prediction of base flow for the groundwater-driven watershed. Overall, we recommend using the GPM IMERG rainfall dataset with the GLUE optimization technique and KGE performance index for optimal flow simulations. The results from this study will help hydrological modelers choose an optimal combination of rainfall dataset, autocalibration technique, and performance index depending on watershed characteristics.

Ganga river basin (GRB) in the Indian subcontinent is one of the most heavily irrigated land in the world. According to a book published in 2005 by Central Water Commission (CWC), 57% of the net irrigated land in India lies inside GRB only. Further GRB is also one of the most populous river basins in the world supporting almost 400 million people of India. With increasing use of fertilizers in agriculture and untreated sewage waste from the booming industries, there is need to assess the water quality and the contamination in surface water. We will use Soil Water Assessment Tool (SWAT) to model the hydrology of the river basin. For water quality analysis, SWAT is able to simulate the impact on hydrology, sediment and nutrients load, due to physical changes brought in the large ungauged river basins. We hypothesize that numerous small, rain-fed rivers in the Indo-Gangetic floodplain that are flowing predominantly through agricultural land are important non-point source of Nitrogen(N) and Phosphorus (P) and will control the nutrient budget of large river system. SWAT model will be used to simulate flow and nutrient/sediment concentrations of nitrogen/nitrates, phosphorus and sediment in the upper reach at Uttarkashi and Rishikesh, in the middle reach at Kanpur, Lucknow and Varanasi, and Farakka at the lower reach. SWAT model will be calibrated at daily/monthly time step for flow and monthly scale for water quality parameters. We will analyze the water quality in the basin using widely used Water Quality Index (WQI) considering pH, TDS, BOD, COD, hardness, nitrates, carbonates and silicates. We will use gridded climate data from Indian Meteorological Department (IMD) and water quality data from CWC. SRTM 90 m DEM, 300 m Land use/land cover map from Climate Change Initiative (CCI) and 7 km soil map from Food and Agriculture Organization (FAO).

This contribution highlights part of training events designed to collect, analyze, and manage water and water-related data (e.g., climate, weather, land, soils) and information products for the purposes of reducing water-related risks, and improving regional responses to environmental emergencies in the Mekong region. In this work, we discuss multiple tools and applications developed by National Aeronautics and Space Administration (NASA) scientists to lower technical barriers through current web technologies and leveraging data sharing capabilities among existing programs and institutions within different parts of the Mekong region. The aim of this training contribution is to leverage a well-established suite of tools that include but are not limited to remote sensing precipitation data adjustment techniques, i.e., the SWATOnline visualization and modeling system, and the NASAaccess data toolkit. The collaborative training events, which this contribution is part of, are administered by the United States Department of State (DOS) and the Ministry of Foreign Affairs - Republic of Korea under the Mekong-US Partnership and its Mekong Water Data Initiative facilitated by Sustainable Infrastructure Partnership (SIP) program. The Mekong Water Data is a DOS Initiative consists of multiple efforts and programs with an overarching objective of building the capacity of Mekong riparian countries and the Mekong River Commission Secretariat (MRCS), National Mekong Committees and line agencies in the Lower Mekong countries to improve the management of the Mekong River.

Satellite-derived data provide useful information about the rationale of Earth’s functioning. While satellite remote sensing has been regarded as the almost only means for observing the entire Earth in near-real-time, errors in satellite observations have limited their direct usage in applications. Merging two or more data sources has been regarded as a simple but effective way to decrease such errors (e. g. minimizing mean square errors between the observation and truth). The principle of data merging is to combine independent information of each data source, improving over each individual product by canceling out random errors, with effectiveness by the degree of independence over the data sources. In the case of linearly combining data, qualitative assessments of the error (i.e. error variance/covariance and data-truth correlation) are essential to calculate the optimal weight for each candidate product. However, such reference “truth” is rarely available in practical. To overcome this limitation, a triple collocation (TC) technique is often used to estimate data error by using a data triplet without the truth. Despite the usefulness and simplicity of the TC-based error estimation, the inherent assumptions (e.g. error independence) in the approach tend to induce sub-optimal results in the error estimation and/or data combination. There have been also further efforts to address the limitation such as quadruple collocation (QC) using a data quadruple to partially estimate error cross-correlation and single/double instrumental variable methods to lessen the difficulty in obtaining multiple datasets. In this presentation, we review the status of error estimation and data merging approaches based on the collocation methods and then present challenges to be addressed through future research.

