Jeffrey C. Davids

Figure provides a schematic representation of the flows, fluxes, and states within the SWEO framework. It gives an overview of the parameters defined by the SWEO model and provides a visual interpretation of the overall framework discussed in this paper. Groundwater is a critical resource for human and ecosystem needs, yet its availability is increasingly stressed by over-abstractions as well as climate change. Monitoring remains challenging due to limited data of well abstractions, regular piezometric readings, and complex subsurface processes. This study presents Scalable Water balance from Earth Observations (SWEO), a novel water accounting framework that estimates groundwater recharge and withdrawals without requiring detailed hydrogeological information, complex groundwater flow models, hydraulic heads, or metered abstractions. The framework is mainly driven by satellite-based remote sensing data of rainfall surplus/deficit and storage changes into spatially distributed key groundwater balance terms by sequentially applying long- and short-term water balances at local and basin scales. River discharge measurements are used to internally calibrate percolation and baseflow. The framework is transparent, scalable, and applicable to large basins with groundwater irrigation. SWEO can be implemented in the cloud, and processing 25 years can be completed within a few days. The groundwater resource information generated by SWEO is currently not available to groundwater policymakers for preparing longer term policies to make aquifer abstractions more sustainable. An accompanying article describes the validation of this new analytical framework for surface water and groundwater accounting.

Figure shows a scatter plot of yearly total groundwater withdrawals per county (Mm³ year-1) from SWEO with USGS reports and average yearly total groundwater withdrawals per county over irrigated areas (mm year-1). The Scalable Water balance from Earth Observation (SWEO) framework proposes an approach to deriving key hydrological fluxes and state variables from remotely sensed data, as a basis for policy formulation and definition of sustainable water allocations. These issues are particularly important in those areas where water is scarce, and current usage depends on unsustainable groundwater use. Validation was carried out across three contrasting basins: the Aconcagua (Chile), Upper North Platte, and Upper San Joaquin (USA) over the period 2003–2019, using the full available dataset. Validation followed four steps combining in situ and independent remote sensing data. First, SWEO monthly total water storage compared well with gravity recovery and climate experiment (R2 = 0.62–0.73). Second, the different water storage components were validated: snow and surface water agreed with United States Department of Agriculture and United States Geological Survey (USGS) observations (R2 = 0.84 and 0.81; MAE = 35.3 mm month−1 and 9.59 Mm3 month−1), and groundwater levels over 17 years matched SWEO (R2 = 0.90; MAE = 10.75 m). Third, SWEO river discharge showed robust agreement with observed streamflow (R2 = 0.73; MAE = 12.48 Mm3 month−1). Fourth, USGS five-year groundwater reports validated total withdrawals per county, agreeing strongly at basin scale (R2 = 0.91; MAE = 13.79 Mm3 year−1) and over irrigated areas (R2 = 0.90; MAE = 36.15 mm year−1), with pixel-level patterns matching well density and literature ratios. Overall, SWEO effectively captures monthly water balance dynamics at 250 m resolution across diverse basins, providing a robust tool for long-term water management, allocation policy, and climate impact assessments.

Strategic planning of water management at the river-basin scale requires (1) measurement and accounting of individual hydrological processes, (2) quantification of water resources, and (3) their optimal allocation. Scalable Water Balances from Earth Observations (SWEO) is an open-access parameterization enabling automated reporting of water footprints and Sustainable Development Goal (SDG) indicators. We present its systematic arrangement and input datasets, and demonstrate its accuracy by independent riverflow measurements. We also review some achievements in remote sensing for hydrology during the last 50 years in quantifying hydrological and water management processes, flows, fluxes and changes in storage from various independent sources; and append mathematical formulations.

High quality citizen science data can be instrumental in advancing science toward new discoveries and a deeper understanding of under-observed phenomena. However, the error structure of citizen scientist (CS) data must be well-defined. Within a citizen science program, the errors in submitted observations vary, and their occurrence may depend on CS-specific characteristics. This study develops a graphical Bayesian inference model of error types in CS data. The model assumes that: (1) each CS observation is subject to a 5 specific error type, each with its own bias and noise; and (2) an observation’s error type depends on the error community of the CS, which in turn relates to characteristics of the CS submitting the observation. Given a set of CS observations and corresponding ground-truth values, the model can be calibrated for a specific application, yielding (i) number of error types and error communities, (ii) bias and noise for each error type, (iii) error distribution of each error community, and (iv) the error community to which each CS belongs. The model, applied to Nepal CS rainfall observations, 10 identifies five error types and sorts CSs into four model-inferred communities. In the case study, 73% of CSs submitted data with errors in fewer than 5% of their observations. The remaining CSs submitted data with unit, meniscus, unknown, and outlier errors. A CS’s assigned community, coupled with model-inferred error probabilities, can identify observations that require verification. With such a system, the onus of validating CS data is partially transferred from human effort to machine-learned algorithms.

Strategic planning of water management at the river-basin scale requires (1) measurement and accounting of individual hydrological processes, (2) quantification of water resources, and (3) their optimal allocation. Scalable Water Balances from Earth Observations (SWEO) is an open-access parameterization enabling automated reporting of water footprints and Sustainable Development Goal (SDG) indicators. We present its systematic arrangement and input datasets, and demonstrate its accuracy by independent riverflow measurements. We also review some achievements in remote sensing for hydrology during the last 50 years in quantifying hydrological and water management processes, flows, fluxes and changes in storage from various independent sources; and append mathematical formulations.

High quality citizen science data can be instrumental in advancing science toward new discoveries and a deeper understanding of under-observed phenomena. However, the error structure of citizen scientist (CS) data must be well-defined. Within a citizen science program, the errors in submitted observations vary, and their occurrence may depend on CS-specific characteristics. This study develops a graphical Bayesian inference model of error types in CS data. The model assumes that: (1) each CS observation is subject to a specific error type, each with its own bias and noise; and (2) an observation’s error type depends on the error community of the CS, which in turn relates to characteristics of the CS submitting the observation. Given a set of CS observations and corresponding ground-truth values, the model can be calibrated for a specific application, yielding (i) number of error types and error communities, (ii) bias and noise for each error type, (iii) error distribution of each error community, and (iv) the error community to which each CS belongs. The model, applied to Nepal CS rainfall observations, identifies five error types and sorts CSs into four model-inferred communities. In the case study, 73% of CSs submitted data with errors in fewer than 5% of their observations. The remaining CSs submitted data with unit, meniscus, and unknown errors. A CS’s assigned community, coupled with model-inferred error probabilities, can identify observations that require verification. With such a system, the onus of validating CS data is partially transferred from human effort to machine-learned algorithms.

Like other cities in the Kathmandu Valley, Bhaktapur faces rapid urbanisation and population growth. Rivers are negatively impacted by uncontrolled settlements in flood-prone areas, lowering permeability, decreasing channels widths, and waste blockage. All these issues, along with more extreme rain events during the monsoon due to climate change, have led to increased flooding in Bhaktapur, especially by the Hanumante River. For a better understanding of flood risk, the first step is a return level analysis. For this, historical data are essential. Unfortunately, historical records of water levels are non-existent for the Hanumante River. We measured water levels and discharge on a regular basis starting from the 2019 monsoon (i.e., June). To reconstruct the missing historical data needed for a return level analysis, this research introduces the Classical Model for Structured Expert Judgment (SEJ). By employing SEJ, we were able to reconstruct historical water level data. Expert assessments were validated using the limited data available. Based on the reconstructed data, it was possible to estimate the return periods of extreme water levels of the Hanumante River by fitting a Generalized Extreme Value (GEV) distribution. Using this distribution, we estimated that a water level of about 3.5 m has a return period of ten years. This research showed that, despite considerable uncertainty in the results, the SEJ method has potential for return level analyses.

High quality citizen science can be instrumental in advancing science toward new discoveries and a deeper understanding of under-observed phenomena. However, the error structure of citizen scientist (CS) data must be well-defined. Within a citizen science program, the error types in submitted observations vary, and their occurrence may depend on a variety of CS-specific variables, such as motivation. This study develops a graphical Bayesian inference model of error types in CS data. The model assumes that: (1) each CS observation is subject to a specific error type, each with its own bias and noise; and (2) an observation’s error type depends on the error community of the CS, which in turn relates to characteristics of the CS submitting the observation. Given a set of CS observations and corresponding ground-truth values, the model can be calibrated for a specific application, yielding (i) number of error types and communities, (ii) bias and noise of each error type, (iii) error distribution of each community, and (iv) the community to which each CS belongs. The model, applied to Nepal CS rainfall observations, identifies seven error types and sorts CSs into four model-inferred communities. In the case study, 79% of CSs committed errors in fewer than 6.3% of their observations. The remaining tended to commit unit, meniscus, and unknown errors. A CS’s assigned community, coupled with the model-inferred error probability, can identify observations that require verification. With such a system, the onus of validating CS data is partially transferred from human effort to machine-learned algorithms.