We studied the temporal and spatial changes in the flood water elevation and variation in the surface extent due to the flooding resulting from Hurricane Florence (September 2018) using the observation from an Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and Sentinel-1. The novelty of this study lies in the estimation of changes in the flood depth during the hurricane, and investigating the best method. Overall, flood depths from SAR were observed to be well-correlated with the spatially distributed ground-based observations (R2 = 0.79 to 0.96). The corresponding change in water level (∂h/∂t) compared well between the remote sensing approach and the ground observations (R2 = 0.90). This study highlights the potential use of SAR remote sensing for inundated landscapes (and locations with scarce ground observations), and it emphasizes the need for more frequent SAR observations during flood to provide spatially distributed and high temporal repeat observations of inundation.

Effective monitoring of groundwater withdrawals is necessary to help mitigate the negative impacts of aquifer depletion. In this study, we develop a holistic approach that combines water balance components with a machine learning model to estimate groundwater withdrawals. We use both multi-temporal satellite and modeled data from sensors that measure different components of the water balance at varying spatial and temporal resolutions. These remote sensing products include evapotranspiration, precipitation, and land cover. Due to the inherent complexity of integrating these data sets and subsequently relating them to groundwater withdrawals using physical models, we apply random forests- a state of the art machine learning algorithm- to overcome such limitations. Here, we predict groundwater withdrawals per unit area over a highly monitored portion of the High Plains aquifer in the central United States at 5 km resolution for the years 2002-2019. Our modeled withdrawals had high accuracy on both training and testing datasets (R≈ 0.99 and R≈ 0.93, respectively) during leave-one-out (year) cross-validation with low Mean Absolute Error (MAE) ≈ 4.26 mm and Root Mean Square Error (RMSE) ≈ 13.57 mm for the year 2014. Moreover, we found that even for the extreme drought year of 2012, we have a satisfactory test score (R≈ 0.79) with MAE ≈ 10.34 mm and RMSE ≈ 27.04 mm. Therefore, the proposed hybrid water balance and machine learning approach can be applied to similar regions for proactive water management practices.

Groundwater plays a crucial role in sustaining global food security but is being over-exploited in many basins of the world. Despite its importance and finite availability, local-scale monitoring of groundwater withdrawals required for sustainable water management practices is not carried out in most countries, including the United States. In this study, we combine publicly available datasets into a machine learning framework for estimating groundwater withdrawals over the state of Arizona. Here we include evapotranspiration, precipitation, crop coefficients, land use, well density, and watershed stress metrics for our predictions. We employ random forests to predict groundwater withdrawals from 2002-2020 at a 2 km spatial resolution using in-situ groundwater withdrawal data available for Arizona Active Management Areas (AMA) and Irrigation Non-Expansion Areas (INA) from 2002-2009 for training and 2010-2020 for validating the model respectively. The results show high training (R2≈ 0.86) and good testing (R2≈ 0.69) scores with normalized mean absolute error (NMAE) ≈ 0.64 and normalized root mean square error (NRMSE) ≈ 2.36 for the AMA/INA region. Using this method, we spatially extrapolate the existing groundwater withdrawal estimates to the entire state and observe the co-occurrence of both groundwater withdrawals and land subsidence in South-Central and Southern Arizona. Our model predicts groundwater withdrawals in regions where production wells are present on agricultural lands and subsidence is observed from Interferometric Synthetic Aperture Radar (InSAR), but withdrawals are not monitored. By performing a comparative analysis over these regions using the predicted groundwater withdrawals and InSAR-based land subsidence estimates, we observe a varying degree of subsidence for similar volumes of withdrawals in different basins. The performance of our model on validation datasets and its favorable comparison with independent water use proxies such as InSAR demonstrate the effectiveness and extensibility of our combined remote sensing and machine learning-based approach.