Citizen science, as a complement to ground-based and remotely-sensed precipitation measurements, is a promising approach for improving precipitation observations. During the 2018 monsoon (May to September), SmartPhones4Water (S4W) Nepal—a young researcher-led water monitoring network—partnered with 154 citizen scientists to generate 6,656 precipitation measurements in Nepal with low-cost (−1 (n = 33), 0.8 mm (n = 99), and 0.3 mm (n = 49), respectively. We recruited citizen scientists by leveraging personal relationships, outreach programs at schools/colleges, social media, and random site visits. We motivated ongoing participation with personal follow-ups via SMS, phone, and site visit; bulk SMS; educational workshops; opportunities to use data; lucky draws; certificates of involvement; and in certain cases, payment. The average citizen scientist took 42 measurements (min = 1, max = 148, stdev = 39). Paid citizen scientists (n = 37) took significantly more measurements per week (i.e., 54) than volunteers (i.e., 39; alpha level = 0.01). By comparing actual values (determined by photographs) with citizen science observations, we identified three categories of observational errors (n = 592; 9% of total measurements): unit (n = 50; 8% of errors; readings in centimeters instead of millimeters); meniscus (n = 346; 58% of errors; readings of capillary rise), and unknown (n = 196; 33% of errors). A cost per observation analysis revealed that measurements could be performed for as little as 0.07 and 0.30 USD for volunteers and paid citizen scientists, respectively. Our results confirm that citizen science precipitation monitoring with low-cost gauges can help fill precipitation data gaps in Nepal and other data scarce regions.

Wise management of water resources requires data. Nevertheless, the amount of streamflow data being collected globally continues to decline. Generating hydrologic data together with citizen scientists can help fill this growing hydrological data gap. Our aim herein was to (1) perform an initial evaluation of three simple streamflow measurement methods (i.e., float, salt dilution, and Bernoulli run-up), (2) evaluate the same three methods with citizen scientists, and (3) apply the preferred method at more sites with more people. For computing errors, we used midsection measurements from an acoustic Doppler velocimeter as reference flows. First, we (authors) performed 20 evaluation measurements in headwater catchments of the Kathmandu Valley, Nepal. Reference flows ranged from 6.4 to 240 L s ⁻¹ . Absolute errors averaged 23 %, 15 %, and 37 % with average biases of 8 %, 6 %, and 26 % for float, salt dilution, and Bernoulli methods, respectively. Second, we evaluated the same three methods at 15 sites in two watersheds within the Kathmandu Valley with 10 groups of citizen scientists (three to four members each) and one “expert” group (authors). At each site, each group performed three simple methods; experts also performed SonTek FlowTracker midsection reference measurements (ranging from 4.2 to 896 L s ⁻¹ ). For float, salt dilution, and Bernoulli methods, absolute errors averaged 41 %, 21 %, and 43 % for experts and 63 %, 28 %, and 131 % for citizen scientists, while biases averaged 41 %, 19 %, and 40 % for experts and 52 %, 7 %, and 127 % for citizen scientists, respectively. Based on these results, we selected salt dilution as the preferred method. Finally, we performed larger-scale pilot testing in week-long pre- and post-monsoon Citizen Science Flow campaigns involving 25 and 37 citizen scientists, respectively. Observed flows ( n =131 pre-monsoon; n =133 post-monsoon) were distributed among the 10 headwater catchments of the Kathmandu Valley and ranged from 0.4 to 425 L s ⁻¹ and from 1.1 to 1804 L s ⁻¹ in pre- and post-monsoon, respectively. Future work should further evaluate uncertainties of citizen science salt dilution measurements, the feasibility of their application to larger regions, and the information content of additional streamflow data.